Big Earth Data in Support of the Sustainable Development Goals (2019) 9782759824427

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Editor-in-Chief

GUO Huadong

Aerospace Information Research Institute Chinese Academy of Sciences China

Map Content Approval Number: GS (2019) 6202 ISBN: 978-7-03-063808-3 Science Press ISBN(print): 978-2-7598-2441-0

ISBN(ebook): 978-2-7598-2442-7

EDP Sciences

© Science Press and EDP Sciences 2019 All rights relative to translation, adaptation and reproduction by any means whatsoever are reserved, worldwide. In accordance with the terms of paragraphs 2 and 3 of Article 41 of the French Act dated March 11, 1957, “copies or reproductions reserved strictly for private use and not intended for collective use” and, on the other hand, analyses and short quotations for example or illustrative purposes, are allowed. Otherwise, “any representation or reproduction—whether in full or in part—without the consent of the author or of his successors or assigns, is unlawful” (Article 40, paragraph 1). Any representation or reproduction, by any means whatsoever, will therefore be deemed an infringement of copyright punishable under Articles 425 and following of the French Penal Code.

Big Earth Data in Support of the Sustainable Development Goals (2019) Editorial Board Editor-in-Chief:

GUO Huadong

Vice Editor-in-Chief:

HAN Qunli

CHEN Fang

JIA Gensuo

HUANG Chunlin

SUN Zhongchang

LIU Jie

BAI Jie

BAO Qing

BIAN Jinhu

CAO Bo

CHEN Lie

CHEN Yu

CHEN Jinsong

CHEN Qiting

CHEN Yaxi

CHENG Kai

CHENG Zhifeng

CUI Yuran

DENG Xinping

DU Wenjie

FAN Xinyue

GAO Feng

GULI Jiapaer

GUO Wanqin

HE Bian

HE Zhonghua

HU Guangcheng

Hu Ziyuan

HUANG Lei

JIA Li

JIA Huicong

JIANG Liangliang

JIANG Xun

Kamal Labbassi

KONG Lingqiao

LEI Guangbin

LEI Liping

LI Ainong

LI Hongzhong

LI Liping

LI Xiaosong

LIANG Dong

LIAO Jingjuan

LIN Yongsheng

LIU Ronggao

LU Qi

LU Shanlong

LUO Lei

MA Keping

Massimo Menenti

MI Xiangcheng

NAN Xi

NIU Zhenguo

OUYANG Zhiyun

QIN Haining

REN Haibao

SHANGGUAN Donghui

SHEN Xiaoli

SONG Xiaoyu

SUN Xiaoxia

WANG Xiao

WANG Bao

WANG Jin

WANG Lei

WANG Li

WANG Jianghao

WANG Juanle

WANG Penglong

WANG Shudong

WANG Xinyuan

WANG Yunchen

WANG Zifeng

WEI Haishuo

WEI Yanqiang

WU Bingfang

WU Wenbin

WU XiaoFei

WU Zaixing

XIAO Yi

XU Weihua

YANG Ruixia

YU Rencheng

YU Xiubo

YUAN Yongquan

Zeeshan Shirazi

ZHANG Lu

ZHANG Miao

ZHANG Jingjing

ZHANG Lirong

ZHANG Shaoqing

ZHANG Zengxiang

ZHAO Lina

ZHAO Xiaoli

ZHENG Yaomin

ZHOU Zhengxi

ZHU Li

ZHU Liang

ZHU Xiulin

ZUO Lijun

Members:

Preface

O

n the 70 th anniversary of the United Nations (UN) in September 2015, heads of state and delegates gathered

at the UN headquarters in New York and adopted the 2030 Agenda for Sustainable Development. This comprehensive sustainability framework was built on the basis of the historical experiences of human society and a shared expectation for the future. It presents a blueprint for countries to pursue global sustainable development in the next 15 years. The 17 Sustainable Development Goals (SDGs) incorporate various social, economic, environmental, and developmental targets and indicators, and have been endorsed by all countries with respective national implementation plans. With the advancement of science, technology, and innovation (STI) accelerating, there is a growing international consensus that STI must play a key role in facilitating the implementation of SDGs. To this end, the UN established the “Technology Facilitation Mechanism” to bring together scientific communities, policy makers, business sectors, and other stakeholders for their collective ideas, insights, knowledge, and wisdom to build societies that are harmonious with the environment. The Chinese Academy of Sciences (CAS), being a member of the international scientific community, has been

ii

Big Earth Data in Support of the Sustainable Development Goals (2019)

mobilizing its research capacities for action. The SDGs consist of 17 goals, 169 targets, and over 230 indicators. Countries have different and very diverse development contexts. The key to success for one goal is often linked to solving issues associated with other goals. The SDGs thus constitute a vast development system that is complicated, diverse, dynamic, and interconnected. This makes effective assessment and monitoring of each and all SDG targets and indicators essential to ensure the implementation of the SDGs. Currently, only about 45% of indicators are supported by both methods and data, about 39% have methods but lack data, and some 16% have neither standard methods nor data. The full implementation of the SDGs will be hampered if these problems are not effectively resolved. CAS addresses these challenges through its Big Earth Data Science Engineering Program (CASEarth) and concentrates on six SDGs, including: SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), SDG 14 (Life below Water) and SDG 15 (Life on Land). CASEarth thus works on 20 associated indicators of these SDGs, especially the indicators that are relatively weak in data or methods. The case studies presented in this report, for instance the assessments of food productivity, urbanization, and land degradation, demonstrate that Big Earth Data and related technologies can provide new analytical tools and data infrastructures for understanding the

Preface

complex and interconnected sustainability issues. The continued effort to develop a Big Earth Data system will provide robust and complementary data services to support and improve SDG indicators. Furthermore, China’s effort on Big Earth Data applications in service of SDGs will likely be of interest to some other developing countries, particularly those lacking technological capabilities. CASEarth research on SDGs is an important contribution of China toward the 2030 Agenda for Sustainable Development. It is a new platform for Chinese scientists and international scientific communities to work together. I would like to thank the CASEarth team led by Prof. GUO Huadong for their efforts towards implementing the SDGs, and I expect them to bring new and more exciting results in coming years.

BAI Chunli President, Chinese Academy of Sciences

iii

Foreword

T

here are four major challenges in the implementation of the United Nations (UN) Sustainable Development Goals

(SDGs): (1) missing data and the evolution of SDG indicators, (2) complementary and non-complementary interconnections between different SDGs, (3) complicated and varied problems in quantifying and monitoring indicators within different national and local FRQWH[WVDQG GLI¿FXOWLHVLQPRGHOLQJLQGLFDWRUVWRPRQLWRU6'*V Particularly, the main challenge in monitoring progress relates to the lack of data available for the development of indicators, and this lack RIGDWDKDVEHHQLGHQWL¿HGIRUPRUHWKDQKDOIRIWKHLQGLFDWRUV In order to achieve the SDGs and effectively assess their progress with the full strength of science, technology, and innovation (STI), the UN has established the “Technology Facilitation Mechanism” (TFM). The TFM consists of three components: the Interagency Task Team on STI for the SDGs (IATT) with a 10-Member Group to support TFM, the collaborative Multi-stakeholder Forum on STI for SDGs (STI Forum), and an online platform as a gateway for information on existing STI initiatives, mechanisms, and programs. Presently, a pressing priority of TFM is to make breakthroughs toward Tier Ĕ indicators (methods established but with poor data) and Tier ĕ indicators (methods under development with either poor data or no data).

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Big Earth Data in Support of the Sustainable Development Goals (2019)

As an important aspect of technological innovation today, big data LVEULQJLQJQHZWRROVDQGPHWKRGRORJLHVWRVFLHQWL¿FUHVHDUFK%DVHG on Earth science, information science, and space science, Big Earth Data derives and integrates data from space-based Earth observations as well as terrestrial, oceanic, atmospheric, and human activity data from other sources. Big Earth Data is therefore characterized in terms of its massive quantity, multiple sources, heterogeneous structure, and high complexity. Big Earth Data can also be non-stationary, unstructured, multi-temporal, and multi-dimensional. Effective use of Big Earth Data has offered a new key to generating knowledge about planet Earth, playing a major role in promoting sustainable development. To this end, the Chinese Academy of Sciences (CAS) launched the “Big Earth Data Science Engineering Program” (CASEarth) in early 2018 and took SDGs as one of its top priorities. SDGs, especially the goals closely related to Earth’s surface, the environment, and natural resources, have the characteristics of being large-scale with cyclical changes. The macroscopic and dynamic monitoring capabilities of Big Earth Data thus fit well as an important means for assessing progress on sustainable development. The main objectives for CASEarth to serve SDGs include converting Big Earth Data into SDG-related information, providing decision support for SDG implementation, constructing a Big Earth Data integration system for SDG indicators, and investigating the inter-linkages among different components of the Earth system.

Foreword

CASEarth has preliminarily sorted out 20 indicators from six SDGs as a priority. These indicators constitute 10% of Tier Ĕ indicators and 11% of Tier ĕ indicators. The six SDGs include: SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), SDG 14 (Life below Water), and SDG 15 (Life on Land). CASEarth supports SDG indicators in three major ways. (1) Big (DUWK'DWDLVXVHGWR¿OOLQPLVVLQJGDWDDQGSURYLGHQHZVRXUFHVRI data for evaluation. (2) New methodologies are created to evaluate SDGs on the basis of Big Earth Data technologies and models. (3) CASEarth provides practice cases of Big Earth Data for SDGs, and aids in monitoring the progress of SDG indicators. CASEarth relies on novel methods for multi-source data acquisition, cloud data analysis, and artificial intelligence technologies to study cases at different scales. These methods also aid in developing global and regional SDG indicator assessment systems based on Big Earth Data for global and national appraisal and reporting. The report Big Earth Data in Support of the Sustainable Development Goals 2019 presents 27 case studies of CASEarth on the development of SDG indicators and sustainability assessments in the above-mentioned six SDGs. These cases provide in-depth, systematic research and evaluation results on the selected SDGs and indicators by means of data, method models, and decision support at global, regional, national, and local scales. The 27 case studies covered 20 indicators, with focus varying from constructing

vii

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Big Earth Data in Support of the Sustainable Development Goals (2019)

databases, building index systems, and evaluating indicator progress. Each case study first clearly lists the corresponding SDG targets and indicators it addresses, and then proceeds with the research methods, data, analysis results, and prospects for future research. It FDQEHVHHQWKDW%LJ(DUWK'DWDDVDQHZVFLHQWL¿FPHWKRGRORJ\KDV started demonstrating its great value and potential for applications in monitoring and evaluating SDGs for China and developing countries around the world. The report concludes with a summary of the major progress in Big Earth Data for SDGs and future research priorities. In The Sustainable Development Goals Report 2019, UN Secretary-General António Guterres said in his foreword that “progress is being made in some critical areas”, and that from these advances, “we know what works”, including “better use of data; and harnessing science, technology and innovation with a greater focus on digital transformation.” Liu Zhenmin, Under-Secretary-General for Economic and Social Affairs, also pointed out in the report that “Most countries do not regularly collect data for more than half of the global indicators”, and “Increased investment is urgently needed to ensure that adequate data are available to inform decision-making on all aspects of the 2030 Agenda.” These messages underline the importance and urgency of data for SDGs, for which Big Earth Data is deemed to make a unique contribution. In September 2019, the main content of the report was released to WKH8QLWHG1DWLRQV,WLVRQHRIIRXURI¿FLDOGRFXPHQWVRIWKH&KLQHVH government for the 74th UN General Assembly and one of the two

Foreword

official documents for the UN Sustainable Development Summit. The report reveals the value of applications and prospects of relevant WHFKQRORJLHVDQGPHWKRGVIRUPRQLWRULQJDQGDVVHVVLQJ6'*V¿OOLQJ the data and methodological gaps for the international community and accelerating the implementation of the 2030 Agenda. The TFM is an important machine for achieving SDGs, fully concurring with China’s concept of and strategy for STI for sustainable development. Big Earth Data as an innovative technology has great potential to this end. It is the plan of CASEarth to continue research on SDGs and publish a report on Big Earth Data in Support of the Sustainable Development Goals every year. For this perspective, CASEarth warmly welcomes cooperation from all research partners, both in China and around the world. On the occasion of this report’s publication, I would like to express my heartfelt thanks for the guidance and advice received from CAS, the Ministry of Foreign Affairs of China, the Ministry of Science and Technology of China, other related departments, and strong support from the Leading Group of CASEarth and the CAS Bureau of Science and Technology for Development. I thank Prof. BAI Chunli, President of CAS, for the Preface, Prof. ZHANG Yaping, VicePresident of CAS, and Prof. XU Guanhua for their strong support. I am also grateful for support from Mr. HUANG Yiyang, Deputy Director-General of the Department of International Economic Affairs of the Ministry of Foreign Affairs of China, Prof. YAN Qing, Director of the CAS Bureau of Science and Technology for

ix

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Development, and Prof. ZHAO Qianjun, Deputy Director of the CAS Bureau of Science and Technology for Development. My sincerest gratitude goes to all of the groups who made this report possible, including the steering group, administrative offices, related project teams, CASEarth Working Groups, and the report’s board members and authors.

GUO Huadong CAS Academician Chief Scientist of CASEarth

Executive Summary

In 2015, the United Nations (UN) adopted the 2030 Agenda for Sustainable Development to promote sustainable development in three dimensions—economic, social, and environmental— in a balanced and integrated manner. This document is an important landmark marking the spirit of consensus and cooperation between different nations toward a sustainable world. However, the implementation of the agenda faces several challenges from a scientific perspective, including data deficiency, imperfect methods, interconnected and mutually constrained targets, diverse localization issues, and other problems restricting the pace of progress. As an important part of big data, Big Earth Data provides the capability to integrate data from multiple sources and helps to produce more relevant, frequent, and accurate information about complex processes that can support decision making and policy formulation. These traits hold potential for important contributions towards the agenda and support various aspects of the UN Sustainable Development Goals (SDGs). Big Earth Data in Support of the Sustainable Development Goals strongly supports and is FRPPLWWHGWRIDFLOLWDWLQJWKHLPSOHPHQWDWLRQRI6'*V,WZLOOIRFXVRQUHVHDUFKWR¿OOLQNQRZOHGJH gaps and develop technologies, methodologies, data products, and open, accessible cloud-based data analysis environments for selected SDGs, including SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), SDG 14 (Life below Water), and SDG 15 (Life on Land).

For SDG 2, this report targets two indicators about food supply, volume of production per labor unit (SDG 2.3.1) and proportion of agricultural area under productive and sustainable agriculture (SDG 2.4.1). The two indicators are evaluated from the global scale and national scale separately by adopting multiple sources of data and employing remote sensing information extraction models, statistical models, and ecological models. Global food production estimation indicates that in the last decade, world crop production per labor unit has increased by 34%; the value of the indicator in developed

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Big Earth Data in Support of the Sustainable Development Goals (2019)

countries is dozens of times more than that in developing countries, and the latter enjoy faster growth. Evaluation of the environmental impacts of food production in China indicates that impacts per unit of production decreased since 2000, showing that China’s cropping systems are becoming more sustainable. Meanwhile, land use change driven by urbanization challenged this trend. By monitoring the progress of these two indicators, this report proposes that further interactions between land use change and farm management are critical to improving the sustainability of global food production systems for SDG 2.

Big Earth Data technologies can directly or indirectly support four SDG 6 indicators, namely, safe drinking water (SDG 6.1.1), water TXDOLW\6'* ZDWHUXVHHI¿FLHQF\6'* DQGZDWHUUHODWHG HFRV\VWHPV6'* 7KLVUHSRUWSUHVHQWV¿YHFDVHVDWWKHUHJLRQDO scale, such as the countries and regions along the Belt and Road, and national scale, such as China, highlighting the potential of Big Earth Data to support SDG 6 by developing data products and technical methods. In these cases, the multi-source data such as satellite remote sensing, Internet data, and statistics were applied. The integration of spatiotemporal data and model simulation helped produce an overall analysis of the provincial and municipal level of drinking water safety and surface water environments LQ&KLQD,WDOVRKHOSHGH[SORUHDQHZPHWKRGIRUHYDOXDWLQJWKHHI¿FLHQF\RIDJULFXOWXUDO water use in a typical irrigation area in the countries and regions along the Belt and Road. Based on satellite remote sensing images, a 30-meter spatial resolution mangrove distribution dataset of Southeast Asian countries from 1990 to 2015 and a 16-meter spatial UHVROXWLRQVXUIDFHZDWHUGLVWULEXWLRQGDWDVHWRI¿YH&HQWUDO$VLDQFRXQWULHVLQZHUH produced to analyze water-related ecosystem changes. During the period of 1990-2018, the mangrove area in Southeast Asia showed a decreasing trend. The surface water area of Central Asia also showed a decreasing trend from 2000 to 2018. The degradation of water ecosystems is a big challenge for the countries and regions along the Belt and Road to achieve SDG 6. China’s mangrove area has steadily increased since 2000, which is mainly due to the implementation of ecological protection policies such as ‘returning ponds to forests’ and ‘returning ponds to wetlands’. These experiences in policy and practice can provide reference for the protection of water-related ecosystems in surrounding developing countries.

Executive Summary

For SDG 11, this report has focused on five indicators, including public transport (SDG 11.2.1), urbanization (SDG 11.3.1), cultural and natural heritage (SDG 11.4.1), PM 2.5 (SDG 11.6.2) and public spaces (SDG 11.7.1). This report utilizes Big Earth Data in place of the traditional statistical data and increases the spatiotemporal resolution of SDG indicator evaluation. This report also develops and employs the Big Earth Data methods for assessing SDG 11 indicators on the global scale, regional scale, and national scale, and takes the lead to assess China’s domestic practices aimed at achieving SDG 11. A global 10-meter spatial resolution impervious surface product with an overall accuracy greater than 86% generated by fusion of optical and synthetic aperture radar (SAR) data provides important data support and resolves data deficiency for the monitoring and evaluation of SDG 11.3.1. Long-term time series urban expansion datasets have been developed for 1,500 cities with populations greater than 300,000 from 1990 to 2015 at five-year intervals in the Belt and Road region, providing foundational material for the sustainable development of cities. The ratio between the land consumption rate and the population growth rate increased from 1.24 in 1990-1995 to 2.67 in 2010-2015 in developing countries in the Belt and Road region, indicating major challenges ahead for urban sustainable development in these countries. Utilizing Big Earth Data, this report has also produced datasets like public transport information, PM2.5 products, and proportion of public spaces in built-up urban areas. These products provide support for comprehensive evaluation of sustainable development in Chinese cities. For SDG 11.4.1, this report has improved the SDG indicator system, stating that it should “increase capital investment per unit area to preserve and protect world cultural and natural heritage”. This report also proposes to modify the SDG 11.3.1 evaluation method to better reflect the relationship between land urbanization and population urbanization in cities with negative population growth or population stagnation.

