Geographic Information Systems [1 ed.] 9781620819050, 9781612099255

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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

ENVIRONMENTAL SCIENCE, ENGINEERING AND TECHNOLOGY

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

GEOGRAPHIC INFORMATION SYSTEMS

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ENVIRONMENTAL SCIENCE, ENGINEERING AND TECHNOLOGY

GEOGRAPHIC INFORMATION SYSTEMS

CHRISTOPHER J. DAWSEN

Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved.

EDITOR

Nova Science Publishers, Inc. New York

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Copyright © 2011 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works.

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Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. Additional color graphics may be available in the e-book version of this book.

Library of Congress Cataloging-in-Publication Data Geographic information systems / editor, Christopher J. Dawsen. p. cm. Includes index. ISBN 978-1-62081-905-0 (eBook) 1. Geographic information systems. I. Dawsen, Christopher J. G70.212.G4255 2011 910.285--dc22 2011005296

Published by Nova Science Publishers, Inc. † New York

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CONTENTS vii 

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Preface Chapter 1

GIS and Spatial Decision Support Xuan Zhu

Chapter 2

Data Base Management System, G.I.S. and Forestry Meliadis Ioannis and Miltiadis Meliadis

35   

Chapter 3

GIS and Spatial Decision Making Khalid A. Eldrandaly

61   

Chapter 4

Using Geographic Information Systems for Range-Wide Species Conservation Planning Kathy A. Zeller and Alan Rabinowitz

85   

Chapter 5

Geological Information System in Rainwater Harvesting Saumitra Mukherjee

107   

Chapter 6

Estimating the Environmental Effects on Residential Property Value with GIS Lubos Matejicek

119   

Modeling of Traffic-Related Environmental Pollution in the GIS Lubos Matejicek and Zbynek Janour

133   

Chapter 7

Index

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147 

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PREFACE A geographic information system (GIS) is any system that captures, stores, analyzes, manages and presents data that are linked to locations. In the simplest terms, GIS is the merging of cartography, statistical analysis and database technology, and can be used in archaeology, geography, cartography, remote sensing, land surveying, public utility management and natural resource management. This new book presents topical research in the study of GIS, including GIS in the spatial decision making process; GIS for wide-range species conservation planning and forestry; GIS in rainwater harvesting, estimating the environmental effects on residential property and traffic-related environmental pollution in the GIS. Chapter 1 - This chapter reviews the use of GIS for spatial decision support, focusing on the design, development and implementation of spatial decision support systems (SDSS) using GIS technology. As the awareness of spatial information in decision making improves significantly, GIS is increasingly becoming an important component of decision support systems. While GIS provides the computational basis for spatial decision support and offers the basic decision aids including spatial data management, spatial analysis and visualization, SDSS is designed to utilize domain specific models and knowledge to solve specific decision problems that have a geographic or spatial component. This chapter discusses the nature of spatial decision making and the role of GIS in the spatial decision making process, reviews the recent advances in the development of SDSS through the integration of GIS with models, decision analysis and knowledge management techniques, advanced visualization and the Internet, introduces examples of successful SDSS systems and examines the conceptual, technical, and organizational issues that must be addressed in the successful implementation of SDSS. Chapter 2 - Planning and developing comprehensive information systems is one of the most difficult and fascinating problems resource managers face today. The rapid implementation of Geographic Information Systems (GIS) and their never ending need for accurate and current spatial data promotes the development of new approaches for automated and fast data acquisition. This technology is very promising for people dealing with a huge amount of data, such as foresters. Forest ecosystem produces many goods and services without the intervention of human management. But since humans began intensively managed forests, the relationship between manufactured goods and production changed. The current pressure on forests and forest areas have a direct impact on the system of human society and will increase further in the coming

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years. It may even be considered that forests which are being created today (artificial or natural) have maturity for logging after 60-70 years. By then, the Earth's population will have increased and cause probable changes that will also effect the weather. The task of Forestry is to collect and maintain accurate and detailed data information in order to address current needs and to provide future situations. Forest management has traditionally been considered as the management of trees for timber. Actually forest management really includes multiple management objectives such as vegetation, land, and people management. GIS has proved to be a suitable tool for managing complex systems, where a great number of variables whose main feature involve spatial location. Knowing the extent of forests, different types and composition of the various categories of use / land cover affect all activities of Forestry Science. The accuracy of measurements and calculations affect successful decision making by managers at local and national levels. Our knowledge for the ecological environment has been increased, but also the methods and the techniques of management changed and they continue to change. The foresters in order to correspond to modern requirements have to improve their place as scientists and as professionals. The foresters continuously process data and information. The development of a Forest Data Base Management System based on the technology of GIS must aim for the reduction of time-consuming duties that are related to the volume of data that they have to process.These advantages would give the opportunity to impose new methods for the processing of data. It is remarkable that the Foresters in various Services tend to automate the existing system for the treatment of data management, and are more willing for the reorganization of current informative systems with the help of new technologies. In this paper the use of GIS in the field of forestry will be discussed and the development of automated Forest Data Base Management System. Also the use and results of research programs involving the use of GIS in Forestry will be presented. Chapter 3 - Spatial decision making problems are multi-facetted challenges. Not only they often involve numerous technical requirements, but may also contain economical, social, environmental and political dimensions that may have conflicting values. Solutions for these problems involve highly complex spatial data analysis processes and frequently require advanced means to address physical suitability conditions while considering the multiple socio-economic variables. Geographic information systems (GIS), Multicriteria Decision Making techniques (MCDM), and Expert Systems (ES) are the most common tools employed to solve these problems. However, each suffers from serious shortcomings. The need for combining the strengths of these techniques has prompted researchers to seek integration of GIS, MCDM and ES. A variety of strategies can be used for integrating GIS and these tools. These strategies range from loose coupling techniques to the recent advanced techniques of software interoperability. In this chapter the complexity of the spatial decision making is highlighted. Both traditional and advanced techniques for software systems integration are presented. Chapter 4 - Geographic Information Systems (GIS) provide important tools for developing comprehensive and effective conservation strategies throughout the entire range of a species. Range-wide conservation strategies have typically used GIS to identify and prioritize populations across a species' distribution. We propose the addition of corridors to these range-wide exercises. Corridors facilitate genetic exchange, can ameliorate the negative effects of demographic and environmental stochasticity, and may increase the survival

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probability of species in the face of climate change. The authors present a new range-wide conservation model for the jaguar (Panthera onca) that uses GIS, expert input, and graphbased connectivity metrics to incorporate corridors into an existing range-wide priority setting exercise. The jaguar is an ideal species for this type of modeling because of extensive genetic exchange across its current distribution. Using expert input, they first developed a movement cost surface for the historic range of the jaguar. The authors then used this cost surface with the 90 known jaguar populations to model least-cost corridors. Results indicate that 78% of historic jaguar range, an area of approximately 14.9 million km2, still holds potential for jaguar movement and dispersal. One-hundred eighty-two corridors were identified between populations, ranging from 3 to 1,607 km in length. The authors then identified three types of priority areas for conservation across jaguar range; populations of ecological importance, populations and corridors of network importance, and vulnerable corridors. Based on our criteria, the authors identified 32 populations of ecological importance, 23 populations and 13 corridors of network importance, and 44 corridors that are vulnerable due to their limited width, and high potential for being break points in the network. These results are novel in that they account for dispersal and genetic exchange between populations throughout the full range of a widely distributed large carnivore species. By prioritizing areas for conservation based on ecological and network importance we developed a more comprehensive and meaningful tool for jaguar conservation across their current distribution. The methods and GIS techniques used here can easily be applied to other wide-ranging species. Chapter 5 - Identification of suitable sites for rainwater harvesting is essential for the successful water resource management. Geological site selection requires identification of lithounits and its structure to ensure the selection of sites for artificial recharge. Use of only panchromatic sensor data of IRS-1D satellite with 5.8-meter spatial resolution has the potential to infer lineaments and faults in this hard rock area. It is essential to identify the location of interconnected lineaments below buried pediment plains in the hard rock area for targeting sub-surface water resources. Linear Image Self Scanning sensor data of the same satellite with 23.5-meter resolution when merged with the panchromatic data has produced very good results in delineation of interconnected lineaments over buried pediment plains as vegetation anomaly. These specific locations of vegetation anomaly were detected as dark red patches in various hard rock areas of Delhi. Field investigation was carried out on these patches by resistivity and magnetic survey in parts of Jawaharlal Nehru University (JNU), Indira Gandhi national Open University, Research and Referral Hospital and Humayuns Tumb areas. Drilling was carried out in eight locations of JNU that proved to be the most potential site with ground water discharge ranging from 20,000 to 30,000 liters per hour with 2 to 4 meters draw down. Further the impact of urbanization on groundwater recharging in the terrain was studied by generating Normalized difference Vegetation Index (NDVI) map which was possible to generate by using the LISS-III sensor of IRS-1D satellite. Selection of suitable sensors has definitely a cutting edge on natural resource exploration and management including groundwater. Chapter 6 - Ongoing research to develop a new generation of decision-making tools for estimating the environmental effects on residential property value has significantly increased the demand for land surface data, information on the state of living environment, and the corresponding need for the advanced use of geographic information systems (GIS). In order to provide the foundation for price estimates, all the existing data focused on building and environment are integrated in the framework of a GIS spatial database. Traditional methods

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mostly emphasize the relationship between the effects of accessibility to central locations, and ignore location-specific attributes of housing. However, little has been done on high-rise, densely populated residential areas. Thus, the paper aims to investigate the neighboring and environmental characteristics of the selected site. A more advanced approach based on spatial analysis and modeling in the GIS environment is used to manage spatio-temporal data, to process aerial images and satellite images, to import measurements from GPS, to create digital terrain models, to analyze topography together with environmental data, and to visualize the results. The partial results based on the traveling time to the closest services and the origin-destination cost matrixes are derived from the network analysis. The landscape characteristics can be demonstrated on animated sequences showing the flight over a landscape model that is based on the digital terrain model with draped images over it to show the area. The living environment contains a few compartments: air pollution, water pollution, waste management, noise assessment, and monitoring of environmental impacts on population health. Air pollution is estimated by continuous map surfaces, predicting the values of pollutants concentration for the selected site in dependence on the sample points at the air quality monitoring stations. Water pollution covers surface water pollution, drinking water supply and quality, waste water, accidental contaminant spills, and optionally, flood control measures. The waste compartment is focused on the system of municipal waste management with sorting of reusable components of municipal waste, and hazardous chemicals information. Noise assessment covers road traffic noise and air traffic noise. The environmental impacts on population health come out from reports of the national institute of public health. In addition to the main compartments, other data can complement the partial inputs to the system (energy prices, public transport schema, locations of neighbor natural reservation sites and historical sites). In the framework of spatio-temporal analysis, the spatial weighting matrixes are used for prediction of the final rating. The final results are represented by the thematic map layers in the GIS project based on ArcGIS and its extensions. The attached case study shows a scenario in dependence on setting the weight parameters. Unexpected findings are caused by rapid changes in the dense living environment and slow conversion of the reality market in the selected site in Prague, the Czech Republic. For all that, the case study brings better understanding of how the residential site rating depends on various environmental attributes. Chapter 7 - The numerical models are based on dispersion modeling and statistical analysis. In case of dispersion modeling, the ISC-AEROMOD View is used for modeling multiple pollutants with the U.S. EPA modeling tool ISCST3. The Mobile View assists as an interface for the U.S. EPA MOBILE6 model that predict arterial street emissions focused on hydrocarbons, carbon monoxide, nitrogen oxides, carbon dioxide, particulate matter, and toxics from cars, motorcycles, light- and heavy-duty trucks under various conditions. The potential impacts of accidental releases are solved by SLAB View that complements the modeling tools by analysis of emissions from accidental releases of toxic gases. Analysis of urban traffic-induced noise pollution is assessed by U.S. FHWA-TNM tools. The GIS is finally used to serve as a common analysis framework for individual modeling tools. In order to display the numerical simulation outputs together with urban area map layers, numerical modeling based on U.S. EPA software tools is integrated into the GIS for spatial interpolations and spatial analysis. It assists to evaluate high levels of air pollution and noise pollution together with the thematic map layers of residential zones, business centers, schools, and hospitals. Finally, finding alternative routes can decreases air pollution and noise

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pollution in selected zones. As a case study, the city of Prague sample data set helps to demonstrate data processing and modeling of traffic-related environmental pollution. The ESRI’s geodatabase is used for implementation a comprehensive information model and a transaction model in the GIS environment. It is also the common application logic used in ArcGIS for accessing and working with all spatial thematic data and simulation inputs/outputs. Spatial interpolation for prediction maps and probability maps complement the existing thematic map layers, which enable cell based modeling for spatial multi criteria decision analysis. The synthesis of environmental models and GISs creates a more complex base for environmental simulation that can support decision-making processes in a more straightforward way.

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Chapter 1

GIS AND SPATIAL DECISION SUPPORT Xuan Zhu* School of Geography and Environmental Science, Monash University, Clayton Campus, Clayton, VIC 3800, Australia

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ABSTRACT This chapter reviews the use of GIS for spatial decision support, focusing on the design, development and implementation of spatial decision support systems (SDSS) using GIS technology. As the awareness of spatial information in decision making improves significantly, GIS is increasingly becoming an important component of decision support systems. While GIS provides the computational basis for spatial decision support and offers the basic decision aids including spatial data management, spatial analysis and visualization, SDSS is designed to utilize domain specific models and knowledge to solve specific decision problems that have a geographic or spatial component. This chapter discusses the nature of spatial decision making and the role of GIS in the spatial decision making process, reviews the recent advances in the development of SDSS through the integration of GIS with models, decision analysis and knowledge management techniques, advanced visualization and the Internet, introduces examples of successful SDSS systems and examines the conceptual, technical, and organizational issues that must be addressed in the successful implementation of SDSS.

INTRODUCTION Spatial decision making is a knowledge-intensive activity that leads to a choice about a course of action, a selection of a strategy for action, or a choice fulfilling a certain objective, which is about the geographic or spatial distribution of resources, facilities or human *

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activities across territory at local or regional scales. Selecting a suitable site for nuclear waste disposal, choosing the best location among several alternative sites for dam building, deciding on a strategy for land resource allocation for sustainable development, and locating a new service outlet in relation to potential customers to maximize its chance for success are examples of spatial decision making. The contribution of GIS in spatial decision making has been far-reaching and still evolving. It is evidenced by the large volume of publications on the use of GIS across a wide range of application areas, including natural resource management, environmental planning, business and service planning (Longley et al. 2005; Wilson and Fotheringham 2007). The value of GIS for spatial decision making increases as the scale and complexity of the decision problem increase. Challenges currently faced by society, such as global warming, resource shortages, and loss of biodiversity, require integrating a wide range of data and information, addressing problems from multiple perspectives and evaluating alternative solutions in a holistic, comprehensive, systematic, analytic, and visual manner. GIS provides digital tools for organizing and integrating data, recognizing spatial patterns, and visualizing spatial patterns and processes that enable decision makers to address these challenges and make effective decisions. It allows various types of data relating to the spatial decision problems and their geographical contexts to be considered together, thus enables the analysis of the problems to have a greater depth and wider scope. With sophisticated and powerful geoprocessing tools, GIS can be used to create new information from various types and sources of data. This information can be displayed with the unique cartographic tools in GIS in a manner that communicates in a clear and compelling fashion not only to decision makers but also to the public. In general, GIS plays a dual role in spatial decision making: to provide geoprocessing tools to process and manipulate spatial data and to integrate disparate spatial and non-spatial data sets. Improving decision making is one of the most compelling reasons to develop a GIS. However, GIS is generally developed as a toolbox. The complex operations offered by GIS, which make the technology so powerful, also make it difficult for decision makers to use without training (Keenan 2008). The use of GIS as a decision support tool has also been hindered by its lack of analytical modeling capabilities and support for multiple decision making strategies (Densham 1991; Openshaw 1991). The limitations with GIS promoted the development of spatial decision support systems (SDSS) to provide specifically defined and intuitive operations on specific decision support needs. The practice of SDSS started in the mid 1980s (Armstrong et al. 1986; Dobson 1986; Densham and Rushton 1987; Densham and Goodchild 1989). A research initiative on SDSS was launched in 1990 by the National Center for Geographic Information and Analysis (Goodchild and Densham 1993). Since then, SDSS has become one of several major expansions in GIS. SDSS, as a subject of research and practice, continues to grow along ever-widening horizons, blending with other expansions in GIS such as spatial analysis and modeling, other forms of information technology, decision science and models drawn from other disciplines. SDSS systems are distinguished from GIS by such capabilities as satisfying specific knowledge needs, performing knowledge discovery, direct accessibility by decision makers and user-specific customization of functionality and interfaces. Spatial decision support is inherently multi-disciplinary and involves integration of a range of data, information, knowledge, skills and methods. This chapter provides a review of SDSS research and practice. It starts with a discussion on the nature of spatial decision problems and the requirements for an SDSS. These

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requirements lead to an examination of various types of SDSS. Each type of SDSS is characterized by its general architecture and development methods or techniques, and illustrated by examples. The key conceptual, technical and organizational issues involved in the successful development and implementation of SDSS are then examined. The chapter concludes by identifying the future directions of SDSS research and development.

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NATURE OF SPATIAL DECISION PROBLEMS Spatial decision making is mainly concerned with achieving integrated, productive and sustainable use of resources at diverse geographic and temporal scales. As our environment becomes increasingly complex in the increasing interconnected world of the 21st century, the need for integrated approaches to management and planning has become widely appreciated. As a consequence, spatial decision making has become a more complex process. Decision makers are expected to be confronted with problems that may have no single ‘right’ solutions, and with problem situations that may be difficult to define, may significantly change in response to some solution, may have no (or few) precedents, and may have many stakeholders (Malczewski 1996; Andrienko et al. 2007). Spatial decision making is to make choices among many decision alternatives whose outcomes are location-based for a complex situation in a complex environment. Location allocation is a typical example of spatial decision making. For instance, decisions regarding bank networks are concerned with where and how many branches should be opened, closed, or re-located; and what services each branch should provide. These decisions require careful considerations of the relationships between the type of services to be offered and spatial and temporal conditions in which the branch is situated, including socio-demographic characteristics of the local area, accessibility to transportation network, distributions of competitors, future growth of the local area, to name but a few. Many other spatial decision making tasks require planning of various actions across the geographic space. For example, coastal zone management decisions may involve identifying which areas are subject to high coastal erosion or flooding risks and which actions should be taken in those areas to protect people and property from coastal hazards during emergencies caused by major coastal storms; ground water management decisions involve making choices of how much water can be taken from each well in different time periods, when and where to restrict the water withdrawals; forest management decisions need to determine where and when timber harvesting or thinning should take place, what will happen before/during/after harvest, and where and when new trees should be planted. All such decision making processes require the examination of many possible spatial distributions of actions or scenarios, analysis, integration and interpretation of complex, heterogeneous spatial and temporal information, such as information about infrastructure, demography, environment, land use and climate. Spatial decision processes are also often collaborative, involving various stakeholders. For example, regional resource management decisions need to address the frequently conflicting demands of agricultural intensification, growing industrial development, increasing tourist activities, and environmental conservation. The decision making process has to be an equitable negotiation and bargaining process that involves mediation of a complex range of perspective involved in regional resource

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management issues from different resource users, managers and planners, who are regional stakeholders. Therefore, spatial decision problems are generally characterized by • • • • •

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• •

a complex geographic context that is space-time related; a large number of decision alternatives that are space-related; multiple evaluation criteria and multiple objectives that may vary over space and time; a large amount of technical information and tacit knowledge related to space and time; multiple stakeholders with various perceptions, interests, preferences and values in relation to the decision criteria and decision making outcomes; many interrelated causative forces; and no single right answer.

As spatial decision problems are complex, it is difficult to anticipate the result of the decisions, and the decision making processes must become as effective as possible. A range of computational methods, implemented with GIS spatial analysis operations, have been applied to deal with the complexity of spatial decision problems and improve the effectiveness of spatial decision making processes. For example, domain specific simulation models and optimization models have been used for generation of possible scenarios (Arentze et al. 2010; Eldrandaly 2010). Multicriteria decision analysis (MCA) tools have been applied to integrate technical information and human value judgments to evaluate the scenarios and to prioritize the decision alternatives (Malczewski 2006; Chakhar and Mousseau 2008). Expert systems or knowledge-based systems are used to capture, represent and store knowledge, and make inferences using the knowledge to derive or evaluate decision alternatives (Witlox 2005; Sikder 2009). The development of SDSS represents an effort to integrate GIS and other computational methods to address complex spatial decision problems and facilitate spatial decision making processes.

SPATIAL DECISION SUPPORT SYSTEMS The concept of SDSS is an extension of decision support systems (DSS) in the information systems discipline, allowing spatial data handling, spatial analysis, spatial modeling, and spatial reasoning. In terms of the roles they play in spatial decision making processes, SDSS systems provide knowledge (including data, models and heuristic knowledge) and/or knowledge management and processing capabilities that are instrumental in making space-related decisions or making sense of space-related decision situations. Like DSS (Power 2002), SDSS aims to enhance the processes and/or outcomes of decision making. An SDSS can be designed to support the examination of spatial decision situations, facilitate the exploration of information and knowledge, help synthesize methods for reaching decisions, develop and evaluate scenarios, provide and synthesize multiple perspectives on issues, or to support the production of semi-structured decisions (to which the issues pertinent are not completely well understood) or structured decisions (for which relevant issues are well understood). The use of an SDSS may greatly enhance the effectiveness and intelligence

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of decision making, improve efficiency, offer greater insight and higher accuracy, provide better and quicker responding ability, and facilitate consensus building. From the technological perspective, an SDSS is an interactive computer-based system with domain-specific knowledge and user-centered interfaces designed to support decision makers in identifying and solving spatial decision problems, making trade-offs and reaching space-related decisions. The early concept of SDSS emphasized on analytical and statistical modeling with spatial data management, analysis and spatial visualization capabilities for solving semi-structured spatial decision problems (Densham 1991). Over the last two decades, SDSS has expanded to incorporate other decision analysis and computer modeling techniques to provide different knowledge management and processing capabilities and to support making structured, semi-structured and unstructured space-related decisions. As shown in Figure 1, an SDSS is an integration of GIS and computer-aided modeling and decision analysis techniques. Simulation is an approach to imitating the behavior of an actual or anticipated human or physical system. Simulation models are often used to predict the likely outcomes of proposed actions. Examples of simulation models include soil erosion models, hydrological models, crop models, spatial interaction models for modeling spatial behavior of travelers in transport planning (Cheung and Black 2005), and cellular automata models for modeling urban growth for investigating scenarios of future urban land use change (He et al. 2007). An optimization model is constructed to maximize or minimize some aspect of the model’s output, for example, maximizing productivity of a land use pattern. Artificial intelligence is a multi-disciplinary field, focused on providing solutions to real life problems, which includes agent-based modeling, expert systems (or knowledge-based systems), neural networks and natural language (Russell and Norvig 2009). Decision analysis techniques include multi-criteria decision analysis methods (such as the analytical hierarchy process and multiattribute utility theory), models for decision-making under conditions of uncertainty, techniques of risk analysis and risk assessment, and techniques for facilitating decisionmaking by groups (Peterson 2009).

Figure 1. SDSS, GIS and other modeling techniques.

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for heuristic knowledge) and employ different methods for processing knowledge. Most SDSS systems have focused on one or two modeling techniques, which results in different types of SDSS. Although SDSS has evolved along with DSS and numerous SDSS systems have been developed for a great variety of application domains, there is not much research work done on the SDSS architectural framework. By extending the basic architectures for DSS introduced by Sprague and Carlson (1982) and Holsapple (2008) and the architecture for knowledge-based SDSS developed by Zhu (1997), this paper proposes a generic architecture for SDSS, which is described below. An SDSS generally consists of four components: a language system (LS), a presentation system (PS), a problem processing system (PPS) and a knowledge system (KS). The LS manages the interactions between the user and the system. It accepts and interprets user requests or problem statements, provides help, and may guide the user through the problem solving process. The PS presents data, information or knowledge retrieved or derived from the system in response to the user requests. Basically, the LS manages the user inputs and the PS deals with system outputs which are presented largely in spatial forms including 2D or 3D maps and images. Both LS and PS systems interact directly with the user through visual (often map-based) interfaces. The visual interfaces make the system intuitively obvious to use without need for indepth commands to operate, yet allowing users to negotiate sophisticated spatial analysis and modeling. The KS is composed of all knowledge contained in the SDSS system. Here, knowledge encompasses spatial and non-spatial data, models and heuristic knowledge. Data are descriptive knowledge that describes the state of some world of interest, mainly represented and organized in map data layers. Models embody knowledge encoded in algorithms that simulate or characterize the behavior or processes of particular types of human or physical system, or provide solutions to specific classes of decision problems, or combine many stages of transformation and manipulation in order to accomplish some problem-solving tasks. Heuristic knowledge is knowledge or judgment derived from experience, which cannot be programmed or expressed in algorithms. The knowledge in the KS is specific to the problem domain for which the SDSS is designed. The PPS is the software engine of an SDSS. It has four key capabilities: knowledge acquisition, problem formulation, knowledge selection/integration/generation, and knowledge assimilation. Knowledge acquisition, carried out through the LS, is to acquire knowledge about what the user wants to do, obtain the facts about a problem or problem situation, and to capture problem parameters and their relationships from user’s problem statements. When a user’s request is for solving a particular problem, the PPS selects the relevant knowledge from the KS as a solution, or integrates different pieces of knowledge stored in the KS to form a solution, or generates new knowledge to derive a solution. Some SDSS systems have a problem formulation function. This function is to structure the problem based on the user’s specifications of the problem parameters and their relationships captured through knowledge acquisition, and formulate a problem model which is used to drive the problem solving process. This problem model is then used by the PPS to select relevant knowledge from the KS through its knowledge selection and knowledge integration functions, and to implement its knowledge generation function to derive solutions. The PPS uses the problem model to determine exactly what knowledge is required and precisely how the knowledge is selected, integrated or generated. The retrieved or derived knowledge is packaged by the knowledge assimilation function, and then presented to the PS for visualization. Knowledge assimilation

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also involves the addition of the newly generated knowledge to the KS. Figure 2. shows the relationships between the four components. Based on the nature of the decision problem to be solved, the type of knowledge embodied in the system and knowledge management and processing techniques used, SDSS can be classified into six types: data-based SDSS, model-based SDSS, knowledge-based SDSS, multi-criteria SDSS, integrated SDSS and collaborative SDSS.

Figure 2. A generic architecture of SDSS.

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DATA-BASED SDSS

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Data-based SDSS mainly derives their functionality from providing users with capabilities to query and then generate reports or maps from a spatial database or data warehouse (Figure 3). They emphasize access to and manipulation of large databases of structured spatial data, especially time-series of internal organizational data and sometimes external data. A simple map data base accessed by query and retrieval tools provides the most elementary level of functionality.

Figure 3. Data-based SDSS.

For instance, a forest inventory system is a data-based SDSS, which provides access to accurate spatial and temporal data about forest lands in a particular region, the ability to generate tables and maps of forest statistics, and the functionality to produce any of the standard or customized reports for a specific area of interest and survey year. The USDA Forest Inventory Data Online (FIDO) is such an example (Nelson et al. 2007). An automated mapping and facilities management (AM/FM) system is another example of data-based SDSS (Meehan 2007). This type of system is designed for use by utility companies to record and keep track of utility network data, such as power lines, gas lines, water and sewerage pipelines, telecommunication networks, valves, meters, and other facilities, and to produce maps of facilities and use them for making management decisions. As technology has evolved, data-based SDSS has become more sophisticated, with the latest being real-time spatial data warehousing and on-line analytical processing (OLAP) (McGuire et al. 2008; Da Silva et al. 2010). Spatial data warehouses allow the manipulation of spatial data by specific tools tailored to specific tasks, while SDSS systems with OLAP

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provide decision support that is linked to analysis of large collections of time series spatial data. Data-based SDSS systems are mostly built using GIS software. They have the following basic features: •

• •

• •

•

data retrieval – helping or guiding users to systematically search for and retrieve spatial data, often using predefined queries, custom filters or query wizards (such as those used in FIDO); data integration – link to a variety of data sources, such as maps, aerial photos, spaceborne remote sensing imagery and field surveys; data summarization – allowing users to set summary attributes (e.g. area of land, tree volume and tree mortality from a forest inventory) and calculate summary statistics for the selected attributes; map layout design and map production – providing map layout templates or allowing users to define map layouts interactively for mapping the retrieved data; report design and generation – providing standard report layout templates or allowing users to define report layouts for presenting the retrieved data in a formal report with tables and charts; metadata management – providing descriptive information about the properties of the data stored in the system, and allowing users to retrieve, edit and add metadata.

Data-based SDSS systems mainly support querying of a large spatial database and production of maps and periodic summary reports.

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MODEL-BASED SDSS A model-based SDSS provides a model-based set of procedures for representing and processing knowledge. It integrates analytical models together with spatial data management, spatial analysis and visualization techniques within the GIS environment. The models may be algebraic or differential equations, optimization, simulation or statistical models designed to address particular types of spatial decision problems. For example, one model-based SDSS may be developed for forecasting, planning, and optimizing land uses, and another has models to simulate climate change and assess the potential impacts of climate change on agriculture. An analytical model is generally represented as a set of mathematical formulae used to simulate a spatial process (such as surface runoff) or explain the relationships among relevant variables of a particular problem. It is encoded in a procedure, a computer program or an algorithm consisting of instructions that a computer can execute in order to solve the problem. The procedure is also called a solver. A model may be solved by integrating several related solvers. For example, an SDSS for land use optimization needs to have an optimization model and a solver that can operate on that model for any selected data set in order to determine the optimum land use pattern for one or more specific objectives for the study area. The selection and collection of available models and associated solvers are centered on some area of problems. We differentiate two types of solvers here. One is model solvers, which model a certain type of process or the relationships among a number of decision variables; and the other is GIS solvers, which are basic GIS spatial analysis tools.

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A model-based SDSS is designed to leverage models and spatial data to help decision makers in manipulating decision variables, exploring decision problems and investigate scenarios without being concerned with the complicated process of constructing, representing, integrating, implementing, evaluating and documenting models. They put emphasis on spatial information access and display and on numeric computation by analytical models. A modelbased SDSS assists decision makers throughout the modeling process by providing model or solver management functions which are concerned with the storage and use of a collection of solvers that generate model solutions. Models can be incorporated into an SDSS in two different ways. First, solvers that run the models can be written as part of the PPS using GIS or modeling programming languages, or built-in GIS spatial analysis and statistical tools. Second, they may be designed as a number of solvers stored in the KS. Upon interpreting a user’s request, the PSS selects solvers from the PSS itself or from the KS, parameterizes the selected solvers, integrates the solvers, executes them, and then chooses a certain type of presentation to output the modeling results. Figure 4. shows a typical architecture of a modelbased SDSS.

Figure 4. Model-based SDSS.

The PPS is often built within a GIS environment coupled with appropriate modeling tools. Generally speaking, three approaches have been used to integrate GIS environment and Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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modeling tools: loose coupling, tight coupling and full integration (Zhu and Healey 1992; Liu and Zhu 2004). With the loose coupling approach, models are written into an independent modeling program. The program and a GIS package are then integrated via data exchange using either ASCII or binary data format without a common interface. Tight coupling embeds certain modeling programs within a GIS package via either GIS macro or conventional programming. This approach can achieve a higher level of integration, and allow the modeling programs to directly access the GIS data through a well-defined interface to the data structures held by the GIS. The full integration approach builds on top of a GIS software package and takes full advantage of built-in GIS functionalities. With this approach, the models can interoperate with the GIS functions and access GIS data without any need to know about the particular data structures in the GIS. LAGA (Landuse Allocation with Genetic Algorithms) is an example of model-based SDSS, which was designed by the author for rural land use allocation. Land use allocation seeks to allocate a certain amount of feasible land for particular types of land use. It needs to determine not only how much but also where particular land uses will take place. The goals of land use allocation are to optimize spatial and temporal patterns of land use and obtain best possible land use strategies, given multiple and often conflicting socio-economic and environmental objectives. LAGA is an integration of ArcView and a genetic algorithm (GA) built with the full integration approach for optimization of rural land use allocation. Currently, the system can be used to optimize land use allocation in terms of economic returns measured in net present values (NPV) over a user-specified period of time at a certain discount rate. The LS, PS, PPS and KS of the system were all built within ArcView software environment. ArcView provides the PPS, which contains the GA solver and related GIS solvers. The solver encoding the objective function for calculating NPV was written as part of the KS. GA is thought of as a model for machine learning in which a population of randomly created individuals goes through a process of evolution simulating the mechanics of natural selection and natural genetics (Reeves and Rowe 2002). In every generation, a new set of artificial creatures is created using bits and pieces of the fittest of the old. An artificial creature is an individual representing a point in the problem’s solution search space. GA effectively exploits historical information to speculate on new search points in a search space with expected improved performance. If we consider that all possible land use patterns in a region constitutes a search space, an individual land use pattern would be a search point. Land use allocation problem becomes a problem of searching for best possible land use patterns in a search space consisting of all possible land use patterns. Therefore, land use allocation can be seen as a search and optimization problem, which could be solved effectively and efficiently using GA. GA uses random choice as a tool to guide a highly exploitive search toward regions of the search space with likely improvement (Reeves and Rowe 2002). A single point in the solution search space is an individual, represented by a chromosome, which consists of genes. Genes are essentially the parameters of the problem to be solved. They can take many forms depending on the problem definition. The fitness of a chromosome is determined by a fitness or an objective function defined prior to the execution of the GA. A fitness function is some measure of profit, utility or goodness to be maximized. However, the fitness of a chromosome tells you nothing about its strength relative to other chromosomes; rather it is a raw evaluation of the chromosome’s fitness. It is at a higher level that fitness is compared and selection

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proceeds according to the rules of a GA. This higher level is the population. A population is a collection of all the chromosomes being evolved in a GA. As new chromosomes are created and reinserted into the population, less fit chromosomes are replaced and only the fittest survive into the next generation. It is here that the process of evolution occurs, as the fitness of the competing chromosomes is compared in order to select parent chromosomes to reproduce. Depending on the search space for a given problem, the population size can range from a few dozen chromosomes to several hundred, several thousand, or more. To use a GA, the first step is to define the problem, which includes defining genes to encode the information needed for problem solving, defining chromosomes to represent single solutions, and defining a fitness function. After the problem is defined, the GA is set into motion. An initial population is randomly created as the first generation. All the chromosomes in the generation are then evaluated using the fitness function. After that, chromosomes are selected from the population according to their fitness function values to ensure that only the fittest chromosomes can survive into the next generation. The selected chromosomes are then combined in a process called crossover to create a set of children. The children are randomly mutated to create a new set of chromosomes to be reinserted into the population. Once enough children chromosomes have been created to replace a population, a generation is said to have passed. For the new generation, the evaluation, selection, crossover, mutation and insertion process starts again. After a number of generations have elapsed, the GA would converge on an optimal solution and stop. The best chromosome is selected from the final population and represents the optimal solution to the problem. Essentially what’s happening is that a random set of solutions to a problem within a given search space is created and evolved over an amount of time to find an optimal solution. Figure 5. shows this process. The GA solver in LAGA implements the process and supports all GA operations, including initialization of the population, evaluation, selection, crossover, mutation and insertion. More information about the GA operations can be found in Reeves and Rowe (2002).

Figure 5. The process of genetic algorithms.

The GA representation of a land use allocation problem in LAGA is shown in Figure 6. Here, the problem parameters for encoding a land use allocation problem include a set of land units, the land use options to be evaluated, the physical suitability of each land unit for those options, and the objectives to be optimized. A gene represents a land unit (e.g. a land block, a unique mapping area or UMA) with the attributes including its location, an assigned land use, the land suitability for all land use options to be examined, and other land attributes required

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for fitness evaluation. A chromosome is an indexed list of all land units with assigned land uses forming a particular land use pattern in a region. LAGA uses the calculated NPV values to determine which land use patterns (chromosomes) are the best among a number of land use patterns (a population).

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Figure 6. A GA representation of a land use allocation problem.

Figure 7. LAGA user interfaces.

Once LAGA is loaded in ArcView, the solvers and the other tools are accessed through pull-down menus, which are introduced in graphical user interfaces (Figure 7). The basic map inputs required for LAGA include digital land suitability maps for all types of land use to be allocated. The system was used for land use allocation in Noosa Shire, Queensland, Australia.

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In that study, three types of land use, orchard, annuals and pasture, were allocated in order to maximize the NPV of agricultural products in the shire over 12 years with an annual discount rate of 4%. Figure 8. shows the two land use allocation scenarios with the optimal NPV values generated by LAGA. As an optimization technique, the GA does not produce a single optimal solution, but several best possible solutions. The two land use scenarios in Figure 8. have different patterns. However, they have similar economic returns.

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Figure 8. Two optimal land use patterns generated by LAGA.

A similar system for rural land use planning that uses GA was reported in Mathews et al. (1999). Modeling techniques incorporated in SDSS systems, by their varying nature, are very diverse. Examples include spatial interaction models (Liu and Zhu 2004), mathematical programming (Diamond and Wright 1988; Ligmann-Zielinska et al. 2008), statistical modeling (Li et al. 2004), Monte Carlo or probabilistic simulation (Marinoni 2005), cellular automata simulation (Jantz et al. 2010), agent-based simulation (Chen et al. 2010; Arentze et al. 2010), and process-based simulation (Di Luzio et al. 2004; Klosterman 2008). The choice of modeling techniques for an SDSS largely depends on the nature of the type of spatial decision problem the system is intended to address, the effectiveness of the techniques in the problem solving, and the availability of the technology.