As for SDG 13, the report has focused on strengthening resilience and adaptive capacity to climate-related hazards and natural disasters in all countries (SDG 13.1) and improving education, awareness, and human and institutional capacity for climate change mitigation, adaptation, impact reduction, and early warning (SDG 13.3). The report makes

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Big Earth Data in Support of the Sustainable Development Goals (2019)

use of datasets and models to monitor and evaluate SDG 13 indicators, contributing data products, methods, models, and decision support to the world. This report produces data products for disaster losses in the countries and regions along the Belt and Road, and analyzes dynamic change in damages caused by various disasters in the countries and regions along the Belt and Road, providing information for the appraisal of all countries to improve efforts to respond to disasters and mitigate losses. This report also builds a method for detecting abnormal variations in global greenhouse emissions based on time-series data fitting and produces continuous spatiotemporal data products for CO2 monitoring with the world’s atmospheric satellites. It also builds a method to produce spatiotemporal monitoring products for glaciers and forecasts Arctic sea ice. This provides important information for all countries to develop mitigation strategies and endeavor to reach the world’s emission-cutting and temperature-control targets.

For SDG 14, this report focuses on marine pollution (SDG 14.1) and PDULQHHFRV\VWHPKHDOWKPDQDJHPHQW6'* %DVHGRQ¿HOGGDWD on nutrient composition, chlorophyll-a concentration, phytoplankton biomass, chemical indexes like dissolved oxygen observed in the coastal waters of China, and the bulletin of the national marine monitoring departments, an integrated eutrophication assessment model and an experimental evaluation model for marine ecosystems were developed. An integrated eutrophication assessment model was developed based on the framework of “Pressure-State-Response”. The model was implemented at different scales of estuaries and bays along China’s coasts to assess their level of eutrophication. The results provide scientific support and decision making for the management of discharged offshore nutrient pollutants and coastal eutrophication. Furthermore, experimental evaluation of the ecosystem health of Jiaozhou Bay was carried out, and a simulation system will be developed to predict possible responses of coastal ecosystems to the changes in marine pollution. By further promoting the operational application of related technologies, it is expected to provide decision making support for offshore environmental protection and management. The system can also effectively promote the realization of the SDG 14 target.

Executive Summary

For SDG 15, this report took the forest area as a proportion of total land area (SDG 15.1.1), proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas (SDG 15.1.2), proportion of land that is degraded over total land area (SDG 15.3.1), mountain green cover index (SDG 15.4.2), and Red List Index (SDG 15.5.1) as research objects. The report presents 10 cases evaluated and monitored at global, regional, national, and local scales. The mountain green cover index in the countries and regions along the Belt and Road, forest cover data of Mainland Southeast Asia, protected forest ecosystem areas in China, and other data products were obtained using Big Earth Data methods. Based on these data products, this report revealed the distribution and changes in forest and green land for the research areas, found regional differences in forest change and the driving factors, and pointed out blank areas in China’s forest protection. This report also proposed an assessment methodology system for global land degradation and indicates that 77 countries had more land degradation area than land restoration area, meaning the realization of SDG 15.3.1 by 2030 is facing serious challenges. The number of net restoration area in China accounted for 18.24% of the global total, which made an important contribution to the zero growth of global land degradation. By evaluating the habitat fragmentation of the giant panda, it was found that panda habitats during the period from 1976 to 2013 became smaller and more fragmented. The report proposes to comprehensively consider the number of protected species and the protection of habitat environments. Based on the evaluation of the Red List Index, it was found that the Red List Index of higher plants and terrestrial mammals in China was on the rise from 2004 to 2017, while the Red List Index of birds was on the decline.

This report cites 27 typical cases centered on 20 SDG indicators of six selected SDGs and conducts in-depth research and appraisal of relevant SDG targets and indicators. It covers the perspectives of data, methods, models, and decision support at four scales—local, national, regional, and global—providing a systematic solution. On the one hand, it demonstrates the great value and potential of Big Earth Data in monitoring SDG indicators; on the other, it is of important UHIHUHQFHYDOXHIRUGHFLVLRQPDNLQJGHSDUWPHQWVDQGUHOHYDQWDFDGHPLF¿HOGVFRQFHUQLQJ6'*V

xv

Map of Cases on Big Earth Data for the SDGs

Area/Country

Global

China

China

China

Sidi Bennour Irrigation District, Morocco

Southeast Asia

Name of case

Global crop production per labor unit assessment based on Big Earth Data

Assessing progress towards sustainable cropping systems: the case of China

Monitoring the proportion of urban population using safely managed drinking water services in China

Analysis of surface water quality in China

Crop water productivity for the SDG assessment of agricultural water use HI¿FLHQF\WKHFDVHRID typical irrigation district in Morocco

Mapping the extent and dynamic change of mangrove forests in Southeast Asia

SDG 2.3.1

SDG 2.4.1

SDG 6.1.1

SDG 6.3.2

SDG 6.4.1

SDG 6.6.1

Indicator

Mangrove distribution dataset for Southeast Asia ranging from 1990 to 2015

—

The change in the extent of waterrelated ecosystems in different countries and regions along the Belt and Road.

Assessment of crop water productivity for optimal water resource management.

Model to estimate crop water productivity for water resource management in irrigated agricultural areas using Big Earth Data —

—

—

The proportion of good ambient water quality at provincial levels in China in 2016 and 2017

Policy suggestions for improving the safety of the drinking water supply in China.

Reveal the progress of sustainable use of cropland in China and the driving forces, and propose suggestions on promoting sustainability of crop production systems.

Methodologies for assessing land productivity, irrigation water consumption, and excess fertilizer application by integrating multisource data and multidisciplinary models

—

Reveal the regional differences in the trend and propose key non-attainment regions.

Decision support

—

Method and model

A multi-source data fusion analysis of the proportion of the population using safely managed drinking water services

—

Global production for major crops per labor unit

Data products

List of Cases on Big Earth Data for the SDGs

List of Cases on Big Earth Data for the SDGs xvii

China

Belt and Road region

Proportion of the population with easy access to public transportation in China

Monitoring and assessing urbanization progress in the Belt and Road region

Preliminary study and suggestions for modifying indicator SDG 11.4.1

SDG 11.2.1

SDG 11.3.1

SDG 11.4.1 China

Central Asia

Area/Country

Surface water changes in Central Asia

Name of case

SDG 6.6.1

Indicator

Decision support is provided for the sustainable development of cities in the Belt and Road region, and data support is provided for comprehensive evaluation of sustainable urban development at the national scale in China

A method is proposed for rapidly extracting the information for global urban impervious surfaces using multisource, ascending/ descending orbits, multitemporal SAR and optical data combined with texture and phenological characteristics. China’s localized practices are evaluated for SDG 11

A method concerning “increasing the capital investment per unit area to preserve and protect world cultural and natural heritage” is proposed

Global 10-meter resolution high-precision spatial distribution information for urban impervious surfaces in 2015 (the base year for SDGs). Remote sensing datasets featuring urban expansion in 1990, 1995, 2000, 2005, 2010, and 2015 of 1,500 cities with a population greater than 300,000 in the Belt and Road region Statistical data of “total per capita expenditure” and “expenditure per unit area” of national scenic spots in eastern, central and western China; 25-year time series datasets on the Huangshan World Heritage Site Remote Sensing Ecological Index (RSEI)

—

Provide data support for comprehensive evaluation of sustainable urban development at the national scale in China

China’s regional public transport data

Surface water dataset for Central Asia in 2018 A simple indicator accounting method is proposed to provide experience and reference for other countries to evaluate and compare the same indicators

Decision support

—

Method and model The change in the extent of waterrelated ecosystems in different countries and regions along the Belt and Road

Data products

(continued)

xviii Big Earth Data in Support of the Sustainable Development Goals (2019)

China

China

Belt and Road Region

Global

Belt and Road Region

Proportion of urban open public space in China

Disaster monitoring and analysis of the SDG 13.1.1 indicator in countries and regions along the Belt and Road

Global atmospheric CO2 concentration changes in response to climate change

Cognition of climate response to glaciers and Arctic sea ice

SDG 11.7.1

SDG 13.1.1

SDG 13.3

SDG 13.3

Area/Country

Monitoring and DQDO\]LQJ¿QH particulate matter (PM2.5) in China

Name of case

SDG 11.6.2

Indicator

Glacier monitoring products in the countries and regions along the Belt and Road; Arctic sea ice prediction products

Global CO2 space-time continuous distribution based on satellite monitoring

The SDG 13.1.1 indicator product in the countries and regions along the Belt and Road

Area indicator evaluation datasets for urban built-up areas in China

China’s 2010-2018 annual average PM2.5 products

Data products

—

Analyzing atmospheric CO2 concentration anomalies caused by extreme events, providing decisionmaking reference for controlling environmental CO2 concentration and environmental protection, improvement, and restoration policies Greenhouse gas (CO2) abnormal change detection method based on time VHULHVGDWD¿WWLQJWKH global scale

Providing decision-making reference for strategic planning such as rational planning of water resources in the glacial recharge area and development of the Arctic channel

Provide decision support to evaluate countries’ resilience and adaptive capacity to climate-related hazards

Data support is provided for comprehensive evaluation of sustainable urban development at the national scale in China

A simple indicator accounting method is proposed to provide experience and reference for other countries to evaluate and internationally compare the same indicators

—

—

Decision support

—

Method and model

(continued)

List of Cases on Big Earth Data for the SDGs xix

Coastal Waters, China

Jiaozhou Bay, China

Southeast Asia

Global

China

Construction and application of an integrated eutrophication assessment model for typical coastal waters of China

Ecosystem health assessment in Jiaozhou Bay, China

Forest cover mapping to monitor terrestrial ecosystems in Southeast Asia

Assessment of conservation priority for global national parks

Study on forest protection proportion indicators

Evaluating the effectiveness of the management of protected areas: an example from Qianjiangyuan National Park in China

SDG 14.1.1

SDG 14.2.1

SDG 15.1.1

SDG 15.1.2

SDG 15.1.2

SDG 15.1.2

Qianjiangyuan National Park, China

Area/Country

Name of case

Indicator

—

—

China’s key forest ecosystem protection areas dataset, and China’s forest ecosystem protection status and protection vacancy dataset

Qianjiangyuan National Park ecosystem and biodiversity datasets

—

—

Forest cover dataset with 30-meter resolution for Mainland Southeast Asia (Vietnam, Laos, Cambodia, Thailand, and Myanmar) —

Build the evaluation index system for typical waters in China

Construct the latest comprehensive assessment system suitable for evaluating coastal eutrophication in China

Method and model

—

—

Data products

Countermeasures for biodiversity conservation and management in Qianjiangyuan National Park

Assessing the proportion of China’s forest ecosystems covered by nature reserves

Global national park conservation priority evaluation report

—

—

Participate in the establishment of marine industry standards for the assessment of coastal eutrophication in China; issue international reports on eutrophication assessment in WKH1RUWKZHVW3DFL¿F$FWLRQ3ODQ (NOWPAP) region together and propose it to UNEP

Decision support

(continued)

xx Big Earth Data in Support of the Sustainable Development Goals (2019)

Global

Central Asia

Belt and Road Region

China

Big Earth Data for global land degradation assessment

The proportion of degraded land to the total land area in Central Asia

Identifying land degradation area and risk control countermeasures in Mongolia and along the China-Mongolia railway

Applications of the Mountain Green Cover Index in countries and regions along the Belt and Road

Evaluation of the Red List Index of threatened species in China

Assessment of giant panda habitat fragmentation

SDG 15.3.1

SDG 15.3.1

SDG 15.3.1

SDG 15.4.2

SDG 15.5.1

SDG 15.5.1

Giant Panda Habitat, China

Mongolia

Area/Country

Name of case

Indicator

—

The data describes the current distribution and past changes in the giant panda habitat in China over the past 40 years

Support is provided to assess evolutionary characteristics and suggestions are offered for protecting giant panda habitats

—

A new grid-based Mountain Green Cover Index calculation model WRUHÀHFWWKHFRQFHQWUDWHG environmental gradient and high spatiotemporal heterogeneity characteristics of mountainous areas —

The Mountain Green Cover Index assessment report in the countries and regions along the Belt and Road

—

Identifying land degradation areas and providing decision making reference for the deployment of restoration plans of the LDN initiative

A new assessment methodology system of the accurate evaluation of land degradation in inland arid regions

Support is provided for analyzing the driving forces of land degradation along the China-Mongolian railway (Mongolian section), discovering key areas of land degradation, and for proposing recommendations for land degradation prevention and control

Global land degradation country evaluation report for 2000-2015

Decision support

Global land degradation Big Earth Data assessment methodology system

Method and model

Chinese species Red List Index data

The Mountain Green Cover Index dataset in the countries and regions along the Belt and Road

Land degradation data for Mongolia and ChinaMongolia railway from 1990 to 2015 with a 30-meter spatial resolution

New data source for evaluating arid regions in Central Asia

Global land degradation dataset

Data products

(continued)

List of Cases on Big Earth Data for the SDGs xxi

xxii

Big Earth Data in Support of the Sustainable Development Goals (2019)

Contents

i

Preface

v

Foreword

xi

Executive Summary

xvi

Map of Cases on Big Earth Data for the SDGs

xvii

List of Cases on Big Earth Data for the SDGs

Chapter 1

Big Earth Data for SDGs / 1 Data-intensive Paradigm / 2 Big Earth Data / 3 Big Earth Data for Implementing SDGs / 4

Background / 10 Contributions / 11 Case Study / 12 Chapter 2 SDG 2 Zero Hunger

Global crop production per labor unit assessment based on Big Earth Data / 12 Assessing progress towards sustainable cropping systems: the case of China / 19 Conclusions / 23

Contents

Background / 28 Contributions / 29 Case Study / 31 Chapter 3 SDG 6 Clean Water and Sanitation

Monitoring the proportion of urban population using safely managed drinking water services in China / 31 Analysis of surface water quality in China / 34 Crop water productivity for the SDG assessment of agricultural water XVHHI¿FLHQF\WKHFDVHRIDW\SLFDOLUULJDWLRQGLVWULFWLQ0RURFFR Mapping the extent and dynamic change of mangrove forests in Southeast Asia / 42 Surface water changes in Central Asia / 46 Conclusions / 50

Background / 54 Contributions / 55 Case Study / 57 Chapter 4 SDG 11 Sustainable Cities and Communities

Proportion of the population with easy access to public transportation in China / 57 Monitoring and assessing urbanization progress in the countries and regions along the Belt and Road / 61 Preliminary study and suggestions for modifying indicator SDG 11.4.1 / 68 0RQLWRULQJDQGDQDO\]LQJ¿QHSDUWLFXODWHPDWWHU302.5) in China / 74 Proportion of urban open public space in China / 77 Conclusions / 81

xxiii

xxiv

Big Earth Data in Support of the Sustainable Development Goals (2019)

Background / 84 Contributions / 85 Case Study / 86 Chapter 5 SDG 13 Climate Action

Disaster monitoring and analysis of the SDG 13.1.1 indicator in countries and regions along the Belt and Road / 86 Global atmospheric CO2 concentration changes in response to climate change / 92 Cognition of climate response to glaciers and Arctic sea ice / 96 Conclusions / 100

Background / 104 Contributions / 105 Case Study / 106 Chapter 6 SDG 14 Life below Water

Construction and application of an integrated eutrophication assessment model for typical coastal waters of China / 106 Ecosystem health assessment in Jiaozhou Bay, China / 110 Conclusions / 115

Contents

Background / 118 Contributions / 119 Case Study / 121 Chapter 7 SDG 15 Life on Land

Forest cover mapping to monitor terrestrial ecosystems in Southeast Asia / 121 Assessment of conservation priority for global national parks / 125 Study on forest protection proportion indicators / 130 Evaluating the effectiveness of the management of protected areas: an example from Qianjiangyuan National Park in China / 134 Big Earth Data for global land degradation assessment / 140 The proportion of degraded land to the total land area in Central Asia / 143 Identifying land degradation area and risk control countermeasures in Mongolia and along the China-Mongolia railway / 146 Applications of the Mountain Green Cover Index in countries and regions along the Belt and Road / 150 Evaluation of the Red List Index of threatened species in China / 155 Assessment of giant panda habitat fragmentation / 159 Conclusions / 164

Chapter 8

Summary and Prospects / 167

References / 172

xxv

2

Data-intensive Paradigm

3

Big Earth Data

4

Big Earth Data for Implementing SDGs

Chapter 1

Big Earth Data for SDGs

Big Earth Data for SDGs

The core of the 2030 Agenda for Sustainable Development is the 17 Sustainable Development Goals (SDGs). China attaches great importance to the implementation of the agenda and makes important contributions to addressing global challenges and achieving common development through practical actions. The 2030 Agenda for Sustainable Development is an ambitious undertaking and requires support from different sectors of society. The role of science and technology in particular is critical to facilitate knowledge-driven decisions and science-based policy development processes. Among numerous technologies and disciplines, big data technology, ZKLFKLVJURZLQJUDSLGO\LVSOD\LQJDGLVWLQFWDQGVLJQL¿FDQWUROH

Data-intensive Paradigm The rapid development of science and technology, with its growing acceptance and social demand, has enabled a revolution focused on collecting, storing, and utilizing data generated from human activities. Semi-structured and unstructured data have emerged in large volumes as the production and storage of data is no longer limited by time and space, and this has triggered an explosive growth of data. The volume and complexity of data is beyond the capabilities of traditional data management systems and processing models, which has given rise to the concept of big data. In today’s world, the United Nations (UN) and governments of many countries attach great importance to the development of big data; in China, the full implementation of the national big data strategy is advancing by leaps and bounds. 7KLVGDWDUHYROXWLRQLQFOXGHVRSHQGDWDÀRZVWKHULVHRIFURZGVRXUFLQJWKHHPHUJHQFH of new data-gathering information and communications technology, the explosive growth RIELJGDWDDYDLODELOLW\DQGWKHSRSXODUL]DWLRQRIDUWL¿FLDOLQWHOOLJHQFHDQGWKH,QWHUQHWRI Things. The data revolution is affecting global production, distribution, and consumption patterns, and is changing human lifestyles, economic mechanisms, and national governance models. Meanwhile, computational science and data sciences have made real-time processing and analysis of big data a reality. New data obtained through data mining can

Chapter 1

Big Earth Data for SDGs

EHXVHGDVDVXSSOHPHQWWRRI¿FLDOVWDWLVWLFVDQGVXUYH\GDWDWRSURPRWHWKHDFFXPXODWLRQ of information on human behavior and other empirical information. The combination of new and traditional data can create high-quality information that is more detailed, timely, and relevant. 'DWDLVRQHRIWKHPRVWVLJQL¿FDQWHOHPHQWVWKDWPD\DIIHFWGHFLVLRQPDNLQJSURFHVVHV Using advanced mining and analysis functions on long-term, macro and micro multisource data obtained through big data technology, it is possible to better monitor and evaluate the progress of implementing SDGs, and propose more scientific and targeted development guidance.

Big Earth Data Big data is of strategic importance in the era of the knowledge economy and a new type of strategic resource for nations around the world. As an important part of big data technology, Big Earth Data is becoming a new frontier of Earth science, and is playing a significant role in promoting the in-depth development of Earth science and major VFLHQWL¿FGLVFRYHULHV Big Earth Data is a new data-intensive research method that consists of big data with a spatial reference, including data related to land, oceans, the atmosphere, and human activities. Big Earth Data is generated by a variety of Earth observation methods, field surveys, and ground sensor networks. It bears the general characteristics of big data— large volume, multi-source, multi-temporal—and additionally has the characteristics of high instantaneity, arbitrary spatiality, and physical correlation, with Earth observation, communication, computation, and network technologies at its core. %LJ(DUWK'DWDLVQRWOLPLWHGWRVFLHQWL¿FUHVHDUFKEXWDOVRFRQWULEXWHVWRWKHVXVWDLQDEOH development of our society and serves the SDGs. An important feature of Big Earth Data is the integration of multi-source data, which can provide more relevant and comprehensive information from complex and frequent analysis. The development of Big Earth Data will usher in more open and transparent data policies, so that humankind can hope for a better future for ourselves and the planet.

3

4

Big Earth Data in Support of the Sustainable Development Goals (2019)

Big Earth Data for Implementing SDGs At present, the UN, national governments, and international organizations are developing systems for monitoring and evaluating SDG indicators. However, one of the biggest challenges in implementing these systems is the lack of data to monitor the progress of various SDGs. Incomplete and inconsistent data statistics and the lack of systems for observing indicators are the main reasons for the lack of data and the low quality of existing data. Furthermore, indicators require multidisciplinary information, which is difficult to integrate and analyze using traditional systems, complicating the monitoring of SDG indicators. Among the SDGs, many targets that are closely related to the environment and resources of the Earth surface are characterized by large-scale, periodic changes. Therefore, the Chinese Academy of Sciences (CAS) launched the Big Earth Data Science Engineering Program (CASEarth), aiming to utilize Big Earth Data to expand upon the capabilities of inter-disciplinary science. The program systemically and holistically studies a series of major scientific issues improving our scientific understanding of the Earth system to produce major breakthroughs, discoveries, and technological innovations in different VFLHQWL¿FGRPDLQV Applying Big Earth Data as a Technology Facilitation Mechanism (TFM) for SDGs, CASEarth proposes to conduct research in relation to SDG 2 (Zero Hunger), SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), SDG 13 (Climate Action), SDG 14 (Life below Water), and SDG 15 (Life on Land). The macro and dynamic monitoring capabilities of Big Earth Data provide an important tool for evaluating sustainable development. It can integrate databases, model libraries, and decisionmaking methods for resources, environment, ecology, and biology, building a sustainable development evaluation index system and decision support platform. These tools can effectively monitor sustainable development within economic, social, and environmental aspects, which helps generate richer, more relevant information for decision support.

Chapter 1

Figure 1-1

Big Earth Data for SDGs

Framework of CASEarth for SDGs.

CASEarth is committed to providing assistance and facilitating SDGs in multiple capacities: (1) Becoming a global data provider by constructing the Big Earth Data Sharing Service Platform to facilitate SDG implementation globally. (2) Constructing an assessment and monitoring system for selected indicators of SDGs 2, 6, 11, 13, 14, and 15 at multiple scales (global, regional, national, and local). (3) Evaluating and demonstrating projects on Big Earth Data utilization for SDGs in three broad aspects: development of data products, development of models and methods, and decision support. (4) Publishing the Series Report on Big Earth Data in Support of the Sustainable Development Goals based on data collection and analysis, conducting regular evaluations of the progress of SDGs, and forming new ideas for global assessment of SDGs. Data sharing is a key element to ensure support of Big Earth Data for SDGs, and therefore demands elimination of data islands and improvements in the efficiency of data exchange and sharing. CASEarth is working to develop and operate the Big Data Cloud Service Platform, Data Integration System, and Data Sharing Service System with 50 PB of storage and 2 PF of

5

6

Big Earth Data in Support of the Sustainable Development Goals (2019)

computing capabilities, to provide stable and secure infrastructures for data sharing (http:// data.casearth.cn). The CASEarth Data Sharing Service Platform breaks the policy barriers of data sharing, promotes the formation of a new model for online data sharing, improves the data sharing evaluation system, and establishes operable intellectual property protection mechanisms. These include data sharing indicators and verified authenticity, accuracy, and timeliness of shared data. The system also ensures that the data is discoverable, accessible, interactive, reusable, and citable. At present, the total amount of data shared on this platform is approximately 5 PB. As the hardware of the platform develops, about 3 PB of data will be updated each year. Big Earth Data is a technological innovation method, and a TFM for SDGs will be developed, improved, and established through a series of major scientific studies with a systematic and holistic concept, contributing to human understanding of Earth, and realizing new breakthroughs in serving global sustainable development.

Global Remote Sensing Imagery

Chapter 1

Big Earth Data for SDGs

7

10

Background

11

Contributions

12 Case Study 23 Conclusions

Chapter 2

SDG 2 Zero Hunger

10

Big Earth Data in Support of the Sustainable Development Goals (2019)

Background The eradication of hunger and the guarantee of food security is one of the fundamental goals of global sustainable development. At present, the proportion of global population suffering from malnutrition has risen after years of continuous decline. Developing countries along the Belt and Road in sub-Saharan Africa and South Asia are among those with a high prevalence of malnutrition. &OLPDWHFKDQJHZDUVFRQÀLFWVDQGXQEDODQFHGHFRQRPLFGHYHORSPHQWLQPDQ\UHJLRQVRIWKHZRUOG account for the trend and place high levels of uncertainty on global food security. SDG 2 aims to end all forms of hunger, achieve food security and improved nutrition and SURPRWHVXVWDLQDEOHDJULFXOWXUH,WLQYROYHVHLJKWVSHFL¿FWDUJHWVDQGWKLUWHHQLQGLFDWRUVUHODWHGWR nutrition demands, sustainable food production, and national actions, for the purpose of guiding governmental regulations and establishing a sustainable food supply system that meets demands. Assessment of the indicators in SDG 2 is led by the Food and Agriculture Organization (FAO), World Health Organization (WHO) and United Nations International Children’s Emergency Fund (UNICEF). Data used in the assessment is mostly acquired through surveys and census by statistical departments of all countries. However, there has been a consensus that the traditional means for surveys and censuses is inadequate in terms of timeliness, spatial resolution, and costs. Earth observation technologies have innate advantages in monitoring indicators related to natural systems, such as food production and environmental impacts. Existing research has adopted Earth observation technologies for long-term monitoring of factors related to food production such as GLVWULEXWLRQRIIDUPODQGVDQGJUDLQ\LHOG6XFKIDFWRUVLQGLUHFWO\UHÀHFWUDWKHUWKDQGLUHFWO\DVVHVV indicators related to SDG 2. CASEarth focuses on two Tier

indicators related to sustainable food production system

estimation (Table 2-1), and builds up methodologies to monitor the spatiotemporal patterns of indicators and sub-indicators by integrating multidisciplinary models based on Big Earth Data. Methods are used to estimate the situations of SDG 2.3.1 and SDG 2.4.1 at the global and national scales, respectively, with emphasis on the developing countries and regions along the Belt and Road for the purpose of supporting the establishment of a sustainable food supply system and realization of the goal of zero hunger.

Chapter 2

Table 2-1

SDG 2 Zero Hunger

SDG 2 indicators included in CASEarth

Target

Indicator

Tier

2.3 By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous SHRSOHVIDPLO\IDUPHUVSDVWRUDOLVWVDQG¿VKHUVLQFOXGLQJ through secure and equal access to land, other productive UHVRXUFHVDQGLQSXWVNQRZOHGJH¿QDQFLDOVHUYLFHVPDUNHWVDQG opportunities for value addition and non-farm employment.

2.3.1 Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size.

Tier ĉ

2.4 By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, H[WUHPHZHDWKHUGURXJKWÀRRGLQJDQGRWKHUGLVDVWHUVDQGWKDW progressively improve land and soil quality.

2.4.1 Proportion of agricultural area under productive and sustainable agriculture.

Tier ĉ

Contributions )RFXVLQJRQWKHGLI¿FXOWLHVLQREWDLQLQJWLPHO\LQIRUPDWLRQRQIRRGSURGXFWLRQV\VWHPVXVLQJ surveys, this study promoted methodologies to assess SDG 2.3.1 and estimate sub-indicators of SDG 2.4.1 by integrating multi-source data including remote sensing data, statistical data, and data from ground surveys. Methods were applied at global and national scales to create a data product to evaluate progress towards sustainable food production systems. This product provides a framework for comparison between countries and regions, revealing sustainability problems and providing decision-making support (Table 2-2). Table 2-2

Cases and their contributions to SDG 2

Indicator

Case

Contributions

2.3.1 Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size.

Global crop production per labor unit assessment based on Big Earth Data.

Data product: Global production for major crops per labor unit. Decision support: Reveal the regional differences in the trend and propose key non-attainment regions.

Assessing progress towards sustainable cropping systems: the case of China.

Method and model: Methodologies for assessing land productivity, irrigation water consumption, and excess fertilizer application by integrating multisource data and multidisciplinary models. Decision support: Reveal the progress of sustainable use of cropland in China and the driving forces, and propose suggestions on promoting sustainability of crop production systems.

2.4.1 Proportion of agricultural area under productive and sustainable agriculture.

11

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Case Study Global crop production per labor unit assessment based on Big Earth Data Scale: Global Study area: Global

The primary task of achieving zero hunger and eradicating poverty is to ensure adequate food, and one important means to ensure adequate food is to improve agricultural productivity. The productivity of small-scale farming operations is easily affected by agro-meteorological conditions, LUULJDWLRQOHYHOVSHVWLFLGHDQGIHUWLOL]HUXVHDQG¿HOGPDQDJHPHQW7KHUHIRUHWKHIRRGSURGXFWLRQ per labor unit of small farms is lower than that of large-scale grain producers, especially for rainfed FURSVDQG\LHOGVIURPWKHVHVPDOOVFDOHRSHUDWLRQVKDYHYHU\KLJKÀXFWXDWLRQV8WLOL]LQJWKHFURS yield monitoring model of the Global Agriculture Monitoring Cloud Platform (CropWatch) system, coupled with population data, the crop production per labor unit can be quickly estimated, which can be of critical importance for the agricultural services of many countries. The system supports the decision-making process by provisioning timely information that is essential for achieving the goal of zero hunger.

Target 2.3: By 2030, double the agricultural productivity and incomes of small-scale food producers, in particular women, indigenous peoples, family farmers, pastoralists, and ÀVKHUVLQFOXGLQJWKURXJKVHFXUHDQGHTXDODFFHVVWRODQGRWKHUSURGXFWLYHUHVRXUFHV and inputs, knowledge, financial services, markets, and opportunities for value addition and non-farm employment.

Indicator 2.3.1: Volume of production per labor unit by classes of farming/pastoral/forestry enterprise size.

Chapter 2

SDG 2 Zero Hunger

Method Aiming at SDG 2, the methodology for this study estimates the production of staple grain and oil crops such as wheat, rice, corn, and soybeans by utilizing multi-source data including: crop yield information from remote sensing monitoring models; crop yields from agro-meteorological stations and meteorological data from ground observation stations; national agricultural population data from national census databases; and crop sample data from crowdsourced data resources. Currently the monitoring of crop production per labor unit for 42 major food producing and exporting countries has been calculated from 2009 to 2018.

Data used in the case Remote sensing data and related products include HJ-1A/B, GF-1, GF-2, ZY-3, Sentinel-1, Sentinel-2, Landsat 8, Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission satellite (TRMM), and Project for On-Board Autonomy-Vegetation (PROBA-V). Statistical data includes national agricultural population data of major global food producing countries. Ground survey data includes yield data from agro-meteorological stations, management information such as fertilization, and crop sample data based on crowdsourced data collection.

Results and analysis The trend of global crop production per labor unit from 2009 to 2018 has been rising gradually with a total increase of 34% at an average annual growth rate of 3.8%. Compared with 2015, the year marks the end of the UN Millennium Development Goals (MDGs) and provides the baseline of the SDGs, global crop production per labor unit increased by 10% in 2018, with an average DQQXDOJURZWKUDWHRI7KLVVXJJHVWVWKDWJOREDOFURSSURGXFWLRQLVEHFRPLQJPRUHHI¿FLHQW Overall, food security pressures have been reduced to some degree; however, there is still a large gap from the goal of doubling agricultural productivity in 2030 set in the SDGs. Crop production per labor unit in Africa and Asia is generally lower than in North America, Oceania, Europe and South America. This is mainly a product of differences in farmland management practices. Developed countries adopt large-scale, intensive farm management and precise agricultural management methods, through a high level of agricultural mechanization. All of these factors make crop production per labor unit in developed countries tens of times higher than that of developing countries. However, crop production in developing countries is growing at a faster rate, and the countries that have doubled their crop production per labor unit in the past 10 years are all developing countries.

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Big Earth Data in Support of the Sustainable Development Goals (2019)

10,000

(a) 2009

10,000

(b) 2012

Chapter 2

SDG 2 Zero Hunger

10,000

(c) 2015

10,000

(d) 2018

Figure 2-1

Spatial distribution of global crop production per labor unit from 2009 to 2018.

15

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Big Earth Data in Support of the Sustainable Development Goals (2019)

(a) 2009-2018

(b) 2015-2018

Figure 2-2

Spatial distribution of changes in different periods of global crop production per labor unit.

Chapter 2

SDG 2 Zero Hunger

)LJXUH $MRLQW¿HOGLQYHVWLJDWLRQRIZKHDW\LHOGLQ(WKLRSLD

From 2009 to 2018, countries with rapid growth in global crop production per labor unit include Angola, Mongolia, Ukraine, and Belarus, all of which are countries along the Belt and Road. Ukraine in particular has a strong growth trend and has maintained sustained and rapid growth. The countries with negative growth include Italy, Sri Lanka, Myanmar, Egypt, Kenya, and RWKHUFRXQWULHVLQ$IULFDPRVWRIZKLFKKDYHDÀXFWXDWLQJFURSSURGXFWLRQSHUODERUXQLWZLWKD declining trend. The growth rate of crop production per labor unit in China is close to the global average, which is between 30%-40%. Closing gaps in crop production per labor unit by advancing agricultural management techniques and the level of agricultural mechanization in appropriate regions is therefore critical to achieve the target of doubling agricultural productivity and income by 2030.

Highlights Global crop production per labor unit increased by 10% from 2015 to 2018, DQGWKHJOREDOFURSSURGXFWLRQV\VWHPLVEHFRPLQJPRUHHIÀFLHQW Crop production per labor unit in developed countries is tens of times KLJKHU WKDQ WKDW RI GHYHORSLQJ FRXQWULHV +RZHYHU GHYHORSLQJ FRXQWULHV HQMR\DIDVWHUJURZWKUDWH&ORVLQJJDSVLQODERUSURGXFWLYLW\E\DGYDQFLQJ agricultural management techniques and level of agricultural mechanization in appropriate regions is therefore critical to achieve the target of doubling DJULFXOWXUDOSURGXFWLYLW\DQGLQFRPHE\

17

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Outlook For technological innovation, it is recommended to optimize crop yield monitoring based on the CropWatch system and further bring in information on the agricultural climate and planting structure at different scales. At the same time, it is important to carry out a coupling analysis of the trend of crop production per labor unit by integrating national socio-economic data, such as per capita income and farm management measures. On the other hand, it is important to strengthen the cloud computing capacity of the CropWatch system to improve data processing and analysis of global crop yields. For application promotion, under the support of the Digital Belt and Road Program (DBAR), participants have realized the customization and system migration of the CropWatch cloud platform in Mozambique first, helping Mozambique to grasp the agricultural monitoring technology. Participants will carry out the promotion and application of monitoring technologies in other countries and regions along the Belt and Road, and improve the accuracy of agricultural monitoring and provide technical support for achieving SDG 2.3.

Chapter 2

SDG 2 Zero Hunger

Assessing progress towards sustainable cropping systems: the case of China Scale: National Study area: China

A major challenge for sustainability is the development of agriculture that ensures a long-term food supply that contributes to economic and social development while minimizing environmental impacts. Quantitatively assessing the sustainability of agricultural systems is therefore critical and requires spatial and temporal monitoring of three key aspects, economic, environmental, and social, and examining the interactions between them. This is a complex undertaking and requires innovative data infrastructure such as the Big Earth Data infrastructure that provides one of the best implementations to accomplish this task.

Target 2.4: By 2030, ensure sustainable food production systems and implement resilient agricultural practices that increase productivity and production, that help maintain ecosystems, that strengthen capacity for adaptation to climate change, extreme ZHDWKHUGURXJKWÁRRGLQJDQGRWKHUGLVDVWHUVDQGWKDWSURJUHVVLYHO\LPSURYHODQG DQGVRLOTXDOLW\

Indicator 2.4.1: Proportion of agricultural area under productive and sustainable agriculture.

Method By integrating remote sensing methodologies, spatial allocation models, global crop water models, and mass balance models, analysis estimates three sub-indicators of SDG 2.4.1—land productivity, water use (represented by irrigation water consumption), and fertilizer pollution risk (represented by excess nitrogen and phosphrous)—for China from 1987 to 2015. The subindicators were calculated for a total of 14 major crops, over 76% of the harvest area in China that accounts for about 87% of kilocalorie production in the region. “Environmental intensity”—environmental impacts per kilocalorie produced—was used to develop a matrix to determine level of sustainability, to facilitate comparison among different agricultural zones and across indicators. Sustainability criteria were proposed according to the

19

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Big Earth Data in Support of the Sustainable Development Goals (2019)

GH¿QLWLRQIURPWKH)$2¶VPHWDGDWDIRU6'*ZKLFKLVEDVHGRQWZRDVSHFWVFXUUHQWVWDWHDQG trends. A decrease in intensity suggests a more sustainable level. The trends of each sub-indicator and the integrative patterns in terms of environmental intensity were estimated for the entire study area.

Data used in this case For this study the Big Earth Datasets are composed of the 1:100,000 National Land Use/ cover Database of China based on Landsat, China-Brazil Earth Resources Satellite (CBERS) and HJ-1 satellite data; MODIS time series vegetation index data; statistical data including national FURSKDUYHVWDUHDDQG\LHOGLUULJDWLRQDUHDDQGIHUWLOL]HUDSSOLFDWLRQ¿HOGVXUYH\GDWDLQFOXGLQJ pollution census data and agricultural census data; and information on crop phenology and fertilizer application rate obtained from literature.