KNOWLEDGE-BASED SDSS Knowledge-based SDSS systems enable the incorporation of specialized knowledge and expertise into the spatial decision making process, and add the capability of heuristic reasoning to GIS. They provide the required knowledge and expertise for solving some aspects of the management problem and/or provide knowledge that can guide users in problem formulation, model selection and integration, and that can enhance the operation of the other SDSS components. A typical knowledge-based SDSS system is built by integrating GIS with expert system or knowledge-based system technology (Figure 9). Expert systems emulate the decision making ability of human experts and perform decision-making tasks by reasoning using domain knowledge. An expert system typically contains an inference engine and a knowledge base (Giarratano and Riley 2005). The

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knowledge base is composed of the knowledge required to solve specific problems. It exposes knowledge about some domain explicitly via symbolic data structures. In a rule-based system, the knowledge base contains knowledge needed to solve problems coded in the form of IFTHEN rules. The rules are activated by facts to produce new facts or conclusions. The following is a simple example.

Figure 9. Knowledge-based SDSS.

Rule: crop-yield IF Precipitation is light AND Soil is sandy loam AND Climate is hot THEN Crop yield is good The inference engine is a set of procedures which operate upon the knowledge base. Users supply facts or other related information to the expert system and receive ‘expert’ advice or expertise in response. The same knowledge encoded in the knowledge base can be simultaneously used for more than one purpose, such as solving a given problem, explaining the solutions produced by the system and offering advice about the problem. Therefore, an expert system has two main functions: drawing conclusions and explaining reasoning.

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In a knowledge-based SDSS, there may be three types of knowledge: descriptive, procedural and domain knowledge in the KS. Descriptive knowledge includes data (spatial and non-spatial) that provide facts. Procedure knowledge includes the knowledge about the structures of the typical problems the system is designed to address, the knowledge describing the procedures of problem solving that is used to guide the problem solving processes, and the knowledge about the GIS operations (GIS solvers) and their effective uses. Domain knowledge is the knowledge within a given domain for problem solving, which cannot be expressed in algorithms like analytical models. Domain knowledge may be either expertise, or knowledge which is generally available from books, journals and experts. The PPS of a knowledge-based SDSS is usually built with an inference engine of an expert system. An example of knowledge-based SDSS is Islay Land Use Decision Support System (ILUDSS) developed to support planners and land managers in strategic land use planning for the island of Islay, off the west coast of Scotland (Zhu et al. 1996). ILUDSS was built by integrating HARDY (a diagramming tool), CLIPS (an expert system development tool) and Arc/Info. It consists of 6 components (Figure 10). The query processing subsystem contains both the LS and PS components. It accepts users’ queries, displays results of land use modeling, and provides explanations and on-line help. This subsystem allows the user to access data from the database and information derived during the modeling process, and to present them in maps or tables. The modeling subsystem is part of the PPS. It is designed to assist the user in structuring a land use problem and building a problem model. The subsystem supports user-assisted and automatic problem formulation. For user-assisted problem formulation, the user may use diagrams to structure land use problems and build problem models, and then submit the problem models to the problem processor for evaluation. It also allows the user to modify the problem models stored in the KS. Automatic problem formulation is designed for tasks of assessing land use potential for agriculture, afforestation and peat-cutting. It uses the knowledge in the domain and meta-data knowledge base modules in the KS to automatically formulate problem models according to the user’s land use interests and his/her preferences and assessments relating to the various criteria and related evaluation factors, such as physical suitability, proximity to desirable and undesirable land features, and the required minimum area of each land unit. The process of automatic problem formulation is driven by computer algorithms. After a problem model is formulated, the problem processor of the PPS designs a solution process, executes the process automatically by integrating the database, knowledge base, GIS tools and utility programs, and derives and presents the modeling results. The execution of the process is characterized by a dialogue with the user. Through the dialogue, the user is made aware of the progress of the system’s operation and how any given inference is made during the operation. The knowledge base contains five modules. The domain knowledge module contains land use models expressed in rules. The meta-data module is composed of information about the data in the data base. The model and utility program module consists of knowledge concerns the effective use of the land use models, GIS tools and utility programs. The process knowledge module includes the knowledge which supports problem formulation, provides user guides in the problem solving process, including directing the dialogue between the user and the system, and providing help messages in the course of consultation. The knowledge base, data base, GIS tools and utility programs constitute the KS. Figure 11. shows the user interfaces of ILUDSS.

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Figure 10. Architecture of ILUDSS (modified from Zhu et al. 1996).

Figure 11. User interfaces of ILUDSS.

A knowledge-based SDSS like ILUDSS can assist users in formulating a problem, designing a solution process, executing the solution process, generating a solution and evaluating the solution, as part of a broader spatial decision making process. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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MULTI-CRITERIA SDSS Space-related decisions don’t rely on analytical models alone (Zhu and Dale 2001). They are often influenced by hard facts, supported by scientific evidence, and dependent on value judgments. In many spatial decision making processes, it is necessary to integrate and incorporate the values of the decision makers, public opinion, government policies and management goals with technical information to examine the overall implications of each alternate plan (Keeny 1981). Multi-criteria decision analysis (MCA) techniques can be used to facilitate such integration. In general, multi-criteria SDSS systems focus on the integration of GIS capabilities and MCA techniques. They aim to support spatial decision making which may involve tangible and intangible social, political and technical factors, several parties, many objectives, criteria and alternatives, and may require bargaining and negotiation. MCA refers to a set of procedures designed to help decision makers investigate a number of choice possibilities in terms of multiple criteria and generate rankings of choice alternatives (Malczewski 1999). It involves the use of data and decision maker’s preferences and the manipulation of the data and preferences according to certain decision rules. MCA has three key components: a number of alternatives or options, a set of criteria by which the alternatives or options are to be judged, and a method for ranking the alternatives or options according to how well they satisfy the criteria. A multi-criteria SDSS supports the structuring of a spatial decision problem in terms of objectives, criteria and choice alternatives, elicitation of value judgments regarding the relative importance of the criteria and relative preferences of the alternatives and synthesis of the value judgments via map-based analysis to derive scores for ranking the alternatives. Figure 12. shows an architecture of multi-criteria SDSS. The PPS could be built within a GIS software environment or in an MCA software tool integrated with some GIS spatial analysis and mapping functions. An example of multi-criteria SDSS is JavaAHP (Zhu and Dale 2001). It is based on an MCA framework called the Analytical Hierarchy Process or AHP (Saaty 1980). Decision analysis using AHP involves three steps. The first step is to decompose a decision problem into a decision hierarchy, in which the overall goal of problem solving is placed at the highest level, the objectives, respective criteria and sub-criteria used to choose among alternatives are located at lower levels, and the alternatives to be evaluated are at the lowest level. Figure 13. is an example of a decision hierarchy for dam site selection. The decision goal is to select the best site for dam construction. There are three potential sites (alternatives), whose suitability is to be assessed from environmental, economic and social perspectives. There are three criteria under the environmental and economic objectives respectively, and four criteria under the social objective. The second step is to determine the relative importance of the criteria or the relative preference of the alternatives with respect to each criterion through pairwise comparison. The AHP provides a fundamental scale of absolute numbers (1 – 9) to express relative importance. (Saaty 1980). The pairwise comparison process may start from the top of a hierarchy and is repeated for all the elements in each level, which results in a set of pairwise comparison matrices. All the judgments are then synthesized in the third step through mathematical estimation to obtain overall priorities or scores for the alternatives of action. They are ratio scale

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numbers derived by calculating eigenvalues and eigenvectors of pairwise comparison matrices (Saaty 1980). JavaAHP implements the three-step analytical process with built-in mapping and basic map analysis functions. It allows users to graphically construct a hierarchy for a decision problem, guides users to make judgments through pairwise comparison, and mathematically estimates the overall priorities for the alternatives of action based on those judgments. It also provides an indicator of consistency of judgments made during the pairwise comparison process, called consistency ratio, a sensitivity analysis tool for examination of how the overall priorities of the alternatives change in respect to the changing priorities of the criteria, and a report on the major aspects of a decision problem (Zhu and Dale 2001). In addition, JavaAHP can perform map-based multi-criteria decision analysis by combing the AHP techniques and the mapping functions. It is achieved through linking interactive maps to an AHP hierarchy and the criterion elements in the hierarchy. The results can also be presented on the alternative map. JavaAHP is a standalone system without being linked to a commercial GIS system, written in Java. Figure 14. shows its user interfaces.

Figure 12. Multi-criteria SDSS.

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Figure 13. A decision hierarchy for dam site selection.

Figure 14. JavaAHP.

Other MCA techniques have also been used in multi-criteria SDSS. For example, Carver and Openshaw (1992) used the simple additive weighting method to develop an SDSS for locating nuclear waste disposal sites. Chakhar and Mousseau (2008) combined GIS (ArcGIS) with the outranking methods to build a multi-criteria SDSS and demonstrated its use for a corridor identification problem. Jankowski and Ewart (1996) developed a multi-criteria SDSS

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for supporting health practitioners in the selection of practices by integrated GIS (Arc/Info) with TOPSIS (an Ideal Point method).

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INTEGRATED SDSS An integrated SDSS integrates several different types of computer-aided modeling and decision analysis techniques with GIS. It can be seen as the combination of two or more single-technique SDSS systems discussed above. Such integration is to improve the capabilities of an application in which each technique performs tasks that the technique is best at, and the techniques complement each other. An integrated SDSS provides capabilities for processing, managing, manipulating and presenting different types of knowledge. VegMan (Zhu et al. 2001) is an example, which is used here to illustrate some features of an integrated SDSS. VegMan was designed as a Web-based decision support system for regional vegetation management in the Central Highlands Region, Queensland, Australia. The system integrates spatial data, management information and decision support tools relevant to regional vegetation management within the Web environment. It was developed as one of the mechanisms for building the capacity of stakeholder groups involved in resource use and management in the region. VegMan combines spatial data management, rule base management and MCA decision analysis with a unified Web interface to provide access to five groups of resources: facts, policies, regional strategy, support tools and external links (Zhu et al. 2001). The Facts group provides information on natural resources in the region, including land systems, soils, vegetation and other biophysical data. All the data are managed and presented as Internet maps. The Policies group provides updated government policies and legislation relevant to vegetation management. These are managed and presented as text documents. The two groups of resources are managed and delivered by a data-based SDSS built with WebMap and ArcView. WebMap is an Internet mapping system developed by the author for map generation, map browsing, spatial query and classification operations that transform the attribute data associated with a single map data layer. It can read and manipulate ESRI shapefiles. ArcView is used to manage the spatial database. The Regional Strategy group provides the information about the current situation and conservation status of every vegetation community in the region, the desired outcomes, proposed guidelines and actions for their management on a Shire basis. The information is provided through a knowledge-based system developed using an expert system tool, JESS. The regional strategy is structured into a set of rules. These rules, along with the basic features of each vegetation community, are stored in the rule base. Users access the regional vegetation strategy by specifying the Shire of interest and the type of vegetation community. Upon receiving a user’s request, JESS looks up applicable rules in the rule base, and extracts the information about the conservation status of the requested vegetation community, the desired outcomes and proposed guidelines and actions for its management. It then interacts with WebMap to analyze the current situation of the vegetation community in the whole region and in the requested shire based on spatial data. All the information extracted from the rule base and the spatial data is assembled by JESS into an HTML document. At the same

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time, WebMap dynamically produces a map image depicting the current distribution of the requested vegetation community in the specified Shire. The map output generated by WebMap, along with the HTML document generated by JESS, is transmitted through the Web server back to the client, and interpreted by and displayed in the Web browser. Indeed, the data-based SDSS and the rule-based system are integrated together into a single tool that allows any capability to be used independently of another, or together with another within a single operation. The Support Tools group provides analytical tools which can be used for environmental tradeoffs, cost-benefit analysis and evaluation of management options in regional vegetation management. JavaAHP, a multi-criteria SDSS discussed above, is embedded in this group to support regional stakeholders in combining best available information and their value judgments to evaluate and prioritize management options in regional vegetation management planning. Therefore, VegMan is an integration of a data-based SDSS, a knowledge or rulebased SDSS, a multi-criteria SDSS and other decision related resources delivered over the World Wide Web (Figure 15). The need for integrated SDSS systems was recognized in the late 1980s. They have been the subject of much research (Loh and Rykiel 1992; Zhu and Healey 1992; Zhu et al. 1998; Zhu et al. 2001; Rutledge et al. 2007). The integration of GIS with other computer-based modeling and decision analysis techniques may avoid some of the limitations and difficulties existing in each of them, and take advantage of their strengths.

Figure 15. Software architecture of VegMan (modified from Zhu et al. 2001).

There are several direct benefits which can be derived from an integrated SDSS for spatial decision making. First, an integrated SDSS provides for storage, integration, interpretation, and delivery of different types of knowledge. For example, it may provide for the use of both qualitative information (including value judgments and rule-based or heuristic knowledge) and quantitative information (from mathematical modeling) in decision making and problem solving. Therefore, an integrated SDSS is a tool for more effective and more efficient use of data, information and knowledge. Second, an integrated SDSS is built within an integrated framework of GIS, knowledge-based systems, decision analysis or analytical

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modeling, therefore can provide complementary information and knowledge to support all stages of the decision making process. For example, using an integrated SDSS, we can determine the suitability for forest treatment through reasoning, evaluate alternatives using analytical models and prioritize the alternatives using MCA. Third, an integrated SDSS may store the knowledge for formulating problem models for particular problems according to decision makers’ preferences over their solutions, and build mechanisms for evaluating the problem models and generating solutions to spatial decision problems by automatically integrating GIS, knowledge-based systems and mathematical modeling tools. Therefore, the system is easy to use, and adaptable to the users’ needs.

COLLABORATIVE SDSS

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Many spatial decision problems are collaborative in nature. A collaborative SDSS is an interactive SDSS designed for facilitating the solution of spatial decision problems by decision makers working together as a group. It is also called group SDSS. A collaborative SDSS can be a data-based SDSS, a model-based SDSS, a knowledge-based SDSS, a multicriteria SDSS, or an Integrated SDSS, with additional tools in the PPS for brainstorming, organizing ideas, gathering information, ranking and setting priorities, voting and other aspects of collaborative work. A collaborative SDSS may provide (Carver et al. 1996; NCGIA 1996; Jankowski and Nyerges 2001): • • • • • • • •

Improved decision making process Improved decision outcomes of groups Increased group participation Collaborative generation of ideas More ideas and more satisfaction Negotiated resolution of conflicts Freedom of expression Preservation and sharing of a group memory

An example of collaborative SDSS is WePWEP, designed for collaborative planning and public participation in the strategic planning of wind farm sites (Simao et al. 2009). The system is a Web-based multi-criteria SDSS, coupled with an argumentation component. This component is a map-centered communication tool, which supports communication and discussion via maps regarding wind farm siting among users and provides feedback to the users. It integrates a map and a discussion forum, allowing users to post questions, suggestions, neutral comments and pro or contra arguments. Users can manipulate maps, select particular wind farm sites, generate their own graphical objects and associate comments or arguments with them, and explore other users’ contributions from the map or the discussion forum. The system can also produce the maps of ‘social classification of feasible sites’ and ‘controversy associated with social classification’. The former shows each feasible wind farm site in its most frequently assigned category; the latter presents the degree of controversy associated with the social classification of each site. In this way, a user can

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compare his or her position on wind farm siting with those of others and understand their differences and reasons behind other people’s judgments. WePWEP was applied for wind farm planning in Norfolk, England. In this case study, 117 sites were identified as feasible locations for wind farms. The system is structured in three tiers: background information, multi-criteria evaluation, and map-centered communication (Simao et al. 2009). They are accessible through a single user interface via the Internet. The background information tier consists of a number of web pages which provide introductory information about the system, and the background information about wind energy, wind farm siting and the planning process. The second tier is the multi-criteria SDSS, which allows users to examine the 117 wind farm sites, assess the performance of each site with regard to 19 decision criteria through weighting, and derive their recommendations (recommended, acceptable and non-acceptable sites). The multi-criteria evaluation process is guided through a sequence of eight web pages. The third tier is used by users to submit and review their questions, suggestions and arguments by using the map-centered communication tool. WePWEP has most of the features of collaborative SDSS listed above. Collaborative SDSS has developed and matured since the mid-1990s (NCGIA 1996; Jankowski et al. 1997; Jankowski 2000; Rinner 2001; Dragicevic and Balram 2004; Dunn 2007; Rinner et al. 2008; Jankowski 2009). In addition to WePWEP, there are many other examples of their successful applications. For instance, Spatial Group Choice, a collaborative SDSS developed by Jankowski et al. (1997), provides tools and a framework that support group decision making in terms of spatial-temporal dimensionality and was successfully applied for prioritizing habitat site development. CommunityViz is a commercial collaborative SDSS designed to support land use planning, which adopts a scenario planning approach and provides interactive 3D models and other tools for public participation and collaboration (Kwartler and Bernard 2001). ParticipatoryGIS is a Web-based collaborative SDSS which supports multicriteria analysis and consensus building (Boroushaki and Malczewski 2009).

SDSS AND ADVANCED VISUALIZATION Spatial visualization in the form of 2D or 3D maps is one of the distinctive features of SDSS. Maps have been widely used to provide the geographical or spatial context for decision making and help decision makers perceive spatial relationships and identify patterns and clusters. It has been recognized that in order to provide more effective decision support, an SDSS should be equipped with techniques and tools of advanced spatial visualization that may help understand the decision problem situation, articulate the decision goal, derive information from the multitude of data, generate plausible scenarios, visualize the impacts of the scenarios and prioritize them for the choice of the right course of action (Andrienko et al. 2007). These techniques or tools can be grouped into two categories: visualization – focusing on visual representation for mapping scientific data and information in graphical forms; and interactive visual interfaces – providing user-interface interactions for effective navigation over displays on a screen. Visualization in SDSS often combines maps with other graphic representations, which is built within an interactive and dynamic environment. Traditionally, spatial visualization has a

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more or less fixed set of options available for mapping of spatial data. Today advanced computer technology provides many ways for generating new forms of visualization of spatial data, such as animation, hyperlinking, immersive environments and multimodal interfaces (Kraak 2009). For example, CommunityViz, an ArcGIS-based SDSS, provides real-time semi-realistic 3D visualization capability (Kwartler and Bernard 2001). This system, combined with a landscape immersion facility, allows decision makers or stakeholders to interactively view and explore the planning area and the represented scenarios in a shared, immersive setting, which has been used for community participatory planning (Salter et al. 2009). User-interface interactions allow users to manipulate the visual objects as well as to select what will be or not be displayed. The interactive functionality of visual interfaces for spatial decision support ranges from map navigation and modifiable symbolization of priority score maps to multiple linked map, graphic and tabular views of the spatial distributions and characteristics of decision alternatives, and highlighting of selected data or objects across the views (Rinner 2007). For example, Rinner (2007) employed multiple choropleth maps to show the multi-criteria assessment outcomes (rankings) of quality of life (QoL) in the City of Toronto derived from different QoL models. Churcher et al. (1997) used fisheye views to assist with visual information overload management in a real-time group SDSS. The fisheye view technique is used to simultaneously provide detailed information in the area of interest and reduced detail in the neighboring areas. In other words, it emphasizes relevant regions of the display, and de-emphasizes less relevant areas without loss of context. This is achieved by transformations which distort the distances between features while preserving connectivity and topological relationships. Zhu and Chen (2005) developed 3D interfaces with interactive animation which allow users to choose their own view angles to observe visual objects, and to retrieve spatial knowledge from geo-referenced textual documents by selecting the visual objects. Recent advances in computer hardware and software allow quick user-interface interaction. Various combinations of visualization techniques and user-interface interaction have been employed (Kwan and Lee 2005; Zhu and Chen 2005). Regardless of the underlying technology, visualization has played a critical role for the success of an SDSS (Andrienko et al. 2007).

IMPLEMENTATION OF SDSS Since the late 1980s, SDSS has been used in decision making processes in a wide range of applications. As with any other computer-based information system, implementation of SDSS is not always a success story. There are many SDSS systems that are never or hardly used (Uran and Janssen 2003). Walker and Zhu (2000) investigated the major factors leading to non-delivery or non-adoption of SDSS. These include conceptual, design, technological and organizational issues. Spatial decision problems are rarely addressed on a procedural and technical basis alone; rather they are embedded in social systems. Many SDSS systems were developed to provide technical information, knowledge and data. Their development was largely based on the assumption that getting ‘all’ the information is an ultimate goal of SDSS and the spatial

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processes (including social, biological and physical) can be completely understood and rationally managed. They failed to recognize that the spatial decision making process is often inherently a political process in which different needs and interests are balanced (Walker 2002). Therefore, information and knowledge is not all that is required. In order to facilitate the use of SDSS in practice and make them to have a significant and persistent impact on practice, SDSS systems need to be responsive to the context of spatial decision making. One of the biggest challenges in the design of SDSS is its relevance to the potential users’ needs and decision making processes. To maximize the likelihood of successful implementation, an SDSS should be easy to use and conceptually accessible to the users by taking into account the capacity for the users to deal with the conceptual structure of the analyses. This conceptual accessibility may provides some advantages, including integrity of deign, easy user training, managerial transparency, and ease of control (Walker and Zhu 2000). Of course, the conceptual accessibility must be tempered with completeness of critical aspects. An SDSS that addresses too small a part of the task of management is unlikely to attain operational acceptance. Situations in which an SDSS reacts too slowly, crashes, is inflexible to respond to evolving needs, or requires technical resources or skills in its use which are not available have been known to create user dissatisfaction (Uran and Janssen 2003; Jarupathirun and Zahedi 2007). Other technical issues that tend to discourage users include the imbalance between hardware and software capabilities, poor graphics, complex manipulation, and inability to deal quickly with changing situations. Some of these issues are mainly due to the limitations of available technology, and others are caused by scarcity of resources. An SDSS project will certainly fail if users are not confident in the reliability of the system and its outputs. While the purpose of an SDSS is to make available information and knowledge that is beyond that of the users, the acceptance of the system relies on confidence in and agreement with the system (Hochman et al. 1994). Highly participatory approaches to system development are necessary to instill user confidence. The successful implementation of SDSS may well depend on organizational culture, management support, user commitment and institutionalization. If the organizational culture is hostile to innovation and managers feel threatened by modern techniques of analysis, introducing SDSS will be difficult. Institutionalization is a process through which an SDSS becomes incorporated as an ongoing part of organizational activities. It may take place when the system is used by successors to the original users, or the system is diffused by other users, or it causes changes in the structure and processes of the organization, or more applications of the system are added throughout the organization. Institutionalization clearly indicates successful implementation. As pointed out by Walker and Zhu (2000), the successful design and implementation of an SDSS requires the system to be compatible with the goals and processes of decision making, the needs and capacity of the users, the available information, the best available technologies and skills, and the organizational and institutional context in which decisions are made.

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CONCLUSION Spatial decision making is a complex process, requiring the integration of a wide range of data and information. Most of the required data and information are spatial in nature. SDSS provides a framework for this integration and offers capabilities for handling both spatial and non-spatial data from various sources with spatial data management, analysis and visualization capabilities offered by the GIS technology. It also provides a valuable mechanism for organizing information in a way that allows managers or decision makers to analyze management strategies in their own decision making styles, and to impose their own values and perceptions on the management process. In addition, spatial decision making tends to involve spatial diversity, temporal variability and large areas. While some problem solving tasks involve predictions from ecological, economic or other models, many others require taking a heuristic approach. Spatial problem solving often involves a large number of repetitive processes to evaluate alternative outcomes. SDSS provides a platform for integrating a variety of knowledge management and processing techniques and for interactive and recursive problem solving. Similar to DSS in business management, an SDSS is designed for a specific problem area, and incorporates domain-specific models or knowledge to guide decision makers through decision making processes. Technically, SDSS is an integration of GIS and other knowledge management and processing techniques. Most of the SDSS systems were built by integrating GIS software systems and other modeling software packages. They were deployed on personal computers as stand-alone systems, or on the Internet via the WWW. Web or the Internet technologies have become the leading edge for building SDSS and will continue to be an important development arena for SDSS. As shown in some SDSS examples presented in this paper, SDSS has already been influenced by the WWW. The Internet provides widespread connectivity between computers; this in turn has facilitated the deployment of SDSS. With the WWW, SDSS can now be delivered to decision makers or users using a web browser like Internet Explorer or Netscape Navigator. The dynamics of the Internet makes it possible for decision makers to go online at any time and allows both wired and wireless access to spatial decision support tools, resources and services. There is no doubt that continued advances in computer hardware and software technology will provide tremendous improvement in many aspects relating to the design, development and implementation of SDSS. However, spatial decision problems and decision making processes remain complex. GIS will continue to be a backbone of SDSS as spatial data and spatial data processing are essential to spatial decision making. However, environmental and social systems and their processes are dynamic and change over time. Many kinds of spatial decision problems require developing and assessing various action plans where actions refer not only to different positions, areas or paths in geographic space, but also to different moments or periods in time (Andrienko et al. 2007). There is a need to introduce the temporal dimension into SDSS to address these types of spatial decision problems. This requires developing methods and tools for analyzing spatio-temporal data and incorporating them into SDSS. Most spatial decision problems involve uncertainties. For example, in rural land use decision making, the factors, such as prices at the time of harvest, government policies, technological change, and weather conditions, all affect land use productivity and income.

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However, these factors are rarely known precisely before they occur. Uncertainties may also arise from limited and inaccurate data, measurement errors, imperfect models, subjective judgments, etc. Introducing the probabilistic description and evaluation of a spatial decision problem into the SDSS framework would enhance the problem solving capabilities of an SDSS system. The use of scenarios as an analytical tool to deal with uncertainties of the future caused by unpredictability of major external forces, such as socio-economic developments or climate change, is common (Pereira et al. 2003). The model supported scenario analysis may prove to be a valuable tool to raise awareness, and to develop adaptation and coping strategies. Therefore, SDSS is no longer viewed as a means to legitimate decisions, but rather to initiate and inform debate, dialogue and deliberation (Schlüter and Rüger 2007). However, the mechanism for probabilistic reasoning with uncertain elements in a spatial context needs to be developed. In addition, for many real problems, most or all probabilities will need to be obtained from expert judgment. Techniques for eliciting and expressing one’s knowledge in terms of probabilities need to be investigated. The primary failure of SDSS was not as much technological as it was social (Walker and Zhu 2000). Space-related decisions are often societal decisions. There is an increasing emphasis on the involvement of users or stakeholders in the design, development and implementation of SDSS. The purpose is not only to make the most of limited information and knowledge about the complex natural and social systems, but also to make the humancomputer communication transparent (Matthies et al. 2007). Considerable interest has been focused on using specialized, well-focused expert systems embedded in SDSS to help users with selecting and parameterizing models and to match technological development options as reported in Zhu et al. (1998). As spatial decision making is a complex socio-technical process, developing SDSS of operational relevance to managers and planners is a demanding process. SDSS can only provide some information useful for decision making. They play a supporting role with an aim to improve the decision making process and improve the quality of decision making. It would never replace decision makers.

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Simao, A. Densham, P.J., and Haklay, M. (2009) Web-based GIS for collaborative planning and public participation: an application to the strategic planning of wind farm sites. Journal of Environmental Management, Vol.90, pp.2027-2040. Sprague, Jr. R.H. and Carlson, E.D. (1982). Building Effective Decision Support Systems. Englewood Cliffs, N.J.: Prentice Hall. Uran, O. and Janssen, R. (2003). Why are spatial decision support systems not used? Some experience from the Netherlands. Computers, Environment and Urban Systems, Vol. 27, pp.511-526. Walker, D. (2002). Decision support, learning and rural resource management. Agricultural Systems, Vol.73, No.1, pp.113-127. Walker, D and Zhu, X. (2000). Decision support for rural resource management. In I. Guijt, J.A. Berdegué, M. Loevinsohn and F. Hall (Eds.), Proceedings of the specialist workshop “Deepening the basis of Rural Resource management” (pp. 23-35). The Hague: ISNAR and RIMISP. Wilson, J. and Fotheringham, A.S. (Eds.) (2007). The Handbook of Geographic Information Science. Blackwell: Wiley. Witlox, F. (2005). Expert systems in land-use planning: an overview. Expert Systems with Applications, Vol.29, pp.437-445. Zhu, B. and Chen, H. (2005). Using 3D interfaces to facilitate the spatial knowledge retrieval: a geo-referenced knowledge repository system. Decision Support Systems, Vol. 40, No. 2, pp.167-182. Zhu, X. (1997). An integrated environment for developing knowledge-based spatial decision support systems. Transactions in GIS, Vol. 1, No. 4, pp.285-300. Zhu, X. and Dale, A.P. (2001). JavaAHP: A Web-based decision analysis tool for natural resource and environmental management. Environmental Modelling and Software, Vol.16, No.3, pp.251-262. Zhu, X. and Healey, R. (1992). Towards intelligent spatial decision support: integrating geographical information systems and expert systems. In Proceedings of GIS/LIS'92, Vol.2 (pp.877-886). San Jose, California. Zhu, X., Aspinall, R.J. and Healey, R.G. (1996). ILUDSS: A knowledge-based spatial decision support system for strategic land use planning. Computers and Electronics in Agriculture, Vol.15, No.4, pp.279-301. Zhu, X., Healey, R.G. and Aspinall, R.J. (1998). A knowledge-based systems approach to the design of spatial decision support systems for environmental management. Environmental Management, Vol. 22, No. 1, pp. 35-48. Zhu, X., McCosker, J., Dale, A. and Bischof, R. (2001). Web-based decision support for regional vegetation management. Computers, Environment and Urban Systems, Vol.25, No.6, pp.605-627.

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Copyright © 2011. Nova Science Publishers, Incorporated. All rights reserved. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

In: Geographic Information Systems Editor: Christopher J. Dawsen

ISBN: 978-1-61209-925-5 © 2011 Nova Science Publishers, Inc.

Chapter 2

DATA BASE MANAGEMENT SYSTEM, G.I.S. AND FORESTRY Meliadis Ioannis1 and Miltiadis Meliadis2 1

Associate Research Scientist. NAGREF-Forest Research Institute, Thessaloniki, Greece 2 M.Sc. student in Forestry. Geographer, Aristotle University of Thessaloniki, Thessaloniki, Greece

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ABSTRACT Planning and developing comprehensive information systems is one of the most difficult and fascinating problems resource managers face today. The rapid implementation of Geographic Information Systems (GIS) and their never ending need for accurate and current spatial data promotes the development of new approaches for automated and fast data acquisition. This technology is very promising for people dealing with a huge amount of data, such as foresters. Forest ecosystem produces many goods and services without the intervention of human management. But since humans began intensively managed forests, the relationship between manufactured goods and production changed. The current pressure on forests and forest areas have a direct impact on the system of human society and will increase further in the coming years. It may even be considered that forests which are being created today (artificial or natural) have maturity for logging after 60-70 years. By then, the Earth's population will have increased and cause probable changes that will also effect the weather. The task of Forestry is to collect and maintain accurate and detailed data information in order to address current needs and to provide future situations. Forest management has traditionally been considered as the management of trees for timber. Actually forest management really includes multiple management objectives such as vegetation, land, and people management. GIS has proved to be a suitable tool for managing complex systems, where a great number of variables whose main feature involve spatial location. Knowing the extent of forests, different types and composition of the various categories of use / land cover affect all activities of Forestry Science. The accuracy of measurements and calculations affect successful decision making by managers at local and national levels.

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Meliadis Ioannis and Miltiadis Meliadis Our knowledge for the ecological environment has been increased, but also the methods and the techniques of management changed and they continue to change. The foresters in order to correspond to modern requirements have to improve their place as scientists and as professionals. The foresters continuously process data and information. The development of a Forest Data Base Management System based on the technology of GIS must aim for the reduction of time-consuming duties that are related to the volume of data that they have to process.These advantages would give the opportunity to impose new methods for the processing of data. It is remarkable that the Foresters in various Services tend to automate the existing system for the treatment of data management, and are more willing for the reorganization of current informative systems with the help of new technologies. In this paper the use of GIS in the field of forestry will be discussed and the development of automated Forest Data Base Management System. Also the use and results of research programs involving the use of GIS in Forestry will be presented.

Keywords: GIS, Database Management System, Management Information Systems, Forest management and planning, Data Base

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INTRODUCTION Geographical Information Systems (GIS) is information technology which can be used in decision-making on environmental and forestry designs. Although this technology exists from the 60’s, the application in the management of forests started during the previous two decades. The GIS and the relevant technologies are powerful tools for maintenance records, analysis and mainly decision-making. Its implementation by the Forest Services provide important information on natural resources and facilitate the planning and management in several areas as with the classification of natural resources into lists allowing for continuous updates, work planning harvest and assessment of produced entry, management of the forest ecosystem and planning of habitats (Baral, 2004). Today, with improved access to computers and current technology it becomes increasingly popular in the management of natural resources. Therefore, many forest managers have become interested with computerized data processing. The usefulness of a management information system is already well known and has increasingly become part of forest management activities. The main reasons for the growing trend toward the use of GIS by the Forest Services and foresters are as follows (Baral, 2004):

1. 2. 3. 4. 5.

Less cost for the hardware and software of computers Technological progress in the material and computer software Most friendly software for the user Availability of trained staff in GIS applications Saving time and money, despite the fact that the original implementation costs may be higher 6. Confidence in the upgrading of technology

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7. Global understanding of the environment 8. Easier update the forest information even though the “wood” is constantly changing

What is GIS A GIS is an organized system of mechanical parts and software appropriate for the collection, storage, information, processing, analysis, and presentation of all types of geographical information. GIS allows the users to view, understand process, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, tables, reports, and charts. The geographical information can be distinguished into two categories as cartographic (=spatial) and descriptive (=attribute). The cartographic information is stored in the geographical data base and the main features recorded are coordinates. The cartographic information referred: •

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•

To information that relates to the position and shape of a phenomenon in land (geometry of space), making it possible for the identification of the phenomenon in the territory, simultaneously providing its shape and scope. To information that describes the amendment of the space; i.e., relations (connectivity, continuity, and proximity) developed between the elements of the environment (space).

The descriptive information concerns the characteristics, quality or quantitatively, of the phenomena occurring in the environment. This information is stored in data bases that are connected with the previous bases of the cartographic data. Geographic object can be shown by four types of representation points, vectors (lines), polygons (areas), and continuous surfaces. Point Data: Points are the simplest type of spatial data. They are zero-dimensional objects with only a position in space but no length. Vector Data: Lines are one-dimensional spatial objects having a position in space and a length. Polygon Data: Polygons are two-dimensional spatial objects with a position in space and a length and also a width. Continuous Surfaces: Continuous surfaces are three-dimensional spatial objects with not only a position in space, it also provides length and width, and a depth or height (defining a volume). A GIS can be regarded as consisting of the following parts (Kalivas, 2001): •

Collection and encoding of the data. The data stored in a GIS are from various sources (e.g., analogue maps, aerial photographs, satellite images, tables, reports, etc.), referring in different places and in different times and can be found in analogue or digital form.

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Meliadis Ioannis and Miltiadis Meliadis •

•

•

Storage and retrieval of data. The control, generally of multi-temporal data in GIS is made with the use of a data base management system, (DBMS), which allows rapid identification by the user for analysis, accurate upgrading and correction. Handling and processing of data. Some of the treatments are: changes in data structure (change the scale, shift under any angle, change the coordinate systems, etc.), topographical analysis of data (neighboring characteristics, couplings, wrong data, etc. ), statistical and modeling analysis, measurements of lines and arcs, recovery territorial and non-data, etc. Presentation of data. The results work on maps or and tables in a variety of instruments, such as paper, slides, monitors, magnetic media, using devices such as printers, laser, inkjet, plotters.

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The spatial information used in data entry for GIS is usually collected at sampling points during measurements in the field. Since the main purpose of the GIS application is for the management of forest production, various working maps require consecutive spatial data (spatial continuous data) for the managed natural resources; and not only as data (point data). Therefore, data should also be assessed by the values of variables (tree volume, tree number, etc.) around the points of each sampling (Li and Heap, 2008). The increase of GIS is explained by its unique ability to assimilate data from widely divergent sources, to analyze any apparent trends over time, and to spatially evaluate impacts caused by development. For an experienced analyst, GIS can be an extension for analytical thinking. The system has no in-built solutions for any spatial problems; it depends upon the analyst. The importance of the different GIS’s factors is listed in decreasing order. • • • • • • • • • •

Spatial Analysis Database Software Hardware A GIS comprises at least five components: Input Analysis Estimation and prediction Decision support Presentation and visualization

The Application of GIS in Forestry There is an increasing recognition world-wide that longer-term sustainability in environmental management often requires consideration of socio-economic issues as well, including the development of methods to assess different options and facilitate stakeholder participation in decision-making processes (Hemmati et al., 2002; Gibson et al., 2005). This, in turn, reinforces the need for multi-disciplinary or interdisciplinary perspectives (Riordan et al., 2000). The literature review reveals a wide range of activities concerning the application

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of GIS in the different sectors of forestry (Baral, 2004; Mckendry et al., 1995). The main topics that GIS can be used in order to help the decision making process to work more scientifically are the following:

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• • • • • • • • • • •

Conservation of biodiversity Planning of wood harvesting Management and rehabilitation of forest ecosystems Inventory of natural resources (which includes their registry and evaluation) Evaluation of the forest health (which includes the control of insect attack) Management of forest fires and natural disasters Planning of road network Management and conservation of wildlife Conservation of soil, runoff and streams Conservation of wetlands Management of forest recreation and ecotourism

Forest management has traditionally been considered management of trees for timber. It really includes vegetation, land, and people management as multiple objectives. As such, forest management is intimately linked with other topics in this volume, most especially those chapters on ecological modeling and human dimensions. The key to responsible forest management is to understand both how forest ecosystems work and how to use this memorandum of understanding to meet community aspirations and prospects. This means to portray forest modeling accurately by the dynamics of forest ecosystems. The complexity of forest management does not stop with the intricate details of the biological system found in a forest. All forest management, established by scientists can also encounter aims or objectives desired by people. Forestry is at its core a social activity. Thus, it demands that we understand the relationships among land owners, professional forest managers, forest-dependent communities and other stakeholders; if we are to model the results of decisions regarding forest management. Individuals and communities have broad interests in the physical, biological, and social goods and services that forest ecosystems provide. Forested properties frequently have poor quality maps and map databases. This is especially critical if market values for forest properties are to be established. Few forest properties have good geo-referenced control, much less good surveys, with many surveys dating back to the last century. The need for better, more accurate field measurements of location coordinates is being driven by rapidly escalating high values of land, and even higher values of the timber resource. GPS offers an opportunity to alleviate many of the problems generated by the poor map base. Analysis and modeling techniques are at the heart of many GIS applications. Successful forest-management modeling finds a means to improve management through accurate representation of all parts of the system. Forest managers should provide effects of implementing various alternatives. Although a large body of scientific knowledge exists on relations between forest structure and pattern, and ecosystem attributes; this information is frequently difficult to interpret and apply. GIS should provide a comprehensive set of information to the decision maker, support the implementation of more timely decisions, and improve the quality of decisions (Meliadis,

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Meliadis Ioannis and Miltiadis Meliadis

2005). As many decisions incorporate a spatial domain, GIS have become a widely used component to facilitate handling of spatial data. The GIS is a management tool and it is essential that the system interface to the user; it must be easy to learn, use, and understand. The current trend in applying computer graphical interfaces improves the convenience by simultaneously requiring minimum training. A GIS is often based on sophisticated technology and is implemented by experts with sophisticated technological skills. However, the user generally has only a minor interest in technical solutions, but requires a system that is efficient, easy to use, flexible, and reliable. In case GIS is used in forestry for decision making the following general requirements should be satisfied: − −

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− − −

the ability to handle multilayered, heterogeneous data bases of spatially-indexed data, the ability to query the data bases about the location and properties of a range of spatial objects, the efficiency in managing such queries in an interactive mode, the ability not only to retrieve but also to create new information, the flexibility in configuring the system in order to accommodate a variety of specific applications.