Results and analysis The study found that from 1987-2015, environmental intensity for land use (-43%), irrigation water consumption (-30%), and excess N application (-24%) decreased whereas excess P application (+66%) increased. However, all indicators declined after 2000. Collectively, the intensity of all four indicators declined across 26% of cropland, meaning that these croplands had achieved at least an acceptable level of sustainability across the four indicators. Environmental intensity was found to have increased in only 3% of cropland for all four indicators collectively. Generally, regions with lower land intensity had greater improvement across all other indicators. 2YHUDOOWKHVHUHVXOWVVXJJHVWWKDW&KLQD¶VIRRGVXSSO\KDVEHFRPHPRUHHQYLURQPHQWDOO\HI¿FLHQW over time. Recognizing drivers of change in sustainability helps develop future strategies. Analysis indicates that farm management explained >90% of changes in crop production and environmental impacts. Precision agricultural management, such as Science Technology Backyard platforms, has helped China’s cropping systems to become more sustainable. Meanwhile, nationwide loss of fertile cropland to urban expansion was offset by cropland expansion in arid and low-productivity northern regions, where land and irrigation water intensity were much higher than the national average. Continued spatial redistribution of croplands, which was already observed, may further challenge China’s food security. Coordinating land-use change and farm management are thus critical for delivering agricultural sustainability in China, and also other rapidly urbanizing regions of the world.

Chapter 2

SDG 2 Zero Hunger

(a)

(b)

(c)

(d)

(e)

Figure 2-4 Spatial distribution of changes in crop kilocalorie production (a), cropland area (b), irrigation water (c), excess N use (d), and excess P use (e) from 1987 to 2015 in China. Inset plots indicate the changes in amount and intensity for these indicators in 1987, 2000, 2010, and 2015 (data for Taiwan Province is missing).

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Figure 2-5

High-standard cropland in Xinjiang Uygur Autonomous Region of China.

Highlights For the period of 1987-2015, about one quarter of croplands in China LPSURYHG LQ HIILFLHQF\ LQ WHUPV RI ODQG XVH LUULJDWLRQ ZDWHU FRQVXPSWLRQ and excess fertilizer application; since 2000, national average intensity of all three indicators decreased, indicating a more environmentally efficient FURSSLQJV\VWHPLQ&KLQD  )  DUPPDQDJHPHQWH[SODLQHG!RIWKHFKDQJHVPHDQZKLOHODQGXVH FKDQJHSULPDULO\GULYHQE\XUEDQL]DWLRQFKDOOHQJHGWKHWUHQGV&RRUGLQDWLQJ land-use change and farm management are critical for delivering agricultural VXVWDLQDELOLW\LQ&KLQDDQGRWKHUUDSLGO\XUEDQL]LQJUHJLRQVRIWKHZRUOG

Outlook Applying the above methodologies in other countries, especially those countries and regions along the Belt and Road, which have a high population density with high food scarcity, can help to gradually improve the efficiency of their productivity and move towards a more sustainable cropping system. Further employing Earth observation technology in the estimation of other sub-indicators, and exploring methodologies for fusing social and economic data with Earth observation data, will help

Chapter 2

SDG 2 Zero Hunger

develop mechanisms to coordinate different sources of data in the estimation. Exploring interconnections between SDG 2.4.1 and other indicators, particularly in SDG 6, 6'*6'*ZLOOSURYLGHLPSURYHGXQGHUVWDQGLQJRIKRZGLIIHUHQWJRDOVLQÀXHQFHHDFKRWKHU and provide valuable insights on underlying factors locally, helping to promote region-specific strategies toward sustainability and to support decision making.

Conclusions Sustainable food production systems are critical for achieving SDG 2. Earth observation technologies have unique advantages in monitoring food production systems, for measuring distribution of agricultural production, food yield and fluctuations, as well as understanding environmental impacts on agricultural production. The high temporal and spatial resolution of Earth observation data coupled with data from statistical surveys and other economic and social data provide progressive monitoring and evaluation of indicators to facilitate actions and decisions towards sustainable agriculture. CASEarth focuses on two Tier Ĕ indicators related to sustainable food supply in SDG 2, namely SDG 2.3.1 and SDG 2.4.1. The two case studies presented in this report propose methods to evaluate indicators and sub-indicators by integrating multi-source data, to improve the monitoring and assessment of sustainability globally and in China. The results show that, over the past decade, global crop production per unit of labor has increased by 34%, indicating that the global production of major crops is becoming more efficient. Developing countries have relatively low crop production per labor unit but higher growth rate; promoting advanced agricultural management technologies and enhancing the level of mechanization is an important avenue for doubling agricultural productivity in developing countries. The estimation of sub-indicators of productive and sustainable agriculture shows that environmental intensity (land use, irrigation water consumption, and excess fertilizer application) in China has been declining since 2000, indicating a movement towards a more sustainable cropping system. Meanwhile, nationwide cropland displacement from high-quality lands to marginal ones primarily driven by urbanization challenged this trend. Farm management and landuse planning must be coordinated to further deliver a sustainable food supply in China and other urbanizing regions. Within this report, CASEarth proposed some key areas, problems, and countermeasures for a sustainable food production system for achieving SDG 2.

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Big Earth Data in Support of the Sustainable Development Goals (2019)

In the future, CASEarth will: (1) Make full use of CASEarth’s international linkages and collaboration networks to cooperate with international organizations and third-party organizations to strengthen the application of Big Earth Data in SDG 2, establish a mechanism for data sharing and technology promotion, and advance the work of indicator evaluations in developing countries. This is particularly applicable to those in Africa and South Asia with serious food problems and relatively poor technical forces. (2) Focus on key issues of the global food production system including small-scale producers and productive, sustainable agriculture; examine interactions between SDG 2 and SDG 13, especially in Africa and South Asia; address challenges to food security due to changing climate; and provide decision making support to achieve global food security within a changing world.

Chapter 2

SDG 2 Zero Hunger

25

28 Background 29 Contributions 31

Case Study

50 Conclusions

Chapter 3

SDG 6 Clean Water and Sanitation

28

Big Earth Data in Support of the Sustainable Development Goals (2019)

Background Water is a key resource for sustaining life on this planet and clean water is essential for the GHYHORSPHQWRIKXPDQVRFLHW\$VLJQL¿FDQWSRUWLRQRIWKHKXPDQSRSXODWLRQVWLOOODFNVDFFHVVWR FOHDQGULQNLQJZDWHU6'*GH¿QHVDQLPSRUWDQWJRDOIRUJOREDOVXVWDLQDEOHGHYHORSPHQWSURSRVLQJ eight targets and eleven indicators, including water resources, water environment, water ecology, and international cooperation related to water. UN-Water, WHO, and other international organizations have jointly implemented the Integrated Monitoring of Water and Sanitation Related SDG Targets program for SDG 6 indicators. 'DWDLVWKHELJJHVWERWWOHQHFNWKDWUHVWULFWVPRQLWRULQJRIWKH6'*LQGLFDWRUV7KHUHDUH¿YH7LHU LQGLFDWRUVDPRQJWKHHOHYHQVSHFL¿FLQGLFDWRUVIRU6'*WKDWKDYHFOHDUPHWKRGVEXWODFNUHOHYDQW data sources. The evaluation methods recommended in the metadata documents and assessment reports for SDG 6 indicators are mainly based on statistical and census data. These methods are limited by the cost and cycle of sampling surveys, which results in limited spatiotemporal resolution. Additionally, differences in statistical systems and methods among different countries create consistency issues, making evaluation of these indicators challenging at global scales. These limitations call for innovative evaluation methods to both improve spatiotemporal accuracies of indicators and provide multi-scale perspectives. This is only possible by diversifying data sources. Big Earth Data therefore becomes highly relevant for indicator evaluation. Presently, a large number of applications utilize satellite remote sensing data and data from ground observations to monitor indicators related to various aspects of water quality and water ecological environments. For example, large-scale dynamic monitoring for indicator SDG 6.3.2 is performed by measuring the chlorophyll content in lakes and large reservoirs through remote sensing data. Moreover, this method is coupled with analysis of samples to determine water quality changes. To enhance the monitoring and assessment accuracy and spatial representation for SDG 6.4.1 and 6'*ZDWHUXVHHI¿FLHQF\DQGDVVHVVPHQWRIHQYLURQPHQWDOSUHVVXUHVRQZDWHUUHVRXUFHVLV performed utilizing multi-source data and multi-model fusion. Spatial change detection of lakes, rivers, and mangrove ecosystems is performed using satellite remote sensing data, and changes in the area of aquatic ecosystems is analyzed for SDG 6.6.1. This report illustrates the supporting role of Big Earth Data for SDG 6 through five cases completed with the support of CASEarth (Table 3-1).

Chapter 3

Table 3-1

SDG 6 Clean Water and Sanitation

SDG 6 indicators included in CASEarth

Target

Indicator

Tier

6.1 By 2030, achieve universal and equitable access to safe and affordable drinking water for all.

6.1.1 Proportion of the population using safely managed drinking water services.

Tier ĉ

6.3 By 2030, improve water quality by reducing pollution, eliminating dumping and minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater and substantially increasing global recycling and safe reuse.

6.3.2 Proportion of bodies of water with good ambient water quality.

Tier ĉ

%\VXEVWDQWLDOO\LQFUHDVHZDWHUXVHHI¿FLHQF\DFURVV all sectors and ensure sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.

&KDQJHLQZDWHUXVHHI¿FLHQF\ over time.

Tier ĉ

6.6 By 2020, protect and restore water-related ecosystems, including mountains, forests, wetlands, rivers, aquifers, and lakes.

6.6.1 Change in the extent of waterrelated ecosystems over time.

Tier Ĉ

Contributions ,QWKHIROORZLQJ¿YHFDVHV%LJ(DUWK'DWDZDVHPSOR\HGLQDVXSSRUWLQJUROHDQGLQFOXGHGGDWD products and methodologies for the realization of SDG 6 targets. Analysis was carried out at two spatial scales in China and the countries and regions along the Belt and Road. Four SDG indicators, SDG 6.1.1, SDG 6.3.2, SDG 6.4.1, and SDG 6.6.1, were monitored at a high resolution by using satellite remote sensing data, network data, statistics, and other data sources (Table 3-2). This was also accomplished using spatiotemporal data fusion and model simulation methods.

29

30

Big Earth Data in Support of the Sustainable Development Goals (2019)

Table 3-2 Indicator

Cases and their contributions to SDG 6 Case

Contributions

6.1.1 Proportion of the population using safely managed drinking water services.

Monitoring the proportion of urban population using safely managed drinking water services in China.

Method and model: A multi-source data fusion analysis of the proportion of the population using safely managed drinking water services. Decision support: Policy suggestions for improving the safety of the drinking water supply in China.

6.3.2 Proportion of bodies of water with good ambient water quality.

Analysis of surface water quality in China.

Data product: The proportion of good ambient water quality at provincial levels in China in 2016 and 2017.

6.4.1 Change in water-use HI¿FLHQF\RYHUWLPH

Crop water productivity for the SDG assessment of agricultural water use HI¿FLHQF\WKHFDVHRID typical irrigation district in Morocco.

Method and model: Model to estimate crop water productivity for water resource management in irrigated agricultural areas using Big Earth Data. Decision support: Assessment of crop water productivity for optimal water resource management.

6.6.1 Change in the extent of water-related ecosystems over time.

Mapping the extent and dynamic change of mangrove forests in Southeast Asia. Surface water changes in Central Asia.

Data product: Mangrove distribution dataset for Southeast Asia ranging from 1990-2015; Surface water dataset for Central Asia in 2018. Decision support: The change in the extent of waterrelated ecosystems in different countries and regions along the Belt and Road.

Chapter 3

SDG 6 Clean Water and Sanitation

Case Study Monitoring the proportion of urban population using safely managed drinking water services in China Scale: National Study area: China

Safely managed drinking water is essential for ensuring a greater quality of life. Globally, 785 million people still lack access to basic drinking water sources and 263 million people spend over 30 minutes per round trip to collect water from an improved source. SDG 6.1.1 encompasses the proportion of the population that uses safely managed drinking water services. It is designed to monitor the safety levels for drinking water at global scales. Currently, this indicator is developed using data obtained from surveys. It not only requires a great deal of human, material, and financial resources, but is also incapable of obtaining high-quality information in a short period of time. The present case employs Big Earth Data analytics to combine statistical and network big data to expedite indicator results. Three sub-indicators were designed for monitoring SDG 6.1.1 in this report, including: the running water household rate, the ZDWHUTXDOLW\VWDQGDUGUDWHIRUGULQNLQJZDWHUVRXUFHVDQGWKHVSDWLDOLPSDFWVFRSHFRHI¿FLHQWIRUVXGGHQ water pollution events. These three sub-indicators were integrated to calculate the proportion of the population with access to safely managed drinking water in 2016 at the city level in China.

Target %\DFKLHYHXQLYHUVDODQGHTXLWDEOHDFFHVVWRVDIHDQGDIIRUGDEOHGULQNLQJ water for all.

Indicator 6.1.1: Proportion of population using safely managed drinking water services.

Method The traditional monitoring method uses questionnaire results to calculate the proportion of the population having access to safely managed drinking water services in a fixed area. In this methodology, three sub-indicators are also calculated to characterize drinking water availability and quality. The area with safe drinking water supply is determined by calculating the rate of

31

32

Big Earth Data in Support of the Sustainable Development Goals (2019)

households with access to running water, the standard water quality of drinking water at sources, DQGDFRHI¿FLHQWWRPHDVXUHWKHVSDWLDOLPSDFWRIVXGGHQZDWHUSROOXWLRQHYHQWV2YHUODSSLQJWKLV information with the population distribution map provides the proportion of the population with access to safely managed drinking water services at the city level in China.

Data used in the case The rate of households with running water was collected from the China Urban Construction Statistical Yearbook. This dataset is used to analyze the availability of drinking water. Water quality standards at drinking water sources were obtained for drinking water safety level analysis from onsite data published on the local environmental protection agency website. The coefficient for measuring the spatial impact of sudden water pollution events was calculated using data collected from online news sources referring to drinking water safety level analysis.

Results and analysis The construction of drinking water infrastructure has greatly improved the availability and safety levels of drinking water in Chinese cities. In 2016, the proportion of urban population using safely managed drinking water services in China was 95.17%. Beijing featured the highest levels of drinking water safety, with 100% of the population having access to safely managed drinking water. The Tibet Autonomous Region had the lowest level of drinking water safety, with 84.08% of the population having access to safe drinking water. Generally, the safety of the drinking water supply in the developed eastern region was better than that in China’s western region. Presently, the proportion of the urban population having access to safe drinking water in China has greatly improved. However, it is also necessary to ensure drinking water management practices meet the objectives outlined by SDG 6.1. Water source quality and unexpected pollution incidents are the main factors affecting regional drinking water safety. The impact scope and time of sudden pollution incidents are generally limited and short. It is suggested that emergency plans should be established in areas where there are long-term and seasonal risks to water quality at source areas. Moreover, backup water sources Figure 3-1 Proportion of the population using safe drinking water in China in 2016.

VKRXOGEHLGHQWL¿HGDQGGHYHORSHG

Chapter 3

SDG 6 Clean Water and Sanitation

Highlights In 2016, the proportion of urban population using safely managed drinking ZDWHUVHUYLFHVLQ&KLQDZDV)XUWKHUPRUHWKHVDIHW\RIWKHGULQNLQJ ZDWHU VXSSO\ LQ WKH GHYHORSHG HDVWHUQ UHJLRQ ZDV VXSHULRU FRPSDUHG WR &KLQD·VZHVWHUQUHJLRQ  :  DWHUUHVRXUFHTXDOLW\LVWKHPDLQIDFWRUDIIHFWLQJUHJLRQDOGULQNLQJZDWHU VDIHW\DQGHPHUJHQF\SODQVDQGEDFNXSZDWHUVRXUFHVDUHQHFHVVDU\IRU FLWLHVWKDWKDYHORQJWHUPDQGVHDVRQDOULVNVWRZDWHUTXDOLW\

Outlook Currently, an effort is being made to improve the coverage of indicators by improving data collection methodologies, especially in rural areas. This is necessary to monitor and evaluate indicator SDG 6.1.1 in China during the 2017-2020 time period. The results from this analysis will be used to provide information and suggestions regarding drinking water management to government departments at the city level. The main challenge for calculating indicator SDG 6.1.1 is the availability of data. Future improvement for this indicator will focus on identifying other relevant sub-indicators at country and regional levels using multi-source data.

33

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Analysis of surface water quality in China Scale: National Study area: China

Surface water quality is important for both human consumption and maintaining a healthy and functioning ecosystem. Pollution is one of the main causes of water quality degradation in surface water bodies throughout the world. The rapid economic development in China during the past 40 years has considerably increased the amount of pollutants released into water bodies. Over the past decade, the Chinese government has made a considerable effort to improve water quality. The Central Government of China has developed national standards for surface water environmental quality, and established a water quality monitoring network covering all major river basins across the country. The national standard LGHQWL¿HVPDLQLQGLFDWRUVZKLFKDOVRSURYLGHWKHEDVLVIRUWKH6'*LQGLFDWRUSURSRVHGE\81 Water. This case study focuses on the water quality of major rivers, lakes, and reservoirs, and uses data collected from national monitoring networks accessible from their website. Herein, a statistical spatial index of surface water quality was developed for China at provincial and municipal scales.

Target %\LPSURYHZDWHUTXDOLW\E\UHGXFLQJSROOXWLRQHOLPLQDWLQJGXPSLQJDQG minimizing release of hazardous chemicals and materials, halving the proportion of untreated wastewater, and substantially increasing recycling and safe reuse globally.

Indicator 3URSRUWLRQRIERGLHVRIZDWHUZLWKJRRGDPELHQWZDWHUTXDOLW\

Method Surface water quality is classified into six categories by Environmental Quality Standards for Surface Water (GB 3838-2002). Classes ĕ, Ĕ, andērepresent progressively higher classes for surface water quality. The water quality data was collected online from websites administered by local environmental protection departments, where the observation data is regularly updated. An index was calculated by determining the proportion of water bodies for each water quality class within sub-national administrative boundaries.

Chapter 3

SDG 6 Clean Water and Sanitation

Data used in the case Observation data for surface water quality is issued online by environmental protection monitoring departments administered by provinces and municipalities.

Results and analysis The above methodology was employed to calculate the proportion of each category of surface water for provincial and municipal administrative units in China during the 2016 and 2017 period. The proportions of water bodies with good ambient water quality in China was 67.8% in 2016 (Figure 3-2). The proportion of Class ē, Ĕ, ĕ, Ė, ė, and inferior ėwater bodies was 2.4%, 37.5%, 27.9%, 16.8%, 6.9%, and 8.6%, respectively, with Class Ĕand ĕ in the majority. The number rose to 67.9% in 2017 (Figure 3-3), and the proportion of inferior ėwater bodies decreased by 0.3% compared to 2016, suggesting that the water quality has improved. Spatially, the surface water quality in the western region of China was superior compared to the central and eastern regions. The Xinjiang Uygur Autonomous Region and Tibet Autonomous Region maintained the highest surface water quality during the 2016 and 2017 periods. After years of centralized treatment, the quality of surface water in China is observed to be gradually improving. However, there are still some provinces where the proportion of substandard water bodies is greater than 50%. There is still much work to be completed in water pollution control, such as differentiated governance and investigating pollution sources.