An historical review about the use of GIS and remote sensing in forestry shows that mapping arose in the 1980s and early 1990s. When linked to the perceived potential of using the Landsat satellite technology combined with GIS, issues were raised by this new combination (Davis et al., 1991; Tappan et al., 1991; Rodriguez-Bachiller and Glasson, 2004). This is partly linked to the development of newer technologies like the Global Positioning System (GPS) (Havens et al., 1997; McWilliam, 1999), and the application of satellite data becomes almost routine, as for example Phinn et al. (1996), who used this type of data to map the biomass distribution in Southern New Mexico; Lammert and Allan (1999) use GIS to relate land-cover and habitat structure to the ecology of fresh water, Geist and Dauble (1998) study in a similar way salmon habitats in large rivers, McMahon and Harned (1998) study the Albemarle-Pamlico drainage basin in North Carolina and Virginia (USA), and Sarch and Birkett (2000) apply it to detecting lake-level fluctuations to manage fishing and farming practices in Lake Chad. Cruickshank et al. (2000) use the CORINE database to estimate the carbon content of vegetation in Ireland, and Akcakaya (1996) integrates fieldwork and GIS to the management of multiple species and, on a different note, Bowker (2000) discusses the problems involved in using GIS to map ecological diversity. Landscape mapping and monitoring is also typical: Higgs et al. (1994) develop a “demonstrator” system of common lands in England and Wales, Isachenko and Reznikov (1994) map the landscapes of the Ladoga region in Russia, and Taylor (1994) does it for the Niagara region in the US; Clayson (1996) monitors landscape change in the Lake District (UK) using remote sensing, Kirkman (1996) also combines GIS and remote sensing to monitor seagrass meadows, and Macfarlane (1998) applies a “landscape-ecology” perspective to the Lake District in the UK. Environmental planning of heritage sites is reported by Wagner (1995) using GIS for a case study in Cambodia. The monitoring and management of forestry – a particularly important component of the landscape – also shows a number of applications: Tortosa and Beach (1993) use “desk-top” portable GIS with GPS to map forest fire hot-spots and lightning strikes on the ground; Dusart et al. (1994) combine GIS with remote sensing in a river valley in Senegal,

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Thuresson et al., (1996) use GIS to visualize landscape changes in the Gulkal forest (Sweden), Jang et al., (1996) use a similar approach to assess global forest changes over time, and Johnson et al., (1999) use the same approach for mapping freshwater wetlands and forests in Australia; Bateman and Lovett (2000) use GIS to estimate the carbon content of forests in Wales.

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Forest Models Environmental applications have long been the core use of GIS. Many of the earliest applications were primarily concerned with matters of inventory and measurement, but from the mid-1980s, a much greater emphasis on statistical analysis and modeling was apparent. In the past decade, innovations such as the use of internet and wireless communications to support Distributed GIS and Location-Based Services (LBS) (Peng and Tsou, 2003; Jiang and Yao, 2006), the availability of higher-resolution GPS and satellite data. Also, by the improvements in computer processing power and interoperability, and the wider adoption of object-orientated software concepts have opened up new opportunities for the dissemination, analysis, and display of spatial data (Maguire et al., 2005). Models used to assist in forest management consist of several types. First and most prevalent are growth and yield models, which predict the development of stands of trees through time. Initially, these models focused on single species, single age stands, such as one would find in plantations. Little wonder, because plantations were the highest investments for forest managers who primarily sought timber value from the forest. Sophistication in the procedure for modeling increased and thus resulted in the development of more accurate models. Multiple species and multiple age and size classes in an individual stand have been included in growth models with varying success. Further developments along a different track have seen modeling techniques used to assist schedule activities, such as harvesting or thinning forests. Linear programming and other techniques that allow a modeler to specify constraints on resources have allowed models to find solutions for sharing problems; again, primarily used for those interested in extracting timber value from forests. As many public and private groups have started to recognize the importance of forests for values for other than timber production and have now focused on a broader range research topics. Further modeling efforts have begun to characterize these natural resources. Wildlife habitat, recreation opportunities, and watershed protection are just a few of the benefits that must be modeled and presented to decision-makers for their evaluation of alternative forest management strategies. These efforts face many challenges due to the inherent complexities of the systems; unfortunately, the attempts to model these systems, most of the time the lack of good quantitative data for many of the factors involved. In addition, the most complex and fuzziest of the factors is that of the role of humans in the forest. Models able to predict human needs and desires and their consequences on the state of forest ecosystems is a great need that remains largely unmet. To have and use both models incorporate the best available knowledge about the biological system and present the results in an interpretable and useful way is a major challenge. Some such integrated models have been developed, and more are in various stages of design. Further, the inclusion of stakeholder knowledge and goals, and the integration of social sciences, theories, and measurement techniques are difficult for many traditionally

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Meliadis Ioannis and Miltiadis Meliadis

trained resource managers. One big challenge today is to develop and test new theories and tools to describe the multiple effects of management decisions and provide a practical, comprehensive choice with decision making. Developing, evaluating, and adapting new decision processes and their supporting software tools is a critically important endeavor in forest management and elsewhere. Some issues are essential for forest managers to helpin the use of GIS: ƒ ƒ

ƒ ƒ ƒ ƒ

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ƒ ƒ ƒ

The administrative boundaries of the forest and the limits of the clusters. Recording natural resources, the creation and use of this data list are essential tools in many areas of forest management, such as the management of the growing stock and protection of certain forest ecosystems. The spatial data usually required for the management of natural resources Topographic characteristic features such as altitude, slope, report, and the digital terrain model. The existing infrastructure as reference to roads and the runoff network, and buildings. The vegetation type and under story vegetation. The characteristics type and form of trees, the form and the density of the clusters, age and the primary production. The form for the management of forests as e.g., dilutions, clear cuttings, etc. The damage by insects, diseases, fires, wind throws Control of growing stock and recording of the changes that are presented over time.

The introduction of the GIS data can create maps that give us useful information for the management stand and clusters. The continuous data update, allows the recording and analysis of the changes in the distribution of trees in each cluster (Meliadis et al., 2009). The display of data may be made more specific, e.g., a specific approach which will present the positions of trees with diseases or wounds. There is the possibility to create maps that can reflect the spatial distribution of trees to be cut in relation to other characteristics, such as territories with steep slope, positions concentration timber and protected areas (Mckendry and Eastman, 2001). Control of natural resources is the next important step when recording these changes into a database GIS list. However, it is necessary for the control of forest resources to document the changes occurring and monitor the data record. For example, the management of growing stock requires compound forestry interventions for the control of the structure and density of clusters, the composition of the species, rotation time and for maintaining the site quality. All this information must be updated in certain periods of time (e.g., intervals of five or ten years). Other changes may result from sudden events or disorders (massive deforestation) and the application of a new managerial measure in the affected region (Mckendry and Eastman, 2001). Others can be more easily monitored because of their slower rate of change such as overgrazing over time. Appropriateness and productivity of the soil is another factor considered in the evaluation of natural resources that includes determining the appropriate biophysical and climatic factors for the regeneration of trees. This factor is important for the artificial planting of trees, or forestation plans, for the resettlement of the indigenous species and harvest timber. Information obtained from the assessment in area productivity can be used to ensure the management of an optimal harvest (Mckendry and Eastman, 2001).

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Management of growing stock, as well as the collection and control of the changes in the data of natural resources are necessary for the management activities of the forest. The literature review for the use of GIS in forest sectors are explored next. A survey by Singh et al., (2004), for the application of GIS in the management of forests estimated growing stock. The use of GPS field data with the delimitation of pilot surfaces (sample plots), as well as the use of aerial photographs to display forest coverage of these surfaces, calculated the growing stock. Then, with the assistance of the GIS technology, specialized maps/models were prepared for the study area. The results of the investigation showed that the subsequent data analysis, and the spatial GIS analysis in the environment, which also included the wastewater existing maps of forest coverage and growing stock; provided the great impetus for the preparation of a forest management plan. In other research, Singh and Moharir (2003) found that the application of GIS is the most effective method in terms of cost and time, for mapping various types’ forest coverage as for the assessment of growing stock. The results of their investigation indicate that the various thematic backgrounds (layers) generated in the working environment of GIS, help in many aspects of mapping, which usually require some time and effort. Mahto (2001) has investigated the possibilities and constraints of GIS for the assessment of growing stock.Mahto (2001) found that the GIS techniques on the election of sample plots in the determination for growing stock, present certain restrictions, and sometimes these surfaces may coincide with inaccessible areas. On another point, this may indicate that there is the possibility of forest between various categories, according to the type and density. Based on its investigation, the accidental sampling of stratified surfaces resulted in an accurate assessment of growing stock at minimum cost, time and sampling intensity, while the digital analysis of the images and the classification of data, created the maps for forest coverage types in this region’s study. Finally, there was the possibility of creating maps with inclination, able to report altitude, and provide a database which necessary for the inventory and the assessment of growing stock. Lyon and Adkins (1995) link a raster-GIS (ERDAS) to a model for the identification of wetlands, and Mackey et al., (1996) reviews issues raised by habitat modeling with GIS. Church et al., (1996) discusses an ecological optimization model for California; Van Horssen (1996) uses regression analysis with GIS for landscape ecological modeling in the Netherlands. Akcakaya (1996) links GIS with models of ecological risk for endangered species, and Kittel et al., (1996) assess terrestrial ecological vulnerability to climate change. Bian (2000) combines GIS and component modeling to represent wildlife movements. In the related area of water ecology, Pierce et al., (2001) combine modeling and GIS and apply the approach to fisheries in the North-East Atlantic. Various aspects of forestry have also attracted interest: Malanson et al., (1996) try to anticipate forest response to climate change, Acevedo et al., (1996) simulate forest dynamics, Mladenoff et al., (1996) extend the simulation into forest management, and Mayaux et al., (1998) combine GIS and modeling techniques to measure the extension of tropical forests. Almeida (1994) uses a model to classify fire risk areas in Portugal and their ecological relevance, which is also an area of obvious practical importance. In the related area of agriculture, Liao and Tim (1994) link a GIS (Arc-Info) to external modules to predict soil loss, sediment yield and phosphorus loading, Collins et al., (1998) link GIS to the simulation of nitrogen leaching from agriculture, and Quiel (1995) uses satellite data to assess (and model) local conditions and water needs for different soils. Lowell (1991) uses a discriminate analysis to model ecological succession between species, Johnston et al., (1996) use GIS to

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model ecological processes, Ortega-Huerta and Medley (1999) use GIS to construct a map algebra model of the jaguar habitats in Mexico; and Khaemba and Stein (2000) combine GIS with Principal Component and Regression analyses to do spatial and temporal analysis of wildlife in Kenya. Arsenau and Lowell (1992) build a monitoring model for forests, Mackey et al., (1996) model boreal forest ecosystems in the Rinker Lake. Johnston (1993) reviews methods of ecological modeling, arguing that GIS functionality can answer questions about “where,” while remote sensing answers questions of “how much.” Lajeunesse et al., (1995) apply map algebra to the management of a regional park in Montreal, Chang et al., (1995) use GIS for habitat analysis in Alaska; and Duguay and Walker (1996) use GIS to monitor an ecological research site. Chou and Soret (1966) study bird distributions in Navarre (Spain), Skidmore et al., (1996) use GIS to classify kangaroo habitats in Australia, Healey et al., (1996) use satellite data for locust forecasting and monitoring, and Karnohan et al., (1998) apply kernel analysis in a GIS to calculate habitat use. Bernert et al., (1997) use GIS map algebra to help define “eco-regions” in the Western Corn Belt plains of the USA, and Harding and Winterbourn (1997) use a similar approach in the South Island (New Zealand). Smallwood et al., (1998) use map algebra to assess habitat quality for a conservation plan for Yolo County (California), Clarke et al., (1999) model re-vegetation strategies for Western Australia, and Carriquiry et al., (1998) use GIS to devise sampling chemes for environmental policy analysis. From a different angle, Carver et al., (1995) evaluated the usefulness of portable field-based GIS for environmental characterization. Davidson (1991) reviews the various methods and GIS technologies available, and Chou (1992) develops an index for fire rotation in the San Bernardino National Forest (California). Hussin et al., (1994) use remote sensing for land cover change detection, and Taylor et al., (1966) apply GIS to test the health of a eucalyptus forest in New South Wales (Australia). Hunter et al., (1999) assess the prospects of riparian forests in Sacramento (California), Bojorquez-Tapia et al., (1999) use the map algebra facility in GRASS to define suitability maps for different types of forest 90 GIS and expert systems for IA land uses in Mexico, Mertens et al., (2001) predict the impact of logging on forests in Cameroon, Gustafson et al., (2001) assess the impact on terrestrial salamanders of different forest-management approaches, and Bocco et al., (2001) study forest quality in an indigenous community in Mexico. Hogsett et al., (1997) assess ozone risks in forests, Kovacs et al., (2001) combine GIS and Landsat data to study forest disturbances, Cassel-Gintz and Petschel-Held (2000) assess the threat to world forests from nonsustainable developments, and Ochoa-Gaona (2001) uses GIS to study forest fragmentation in Chiapas (Mexico). On a different note, Wing and Johnson (2001) use GIS to quantify forest visibility in McDonald Forest (Oregon). For a more general area of landscape and land cover, Cihlar et al., (1989) combined satellite pictures with other maps and variables to analyze their correspondence in the growth season (by overlay, using Arc Info), Amissah-Arthur et al., (2000) use a similar approach to assess land degradation and farmland dynamics in Nigeria, and Petit and Lambin (2001) combine GIS and multi-source remote sensing information to detect land-cover changes in Zambia. Peccol et al., (1996) use GIS to assess the influence of planning policies on landscape change, and Namken and Stuth (1997) analyze and model (using map algebra) the effects on landscape due to grazing pressures on the land. Mendonca-Santos and Claramunt (2001) use a similar map algebra approach to integrate landscape and local analysis of landcover changes. Gustafson and Crow (1996) use ERDAS to simulate the effects of different

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landscape-management strategies in Hoosier National Forest (Indiana), and Baskent and Yolasigmaz (1999) review the literature concerning forest landscape management. The analysis of visibility areas (one of the most sophisticated GIS functions) has also been put to good use, usually for landscape assessment (not linked to IA): Uchida et al., (1997) analyze the visual potential of woodlands as seen from the city of Yamada (Japan), Sato et al., (1995) use this type of analysis to characterize the landscape views into the natural environment from 76 City Halls in Japan. On a related note, O’Sullivan and Turner (2001) develop a methodology to combine “visibility graphs” with GIS for landscape-visibility analysis.

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DATA BASE MANAGEMENT SYSTEM The GIS has been used in the monitoring and protection of the environment successfully. As they have created support systems decisions to assist the politicians, together with the experts to evaluate the effectiveness of any measures taken to protect the environment. These systems are known as support systems decisions (Fedra and Reitsma, 1990; Guariso and Werthner, 1989). The very critical case of sustainability of the environment is now studied by the use of the implementation of GIS (Despotakis et al., 1992; Nijkamp, 1990; Archibugi and Nijkamp, 1989; Barbier et al., 1990; ven Velden and Kreuwel, 1990). Recent efforts led toward setting environmental sustainability indicators quantified by the GIS (Opschoor and Reijnders, 1991). The technology of GIS has been applied with success for the management of very valuable assets, such as water, one resource that always super-consumed. More specifically, various projects relating to optimum use of water resources have already been completed successfully with the use GIS (Broers et al., 1990; O’ Callaghan and Mark, 1984; Damoiseaux, 1990; Deursen and Kwadijk, 1990). The application of GIS for management of water resources in major insular ecosystems has also demonstrated (Despotakis, 1994a,b). A Database Management System (DBMS) is a set of computer programs that controls the creation, maintenance, and the use of a database. It allows organizations to place control of database development in the hands of database administrators (DBAs) and other specialists. A DBMS is a system software package that helps the use of integrated collection of data records and files known as databases. It allows different user application programs to easily access the same database. DBMSs may use any of a variety of database models, such as the network model or relational model. In large systems, a DBMS allows users and other software to store and retrieve data in a structured way. Instead of having to write computer programs to extract information, user can ask simple questions in a query language. Thus, many DBMS packages provide Fourth-generation programming language (4GLs) and other application development features. It helps to specify the logical organization for a database and access and use the information within a database. It provides facilities for controlling data access, enforcing data integrity, managing concurrency, and restoring the database from backups. A DBMS also provides the ability to logically present database information to users (http://en.wikipedia.org/wiki/ Database management system). The Primary objective of a DBMS is to provide a convenient environment to retrieve and store database information. DBMS has to protect database against unintentional changes that could be caused by users.

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Data Definition D n: It is a methhod of data definition and sttorage. Data in i a database is i stored in the form of o tables. A taable is a Row--and-Column arrangement of information. It stores information n pertaining too similar groupps of data. D Maintenan Data nce: It checkss whether eachh record has fields f containing all informaation about one particu ular item. Thiss is done in order to prevennt duplicate orr null values from f being entered from the primaryy fields. D Data Manipulaation: Only authorized a peoople (Databasse administraator) can inseert, update, delete and sort the “datta base.” Whhenever a userr attempts to modify the database, d a mirror imaage of the origginal database is made available to the user and modiffication are made in th he mirror imaage. The origginal databasee in technical terms is knoown as the “Remote Cluster.” C If daatabase adminnistrator is saatisfied with the changes, the mirror image is ro olled back in too the remote cluster c and thuus making channges permanent. D Display: Displaying Data D thee data based onn the search quueries given by b the user. D Integrity: This ensures the Data t accuracy of o the data. m more eaasily as the When a DBMS is used,, information systems can be changed much orrganization's information i reequirements change. New categories c of data d can be addded to the daatabase withou ut disruption to t the existingg system. Decision making m can be defined as thhe process of making m a choiice between alternatives. a The componen nts of this proocess are: data, decision models, m the deecision enviroonment and o which alterrnative gets peeople (Figure 1). Each of thhese componeents has a direect influence on chhosen.

Fiigure 1. The Co omponents of Decision-Making D g in Forestry.

Better deccision-making takes place when we acctually implem ment a more satisfying allternative thatt is consistentt with the statted objectivess of the problem under connsideration. These objectiv ves, which are a conscioussly or unconnsciously infl fluenced by our social orm the range of possible alternatives andd evaluate eacch one. The evvaluation is ennvironment, fo doone with the help h of decisioon models whiich in turn aree limited by thhe available daata. Finally, onne must remeember that all decisions aree made by peoople (human agents), a actingg rationally buut eternally bo ounded by speecific structuraal elements.

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The main features of the decision-making process for spatial problems are the following: • • • • • • •

the number of alternative solutions are large the results or consequences of alternative solutions change in the area any alternative solution is set on several criteria some of the criteria may be quality, while some other quantitative usually there are more than one decision-making person (or groups of people concerned) who are involved in decision-making process the beneficiaries of the decision have different preferences in respect of the relative weight of criteria used in the evaluation and consequences of the decision decisions usually characterized by a large degree of uncertainty

Considering the three main stages of decision-making process: mental – design – selection, the question raised is how and to what extent the GIS involved in these The Mental phase ƒ ƒ ƒ ƒ

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ƒ

this phase is applied in the research of the environment for identifying cases requiring a unique solution / decision requires exploratory analysis of the problem which requires decision the GIS can play a vital role in this early stage of decision-making process system can help in the coordination of the analysis of the problem for which a decision, because the capacity to incorporate and investigate data and information coming from a wide range of sources the GIS may present information effectively and with a way that is understandable to the person who decides

The Design phase ƒ ƒ ƒ

ƒ

ƒ

This phase comprises the creation, developing and analysis of all the alternatives solutions for the problem, as the latter established during the previous phase a model normally is used to provide the person who will decide support for the creation of all alternatives while an increasing number GIS described as systems supporting the process design and evaluation alternatives which relate to area, the more commercially 'package' of software GIS do not have those types spatial analysis and modeling required by specific scientists (e.g., foresters, agronomists, ecologists, etc.) to decide the possibilities of GIS for a total alternative solutions main based on the principles spatial correlation relating to connectivity, continuity, proximity, and overlay methods in current GIS models for the creation of alternative solutions operating behind the scenes, irrespective of specialist knowledge of the environment

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Meliadis Ioannis and Miltiadis Meliadis The Selection phase ƒ ƒ ƒ

ƒ

choice of a specific concerns in alternative solution than those available any alternative solution assessed and analyzed in comparison with other on the basis of the already identified rule decision rules decision shall be used to classify the under examination alternatives − the classification depends on the preferences of the person who decides and relates to the importance the criterion for assessing critical for the use of the GIS at this stage is the possibility for the integration of the aforementioned user preferences in decision-making process, while generally it should be observed that the GIS does not provide a mechanism for flexible integration of all these preferences

The Spatial Decisions Support Systems

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The spatial decisions support systems, based on the PC, are designed to support one user (or a group of users) to increase the effectiveness of the decision-making process for the solution to a semi-structured spatial problem. The three previously mentioned conditions (semi-structural problem, effectiveness, and support decision) attach the concepts in the spatial decisions support systems. More specifically, each condition is separately mentioned here:

Semi-structural decision problems The structured problems decision may be programmed and can be resolved by the PC. The unstructured decision problems it is not possible to organize and must be solved by the responsible person without any help from a PC. Most spatial decision problems (if not all) are located between the earlier mentioned extreme categories, semi-structural decision problems. Essentially the structured part of the problem is possible to be automated, while the unstructured must be addressed by the same people who make decisions. Effectiveness of decision-making process The objective of the system is to increase the effectiveness, despite the profitability of decision-making process. The a high degree of efficiency can be achieved with the incorporation of the crisis person who decides and on the programs of PC used in the decision-making process. System in order to be effective should be easy to use. The support of the decision The system helps users to search the problem to decide in an interactive way, all stages of the procedure for the decision.

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DBMS and Forestry Decision makers, foresters, manager of wild life need the ability to integrate and correlate information from many different sectors, which most of the time do not seem relevant to each other. To access the potentials and the carrying capacities of the environmental ecosystems, to monitor trends, to make future projections, to test different solutions, the managers require accurate, up-to-date and detailed information and an analytical support for decision making. DBMS (Database Management Systems), GIS (Geographical Information System) and RS (Remote Sensing) are some of the recent computer tools that are being invariably used in forestry. Several databases are available on forestry throughout the world for the benefit of research workers and those who are in need of such scientific information on the subject (discuused more in review topics). By the rapid expansion of computers and Internet, it is now possible to access the databases globally which has resulted in to the availability of instant information atmost immediately. Reliable information is necessary for effective management planning. To some extent a data base management system (DBMS) often provides the underlying structure for a management information system (MIS). Many computerized systems can be grouped under the MIS umbrella. This is the case of GIS (geographic information system) that is optimized to store, retrieve, and update geographic information. Such developing technologies that are being used more and more for the planning, monitoring, and evaluation of management activities at different levels must take into account requirements of the forest managers and technical implications. Forest inventory and forest management activities are among the best designed fields for applying the DBMS concept. Any forest survey makes use of a great volume of detailed and up-to-date information which requires data base management, as well as spatially referenced data (Clinch, 1989; Sheffield and Royer, 1989). A data base management system (DBMS) that operates efficiently as a data base often relies on a component or the elementary structure of an MIS and satisfies the main following objectives (Rondeux, 1991): -

to provide information support for decision-making and planning activities at top management level (useful for a forest service) to provide information support for all levels of management, in planning, and management control (useful for a forester) to deliver information in a required time frame (when the manager needs it).

The main expected benefits can be summarized as follows: -

development of minimum data sets (collection of variables able to provide an answer to a question either directly or indirectly) and standardized procedures, establishment of uniform standards for measurements, coordination of research to save both time and energy, efficiency of exchange and transfer of research information.

An information system must begin by defining the necessary and sufficient conditions for the data (Rose , 1989). Some important needs are to: Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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Meliadis Ioannis and Miltiadis Meliadis -

define completely and concisely the information required, identify what and how to measure to provide this, identify the accuracy required, ensure suitable computer support (hardware, software), make full use of existing and checked information.

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DISCUSSION When we talk about forests, we talk about 30% of our planet's land surface area. In 2000, there was an estimated 3,870 x l06 ha of forest worldwide, of which 5% is in forest plantations and 95% in natural forests (FAO, 2003). Forests are not only a source for timber; they also generate significant non-wood goods and services,mitigate climate change, conserve biological diversity, provide protection from natural hazards. The availability of timber and non-wood goods and services is waning as deforestation and degradation of forests continue, and the desertification of Mediterranean forests increase every year. While forest area has stabilized or is slightly increasing in the boreal and temperate regions, the annual loss of forest area in the tropics and subtropics is decreasing. Between 1990 and 2000, the annual rate of deforestation was estimated to be 14.6 x l06 ha (approximately 0.38%) and took place mainly in tropical and subtropical forests (FAO, 2003). The net annual rate of change is about 9.4 x l06 ha (0.2%). Maintaining and enhancing forest areas and the vitality of forest ecosystems is a widely accepted political goal, which is often opposed by conflicting demands of various stakeholders. Solutions to conflicts of this nature require actions at different scales ranging from managing demands of local communities to resolutions of transboundary problems such as global climate change (Jackson and Ingeles, 1998; Mayers and Bass, 2004; Sliggers and Kakabeeke, 2005). Decisions on policy measures and local management issues will not be effective if it is not based on reliable, timely, and readily available information. Forest inventories offer a tool to provide objective and reliable information about the multiple functions of forest ecosystems and their potential to satisfy various demands (Kohl et al., 2006). The GIS has proved to be a suitable tool for managing complex systems, where a great number of variables whose main feature is spatial location are involved. Within the past decade, many mapping agencies have digitized their original paper-based information. The results are placed into a data management system and they become the definitive version, from which subsequent paper and digital mapping products are produced. Many mapping agencies now consider themselves as data rather than map providers. Today, the integration of GIS, remote sensing, and GPS has created new situations to the foresters for the management of the environment. The data in digital form can be processed more easily, accurate, and can be updated well. This integration can be said to be amajor help to foresters. This integration process notably increases the GIS and Remote Sensing tool capabilities and usefulness. The GIS capabilities increase because it accesses and manages a great amount of information whose acquisition would be economically unapproachable by other ways, since it

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deals with large land surfaces. Furthermore, it facilitates the checking of fieldwork plot by plot, and the plots can be grouped as exploitations, which are the elementary aquifer management units, allowing the monitoring of exploitations internally. Remote Sensing also increases its potential as an information source for the monitoring and control of irrigated crops. The strategy for integrating environmental simulation models with GIS can range from loose coupling through the exchange of data files, to tight coupling, where model algorithms are embedded within the GIS using programming languages.

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RESULTS Resource managers are concerned with very complex systems, where they are presented with an essentially infinite variety of choices. It is important that managers do not just consider the cost associated with improving the quality of information available to support decision making, but that they also understand and take into consideration the risk associated with making an insufficient decision. The sustainable management of forest ecosystems requires decisions that have to incorporate a diversity of specialized knowledge and are subject to long-term impacts. GIS is a modern tool that provides decision makers with comprehensive information and results in better management decisions. The increasing amount of available data, the need for spatial reasoning, the desire to share distributed information, and the impressive potential of modern computer technology needs to be made available for the thorough management of forests. GIS offer the potential for informed decisions and support the maintenance and enhancement of the multiple forest functions. Though the technology is being employed extensively in various types of studies by researchers, it is yet to be effectively used for decision making in the field level. The field applications have remained largely confined to preparation of Working Plans and preparation of some thematic maps. Monitoring the current and changing conditions or ecological resources, understanding more fully the structure and function of ecosystems to develop improved management options, modeling the response of ecosystems to human induced stresses, and assessing the social and economic implications of management actions. If, in the future, human requirements are to be met in a sustainable manner, it is now essential to resolve these conflicts and move towards more effective and efficient use of land and its natural resources. Integrated physical and land use planning and management are an eminently practical way to achieve this. By examining all uses of land in an integrated manner, it makes it possible to minimize conflicts, to make the most efficient tradeoffs and to link social and economic development with environmental protection and enhancement, thus helping to achieve the objectives of sustainable resource management.

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REFERENCES Acevedo, M.F., Urban, D.L., and Ablan, M. (1996). Landscape Scale Forest Dynamics: GIS, Gap, and Transition Models. In M.F.Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning (eds.), op. cit., Ch. 33, pp. 181-185. Akcakaya, H.R. (1996). Linking GIS with Models of Ecological Risk Assessment for Endangered Species. In M.F. Goodchild et al., op. cit. Almeida, R. (1994). Forest fire risk areas and definition of the prevention priority planning Actions using GIS, Proc. EGIS/MARI ’94 Conf., Paris (March 29-April 1), Vol. 2, p. 1700. Amissah-Arthur, A., Mougenot, B., and Loireau, M. (2000). Assessing farmland dynamics and land degradation on Sahelian landscapes using remotely sensed and socioeconomic data, Intl J Geograph Info Sci,14(6): 583-599. Archibugi, F., and Nijkamp, P. (1989). Economy and Ecology: Towards Sustainable Development, Kluwer Academic Publishers, Dordrecht. Arsenau, G. and Lowell, K. (1992) Elements de Gestion Pour les Suivis de la Planification Forestiere au Quebec a l’Aide de GIS, Proceedings of the Canadian Conference on GIS ’92, Ottawa, Canada (March 24–6), pp. 303–16. Baral, H. (2004). Applications Of GIS In Community-Based Forest Management In Australia (and Nepal), A thesis, Master of Forest Science, School of Forest and Ecosystem Science Institute of Land and Food Resources The University of Melbourne. Barbier, E.B., Markandya, A., and Pearce, D.W. (1990). Environmental sustainability and costbenefit analysis, Envir & Planning A, 22, 1259-1266. Baskent, E.Z., and Yolasigmaz, H.A. (1999). Forest landscape management revisited, Envir Mgmt, 24(4):437-–448. Bateman, I.J., and Lovett, A.A. (2000). Estimating and valuing the carbon sequestered in softwood and hardwood trees, timber products and forest soils in Wales, J Envir Mgmt 60(4): 301-323. Bernert, J.A., Eilers, J.M., Sullivan, T.J., Freemark, K.E., and Ribic, C. (1997). A quantitative method for delineating regions: An example for the Western Corn Belt Plains ecoregion of the USA, Envir Mgmt 21 (3): 405-420. Bian, L. (2000). Component modeling for the spatial representation of wildlife movements, J Envir Mgmt 59 (4): (235-245. Bocco, G., Rosete, F. Bettinger, P., and Velazquez, A. (2001). Developing a GIS program in rural Mexico: Community participation equals success. J Forestry 99 (6):14-20. Bojorquez-Tapia, L.A., Diaz-Mondragon, S., and Gomez-Priego, P. (1999). GIS approach for land suitability assessment in developing countries: A case study of forest development project in Mexico.In J.-C. Thill, (ed.), op. cit., Ch. 14, pp. 335-352. Bowker, G.C. (2000). Mapping biodiversity, Intl J Geograph Info Sci 14 (8): 739-754. Broers, H. P., Peters, S.W.M., and Biesheuvel, A. (1990). Design of a groundwater quality network with GIS and remote sensing. In J. Haarts, H.F.L. Ottens, and H.J. Scholten, (eds.), EGIS '90, First European Conf GIS, EGIS Foundation, pp. 95-105. Carriquiry, A., Breidt, F.J., and Lakshminarayan, P.G. (1998). Sampling schemes for policy analyses using computer simulation experiments, Envir Mgmt 22 (4): 505-515.

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Carver, S., Heywood, I., Cornelius, S., and Sear, D. (1995). Evaluating field-based GIS for environmental characterization, modeling and decision support, Intl J Geograph Info Syst 9 (4): (475-486). Cassel-Gintz, M., and Petschel-Held, G. (2000). GIS-based assessment of the threat to world forests by patterns of non-sustainable civilization nature interaction, J Envir Mgmt 59 (4): 279-298. Chang, K.-T., Verbyla, D.L. and Yeo, J.J. (1995) Spatial Analysis of Habitat Selection by Sitka Black-Tailed Dear in Southeast Alaska, USA, Environmental Management, Vol. 19, No. 4 (July/August), pp. 579–89. Chou, Y.-H. (1992). Management of wildfires with a Geographical Information System, Il J Geograph Info Syst 6 (2): 123-140. Chou, Y.-H., and Soret, S. (1996). Neighborhood effects in bird distributions, Navarre, Spain, Envir Mgmt 20 (5): 675-687. Church, R., Stoms, D., Davis, F., and Okin, B.J. (1996). Planning management activities to protect biodiversity with a GIS and an integrated optimization model. In M.F. Goodchild et al., op. cit. 100 GIS and expert systems for IA Cihlar, J., D’Iorio, M., Mullins, D., and St-Laurent, L. (1989). Use of satellite data and GIS for environmental change studies, Proc Nat Canadian Conf GIS “Challenge for the 1990s,” Ottawa, Canada (February 27-March 3), pp. 933-943. Clarke, C.J., Hobbs, R.J., and George, R.J. (1999). Incorporating geological effects in modeling of revegetation of strategies for salt-affected landscapes, Envir Mgmt 24 (1): 99-109. Clayson, J. (1996). On a wing and a prayer, Mapping Awareness (November), pp. 24-26. Clinch, P.G. (1989). How to convert a traditional stand inventory to GIS. Proc Global Natural Resource Monitoring and Assessments: Preparing for the 21st Century. Amer Soc Photogrammetry & Remote Sensing 3: 1183-1190. Collins, R.P., Jenkins, A., and Sloan, W.T. (1998). A GIS framework for modeling nitrogen leaching from agricultural areas in the Middle Hills, Nepal, Intl J Geograph Info Sci 12 (5): 479-490. Cruickshank, M.M., Tomlinson, R.W., and Trew, S. (2000). Application of CORINE landcover mapping to estimate carbon stored in the vegetation of Ireland, JEnvir Mgmt 58 (4): 269-287. Damoiseaux, M.A. (1990). From a Water management map towards a WIS. In J. Haarts, H.F.L. Ottens, and H.J. Scholten, (eds.), EGIS '90, First European Conf GIS, EGIS Foundation, pp. 230-235. Davidson, D.A. (1991). Forestry and GIS, Mapping Awareness 5 (5): 43-45. Davis, F.W., Quattrochi, D.A., and Ridd, M.K. (1991). Environmental analysis using integrated GIS and remotely sensed data: Some research needs and priorities, Photogrametric Engng & Remote Sensing 57: 689-697. GIS and environmental management 101 Despotakis, V., Nijkamp, P., and Giaoutzi, M. (1992). GIS and DSS as tools for modeling sustainable development. Presented at the Third European Geograph Info Syst Conf (EGIS '92), Munich, Germany. Despotakis, V. (1994a). Geographical data base of the integrated water resources management system of eastern Crete. Presented at the Sci Conf “ntegrated Water Res Mgmt Syst Eastern Crete, Iraklion, Crete, Greece, November 7.

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Despotakis, V. (1994b). Development of multi-dimensional spatial information for the island of Crete, Greece. Presented at the Fourth Arc/Info Users Mtg, Athens, Greece, November 9 and 10. Deursen, W.P.A., and Kwadijk, van J. (1990). Using the watershed tools for modeling the Rhine catchment. In J. Haarts, H.F.L. Ottens, and H.J. Scholten, (eds.), EGIS '90, First European Conf GIS, EGIS Foundation, pp. 254-262. Duguay, C.R., and Walker, D.A. (1996). Environmental modeling and monitoring with GIS: Niwot Ridge long-term ecological research site. In M.F. Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning, (eds.), op. cit., Ch. 41, pp. 219-223. Dusart, J., Toure, A., and Diop, S. (1994). Cartes du couvert vegetal, d’utilisation et d’occpation des sols par teledetection dans le cadre de l’amenagement des forets naturelles du gonakie dans la vallee du Fleuve Senegal, Proc EGIS/MARI ’94 Conf, Paris (March 29–April 1), 1 :579-588. FAO, (2003). State-of-the-worlds’s forests. FAO Rome. Fedra, K., and Reitsma, R.F. (1990). Decision support and geographical information systems. In H. Scholten and J. Stillwell, (eds.), Geographical Information Systems for Urban and Regional Planning, Kluwer Academic Publishers, pp. 177-188. Geist, D.R. and Dauble, D.D. (1998). Redd site selection and spawning habitat use by Fall Chinook salmon: The importance of geomorphic features in large rivers, Envir Mgmt 22 (5): 655-669.Gibson, R.B., et al., (2005). p. 4 Guariso, G., and Werthner, H. (1989). Environmental Decision Support Systems, Ellis Horwood, Chichester. Gustafson, E.J., and Crow, T.R. (1996). Simulating the effects of alternative forest management strategies on landscape structure, J Envir Mgmt 46 (1): 77-94. Gustafson, E.J., Murphy, N.L., and Crow, T.R. (2001). Using a GIS model to assess terrestrial salamander response to alternative forest management plans J Envir Mgmt 63 (3): 281-292. Harding, J.S., and Winterbourn, M.J. (1997). An ecoregion classification of the South Island, New Zealand J Envir Mgmt 51 (3): 275-287. Havens, K.J., Priest III, W.I., and Berquist, H. (1997). Investigation and long-term monitoring of Pragmites australis within Virginia’s constructed wetland sites, Envir Mgmt 21 (4): 599-605. Healey, R.G., Robertson, S.G., Magor, J.I., Pender, J., and Cressman, K. (1996). A GIS for desert locust forecasting and monitoring, Intl J Geograph Info Syst 10 (1): 117-136. Hemmati, M., Dodds, F., Enayati, J., and McHarry, J., Multi-Stakeholder. (2002). Processes for Governance and Sustainability: Beyond Deadlock and Conflict, Earthscan Publications, London, 2002. Higgs, G., Aitchison, B.I., Crosweller, H., and Jones, P. (1994). The national GIS demonstrator of common lands for England and Wales, J Envir Planning & Mgmt 37 (1):33-51. Hogsett, W.E., Weber, J.E., Tingey, D., Herstrom, A., Lee, E.H., and Laurence, J.A. (1997). An approach for characterizing tropospheric ozone risk to forests, Envir Mgmt 21 (1): 105-120.

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Hunter, J.C., Willett, K.B., McCoy, M.C., Quinn, J.F., and Keller, K.E. (1999). Prospects for preservation and restoration of Riparian forests in the Sacramento Valley, California, USA, Envir Mgmt 24 (1): 65-75. Hussin, Y.A., de Gier, A., and Hargyono, □. (1994). Forest cover charge detection analysis using remote sensing – A test for the spatially resolved area prediction model, Proc EGIS/MARI ’94 Conf, Paris (March 29-April 1), 2: 1825-1834. Isachenko, G.A., and Reznikov, A.I. (1994). Natural anthropogenic dynamics of landscape: Information systems of simulation for Ladoga Region, Proc EGIS/MARI ’94 Conf, Paris (March 29-April 1), 1: 661-667. Jackson, W.J., and Ingeles, A.W. (1998). Participatory Techniques for Community Forestry.A Field Manual. IUCN, Gland. Jang, C.J., Nishigami, Y., and Yanagisawa, Y. (1996). Assessment of global forest change between 1986 and 1993 using satellite-derived terrestrial net primary productivity, Envir Conserv 23(4):315-321. Jiang, B., and Yao, X. (2006). Location-based services and GIS in perspective, Computers, Envir & Urban Syst, 30: 712-725. Johnson, A.K.L., Ebert, S.P., and Murray, A.E. (1999). Distribution of coastal freshwater wetlands and Riparian forests in the Herbert River catchment and implications for management of catchments adjacent the Great Barrier Reef Marine Park, Envir Conserv 26 (3): 229-235. Johnston, C.A. (1993) Introduction to Quantitative Methods and Modelling in Community, Population, and Landscape Ecology, in Goodchild, M.F., Parks, B.O. and Steyaert, L.T. (eds) op. cit., Ch. 25, pp. 276–83. Johnston, C.A., Cohen, Y. and Pastor, J. (1996) Modeling of Spatially Static and Dynamic Ecological Process, in Goodchild, M.F., Steyaert, L.T., Parks, B.O., Johnston, C., Maidment, D., Crane, M. and Glendinning, S. (eds) op. cit., Ch. 27, pp. 149–54. Kalivas D.P. (2001) Geographical Information Systems in Water Resources Management. Proceedings (CD-ROM) of the Congress “Aegean – Water – Sustainable Development” Paros 6-7 July 2001. Karnohan, B.J., Millspaugh, J.J., Jenks, J.A., and Naugle, D.E. (1998). Use of an adaptive Kernel home-based estimator in a GIS environment to calculate habitat use, J Envir Mgmt 3 (1): 83-89. Kirkman, H. (1996). Baseline and monitoring methods for Seagrass Meadows, JEnvir Mgmt 47 (2): 191-201. Kittel, T.G.F., Ojima, D.S., Schimel, D.S., McKeown, R., Bromberg, J.G., Painter, T.H., Rosenbloom, N.A., Parton, W.J., and Giorgi, F. (1996). Model GIS integration and data set development to assess terrestrial ecosystem vulnerability to climate change. In M.F. Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning, (eds.), op. cit., Ch. 55, pp. 293-297. Khaemba, W.M. and Stein, A. (2000) Use of GIS for a Spatial and Temporal Analysis of Kenyan Wildlife with Generalised Linear Modelling, International Journal of Geographical Information Science, Vol. 14, No. 8 (December), pp. 833–53. Kohl, M., Manussen, S.S., and Marchetti, M. (2006). Sampling Methods, Remote Sensing and GIS Multiresource Forest Inventory. Springer-Verlag Berlin Heildelberg p. 373. Kovacs, J.M., Wang, J., and Blanco-Correa, M. (2001). Mapping disturbances in a Mangrove Forest using multi-date Landsat TM imagery, Envir Mgmt 27: (5): 763-776.

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Lajeunesse, D., Domon, G., Drapeau, P., Cogliastro, A., and Bouchard, A. (1995). Development and application of an ecosystem management approach for protected natural areas, Envir Mgmt 19(4): 481-495. Lammert, M., and Allan, J.D. (1999). Assessing biotic integrity of streams: Effects of scale in measuring the influence of land use/cover and habitat structure on fish and macroinvertebrates, Envir Mgmt 23: (2): : 257-270. Liao, H.-H., and Tim, S. (1994). Interactive water quality modeling within a GIS environment, Computers, Envir & Urban Syst 18 (5):343-363. Li, J., and Heap, A. (2008). A Review of Spatial Interpolation Methods for Environmental Scientists. Geoscience Australia. Lovett, A., and Appleton, K. (2008). GIS for Environmental Decision-Making.p. 253. Taylor and Francis Group, 6000 Broken Sound Parkway NW, Suite 300 Boca Raton, FL 33487‑2742. Lowell, K. (1991). Utilizing discriminated function analysis with a Geographical Information System to model ecological succession spatially, Intl J Geograph Info Syst 5 (2): 175191. 108 GIS and expert systems for IA Lyon, J.G., and Adkins, K.F. (1995). Use of a GIS for wetland identification, the St. Clair Flats, Michigan. In J.G. Lyon and J. McCarthy, (eds.), op. cit., Ch. 5, pp. 49-60. Macfarlane, R. (1998). Implementing agri-environmental policy: A landscape ecology perspective, J Envir Planning & Mgmt 41 (5): 575-596. Mackey, B.G., Sims, R.A., Baldwin, K.A., and Moore, I.D. (1996). Spatial analysis of Boreal forest ecosystems: Results from the Rinker Lake case study. In M.F. Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning, (eds.), op. cit., Ch. 34, pp. 187-190. Maguire, D.J., Batty, M., and Goodchild, M.F. (2005). (eds.), GIS, Spatial Analysis and Modeling, ESRI Press, Redlands, California. Mahto Bharat.(2001).Quantification of Forest Growing stock using Remote Sensing data for management planning- A case study of Tikauli Forest in Chitwan District of Nepal in the proceedings of 22nd Asian Conference on Remote Sensing, 2001. Malanson, G.P., Armstrong, M.P., and Bennett, D.A. (1996). Fragmented forest response to climatic warming and disturbance. In M.F. Goodchild, L.T. Steyaert, B. O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning, (eds.), op. cit., Ch. 46, pp. 243247. Mayaux, P., Achard, F., and Malingreau, J.-P. (1998). Global tropical forest area measurements derived from coarse resolution satellite imagery, Envir Conserv 25 (1): 3752. Mayers, J., and Bass, S. (2004). Policy that Works for Forests and People; Real Prospects for Governance and Livelihoods. Earthscan, London. Mckendry. J. E., J. R. Eastman, K. S. Martin and M. A. Fulk.(1995). Exploration in geographic information systems technology. V.2 (Applied in Forestry). UNITAR Switzerland. 1995. Mckendry. J. E. and J. R. Eastman, .(2001). Application of GIS in Forestry: A review. McMahon, G., and Harned, D.A. (1998). Effect of environmental setting on sediment, nitrogen, and phosphorus concentrations in Albemarle-Pamlico Drainage Basin, North Carolina and Virginia, USA, Envir Mgmt 22 (6): 887-903.

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McWilliam, F. (1999). Blinded with vision, GeoEurope, pp. 26-29.

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Μeliadis Ι. (2005). Correlations of environmental parameters with the use of satellite technology and G.I.S. Forest Research Volume 17:19-26. Meliadis, I., and Karteris, M. (1997). The possibilities of utilization of anautomated software in the management of forest information. Proc Workshop G.I.S. & Spatial Analysis. June 1997, p. 9 (In Greek). Meliadis, I., and Xatzilakou, D. (2000). Spatial analysis and decision making with the use of G.I.S. ProcNinth Nat Forestry ConfNatural Environment Protection and Restoration of Disturbed Areas. Kozani, November 18-20 2000, 592-597pp. (In Greek). Meliadis, I., and Kassioumis, K. (2001). The combined use of remote sensing and G.I.S. for the study of a special protected area in Greece. Proc Third Intl ConfEcosyst & Sustainable Dev., Alicante Spain. pp. 409-417. Meliadis, I., Platis, P., Ainalis, A., and Meliadis, M. (2009). Monitoring and analysis of natural vegetation in a special protected area of mountain Antichasia-Meteora, Central Greece. Envir Mgmt (Springer), 153 (1-4):x. Mendonca-Santos, M.L., and Claramunt, C. (2001). An integrated landscape and local analysis of land cover evolution in an alluvial zone, Computers, Envir & Urban Syst 25 (6): 557-577. Mertens, B., Forni, E., and Lambin, E.F. (2001). Prediction of the impact of logging activities on forest cover: A case-study in the East Province of Cameroon, J Envir Mgmt 62 (1): 21-36. Mladenoff, D.J., Host, G.E., Boeder, J., and Crow, T.R. (1996). LANDIS: A spatial model of forest landscape disturbance, succession, and management. In M.F. Goodchild, L.T. Steyaert, B.O. Parks, C. Johnston, D. Maidment, M. Crane, and S. Glendinning, (eds.), op. cit., Ch. 32, pp. 175-179. Namken, J.C., and Stuth, J.W. (1997) A prototype graphic landscape analysis system: Part 1. Predicting spatial patterns of grazing pressure using GIS, Intl J Geograph Info Sci 11 (8): 785-798. Nijkamp, P. (1990). Regional sustainable development and natural resource use. Presented in the World Bank Ann Conf Dev Economics, Washington D.C. O' Callaghan, J.F., and Mark, D.M. (1984). The extraction of drainage networks from digital elevation data, Computer Vision & Image Processing. Ochoa-Gaona, S. (2001). Traditional land-use systems and patterns of forest fragmentation in the highlands of Chiapas, Mexico, Envir Mgmt 27 (40): 571-586. Opschoor, J.B., and Reijnders, L. (1991). Towards sustainable development indicators. In O. Kuik and M. Verbruggen, (eds.),Search of Sustainable Development Indicators, Kluwer Academic Publishers, Dordrecht, pp. 7-29. Ortega-Huerta, M.A. and Medley, K.E. (1999) Landscape Analysis of Jaguar (Panthera onca) Habitat Using Sighting Records in the Sierra de Tamaulipas, Mexico, Environmental Conservation, Vol. 26, No. 4 (December), pp. 257–69. O’Sullivan, D., and Turner, A. (2001) Visibility graphs and landscape visibility analysis, Intl J Geograph Info Sci 15 (3): 221-237. Peccol, E., Bird, A.C., and Brewer, T.R. (1996). GIS as a tool for assessing the influence of countryside designations and planning policies on landscape change, J Envir Mgmt 47 (4): 355-367.

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Peng, Z-R., and Tsou, M-H. (2003). Internet GIS: Distributed Geographic Information Services for the Internet and Wireless Networks, Wiley, Chichester. Petit, C.C., and Lambin, E.F. (2001). Integration of multi-source remote sensing data for land cover change detection, Intl J Geograph Info Sci 15 (8): 785-803. Phinn, S., Franklin, J., Hope, A., Stow, D., and Huenneke, L. (1996). Biomass distribution mapping using airborne digital video imagery and spatial statistics in a semi-arid environment, J Envir Mgmt 47 (2): 139-164. Pierce, G.J., Wang, J., Zheng, X., Bellido, J.M., Boyle, P.R., Denis, V., and Robin, J.P. (2001). A Cephalopod Fishery GIS for the Northeast Atlantic, Intl J Geograph Info Sci 15 (8): 763-784. Quiel, F. (1995). Modeling and optimizing water usage in irrigated areas in a GIS, Proc Joint European Conf Exhibit on Geographic Info JEC-GI ’95, The Hague (March 26-31), 1: 415-420.Riordan, O., et al. (2000). p. 4 Rodriguez-Bachiller, A., and Glasson, J. (2004). Information derived from the expert systems and GIS for impact assessment.Taylor and Francis, 11 New Fetter Lane, London EC4P 4EE 400 pp. Rondeux, J. (1991). p. 16 Rose, D.W. (1989). Development of management information systems for natural resource research. ProcGlobal Natural Resource Monitoring and Assessments: Preparing for the 21st Century. Amer Soc Photogrammetry & Remote Sensing, 3:1145-1154. Sarch, M.-T., and Birkett, C. (2000). Fishing and farming at Lake Chad: Responses to Lakelevel fluctuations, The Geographical J 166 (2): 156-172. Sato, S., Arima, T., Hsiao, N., Hagashima, S., and Sugahara, T. (1995). Using GIS topographic data for quantitative landscape analysis of City Hall views in 76 Kyushu Cities, Proc Fourth Intl Conf Computers in Urban Planning and Urban Mgmt, Melbourne, Australia (July 11-14), 1: 463-476. Sheffield, R.N., and Royer, L.A. (1989). GIS - A broad scale inventory perspective. In Forestry on the Frontier. Proc. Soc Amer Foresters Nat Convention. Spokane, pp. 38-42. Singh I. J. and Sanjay Moharir (2003). Forest management using remote sensing and GIS in Barbatpur range, Betul forest division. Journal o f the Indian Society o f Remote Sensing, Vol. 31, No. 3, 2003 Singh A. Kumar, U.K. Chopra, D.K. Das and R.N. Garg (2004).Geoinformatics applications for sustainable development, jointly published by the Indian Society of Agrophysics and Indian Society of Remote Sensing. Skidmore, A.K., Gauld, A., and Walker, P. (1996). Classification of kangaroo habitat distribution using three GIS models, Intl J Geograph Info Syst 10 (4): 441-454. Sliggers, J., and Kakabeeke, W. (2005). Clearing the Air: 25 Years of the Convention on Long-range Transboundary Air Pollution. United Nations, New York. Smallwood, K.S., Wilcox, B., Leidy, R., and Yarris, K. (1998). Indicators assessment for habitat conservation plan of Yolo County, California, USA, EnvirMgmt 22 (6): 947-958. Tappan, G.G., Moore, D.G., and Knausenberger, W.I. (1991). Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology, Intl J Geograph Info Syst 5 (1): 123-135. GIS and environmental management 113. Taylor, J.R. (1994). Application of GIS to cultural landscape assessment within the Niagara escarpment planning area, Proc Canadian Conf GIS ’94, Ottawa, Canada (June 6-10), 1: 742-750.

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Taylor, P.J., Walker, G.R., Hodgson, G., Hatton, T.J., and Correll, R.L. (1996). Testing of a GIS model of Eucalyptus largiflorens health on a semi-arid saline floodplain, Envir Mgmt 20 (4): 553-564. Thuresson, T., Nasholm, B., Holm, S., and Hagner, O. (1996). Using digital image projections to visualize forest landscape changes due to management activities and forest growth, Envir Mgmt 20 (1): 35-40. Tortosa, D., and Beach, P. (1993). Application of portable GPS/desktop mapping- GIS for fire management support, Proc Canadian Conf GIS ’93, Ottawa, Canada (March 23-25), pp. 802-819. Uchida, A., Satani, N., Nakano, H., Deguchi, A., and Hagishima, S. (1997). Study on method for visual evaluation of sloped wooded area, Proc 5th Intl Conf Computers in Urban Planning & Urban Mgmt, Bombay (India), 1: 146-156. Van Horssen, P. (1996). Ecological Modelling in GIS. In M.F. Goodchild et al., op. cit. Velden, H.E. ven, and Kreuwel, G. (1990). A geographical information system based decision support system for environmental zoning. In □. Scholten and □. Stillwell, (eds.), Geographical Information Systems for Urban and Regional Planning, Kluwer Academic Publishers, pp. 119-128. Wagner, J. (1995) Environmental Planning for a World Heritage Site: Case Study of Angkor, Cambodia, Journal of Environmental Planning and Management, Vol. 38, No. 3 (September), pp. 419–34. Wing, M.G., and Johnson, R. (2001). Quantifying forest visibility with spatial data, Envir Mgmt 27 (3): 411-420.

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In: Geographic Information Systems Editor: Christopher J. Dawsen

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Chapter 3

GIS AND SPATIAL DECISION MAKING Khalid A. Eldrandaly* Faculty of Computers and Informatics, Zagazig University, Egypt

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ABSTRACT Spatial decision making problems are multi-facetted challenges. Not only they often involve numerous technical requirements, but may also contain economical, social, environmental and political dimensions that may have conflicting values. Solutions for these problems involve highly complex spatial data analysis processes and frequently require advanced means to address physical suitability conditions while considering the multiple socio-economic variables. Geographic information systems (GIS), Multicriteria Decision Making techniques (MCDM), and Expert Systems (ES) are the most common tools employed to solve these problems. However, each suffers from serious shortcomings. The need for combining the strengths of these techniques has prompted researchers to seek integration of GIS, MCDM and ES. A variety of strategies can be used for integrating GIS and these tools. These strategies range from loose coupling techniques to the recent advanced techniques of software interoperability. In this chapter the complexity of the spatial decision making is highlighted. Both traditional and advanced techniques for software systems integration are presented.

Keywords: GIS, spatial decision making, expert systems, MCDM, systems integration techniques, interoperability

1. INTRODUCTION Spatial decision making is a routine activity that is common to individuals and to organizations. People make decisions influenced by location when they choose a store to shop, a route to drive, or a neighborhood for a place to live, to name but a few. Organizations are not much different in this respect. They take into account the realities of spatial *

E-mail: [email protected]

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organization when selecting a site, choosing a land development strategy, allocating resources for public health, and managing infrastructures for transportation or public utilities (Jankowski and Nyerges 2001). Spatial decision making is a highly complex process of choosing among alternatives to attain an objective or a set of objectives under constraints. It can be a structured process involving problems with standard solution procedures, or an unstructured process consisting of problems with no clear-cut solution procedures, or even semi-structured problems for which combinations of standard procedures and individual judgments have to be used to find a solution. All these processes generally involve voluminous spatial and aspatial information, structured and unstructured knowledge, and human valuation and judgment (Leung 1997). Spatial decision-making problems are multi-facetted challenges. Not only do they often involve numerous technical requirements, but they may also contain economical, social, environmental and political dimensions that could have conflicting objectives. Malczewski (1999) defined the main characteristics of spatial decision problems as follows: A large number of decision alternatives. The outcomes or consequences of the decision alternatives are spatially variable. Each alternative is evaluated on the basis of multiple criteria. Some of the criteria may be qualitative while others may be quantitative. There are typically more than one decision maker (or interest group) involved in the decision-making process. 6) The decision makers have different preferences with respect to the relative importance of evaluation criteria and decision consequences. 7) The decisions are often surrounded by uncertainty.

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1) 2) 3) 4) 5)

Solving this complex type of decision problems usually requires an intelligent and integrative use of information, domain specific knowledge and effective means of communication (Leung 1997). Geographic information systems (GIS), multicriteria decision making techniques (MCDM), and Expert Systems (ES) are the most common tools employed to solve these problems. However, each suffers from serious shortcomings. GIS is a great tool for handling physical suitability analysis. However, it has limited capabilities of incorporating the decision maker’s preferences into the problem solving process. MCDM is the proper tool for analyzing decision problems and evaluating alternatives based on a decision maker’s values and preferences. However, it lacks the capability of handling spatial data (e.g., buffering and overlay) that are crucial to spatial analysis. Also ES, which is capable of addressing heuristic analysis, lacks the capability of handling spatial data/knowledge. The need for combining the strengths of these techniques has prompted researchers to seek integration of GIS, MCDM and ES. Numerous mechanisms enabling interoperability between GIS and theses tools have appeared over the years. Examples range from primitive (although widely used) solutions such as simple, loose coupling to much more sophisticated approaches, such as COM technology. In the following sections, brief descriptions of GIS, ES, and MCDM are presented, and the different techniques for integrating these tools are discussed.

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2. GEOGRAPHIC INFORMATION SYSTEMS

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2.1. Brief History of GIS The day-to-day necessity of dealing with space and spatial relationships represents one of the basic facets of human society. Geographic information systems evolved as a means of assembling and analyzing diverse spatial data. These systems evolved from centuries of mapmaking and the compilation of registers. The earliest known maps were drawn on parchment to show the gold mines at Coptes during the reign (1292- 1225 B.C.) of Rameses II of Egypt. At a later date, the Greeks acquired cartographic skills and compiled the realistic maps. The Greek mathematician, astronomer, and geographer Eratosthenes (ca. 276-194 B.C.) laid the foundations of the scientific cartography i.e., the science, art, and technology of making, using, and studying maps. The Arabs were the leading cartographers of the Middle Ages. The Arabian geographer Al-Idrisi made a map of the world in 1154. European cartography degenerated as the Roman Empire fell. Until the nineteenth century, geographical information was used mostly for trade and exploration by land and sea and for tax collection and military operations. New needs arose in step with evolving infrastructures, such as roads, railways, etc., because planning these facilities required information about the terrain beyond that commonly available. As planning advanced, specialized maps became more common. In 1838, the Irish government compiled a series of maps for the use of railway engineers, which may be regarded as the first manual geographic information system. By the late 1950s and early 1960s, second–generation computers using transistors became available and the first computerized geographic information system appeared. The first GIS was the Canada Geographic Information System (CGIS), designed in the mid 1960s as a computerized map measuring system. CGIS was developed by Roger Tomlinson and colleagues for Canadian land inventory. This project pioneered much technology and introduced the term GIS. The rapid development of powerful computers led to an increasing acceleration in the use of GIS. In the 1970s and 1980s, various systems evolved to replace manual cartographic computations. Workable production systems became available in the late 1970s. GIS really began to take off in the early 1980s, when the price of computing hardware had fallen to a level that could sustain a significant software industry and cost-effective applications. The market for GIS software continued to grow, computers continued to fall in price, and increase in power, and the software industry has been growing ever since (Clarke 2001;Bernhardsen 2002; Longley et al. 2005).

2.2. Definitions of GIS Many definitions of GIS have been suggested over the years in different areas and disciplines. All GIS definitions recognize that spatial data are unique because geographic location is an important attribute of activities, policies, strategies, and plans. Following are some of these definitions: Ducker (1979) defined GIS as “ a special case of information systems where the database consists of observations on spatially distributed features, activities or events, which are

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definable in space as points, lines, or areas. A geographic information system manipulates data about these points, lines, and areas to retrieve data for ad hoc quires and analysis”. Star and Estes (1990) defined GIS as “an information system that is designed to work with data referenced by spatial or geographic coordinates. In other words, a GIS is both a database system with specific capabilities for spatially–referenced data, as well as a set of operations for working with the data”. Burrough and McDonnell (1998) defined GIS as “a powerful set of tools for storing and retrieving at will, transforming and displaying spatial data from the real world for a particular set of purposes”. Clarke (2001) defined GIS as “an automated system for the capture, storage, retrieval, analysis, and display of spatial data”. Davis (2001) defined GIS as “A computer–based technology and methodology for collecting, managing, analyzing, modeling, and presenting geographic data for a wide range of applications.” Worboys and Duckham ( 2004) defined GIS as “A computer-based information system that enables capture, modeling, storage, retrieval, sharing, manipulation, analysis, and presentation of geographically referenced data”. Whereas Longley et al. (2005) defined GIS as "A special class of information systems that keep track not only of events, activities, and things, but also of where these events, activities, and things happen or exist."

2.3. Major Components of GIS

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Any functional GIS has six major components as shown in figure 1 (Zeiler 1999; Longley et al.2005). These components are: 1) People - People are the most important component of a GIS. People must develop the procedures and define the tasks the GIS will perform. People can often overcome shortfalls in other components of the GIS, but the opposite is not true. The best software and computers in the world cannot compensate for incompetence. 2) Data - Data, which are quite critical to GIS, contains both geographic and attribute data. The availability and accuracy of data affect the results of queries and analysis. 3) Hardware - Hardware is the devices that the user interacts directly in carrying out GIS operations, such as the computer, digitizer, plotter, etc. Hardware capabilities affect processing speed, ease of use, and the types of available output. 4) Software - This includes not only GIS software, but also various database, drawing, statistical, imaging, and other software programs. 5) Procedures - GIS analysis requires well-defined, consistent methods to produce correct and reproducible results. 6) Network - Network allows rapid communication and sharing digital information. The internet has proven very popular as a vehicle for delivering GIS applications.

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Fiigure 1. Basic components c of GIS G (reproducedd with permissiion, John Wileyy and Sons, Ltd. Longley et all., Geographic Information I Sysstems and Sciennce, 2nd edition, 2005).

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2.4. GIS Data y of GIS to handle h and proocess geograpphically refereenced data distinguishes The ability GIS from otheer information systems. Geographically G G y referenced data describee both the loocation and ch haracteristics of o spatial feattures on the eaarth’s surface. GIS thereforre involves tw wo geographicc data componnents: spatial data relate too the geometryy of spatial feeatures and atttribute data give g the inforrmation aboutt the spatial features. f A GIS G organizes and stores innformation ab bout the worlld as a colleection of them matic layers that can be linked by geeography. Eacch layer contaiins features haaving similar attributes, likee streets or citties that are loocated within the same geoggraphic extentt. This simplee but extremely powerful annd versatile cooncept has pro oven invaluablle for solving many real-woorld problems— —from trackinng delivery veehicles to recording detaiils of planninng applicatioons to modeling global atmospheric ciirculation (Bolstad 2002). Data D collectionn is one of thee most time-coonsuming andd expensive G activities. There are many GIS m diverse sources of geographic data d and many methods avvailable to entter them into a GIS such as digitizing andd scanning of maps, image data, d direct daata entry usin ng GPS and suurveying instrruments, and transfer t of data from existiing sources (B Bernhardsen 2002; 2 Bolstad 2002; 2 Longleyy et al. 2005).

2.5. GIS Data Models w is far tooo complex to model m in its enntirety within any informatiion system, The real world soo only speciffic areas of interest i shoulld be selectedd for inclusioon within a given GIS appplication.

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Figure 2. Vector and Raster Data Models (adapted from Bolstad 2002).

Once a particular application area has been chosen, the next task is to select those features which are relevant to the application and to capture information about their locations and characteristics. In order to bring the real world into GIS, one has to make use of simplified models of the real world. A geographic data model is a set of constructs for describing and representing selected aspects of the real world in a computer. There are two basic data models used in GIS; these models are (Zeiler 1999; Davis 2001; Bolstad 2002; Bernhardsen 2002; Longley et al. 2005): Vector Data Model: The basis of the vector model is the assumption that the real world can be divided into clearly defined elements (features) each element consists of an identifiable object with its own geometry of points, lines, or areas. Vector data represents the shapes of features precisely and compactly as an ordered set of coordinates with associated attributes. Points (e.g., wells) are recorded as single coordinate pairs, lines (e.g., roads) as a series of ordered coordinate pairs, and polygons (e.g., census tracts) as one or more line segments that close to form a polygon area. Vector models are particularly useful for representing and storing discrete features such as buildings, pipes, or parcel boundaries. Raster Data Model: In a raster model, the world is represented as a surface that is divided into a regular grid of cells. The x, y coordinate of at least one corner of the raster are known, so it can be located in geographic space. Raster models are useful for storing and analyzing data that is continuous across an area. Each cell contains a value that can represent membership in a class or a category, a measurement, or an interpreted value. Raster data includes images and grids. Images, such as an aerial photograph, a satellite image, or a scanned map, are often used for generating GIS data. Grids represent derived data and are often used for analysis and modeling. They can be created form sample points or by converting vector data. The smaller the cell size for the raster layer, the higher the resolution and the more detailed the map. Both vector and raster data models are shown in figure 2.

2.6. GIS Functions Any geographic information system should be capable of six fundamental operations in order to be useful for finding solutions to real-world problems. A GIS should be able to capture, store, query, analyze, display, and output data (Zeiler 1999, Bolstad 2002).

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Capturing data - Data describing geographic features is contained in a geographic database. The geographic database is an expensive and long-lived component of a GIS, thus data entry is an important consideration. A GIS must provide methods for entering geographic (coordinate) and tabular (attribute) data. The more input methods available, the more versatile the GIS. Storing data - There are two basic models used for geographic data storage: vector and raster. A GIS should be able to store both types of geographic data. Querying data - A GIS must provide tools for finding specific features based on their location or attributes. Queries, which are often created as logical statements or expressions, are used to select features on the map and their records in the database. Analyzing data - Geographic analysis usually involves more than one geographic dataset and requires working through a series of steps to reach a final result. A GIS must be able to analyze the spatial relationships among multiple datasets to answer questions and solve problems. There are many types of geographic analysis. The two common types of geographic analysis are described below: A. Proximity analysis - Proximity analysis uses the distance between features to answer questions like: 1) How many houses lie within 100 meters of this water main? 2) What is the total number of customers within 10 kilometers of this store? 3) What proportion of the alfalfa crop is within 500 meters of the well?

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GIS technology often uses a process called buffering, defining a zone of a specified distance around features, to determine the proximity relationship between features. B. Overlay analysis - The integration of different data layers involves a process called overlay. At its simplest, this could be a visual operation, but analytical operations require one or more data layers to be joined physically (i.e., combined into one layer in the database). Overlay analysis could be used to integrate data on soils, slope, and vegetation or land ownership data with tax assessment data. Displaying data - A GIS also needs tools for displaying geographic features using a variety of symbology. For many types of geographic analysis operations, the end result is best visualized as a map, graph, or report. Outputting data - Sharing the results of your geographic labor is one of the primary justifications for spending resources on a GIS. Taking displays created through a GIS (maps, graphs, and reports) and outputting them into a distributable format is a great way to do this. The more output options a GIS can offer, the greater the potential for reaching the right audience with the right information.

2.7. GIS Software GIS software is constructed on the top of basic computer operating capabilities such as security, file management, peripheral drivers, printing, and display management to provide a

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controlled environment for geographic information collection, management, analysis, and interpretation. The GIS software employed in a GIS project has a controlling impact on the type of studies that can be undertaken and the results that can be obtained. There are also far reaching implications for user productivity and project costs. Today, there are many types of GIS software product to choose from and a number of ways to configure implementations. Longley et al. (2005) classify the main GIS software packages into four main types as follows: Desktop GIS software: Desktop GIS software owes its origins to the personal computer and Microsoft Windows operating system and is considered the mainstream workhorses of GIS today. It provides personal productivity tools for a wide Varity of users across a broad cross section of industries. The desktop GIS software category includes a range of options from simple viewers (such as ESRI ArcReader, Intergraph GeoMedia Viewer and MapInfo ProViewer) to desktop mapping and GIS software systems (such as Autodesk Map 3D, ESRI ArcView, Intergraph GeoMedia, and MapInfo Professional), and at the high-end, full-featured professional editor/analysis systems (such as ESRI ArcGIS ArcInfo, Intergraph GeoMedia Professional, and GE Smallworld GIS). Desktop GIS software prices typically range from $1000- $20000 per user. Server GIS: Server GIS runs on a computer server that can handle concurrent processing requests from a range of networked clients. Initially, it focused on display and query applications, but now offers mapping, routing, data publishing, and suitability mapping. Third generation server GIS offers complete GIS functionality in a multiuser server environment. Examples of server GIS include AutoDesk MapGuide, ESRI ArcGIS Server, GE Spatial Application Server, Intergraph GeoMedia Webmap, and MapInfo MapXtreme. The cost of server GIS products varies from around $5000-25000, for small to medium-sized systems, to well beyond for large multifunction, and multiuser systems. Developer GIS: Developer GIS are toolkits of GIS functions (components) that a reasonably knowledgeable programmer can use to build a specific-purpose GIS application. They are of interest to developers because such components can be used to create highly customized and optimized applications that can either stand alone or can be embedded with other software systems. Examples of component GIS products include Blue Marble Geographics GeoObjects, ESRI ArcGIS Engine, and MapInfo MapX. Most of the developer GIS products from mainstream vendors are built on top of Microsoft’s COM and .Net technology standards, but there are several cross platform choices (e.g., ESRI ArcGIS Engine) and several Java-based toolkits (e.g., ObjectFX Spatial FX ). The typical cost for a developer GIS product is $1000 - $5000 for developer kit and $100-500 per deployed application. Hand-held GIS: Hand-held GIS are lightweight systems designed for mobile and field use. A very recent development is the availability of hand-held software on high-end socalled ‘smartphones’ which can deal with comparatively large amounts of data and sophisticated applications. These systems usually operate in a mixed connected/disconnected environment and so can make active use of data and software applications held on the server and accessed over a wireless telephone network. Examples of Hand-held GIS include Autodesk OmSite, ESRI ArcPad, and Intergraph Intelliwhere. Costs are typically around $400-$600.

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3. GIS, ES, MCDM AND SPATIAL DECISION MAKING 3.1. GIS and Spatial Decision Making The ultimate aim of GIS is to support spatial decision-making. A GIS system typically has the following capabilities (Zhu and Healey 1992): 1. 2. 3. 4.

Describing spatial problems and their spatial relationships. Storing and managing large quantities of complex and heterogeneous spatial data. Using geographical data models for structuring the available information. Providing spatial data handling and displaying facilities.

Malczewski (1999) analyzed the GIS capabilities for supporting spatial decisions in the context of Simon’s decision making process framework which divides any decision making process into three major phases: intelligence (is there a problem or opportunity for change?), design (what are the alternatives?), and Choice (which alternative is best?). Malczewski mentioned the following conclusions:

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1. Commercially available GIS systems tend to focus on supporting the first phase of the decision- making process through its ability to integrate, explore, and effectively present information in a comprehensive form to the decision makers, 2. These available GIS systems have limited capabilities of supporting the design and choice phases of the decision- making process, and 3. These systems provide a very static modeling environment and thus reduce their scope as decision support tools- especially in the context of problems involving collaborative decision-making. Today, geographic information systems incorporate many state-of-the-art principles such as relational database management, powerful graphics algorithms, and elementary spatial operations such as proximity analysis, overlay analysis, interpolation, zoning and network analysis. However, the lack of analytical modeling functionality and the low level of intelligence in terms of knowledge representation and processing are widely recognized as major deficiencies of current systems (Fischer 1994).

3.2. Expert Systems and Spatial Decision Making Expert systems are fast becoming the leading edge of artificial intelligence (AI) technology because of the need for such systems in commercial and scientific enterprises and also because AI technology has evolved to the point where expert systems development has become well understood and feasible in many domains. An expert system is a computer program that embodies the expertise of one or more experts in some domain and applies this knowledge to make useful inferences for the user of the system (Hayes-Roth et al 1983). Firebaugh (1988) defined expert systems as" a class of computer programs that can advise, analyze, categorize, communicate, consult, design, diagnose, explain, explore, forecast, form concepts, identify, interpret, justify, learn, manage, monitor plan, present, retrieve, schedule,

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test, and tutor. They address problems normally thought to require human specialists for their solution." Expert systems (ES) perform decision-making tasks by reasoning using domain specific rules that have been judged by an expert in his domain to be true. They are best suited for ill-structured problems. The distinctive strength of ES can be summarized as (Jackson 1990):

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1. 2. 3. 4.

Handling imprecise data, incomplete and inexact knowledge. Exploiting knowledge at the right time. Explaining and justifying the reasoning that lead to a conclusion. Changing or expanding knowledge relatively easily.