0

500 1,000 1,500 2,000 km

0

500 1,000 1,500 2,000 km

0-38.31%

38.32%-51.71%

51.72%-72.24%

72.25%-90.67%

90.68%-100%

No data

Figure 3-2 Proportion index for water quality at provincial scales in China in 2016.

Figure 3-3 Proportion index for water quality at provincial scales in China in 2017.

35

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Highlights  ,Q  DQG  WKH SURSRUWLRQV RI ERGLHV RI VXUIDFH ZDWHU ZLWK JRRG DPELHQWZDWHUTXDOLW\LQ&KLQDZHUHDQGUHVSHFWLYHO\  7  KHVXUIDFHZDWHUTXDOLW\LQ&KLQD·VZHVWHUQUHJLRQZDVVXSHULRUFRPSDUHG WRWKHFHQWUDODQGHDVWHUQUHJLRQV

Outlook The monitoring result will be updated each year. A more comprehensive analysis is being planned by adding data from 2018 to 2020. This will allow for estimation of high surface water quality trends for ambient water quality. A comparative analysis of water quality monitoring standards is also being designed for other countries and regions. This is necessary to explore the feasibility of using networks to acquire observation data for surface water quality analysis. This will allow for additional applications in countries and regions that maintain a water quality database.

Chapter 3

SDG 6 Clean Water and Sanitation

Crop water productivity for the SDG assessment of DJULFXOWXUDOZDWHUXVHHI¿FLHQF\WKHFDVHRIDW\SLFDO irrigation district in Morocco Scale: Local Study area: Sidi Bennour Irrigation District, Morocco

Many countries and regions along the Belt and Road are facing problems of uneven spatiotemporal distribution of water resources, water shortages, and over-exploitation of groundwater. Globally, agricultural water use occupies about 70% of freshwater withdrawal. Therefore, improving water use HI¿FLHQF\LQDJULFXOWXUHLQSDUWLFXODULQZDWHUVFDUFHUHJLRQVLVFULWLFDOIRUHQVXULQJWKHVXVWDLQDEOHXVH of water resources and agricultural development. There are many ways to evaluate agricultural water use HI¿FLHQF\WKDWUHODWHWRFXOWLYDWLRQDQGLUULJDWLRQSUDFWLFHVDQGWKHOHYHORILUULJDWLRQZDWHUPDQDJHPHQW Water productivity is a widely used indicator that refers to the yield or converted economic value SURGXFHGSHUXQLWYROXPHRIZDWHUFRQVXPHG7KHUHIRUH&URS:DWHU3URGXFWLYLW\&:3 GH¿QHVFURS ZDWHUXVHHI¿FLHQF\LQWHUPVRISURGXFWLRQDQGHFRQRPLFEHQH¿WV &URS:DWHU3URGXFWLYLW\DVVHVVPHQWXVLQJ%LJ(DUWK'DWDFDQSURYLGHVFLHQWL¿FVXSSRUWWRRSWLPDO management of water resources and use for sustainable development, and can provide methods and data under the framework of SDGs. A Big Earth Data-based crop water productivity assessment method was applied to the Sidi Bennour Irrigation District in Morocco to provide scientific and technological support for improving irrigation ZDWHUSURGXFWLYLW\DQGDJULFXOWXUDOZDWHUXVHHI¿FLHQF\7KHVWXG\ZDVFRQGXFWHGZLWKWKHVXSSRUWRIWKH DBAR International Center of Excellence in El Jadida.

Target %\VXEVWDQWLDOO\LQFUHDVHZDWHUXVHHIÀFLHQF\DFURVVDOOVHFWRUVDQGHQVXUH sustainable withdrawals and supply of freshwater to address water scarcity and substantially reduce the number of people suffering from water scarcity.

Indicator &KDQJHLQZDWHUXVHHIÀFLHQF\RYHUWLPH

37

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Method Crop water productivity is measured as the ratio between crop yield and crop water consumption in the unit of kg per cubic meter (m3). This case presents an assessment of crop water productivity based on the estimation of crop yield and crop water consumption (evapotranspiration) using Big Earth Data. The model was established upon theories of energy balance, water balance and plant SK\VLRORJ\RIDFURSVRLOV\VWHPWRHVWLPDWHWKHWUDQVIHURIZDWHUDQGFDUERQÀX[HVLQGLIIHUHQW HQHUJ\DQGZDWHUVXSSO\FRQGLWLRQV0HWHRURORJLFDOFRQGLWLRQVVXFKDVUDGLDWLRQÀX[SUHFLSLWDWLRQ wind speed, air temperature, humidity, pressure, and a variety of biophysical parameters retrieved from multi-source remote sensing data were used to drive the model.

Data used in the case Multi-source satellite remote sensing data from Sentinel-2, Landsat 8, and MODIS were used to UHWULHYHELRSK\VLFDOSDUDPHWHUVLQFOXGLQJVXUIDFHDOEHGROHDIDUHDLQGH[YHJHWDWLRQFRYHUDJHODQG VXUIDFHWHPSHUDWXUHVRLOPRLVWXUHODQGFRYHUDQGFURSW\SHFODVVL¿FDWLRQ7KH¿IWKJHQHUDWLRQ ECMWF Reanalysis (ERA5) data—a global near-surface atmospheric reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF)—includes air temperature, dew point temperature, air pressure, wind speed, precipitation, downward shortwave radiation, and downward longwave radiation.

Results and analysis The Sidi Bennour Irrigation District in Morocco is one of the main agricultural areas in Morocco and plays an important role in the regional development and food security of the country. Irrigation water is provided by the Al Massira Dam, the second largest reservoir in Morocco. The main crop types in this irrigation area are wheat (52%), alfalfa (12%), and sugar beets (20%). The water productivity of wheat (1.3 kg/m3) is significantly lower than that of alfalfa (4.1 kg/m3) and sugar beets (10.5 kg/m3) based on a three-year study from 2016 to 2018 (Figure 3-4; Figure 3-5; Figure 3-6). However, there are significant differences in the water productivity for the same crop type in different plots due to differences in field management (e.g., irrigation and fertilization). The standard deviations in water productivity for wheat, alfalfa, and sugar beets over all plots of the Sidi Bennour Irrigation District are 0.34 kg/m3, 2.05 kg/m3, and 1.67 kg/m3, respectively. Advancements in irrigation methods such as using sprinkler irrigation or drip irrigation systems could be one of the solutions for saving water so that pressure on the water supply from the Al Massira Dam can be UHGXFHG7KHVHLPSURYHPHQWVDUHH[SHFWHGWRUHGXFHZDWHUFRQVXPSWLRQDQGLQFUHDVHFURSZDWHUXVH HI¿FLHQF\ZKLOHPDLQWDLQLQJWKHFURS\LHOGVQHFHVVDU\IRUIRRGVHFXULW\

Chapter 3

SDG 6 Clean Water and Sanitation

2016

2017

2018

Figure 3-4 Spatial distribution of crop water productivity (CWP) in the Sidi Bennour Irrigation District, Morocco, 2016-2018.

39

Big Earth Data in Support of the Sustainable Development Goals (2019)

wheat

15

alfalfa sugar beets CWP(kg/m3)

40

10

5

0

2016

2017

2018

Figure 3-5 Water productivity for major crop types in the Sidi Bennour Irrigation District, Morocco, 2016-2018.

50cN

40cN

30cN

30cN

10cW

Figure 3-6

0c

10cE

20cE

Spatial distribution of major crop types in the Sidi Bennour Irrigation District, Morocco.

Chapter 3

SDG 6 Clean Water and Sanitation

Highlights  , Q WKH 6LGL %HQQRXU ,UULJDWLRQ 'LVWULFW LQ 0RURFFR WKH ZKHDW ZDWHU SURGXFWLYLW\LVNJP3ZKLFKLVVLJQLÀFDQWO\ORZHUWKDQDOIDOIDNJP3) DQGVXJDUEHHWVNJP3   7  KHUHLVDQHHGWRUHGXFHWKHLQHIIHFWLYHZDWHUFRQVXPSWLRQRIFURSODQGV E\ LPSURYLQJ WKH DJULFXOWXUDO ZDWHUVDYLQJ LQIUDVWUXFWXUH WKURXJK VKLIWLQJ WRPRUHDGYDQFHGLUULJDWLRQSUDFWLFHVHJVSULQNOHULUULJDWLRQRUWKHPRUH HIÀFLHQWGULSLUULJDWLRQ6XFKHIIRUWVDUHQHFHVVDU\WRPDLQWDLQZKHDWSODQWLQJ DUHDVIRUIRRGVHFXULW\XQGHUWKHSUHVVXUHRIZDWHUUHVRXUFHVFDUFLW\

Outlook SDG 6.4.1 addresses “change in water-use efficiency over time”, which is relevant to all sectors, including the primary, secondary and tertiary industries. Though SDG 6.4.1 is claimed as a Tier ĉ indicator, proper data for evaluation is absent. The crop water productivity obtained by XVLQJ%LJ(DUWK'DWDFDQFRQWULEXWHWRJHQHUDWLQJWLPHVHULHVGDWDRQFURSZDWHUXVHHI¿FLHQF\ under the primary industry (agriculture) to support the evaluation of SDG 6.4.1. This method FDQTXDQWLWDWLYHO\DQGHI¿FLHQWO\HYDOXDWHUHJLRQDOFURSZDWHUFRQVXPSWLRQ\LHOGDQGZDWHUXVH HI¿FLHQF\DVSUHVHQWHGLQWKLVFDVH6XFKDPHWKRGRORJ\FDQEHDGRSWHGIRURWKHUDJULFXOWXUDODUHDV in countries where agriculture relies on scarce water resources.

Agricultural irrigation diversion canal in Morocco

41

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Mapping the extent and dynamic change of mangrove forests in Southeast Asia Scale: Regional Study area: Southeast Asia

Mangrove forests are swampy woody plant communities that grow on the intertidal zone of tropical and subtropical coasts. They have special sea-land characteristics and enormous ecological, economic, and VRFLDOEHQH¿WV0DQJURYHIRUHVWVSOD\DQLPSRUWDQWUROHLQKLJKOLJKWLQJJOREDOFOLPDWHDQGHQYLURQPHQWDO changes. Additionally, they are important for maintaining biodiversity, protecting coastal environments, purifying coastal waters, and protecting farmland and villages from natural disasters such as hurricanes and tsunamis. The distribution of mangroves along the Maritime Silk Road accounts for more than half of the world’s total mangrove forest area. They are mostly located in tropical-subtropical regions with Southeast Asia having a large mangrove forest area. Currently, mangrove forests in the coastal areas of Southeast Asia are under threat from ecological disturbances and are experiencing continuous degradation.

Target 6.6: By 2030, protect and restore water-related ecosystems, including mountains, IRUHVWVZHWODQGVULYHUVDTXLIHUVDQGODNHV

Indicator 6.6.1: Change in the extent of water-related ecosystems over time.

Method This case study employed long-term time series remote sensing data obtained from Landsat satellites to monitor dynamic changes in mangrove forests in Southeast Asia from 1990 to 2015. The satellite images were first pre-processed using radiometric calibration, and atmospheric correction and registration. The mangrove areas were then identified using their spectral characteristics, textural features, and spatial configuration with references from Google Earth images for the same period. These areas were then vectorized and mapped. The results of this study were compared with the existing mangrove datasets provided by FAO and World Mangrove Forests (WMF).

Chapter 3

SDG 6 Clean Water and Sanitation

Data used in the case A total of 1,836 Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images from 1990 to 2015 were used for long-term change monitoring of mangrove forests. FAO and WMF mangrove datasets were used to verify the accuracy of results.

Results and analysis The top three countries with the largest area of mangroves in Southeast Asia were Indonesia (68%), Malaysia (10%), and Myanmar (9%) (Figure 3-7). During the period from 1990 to 2015, the area of mangrove forests in Southeast Asia revealed a decreasing trend (Figure 3-8). The mangrove areas in Vietnam, the Philippines, Thailand, Myanmar, Indonesia, Malaysia, and Cambodia were observed to continue to decrease, while the areas in Singapore remained unchanged. China’s mangrove area was observed to slightly increase in recent years (Figure 3-8). The mangrove forest area in Southeast Asia was observed to decrease from 1990 to 2015, especially in Vietnam, the Philippines, Thailand, Myanmar, Indonesia, Malaysia, and Cambodia. This was caused by the rapid economic development in Southeast Asia, especially in Vietnam and Thailand. Aquaculture has been vigorously developed, resulting in the accelerated loss of mangrove wetlands and their transformation into aquaculture ponds. Meanwhile, aquaculture has enabled environmental pollution, further damaging mangrove wetland habitats and decreasing

Legend unchanged decreased increased

Figure 3-7 The change in mangrove area in Southeast Asia from 1990 to 2015.

43

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Big Earth Data in Support of the Sustainable Development Goals (2019)

their total surface area. Conversely, China’s mangrove area has been observed to be increasing LQUHFHQW\HDUVUHÀHFWLQJWKHQDWLRQ¶VHPSKDVLVRQHFRORJLFDODQGHQYLURQPHQWDOSURWHFWLRQDQG implementation of relevant policies. Such policies include returning ponds to forests and wetlands, which leads to an increase in the area of mangrove wetlands.

Vietnam 2%

China Cambodia 21% 0

in the eastern cities was higher than in the central and western cities. Moreover, provincial

500 1,000 1,500 2,000 km

capitals had higher proportions of open spaces compared to other cities in the province. The

Figure 4-12

proportion of open public spaces in the Beijing-

Proportion of open public space in major prefecture-level cities in China. (data for Taiwan Province is missing)

Tianjin-Hebei urban agglomeration, Yangtze River Delta urban agglomeration, the Pearl River Delta urban agglomeration, Sichuan Basin urban agglomeration, and the Yunnan-Guizhou urban agglomeration were higher than in the surrounding cities. Urban open public spaces were mainly composed of parks, squares, green spaces, and other public spaces and roads. The largest urban open spaces based on the density of urban roads were found in the Beijing-Tianjin-Hebei urban agglomeration, Yangtze River Delta urban agglomeration, and the Pearl River Delta urban agglomeration. Green public spaces were mostly found in the Sichuan Basin urban agglomeration and the Yunnan-Guizhou urban agglomeration.

Highlights At the provincial level, the average proportion of open public spaces in the built-up areas of Chinese cities was 17.98% (excluding Hong Kong, Macao, and Taiwan). Beijing had the highest proportion of open public space (29.18%), while Guangxi had the lowest proportion (10.82%). At the city level, the proportion of open public space in eastern cities was higher than in the central and western cities. Moreover, the provincial capitals had a higher proportion of open space than other cities in the province.

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Outlook The navigation data for China can be updated in real time, and land use products are updated every 3-5 years. This is enough to meet the requirements for future high temporal resolution evaluation. The method adopted in this case is simple, and navigation and land use data are relatively easy to obtain. This enables the methodology to be easily reproducible in other countries and allows for international comparison. The open space described in this study does not consider diverse categories of open space, and this needs to be addressed in the future. It is still challenging to classify information based on gender, age, disability, and other demographic characteristics. The next step is to develop spatial data products for population based on different groups utilizing big data such as mobile phone platforms and Internet sources.

Chapter 4

SDG 11 Sustainable Cities and Communities

Conclusions These case studies propose methods for rapidly extracting global impervious surfaces using multi-source Big Earth Data. In this study, remote sensing datasets were produced for urban expansion in 1990, 1995, 2000, 2005, 2010, and 2015 and included 1,500 cities with a population greater than 300,000 in countries and regions along the Belt and Road. Basic data was provided to track the sustainable development of these cities. A new method was proposed to estimate nearsurface PM2.5 (SDG 11.6.2, Tier Ĉ ) using aerosol optical thickness retrieved from satellites. This method was observed to improve the accuracy and spatiotemporal resolution of PM2.5 remote sensing estimation and provides a new data product for the evaluation of urban air quality. High-resolution remote sensing and navigation vector data were examined in terms of urban public transport (SDG 11.2.1, Tie ), urban land use efficiency (SDG 11.3.1, Tier ), and urban public space (SDG 11.7.1, Tier ) indicators. These datasets were used to develop a high spatiotemporal resolution method for extracting information related to urban public transport, open public spaces (e.g., green space, squares, roads), urban built-up areas, and population distributions. This allowed for the creation of high-resolution regional data products for China. In this chapter, a new indicator was proposed for preserving and protecting world cultural and natural heritage sites (SDG 11.4.1, Tier Ċ). This indicator is defined as the “increase in capital investment per unit area to preserve and protect world cultural and natural heritage”. A corresponding evaluation model was developed and a case study was conducted in China. Sustainable development in urban areas is crucial for resource and environmental disaster management challenges and for the future development in cities. The following studies are being planned to develop methodologies for comprehensively evaluating indicators. (1) There are plans to further tap into the potential of Big Earth Data by developing new evaluation models and producing high-quality evaluation datasets. (2) Multi-indicator, coordinated research will be carried out around city-related indicators with SDGs as the framework. Active cooperation with government departments will be carried out to comprehensively evaluate the sustainability of major Chinese cities and serve government decision-making. (3) SDG 11 indicator evaluation models and methods supported by Big Earth Data will be standardized, and new data and methods will be promoted to the international community. Data and technical support will be provided for the countries and regions along the Belt and Road to help related countries monitor and comprehensively evaluate SDG 11 indicators.

81

84

Background

85

Contributions

86

Case Study

100 Conclusions

Chapter 5

SDG 13 Climate Action

84

Big Earth Data in Support of the Sustainable Development Goals (2019)

Background Climate change is now affecting every country in the world, extreme weather events are becoming worse and worse, and greenhouse gas emissions have reached an all-time high. If no action is taken, the world’s average surface temperature may rise by more than 3°C in this century. To strengthen the global response to the threat of climate change, countries adopted the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Paris Agreement in 2015. In these agreements, all countries agreed to work together to limit global temperature rise to well below 2°C. In 2015, the Third UN World Conference on Disaster Risk Reduction adopted the Sendai Framework for Disaster Risk Reduction 2015-2030, aiming to resist and reduce disaster losses caused by extreme weather events. SDG 13, Climate Action, has also set up five targets and eight indicators, including resilience and adaptive capacity-building to climate-related hazards, implementing the UNFCCC, and raising awareness of climate change. At present, the monitoring and implementation of specific indicators rely on the surveys conducted by governmental statistical departments and international organizations. Due to the differences in priorities and interpretations of regional and global challenges DPRQJJRYHUQPHQWVVRPHFRXQWULHVKDYHKDGDQLQVXI¿FLHQWUHVSRQVHWRFOLPDWHFKDQJHPDNLQJ GDWDDFTXLVLWLRQGLI¿FXOW%DVHGRQWKHWHFKQRORJLFDODGYDQWDJHVRI&$6(DUWKWKLVUHSRUWIRFXVHV on SDG 13.1, strengthening resilience and adaptive capacity to climate-related hazards and natural disasters, and SDG 13.3, improving education, awareness and human and institutional capacity for climate change mitigation, adaptation, impact reduction, and early warning (Table 5-1). The report provides data support and decision support for monitoring and implementing SDG 13.