There is ample scope for applying ES technology in decision making processes. For example, we may use knowledge representation techniques to characterize decision-making domains, use heuristic methods to generate and evaluate decision options, apply inference and reasoning to explain and justify decisions, etc. Spatial decision making with expert systems began in the late of 1980s. Many expert systems have been developed to solve various site selection problems that are heavily dependent on human judgment and experience. These systems use symbolic knowledge to construct human understanding of problems in the area of site selection and evaluation. Because symbolic knowledge is not well suited to describe the spatial nature of site selection problems, expert systems lack a mechanism to derive solutions based on spatial knowledge (or knowledge about positional and topological characteristics) of different sites. Spatial knowledge is critical to spatial reasoning and decision making in many site selection applications (Jia 2000). Unfortunately, current expert systems can’t handle spatial knowledge. They don’t have an appropriate method to encode and represent the spatial nature of knowledge. Furthermore, they can’t deal with locators, spatial relations, and spatial reference actions involved in spatial knowledge. Zhu and Healey (1992) asserted that expert systems technology alone does not adequately support spatial decision making because it has the following limitations: 1. Spatial decision making requires large volumes of spatial data. These data mainly reside in GIS and not in ES. ES lack facilities for handling large-scale data sets. 2. Expert systems are concentrated on symbolic reasoning and do not provide good arithmetic capabilities. Yet, arithmetic operations are required in spatial data handling. 3. Expert systems lack spatial data handling capabilities such as buffering and overlay which are unique and important to spatial analysis. 4. Expert systems do not provide facilities for spatial data representation and visualization. Table 1. contrasts the strengths and weakness typically observed in expert systems and GIS .The advantages of integrating a GIS with an expert system have been recognized by a number of authors (Zhu and Healey 1992, Fischer 1994, Lilburne et al.1996, Moore 2000). Zhu and Healey (1992) argued that the integration of GIS and ES may avoid some of the limitations and difficulties existing in each of them and the spatial decision process can be made more effective within such integrated systems. They also mentioned that a conventional GIS is very suitable to well-structured spatial problem solving, while the integration of GIS

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and ES offers a best approach to solving ill-structured spatial problems and to providing knowledge of how to use and run the GIS. Lilburne et al. (1996) mentioned that domain knowledge represented in an expert system together with spatial data found in GIS can provide a decision support environment in which users are guided by the integrated system towards useful recommendations. Fischer (1994) asserted that there were no longer any questions that expert systems would be integral in building the next generation of intelligent GIS. Moore (2000) has noted that the reason why there is plenty of scope for use of expert systems in this subject area is that GIS without intelligence have a limited chance to effectively solve spatial decision support problems in a complex or imprecise environment.

3.3. MCDM and Spatial Decision Making

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Almost all decision problems involve the simultaneous consideration of several different objectives that are often in conflict. Multicriteria problems with conflicting objectives have encountered in several applications, such as facility location. The development of multicriteria methods is actually relatively recent. Over the past 20 years there has been a plethora of tools and techniques developed for solving these problems such as Analytic Hierarchy Process (AHP), Goal Programming, Data Envelopment Analysis, etc. MCDM techniques are decision support tools designed to analyze decision problems, generate useful alternative solutions, and evaluate alternatives based on the decision maker’s values and preferences. The general objective of these methods is to assist the decision-maker in selecting the best alternative from the number of feasible alternatives under the presence of multiple choice criteria and diverse criteria priorities (Eldrandaly, 2010). These techniques, however, assume homogeneity within the study area, which is unrealistic in many spatial decision making situations such as site selection problems. Table 1. Comparison of some GIS and expert systems capabilities (adapted from Lilburne et al.1996) GIS Quantitative and Suited to structured problems Use geometric primitives, e.g., point, line, polygon Integrate data Do not easily handle incomplete data Spatially capable Cope with large volume of data No explanation facility Can not represent and manage knowledge No inference or reasoning capabilities Algorithmic Variety of output maps/graphics Can efficiently perform geometrical operations

Expert Systems Qualitative and Suited to unstructured problems Use symbols Integrate knowledge Handles incomplete data and knowledge No spatial capability Do not cope well with lots of data Explanation facility Can represent and manage knowledge Have inference engines Opportunistic No mapping capability Can not efficiently perform geometrical or arithmetical operations

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Figure 3. Framework for spatial multicriteria decision analysis (adapted from Malczewski ,1999).

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It also, lacks the capability of handling spatial data (e.g., buffering and overlay) that are crucial to spatial analysis. Malczewski (1999) suggested that there is a need for an explicit representation of geographical dimension in MCDM techniques. The combination of GIS and MCDM capabilities could effectively solve this problem. Malczewski (1999) has proposed a framework for spatial multicriteria decision analysis, as shown in figure 3.

4. INTEROPERABILITY AND SYSTEMS INTEGRATION TECHNIQUES The concept of software interoperability is one of those buzzwords in the computer field that means different things to different people (Eddon and Eddon 1998). According to Goodchild et al. (1999) Interoperability means openness in the software industry, because open publication of internal data structures allows software users to use different software components from different developers to build their applications. It also means the ability to exchange data freely between systems, because each system would have knowledge of other systems’ formats. Interoperability also means commonality in user interaction, as system designers build interfaces that can be customized to a look and feel similar to the user. Wegner (1996) defined interoperability as “is the ability of two or more software components to cooperate despite differences in language, interface, and execution platform”. Interoperable systems are systems composed from autonomous, locally managed, heterogeneous components, which are required to cooperate to provide complex services (Finkelstein 1998). Although, Interoperability has been a basic requirement for modern information systems environment for over two decades (Sheth 1999), it is a recent research agenda element of geographic information science. To GIS users, interoperating GIS refers to the ability to exchange GIS data and functionality free among systems. Such interoperability can be

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achieved at three levels: technical (e.g. compatible data formats between systems), semantic (e.g. consistent meanings of data across systems), and institutional (e.g. legal and economic support for data sharing across organizations) (Goodchild et al. 1999). Major efforts in GIS interoperability are associated with organizations such as Open GIS Consortium and ISO/TC211 (Feng and Sorokine 2001). The development and deployment of successful interoperability strategies requires standardization that provides the lingua franca needed for the exchange and integration of information (Vckovski 1998). In the GIS community, the Open GIS Consortium has used the well known industry-wide specifications for exchanging data and functionality, such as COM (Component Object Model) by Microsoft and CORBA (Common Object Request Broker Architecture) by the Object Management Group, as the base standards to develop specifications for exchanging GIS data and functionality. Interoperability is sometimes distinguished from integration, but at other times the two terms are used almost interchangeably. Dictionary definitions suggest that any significant difference between them lies in the degree of coupling between the entities. Thus, an integrated system is sometimes considered to be more tightly coupled than a system composed of interoperable components. Yet even this distinction suggests that perspective is a key factor in discussing interoperability. Thus, when looked on from a distance, a system is perceived to be integrated, but from the perspective of its constituent elements, they are interoperating with each other. The issue of perspective is recursive, because the interoperable entities themselves may be an integration of other constituents. Thus, the relationship of the observer to the constituent makes a difference as to whether the appropriate term is integration or interoperability (Brownsword et al 2004). We shall not make any further distinction between these terms in the remainder of this study. An extensive body of literature published since 1990 documents the need for additional GIS functionality (Lilburne 1996). To fulfill this need, researchers give considerable interest in integrating geographical information systems (GIS) with other specialist systems to meet the requirements of advanced applications. Also, the integration of GIS and other technologies such as expert systems will lead to new, richer approaches to problem solving (Abel and Kilby 1994). In the context of a particular problem, systems integration essentially seeks to fuse capabilities available in the individual systems and to provide some desired level of usability (Chou and Ding 1992). Abel and Kilby (1994) argued that the available GIS and modeling systems are complex systems which would be costly to re-implement or to remodify and consequently, there is some value in determining the limits of possible integration using existing systems. They defined the system integration problem as “the problem which concerned with the coupling of pre-existing systems (the components of the integrated system) to fuse a desired set of capabilities with some targeted degree of usability of the integrated system. While the pre-existing systems (components) themselves are to be taken as not to be modified, systems integration typically involves the design of some specialist components linkage components to facilitate coupling. Identifying the types of the linkage components needed is then a core issue in the system integration problem.” Coupling is a measure of the degree to which functions in one software package can be controlled directly from another. It refers to the physical and logical connection between software packages in the system implemented (Malczewski 1999). The degree of interoperability between an expert system and a GIS will affect the ability of an integrated system to model the complexity of the real world (Linlburne et al 1996). Numerous mechanisms enabling interoperability between GIS and ES have appeared over the years. Examples range from simple solutions

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such as, loose coupling techniques to much more sophisticated approaches, such as Component Object Model (COM) technology and Ontology.

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4.1. Loose (Shallow) Coupling In this approach GIS and ES support each other to solve specific problems through sharing data files written in ASCII or other standard file format by the use of file transfer utilities (Goodchild et al. 1992). Using this approach, the GIS serves as a preprocessor or postprocessor to the expert system and the expert systems could access to the data stored in the GIS or produced by the GIS. However, this approach does not provide the ES with the spatial data handling capabilities of the GIS. At this level of integration, each tool runs independently, the user interfaces continue to be separated, and there is no need to write extra software, only the file formats have to be adapted. However, manipulating the exchange files tends to be cumbersome and error prone (Fedra 1996, Jun 1997). In addition, the approach may not work if the data sets extracted from the GIS become too large to fit into the ES database (Zhu and Healey 1992). The following paragraphs summarize some of the systems developed using this approach. Kirkby (1996) used loose coupling to integrate a GIS with an expert system to identify and manage dry land salinization in South Australia. The developed system, Salt Manager, is a UNIX-based computer software that integrates “off the shelf” commercially available GIS (ARC/INFO), RDBMS (ORACLE), and ES (Harlequin Lisp works/ Knowledge Works Environment) Software. The communication between the RDBMS and both the ES and GIS is conducted via a standard interface file, while the communication between the GIS and ES is conducted via an ASCII text file. Jun (1997) designed an expert geographic information system for industrial site selection by integrating GIS (ARC/INFO 7), expert system (CLIPS), and MCDM (AHP). The software integration between all the modules is based on loose coupling and is handled by the ASCII file transfer method. Yialouris et al. (1997) followed loose coupling strategy to develop EXGIS, an integrated expert geographical information system for soil suitability and evaluation. EXGIS consists of two components: GIS (ARC/INFO) and Expert system shell. The expert system shell was implemented in CLIPPER because the files produced by it (dBase III+ files) can subsequently be processed by ARC/INFO.

4.2. Tight (Deep) Coupling Tight (deep) integration means that one system provides a user interface for viewing and controlling the application, which may be built from several component programs (Pullar and Springer 2000). That is, tight coupling is to integrate ES with GIS using communication links in such a way that the GIS appears to the ES as an extension of its own facilities, or vice versa. One appears as the shell around the other. The system developed by this approach is called a "tight coupled standalone system". A "tight coupled standalone system" can be either a merged system with expert systems as a subsystem of GIS Functionality, or an embedded system, where existing GIS facilities are enhanced with expert system functionality. The

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second type of tight coupling is "expert command languages", where expert system reasoning is added to GIS macro or command languages (Zhu and Healey 1992). Compared with loose coupling, tight coupling is considered to be a more effective integration method as the decision problem can be modeled using generic tools on a single integrated database. However, the computations will not be optimal. Also, it sometimes causes serious problems due to the complicated communication between GIS macro language and the user-developed expert systems. The following paragraphs summarize some of the systems developed using this approach. Kristijono (1997) followed a tight coupling strategy to develop a knowledge-based GIS for landscape suitability. The author used the available tools of ARC/INFO GIS to build his system entirely within the GIS environment The following ARC/INFO tools were used to design the system :( 1) ARCEDIT environment, (2) a combination of the logical expressions and the commands of the ARCEDIT, and (3) the AML (ARC Macro Language) facilities. The first tool was used to perform as a rule editor, the second tools were used to transform the IF … THEN form of the production rules, and the third tool was used to control the whole operation of the entire transformed production rule. Corner et al (2002) used tight coupling in developing EXPECTOR, a method of combining data and expert knowledge within a GIS to provide information on the occurrence of spatially distributed attributes. The method has been implemented as a stand-alone "Knowledge Editing" module coded in Visual Basic and interfaced with a GIS (ArcView) both to derive information about the input spatial data and to communicate back the results of its calculations. Total Probability Rule and Bayes Theorem were used as knowledge representation mechanism and as the inference engine. The data processing and combination phases are carried out in ArcView using routines written in Avenue (the ArcView scripting language). Yang et al (2006) developed a GIS expert system for modeling distribution of matsutake mushrooms using tight coupling approach. The system was developed under ENVI-IDL environment and Bayesian theory was used as the inference engine

4.3. Client/Server Client/server technology refers to the software that allows a process to receive messages from another process. These messages request services of the receiving system (the server). The service might be to perform a specified action or to return some information to the requesting system (the client). Both processes remain in memory concurrently, avoiding the loss of performance that occurs when loading a system into memory every time of one of its functions is required. There is no limit to the number of requests, nor are there any restrictions on the types of requests that can be made. In client/server integration approach GIS and ES communicate via a standard protocol such as DDE, OLE or PRC which enables them to send and receive messages from other concurrently running systems. Functionality is interleaved, dynamic and relatively full. Data may be transferred or shared and there may be one or two interfaces (Lilburne 1996). The following paragraphs summarize some of the systems developed using this approach. Lilburne et al. (1996) used client/server technology in developing a spatial expert system shell (SES) that integrates two commercial products: the GIS ARC/INFO v7 and the expert

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system shell, Smart Elements. ARC/INFO v7 includes some commands which create a framework for client/server communication with another process. Once a connection has been initialized, messages can be sent between the processes. Smart Elements is a combination of a hybrid frame, rule-based expert system called Nexpert Object, and a GUI developer kit called Open Interface. It has an Application Programming Interface (API) which allows C routines to access Smart Elements functions. SES was developed on a Solaris SUN Workstation platform. Both ARC/INFO v7 and Smart Elements use Sun's ONCRPC client/server protocol. Smart Elements is the client and ARC/INFO is the server. A combination of C and ARC/INFO's macro language AML is used to develop the client/server interface between ARC/INFO and Smart Elements. Jia (2000) developed a conceptual framework for integrating ES and GIS using Transmission Control Protocol/ Internet Protocol and Remote Procedure Calling technologies. A software system (called IntelliGIS) implementing the method has been developed. IntelliGIS was implemented by enhancing CLIPS (a rule and object-based expert system shell) with ARC/INFO GIS and the ES-GIS interface developed in the research. ARC/INFO performs as the GIS server and its Inter-Application Communications (IAC) function is used for developing the spatial reference engine because the IAC function allows direct "talk" between CLIPS and ARC/INFO, and it does not require text files for the talk. The ES-GIS interface used Transmission Control Protocol/Internet Protocol (TCP/IP) and Remote Procedure Calling technologies to integrate CLIPS and ARC/INFO. Fedra and Winkelbauer (2002) developed a client/server DSS framework, RealTime eXPert System (RTXPS), which integrates a forward chaining expert system and a backward chaining system with simulation models and GIS for environmental and technological risk assessment. To integrate the various information resources in an operational decision support system, flexible client-server architecture is used, based on TCP/IP and the http protocol. The central system, which runs the RTXPS expert system as the overall framework is connected to a number of conceptual servers that provide high-performance computing and data acquisition tasks.

4.4. Component Object Model Technology (COM) To achieve interoperability between the systems, one must proceed with the decomposition of the software system into small components that are available to other applications (Bian 2000). Leading commercial software vendors have adopted component-based software development (CBSD) approach in their software design. CBSD approach focuses on building large software systems by integrating previously existing software components as a way to reduce development costs, improve productivity, and provide controlled systems upgrade in the face of rapid technology evolution (Brown 2000). This approach, which is also called componentware, is a further development of the object oriented programming. It adds to the object oriented programming the concept of a highly reusable components. In CSBD, the notion of building a system by writing code has been replaced with building a system by assembling and integrating existing software components (Karlsson 1995). Brown (2000) defined a software component as “an independently deliverable piece of functionality providing access to its services through interfaces”.

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A component is a reusable piece of software in binary form that can be plugged into other components from other vendors with relatively little effort (Eddon and Eddon 1998). A rather small group of objects is joined into the component with a well defined interface. Inside the component the objects can communicate with each other without any restrictions, but communication with the outside world is only possible through the component interface. A component acts like a black-box: the inner structure is hidden and protected from the outside world by the component interface (Rebolj and Sturm 1999). That is, with COM, applications interact with each other and with the system through collections of functions calls known as interfaces as shown in figure 4.(a). An interface is a strongly typed contract between a software component and a client that describes the component's functionality to the client without describing the implementation at all (Eddon and Eddon 1998). Each component can act as both client and server as shown in figure 4.(b). A server is a component that exposes interfaces and therefore a list of functions that a client can call (Lewis 1999). The main goal of the COM is to promote interoperability. COM supports interoperability by defining mechanisms that allow applications to connect (Eddon and Eddon 1998). COM specifies an object model and programming requirements that enable COM objects to interact with other COM objects. These objects can be within a single process, in other processes, or even on remote machines. They can be written in other languages and may have been developed in very different ways. COM allows these objects to be reused at a binary level, meaning that third party developers don’t require access to source code, header files, or object libraries in order to extend the system (Zeiler 2001). Leading commercial GIS software vendors have adopted component-based software development (CBSD) approach in their software design and choose COM as the component technology for their products. For example, ArcGIS Desktop (an integrated suite of professional GIS application) developed by Environmental systems Research Institute (ESRI), is based on a common modular component-based library of shared GIS software components called ArcObjects. ArcObjects includes a wide variety of programmable components which aggregate comprehensible GIS functionality for developers (Zeiler 2001). Also, Leading commercial ES software vendors have adopted COM technology in designing their software. Visual Rule Studio® (an object-oriented COM-compliant expert system development environment for windows) developed by RuleMachines is an example. Visual Rule Studio® solves the problem of software interoperability by allowing the developers to package rules into component reusable objects called RuleSets. By fully utilizing OLE and COM technologies, RuleSets act as COM automation servers, exposing RuleSet objects in a natural COM fashion to any COM compatible client.

Interface Requests Client

Interface COM Object

(a)

Server Service

(b)

Figure 4. COM Architecture (adapted from Lewis 1999).

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Visual Rule Studio installs as an integral part of MS Visual Basic 6.0, professional or enterprise editions, and appears within the visual Basic as an ActiveX Designer. RuleSets can be complied within Visual Basic .EXE, .OCX, or .DLL executables and used in any of the ways the developers normally use such executables (RuleMachines 2002). The following paragraphs summarize some of the systems developed using COM technology. Eldrandaly et al (2003) used COM technology to develop an intelligent GIS-based spatial decision support system for industrial site selection. A prototype was developed using three COM-compliant commercially available software packages: Visual Rule Studio®, ArcGIS® 8.2, and Microsoft® Excel 2002. Visual Rule Studio® was used to develop the expert system component. ArcGIS® provided the GIS platform to manage the spatial data and conduct the required spatial analysis operations. Microsoft® Excel provided the tools to implement the AHP component. In addition, Microsoft® Visual Basic® 6.0 was used to provide the shell for the COM integration and to develop the system’s user interface. Tsamboulas and Mikroudis (2005) used COM technology in developing a DSS (TRANSPOL) for evaluating transportation polices and projects. It was developed using four COMcompliant commercially available software packages: Microsoft Visual Basic, Microsoft Access, ESRI MapObjects, and Amzi Prolog. Eldrandaly (2006) developed a COM-based expert system to assist the GIS analysts in selecting suitable map projection for their application in ArcGIS software package. Visual Rule Studio® (an object-oriented COM-compliant expert system development environment for windows) was used to develop the expert system. The COM technology was used for integrating the expert system with ArcGIS ® 9.0, a COM-complaint GIS software package. Its built in macro language, Visual Basic for Application (VBA), was used to develop the Map Projection application that implements the expert system using Automation Technology.

4.5. Ontology: A Promising Interoperability Approach Ontologies are expected in various areas as promising tools to improve communication among people and to achieve interoperability among systems (Lee et al. 2006). Ontology for a philosopher is the science of beings, of what is, i.e., a particular system of categories that reflects a specific view of the world. For the Artificial Intelligence (AI) community, Ontology is an engineering artifact that describes a certain reality with a certain vocabulary, using a set of assumptions regarding the intended meaning of the vocabulary words (Fonseca et al. 2oo2). Ontology defines the terms and relationships among terms that represent an area of knowledge. In software engineering, computer-readable Ontologies are growing in importance for defining basic concepts within a domain. If multiple-domain applications are developed utilizing a shared Ontology, or if their distinct Ontologies can be related, then the applications can have a common understanding of data, and semantic interoperability is enhanced. In addition, Ontologies can be developed that relate information across domains, opening up new possibilities for interoperability (Carney et al. 2005). The importance of Ontologies in GIScience has been established over the past decade as scholars have demonstrated their value in multiple geospatial and reasoning contexts (Schuurman and Leszczynski 2006). The use of Ontology, translated into an active information system component, leads to Ontology-Driven Information Systems and, in specific case of GIS, leads to what is called Ontology-Driven Geographic Information Systems- ODGIS (Fonseca et.

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al.2002). ODGIS are built using software components derived from various Ontologies. These software components are classes that can be used to develop new applications. Being Ontology-derived, these classes embed knowledge extracted from Ontologies (Fonseca et al. 2002). Ontologies aim at modeling and structuring domain knowledge and an Ontology development follows a cycle containing several phases, ranging from the requirements analysis and initial Ontology design to conceptual refinement, evaluation and evolution as shown in figure 5 (Linkova et. al. 2005). Software tools are available to accomplish most aspects of Ontology development. Today's most Ontology languages are based on the XML syntax such as OWL (Web Ontology Language). OWL (Linkova et. al. 2005) is a product of W3C (the World Wide Web Consortium) and is presented as an Ontology language for the semantic web. It allows representing not only concepts, taxonomies, binary relations, but also cardinalities, richer type definitions and other characteristics. Most of the Ontology languages are supported by tools such as the widely used Protégé system which provides OWL support. Protégé is a free, open-source platform that provides a growing user community with a suite of tools to construct domain models and knowledge-based applications with Ontologies. At its core, Protégé implements a rich set of knowledge-modeling structures and actions that support the creation, visualization, and manipulation of Ontologies in various representation formats. Protégé can be customized to provide domain-friendly support for creating knowledge models and entering data. Further, Protégé can be extended by way of a plug-in architecture and a Java-based Application Programming Interface (API) for building knowledge-based tools and applications. The Protégé platform supports two main ways of modeling Ontologies: The Protégé-Frames editor that enables users to build and populate Ontologies that are framebased, in accordance with the Open Knowledge Base Connectivity protocol (OKBC) and The Protégé-OWL editor that enables users to build Ontologies for the Semantic Web(Protégé 2006). The following paragraphs describe two of the systems developed using Ontology. Moore et al. (2001) established an ontological basis for geography and environmental science (feeding into coastal zone management), and GeoComputation from a holistic viewpoint. This Ontology serves as the foundation for the development of COAMES (COAstal Management Expert System), which uses the Dempster-Shafer theory of evidence to model holism. COAMES is an object-oriented expert system, consisting of a user interface, a database, an object-oriented knowledge base (incorporating both the expert’s factual knowledge and the process knowledge embodied in models) and most importantly an inference engine. Within the inference engine are algorithms to calculate belief with uncertainty through the Dempster-Shafer Theory of Evidence. COAMES achieves technological holism, as it brings together expert systems and GIS, as well as remotely sensed data and GPS measurements. Niaraki and Kim (2009) developed a generic ontology-based architecture using a multi-criteria decision making technique to design a personalized route planning system. The objective of their research is to determine an impedance model of road GIS and Intelligent Transportation Systems (ITS) for a personalized ontology-based route planning system using a multiple criteria decision making method. The impedance model aims to distinguish the appropriate user-centric criteria and combine them in order to obtain the impedance function to be employed in a route finding algorithm.

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Khalid Eldrandaly Requirements Analysis

Initial Design

Refinement Evolution Evaluation

Ontology

Figure 5. Ontology Lifecycle (adapted from Linkova et. al. 2005).

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CONCLUSION In this study, we have attempted to highlight the complexity of spatial decision making, system integration problems, and interoperability and to provide an overview of the different strategies for integrating GIS, MCDM, and Expert Systems. From the above discussion, it is clear that spatial decision making is a highly complex process and most spatial decision problems are complex and ill structured. GIS, MCDM, and ES are required for solving these problems but each of them has its own limitations and drawbacks in dealing with spatial decision making. The integration of these tools may avoid some of the limitations and difficulties existing in each of them and provide the decision maker with an efficient tool for solving these problems. The degree of interoperability between these tools and a GIS will affect the ability of an integrated system to model the complexity of the real world. Numerous mechanisms enabling interoperability between GIS and these tools have appeared over the years. Examples range from simple solutions such as, loose coupling techniques to much more sophisticated approaches, such as Component Object Model (COM) technology and Ontology. Although the simple techniques (loose and tight coupling) have achieved considerable success in integrating GIS and these tools and they are still used now, these techniques have many drawbacks and limitations. These drawbacks can be eliminated or at least reduced by applying the recent approaches of software interoperability such to be COM technology and Ontology. Ontologies are expected as promising tools to achieve and open up new possibilities for software interoperability.

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Bernhardsen, T., 2002, Geographic Information Systems: An Introduction (New York: John Wiley and Sons). Bolstad, P., 2002, GIS Fundamentals (Minnesota: Eider Press). Bian, L., 2000, Component modeling for the spatial representation of wildlife movements. Journal of environmental management, 59, 235-245. Brown, A., 2000, Large – scale, component –based development (New Jersey: Prentice Hall). Brownsword, L., Carney, D., and Wrage, L., 2004, Current Perspectives on Interoperability, Technical Report, CMU/SEI-TR-009. Burrough, P., and McDonnell, R., 1998, Principles of Geographical Information Systems (New York: Oxford University Press). Carney, D., Fisher, D., and Place, D., 2005, Some Current Approaches to Interoperability, Technical Report, CMU/SEI-TN-033. Chou, H. and Ding, Y., 1992, Methodology of integrating spatial analysis/modeling and GIS. In Proceedings of 5th International Symposium on Spatial Data Handling (South Carolina), pp.514-523. Clarke, K., 2001, Getting Started with Geographic Information Systems (Upper Saddle River, NJ: Prentice-Hall). Corner, R. and Hickey, R., 2002, Knowledge Based Soil Attribute Mapping in GIS: The Expector Method, Transactions in GIS, Vol.6, No.4, pp.383-402. Davis, B., 2001, GIS: A Visual Approach (Canada: Onword Press). Dueker, K., 1979, Land resource information system: a review of fifteen years’ experience. Geo-Processing, 1, 105-128. Eddon, G. and Eddon, H., 1998, Programming Components with Microsoft Visual Basic 6.0(Redmond: Microsoft press). Eldrandaly, K., Eldin, N., and Sui, D., 2003, A COM-based Spatial Decision Support System for Industrial Site Selection, JGIDA, Vol.7, No.2, pp.72-92. Eldrandaly, K. ,2006, A COM-based expert system for selecting map projection in ArcGIS, Expert Systems with Applications, Vol.31, pp.94-100. Eldrandaly, K., (2010), Spatial Decision Making: An Intelligent GIS-Based Decision Analysis Approach’, (Germany: VDM Verlag). Fedra, K., 1996, Distributed models and embedded GIS: integration strategies and case studies. In GIS and Environmental Modeling: progress and research issues, edited by Goodchild, Steyeart, and Parks (Fort Collins, GIS World Books), PP.413-417. Fedra, K. and Winkelbauer, L. ,2002, A Hybrid Expert System, GIS, and Simulation Modeling for Environmental and Technological Risk Management, Computer-Aided Civil and Infrastructure Engineering, Vol.17, pp.131-146. Feng,C., and Sorckine,A.,2001, Incorporating hydrologic semantic information for interoperable GIS with hydrologic model. In Proceedings of the Ninth ACM International Symposium on Advances in Geographic Information Systems (Atlanta), pp.59-63. Finkelstein, A., 1998, Interoperable Systems: An introduction. In Information Systems interoperability, edited by Kramer, Papazoglou, and Schmidt (England: Research studies press), pp. 1-9. Fischer, M.M., 1994, From conventional to knowledge-based geographic information systems, Computers, Environment, and Urban Systems, Vol.18, No.4, pp.233-242. Firebaugh, M., 1988, Artificial Intelligence: A knowledge- based Approach (New York: Boyd and Fraser).

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Fonseca, F., Egenhofer, M., Agouris, P., and Camara, G., 2002, Using Ontologies for Inegrated Geographic Information Systems, Transactions in gis, 6(3), 231-257. Fonseca, F., Egenhofer, M., Davis,c., and Camara, G., 2002, Semantic Granularity in Ontology-Driven Geographic Information Systems, AMAI annals of Mathematics and Artificial Intelligence, 36(1-2), 121-151. Goodchild, M., Haining, R., and Wise, S., 1992, Integrating GIS and spatial data analysis: problems and possibilities. International Journal of Geographical Information Systems, 6, 407-423. Goodchild, M., Egenhofer, M., Fegeas, R., and Kottman, C.,1999, Interoperating Geographic Information Systems (Massachusetts: Kluwer Academic publishers). Hayes-Roth, F., Waterman, D.A., and Lenat, D., 1983, Building Expert Systems (Reading: Addison-Wesley). Jackson, P., 1990, Introduction to Expert Systems, Addison-Wesley. Jankowski, P., and Nyerges, T., 2001, Geographic Information Systems for Group Decision Making (London: Taylor and Francis). Jia, X., 2000, INTELLIGIS: Tool for representing and reasoning spatial knowledge. Journal of computing in civil engineering, 14, 51-59. Jun, C., 1997, Incorporating decision preferences into an expert geographic information system for industrial site selection. PhD Dissertation, Texas AandM University, College Station, USA. Karlsson, E.A., 1995, Software Reuse: A Holistic Approach (Chichester: John Wiley and sons). Kirkby, S., 1996, Integrating a GIS with an expert system to identify and manage drayland salinization. Applied Geography, 16, 289-303. Kristijono, A., 1997, Modeling a Knowledge–based Geographic Information System For Landscape Suitability: Siberut Island, Indonesia. PhD Dissertation, Texas AandM University, College Station, USA. Lee, J., Chae, H., Kim, K., and Kim, C., 2006, An Ontology Architecture for Integration of Ontologies, In R.Mizoguch, Z.Shi, and F.Giunchiglia (Eds.): ASWC-LCNS4185, pp.205211. Leung, Y., 1997, Intelligent Spatial Decision Support Systems (Berlin: Springer). Lewis, T., 1999, VB COM (UK: Wrox Press). Lilburne, L., 1996, The Integration Challenges. In Proceedings of the spatial information research center’s 8th colloquium (New Zealand), pp.85-94. Lilburne, L., Benwell, G. and Buick, R., 1996, GIS, Expert Systems, and Interoperability. In Proceedings of the 1st international conference in GeoComputation (Leeds, UK), pp.527-541. Linkova, Z., Nedbal, R, and Rimnac, M., 2005, Building Ontologies for GIS, Technical Report No.932, Institute of Computer Science, Academy of Sciences of the Czech Republic. Longley, P., Goodchild, M., Maguire, D., and Rhind, D., 2005, Geographic Information Systems and Science (New York: Wiley). Malczewski, J., 1999, GIS and Multicriteria Decision Analysis (New York: John Wiley and Sons). McLeod, R. and Schell, G., 2001, Management Information Systems (Upper Saddle).

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Moore, T., 2000, Geospatial expert systems" In Openshaw, S. and Abrahart, R., GeoComputation, Taylorand Francis. Moore, A., Jones, A., Sims, P., and Blackwell, G., 2001, Integrated Coastal Zone Management's Holistic Agency: An Ontology of Geography and GeoComputation, The 13th Annual Colloquium of the SIRC, University of Otago, New Zealand. Niaraki, A., and Kim, K., 2009, Ontology based personalized route planning system using a multi-criteria decision making approach. Expert Systems with Applications, 36, 22502259. Pullar, D. and Springer, D., 2000, Towards integrating GIS and catchment models. Environmental Modeling and Software, 15, 451- 459. Rebolj, D., and Sturm, P., 1999, A GIS based component-oriented integrated system for estimation, visualization, and analysis of road traffic air pollution. Environmental modeling and software, 14, 531-539. Protégé , 2006, Protégé OverView, http://protege.stanford.edu/overview/ RuleMachines, 2002, Visual Rule Studio Developer’s Guide (Canada: OnDemandManuals). Schuurman, N., and eszczynski, a., 2006, Ontology-Based Metadata, Transactions in gis, 10(5), 709-726. Sheth, A., 1999, Changing focus on interoperability in information systems: from systems, syntax, structure to semantics. In Interoperating Geographic Information Systems, edited by Goodchild, Egenhofer, Fegeas, and Kottman, (Massachusetts: Kluwer Academic publishers). Star, J., and Estes, J., 1990, Geographic Information Systems: An Introduction (Englewood Cliffs, NJ: Prentice Hall). Vckovski, A., 1998, Interoperable and distributed processing in GIS (London: Taylor and Francis). Wegener, P., 1996, Interoperability. ACM Computing Surveys, 28, 285-287. Worboys, M., and Duckham, M., 2004, GIS: A Computing Prespective, CRC Press. Yang, X., Skidmore, A., Melick, D., Zhou, Z., and Xu, J. ,2006, Mapping non-wood forest product (matsutake mushrooms) using logistic regression and a GIS expert system, Economical Modeling, doi:10.1016/j.ecolmodel.2006.04.001. Yialouris, C., Kollias, V., Lorentzos, N., Kalivas, D., and Siderdis, A., 1997, An integrated expert geographical information system for soil suitability and soil evaluation. Journal of Geographic Information and Decision Analysis, 1, 90-100. Zeiler, M., 1999, Modeling Our World: The ESRI Guide to Geodatabase Design (Redlands: ESRI Press). Zeiler, M., 2001, Exploring ArcObjects (Redlands: ESRI Press). Zhu, X. and Healy, R., 1992, Towards Intelligent Spatial Decision Support: Integrating Geographic Information Systems and Ex.

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Chapter 4

USING GEOGRAPHIC INFORMATION SYSTEMS FOR RANGE-WIDE SPECIES CONSERVATION PLANNING Kathy A. Zeller and Alan Rabinowitz Panthera, 8 West 40th Street, Floor 18, New York, NY 10018, USA

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ABSTRACT Geographic Information Systems (GIS) provide important tools for developing comprehensive and effective conservation strategies throughout the entire range of a species. Range-wide conservation strategies have typically used GIS to identify and prioritize populations across a species' distribution. We propose the addition of corridors to these rangewide exercises. Corridors facilitate genetic exchange, can ameliorate the negative effects of demographic and environmental stochasticity, and may increase the survival probability of species in the face of climate change. We present a new range-wide conservation model for the jaguar (Panthera onca) that uses GIS, expert input, and graph-based connectivity metrics to incorporate corridors into an existing range-wide priority setting exercise. The jaguar is an ideal species for this type of modeling because of extensive genetic exchange across its current distribution. Using expert input, we first developed a movement cost surface for the historic range of the jaguar. We then used this cost surface with the 90 known jaguar populations to model least-cost corridors. Results indicate that 78% of historic jaguar range, an area of approximately 14.9 million km2, still holds potential for jaguar movement and dispersal. One-hundred eighty-two corridors were identified between populations, ranging from 3 to 1,607 km in length. We then identified three types of priority areas for conservation across jaguar range; populations of ecological importance, populations and corridors of network importance, and vulnerable corridors. Based on our criteria, we identified 32 populations of ecological importance, 23 populations and 13 corridors of network importance, and 44 corridors that are vulnerable due to their limited width, and high potential for being break points in the network. These results are novel in that they account for dispersal and genetic exchange between populations throughout the full range of a widely distributed large carnivore species. By prioritizing areas for conservation based on ecological and network importance we developed a more comprehensive and meaningful tool for jaguar conservation

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across their current distribution. The methods and GIS techniques used here can easily be applied to other wide-ranging species.

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INTRODUCTION Because species are inextricably linked to the spatial environment in which they live, Geographic Information Systems (GIS) have been exceptionally powerful tools for wildlife research and conservation. GIS is used for mapping wildlife-related data such as population locations, habitat use and availability, and changes in habitat. GIS also gives us the ability to assess, not just individual populations of wildlife or discrete habitat patches, but the entire range of a species -- providing a relatively new perspective for wildlife research and a much more effective platform from which strategic conservation planning can take place. It has long been recognized that protected areas in themselves are not sufficient for the conservation of many large mammals. Even the biggest protected areas in East Africa and North America are too small or too isolated to maintain healthy populations of wide-ranging species (Newmark 1987; Newmark 1995; Gurd et al. 2001). Furthermore, focusing conservation on a few discrete protected areas across the entirety of a species' range does not prevent the persistent contraction of their geographic distribution (Sanderson et al. 2002). Scaling up our perspective allows us to identify ecological distinctions across a species' range and aim to conserve populations that have unique behavioral, demographic, and ecological characteristics (Wikramanayake et al. 1998). This approach elevates ecology above any sort of political boundary, so we can examine a species' needs regardless of whether a population spans an international border or is thriving beyond the boundaries of a national park. A range-wide perspective also facilitates the identification of large-scale patterns of threats and anthropogenic development, allowing us to determine which areas are most highly threatened today, and predict which areas will face significant threats in the future. Finally, range-wide planning may be important in preventing extinction due to climate change. It has been shown that species that are more widely distributed, and occupy a diverse climactic envelope, are less threatened by a changing climate (Harte et al. 2004; Barnosky 2008; Faith and Surovell 2009). In the late 1990s, wildlife biologists began applying this range-wide thinking to species conservation in what are now commonly referred to as Range-wide Priority-Setting (RWPS) exercises. RWPS exercises have played a crucial role in shifting the traditional conservation paradigm from discrete populations to a consideration of how aggregate populations contribute to the survival of a species across its entire geographic range. These exercises, which use a combination of expert opinion and field data, are built within a geographic framework and rely heavily on GIS tools. The exercise takes place in a hierarchical fashion where first the historic range is mapped, then the current range is delineated, and finally the core populations, or Conservation Units, are identified. Conservation Units in each distinct ecotype across the range of a species are prioritized based on their ability to contribute to long-term conservation. To date, RWPS exercises have been performed for a variety of species, from tigers (Wikramanayake et al. 1998, 2004) and jaguars (Sanderson et al. 2002; Zeller 2007) to

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crocodiles (Thorbjarnarson et al. 2005) and chimpanzees (Plumptree et al. 2010). These RWPS exercises have been powerful and effective conservation tools because they provide a single framework for the entirety of a species from which conservation planning and research can take place. However, for wide-ranging species and species for which dispersal is an innate biological characteristic, significant improvements can be made to these exercises through the addition of movement corridors. Though mentioned in previous RWPS exercises, corridors have not been explicitly identified as part of any range-wide conservation strategies to date, with the exception of one corridor that was identified for chimpanzees (Sanderson et al. 2006; Plumptre et al. 2010). Corridors have been shown to strengthen efforts to protect species and habitats by maintaining or enhancing connectivity between populations (Noss et al. 1996; Crooks and Sanjayan 2006; Gilbert-Norton et al. 2010). Connectivity can contribute to the survival of species by allowing for the dispersal of individuals from their natal ranges to new territories, which, in turn, facilitates the exchange of genetic material among otherwise isolated populations. Without genetic exchange, genetic drift and inbreeding can occur (Soulé and Mills 1998; Young and Clarke 2000). Other negative consequences include potential deleterious effects on sperm production, mating ability, female fecundity, and juvenile survival (Frankham et al. 2002). Such effects can eventually compromise the ability of individuals to adapt to changing conditions and climate (Saccheri et al. 1998; Lehmann and Perrin 2006), reduce fitness, and contribute to extinction risk for a population (Frankham 2005). In facilitating the movement of individuals between populations, corridors may also play a role in ameliorating the negative effects of demographic and environmental stochasticity (Brown and Kodric-Brown 1977; Hilty et al. 2006). Furthermore, corridors may be vital for species persistence by allowing for range shifts in response to climate change (Noss and Daly 2006; Soulé et al. 2006; Krosby et al. 2010). A recent climate change simulation experiment projected a drastically lower number of extinctions when dispersal habitat was available compared to scenarios without dispersal habitat (Thomas et al. 2004). In addition to the important roles corridors play in species survival, the incorporation of corridors allows us to monitor and address habitat fragmentation across a species range. RWPS exercises have no way to account for large disturbances outside of Conservation Units that may ultimately affect species survival by leading to irreversible habitat loss and isolation of populations. RWPS exercises also do not account for the importance of populations based on their location in a network or meta-population. When corridors are considered, a linked, geographic network is formed that can make range-wide conservation planning more effective and conservation strategies more meaningful (Keitt et al. 1997). Take, for example, ten populations connected by corridors (Figure 1a). This conglomerate of populations and corridors forms a network where species can move between populations and populations retain their genetic structure and viability. From this perspective, we can identify the populations and corridors that are essential to maintaining the connectivity of the whole network. If, as displayed in Figure 1b, the population, habitat, and corridors associated with Population R were lost, Populations S, T, and U are completely isolated from the rest of the network as are the sub-networks of

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Populations P, Q, and U and V, X, and Y. This simplified example illustrates how, through the identification of populations and corridors that are important for maintaining the integrity of the network, the prioritization scheme from RWPS exercises can be enhanced, and isolation of populations can be prevented.

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Figure 1. Populations (circles) and corridors (lines). Part A shows a connected network. Part B shows the network if the population, habitat, and connections associated with Population R were lost.

A network perspective may also highlight the importance of small populations in maintaining network connectivity, which have traditionally been given a low priority in RWPS exercises. In this chapter, we will use the jaguar (Panthera onca) to illustrate how we employed GIS tools to incorporate corridors into a RWPS exercise and to develop a conservation strategy across the entire range of this species. For details on using GIS in the RWPS process and the complete results of the exercise, please see Sanderson et al. (2002) and Zeller (2007). The jaguar, a near-threatened species (IUCN 2009) and the largest cat in the New World, historically occupied a continuous range from the southern United States to central Argentina (Swank and Teer 1989). By the end of the 20th century, hunting for the fur trade, persecution for livestock depredation, and habitat loss caused an estimated 54% reduction in the historic range of the jaguar, with high levels of habitat fragmentation (Sanderson et al. 2002). Yet studies of genetic variation among jaguars using mitochondrial DNA showed little evidence of significant geographical partitions or barriers to gene flow range-wide (Eizirik et al. 2001; Johnson et al. 2002; Ruiz-Garcia et al. 2006). The jaguar is an ideal candidate for this type of analysis because it is a wide-ranging species with a very large distribution whose habitat has not yet been fragmented to the point where movement is restricted between populations.

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METHODS Previous RWPS exercises identified 90 core jaguar populations, or Jaguar Conservation Units (JCUs) (Sanderson et al. 2002; Zeller 2007). These populations, identified by jaguar biologists throughout the range of the species, are either thought to have at least 50 breeding individuals and sufficient habitat and prey to survive for the next 100 years, or have sufficient habitat and prey such that, if threats were alleviated, the breeding population could increase to 50 individuals. We used these JCUs as the source and destination patches for our corridor analysis. Corridors can be identified in any number of ways, from the 'seat of the pants' approach to the most complex and spatially explicit individual-based models (Noss and Daly 2006). Because of the large scale of our study area and the lack of empirical data on jaguar movement during dispersal events, we chose to identify linkages between JCUs with a least-cost corridor model (Adriaensen et al. 2003; Epps et al. 2007). Least-cost corridor models are simple enough to use expert opinion to inform parameters relating to dispersal and movement behavior, yet are complex enough to predict functional areas of connectivity. Functional connectivity takes into account both landscape structure the response of individuals to this landscape structure (Pither and Taylor 1998). The foundation for most least-cost corridor models is a grid-based matrix which quantifies the landscape into varying levels of resistance, or costs, to movement (Bélisle 2005; McRae 2006). The matrix is constructed by assigning a movement cost to each chosen landscape element (Ray et al. 2002). Using such a matrix allows us to expand beyond the simple notion of habitat connectivity, where two patches are connected by a swath of similar habitat, by quantifying all the varying landscape features that a large carnivore such as a jaguar might use (Singleton et al. 2002). The use of certain anthropogenic landscape layers also allows us to incorporate the notion of safety and survival into the matrix. After constructing the landscape matrix, a least-cost-corridor analysis can be performed between populations.

GIS Layer Compilation Using ArcGIS v9 software, we chose six GIS-based landscape characteristics considered to most affect jaguar movement and survival: land cover type, percent tree and shrub cover, elevation, distance from roads, distance from settlements, and human population density (Table 1). Land cover class, percent tree and shrub cover, and elevation are closely related to movement behavior in most large mammal species (Carroll et al. 2003; Naves et al. 2003; Dickson et al. 2005), whereas distance from roads, distance from settlements, and human population density were considered to be correlated with human persecution of jaguars, including direct mortality (Naves et al. 2003; Rabinowitz 2005; Woodroffe et al. 2005). The roads and settlements vector layers were converted into distance grids using the Spatial Analyst Euclidean Distance function. Layers were standardized to a Lambert Azimuthal Equal Area projection and re-sampled to a 1 km2 grid.

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Kathy A. Zeller and Alan Rabinowitz Table 1. Geographic data bases used for creating the jaguar movement cost surface

Data Base Elevation

Land cover type Percent tree and shrub cover Settlements Roads Human population density

Dataset name and scale Global 30 arc-second elevation data set, 1 km resolution Global land cover 2000, 1 km resolution Continuous vegetation fields, 500 m resolution Vector map level 0, 1:1,000,000 scale Vector map level 0, 1:1,000,000 scale Gridded population of the world v3, 2.5 min resolution

Year of data 1996

1999–2000

Source Center for earth resources observation and science (EROS) Global land cover 2000

2000

Global land cover facility

1960s–1990s

National imagery and mapping agency (NIMA) National imagery and mapping agency (NIMA) Center for international earth science information network (CIESIN)

1960s–1990s 2000

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Creation of Cost Surface Least-cost corridor analyses depend on an understanding of how individuals move (Dickson et al. 2005). Since scientific data on jaguar dispersal was not available, we asked 15 jaguar experts throughout jaguar range to assign cost values to the attributes of the individual landscape layers based on how costly a particular attribute would be to jaguar movement. Cost values ranged from 0 (no cost to jaguar movement) to 10 (a high cost for jaguar movement). Attributes could be assigned an N/A if the physical characteristics of that cell would prevent a jaguar from moving through it. Experts also provided a value representing the cumulative cost of all the layers beyond which a jaguar would not likely travel. We averaged the values across the jaguar’s range to obtain an overall movement cost for the attributes of each landscape layer (Table 2). Movement costs were then applied to each cell of the six grids (Figure 2) and the grids were combined into one layer using the Raster Calculator. To create the final cost surface, we reclassified the output from the Raster Calculator so that all the pixels whose sums were above 25 (the average cumulative score indicating a barrier to movement) represented a break in the matrix.

Corridor Delineation To determine optimal routes of travel across the cost surface, we used the Cost-Distance function in Spatial Analyst to create movement cost grids from each of the 90 JCUs. This tool accumulates costs as it moves away from a population, taking into account both distance and direction. These cost-distance grids were used as inputs in the Corridor function in Spatial Analyst. We used the Corridor function between all proximate pairs of jaguar populations to derive least-cost corridors between these populations. To combine all overlapping corridors

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and display the best routes for jaguar movement, we used the minimum mosaic method and then extracted the lowest 0.1% of grid cell values.

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Prioritization We identified three priority classes across the range of the jaguar. One priority class was based on the importance of JCUs and corridors in maintaining the connectivity of the network. A second priority class was based on a population's ecological importance across jaguar's range. A third priority class was based on the vulnerability of jaguar corridors to losing their functionality as conduits to movement. In order to determine the importance of populations in maintaining connectivity across the jaguar's range, we performed an analysis where JCUs were evaluated based on their significance in maintaining the overall connectivity of the network (Keitt et al. 1997; PascualHortal and Saura 2006; Saura and Pascual-Hortal 2007). We used CONEFOR Sensinode 2.2 software to calculate the integral index of connectivity (IIC) and the change in IIC (dIIC) for each population (Pascual-Hortal and Saura 2006; Saura and Pascual-Horal 2007). The IIC calculates the extent of habitat connectivity in a network, incorporating the area of each population and the number of links between populations. The dIIC for a population is simply the change between the original IIC value and the IIC value of the network when that population is removed. The higher the dIIC value, the higher the importance for maintaining the network (Pascual-Hortal and Saura 2008). We also identified corridors that were important for maintaining the connectivity of the network. Any corridor that was the sole linkage to a population with a high dIIC value was also considered a priority since losing these connections would also result in severing the connectivity of the network. Ecologically-important JCUs were previously identified in the RWPS exercise. JCUs were separated by eco-region and the JCUs in each eco-region were ranked against one another in terms of their ability to provide for long term jaguar conservation. JCUs which had large areas, stable or increasing populations, good quality habitat, frequent dispersal, and little or no hunting were ranked higher than other JCUs in their eco-region. We used the 32 populations that had the highest possible rank as our ecologically-important JCUs (Zeller 2007). The corridor vulnerability analysis was based on the width of the corridors. While no empirical data exist on the width at which corridors fully lose their functionality, corridor width likely becomes more important as the corridor length increases. Beier (1993) suggested cougar corridors to be at least 400 m wide, while Florida panthers disperse through areas 3-7 km wide (Kautz et al. 2006). Therefore, we identified any corridor that measured less than 10 km in width at any point along its length as vulnerable to being lost. We combined the three priority classes to identify all areas of conservation importance across jaguar range.

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Table 2. Classes of landscape layers and expert-determined cost values for jaguar movement. Possible values ranged from 0 (no cost to jaguar movement) to 10 (a high cost for jaguar movement). A class could also be assigned an N/A if its characteristics would create a barrier to jaguar movement Land Cover Type

Class Tree Cover, broadleaved, evergreen Tree Cover, broadleaved, deciduous Tree Cover, needle- leaved, evergreen Tree Cover, mixed leaf type Tree Cover, regularly flooded, fresh water Tree Cover, regularly flooded, saline water Mosaic: Tree cover/other natural vegetation Shrub Cover, evergreen Shrub Cover, deciduous Herbaceous Cover Sparse herbaceous or Sparse shrub cover Regularly flooded shrub and/or herbaceous cover Cultivated and managed areas Mosaic: Cropland/Tree Cover/ Other atural Vegetation Mosaic: Cropland/Shrub or grass cover Bare areas Water Bodies

Percent Tree and Shrub Cover Cost Value 0 0 1 0 2 2 1 2 3 5 6 5 8 5 7 8 6

Class 0 – 10% 10% - 20% 20% - 40% 40% - 60% 60% - 80% 80% 100%

Cost Value 9 7 5 2 0

Human Population Density (people/km2) Class Cost Value 0-20 1 20-40 5 40-80 7 80-160 9 160-320 10

0

>320

N/A

Elevation (meters) Class 0 – 1000 1000–2000 2000 – 3000 3000– 5000 >5000

Cost Value 0 2 7 10 N/A

Distance from Roads (kilometers)

Distance from Settlements (kilometers)

Class

Class

0 to 2 2 to 4 4 to 8 8 to 16 > 16

Cost Value 7 4 2 1 0

0–2 2–4 4–8 8 – 16 > 16

Cost Value 8 5 4 1 0

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Figure 2. Expert-derived movement costs applied to the six landscape layers. The costs were averaged across all the experts and were restricted to the historic range of the jaguar.

RESULTS Additive cost values from the 6 landscape layers yielded movement costs ranging from 154. Any value over 25 was considered a barrier to movement. The final permeability matrix

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from this analysis (Figure 3) represents areas that could potentially be used by a dispersing jaguar. The results indicate that 78% of the jaguar’s historic range still has the potential to allow for jaguar dispersal.

Figure 3. Permeability matrix. Cumulative values from the 6 landscape layers yielded movement costs from 1-54. Values above 25 were considered barriers to movement.

The 90 JCUs from the RWPS exercises are shown in Figure 4a. The least-cost corridor analysis resulted in corridors connecting all 90 JCUs except two, between the Sierra de las Minas JCU in southern Guatemala and the Pico Bonito/Texiguat JCU in north central Honduras. Figure 4b portrays the 182 resultant corridors. The total area of all 90 JCU’s is 1.9 million km2 (Zeller 2007), while the total area of the corridors connecting these JCUs is 2.6 million km2. For Mexico and Central America, the average corridor length between known

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jaguar populations is 174.42 km (range: 3 - 1102 km) compared to South America, where the average corridor length is 489.14 km (range: 12-1607 km).

Figure 4. Jaguar Conservation Units and Corridors.

The network importance analysis resulted in dIIC values ranging from 1-20 (median 2.75). For prioritization purposes, we chose all 23 populations above the 75th percentile as being the most important for maintaining the integrity of the network. The JCU that ranked the highest in importance was the Choco-Darien JCU spanning the border of Panama and Colombia. Of the highest ranked JCUs, 18 were in Mexico and Central America and the

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remainder were in South America. Any corridors that were the sole linkage for a JCU with a high dIIC value were also prioritized as being important for maintaining the integrity of the network. This resulted in 13 corridors, all located in Mexico and Central America. Results of the network importance analysis are displayed in Figure 5a.

Figure 5. Prioritization Results.

Figure 5b displays the 32 JCUs that were identified in the RWPS exercise as being the highest priority for their ecological importance and ability to contribute to long-term jaguar survival.

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We identified 44 corridors which were vulnerable to being lost. Including the Guatemala/Honduras connection, five of these corridors were in Central America and Mexico and 39 were in South America (Figure 5c). Figure 5d shows the assemblage of all three priority categories summarized above. There were eight JCUS that were both important for the integrity of the network and are important ecologically. There were nine corridors that were both vulnerable to fragmentation and important for network integrity. Clusters of JCUs that are important for connectivity, JCUs that are a priority ecologically, and corridors that are vulnerable are found in the extreme northern and southern parts of jaguar range as well as in Central America and Colombia. These clusters point to areas where efforts would significantly contribute to a range-wide jaguar conservation strategy.

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CONCLUSION The results of our least-cost corridor analysis are supported by previous genetic studies (Eizirik et al. 2001; Johnson et al. 2002; Ruiz-Garcia et al. 2006) in that linkages were identified between all populations, with the exception of one linkage across northwestern Honduras -- likely a reflection of recent habitat change. The corridors represent areas with both the shortest distance and least dispersal cost between jaguar populations. It is important to note that these corridors are not predictive corridors. They do not identify where an individual jaguar might travel, but instead offer a functional conduit for movement which, if chosen by a jaguar, would offer the shortest route through the best habitat and offer the highest chance of survival. While scientific data on jaguar dispersal or long range movements are lacking, de Almeida (1990) cites jaguars moving 15 km or more in a single night on hunting patrols in the Brazilian Pantanal. Crawshaw and Quigley (1991) and Crawshaw (1995) documented dispersal distances of 30 and 64 km respectively for male jaguars in different areas of Brazil. One jaguar dispersed for three months, a second for eight months before being killed. Leopold (1959) speculated that a jaguar killed in California in the 1950’s had traveled more than 800 km from its point of origin. Clearly, the occasional jaguar traversing corridors ranging from three to 1,607 km in length is not entirely unlikely. However, as distances between core jaguar populations increase, relatively small patches of habitat that might not normally support even a single resident jaguar take on greater importance. Such stepping stone islands, where an individual jaguar might rest and/or feed, greatly increase the ability of the species to disperse (Sondergerath and Schroder 2001) and thus become important landscape features for possible connectivity (Harris1984). The identification and maintenance of these stepping stones will be an integral element in corridor conservation and planning. It is widely acknowledged that carnivores are often killed once they travel outside of protected areas (Naves et al. 2003; Rabinowitz 2005; Woodroffe et al. 2005). Jaguars traveling in corridor areas are exposed to this type of persecution. However, the corridors identified in this study were designed to keep threats to jaguars to a minimum. In addition, corridors will likely remain functional even if successful dispersal is a rare occurrence. Mills and Allendorf (1996) suggested that populations needed at least one migrant but not above 10

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migrants per generation to preserve genetic vigor. Recent data indicate that isolated jaguar populations that have shown divergent genetic patterns are eventually “rescued” by the occasional immigrant that disperses into these populations (E. Eizirik, personal communication; Hanski 2001). Computer simulations also predict that subdivided populations typically preserve more alleles and maintain more heterozygotes over the long term than do intact populations with the same total numbers of individuals (Boecklen 1986) -suggesting that smaller populations connected by occasional migrants may have genetic advantages. To account for demographic and environmental stochasticity and markedly increase the probability of population persistence, it was found that as few as one to four cougars per decade are needed to immigrate into a small population (~20 animals) (Beier 1993). The maps and analyses presented here represent a practical range-wide conservation strategy for the jaguar as well as a platform for regional and site-based actions for the species. The population importance analysis allowed us to analyze population 'hubs' across the entire range of the jaguar. These hubs, if lost, would have severe negative impacts on the connectivity of the rest of the network. It is not surprising then, that these connectivity hubs are concentrated in Central America. Spread along an already-restricted isthmus, JCUs and corridors mostly form a single chain which, if broken, would divide the network. Colombia also contained a concentration of important populations for connectivity and vulnerable corridors, being at the nexus of Central and South America. JCUs of ecological importance were spread throughout the range of the jaguar, but concentrations of these JCUs and vulnerable corridors were identified in the southernmost portions of jaguar range. These priority areas have formed the basis for our on-the-ground research and conservation activities. We have established programs in Belize, Honduras, Nicaragua, Costa Rica, Panama, and Colombia and are also supporting research activities in northern Mexico and in the southern edges of jaguar range. By using the framework presented in this chapter, the work of other jaguar biologists will have a multiplying effect, increasing the importance of individual actions and making site-based conservation initiatives more meaningful. Because one of our goals for jaguar conservation is to petition governments to implement jaguar corridors through a variety of measures -- from reducing jaguar-livestock conflicts and promoting sustainable livelihoods, to implementing land use zoning and establishing new protected areas -- we opted to verify the GIS-based corridors with field data. We are currently collecting data on the presence of jaguars and their prey in the landscape matrix between populations. These data are being analyzed within an occupancy model to predict habitat use of our target species and refine the boundaries of the least-cost corridors (Zeller et al. 2011). To date, we have assessed ten corridors in six countries which cover over 20,000 km2. The field assessment not only provides a baseline of data from which future conservation activities can be monitored, it allows us to include the presence of prey species in the identification of corridors, making these connections important not only for jaguars, but for a suite of species, thereby increasing their contribution to biological conservation throughout jaguar range. An improvement to the analysis presented in this chapter would be the use of empirical data for developing the permeability matrix. Studies have shown that empirical data on wildlife habitat use outperforms expert opinion (Johnson and Gillingham 2005). Admittedly, empirical data, such as telemetry data or genetic data are often hard to come by for large carnivores, especially over very large study areas. However, if available, there are a variety of

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methods in which empirical data can be incorporated into resistance surfaces. Chetkiewicz et al. (2006), describe the use of telemetry data and resource selection functions to develop resistance surfaces. Another example of using telemetry data is from Cushman et al. (2010), where path-level spatial randomization is used to construct resistance surfaces. Finally, Cushman et al. (2006) provide an example of how individual genetic distances can be used to develop resistance surfaces. Other corridor models can also be used to model connectivity across a cost surface. FunConn (Theobald et al. 2006), a graph-based connectivity model calculates connectivity across all patches in a network, not just proximate pairs. Though FunConn is based on leastcost principles it allows for the identification of multiple pathways between populations. CircuitScape (McRae 2006), uses the principles of circuit theory to determine genetic distances and areas of connectivity between populations. Finally, Connectivity Analysis Toolkit (Carroll et al., in review), a relatively new corridor model, uses 'centrality' analysis to identify and prioritize corridors and populations. Resistance surface development and corridor model selection will depend on the extent and availability of input data and the ecology of the target species. A sensitivity analysis should be performed on any chosen model, especially if ground-based validation efforts are not feasible (Beier et al. 2008). Sensitivity analyses and/or ground-based validation are recommended since modeled corridors for generalist species, like many large cats and other carnivores, often have a high degree of uncertainty (Beier et al. 2009) or do not align with areas animals are using on the ground (Zeller et al. 2011). Additional GIS-based analyses can be performed to augment a long-term species conservation agenda. These include simulation analyses where GIS-based anthropogenic and climate change data are used to predict future changes in the range of the species. These analyses can aid in the identification of areas that will likely come under threat in the future and can provide an approximate timeline for effective prioritization of conservation actions. The range-wide conservation framework presented in this chapter is a new paradigm for species conservation. We preserve the tenets of traditional RWPS exercises by maintaining the Conservation Unit framework. However, we significantly increase the conservation value of the RWPS exercise by adding corridors. Conservation biology theory suggests that corridors between isolated habitat patches may maintain levels of genetic exchange through inter-population dispersal (Hanski and Ovaskainen 2000; Mech and Hallett 2001) and may contribute positively to demographic factors and meta-population dynamics (Gilpin and Hanski 1991; Hanski 1998). While corridor cost and functionality has been questioned (Simberloff and Cox 1987; Simberloff et al. 1992; Horskins et al. 2006), a growing body of literature supports corridors as valuable conservation tools (Beier and Noss 1998; GilbertNorton et al. 2010) that can help preserve the viability of a species (Gilpin and Soulé 1986; Noss 1987; Lidicker Jr. and Koenig 1996; Mech and Hallett 2001; Coulon et al. 2004; Wikramanayake et al. 2004; Hilty et al. 2006). Corridors also enhance long-term species conservation by allowing movement between populations and ecosystems. Species that have wider distributions and can utilize dispersal corridors are much more likely to survive in a changing climate (Harte et al. 2004; Thomas et al. 2004). Finally, corridors can help in reducing the steady decline of habitat across the range of a species. The negative effects of habitat loss and fragmentation, particularly on large bodied, wide-ranging, solitary carnivores is well documented (Crooks 2002), and the identification

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and conservation of corridors helps reduce the prevalence of these threats. Furthermore, corridors can prevent the irreversible isolation of populations by preventing habitat fragmentation in key areas outside of Conservation Units. Figure 4 provides a visual example of the difference between the traditional RWPS exercise (4a) and the RWPS with the addition of corridors (4b). It is evident that a wider, more comprehensive conservation agenda is present in the latter. We recommend the inclusion of corridors in RWPS exercises for large, wide-ranging species such as the jaguar. Though the jaguar is likely a unique case -- with populations and corridors creating one unbroken network across its entire distribution -- corridors for other wide-ranging species are crucial for their long-term conservation. Identifying areas where connectivity still exists or can be re-established between even a few populations will aid in enhancing effective population sizes and in maintaining genetic and demographic viability and should be considered as part of any comprehensive species conservation plan. The GIS tools used in this chapter can easily be adapted to other species to identify movement corridors between pockets of protected areas and across landscapes where habitat fragmentation is not yet extensive. By enhancing RWPS exercises with corridors, a truly comprehensive perspective on long-term species survival can be created and effective conservation strategies for wide-ranging species can be implemented.

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ACKNOWLEDGMENTS We would like to thank the following jaguar experts for their input on the dispersal parameters used in this analysis: M. Aranda, S. Cavalcanti, A. Gonzalez-Fernandez, B. Harmsen, M. Kelly, A. Kuroiwa, R. McNab, B. Miller, R. Moreno, A. Noss, J. Polisar, O. Rosas Rosas, S. Silver, and R. Wallace. We are grateful to the Liz Claiborne and Art Ortenberg Foundation, the Wildlife Conservation Society, and the United States Department of State for the funding and support of this work. We wish to thank H. Quigley, L. Hunter for critical review of the manuscript.

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Wikramanayake, E., Dinerstein, E., Robinson, J.G., Karanth, U., Rabinowitz, A., Olson, D., Mathew, T., Hedao, P., Conner, M., Hemley, G., and Bolze, D. (1998). An ecology-based method for defining priorities for large mammal conservation: the tiger as case study. Conservation Biology, 12, 865-878. Wikramanayake, E., McKnight, M., Dinerstein, E., Joshi, A., Gurung, B., and Smith, D. (2004). Designing a conservation landscape for tigers in human-dominated environments. Conservation Biology, 18, 839-844. Woodroffe, R., Thirgood, S., and Rabinowitz, A. (2005). People and wildlife: conflict and coexistence. Cambridge: Cambridge University Press. Young, A.G., and Clarke, G.M. (2000). Conclusions and future directions: What do we know about the genetic and demographic effects of habitat fragmentation and where do we go from here? In: A.G. Young and G.M. Clarke (Eds.), Genetics, Demography and Viability of Fragmented Populations (pp.361-366). Cambridge: Cambridge University Press. Zeller, K.A. (2007). Jaguars in the New Millennium Data Set Update: The State of the Jaguar in 2006. Bronx, NY: Wildlife Conservation Society. Zeller, K.A., Nijhawan, S., Salom-Pérez, R., Potosme, S.H., and Hines, J.E. (2011). Integrating occupancy modeling and interview data for corridor identification: a case study for jaguars in Nicaragua. Biological Conservation, 144, 892-901.

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In: Geographic Information Systems Editor: Christopher J. Dawsen

ISBN: 978-1-61209-925-5 © 2011 Nova Science Publishers, Inc.

Chapter 5

GEOLOGICAL INFORMATION SYSTEM IN RAINWATER HARVESTING Saumitra Mukherjee* School of Environmental Sciences, Jawaharlal Nehru University, New Mehrauli Road, New Delhi-110067, India

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ABSTRACT: Identification of suitable sites for rainwater harvesting is essential for the successful water resource management. Geological site selection requires identification of lithounits and its structure to ensure the selection of sites for artificial recharge. Use of only panchromatic sensor data of IRS-1D satellite with 5.8-meter spatial resolution has the potential to infer lineaments and faults in this hard rock area. It is essential to identify the location of interconnected lineaments below buried pediment plains in the hard rock area for targeting sub-surface water resources. Linear Image Self Scanning sensor data of the same satellite with 23.5-meter resolution when merged with the panchromatic data has produced very good results in delineation of interconnected lineaments over buried pediment plains as vegetation anomaly. These specific locations of vegetation anomaly were detected as dark red patches in various hard rock areas of Delhi. Field investigation was carried out on these patches by resistivity and magnetic survey in parts of Jawaharlal Nehru University (JNU), Indira Gandhi national Open University, Research and Referral Hospital and Humayuns Tumb areas. Drilling was carried out in eight locations of JNU that proved to be the most potential site with ground water discharge ranging from 20,000 to 30,000 liters per hour with 2 to 4 meters draw down. Further the impact of urbanization on groundwater recharging in the terrain was studied by generating Normalized difference Vegetation Index (NDVI) map which was possible to generate by using the LISS-III sensor of IRS-1D satellite. Selection of suitable sensors has definitely a cutting edge on natural resource exploration and management including groundwater.

*

Email: [email protected] ; Tel: (91-11-26704312)

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Keywords: groundwater, aravalli quartzite, lineaments, buried pediment plains, NDVI

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1. INTRODUCTION In view of the scarcity of piped water in the city of Delhi increasingly the dependence for non-drinking requirements has been on groundwater availability. The groundwater table has declined and the extraction of ground water is not sustainable. As a result there has been reported draw down in the wells/tube wells/submersible pumps and some of them having been abandoned due to poor yields or gone dry. A number of Non Government Organizations are working in India for water conservation, especially in Aravalli formation, which is facing serious problems of depletion of water level. Funding in this sector is generous by government and international agencies, which has attracted even non-specialists of water conservation to work in this terrain. It is necessary to understand the geology and geophysics of this terrain for the scientific water resource management. It is essential to study the information on the existing land use pattern, the spatial distribution and its changes is required for planning, utilization and formulation of policies and programs for sustainable development [3, 5, 6, and 7]. Humans transform the land for different activities based on their needs and quantifiable information on these dominating activities is necessary to develop future plans. Soil properties and land use patterns are major contributing factors to the hydromorphogeology of a particular area. Historical monuments and other urban features have been analyzed by satellite imagery. Remote sensing data are helpful in the studies changes in land use patterns, which are located in the recharge areas of elevated parts of Delhi Super group of Aravalli hill region [1, 9]. It is essential to identify suitable location in this area by using multisensor satellite data for groundwater replenishment [4]. Multispectral and multitemporal data from SPOT, IRS1A, IRS- 1B and IRS -1C when integrated with Land use, Geological, Geomorphologic, Hydro-geological and magnetic data have potentiality for identification of suitable areas for construction of check dams. These geological information were found useful to predict the possibility to infer the sites suitable for rainwater harvesting. [10]. Interception of surface runoff by check dams across drainage at appropriate locations is one method for artificial recharge. Leading institutes, hospitals and buildings of archeological importance covers substantial portion of Delhi-Aravalli ridge area. Jawaharlal Nehru University (J.N.U.), Research and referral Hospital (RRH), Indira Gandhi National University (IGNOU), Humayuns Tomb (HT) areas are lacking sufficient surface water bodies, and palaeochannels. Very thin soil cover in this area is also not supporting the dug well structures. Groundwater occurrence is restricted to the deep-seated fracture zones only [2, 12, and 13]. High-resolution satellite data has the potential to infer buried pediment plains and interconnected fracture zones for the selection of groundwater exploration and artificial recharge sites. IRS-1D PAN and LISS-III data has better capability to infer these landform units in comparison to the IRS-P6-AWIFS data of limited spatial and spectral resolution (Figure 1 and Figure 2).

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Figure.1.AWIFS sensor showing lineaments (L) in south Delhi but detailed information of hydrogeology is not possible to infer for water resource management.

Figure.2. IRS-1D LISS-III sensor data using ERDAS software showing details of lineaments (L) and detailed hydrogeological information of whole Delhi for ground water management.

Extraction of groundwater from non-potential aquifer may lead to land subsidence and destruction of vegetation in surrounding areas [8]. For selection of artificial recharge areas the radiance values of pixels in near infrared region of IRS satellite data were studied. Due to high recharge the soil moisture were less as well as the vegetation also. Lineaments are passing through the proposed check dams, which were selected based on their low spectral reflectance and low magnetic values over the weathered ferruginous quartzite, which are prevailing geologic suite of the study area.

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Figure.3. IRS-1D Panchromatic sensor data using ERDAS software showing detailed landuse useful in pinpoint location of groundwater recharge points.

Figure.4. Change in NDVI study by IRS-1D LISS-III and PAN sensor data showing more vegetation as black patch in JNU, a part of Aravalli Quartzite ridge in New Delhi, India. This data was used for selection and monitoring of groundwater recharge (check dam).

Groundwater level was gone down in this area, which increased after the artificial recharge. Plantations of suitable area specific species are restoring eco-conservation in the stressed aravallis (Figure 4).

1.1. Investigation Area: Keeping in view the identical geological and spectral reflectance of the space sensor data, JNU area has been selected as type area for the investigation in this Aravalli ridge. The area under study is a 2km.X2.5 km. region situated on a low relief hill, northwest of Mehrauli and southwest of Hauz Khas in South Delhi, India. Satellite data products inferred Land use, Geological, Hydromorphogeological and ecological information. The satellite imageries

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covering the area were analyzed digitally to prepare a Geomorphologic map on 1:50,000 scales.

2. METHODOLOGY 2.1. Landuse studies Environmental impact of anthropogenic activities on its surrounding area is dramatic. Land use change and over exploitation of groundwater has reduced the water table in ridge areas of Aravalli quartzite terrain of Delhi, India. These changed land use patterns were inferred from the high albedo structure in satellite images [11]. Fluctuation in water level in a part of South Delhi has been noticed since several years. Fall in water level was found during 1994-95 in RR Hospital, IIMC, Munirka Vihar, JNU old campus, Ber Sarai, JNU and Vasant Kunj of Aravalli ridge area. The recharge area for these points is Masoodpur and surroundings, which is inferred by lineament and geological attitude of Quartzite rocks. Further changes in these features may lead to lowering down of the water level which may even lead to land subsidence due to increased effective geostatic pressure.

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2.2. Geological Studies: With ferruginous stains or siliceous character containing bands of mica schist, grits and argillaceous material. Intrusions of pegmatites are present in quartzite along the shear zones and when altered, they give rise to morrum (coarse grained sand) and China clay. Geological formation exposed in the southern ridge area belongs to the Alwar group of the Delhi Super Group of the Precambrian age. They are predominantly, thick bedded, massive, compact, Grey colored quartzites with subordinate argillites. They are fine to medium grained in general, but form gritty, arcosic and ferruginous, often giving brownish to reddish colors and streaks.

2.3. Structural Geology by Remote Sensing Techniques: The formation of the study area strikes along NS to NE-SW and dip steeply on either side indicating isoclinal folds. In ridge area satellite images show a series of folds in the western parts with N-S axes The quartzites are characterized by several sets of well-developed fractures and joints. Of these the following sets are more prominent in the study area. • • •

NNE-SSW (parallel to bedding) with steep easterly dips E-W to ENE-WSW, vertical NE-SE, vertical. Perpendicular to strike.

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A few lineaments have also been identified in the area from satellite images. Of these, the two nearly E-W trending lineaments traced in the northern part of the campus are prominent. The less conspicuous lineaments /fractures identified in the images are along NW-SE. These lineaments have a control over the drainage in the area. Vein quartz and pegmatites have been emplaced along fractures and joints in quartzite. Pegmatites contain feldspars, muscovite, quartz and tourmaline. Clay (kaolin) derived from weathering of pegmatite is extensively mined in the area west of the campus. The compact, Grey quartzites are highly resistant to weathering and they stand out as jointed, blocky or sub rounded boulders as a result of intersecting joints. But weathering is more prominent along sheared gritty, arcosic and ferruginous quartzites, which result in sandy or gritty, red soil. Over the eroded surfaces of Alwar quartzites and in the elongated depressions between the quartzite ridges, are deposited the weathered agleam and alluvial materials and soil, which include clay, loam, silt, grit, gravel and kankar of recent to subrecent origin. Delhi ridge lineaments were inferred by using RESOURSESAT-1 (IRS-P6 SensorAWIFS data show a strong trend in NE-SW direction only further the area was studied by using IRS-1D Panchromatic and LISS-III sensor has shown detailed lineament which passes though Asola Bhati sanctuary, J.N.U. sports, complex (stadium), RR Hospital and extends up to Bahadurgarh in Haryana (adjacent to north west Delhi).

Figure.5. Merged data (PAN+ LISS-III sensor) of RR Hospital area, part of Aravalli ridge showing higher value of Normalized Difference Vegetation Index (NDVI) in increasing suitability for groundwater exploration with increasing red colour of satellite image.

Panchromatic sensor shows detailed land use patterns of Delhi area, which are useful information in water resource management (Figure.3). Measurements were taken along this lineament, which is inferred as sudden decrease in magnetic values (average magnetic value of Delhi region is 47,000 gammas). Groundwater recharge map of whole Delhi and specifically ridge area has been prepared by using multisensor satellite data and geophysical investigations (Figure 5 and Figure 6). Based on the spot magnetic values in and around J.N.U. eleven profiles were done. Contour maps were made along the profile. Contour lines were drawn at every ten gammas interval; Low magnetic values were noticed in lineaments

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on ferruginous quartzite. Selection of check dams was based on the points inferred by magnetometer showing low magnetic values and interconnected lineaments. A Geographic Information system map was prepared for J.N.U. area showing geology, lineament and magnetic anomaly.

Figure.6. Merged sensor data shows high accuracy groundwater recharge site map of whole Delhi using digital image processing and GIS techniques.

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2.4. Surface Water Management: Rain Water Harvesting: Interception of surface run-off by check dams across drainage at appropriate locations is one method of surface water management. Three check dams have been constructed in JNU. In JNU campus, a series of check bunds with pipe outlets have been proposed to construct for rainwater harvesting. Locations for such structures have been selected where the valley section is narrow and maximum spreading up of steam, with least bearing on the height of the structure is possible. Thus 14 sites have been selected for check dams extend outside the campus. Out of these three check dams are already constructed in western stream and another in northern stream. The rise in water level in 16 piezometers is being monitored every month. The results are encouraging. It is observed that there is substantial increase in water level in the JNU campus after the recharge through check dams. Merged sensors data of IRS-PAN and LISS-III has been interpreted using ERDAS image processing and Arc GIS software for the whole Delhi area.