Table 5-1

SDG 13 targets included in CASEarth Targets

Tier

13.1 Strengthening resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.

Tier ĉ, Tier Ĉ

13.3 Improving education, awareness, and human and institutional capacity for climate change mitigation, adaptation, impact reduction, and early warning.

Tier Ċ

Chapter 5

SDG 13 Climate Action

Contributions 7KHPRQLWRULQJDQGHYDOXDWLRQRIVSHFL¿FLQGLFDWRUVDUHSHUIRUPHGEDVHGRQWKHGDWDVHWVPRGHOV and methods developed by CASEarth. Reducing the loss of life from extreme disasters triggered by climate change and strengthening climate change response are the two key areas. The data products and methodologies are developed for different scales of application. The specific case names and their key contributions in 2019 are shown in Table 5-2. Table 5-2

Cases and their contributions to SDG 13

Target

Case

13.1 Strengthening resilience and adaptive capacity to climaterelated hazards and natural disasters in all countries.

Disaster monitoring and analysis of the SDG 13.1.1 indicator in countries and regions along the Belt and Road.

Data product: The SDG 13.1.1 indicator product in the countries and regions along the Belt and Road. Decision support: Provide decision support to evaluate countries’ resilience and adaptive capacity to climate-related hazards.

Global atmospheric CO2 concentration changes in response to climate change.

Data product: Global CO2 space-time continuous distribution based on satellite monitoring. Method and model: Greenhouse gas (CO2) abnormal change GHWHFWLRQPHWKRGEDVHGRQWLPHVHULHVGDWD¿WWLQJWKHJOREDO scale. Decision support: Analyzing atmospheric CO2 concentration anomalies caused by extreme events, providing decisionmaking reference for controlling environmental CO2 concentration and environmental protection, improvement, and restoration policies.

Cognition of climate response to glaciers and Arctic sea ice.

Data product: Glacier monitoring products in the countries and regions along the Belt and Road; Arctic sea ice prediction products. Decision support: Providing decision-making reference for strategic planning such as rational planning of water resources in the glacial recharge area and development of the Arctic channel.

13.3 Improving education, awareness, and human and institutional capacity on climate change mitigation, adaptation, impact reduction, and early warning.

Contributions

Tiger Valley Glacier

85

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Case Study Disaster monitoring and analysis of the SDG 13.1.1 indicator in countries and regions along the Belt and Road Scale: Regional Study area: Belt and Road Region

The disaster data used in this case was obtained from the International Disaster Database Emergency Events Database (EM-DAT), for the duration from 1980 to 2018. The EM-DAT database is widely used in disaster management and research at the national scale. It is categorized according to different disaster events, which is a convenient way to manage and analyze natural disasters. The database is used by many journals, newspapers, and UN agencies, including the Economic and Social Commission for Asia and the Pacific (ESCAP) and WHO. It is used for research purposes in order to better understand and deal with disasters. The frequency of different types of natural disasters, the casualties, and the number of affected people are calculated based on values from the database. Changes in global disaster losses after the SDG agenda was proposed were monitored to understand spatiotemporal patterns, key time points, and critical areas.

Target 13.1: Strengthening resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.

Indicator 13.1.1: Number of deaths, missing persons and persons affected by disasters per 100,000 people.

Method Based on indicator 13.1.1, statistics were obtained for the types of natural disasters, the frequency of occurrence, the number of deaths, and the number of missing and affected people in countries and regions along the Belt and Road from 1980-2018. The SDG 13.1.1 indicator was then calculated for each corresponding country. The proportion of deaths from natural disasters was used to assess the effectiveness of disaster risk reduction in each country.

Chapter 5

SDG 13 Climate Action

Data used in the case The international disaster database EM-DAT (https://www.emdat.be/).

Results and analysis The analysis of available data shows that the total number of people affected by disasters in Asia, Africa, Oceania, and Europe between 1980 and 2018 was 6 billion, among which 1,840,000 were deaths. Asia has the highest number of people affected, reaching 5 billion and accounting for 86.93% of the total number of people affected by disasters. In order to explore the situation in Asia and Africa over the past 40 years, based on the annual population data of each region, the changing trends in Asia and Africa from 1980 to 2018 were analyzed, as shown in Figure 5-1. Overall, countries affected severely by natural disasters (SDG 13.1.1

1,000) are mainly located in

Southeast Asia and central and southern Africa, including developing and underdeveloped countries like China, India, Pakistan, Myanmar, Laos, the Philippines, Namibia, Sudan, Mozambique, and Ethiopia. These countries were affected severely by disasters, including large death tolls, so they have taken on a heavy responsibility and a long course of prevention and mitigation for disasters. Indicator performance for all countries before and after 2000 shows that the overall values in the countries and regions along the Belt and Road have declined, which means most countries have achieved some success in their disaster reduction policies (Figure 5-2). Among the 20 high-risk countries, the indicator value declined in 13 countries after 2000. In other countries, like China, the indicators have increased but the rate of deaths from disasters dropped considerably. In addition, China’s rate of deaths is lowest among 20 high-risk countries, and it dropped from 700/100,000 to 6/100,000 after 2000. At the regional level, the indicator in Asia, Africa, Oceania, and Europe KDYHDOOGHFOLQHGVLJQL¿FDQWO\DIWHU,QRIWKHUHJLRQVWKHLPSDFWRIGLVDVWHUVKDVEHHQ controlled to some extent. However, the indicators in Central Asia, Southeast Asia, South Asia, Southern Africa, Eastern Europe, and Melanesia in Oceania were still rising. Disaster reduction is most effective in East Asia and Oceania. In short, countries and regions with higher levels of economic development are better able to withstand and reduce disaster losses than countries with a lower economic development level.

87

Figure 5-1

SDG 13.1.1 of countries and regions along the Belt and Road during 1980-2018.

1,000-2,000 >2,000

500-1,000

100-500

0-10 10-100

No data

Legend

88 Big Earth Data in Support of the Sustainable Development Goals (2019)

Figure 5-2

SDG 13.1.1 of countries and regions along the Belt and Road during 1980-2000 (a) and 2000-2018 (b).

(a) 1980-2000

1,000-2,000 >2,000

500-1,000

100-500

0-10 10-100

No data

Legend

Chapter 5 SDG 13 Climate Action

89

Figure 5-2

SDG 13.1.1 of countries and regions along the Belt and Road during 1980-2000 (a) and 2000-2018 (b) (continued).

1980-2001 2000-2020

4000

Mortality Rate / 100,000

(b) 2000-2018

1,000-2,000 >2,000

500-1,000

100-500

0-10 10-100

No data

Legend

90 Big Earth Data in Support of the Sustainable Development Goals (2019)

Chapter 5

SDG 13 Climate Action

Highlights  2  YHUDOOVLQFHWKHFRXQWULHVPRVWLPSDFWHGE\QDWXUDOGLVDVWHUVKDYH been mainly located in Southeast Asia and central and southern Africa, and WKHVHFRXQWULHVDQGUHJLRQVKDYHDORQJZD\WRJRLQWHUPVRIUHVLOLHQFHDQG UHGXFWLRQRIGLVDVWHUORVVHV  &  RPSDUHG ZLWK WKH 6'*  LQGLFDWRUV RI FRXQWULHV DURXQG  WKH overall indicators of the countries and regions along the Belt and Road KDYHGHFOLQHG$PRQJWKHKLJKULVNFRXQWULHVWKHLQGLFDWRUVLQKLJK ULVN FRXQWULHV KDYH GHFUHDVHG DIWHU  ,Q RWKHU FRXQWULHV OLNH &KLQD the indicators have increased but the proportion of deaths caused by QDWXUDOGLVDVWHUVGURSSHGFRQVLGHUDEO\$OORIWKHVHGDWDLQGLFDWHGWKDWPRVW FRXQWULHVDQGUHJLRQVKDYHDFKLHYHGVRPHUHVXOWVLQGLVDVWHUUHGXFWLRQ

Outlook Generally, the impacts of disasters are closely related to the economic development of each country. For countries with high frequency of disasters and high casualties, while developing their own economies, they should also cooperate with the UN’s disaster reduction agency to strengthen capacity building in resisting and reducing disaster losses. In the future, CASEarth will continue to monitor the SDG 13.1.1 indicator in various countries and regions. Meanwhile, remote sensing and geographic information technology will be used to produce spatial products for typical climate disasters in countries and regions along the Belt and Road, and the results can provide technological support for disaster reduction.

91

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Global atmospheric CO2 concentration changes in response to climate change Scale: Global Study area: Global

Controlling and reducing anthropogenic emissions of CO2 is an important way for humankind to mitigate climate change. Surface measurements indicate that annual increase in global CO2 concentration is fluctuating, even though the interannual variability of anthropogenic CO2 emissions is stable (especially during 2010-2016). These fluctuations in CO2 increase could be related to abnormal CO2 absorption and emission of terrestrial ecosystems resulting from extreme climate change. Extreme weather events increase with global warming, caused by the increase in atmospheric CO2 concentration and its radiative forcing, and promote atmospheric CO2 increases with feedback to terrestrial ecosystems. Fully recognizing the increase and decrease in atmospheric CO2 concentration caused by this natural change has important reference value for us to formulate environmental protection, improvement, and restoration policies for controlling the increase in atmospheric CO2 concentration.

Target 13.3: Improve education, awareness, and human and institutional capacity for climate change mitigation, adaptation, impact reduction, and early warning.

Method Spatiotemporal geostatistical modeling methods were used to estimate global continuous spatiotemporal atmospheric CO2 concentration at a spatial resolution of 1×1 degree and three-day temporal resolution, using the Greenhouse Gases Observing Satellite (GOSAT) for the period from June 2009 to May 2016. Harmonic function composition was used to obtain abnormal residuals, QRUPDOL]DWLRQZDVSHUIRUPHGZLWK=VFRUHDQGÀRRG¿OOLQJDOJRULWKPVZHUHXWLOL]HGWRH[WUDFWSHULRGV and regions of XCO2 (column-averaged CO2 dry air mole fraction) anomalies. GM-XCO2 (global land maps of XCO2) and CT-XCO2 (XCO2 calculated from the CarbonTracker atmospheric distribution of CO2) were applied together with anomaly change detection methods for comparing results. $VSDWLRWHPSRUDODQRPDO\FKDQJHGHWHFWLRQPHWKRGZDVGHYHORSHGEDVHGRQWLPHVHULHVGDWD¿WWLQJ to detect regions and periods of abnormal increases in atmospheric CO2 concentrations from 2009 to 2016. The study also adopted global surface skin temperature and inversion parameters from satellite

Chapter 5

SDG 13 Climate Action

observations (drought index, biomass burning area, gross primary productivity) for attribution analysis over the area and a period of abnormal increase of atmospheric CO2 concentration.

Data used in the case The study used a dataset derived from the satellite observations and model simulation during 20092016 listed as follows. Global atmospheric column CO2 concentration (XCO2) mapping data (GM-XCO2) produced by Big Earth Data methods using ACOS-GOSAT (v7.3) data from the Atmospheric CO2 Observations from Space (ACOS). The land cover type Climate Modeling Grid (CMG) product from MODIS (MCD12C1). Land surface skin temperature (AIRSX3STM v6.0, GES DISC). Self-calibrating Palmer Drought Severity Index (scPDSI, RU TS 3.25). Burned area (BA) (GFED v4.0). Gross primary production (GPP) (MOD17A2 v5, MODIS). Atmospheric transport model simulated XCO2 (CarbonTracker 2016).

Results and analysis Extreme climate events enhance CO2 emissions in some terrestrial ecological regions, which is consistent with the fluctuations of atmospheric CO2. The period of abnormal CO2 changes corresponds to extreme climate change, and the geographical extent shows abnormality in local temperature, drought index, biomass burning, and GPP. It was revealed that the occurrence of extreme weather leads to an increase in terrestrial ecological carbon emissions, which leads to an increase in atmospheric CO2 concentration. As a result, not only do anthropogenic CO2 emissions need to be controlled, but sensitive terrestrial ecosystems need to be repaired and managed to reduce the impact of extreme climate change on these ecosystems. Both satellite observations and model simulations have shown an abnormal increase in global XCO2 during the summer of 2010, middle of 2013 and early 2016 that lasted for about 3 months (Figure 5-3). The anomaly was detected in grasslands and forest areas mostly (Figure 5-4; Table 5-3), with local abnormal XCO2 between 1.3 and 1.8 ppm. The period of XCO2 anomaly corresponds to the period of extreme weather events such as heat waves and extreme drought caused by El Niño or abnormal biomass burning in 2013. The interannual increase in global atmospheric CO2 concentrations during 2010, 2013, and 2016 are also higher than the average increase in the previous 10 years (2.3 ppm in 2010, 2.9 ppm in 2013, 3.3 ppm in 2016), while the anthropogenic carbon emissions in these periods did not exhibit any increasing trends. Particularly, in 2016 the enhancement of CO2 was up to 3.3 ppm, which is consistent with the abnormal increase detected

93

94

Big Earth Data in Support of the Sustainable Development Goals (2019)

Figure 5-3 National Oceanic and Atmospheric Administration (NOAA) SOI (Southern Oscillation Index) (top), number of grids with extremely high XCO2 (Z-score>1.96) detected by satellite (GOSAT) and model (CarbonTracker) simulation for global land (bottom), and the ground-measured annual enhancement of CO2 from NOAA. Gray dashed lines are the time of detected regions with continuous extremely high XCO2 in space and time as shown in Figure 5-4.

Figure 5-4

Location of extremely high XCO2 with the continuous grid in space and a time span detected by a ÀRRG¿OODOJRULWKPZKHUHWKHIHDWXUHRIHDFKUHJLRQLVVKRZQLQ7DEOH

Chapter 5

SDG 13 Climate Action

in satellite-observed XCO2 in several regions. It is shown from attribution analysis that abnormal XCO2 increase is associated with the abormal CO2 release of terrestrial ecosystem, induced by extreme climate. Table 5-3

Legend

Characteristics of the extreme XCO2 regions

Label in Figure 5-4 period of extreme XCO2 (year-month-number)

Abnormal XCO2 (ppm)

16Mar-1

1.5±0.3

4

36

17

39

16Mar-2

1.6±0.3

53

16

30

0

16Feb-3

1.3±0.3

20

49

12

12

16Feb-4

1.4±0.3

0

89

1

5

16Jan-5

1.3±0.2

1

94

1

5

15Nov

1.4±0.23

3

79

8

0

13Apr

1.4±0.3

41

35

6

0

10Jul-1

1.8±0.4

24

37

16

9

10Aug-2

1.7±0.3

45

15

26

0

Fraction of land cover (forest-grass & shrub-crop-bareland)

Highlights Extreme climate change has enhanced CO2 release in some terrestrial ecological areas, which is consistent with the fluctuations of atmospheric CO2 data. While controlling human-made CO 2 emissions, it is necessary to VFLHQWLÀFDOO\SURWHFWDQGPDQDJHVHQVLWLYHWHUUHVWULDOHFRV\VWHPVLQRUGHUWR reduce the impact from extreme climate.

Outlook In the future, methods should be developed for GOSAT and OCO-2 observations to generate longer sequences of continuous spatiotemporal global atmospheric XCO2 (GM-XCO2). Spatiotemporal anomaly change detection methods could be applied to updated GM-XCO2 to detect extreme fluctuations in XCO2. Carbon-related datasets from remote sensing could be collected and used for analysis of terrestrial ecological carbon absorption characteristics in detected extremes. In the end, information on abnormal increases/decreases in XCO2 should be adopted for evaluating terrestrial ecological carbon source/sink change and sensitivity to extreme climate change.

95

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Cognition of climate response to glaciers and Arctic sea ice Scale: Regional Study area: Belt and Road Region

Glaciers are very sensitive to climate change and are a natural indicator of climate change. During the past 100 years, in response to increasing global and regional temperatures, glaciers around the world have been characterized by retreat and thinning. The countries and regions along the Belt and Road have numerous glaciers with a glacial area of 100,000 km2. The Tianshan Glaciers provide fresh water for 10 rivers, including two Pan-Central Asian transboundary rivers, such as the Ili and Aksu, which are essential for the survival of nearly 70 million people in the region, their economic and social development, and also for the preservation of ecological systems along these rivers. Arctic sea ice is another important part of the climate system. Changing sea LFHKDVDVLJQL¿FDQWLPSDFWRQWKHUHJLRQDQGHYHQPRUHSURQRXQFHGLPSDFWDWWKHJOREDOVFDOHE\ affecting weather and climate through complex feedback processes. Thus, any change in sea ice is an important indicator of global climate change. Since 1979 when satellite data became available, sea ice in the Arctic has been melting at increasing rates, with completely ice-free areas appearing in the Arctic during summer season. Therefore, monitoring and forecasting changes in glaciers and VHDLFHLQWLPHDQGVSDFHKDVLPSRUWDQWVFLHQWL¿FDQGDSSOLHGYDOXH

Target 13.3: Strengthening education and advocacy on climate change mitigation, adaptation, impact reduction and early warning, and strengthening the capacity of personnel and institutions in this regard.

Method The historical glacier inventory before the availability of satellite images is mainly based on topographic maps, and the glacier boundaries were manually digitized by correcting aerial photographs. For years after the availability of satellite images, Landsat images were used to extract glacier extension by the band ratio method (TM3/TM5) combining the morphological opening and closing operation method, and then referring to topographic maps, ASTER images, Google Earth, and the first glacial inventory data to revise glacier vector boundaries and to ensure that glacier boundaries, especially debris covered glaciers, were accurately identified. DEM data was then used to create watershed boundaries and ridgelines were extracted, and an LQGLYLGXDOJODFLHUZDVLGHQWL¿HG)RUVHDLFHSUHGLFWLRQWKH)*2$/6IVXEVHDRQDOWRVHDVRQDO

Chapter 5

SDG 13 Climate Action

realtime forecasting system is adopted. The system was developed by the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), CAS Institute of Atmospheric Physics (IAP). This system includes the atmospheric-ocean-land-sea ice four-component model and a coupler; the atmospheric component uses the atmospheric circulation model FAMIL2.1, which was independently developed by IAP-LASG. The model uses a dynamic core with a finite volume cubic spherical grid with a horizontal resolution of approximately 1 degree, a vertical resolution of 32 layers, and a top of 2.16 hPa (approximately 45 km). The prediction system uses the nudging method to assimilate the reanalysis data of the atmosphere and oceans in real time. The assimilation data are the United States Global Forecast System (GFS) reanalysis data and the Optimum Interpolation Sea Surface Temperature (OISST) real-time ocean data, and the time resolution and assimilation time window are 6 hours. The atmospheric analysis ¿HOGLQFOXGHVVWDQGDUGLVREDULFVXUIDFHZLQG¿HOGWHPSHUDWXUH¿HOGDQGKHLJKW¿HOGGDWDDQGWKH RFHDQ¿HOGLQFOXGHVVHDVXUIDFHDQDO\VLVGDWD

Data used in the case 1:100,000 & 1:50,000 topographic maps. Landsat Multispectral Scanner (MSS) / TM / Enhanced Thematic Mapper (ETM) / OLI data. RGI 6.0 (Randolph Glacier Inventory 6.0), the first Chinese Glacial Inventory data and the second Chinese Glacial Inventory data. GFS real-time reanalysis data, OISST real-time ocean data.

Results and analysis The changes in glaciers in 15 basins in countries and regions along the Belt and Road [not including the Amu Darya River and Salween River (part of the river in China is called Nu Jiang)] have been monitored since 1969, and the overall glacier area has been reduced by 9,300 km2, accounting for 17.2%. The glacier area in the inland basin of the Ili River and Hexi Corridor has decreased rapidly with rate over 0.8% a-1XQGHUWKHLQÀXHQFHRIWKHQRUWKHUQEUDQFKRIWKHZHVWHUO\IROORZHGE\WKH*DQJHV the Yarlung Zangbo River, and the Mekong River (part of the river in China is called Lancang Jiang) XQGHUWKHLQÀXHQFHRIWKH,QGLDQPRQVRRQ7KHDQQXDOFKDQJHUDWHRIJODFLHUVLQWKH7DULP5LYHUDQG Qinghai-Tibetan Plateau has decreased slowly. However, the glacier area of the Indus River Basin, which intersects the southern branch of the westerly with the Indian monsoon, has little change or even an increasing trend (Figure 5-5). More detailed data shows that according to the completed survey data of the Tianshan and Qilian Mountains in China, the total glacier areas in the Tianshan in China and Qilian Mountains around 1969 were 8,487 km2 and 1,986 km2, respectively. China’s Tianshan glaciers shrank by 16.5% between 1969 and 2013. Glaciers shrank by 9% between 1969 and 2000 and slowed to

97

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Big Earth Data in Support of the Sustainable Development Goals (2019)

0

500

1000 km

Figure 5-5 Annual glacier area changes in 15 basins in the countries and regions along the Belt and Road.

about 7% between 2000 and 2013. It also found differences between the size and distribution of glaciers due to climate change. That is, the shrinkage rate of glaciers in large glacier basins, such as the Aksu River and Weigan River (larger than 1.0% a-1), is different from that in smaller watersheds, such as the Baiyang River and Kaidu River (less than 0.5% a-1 ZKLFKUHÀHFWVWKDWVPDOOJODFLHUVDUHPRUHVHQVLWLYH to climate change. Glaciers in Qilian Mountain were reduced by 683.674 km2 between 1970 and 2018, accounting for 34%, or 37% a-1. Based on the sea ice prediction data reported by the FGOALS-f2 seasonal forecasting system on June 1, 2019, the Arctic sea ice extent was 9.28 million square kilometers on June 30. The sea ice extent distribution is shown in Figure 5-6 (right). Compared with the National Snow and Ice Center (NSIDC) data, the FGOALS-f2 prediction results are highly similar and can provide reference information for Arctic channel development and other related strategic planning. The methodology presented in this case and the production of sea ice forecast data can provide spatial data and decision support for the 2030 Agenda for Sustainable Development.

Chapter 5

SDG 13 Climate Action

Figure 5-6 Arctic sea ice extent observed by NSIDC on June 30, 2019 (left), Arctic sea ice extent predicted by the FGOALS-f2 prediction system on June 30, 2019 (right). Source:National Snow and Ice Data Center

Highlights  6  LQFHWKHJODFLHUVLQWKHEDVLQVDORQJWKH%HOWDQG5RDGKDYHVKUXQN H[FHSWWKH,QGXV5LYHUDQGDUHDKDVGHFUHDVHGE\7KHUHGXFWLRQUDWH RIJODFLHUDUHDIURPWKH,OL5LYHUWRWKH+H[L,QODQG%DVLQXQGHUWKHLQÁXHQFHRI WKHQRUWKHUQEUDQFKRIWKHZHVWHUO\FLUFXODWLRQLVPRUHWKDQD-1. Based on the FGOALS-f2 seasonal prediction system, the sea ice distribution ranges from 1 June to 30 June, 2019, were predicted successfully.

Outlook Understanding the impact of glacial changes on water resources is important for glacial water resource management, and can provide insight into regional infrastructure development and FDSDFLW\EXLOGLQJ2YHUXWLOL]HGULYHUVVKRXOGEHVSHFL¿FDOO\IRFXVHGRQIRUSUHVHUYDWLRQRIZDWHU resources and ecological environments.

99

100

Big Earth Data in Support of the Sustainable Development Goals (2019)

In the future, the study will release and update Arctic sea ice prediction products at daily intervals. Research will be carried out to understand Arctic sea ice ablation and its physical mechanism for climate feedback and the evolutionary trend of sea ice in the future, and Arctic seaLFHDLULQWHUDFWLRQVDQGWKHLULQÀXHQFHRQZHDWKHUDQGFOLPDWH

Conclusions At present, climate change is affecting human and natural systems on all continents and in all countries, resulting in frequent extreme anomalous climate disasters. On the regional scale of the Belt and Road and on the global scale, this report utilized Big Earth Data through integrated highresolution satellite remote sensing and statistical surveys to calculate the SDG 13.1.1 indicator for monitoring and analyzing CO2 concentration and its response to climate change. The response of glaciers to climate change was also studied. On the basis of these cases, the following topics will be the focus of future work: (1) Producing datasets related to monitoring different indicators of SDG 13 at different spatial scales, including: spatial distribution products of different climate disaster types and disaster assessment indicator products around SDG 13.1; continuous monitoring products for CO2, CH4, other greenhouse gases, ice, and snow; and Arctic sea ice prediction products. (2) Climate change affects all aspects of SDG 2, 6, 14, and 15. The interconnections between SDG 13 and the other SDG targets will therefore be explored through research and analysis of the response of each indicator to climate change. Such research can enhance our understanding of climate change response and provide data references and technical support for decision making.

Chapter 5

SDG 13 Climate Action

101

104 Background 105

Contributions

106 Case Study 115

Conclusions

Chapter 6

SDG 14 Life below Water

104

Big Earth Data in Support of the Sustainable Development Goals (2019)

Background Oceans are an important part of the global ecosystem. They provide food and livelihoods for billions of people, absorb atmospheric heat and more than a quarter of carbon dioxide, and produce about half of the oxygen in the atmosphere. In recent decades, human activities and global climate change have adversely affected the stability of marine ecosystems, especially the coastal-ocean HFRV\VWHP(QYLURQPHQWDOSUREOHPVOLNHDFLGL¿FDWLRQK\SR[LDDQGHXWURSKLFDWLRQKDYHLQFUHDVHG FRQVLGHUDEO\ZKLOHHFRORJLFDOGLVDVWHUVVXFKDVKDUPIXODOJDOEORRPV+$%V DQGMHOO\¿VKEORRPV occur more frequently. Coastal fishery resources are being exhausted extensively, and marine ELRGLYHUVLW\LVDOVRXQGHUVWUHVVIURPRFHDQDFLGL¿FDWLRQDQGWHUULJHQRXVSROOXWDQWV7KHUHIRUHPDULQH ecosystems and environments are under major threats and the sustainable development of coastal areas and their economic outcomes face serious challenges. Policies and treaties that encourage the responsible exploitation of marine resources are critical to address these threats. Several large marine studies have been initiated in China and helped gather data and improve theoretical understanding of marine ecosystems. However, there are still shortages in the FRPSUHKHQVLYHDVVHVVPHQWRIPDULQHSROOXWLRQDFLGL¿FDWLRQFRDVWDOHFRV\VWHPKHDOWKPDQDJHPHQW and sustainable utilization of marine resources. It is hard to meet SDG 14, “conserve and sustainably use the oceans, seas and marine resources for sustainable development”, at the present stage. This research is targeting development of basic data products and data platforms to facilitate access to information for sustainable development in marine environments. Table 6-1

Focused SDG 14 indicators

Target

Indicator

Tier

%\SUHYHQWDQGVLJQL¿FDQWO\UHGXFHPDULQHSROOXWLRQ of all kinds, in particular from land-based activities, including marine debris and nutrient pollution.

14.1.1 Eutrophication index and FRQFHQWUDWLRQRIÀRDWLQJSODVWLF pollutants.

Tier Ĕ

14.2 By 2020, sustainably manage and protect marine and coastal HFRV\VWHPVWRDYRLGVLJQL¿FDQWDGYHUVHLPSDFWVLQFOXGLQJE\ strengthening their resilience, and take action for their restoration in order to achieve healthy and productive oceans.

14.2.1 Proportion of national special economic zones implementing ecosystem-based management measures.

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Chapter 6 SDG 14 Life below Water

Contributions The research on Big Earth Data for SDGs is committed to realizing SDG 14 through studies focused on four key priority areas, namely: assessment of marine pollution, detection of ocean acidification, marine ecosystem health management and sustainable exploitation of marine resources. The research developed new datasets, models, and methods to calculate specific indicators for these four key areas (Table 6-2). Table 6-2 Indicator

Cases and their contributions to SDG 14 Case

Construction and application 14.1.1 Eutrophication index of an integrated DQGFRQFHQWUDWLRQRIÀRDWLQJ eutrophication assessment model plastic pollutants. for typical coastal waters of China. 14.2.1 Proportion of national special economic zones implementing ecosystembased management measures.

Contributions Method and model: Construct the latest comprehensive assessment system suitable for evaluating coastal eutrophication in China. Decision support: Participate in the establishment of marine industry standards for the assessment of coastal eutrophication in China; issue international reports on HXWURSKLFDWLRQDVVHVVPHQWLQWKH1RUWKZHVW3DFL¿F$FWLRQ Plan (NOWPAP) region together and propose it to UNEP.

Ecosystem health Method and model: Build the evaluation index system for assessment in typical waters in China. Jiaozhou Bay, China.

Yellow River Estuary

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Case Study Construction and application of an integrated eutrophication assessment model for typical coastal waters of China Scale: Local Study area: Coastal Waters, China

Land-based human activities are increasing and producing a large amount of pollutants, which have led to rapid deterioration of the eutrophication status in coastal areas via river discharges, underground waste input, or atmospheric deposition. Confronted with nutrient pressure, coastal ecosystems have responded through ecological signals, such as hypoxia and HABs. Based on the framework of “Pressure-State-Response”, an integrated eutrophication assessment model was GHYHORSHGZKLFKUHÀHFWVERWKZDWHUTXDOLW\WKHSUHVVXUHSDUW DQGHFRORJLFDOHIIHFWWKHUHVSRQVH part). By using this model, estuaries and bays along China’s coast were assessed at multiple VFDOHVQRWRQO\WRXQGHUVWDQGWKHKXPDQSUHVVXUHDQGHFRORJLFDOV\PSWRPVEXWDOVRWRGH¿QHD comprehensive eutrophication status. Such a model and evaluated results provide scientific and technical support for decision making processes in eutrophication management.

Target 14.1: By 2025, prevent and significantly reduce marine pollution of all kinds, particularly from land-based activities, including marine debris and nutrient pollution.

Indicator (XWURSKLFDWLRQLQGH[DQGFRQFHQWUDWLRQRIÁRDWLQJSODVWLFSROOXWDQWV

Method 7KHPRGHOFRQVLGHUVGLIIHUHQWVHQVLWLYLWLHVDQGK\GURORJLFFRQGLWLRQVLQGLIIHUHQWDUHDVWRUHÀHFW the characteristics of different anthropogenic pressures. The study considered primary and secondary ecological responses to detect the degree and estimate the stage of coastal eutrophication. The human management response part was added in the model by combining data on human pressures with ecological responses to comprehensively evaluate the trophic status in varied coastal areas. The framework of the model ensured the comprehensive and objective assessment of coastal areas.

Chapter 6 SDG 14 Life below Water

Data used in the case Indicators of nutrients in typical coastal areas of China—Chl-a, biomass, and dissolved oxygen—were obtained from the CAS Big Earth Data Program database, and some other data were collected from published literature, as well as related bulletins of China.

Results and analysis In each case, the anthropogenic pressures and the ecological responses were individually evaluated initially. For example, in Jiaozhou Bay and adjacent areas, the results (human pressure, Figure 6-1 left, and ecosystem symptom HABs, Figure 6-1 right) indicate that there were spatial shifts in ecosystem symptoms, as the area experiences frequent HAB starting within the bay moving outwards in response to human activities that were also observed to have rapidly increased within the same period outside the bay. These results provide valuable information on HAB timings and patterns to devise a management plan. Furthermore, as shown in Figure 6-2, the eutrophication status of each case was evaluated comprehensively, considering both human pressures and ecological responses. The results also indicate that the eutrophication problems in the inner bays and big estuaries were very serious where anthropogenic activities are concentrated in China. These areas include Bohai Bay, Jiaozhou Bay, Laizhou Bay, and Changjiang River Estuary, and more. In these areas, response strategies should be developed to reduce terrigenous nutrients and facilitate ecological restoration.

Figure 6-1 Assessment of human pressure and typical eutrophication symptoms (HABs) in Jiaozhou Bay, China (JB: Jiaozhou Bay; OJB: Waters outside Jiaozhou Bay).

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High Good Moderate Poor Bad

Figure 6-2

Evaluation of the trophic status in typical coastal waters of China.

Chapter 6 SDG 14 Life below Water

Highlights Develop an integrated eutrophication assessment model reflecting both water quality (the pressure part) and ecological effect (the response part). Scientifically evaluate the eutrophication status of typical coastal waters. The indicators of both human activity-derived pressures and ecological responses are relatively important and should all be taken into consideration.  ,QW\SLFDOZDWHUVDORQJ&KLQD·VFRDVWWKHHXWURSKLFDWLRQSUREOHPLQWKHLQQHU bays and big estuaries were very serious where anthropogenic activities ZHUHGHQVHDQGWKHHFRORJLFDOUHVSRQVHVZHUHDOVRVHULRXV

Outlook This method has been listed as a marine standard for the assessment of coastal eutrophication in China, and it will ultimately be published in the near future. Representing China in the framework of NOWPAP, six international reports were proposed to UNEP, and such eutrophication detection methods will be continuously involved in the future plans issued by NOWPAP. The relevant data will be updated continuously, to further contribute to the realization of SDGs through evaluation results and decision support.

Large scale HAB occurrences in a typical eutrophicated coastal area of China

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Ecosystem health assessment in Jiaozhou Bay, China Scale: Local Study area: Jiaozhou Bay, China

One of the most important approaches to ensuring the protection and sustainable development of marine environments and resources is to establish ecosystem-based management practices that maintain a healthy ocean ecosystem. Marine ecosystem health assessments can be used as important decision support tools to provide direct, high-quality information and to guide sustainable coastal use and development. It can also help to improve an ecologically sound management strategy for sustainable use and development of coastal areas. Industrialization, urbanization, aquaculture, agriculture, tourism, and other human activities, combined with global changes, are compounding pressures on coastal ecosystems. Ecosystem health assessment needs to integrate relevant data sources from different aspects describing ecosystem conditions and impacts of existing pressures. A coordinated way of integrating land and marine data/information is hence necessary to assess current cumulative pressures and impacts. The research is working to develop new approaches based on multi-source data, big data analysis, and machine learning technologies.

Target 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems to DYRLGVLJQLÀFDQWDGYHUVHLPSDFWVLQFOXGLQJE\VWUHQJWKHQLQJWKHLUUHVLOLHQFHDQGWDNH action for their restoration, to achieve healthy and productive oceans.

Indicator 14.2.1: Proportion of national exclusive economic zones managed using ecosystembased approaches.

Method Taking Jiaozhou Bay as a case study, a primary assessment framework was established based on long-term studies that focused on variations in meteorological, hydrological, chemical, and biological elements and key processes, as well as their impacts on marine ecosystem evolution. With a focus on SDG 14.2, a selection of indicators and guideline settings were reviewed to gain a better understanding of ecosystem structure, services, functions, and ecological disasters and diseases. The present case study uses data mining to improve guidelines, thresholds, and reference settings in existing health assessments using machine learning techniques. The goal is to translate monitoring, observation, and research results into information that can be understood easily by the public and policy makers.

Chapter 6 SDG 14 Life below Water

Data used in this case January 2006-December 2015, observation data in Jiaozhou Bay, including: phytoplankton, zooplankton, benthos, bacteria, hydrological, physical, and chemical factors. Aquaculture production, area, and other environmental data from 2006-2015 environmental situation bulletins.

Results and analysis The 2006-2015 long-term variations in the meteorological, hydrological, chemical, and biological elements at Jiaozhou Bay showed that the marine ecosystem in the bay was experiencing a change. Nutrient concentrations in the bay exhibited a decreasing trend, suggesting that the water quality in the bay has been improving; while the health conditions of the plankton community, as represented by the phytoplankton/zooplankton community compositions and size distributions, exhibited an ascending trend (Figure 6-3). The long-term changes in nutrient concentration and structure led to an increasing number of nutrient limitations in the bay (Figure 6-4), which has potential impacts on phytoplankton communities and water quality. The results of the pilot health assessment in Jiaozhou Bay show that the health condition of the -LDR]KRX%D\HFRV\VWHPLVFODVVL¿HGDVJUDGH³%´ZKLFKPHDQVWKDWWKHHFRV\VWHPLVLQUHODWLYHO\ good condition (Figure 6-5). Environmental conditions during the study duration show an improving trend, consistent with the varying trend in nutrients. The biota condition in the bay had a slight declining trend, similar to the plankton community condition indicators. The assessment results show nonlinear characteristics in the long-term variations of indicators at different levels, partly consistent with historical trends, with only a few indicators showing a different trend over the past decade.

Figure 6-3 Variations in plankton abundance and nutrient concentration in Jiaozhou Bay from 2006 to 2015.