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2.5. Eco-Conservation of Using Multisensor Data:

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The Delhi master plan of August 1990 clearly states that the ridge " should be conserved with utmost care and should be afforested with indigenous species with minimum of artificial landscape. Within urbanized areas of southern ridge some areas of the ridge have successfully retained and regenerated their bio-diversity and also sustained a considerable variety of fauna, including some rare species, other parts have suffered substantial denudation, soil erosion, etc. The water level in some parts of JNU, RR Hospital has already risen by over ten meters in less than two years and the water discharge in bore wells in neighboring areas of JNU has shown remarkable improvement. Also the check dams are creating water bodies, which the master plan says, “should be developed to act as major lung spaces and to attract migratory birds and for improving the microclimate". Multi-date satellite images infer changes in vegetation canopy cover from 1987 to 1997. This appears to be due to increase in soil moisture by artificial recharge in JNU campus. There has been about 1.104 square kilometer (46.96 percent) increase in dense vegetation canopy cover due to increase in groundwater level. This increase is mainly reported in JNU valley and Sanjay Van and its eastern parts. It is now planned to recreate the forest cover of the large denuded parts of the JNU ridge through planting of new trees of indigenous varieties, which are native to the ridge. Based on the soil type, thickness moisture retaining capacity and slope of the area plantation scheme has already started. The areas where it was decided to do planting on a priority basis were: a) On sides of roads where no trees have been planted so far or where trees have died. b) In selected forest areas especially around the check dams so as to increase the percolation of water and to prevent silting through soil erosion. c) Any areas where excessive denudation has occurred. Being situated on a structural hill made up of hard, massive quartzite rocks and buried pediment, the flora of this campus is unique. An attempt was made to classify the suitable Hydromorphogeological niche for new plantation. IRS-1C and SPOT data were used for this purpose. Ground truth was done by resistively meter and ground truth radiometer. In areas where resistively value is ranging in between 20 to 300 ohm meter the geomorphologic unit is classified as buried pediment. Pediments and ridges show relatively higher resistivity values. A new terminology is being introduced here "Eco-hydromorphogeology" which infers suitable hydrology, geology, morphology for the ecology of the area. Some species selected for roadside planting are Prosopis cineraria (Khejri), for shallow buried pediment Acacia leucophloea (safed kikar), Acacia senegal (kumta) Cordia rothii (gondi), etc., Suitable plants for sandy soil near Check dam sites are Salvadora persica (Pilu), Boswellia serrata (salai) etc. Suitable draught resistant plant would be Acacia modesta (phulanhi). Fast growing plants in Buried pediment plain (Deep) are suitable for Zizyphus nummularia (Kokanber). The eco-hydromorphogeology of Aravalli ridge (Table 1) gives a clear guideline of suitability of plant species in this varied terrain.

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Table 1. Sensor specific Eco-hydromorphogeology of Aravalli ridge area Geomorphic Unit Residual/ structural hills

Landform

Hydrogeology

Suitable flora

Sensor

Rocky ridges tors and mounds.

Massive compact jointed quartzites. Poor ground water

Prosopisjulfiflora (Kabulikikar) Azadirachta indica (Neem) Miragyna parviflora (Kadamb)

Pediment

Undulating, eroded and dissected, shallow buried pediment with rock exposures. Thickly vegetated with scrub.

Buried Pediment

Plain to gently sloping ground with occasional rock outcrops.

Weathered coarse gritty or arcosic quartzite with cover of clayey and silty soil along stream course. Moderate to good ground water prospects along fracture and shear zones. Silty clayey and at places gravelly soil derived from weathering of arcosic and gritty quartzite. Good ground water prospects

Acacia senegal (Kumta) Wringhitia tinctoria (Dudhi) Balanites aegyptiaca (Hingot) Streculia urens (Kullu) Boswellia serrata (Salai) Ficus benghalensis (Bargad) Cassia fistula (Amaltas) Albizzia lebbeck (Siras) Fics religiosa (Peepal) Ficus infectoria (Pilkhan) Terminalia arjuna (Arjun) Bauhinia variegata (Kachnar)

Lineaments intersection and species identification is possible with IRS1D LISS-III merged with PAN data. Groundwater potential Pediment and NDVI based species identification possible in AWIFS, IRS-LISSIII and PAN data.

Buried Pediment delineation with Pediment is possible by using IRS-PAN and LISS-III merged data but the landform units are difficult to infer by using AWIFS data.

2.6. Qualitative Improvement in Groundwater by Rainwater Harvesting: The quality of water for domestic consumption is of paramount significance as the chemical and microbiological contamination of potable water can lead to serious health hazards/body disorders through a waterborne disease of toxic chemicals. Qualitative improvement in groundwater quality in JNU campus can be quantified by pre and post rainwater harvesting water quality studies. Table 2 shows the qualitative improvement of groundwater after artificial recharge. This work has a long-term economic importance as well as useful for improvement of groundwater quality For selection of artificial recharge areas the radiance values of pixels in near infrared region of IRS satellite data were studied, otherwise, the cost of water treatment would have levied measurable recurring financial burden on the University.

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Table 2. Improvement in groundwater quality in JNU campus after artificial recharge pH

Prior

After

EC micromohos/ \cm Prior After

8.5

7.0

694

137

Hardness CaCO3(mg/l)

Ca (mg/l)

Mg(mg/l)

NO3(mg/l)

F(mg/l)

Prior

After

Prior

After

Prior

After

Prior

After

Prior

After

460

394.5

92

148

73.3

22.06

296

148.2

0.9

0.9

3. RESULTS

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From 1996 to 2009 four rainfall cycles were identified each year in Delhi ridge area and their effects of recharging to groundwater regime were observed in sixteen piezometers in JNU campus, tapping shallow and deep aquifers. These piezometers were installed downstream along its course and lateral to it. The depth to water level in the area prior to check dam construction was ranging in between 17-22 meter below land surface. From 1996, the rise of water level was noticed between 5.26 and 6.50 meter in the piezometers about 166 and 44 meters from the dam respectively. It was computed that 90, 000 cubic meter of water was recharged per year to the shallow and deep aquifers and 2000 cu.m were lost due to evaporation. Out of total quantity of water resource available, about 98% had been recharged to the aquifers. Due to increase in soil moisture, the total area under dense and sparse vegetation canopy cover has increased. Dense vegetation has increased 46.96%; sparse vegetation has increased 24.30%. Area without vegetation has shown 2.14% vegetation.

CONCLUSION Selection of rainwater harvesting and drilling sites for ground water was done by using geological information system. Based on spectral reflectance from multispectral highresolution satellite data for identification of interconnected fractures exposed and hidden within the soil and vegetation cover. Normalized Difference Vegetation Index (NDVI) was generated in vegetated areas and relative soil moisture in buried pediment plains and soil cover were detected from the non vegetated surface areas. Based on the results of the multispectral sensor data interpretation water resource management in the Aravalli ridge area was successfully implemented. Eco-hyrdromorphogeological plantations in this area will be useful in reducing the air pollution. Other parts of Delhi also require similar type of rainwater harvesting practices by experienced geologists and geophysicists to improve groundwater environment and for Ecoconservation.

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ACKNOWLEDGMENT Author is grateful to Central Ground Water Board , Government of India for help in drilling and construction of check dams in various parts of Delhi including Jawaharlal Nehru University with technical support of the author during 1993-1997 period.

REFERENCES [1]

[ 2]

[3] [4]

[ 5]

[6]

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Bhanumurty, Y.R., Dimri, D.B. Hassan, Y. Geophysical investigations for the geotechnical project of Delhi metropolitan area. Geological Survey of India, 1978 Misc. Pub. No. 43 pp. 35-43. Jaisawal, R.K and Krishnamurthy and Mukherjee, S. Role of Remote sensing and GIS techniques for generation of groundwater prospect zones towards rural development- an approach. International Journal of Remote Sensing, 2003 Vol.24, No.5, 993-1008. Kale, P. Sustainable development: critical issues, Journal of Indian Society of Remote Sensing 1992, 20(4): 183-186. Mishra, J.K., Aarthi, R., Joshi, M.O., Remote sensing quantification and change detection of natural resources over Delhi; Atmospheric Environment, 1994 28(19): 3131-3137. Mukherjee, S. Change in groundwater environment with land use pattern in a part of South Delhi: a remote sensing approach, Asian-Pacific Remote Sensing and GIS journal, 1997 9(2): 9-14. Mukherjee, S., (2008) Role of Satellite Sensors in Groundwater Exploration. Sensors Journal 2008, 8 pp 2006-2017. Mukherjee, S. Eco-conservation of a part of JNU campus, by GIS analysis, Proceeding National Symposium on artificial recharge of groundwater 1998 New Delhi, India, pp. Mukherjee, S. Land subsidence in middle Andaman: A case study. Hydrology journal 1986, 13(3): 150-156. Mukherjee, S. Text Book of Environmental remote Sensing. Macmillan India Limited New Delhi India 2004 ISBN: 1403922357. Pp.139-148. Murthy, K. S. R. Groundwater potential in a semi-arid region of Andhra Pradesh: A geographical Information System approach, International journal of Remote Sensing 2000 Vol. 21 No. 9, pp.1867-1884. Rao, L. K. M., Remote sensing for land use planning; International Journal of Remote Sensing, 1995, 16(1): pp.53-60 Saraf, A.K. and Chaudhary, P.R. Integrated remote sensing and GIS for groundwater exploration and identification of artificial recharges sites, International Journal of Remote Sensing 1998, Vol. 19, No. 10, pp.1825-1841 Singh, A.K., Prakash, S.R. Delineation of groundwater potential zones in Bakhar subwatershed Mirzapur and Sonebhadra districts, U.P., using integrated studies of Remote Sensing, Geoelectrical and GIS techniques, Proceeding of ISRS National symposium, 2000, held at Kanpur, India pp. 320-329.

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Chapter 6

ESTIMATING THE ENVIRONMENTAL EFFECTS ON RESIDENTIAL PROPERTY VALUE WITH GIS Lubos Matejicek Institute for Environmental Studies, Charles University in Prague, Faculty of Natural Science, Benatska 2, 128 01, Prague, Czech Republic

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ABSTRACT Ongoing research to develop a new generation of decision-making tools for estimating the environmental effects on residential property value has significantly increased the demand for land surface data, information on the state of living environment, and the corresponding need for the advanced use of geographic information systems (GIS). In order to provide the foundation for price estimates, all the existing data focused on building and environment are integrated in the framework of a GIS spatial database. Traditional methods mostly emphasize the relationship between the effects of accessibility to central locations, and ignore location-specific attributes of housing. However, little has been done on high-rise, densely populated residential areas. Thus, the paper aims to investigate the neighboring and environmental characteristics of the selected site. A more advanced approach based on spatial analysis and modeling in the GIS environment is used to manage spatio-temporal data, to process aerial images and satellite images, to import measurements from GPS, to create digital terrain models, to analyze topography together with environmental data, and to visualize the results. The partial results based on the traveling time to the closest services and the origin-destination cost matrixes are derived from the network analysis. The landscape characteristics can be demonstrated on animated sequences showing the flight over a landscape model that is based on the digital terrain model with draped images over it to show the area. The living environment contains a few compartments: air pollution, water pollution, waste management, noise assessment, and monitoring of environmental impacts on population health. Air pollution is estimated by continuous map surfaces, predicting the values of pollutants concentration for the selected site in dependence on the sample points at the air quality monitoring stations. Water pollution covers surface water pollution, drinking water supply and quality, waste water, accidental contaminant spills, and optionally, flood control measures. The waste compartment is focused on the system of municipal waste management with sorting of reusable components of municipal waste, and

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Lubos Matejicek hazardous chemicals information. Noise assessment covers road traffic noise and air traffic noise. The environmental impacts on population health come out from reports of the national institute of public health. In addition to the main compartments, other data can complement the partial inputs to the system (energy prices, public transport schema, locations of neighbor natural reservation sites and historical sites). In the framework of spatio-temporal analysis, the spatial weighting matrixes are used for prediction of the final rating. The final results are represented by the thematic map layers in the GIS project based on ArcGIS and its extensions. The attached case study shows a scenario in dependence on setting the weight parameters. Unexpected findings are caused by rapid changes in the dense living environment and slow conversion of the reality market in the selected site in Prague, the Czech Republic. For all that, the case study brings better understanding of how the residential site rating depends on various environmental attributes.

Keywords: Building; living environment; spatial modeling; GIS

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1. INTRODUCTION Throughout history, the residential environment affects our health and well-being and we, through our activities, reshape the residential environment. This environment is interconnected to natural, social, and economic processes that run on the local, regional, and global scales. But, different neighborhoods or communities have different demographic characteristics, because of the economic and social processes that structure age, and raceethnicity of the population. The residence represents an important activity site in the environment where a person spends time. In addition to residence sites, the activity space is larger and depends on other activity sites like workplaces, stores, recreational areas, and the pathways traveled to and from the residential site. The size and shape of an individual person vary depending on its profession, the modes of transportation available and the geographical locations where all the activities take place. The residential activity sites together with others represent the zones where the individuals can be exposed to risk or resources, which affect the residential property value. It can be documented by disease mapping that has made contributions to public health and epidemiology for centuries. A common practice in mapping the residential property value by health events has been to calculate health event rates for political or administrative units like towns or tracts, because population and health outcome data are mostly reported for these areas. An obstacle with this approach is evident in arbitrary partitioning the underlying distribution of population and health events. Also, the risk factors, like population at risk, are not located at individual residential sites. Selected contaminants like air pollution, water pollution, waste, noise and biological agents are present in our living environment in various local and global scales. The temporal and spatial patterns are needed to model the distribution of known or potential risk factors.

2. GIS AND ASSOCIATED SYSTEMS Geographic Information Systems (GISs) are being used in pollution environmental studies to model pollution dispersion and its effects on residential areas. The ability to

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estimate the environmental effects on residential property values is important for decisionmaking processes in order to start health public intervention activities [1]. Spatio-temporal modeling, in the framework of GIS, occurs whenever operations attempt to emulate geographic processes in the real world, at selected points in time or over extended periods. In the past, many other modeling applications have been coupled with GIS for high performance in dynamic modeling, remote sensing calculations and database management. But with the increasing power of GIS hardware and software, it is now possible to reconsider this relationship and to develop multifunctional tools [2]. Thus, the spatio-temporal modeling is trying to integrate several different contexts in the GIS world. There are two particularly important meanings. First, spatial modeling by itself is focused on data models that include a set of expectations about data extended by spatial analysis. The data models are mostly represented by templates into which the data needed for particular applications are fitted together with topological rules and spatial analysis results. For example, the ESRI’s geodatabase supports models of topologically integrated feature classes with extensions for complex networks, topologies, relationships among feature classes, and other object-oriented features. The geodatabase is implemented on standard relational databases complemented by spatial database extensions [3]. The way feature classes are often used in GIS, the features correspond to a group or class of real-world features, such as buildings, roads, railways, rivers, bus lines, dominant sources of environmental pollutions, and monitoring networks. In essence, GIS assists to develop the data models and to create map composition and visualization based on data models [4]. Second, temporal modeling deals with representation of processes that modify or transform some aspects of the Earth’s surface through time. For example, contemporary environmental pollution forecasts are based on dynamic models of the particle dispersion in air, water and soil. Temporal models of vehicles behavior are used to predict traffic congestions. The strategies of integration spatially distributed environmental models into GIS range from simple linkage through shared data to building temporal models as analytical extensions into fully functional GISs. In case of shared data, two separate systems, the GIS and the simulation system, just exchange data through common files or a shared database [5]. GISs, computer based systems for the management and analysis of geographic data, integrate spatial data that originate from observation and measurement of the earth phenomena referenced to their locations on the earth’s surface [6]. Whenever urban planners or environmental authorities use residential registration with address information, consider the location of pollution sources, or look at air quality and water quality reports from monitoring networks, they are working with geographic data. Thus, the GISs are considered as tools applicable to the integration and analysis of many different types of spatio-temporal data by people in different organizational setting asking various questions [7]. Thus, the GIS is related to other types of software that are focused on spatial data and spatial analysis [8].

2.1. GIS Mapping and Standard Digital Data Sources The process and object views are expressed in the GIS by two main data structures: raster and vector. These data structures have different properties for storage and processing of

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spatial data. Majority of the recent GISs can manage these data structures, but many various data formats implemented in GISs require advanced data transformation methods [9]. In a raster system, each raster layer matches to a single attribute for a unit of space. For example, elevation or pollutant concentration at a particular place corresponds to a single attribute. Raster data are represented by an array of cells corresponding to particular places. The setting of the cell size affects the size of an object that can be discerned in a digital image, which determine the spatial resolution of the data. Landscape feature such as a singlefamily detached residence has to be discerned in high resolution raster images like the 10meter spatial data, because the feature could be smaller than the raster cell size. On the other hand, low resolution is used to be provided for large geographical areas where the limited detail of landscape features is sufficient. In a vector system, traditional GISs use layers that correspond to a single class of objects with the same dimensionality. Thus, the point layers can represent locations of residential buildings, sources of pollution, a network of air quality monitoring stations or locations of water quality measurements. The line layers represented by a sequence of straight-line segments interconnected with vertexes are used for traffic networks, surface water networks or line sources of pollution. The polygon layers formed by line boundaries can represent residential buildings in a more detailed view, different types of land-cover or land-use. In addition to the spatial location of vector objects, many attached attributes stored in tables contain other information. While the raster topology is based on regularly organized cells in grids, the vector topology has to reflect the neighborhood relationships among various types of vector objects. In order to effectively store spatial data and their spatial relationship, a few advanced vector formats have been developed during last decades. For example, the ESRI’s coverage model has been used for a few years. An extension of the coverage model is the ESRI’s geodatabase that support complex networks, relationship among feature classes, and other object-oriented features. Both the models are also implemented on standard relational databases in the framework of the ArcSDE application server [10]. Thus, the individual environmental effects can be studied in dependence on location and time [11]. Mostly, they are represented by main environmental compartments like air pollution, water pollution, landscape, waste management, and noise assessment. The air pollution compartment contains layers focused on emissions, air quality monitoring, and predictions of pollutant concentrations. The water compartment can include layers with monitoring of surface water, drinking water, waste water, accidental contaminant spills, and flood control measures. The landscape compartment usually includes a wide range of thematic map layers focused on land cover, nature conservation, landscape protection, city greenery, and protected areas. The layers dealing with waste management include information about the system of municipal waste management. The noise assessment compartment include mapping of road and air traffic noise.

2.2. Remote Sensing and Field Mapping by GPS Remote sensing deals with the acquisition of information about the entity without being in physical contact [12]. The data are collected from sensors on satellites or aircrafts. In case

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of Earth observations, two major groups of sensors can be used for collection of images in dependence on the source of electromagnetic spectrum. Passive sensors detect natural energy that is emitted by the surface objects. Examples of passive remote sensors include airborne cameras or sensors carried by Landsat 7, Aster, Ikonos, and other existing satellites. Aerial images can assist in the high resolution mapping of residential zones and their surrounding areas. Satellite images offer to collect and transmit data from various parts of the electromagnetic spectrum (red-green-blue visible spectrum, near-middle-far infrared invisible spectrum), which in conjunction with aerial or groundbased sensing provides Earth science research with more information to monitor trends such as evolution of environmental and climatic changes. Active sensors, on the other hand, emit energy in order to scan objects whereupon passive sensors then detect the radiation that is reflected from the target objects. Examples of these sensors represent 3D surface laser scanners or sensors carried by Radarsat. Radar/Lidar systems offer active remote sensing where the time delay between energy emission and its detection is measured in order to acquire the location and shape of the surface objects. It is often used for digital terrain mapping, which include the construction of a digital terrain model (DTM) [13]. In such a process, points are sampled by remote sensing sensors or by a global positioning system (GPS) from the terrain to be modeled with a certain observation density and accuracy. The terrain surface is then represented by the set of sample points that can be complemented by spatial interpolation if attributes on locations on the digital surface other than sample points is needed. In case of visualization, grid- and triangle-based modeling approaches are widely used than point-based approaches. For grid-base modeling, a grid network needs to be formed through spatial interpolation, if the original data are not in a grid form. For triangle-based modeling, a triangulation network is formed from point-based approaches through a triangulation procedure in order to create the triangular irregular network (TIN). Finally, the digital terrain model together with the draped satellite or the aerial images are included into other GIS layers for landscape analysis and visualization that are linked to the residential property value mapping [14, 15, 16].

3. MAPPING OF THE ENVIRONMENTAL POLLUTION The success of environmental-related GIS studies depends on having access to accurate, timely, and compatible spatio-temporal data. Map layers focused on living environment mostly complement spatial data sets for a wide range of policy and planning issues. In case of mapping environmental pollution, their value extends well beyond the scope of the original projects for which they were created, and it increases as the data sets are used and updated. In order to use environmental data sets in GIS, the data sets must be captured and linked to the existing map layers in the framework of a foundation spatial database. In many cases, the digital data need to be imported into GIS from exiting information systems. Thus, the complex process of scanning, digitizing, entering field data and address matching can be reduced or even omitted. The mapping of the environmental pollution together with other spatial data brings together four key elements: the spatio-temporal data stored in spatial database, the

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representations of that information on map layers, queries about the spatio-temporal data, and the analysis that creates new information and new map layers.

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4. SPATIAL ANALYSIS AND VISUALIZATION The environmental effects on residential property value affect agents that produce adverse health outcomes in humans. At residential zones, these agents can be physical (noise), chemical (ozone, nitrogen oxides, sulphur dioxide, particle fraction PM10) or biological (cryptosporidium) in nature. Human population encounters these agents by breathing, eating, and drinking, or coming into physical contact at built environment. The processes by which agents in the living environment produce an adverse health outcome can be modeled as hazard-exposure-outcome processes by risk assessment methods [17]. In the last decade, many models have been carried out in the field of noise impacts [18, 19], air pollution [20, 21, 22, 23], water pollution [24, 25], waste management [26] and land use/land cover changes [27]. In order to link spatio-temporal data drawn from many sources into the GIS, time and space must be the basis for data integration in a way that models hazard-exposure-outcome processes. Besides the integration of digital map layers focused on the location of residential buildings, hazardous agents need to be identified and their source locations and associated contamination fields must be estimated. In spite of that human experience is an important source of awareness of the harmful effects of many toxicants, there is still the need to carry out long term research in the framework of quantitative risk assessment that deals with the process of characterizing the health effects expected from exposure to an agent and estimating the probability of occurrence of health effects. Many case studies and reports are annually published by U.S. EPA. Subsequent legislation represents the major impetus for conducting risk assessment, but the responsibility for determining acceptable risk is in the range of state authorities and local agencies that implement the legislation. In this case, the methods of hazard identification where the GISs make the strong contribution include spatio-temporal association of adverse environmental residential effects [28, 29, 30].

5. A CASE STUDY: MAPPING OF ENVIRONMENTAL POLLUTION IN PRAGUE WITH RESPECT TO ENVIRONMENTAL EFFECTS ON RESIDENTIAL PROPERTY VALUE The mapping of environmental pollution in the city Prague aimed to evaluate the application of spatio-temporal analysis with estimating the environmental effects on residential property value. The project was created in the framework of ESRI’s ArcGIS and the spatio-temporal data were mostly saved in ESRI’s geodatabase, Figure 1.

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Figure 1. The data structure of the GIS project for estimating of the environmental effects: the list of the map layers is on the left site, the whole city from the Landsat 7 is in the preview mode.

As an example, measurements of air pollution are determined by monitoring of pollutants (SO2, PM10, NOx) in the ground-level strata of atmosphere, and measurements of surface water quality are taken at fixed sampling points in the urban surface water network. Information about the ground-level air pollution is based on the public data accessible through the Internet (http://www.premis.cz , December, 2008). The actual configuration of the monitoring network is 15 monitoring stations that measure 12 main pollutants. Data are recorded in one hour period. The data are archived in the database of the Air Quality Information System of the Czech Republic. The surface water quality assessment is performed according to the local standards. The recommended monitoring frequency is 12 samples taken per a year. Surface water is classified into five classes based on quality parameters over a longer period, usually a two-year period in order to have 24 data series

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(http://envis.prague-city.cz , December, 2008). The selected measurements are stored in the geodatabase (EnviInfo) and linked to the map layers. The network of sampling sites is in Figure 2, which also includes, as an example, measurements of the biological oxygen demand (BOD) in the attached column diagrams.

Figure 2. The measurements of water quality at the sampling points linked to the GIS layer. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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In case of air pollution, the prediction map layers for selected pollutants (SO2, PM10, NOx) were created by examining the relationships between all of the sample points and producing a continuous surface of concentration. In order to produce the predictions maps, there are two main groupings of interpolation techniques: deterministic and geostatistical. In spite of that the geostatistical methods offer not only prediction surfaces but also error or uncertainty surfaces to give an indication of how good the predictions are, the deterministic method Inverse Distance Weighted (IDW) was selected considering to the limited number of the sample points-monitoring stations. This interpolation method forces the resulting surface to pass through the data value at sampling points and assumes that each measured point has a local influence that diminishes with distance.

Figure 3. The prediction map of PM10 concentrations in the city Prague and its central part. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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It weights the pollutant concentration at the points closer to the prediction location greater than those farther away. An example of the spatial interpolations based on IDW for PM10 concentrations is in Figure 3. Similarly, the spatial interpolation derived from the same sampling locations and based on IDW for NOx is in Figure 4. In order to explore the results of the PM10 prediction map, the map composition in Figure 3 contains the map layers of the monitoring stations, the road network, and the potential sources of air pollution based on the national Air Pollution Sources Register (large and the mid-sized pollution sources: REZZO 1 and REZZO 2). The NOx prediction map in Figure 4. is complemented by the map layers of the monitoring stations and the road network that is considered to have the high influence on the NOx concentration in urban areas together with ozone and other compounds [31].

Figure 4. The prediction map of NOx concentrations in the city Prague and its central part. Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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The next step in Figure 5 demonstrates using of the GIS spatial analysis to produce a suitability map by combining prediction maps of air pollutants (NOx, PM10, SO2). The prediction maps are reclassified to a common scale, weighted according to a percentage influence and, finally, combined to produce a map displaying suitable locations for residential building. As an example, the datasets from the air pollution compartment have been selected to document the GIS ability for creating the spatial weighting matrixes for prediction of the final rating. Similarly, the map layers from other environmental compartments dealing with water pollution, waste management, noise assessment, and monitoring of environmental impacts on population health can be rated or complement existing results [32, 33]. Other GIS rating can include the calculation of the traveling time to the closest services and the origindestination cost matrixes. Multimodal network datasets in the GIS enable to model multiple forms of transport across rail stations, subway stations or bus stops that form the linkages between several different forms of transportation. The temporal changes can be identified by comparison of a few spatial weighting matrixes linked to the selected time events.

Figure 5. Mapping of suitable locations for residential building based on estimating the environmental effects: the light sites in the average weighted map layer indicate higher levels of mixed air pollution derived from spatial weighting matrixes for prediction of the final rating, the bottom layers represent the DTM with the draped surface water network and the street network.

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CONCLUSION The presented research shows various possibilities of using the GIS and its associated systems in the framework of estimating the environmental effects on residential property value. Considering to the complexity of this research, the attached case study is mainly focused on environmental compartments linked to the air pollution and partially to the surface water pollution. The spatial analysis demonstrates the weighting matrixes for prediction of the final rating, which can be extended to the whole range of environmental compartments and other phenomena. In order to provide the described complex analysis, a wide range of spatiotemporal data is needed to create a GIS project and to start database tasks. But, a great part of the digital data can be accessible from the U.S. EPA, landscape and urban planning departments, or local environmental agencies. Also, it is expected that the future GIS development will bring a better access to environmental data and insights to the research focused on building and environment.

ACKNOWLEDGMENT

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The presented research was carried out in the framework of the project supported by Academy of Sciences in the Czech Republic (AVCR 1ET400760405). The spatio-temporal data and terrain measurements have been processed by ArcGIS, ERDAS Imagine in the GIS Laboratory at the Faculty of Natural Science, Charles University in Prague. The spatiotemporal data were captured via the Internet and from the annual reports with information on the Prague environment published by the city hall.

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[33]

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Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

In: Geographic Information Systems Editor: Christopher J. Dawsen

ISBN: 978-1-61209-925-5 © 2011 Nova Science Publishers, Inc.

Chapter 7

MODELING OF TRAFFIC-RELATED ENVIRONMENTAL POLLUTION IN THE GIS Lubos Matejicek1 and Zbynek Janour2 1

Institute for Environmental Studies, Charles University in Prague Benatska 2, 128 01, Prague, Czech Republic 2 Institute of Thermomechanics, Academy of Sciences Dolejskova 5, 182 00, Prague, Czech Republic

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ABSTRACT The numerical models are based on dispersion modeling and statistical analysis. In case of dispersion modeling, the ISC-AEROMOD View is used for modeling multiple pollutants with the U.S. EPA modeling tool ISCST3. The Mobile View assists as an interface for the U.S. EPA MOBILE6 model that predict arterial street emissions focused on hydrocarbons, carbon monoxide, nitrogen oxides, carbon dioxide, particulate matter, and toxics from cars, motorcycles, light- and heavy-duty trucks under various conditions. The potential impacts of accidental releases are solved by SLAB View that complements the modeling tools by analysis of emissions from accidental releases of toxic gases. Analysis of urban traffic-induced noise pollution is assessed by U.S. FHWA-TNM tools. The GIS is finally used to serve as a common analysis framework for individual modeling tools. In order to display the numerical simulation outputs together with urban area map layers, numerical modeling based on U.S. EPA software tools is integrated into the GIS for spatial interpolations and spatial analysis. It assists to evaluate high levels of air pollution and noise pollution together with the thematic map layers of residential zones, business centers, schools, and hospitals. Finally, finding alternative routes can decreases air pollution and noise pollution in selected zones. As a case study, the city of Prague sample data set helps to demonstrate data processing and modeling of trafficrelated environmental pollution. The ESRI’s geodatabase is used for implementation a comprehensive information model and a transaction model in the GIS environment. It is also the common application logic used in ArcGIS for accessing and working with all spatial thematic data and simulation inputs/outputs. Spatial interpolation for prediction maps and probability maps complement the existing thematic map layers, which enable cell based modeling for spatial multi criteria decision analysis. The synthesis of

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Lubos Matejicek and Zbynek Janour environmental models and GISs creates a more complex base for environmental simulation that can support decision-making processes in a more straightforward way.

Keywords: Road traffic; air pollution; noise assessment; spatial modeling; GIS

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1. INTRODUCTION The increasing traffic in growing urban areas amplifies certain problems. It affect human daily life, because the traffic related emissions and noise are today’s leading cause of environmental pollution due to the overall success in reducing emissions of pollutants from other industrial sources in the past decades. Numerous studies have identified many associations between traffic-related air pollution merged with other negative phenomena (noise, traffic accidents, traffic jams), and adverse potential health effects indicated either by characterizing exposures to specific pollutants using measurements from a few ambient sites or by some estimates of traffic [1, 2]. Despite ignoring the local contribution of indoor sources, the local effect of residential ventilation and the accuracy to estimate personal exposures, home-specific outdoor measurements are often used for initial estimates. In lieu of using these measurements, which requires nearly labor-intensive steps as indoor monitoring, some factors can be generated from the geographic information system (GIS) [3, 4]. It includes using distance from roads, complex digital elevation models, land use, and population density in combination with central site monitoring data to derive estimates of ambient exposures. Questionnaire data focused on individual building characteristics (air conditioning usage, opening windows and local indoor emission sources) can be used to particularize local environmental pollution and potential health effects. Many current studies deal with publicly available data from monitoring networks and questionnaire responses managed by state agencies and local authorities. Some of them provide directions how publicly available data can be utilized, in order to predict residential indoor exposures in the absence of measurements. For example, information on traffic applied in the GIS framework in combination with ambient monitoring data focused on nitrogen dioxide, fine particulate matter and elemental carbon is used as a substitute for home-specific outdoor measurements and consecutively as a particular estimate for indoor exposures of outdoor dominated pollutants. Thus, models based on regression analysis and Bayesian approaches are often used for predictions [5, 6, 7]. Another research is represented by using methods of numerical modeling [8] or physical modeling [9]. Numerical models based on partial differential equations can solve dynamic phenomena of pollutant dispersion in dependence on wind flows above complex terrain. In addition to case oriented numerical models dedicated to special mathematical tasks, many software tools are accessible from U.S. EPA in the framework of the given guidelines. Physical models, exploring the relation between three-dimensional morphology and windiness in wind tunnel experiments, are usually used for prediction of pollutant dispersion in the idealized city models. A relatively new approach is represented by statistical tools and numerical models integrated in the GIS environment. The GIS offers many tools for exploratory spatial data analysis and management, which can support numerical models in a more efficient way [10]. Environmental pollution by noise brings health effects and behavioral problems. Noise pollution can cause hypertension, high stress levels, hearing loss and sleep disturbances. In

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case of animals, noise causes stress and increases risk of mortality. Mitigation of traffic noise is provided by noise barriers, limitation of vehicle speed and acceleration. Applying these strategies optimizes computer models based on physical rules and local topography [11]. Again, GISs offer a wide range of tools for data management and noise modeling support. Other potential sources of environmental pollution are impacts of accidental releases. They can cause releases of toxic gases that may expose residential zones, schools and hospitals in urban areas. Thus, the potential accidents at selected sites of crossroads and arterial roads are also included in modeling of traffic-related environmental pollution [12].

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2. TRAFFIC-RELATED ENVIRONMENTAL POLLUTION Environmental pollution originated from traffic comes from a wide variety of individual moving sources. Thus, in order to provide computer simulations, the crossroads are mostly identified as the point sources, the highways and city streets as the line sources, and the parking places or arterial roads as the area sources. In case of this paper, the environmental pollution includes air pollution, air pollution from potential car accidents and noise. The air pollutants emitted directly into the atmosphere represent primary pollutants. The air pollutants formed in the air as a result of chemical reactions are known as secondary pollutants. For example, carbon monoxide (CO) and sulfur dioxide (SO2) are primary pollutants, while ozone is a secondary pollutant. Nitrogen dioxide (NO2) and some particulate matter represent both primary and secondary pollutants, because they are emitted directly into the atmosphere, and formed from other pollutants. In case of traffic emissions, some nitrogen dioxide is emitted directly from vehicle exhaust, but most is formed by oxidation of nitric oxide (NO) in the atmosphere. Similarly, fine particular matter is emitted directly by moving vehicles as well as formed in the air. In case of primary pollutants, the ambient concentrations indicate approximately proportional relationship to emissions. In case of secondary pollutants, the relationship is more complex. For example, reducing local emissions of nitrogen oxides can lead to an increase in local ozone concentration and other consequent reactions [13]. The emissions of carbon dioxide (CO2), SO2 and black smoke can be estimated from petrol and diesel consumption. Air pollution caused by SO2 usually varies in dependence on the sulfur content of the fuels. Black smoke emissions are estimated in accordance with soiling factors for different fuels and vehicle types. The estimates of emissions of NOx, CO and non-methane volatile organic compounds (NMVOCs) are often based on vehicle performance (emissions from cold starts, hot engines and evaporative losses) rather than fuel consumption. Thus, the emissions from individual motor vehicles mainly depend on the type of vehicle, the fuel used, and the vehicle performance that depends on configuration of the traffic network, especially in urban areas. In order to obtain required information, complex exploration and measurements of a number of various vehicles together with vehicle kilometers driven in roads are needed. In case of using the GIS, its spatial database can support mapping of traffic networks, classification of road types (urban, rural single or dual carriageway, motorway), mapping of vehicles categories (petrol or diesel cars, light goods or heavy duty vehicles, buses or coaches, and motorcycles) in dependence on spatial location. In addition to these categories, the vehicle type can be split according to the required regulation in the last years. The emission factors are mostly based on on-road measurements of

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emissions. Due to large variations from vehicle to vehicle, the emission factors used for calculations are represented by means of the measured emissions. Besides these uncertainties, a number of other implicit assumptions are needed like, for example, impacts of load, road gradient, etc. Particulate matters like PM10 need slightly different estimates in dependence on the type of vehicles and catalyst technology. The prediction of future emissions based on specified factors also requires estimates of economic activity and demographic influence together with repeated terrain measurements. Information about emission forecasting models, annual reports and methodological recommendation are regularly published by U.S. EPA and by the European Environmental Agency (EMEP/CORINAIR Emission Inventory Guidebooks). Traffic noise represents other part of environmental pollution. Various methodologies are used for risk assessment in dependence on recommendations of government authorities. Traffic noise prediction models mostly assist in simulation of sound pressure levels, specified in terms of the equivalent continuous level (Leq) over a chosen period and under interrupted or varying flow conditions. The early models were focused on prediction of linear levels whereas the later models on prediction of A-weighted levels. For setting of the noise sources, single point sources, short line sources or multi point/line sources are taken into calculations, optionally, some with different spectra. Among many prediction models and case oriented studies [14, 15, 16], there is a few modeling tools recommended by national regulations (the FHWA model in the US, the CRTN model in the UK, the RLS90 model in Germany, the OAL model in Austria, the EMPA model in Switzerland, the ASJ model in Japan).

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3. DESCRIPTION OF SOFTWARE TOOLS USED FOR MODELING OF TRAFFIC-RELATED ENVIRONMENTAL POLLUTION In the framework of the attached case study a number of software tools are used for modeling of traffic-related environmental pollutants in the selected urban area. In order to merge all the needed data for risk assessment, the GIS is utilized for data management, spatial analyses and visualization. Sharing data between the GIS and the individual modeling software tools is mostly through the shared data files. Industrial Source Complex Short Term (ISCST), originally developed in the 1970’s, is the US EPA’s regulatory tool based on a steady-state Gaussian plume algorithm. The current version (ISCST3) contains many enhancements that include an improved area source algorithm, the complex terrain screening algorithms, a revised dry and wet deposition tools, and many revisions intended for air toxics applications. The ISCST3 is applicable for estimating ambient impacts from point, area and volume emission sources out to a distance of approximately 50 kilometers. In spite of that the ISCST3 is primarily dedicated to the dispersion modeling of the stationary sources, it can by applied together with other modeling tools for dominant traffic-related sources (sites with continuous traffic jams, larger parking places, and arterial roads during rush hours) [17, 18, 19]. The MOBILE6 includes a revised emission factor model for estimation of gram per mile emissions of hydrocarbons (HC), carbon monoxide (CO), nitrogen oxides (NOx), carbon dioxide (CO2), particulate matter (PM), and toxics from cars, trucks, and motorcycles under various conditions affecting in-use emission levels. Thus, the ambient temperatures and

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average traffic speeds can be specified by users to develop emission inventories and control strategies. Due to the new vehicles types and progress in traffic systems, the emission factor model has been repeatedly improved and revised since its initial version. Many case studies bring new ideas into research of the traffic-related emission estimates by adopting new technology [20, 21] and regional driving differences [22]. The FHWA Traffic Noise Model (FHWA TNM) for highway traffic noise prediction and analysis was initially released in 1998. Actual version, FHWA TNM 2.5, contains many improvements and revisions. As sources of noise, it includes noise emission levels for automobiles, medium tracks, heavy trucks, buses, and motorcycles. Noise emission levels consist of A-weighted sound levels (one-third octave-band spectra), and subsource-height strengths for a few pavement types (dense-graded asphaltic concrete, Portland cement concrete, open-graded asphaltic concrete, and composite pavement types consisting of previous listed types). It includes full-throttle noise emission levels for vehicles on upgrades, and vehicles accelerating away from stop signs, toll booths, traffic signals and on-ramp start points. Upcoming case studies are mostly focused on evaluation the effects of noise barriers, and assessing the model sensitivity to parameter variations [23, 24]. The computer model focused on the potential impacts of accidental releases, SLAB, complements the list of the selected models for presented complex modeling of traffic-related environmental pollution. The SLAB model solves the atmospheric dispersion of denser-thanair releases over flat. All the source input conditions have to be determined externally by approximate estimates or terrain measurements. A few release types are treated. They include continuous release, finite duration release, instantaneous release, ground-level evaporating pool, elevated horizontal jet, stack or elevated vertical jet and instantaneous volume source. The SLAB model was developed in 1990 by D.L. Ermak of the Lawrence Livermore National Laboratory with support from U.S. Department of Energy, USAF Engineering and Services Center, and the American Petroleum Institute [25]. A wide range of various data based on terrain measurements, existing projects, and annual reports needs to be managed and integrated together with modeling tools, in order to enable the exploration of traffic related environmental pollution in dependence on spatial scales, time scales, and other attributes. Thus, the GISs are used for most of the tasks. The described U.S. EPA modeling tools (ISCST3, MOBILE6, FHWA-TNM 2.5, and SLAB) complement the risk assessment information system as the case oriented modeling tools. Integration of the GIS and spatially distributed environmental models is based on pre- and postprocessor linkage through shared data files. Despite building models as analytical functions into the fully functional GIS, the U.S. EPA modeling tools and the GIS form standalone software systems. The preprocessed GIS data are imported into U.S. EPA models, and simulation outputs are backward exported into the GIS to provide spatial analysis and visualization. Thus, the GIS project is finally used to serve as a common analysis framework for individual U.S. EPA modeling tools. In addition to standard GIS functionality (entering, storing, retrieving, transforming, measuring, combining, subsetting, and displaying spatial data that are registered to a common coordinate system) [26], managing 3D data for complex digital terrain models (DTMs extended by buildings, barriers, and other surface objects) and advanced spatial interpolation algorithms are needed to be carry out for final display and visualization. To accomplish all the needs, the ESRI’s geodatabase offers to implement a comprehensive information model and a transaction model in the GIS environment. Thus, the ArcGIS tools can assess and work with all spatial thematic data and simulation

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inputs/outputs. The GIS can also efficiently handle data from remote sensing and global positioning systems (GPSs). Thus, the aerial images and terrain observations complement the existing map layers in the framework of the final visualization. The satellite images can extend the map layers by data from other infrared spectral bands. GPSs, frequently used for car navigation, extend existing map layers by location of terrain measurements.

4. A CASE STUDY FOR MODELING OF TRAFFIC-RELATED ENVIRONMENTAL POLLUTION

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In order to demonstrate modeling of the traffic-related environmental pollution, the GIS project is created to integrate dispersion of pollutants, noise assessment and potential accident releases together with terrain measurements, aerial images, satellite images, DTMs, annually reported data and other existing spatial data. The data processing and modeling in the framework of the GIS project is illustrated in Figure 1.

Figure 1. The flow diagram for data processing and modeling of the traffic-related environmental pollution in the GIS environment.

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The upper part of the schema includes the main environmental data inputs. In case of air pollution, the meteorological data contain local estimates of the wind speed and the wind direction. In addition to importing the DTM into the modeling tools (ISC AEROMOD View Terrain Processor, FHWA-TNM and SLAB), the simplified shapes of buildings are used for US EPA BPIP Model, in order to effectively and quickly complete the building downwash analysis in the framework of running the ISCST3. The Computer Aided Design (CAD) files include the DTM complemented by surface objects. Traffic related data describe traffic intensity and vehicle types for setting of the modeling tools based on the MOBILE6 and the FHWA-TNM. The next phase includes pre-processing of the input data into the data formats for inputs of the selected models (ISCST3, MOBILE6, FHWA_TNM, and SLAB). In case of the ISCST3, the PC-RAMMET is used for pre-processing of meteorological data and the BPIP for adopting of spatial data (DTM and buildings). While the described data processing can be carried out in the GIS (with exception of the PC-RAMMET, and the BPIP), the models solved by ISCST3, MOBILE6, FHWA-TNM and SLAB are implemented in the standalone software tools. In spite of that they also include display functions, the simulation outputs are imported backward into the GIS project that manages a wide range of spatial analyses, and provide more complex outputs for risk assessment. In addition to the spatial data management, the spatial interpolations by deterministic techniques (Inverse Distance Weighted-IDW, and polynomial interpolations) and geostatistical methods (kriging and cokriging) assist for estimations of air pollution concentrations in the neighborhood of the sites predicted by models, and existing monitoring stations [27, 28]. The GIS also contains many spatial exploratory statistical tools such as histograms for checking normal distribution, quantile plots, trend analysis and semivariograms. The normal distribution of input data is required for geostatistical interpolation methods that produce not only prediction surfaces but also uncertainty surfaces, give an indication of prediction quality and can generate probability and quantile output maps depending on spatial modeling needs. Unsuitable sites can be located by combining layers focused on air pollution and noise assessment. After the deriving the air pollution layers and the noise level layer to a common scale, the cell based modeling can assist in calculation the output map for complex risk assessment.

4.1. Mapping of the Emission Sources As an example, the urban site in Prague, the Czech Republic, characterized by high population density is selected for modeling of traffic-related environmental pollution, Figure 2. The accumulation of traffic in the last decade has indicated an increasing pressure in the air quality and noise. The more detailed view of the main crossroad from the aerial image is in Figure 3. The image was not taken during the rush hours, which causes a small percentage of the vehicles on the roads. The significant spatial and temporal variability of traffic during the day also results in variability of traffic-related environmental pollution. A number of environmental studies have raised the question of how representative the site and time period of air quality actually can be in comparison with other sites and different time periods [29]. In order to provide validation of simulation outputs, data from surface monitoring stations and local mobile DIAL-LIDAR measurements can be used for the selected site [30].

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Figure 2. The selected site for modeling of traffic-related environmental pollution with a main crossroad in the middle part and arterial roads (Google Earth 2008, Image © GEODIS, Brno).

Figure 3. Simulation results of the ammonia horizontal jet release caused by a traffic accident at the main crossroad displayed together with the aerial images in the GIS environment.

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In case of the presented case study, estimates of emissions from the motor vehicles are based on the mobile sources emission factor model implemented in the U.S. EPA MOBILE6. The setting of the parameters reflects the rush hours, in order to explore scenarios focused on expected higher levels of air pollution and noise. The traffic volumes, average vehicle speeds, humidity, and meteorological parameters are based on terrain observations and regular reports.

4.2. Simulation of Traffic-Related Environmental Pollution

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As an example, the ammonia horizontal jet release caused by the traffic accident at the main crossroad is simulated and displayed together with other spatial data in the GIS environment, Figure 3. The SLAB model simulates the atmospheric dispersion in accordance to the spill source type, source properties, spill parameters, local field parameters and meteorological conditions. The spread of a plume caused by an accidental release of a chemical is drifted above the neighbor residential zone. Line contours give an approximate concentration levels. Simulation of atmospheric dispersion originated from vehicles is demonstrated by the ISCST3 in the framework of the AEROMOD View. As an example, the simulation results based on a steady-state Gaussian plume algorithm are used for the approximate spatial prediction of NOx in Figure 4.

Figure 4. Simulation results of the ammonia horizontal jet release together with the traffic related air pollution (NOx) displayed in the GIS environment.

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Figure 5. Simulation results of the ammonia horizontal jet release together with the traffic related air pollution (NOx), and noise assessment displayed in the GIS environment.

But, many other traffic-related compounds (CO, VOCs, and dust emissions caused by heavy transport) can be included into the simulation. The spatial distribution of the pollutant concentration shows the higher concentration in the neighborhood of the crossroads and the arterial roads that can be shifted in dependence on the wind speed and the wind direction. In order to provide more complex risk assessment, the traffic noise model (FHWATNM2.5) complements used models. The simulation results based on prediction of hourly Aweighted equivalent sound level (LAeq1h) are illustrated together with air pollution in Figure 5. Again, the simulation outputs are integrated in the framework of the GIS project. The higher noise levels are at the main crossroads and arterial roads, but optionally, can be decreased by barriers in their neighborhood. Considering to sharing other map layers, the GIS approach offers a more efficient way in design of the barriers. In spite of that the displayed map layers are based on the dense network of individual receptors or grids of receptors, the spatial interpolations (IDW or the ordinary kriging, for normal input data distributions) are needed to produce continuous surfaces of pollutant concentrations or noise levels. In this case, the GIS provides a comprehensive set of tools for creating surfaces that are used for visualization and analysis.

CONCLUSION In spite of that many environmental studies focused on air pollution, noise assessment and other environmental phenomena have been carried out [31, 32, 33], there is still need to develop research tools for complex risk assessment. Different techniques of terrain

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measurements, different methodology of data processing, and finally, various decisionmaking processes have created a wide range of individual procedures in dependency on spatial and temporal resolution of the areas of interest. In order to unify some common procedures of data processing, the GISs can be used as a framework for running and development of powerful, efficient and complex tools in air pollution exploration and modeling [34, 35, 36]. In addition to spatial database management, many extensions focused on network analysis, spatial analysis, 3D spatial modeling and geostatistical methods can support modeling of the traffic-related environmental pollution. The used case oriented modeling tools for the accidental releases, the pollutants dispersion, and the noise assessment have shown that new ways of integration are needed. In case of environmental data, the unified data storage complemented by spatial information from GPS or remote sensing can be realized by the spatial database in the GIS environment. It is designed to offer best available knowledge to bear on environmental planning and policy making, reach a broad audience, be easy to use and understand and help to explore a wide range of options. Thus, the close integration of environmental models into the GIS will help to explore the traffic-related environmental pollution in a more sophisticated way, and will reach a broad audience.

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ACKNOWLEDGMENTS The input spatial data included into the case study are originally provided by the Institute of Municipal Informatics of Prague. The map layers of emission sources and the temporal measurements of the surface air pollution transferred from the Internet and from the annual reports are administrated by the IT Department of the Prague City Hall. The spatio-temporal data were processed by ESRI-ArcGIS and Leica GeosystemsERDAS Imagine in the GIS Laboratory, Faculty of Natural Science, Charles University in Prague in the framework of the project AVCR 1ET400760405 supported by the Information Society, Academy of Sciences in the Czech Republic.

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[21] Kuhns, H.D., Mazzoleni, C., Moosmüller, H., Nikolic, D., Keislar, R.E., Barber, P.W., Li, Z., Etyemezian, V. and Watson, J.G. (2004). Remote sensing of PM, NO, CO and HC emission factors for on-road gasoline and diesel engine vehicles in Las Vegas, NV. Science of the Total Environment 322, 123-137. [22] Lin, J. and Niemeier, D.A. (2003). Regional driving characteristics, regional driving cycles. Transportation Research Part D 8, 361-381. [23] El-Fadel, M., Shazbak, S., Baaj, M.H. and Saliby E. (2002). Parametric sensitivity analysis of noise impact of multihighways in urban areas. Environmental Impact Assessment Review 22, 145-162. [24] Li, B., Tao, S., Dawson, R.W., Cao, J. and Lam, K. (2002). A GIS based road traffic noise prediction model. Applied Acoustics 63, 679-691. [25] Thé, J.L., Thé, C.L. and Johnson, M.A. (2004). Slab View. User’s Guide. Ontario, Canada: Lakes Environmental. [26] Johnston, C.A. (1998). Geographic Information Systems in Ecology. London: Blackwell. [27] Cressie, N.A.C. (1993). Statistics for Spatial Data. New York: Wiley. [28] Johnston, K., Ver Hoef, J.M., Krivoruchko, K. and Lucas, N. (2001). Using ArcGIS Geostatistical Analyst. Redlands, California: ESRI Press. [29] Vardoulakis, S., Gonzalez-Flesca, N., Fisher, B.E.A. and Pericleous, K. (2005). Spatial variability of air pollution in the vicinity of a permanent monitoring station in central Paris. Atmospheric Environment 39, 2725-2736. [30] Matejicek, L., Janour, Z. and Strizik, M. (2008). Spatial Modeling of Air Pollution Based on Traffic Emissions in Urban Areas. In S.E. Paterson, L.K. Allan (Ed.), Road Traffic: Safety, Modeling, and Impacts (in press). New York: Nova Science Publisher, Inc. [31] Mower, B. (1998). A multiple source approach to acute human health risk assessment. Waste Management 18, 377-384. [32] Borrego, C., Tchepel, O., Barros, N. and Miranda, A.I. (2000). Impact of road traffic emissions on air quality of the Lisbon region. Atmospheric Environment 34, 4683-4690. [33] Borrego, C., Tchepel, O., Costa, A.M., Martins, H., Ferreira, J. and Miranda, A.I. (2006). Traffic-related particulate air pollution exposure in urban areas. Atmospheric Environment 40, 7205-7214. [34] Matejicek, L. (2005). Spatial modelling of air pollution in urban areas with GIS: a case study on integrated database development. Advances in Geosciences 4, 63-68. [35] Matejicek, L., Engst, P. and Janour, Z. (2006). A GIS-based approach to spatiotemporal analysis of environmental pollution in urban areas: A case study of Prague’s environment extended by LIDAR data. Ecological Modelling 199, 261-277. [36] Grünfeld, K. (2005). Integration spatio-temporal information in environmental monitoring data-a visualization approach applied to moss data. Science of the Total Environment 347, 1-20.

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INDEX #  20th century, 88 21st century, 3 9/11, 31

B 

A 

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automate, viii, 36 automation, 77

access, 8, 10, 11, 16, 21, 27, 36, 45, 49, 74, 76, 77, 123, 130 accuracy, 123, 134 accurate models, 41 air, x, 119, 120, 121, 122, 124, 125, 127, 129, 130, 131, 133, 134, 135, 136, 137, 139, 141, 142, 143, 144, 145 air pollutant(s), 129, 135, 144 air quality, x, 119, 121, 122, 139, 144, 145 air toxics, 136 air traffic, x, 120, 122 alfalfa, 67 algorithm, 9, 11, 79, 136, 141 alternative, x, 133, 144 ammonia, 140, 141, 142 amphibia, 104 aquifers, 116 ARC, 74, 75, 76 Argentina, 88, 101 artificial intelligence, 69 assessment, x, 25, 29, 32, 36, 42, 43, 45, 52, 53, 58, 67, 98, 102, 103, 119, 122, 124, 125, 129, 131, 132, 134, 136, 137, 138, 139, 142, 145 assets, 45 assimilation, 6 atmosphere, 125, 135 authorities, 121, 134, 136 automata, 5, 14, 30

background information, 24 barriers, 88, 94, 135, 137, 142 base, xi, 8, 14, 15, 16, 21, 31, 37, 38, 39, 46, 49, 53, 73, 79, 123, 132, 134, 143, 144, 145 Bayesian theory, 75 benefits, 22, 41, 49 biodiversity, 2, 39, 52, 53, 102 biogeography, 101, 102 biomass, 40, 100 biotic, 56, 102 boreal forest, 44 Brno, 140

C  CAD, 139 carbon, x, 40, 52, 53, 133, 134, 135, 136, 144 carbon dioxide, x, 133, 135, 136 carbon monoxide, x, 133, 135, 136 carnivores, 97, 98, 99, 101 case study(ies), x, xi, 24, 40, 52, 56, 81, 104, 105, 117, 120, 124, 130, 132, 133, 136, 137, 141, 143, 145 challenges, viii, 2, 26, 41, 61, 62, 131 chemical(s), x, 115, 120, 124, 135, 141 chemical reactions, 135 chromosome, 11, 12, 13 classes, 6, 41, 79, 91, 121, 122, 125 classification, 21, 23, 36, 43, 48, 54, 135 climate, ix, 3, 9, 28, 43, 50, 55, 85, 86, 87, 99, 104

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148

Index

climate change, ix, 9, 28, 43, 50, 55, 85, 86, 87, 99, 104 climatic factors, 42 clusters, 24, 42, 97 CO2, 135, 136 Colombia, 95, 97, 98, 104 commercial, 19, 24, 69, 75, 76, 77 communication, 23, 24, 28, 62, 64, 74, 76, 77, 78 computer, 5, 9, 16, 21, 22, 25, 27, 28, 36, 40, 41, 45, 49, 50, 51, 52, 64, 66, 67, 68, 69, 72, 74, 78, 121, 135, 137 computer simulations, 135 computer software, 36, 74 computer technology, 25, 51 computing, 63, 76, 82 concentration, x, 119, 122, 127, 135, 141, 142, 144 connectivity, ix, 25, 27, 37, 47, 85, 87, 88, 89, 91, 97, 98, 99, 100, 101, 102, 103, 104 conservation, vii, viii, 3, 21, 39, 44, 58, 85, 86, 87, 88, 91, 97, 98, 99, 100, 101, 102, 103, 104, 105, 108, 110, 116, 117, 122 constituents, 73 construction, 18, 108, 116, 117, 123 contaminant(s), x, 119, 120, 122 contamination, 115, 124 contiguity, 29 continuous data, 38, 42 CORBA, 73 cost, ix, x, 22, 36, 43, 51, 52, 63, 68, 85, 89, 90, 92, 93, 94, 97, 98, 99, 100, 115, 119, 129 cost-benefit analysis, 22, 52 cryptosporidium, 124

D  data analysis, viii, 43, 61, 82, 134 data distribution, 142 data processing, xi, 27, 36, 75, 133, 138, 139, 143 data set, xi, 2, 9, 49, 55, 70, 74, 90, 123, 133 data structure, 11, 15, 38, 72, 121, 125 database, vii, ix, 8, 9, 16, 21, 40, 42, 43, 45, 46, 63, 64, 67, 69, 74, 75, 79, 119, 121, 123, 125, 130, 135, 143, 145 database management, 69, 121, 143 decision makers, 2, 5, 10, 18, 23, 24, 25, 27, 28, 51, 62, 69 decision-making process, xi, 38, 47, 48, 62, 121, 134, 143 decomposition, 76 deforestation, 42, 50 degradation, 44, 50, 52 demographic characteristics, 3, 120 demographic factors, 99

Department of Agriculture, 104 Department of Energy, 137 detection, 44, 55, 58, 102, 117, 123 developing countries, 52 diesel, 135, 145 differential equations, 9, 134 dimensionality, 24, 122 dioxins, 144 dispersion, x, 120, 121, 133, 134, 136, 137, 138, 141, 143, 144 distribution, viii, 1, 22, 40, 42, 58, 75, 85, 86, 88, 100, 102, 104, 108, 120, 139, 142, 144 diversity, 27, 40, 50, 51, 102, 114 drainage, 40, 57, 108, 112, 113 dust, 142

E  earth, 121 ecological information, 110 ecology, 40, 43, 56, 86, 99, 101, 102, 105, 114 economic activity, 136 economic development, 28, 51 ecosystem, vii, 35, 39, 55, 56 electromagnetic, 123 emergency response, 31 emission, 123, 134, 135, 136, 137, 139, 141, 143, 144, 145 encoding, 11, 12, 37 endangered species, 43 energy, x, 24, 49, 120, 123 energy emission, 123 energy prices, x, 120 engineering, 78, 82 environment, viii, ix, xi, 3, 9, 10, 11, 18, 21, 24, 33, 36, 37, 43, 45, 46, 47, 50, 55, 56, 58, 68, 69, 71, 72, 75, 77, 78, 86, 101, 116, 117, 119, 120, 123, 124, 130, 131, 132, 133, 134, 137, 138, 140, 141, 142, 143, 144, 145 environmental change, 53 environmental characteristics, x, 119 environmental effects, vii, ix, 119, 121, 122, 124, 125, 129, 130 environmental impact, x, 119, 129 Environmental Impact Assessment, 131, 144, 145 environmental management, 33, 38, 53, 58, 81 environmental policy, 44, 56 environmental protection, 51 environmental sustainability, 45 EPA, x, 124, 130, 133, 134, 136, 137, 139, 141, 144 evolution, 11, 12, 57, 76, 79, 123 execution, 11, 16, 72

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Index Expert Systems (ES), viii, 5, 28, 30, 31, 32, 33, 44, 53, 56, 58, 61, 62, 69, 70, 71, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83 expertise, 14, 15, 16, 69 exploitation, 111 exposure, 124, 131, 145 extinction, 86, 87, 101, 102, 103, 104

F  factual knowledge, 79 farmland, 44, 52 FHWA, x, 133, 136, 137, 139, 142 filters, 9 forest areas, vii, 114 Forest Data Base Management System, viii forest ecosystem, vii, 36, 39, 41, 42, 50, 51, 56 forest fire, 39, 40 forest management, viii, 3, 35, 36, 39, 41, 42, 43, 49, 54 forest resources, 42 freshwater, 41, 55 fuel, 135 fuel consumption, 135 function values, 12 funding, 100

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G  gasoline, 145 Geographic Information System (GIS), ix, x, 119, 120, 121, 123, 124, 125, 126, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145 geography, vii, 65, 79 geology, 108, 113, 114 geometry, 37, 65, 66 global climate change, 50 global scale, 120 global warming, 2 goods and services, vii, 35, 39, 50 GPS, x, 39, 40, 41, 43, 50, 59, 65, 79, 119, 122, 123, 143 graph, ix, 67, 85, 99, 103 grass, 92 grazing, 44, 57 greenhouse, 143 greenhouse gas, 143 grids, 66, 89, 90, 122, 142 ground-based, 123 groundwater, ix, 52, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117

149

H  habitat(s), 24, 36, 40, 41, 43, 44, 54, 55, 56, 58, 86, 87, 88, 89, 91, 97, 98, 99, 100, 101, 103, 104, 105 habitat quality, 44 harmful effects, 124 harvesting, vii, ix, 3, 39, 41, 107, 108, 113, 115, 116 header files, 77 Humayuns Tumb, ix, 107 Hunter, 44, 55, 100 hunting, 88, 91, 97, 101 hybrid, 76 hydro, x, 133, 136 hydrocarbons, x, 133, 136 hydrology, 131 hyperlinking, 25 hypertension, 134

I  imagery, 9, 55, 56, 58, 90, 108 Impact Assessment, 58, 131, 144, 145 implementation, xi, 133 improvements, 41, 87, 137 inbreeding, 87 Indira Gandhi national Open University, ix, 107 information system(s), ix, 119, 123, 125, 130 information technology, 2, 36 interface, x, 11, 21, 24, 25, 40, 72, 74, 76, 77, 78, 79, 133 interoperability, viii, 41, 61, 62, 72, 73, 76, 77, 78, 80, 81, 83 inventories, 137, 143 investments, 41, 104 IPCC, 143 ISC, x, 133, 139

J  jaguar (Panthera onca), ix, 85, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 100, 101, 102, 104 Jawaharlal Nehru University (JNU), ix, 107, 108, 110, 111, 113, 114, 115, 116, 117

K  knowledge acquisition, 6

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

150

Index L 

labor-intensive, 134 land use, 122, 124, 134 Land Use Policy, 132 Landsat 7, 123, 125 landscape(s), x, 25, 40, 43, 44, 45, 52, 53, 54, 55, 56, 57, 58, 59, 75, 89, 90, 92, 93, 94, 97, 98, 100, 101, 102, 103, 104, 105, 114, 119, 122, 123, 130 leaching, 43, 53 lead, 3, 70, 73, 109, 111, 115, 135 legislation, 21, 124 light, x, 15, 129, 133, 135 linear, 136 livestock, 88, 98 living environment, ix, 119, 120, 123, 124 local authorities, 134 local conditions, 43 location, x, 119, 121, 122, 123, 124, 128, 135, 138 logging, viii, 35, 44, 57

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M  machine learning, 11 manufactured goods, vii, 35 mapping, 8, 9, 12, 18, 19, 21, 24, 25, 40, 43, 50, 53, 58, 59, 68, 71, 86, 90, 120, 122, 123, 124, 135, 143 mathematical programming, 14 matrix(es), x, 89, 90, 93, 94, 98, 104, 119, 129, 130 meteorological, 139, 141 meter, ix, 107, 114, 116, 122 methane, 135 methodology, 45, 64, 103, 143 microclimate, 114 missions, 135, 142, 145 mitochondrial DNA, 88 modules, 16, 43, 74 moisture, 109, 114, 116 morphology, 114, 134, 144 mortality, 9, 89, 135 motor vehicle emissions, 144 motorcycles, x, 133, 135, 136, 137 Multicriteria Decision Making techniques (MCDM), viii, 61, 62, 69, 71, 72, 74, 80 mutation, 12

N  national parks, 103 natural disaster, 39

natural disasters, 39 natural hazards, 50 natural resource management, vii, 2 natural resources, 21, 36, 38, 39, 41, 42, 51, 117 natural selection, 11 nature conservation, 122 negative consequences, 87 negative effects, viii, 85, 87, 99 net present values, 11 network, x, 119, 122, 123, 125, 128, 129, 135, 142, 143 neural networks, 5 nitric oxide(s) (NO), x, 124, 133, 135, 136, 145 nitrogen, x, 43, 53, 56, 124, 133, 134, 135, 136, 144 nitrogen dioxide, 134, 135, 144 noise, x, 119, 120, 122, 124, 129, 131, 133, 134, 135, 136, 137, 138, 139, 141, 142, 144, 145 normal distribution, 139 Normalized Difference Vegetation Index (NDVI), ix, 107, 108, 110, 112, 115, 116

O  OAL, 136 observations, 123, 138, 141 oceans, 104 on-ramp, 137 operating system, 68 operations, 2, 4, 12, 16, 21, 63, 64, 66, 67, 69, 70, 71, 78, 121 optimization, 4, 5, 9, 11, 14, 31, 43, 53 organic compounds, 135 organizational culture, 26 overgrazing, 42 overlay, 44, 47, 62, 67, 69, 70, 72 oxidation, 135 oxygen, 126 ozone, 44, 54, 124, 128, 135

P  Pacific, 104, 117 PAN, 108, 110, 112, 113, 115 parameter, 137 partial differential equations, 134 particulate matter, x, 133, 134, 135, 136, 144 pasture, 14 pathways, 99, 120 peat, 16 percentile, 95 percolation, 114 permeability, 93, 98, 104

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

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Index personal communication, 98 personal computers, 27 Petroleum, 137 pH, 116 phosphorus, 43, 56 photographs, 37, 43 physical characteristics, 90 planning, 123, 130, 132, 143 plants, 114 platform, 27, 32, 68, 72, 76, 78, 79, 86, 98 point of origin, 97 pollutant(s), x, 119, 122, 125, 127, 128, 129, 133, 134, 135, 136, 138, 142, 143, 144 pollution, vii, x, 29, 83, 116, 119, 120, 121, 122, 123, 124, 125, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 145 population, viii, x, 11, 12, 13, 35, 86, 87, 88, 89, 90, 91, 98, 99, 100, 101, 102, 104, 119, 120, 124, 129, 134, 139 population density, 89, 90, 134, 139 population size, 12, 100, 102 PRC, 75 prediction, x, xi, 120, 127, 128, 129, 130, 133, 134, 136, 137, 139, 141, 142, 144, 145 prediction models, 136, 144 probabilistic reasoning, 28 probability, ix, xi, 85, 98, 124, 133, 139 problem solving, 6, 12, 14, 16, 18, 22, 27, 28, 62, 70, 73 programming languages, 10, 51 project, x, 26, 52, 63, 68, 117, 120, 124, 125, 130, 131, 137, 138, 139, 142, 143 protected area(s), 42, 86, 97, 98, 100, 122 protection, 41, 42, 45, 50, 122 prototype, 57, 78 public, x, 120, 121, 125 public health, x, 62, 120 public opinion, 18 pumps, 108

Q  quality of life, 25, 32 quantification, 117 quartz, 112 query, 8, 9, 16, 21, 40, 45, 66, 68

R  radiation, 123 rail, 129

151

rainfall, 116 rainwater harvesting, vii, ix, 107, 108, 113, 115, 116 range, 121, 122, 123, 124, 130, 131, 135, 137, 139, 143 reasoning, 4, 14, 15, 23, 51, 70, 71, 75, 78, 82 receptors, 142 Red List, 102 regeneration, 42 regression analysis, 43, 134 rehabilitation, 39 relational database, 121, 122 relational model, 45 remote sensing, vii, 9, 40, 44, 50, 52, 55, 57, 58, 117, 121, 123, 138, 143 requirements, viii, 2, 36, 40, 46, 49, 51, 61, 62, 73, 77, 79, 108 Research and Referral Hospital, ix, 107 reserves, 101, 102 resettlement, 42 residential, ix, x, 119, 120, 121, 122, 123, 124, 129, 130, 133, 134, 135, 141, 144 residential buildings, 122, 124 resistance, 89, 99, 103 resolution, ix, 23, 41, 56, 66, 90, 107, 108, 116, 122, 123, 143 resource allocation, 2 resource management, ix, 3, 31, 33, 51, 107, 108, 109, 112, 116 response, 3, 6, 15, 31, 43, 51, 54, 56, 87, 89 restoration, 30, 55 restrictions, 43, 75, 77 restructuring, 131 risk assessment, 5, 76, 124, 131, 136, 137, 139, 142, 145 risk factors, 120 rules, 12, 15, 16, 18, 21, 48, 70, 75, 77, 121, 135 runoff, 9, 39, 42, 108 rural, 135 rural development, 117

S  saline water, 92 salmon, 40, 54 sample, x, xi, 119, 123, 127, 133 sampling, 125, 126, 127 satellite, x, 119, 123, 138 satellite technology, 40 scarcity, 26, 102, 108 schema, x, 120, 139 science, 2, 63, 72, 78, 79, 90, 102, 123 scientific knowledge, 39 scope, 2, 37, 69, 70, 71, 123

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152

Index

search space, 11, 12 sediment, 43, 56 semantic information, 81 semantics, 83 sensing, 40, 44, 108, 117, 121, 122, 123, 138, 143, 145 sensors, ix, 107, 113, 122, 123 services, x, 3, 27, 55, 72, 75, 76, 119, 129 shape, 37, 120, 123 sharing, 142 shear, 111, 115 signals, 137 signs, 137 simulation, x, 4, 5, 9, 14, 28, 32, 43, 51, 52, 55, 76, 87, 99, 104, 121, 133, 136, 137, 139, 141, 142 simulations, 98, 135 single chain, 98 sites, x, 120, 126, 129, 134, 135, 136, 139 sleep disturbance, 134 SO2, 125, 127, 129, 135 social class, 23 social environment, 46 social sciences, 41 society, vii, 2, 35, 63 software, viii, x, 6, 9, 11, 18, 25, 26, 27, 36, 37, 41, 42, 45, 47, 50, 57, 61, 63, 64, 67, 68, 72, 73, 74, 75, 76, 77, 78, 80, 83, 89, 91, 109, 110, 113, 121, 133, 134, 136, 137, 139 soil, 121, 144 soil erosion, 5, 114 soil type, 114 solid waste, 131 solution, 3, 6, 11, 12, 14, 16, 17, 23, 47, 48, 62, 70 space-time, 4 spatial, ix, x, 119, 120, 121, 122, 123, 128, 129, 130, 133, 134, 135, 136, 137, 138, 139, 141, 142, 143 spatial analysis, vii, x, 1, 2, 4, 6, 9, 10, 18, 32, 62, 70, 72, 78, 81, 119, 121, 129, 130, 133, 137, 143 spatial data management, vii, 1, 5, 9, 21, 27, 139 spatial decision support, vii, 1 spatial decision support systems (SDSS), vii, 1, 2, 4, 5, 6, 7, 8, 9, 10, 11, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 30, 31, 32, 33 spatial information, vii, 1, 10, 29, 38, 54, 82, 143 spatial location, viii, 35, 50, 122, 135 specialists, 45, 70, 108 species, vii, viii, 40, 41, 42, 43, 85, 86, 87, 88, 89, 97, 98, 99, 100, 102, 103, 110, 114, 115 specific knowledge, 2, 5, 62 specifications, 6, 73 spectrum, 123 spills, x, 119, 122 stakeholder groups, 21

stakeholders, 3, 4, 22, 25, 28, 39, 50 state authorities, 124 statistical analysis, x, 133 storage, 10, 22, 37, 46, 64, 67, 121, 143 storms, 3 strategic planning, 23, 33 strategies, 121, 135, 137 stress level, 134 structure, ix, 6, 16, 26, 29, 39, 40, 42, 49, 51, 54, 56, 77, 83, 87, 89, 107, 111, 113, 120, 125 structuring, 16, 18, 69, 79 subjective judgments, 28 subtropical forests, 50 sulfur, 135 sulfur dioxide, 135 sulphur, 124 Sun, 76 supply, x, 119 surface area, 50, 116 surface water, x, 119, 122, 125, 129, 130 survival, viii, 85, 86, 87, 89, 96, 97, 100 sustainability, 38, 45, 52 sustainable development, 2, 53, 57, 108 synthesis, xi, 18, 133

T  target, 98, 99, 123 technical support, 117 techniques, vii, viii, ix, 1, 3, 5, 7, 9, 14, 18, 19, 20, 21, 22, 24, 25, 26, 27, 29, 36, 39, 41, 43, 61, 62, 70, 71, 72, 74, 80, 86, 113, 117, 127, 139, 142 technological change, 27 technology(ies), vii, viii, 1, 2, 8, 14, 25, 26, 27, 35, 36, 40, 43, 44, 45, 49, 51, 58, 62, 63, 64, 67, 68, 69, 70, 73, 74, 75, 76, 77, 78, 80, 136, 137 temporal, x, 119, 120, 121, 123, 124, 129, 130, 131, 132, 139, 143, 145 territory, 2, 37 testing, 32, 144 threats, 86, 89, 97, 100 three-dimensional, 134 timber production, 41 topology, 122 tourmaline, 112 toxic gases, x, 133, 135 trade, 5, 63, 88 trade-off, 5 traffic, x, 120, 121, 122, 131, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145 traffic flow, 144 training, 2, 26, 40 transparency, 26

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,

Index transport, x, 5, 29, 120, 129, 131, 142 transportation, 3, 31, 62, 78, 120, 129 treatment, viii, 23, 36, 115 triangulation, 123 tropical forests, 43 trucks, x, 133, 136, 137

U  urban, x, 5, 29, 30, 31, 32, 108, 121, 125, 128, 130, 131, 132, 133, 134, 135, 136, 139, 143, 144, 145 urban areas, 128, 131, 132, 134, 135, 144, 145 urbanization, ix, 107 user-interface, 24, 25

V 

visualization, vii, 1, 5, 6, 9, 24, 25, 27, 32, 38, 70, 79, 83, 121, 123, 136, 137, 142, 145 volatile organic compounds, 135

W  waste, x, 2, 20, 29, 119, 120, 122, 124, 129, 131 waste disposal, 2, 20, 29, 131 waste disposal sites, 20, 29 waste management, x, 119, 122, 124, 129 waste water, x, 43, 119, 122 water, ix, x, 3, 8, 32, 40, 43, 45, 53, 56, 58, 67, 92, 107, 108, 109, 111, 112, 113, 114, 115, 116, 119, 120, 121, 122, 124, 125, 126, 129, 130, 131 water quality, 56, 115, 121, 122, 125, 126 water resources, ix, 32, 45, 53, 107 watershed, 29, 41, 54, 117 wetlands, 39, 41, 43, 55 wildlife, 39, 43, 44, 52, 81, 86, 98, 100, 102, 103, 104, 105 wildlife conservation, 103 wind, 134, 139, 142, 144 wind farm, 23, 24, 33 wood, 37, 39, 50, 83

X  XML, 79

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validation, 99, 139 variability, 131, 139, 145 variables, viii, 9, 10, 35, 38, 44, 49, 50, 61 variations, 136, 137 varieties, 114 vector, 66, 67, 89, 121, 122 vegetation, viii, ix, 21, 22, 33, 35, 39, 40, 42, 44, 53, 57, 67, 90, 92, 101, 107, 109, 110, 114, 116 vehicles, 65, 121, 135, 137, 139, 141, 145 ventilation, 134 visible, 123

153

Geographic Information Systems, edited by Christopher J. Dawsen, Nova Science Publishers, Incorporated, 2011. ProQuest Ebook Central,