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N-AbsLimit N-RelaLimit P-AbsLimit P-RelaLimit RedTide Si-AbsLimit Si-RelaLimit (a) 2006 0 N-AbsLimit N-RelaLimit P-AbsLimit P-RelaLimit RedTide Si-AbsLimit Si-RelaLimit (b) 2008 0

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N-AbsLimit N-RelaLimit P-AbsLimit P-RelaLimit RedTide Si-AbsLimit Si-RelaLimit (c) 2012 0 N-AbsLimit N-RelaLimit P-AbsLimit P-RelaLimit RedTide Si-AbsLimit Si-RelaLimit (d) 2015

Figure 6-4

Frequencies of nutrient limitations and ecological disasters in Jiaozhou Bay for 2006, 2008, 2012, and 2015.

Chapter 6 SDG 14 Life below Water

Figure 6-5

Evolution of marine ecosystem conditions from 2007 to 2015, and result of pilot health assessment in Jiaozhou Bay.

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Highlights The case study improved ecosystem health assessment research by reviewing a selection of indicators and guideline settings to gain a better XQGHUVWDQGLQJ RI HFRV\VWHP VWUXFWXUH VHUYLFHV IXQFWLRQV DQGHFRORJLFDO disasters and diseases. By applying machine learning-based data mining WHFKQRORJ\ WKH JXLGHOLQH WKUHVKROG DQG UHIHUHQFH VHWWLQJV LQ H[LVWLQJ health assessments can be improved. The improved framework was used to conduct a pilot marine ecosystem health assessment in Jiaozhou Bay. The overall health condition of Jiaozhou Bay is relatively good: HQYLURQPHQWDOFRQGLWLRQVGXULQJWKHVWXG\GXUDWLRQVKRZDQLPSURYLQJWUHQG and biota conditions in the bay had a slight declining trend. The assessment results show nonlinear characteristics in the long-term YDULDWLRQV RI LQGLFDWRUV DW GLIIHUHQW OHYHOV SDUWO\ FRQVLVWHQW ZLWK KLVWRULFDO WUHQGV ZLWK RQO\ D IHZ LQGLFDWRUV VKRZLQJ D GLIIHUHQW WUHQG RYHU WKH SDVW decade.

Outlook The current case study improves existing coastal ecosystem health assessment research in the following aspects: selecting indicators from ecosystem structure and changes; establishing reference and guideline values for indicators using machine learning technology; and improving assessment methods according to data types and characteristics. The marine ecosystem health assessment will be further developed, including diagnostic models and scenario simulation models to analyze stress factors on marine health and to diagnose marine ecosystem health. By integrating diagnostic, water quality, ecological, and hydrodynamic modules into a model platform, it will be able to simulate different scenarios and predict possible responses of the marine ecosystem. This structured research methodology will be repeated to study other coastal regions and improve our model for a wide variety of environments. With collaborating partners such as the Australian Institute of Marine Science and the Indonesian Institute of Sciences, the current research is being considered for application to other areas. Advisory reports as decision support tools will be developed with the aim to support national strategic objectives and to facilitate targeted end-users towards restoration and protection of coastal and marine environments.

Chapter 6 SDG 14 Life below Water

Conclusions Focusing on marine pollution and marine ecosystem health management, an integrated eutrophication assessment model and an experimental evaluation model for marine ecosystems were developed based on data provided through the CAS Big Earth Data Program. Reflecting both water quality and ecological effects, the eutrophication assessment model was applied to evaluate the estuaries and bays along China’s coast at multiple scales, and the research reports on eutrophication assessment in China were proposed to UNEP. By participating in the establishment of marine industry standards for coastal eutrophication assessment in China, such models and evaluated results provide scientific and technical support for the management of discharged offshore nutrient pollutants and coastal eutrophication. Experimental evaluation of the ecosystem health of Jiaozhou Bay was also carried out, and a scenario simulation system will be developed to predict possible responses of coastal ecosystems to changes in marine pollution. The operational application of related technologies will be further promoted to provide decision-making support for coastal environmental protection and management, and effectively evaluate the SDG 14.2.1 indicator and realize the SDG 14 target.

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Chapter 7

SDG 15 Life on Land

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Background Terrestrial ecosystems are an important component of the Earth system and provide diverse habitats for humans. The resources and services provided by terrestrial ecosystems have enabled humans to thrive and develop over thousands of years. Unfortunately, excessive exploitation of resources, pollution, and unplanned development have led to land degradation and threatened the sustainability of terrestrial ecosystems. Cropland is currently being lost at rates 30 to 35 times higher compared to WKHKLVWRULFDOUHFRUG'URXJKWIUHTXHQF\DQGGHVHUWL¿FDWLRQH[WHQWDUHDOVRUDSLGO\LQFUHDVLQJUHVXOWLQJ in the loss of 12 million hectares of cropland. This development mostly affects poor and vulnerable communities around the world. Additionally, among the 8,300 animal breeds known, 8% are extinct and 22% are at risk of extinction. The impacts of humans on ecosystems over the past 50 years have been more rapid and widespread than at any other time in history, which has led to huge irreversible losses of biodiversity on Earth. Sustainable management of terrestrial ecosystems is now more critical due to the effects of climate change and urgent action is required to mitigate its impact. SDG 15 indicators included in CASEarth are showed in Table 7-1.

Table 7-1 SDG 15 indicators included in CASEarth Target 15.1 By 2020, ensure the conservation, restoration, and sustainable use of terrestrial and inland freshwater ecosystems and their services. This refers to forests, wetlands, mountains, and drylands, and is in line with international agreements. %\FRPEDWGHVHUWL¿FDWLRQDQGUHVWRUH degraded lands and soils, including land affected by GHVHUWL¿FDWLRQGURXJKWDQGÀRRGVDQGVWULYHWRUHGXFH global land degradation. 15.4 By 2030, ensure the conservation of mountain ecosystems, including their biodiversity, to enhance WKHLUFDSDFLW\IRUSURYLGLQJEHQH¿WVWKDWDUHHVVHQWLDOIRU sustainable development.

Indicator

Tier

15.1.1 Proportion of the forest area to the total land area.

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15.1.2 Proportion of sites for terrestrial and freshwater biodiversity that are covered by protected areas.

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15.3.1 Proportion of land that is degraded over the total land area.

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15.4.2 Mountain Green Cover Index.

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15.5 By 2020, take urgent action to reduce the degradation of natural habitats, halt the loss of biodiversity, and 15.5.1 Red List Index. protect and prevent the extinction of threatened species.

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Chapter 7

SDG 15 Life on Land

Contributions CASEarth aims to use Big Earth Data approaches to support monitoring and evaluating SDG 15 at global, regional (e.g., the countries and regions along the Belt and Road), national (e.g., China), and sub-national scales. The program will provide a platform for linking Chinese expertise DQGH[SHULHQFHLQNH\¿HOGVUHODWHGWR6'*ZLWKWKHLQWHUQDWLRQDOFRPPXQLW\7DEOH 7KH program has multiple objectives, including developing new and improving upon existing methods and models. For example, the program will improve monitoring methods of degradation processes in inland arid regions using Earth observation data integrated with relevant multi-source data. This ZLOOIDFLOLWDWHPDQDJHPHQWDQGFRQWURORIGHVHUWL¿FDWLRQLQ&HQWUDO$VLDQFRXQWULHVDORQJWKH%HOW and Road. An additional objective is to develop and improve other related data products, such as the global land degradation dataset, the Mountain Green Cover Index in the countries and regions along the Belt and Road, and the Species Red List Index in China. Lastly, CASEarth will provide decision support through published reports and assessments, and access to data and information.

Table 7-2 Indicator 15.1.1 Forest area in proportion to the total land area.

15.1.2 Proportion of important sites and ecosystems for terrestrial and freshwater biodiversity that are covered by protected areas.

Cases and their contributions to SDG 15

Case

Contributions

Forest cover mapping to monitor terrestrial ecosystems in Southeast Asia.

Data product: Forest cover dataset with 30-meter resolution for Mainland Southeast Asia (Vietnam, Laos, Cambodia, Thailand, and Myanmar).

Assessment of conservation priority for global national parks.

Decision support: Global national park conservation priority evaluation report.

Study on forest protection proportion indicators.

Data product: China’s key forest ecosystem protection areas dataset, and China’s forest ecosystem protection status and protection vacancy dataset. Decision support: Assessing the proportion of China’s forest ecosystems covered by nature reserves.

Evaluating the effectiveness of the management of protected areas: an example from Qianjiangyuan National Park in China.

Data product: Qianjiangyuan National Park ecosystem and biodiversity datasets. Decision support: Countermeasures for biodiversity conservation and management in Qianjiangyuan National Park.

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(continued) Indicator

Case

Contributions

Data product: Global land degradation dataset. Method and model: Global land degradation Big Earth Data Big Earth Data for global assessment methodology system. land degradation assessment. Decision support: Global land degradation country evaluation report for 2000-2015. Data product: New data source for evaluating arid regions in Central Asia. Method and model: A new assessment methodology system The proportion of degraded for the accurate evaluation of land degradation in inland arid 15.3.1 Proportion land to the total land area in regions. of degraded land Central Asia. Decision support: Identifying land degradation areas and to the total land providing decision-making reference for the deployment of area. restoration plans of the LDN initiative. Data product: Land degradation data for Mongolia and ChinaMongolia railway from 1990 to 2015 with a 30-meter spatial Identifying land degradation resolution. area and risk control Decision support: Support is provided for analyzing the countermeasures in driving forces of land degradation along the China-Mongolian Mongolia and along the railway (Mongolian section), discovering key areas of land China-Mongolia railway. degradation, and for proposing recommendations for land degradation prevention and control. Data product: The Mountain Green Cover Index dataset in the countries and regions along the Belt and Road. Method and model: A new grid-based Mountain Green Applications of the Mountain &RYHU,QGH[FDOFXODWLRQPRGHOZDVGHYHORSHGWRUHÀHFWWKH 15.4.2 Mountain Green Cover Index in concentrated environmental gradient and high spatiotemporal Green Cover countries and regions along heterogeneity characteristics of mountains areas. Index. the Belt and Road. Decision support: The Mountain Green Cover Index assessment report in the countries and regions along the Belt and Road. Evaluation of the Red List Index of threatened species Data product: Chinese species Red List Index data. in China. Data product: The data describes the current distribution and 15.5.1 Red List past changes in the giant panda habitat in China over the past Index. 40 years. Assessment of giant panda Decision support: Support is provided to assess evolutionary habitat fragmentation. characteristics and suggestions are offered for protecting giant panda habitats.

Chapter 7

SDG 15 Life on Land

Case Study Forest cover mapping to monitor terrestrial ecosystems in Southeast Asia Scale: Regional Study area: Southeast Asia

)RUHVWVQRWRQO\SURYLGHVLJQL¿FDQWSURGXFWVDQGVHUYLFHVIRUKXPDQVXUYLYDODQGGHYHORSPHQW but also are critical in mitigating climate change due to their carbon sequestration effect. Therefore, forest management, restoration, and conservation are extremely important to human civilization. The creation of a forest resource inventory is an essential step in sustainable forest management. It is necessary to track the changes in forest resources to develop effective policy and management guidelines for public and private investments in these areas. Such policies are required to protect forest resources and promote social and economic development and ensure ecosystem stability. This enables forest resources and services to be preserved for future generations. Currently, there are limitations in the temporal and spatial resolutions for forest cover mapping, which makes it difficult to accurately map land cover changes in forests. High temporal and spatial resolution data with high accuracy can be used to overcome these problems and provide data support for the sustainable development of forest resources.

Target 15.1: By 2020, protect, restore, and promote the sustainable use of land and inland freshwater ecosystems, especially forests, wetlands, foothills and dry lands.

Indicator 15.1.1: The proportion of forest area to the total land area.

Method This case study presents a process for monitoring woodlands in Mainland Southeast Asia using the Google Earth Engine (GEE) cloud computing platform. A set of automated algorithms was designed to compare the remote sensing images from the MODIS and Landsat systems. This is necessary since remote sensing images of tropical regions tend to have high percentages of cloud

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cover. The high temporal resolution of MODIS data aids in screening Landsat images with poor cloud removal effects, and helps to avoid manual examination of images. This was followed by multi-temporal data fusion on the GEE platform, which can reduce the interference of sensors, imaging conditions, and other factors, and generates stable and continuous NDVI sequences. )XUWKHUPRUHYHJHWDWLRQFDQEHH[WUDFWHGXVLQJHVWDEOLVKHGUXOHV)LQDOO\DFODVVL¿FDWLRQVDPSOH library was developed using ground survey samples and visual interpretation results of highresolution Google Earth images. Forests were extracted after classifying vegetation areas using the IXVLRQRI/DQGVDWDQG6HQWLQHOPXOWLEDQGGDWD7KHH[WUDFWLRQUHVXOWVZHUHYHUL¿HGXVLQJKLJK UHVROXWLRQ*RRJOH(DUWKLPDJHU\DQG¿HOGVDPSOHGDWD7KHUHVXOWVUHYHDOWKDWWKHPHWKRG FDQ effectively reduce errors and improve the accuracy of forest extraction using single-temporal data, and (2) the method also achieves rapid high-resolution extraction of forests.

Data used in the case Landsat series remote sensing images, MODIS images (NBAR products), and Sentinel-2 optical images for forest cover mapping. +LJKUHVROXWLRQ*RRJOH(DUWKLPDJHU\DQG¿HOGVDPSOHGDWDIRUGDWDYDOLGDWLRQ

Results and analysis Figure 7-1 shows the distribution of forests in Mainland Southeast Asia and woodlands that ZHUHDOWHUHGRYHUWKHFRXUVHRIWKUHH\HDUVGXHWRGHIRUHVWDWLRQIRUHVW¿UHVDQGWKHJURZWKRIQHZ woodlands. There was a relatively high proportion of forested areas in the five countries located in Mainland Southeast Asia. Laos was observed to have the greatest proportion of forested land, while Cambodia had the lowest. Cambodia is located at a relatively high altitude and has large areas consisting of sparse forests, sparse trees, and weeds that were excluded from our analysis. The changes in woodland land cover was compared with high-resolution historical images from Google Earth. Results revealed that the changes in woodland area were mainly caused by human activities, including deforestation and the construction of new plantations in Vietnam and Thailand, and mining and quarrying in Laos and Cambodia.

Chapter 7

SDG 15 Life on Land

Legend Forest Changes Non-Forest Forest

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Highlights Forest cover dataset of Mainland Southeast Asia from 1990 to 2018 was obtained. Abundant forest resources were discovered in the Mainland Southeast $VLD/DRVIHDWXUHGWKHODUJHVWIRUHVWHGDUHDZKLOHWKHVPDOOHVWDUHDZDV observed in Cambodia.  +  XPDQDFWLYLWLHVVXFKDVGHIRUHVWDWLRQPLQLQJDQGTXDUU\LQJRIIRUHVWHG areas were the major factors causing changes in the distribution of forest land in the region.

Outlook The forest extraction algorithm will be extended to other countries in Southeast Asia, and the high-resolution and high-precision forest remote sensing products in Southeast Asia will be UHOHDVHGDQGXSGDWHGHYHU\WKUHHWR¿YH\HDUV Changes in forested areas will be monitored in real time using remote sensing products. A quota will be developed to monitor and measure forest resources according to SDG 15.1.1, or the “proportion of forested area to land area”.

Chapter 7

SDG 15 Life on Land

Assessment of conservation priority for global national parks Scale: Global Study area: Global

A total of 217,834 protected areas have been established throughout the world since the HVWDEOLVKPHQWRIWKHZRUOG¶V¿UVWQDWLRQDOSDUNLQWKH8QLWHG6WDWHVLQ7KHVHSURWHFWHGDUHDV FRYHUDWRWDODUHDRIî7NP2DQGDFFRXQWIRUDERXWRI(DUWK¶VODQGVXUIDFH1DWLRQDOSDUN LVWKHPRVWLPSRUWDQWFDWHJRU\RISURWHFWHGDUHDV*OREDOO\WKHUHDUHQDWLRQDOSDUNVZLWKDQ DUHDRIî6NP21DWLRQDOSDUNVUHSUHVHQWERWKRIWKHQXPEHURIDOOSURWHFWHGDUHDVDQG RIWKHWRWDODUHDIRUDOOSURWHFWHGDUHDV ,QWKH7KLUG3OHQDU\6HVVLRQRIWKHth&HQWUDO&RPPLWWHHRI&KLQD¶V&RPPXQLVW3DUW\ SURSRVHGWRHVWDEOLVKDV\VWHPRIQDWLRQDOSDUNVZKLFKKDVQRZEHFRPHDQDWLRQDOSULRULW\7KH &KLQHVHJRYHUQPHQWKDVFRQVWUXFWHGSLORWQDWLRQDOSDUNVDFURVVSURYLQFHVZLWKDWRWDODUHD RIDERXWî5NP2LQFOXGLQJ6DQMLDQJ\XDQ*LDQW3DQGD7LJHUDQG/HRSDUG6KHQQRQJMLD 4LDQMLDQJ\XDQ1DQVKDQ:X\LVKDQWKH*UHDW:DOO3XGDFXRDQG4LOLDQ0RXQWDLQ A quantitative monitoring and evaluation mechanism for protected areas can provide an important assessment for the government to improve conservation efforts and scientific PDQDJHPHQW7KLVDVVHVVPHQWUHTXLUHVTXDQWLI\LQJPXOWLSOHSDUDPHWHUVUHODWHGWRWKHSURWHFWHG DUHD¶V VWDWXV SUHVVXUH DQG IHHGEDFN DQG UHTXLUHV TXDQWLI\LQJ WKHLU LQWHUUHODWLRQVKLSV 6XFK DVVHVVPHQWVDLGLQUH¿QLQJSULRULWLHVDQGSROLFLHVWRLPSURYHWKHPDQDJHPHQWDQGFRQVHUYDWLRQRI SURWHFWHGDUHDV7KLVUHSRUWHYDOXDWHVWKHFRQVHUYDWLRQSULRULWLHVRIWKHWRSQDWLRQDOSDUNVWR VXPPDUL]HEHVWSUDFWLFHQDWLRQDOSDUNPDQDJHPHQWH[SHULHQFHV

Target 15.1: By 2020, ensure the conservation, restoration, and sustainable use of terrestrial and inland freshwater ecosystems and their services. This includes forests, wetlands, mountains, and drylands, and is in line with obligations under international agreements.

Indicator 15.1.2: The proportion of important sites and ecosystems for terrestrial and freshwater biodiversity that are covered by protected areas.

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Method 7KLVUHSRUWLQWHJUDWHVJOREDOSURWHFWHGDUHDUHPRWHVHQVLQJSRSXODWLRQOLJKWLQJDQGURDGGDWD DQGXVHVWKHSUHVVXUHVWDWHIHHGEDFNPRGHOWRTXDQWLI\WKHFRQVHUYDWLRQSULRULW\IRUSURWHFWHGDUHDV EDVHGRQWKH6'*LQGLFDWRU7KHUHSRUWLQYROYHVDQDVVHVVPHQWRIWKHFRQVHUYDWLRQSULRULW\ IRUWKHWRSQDWLRQDOSDUNV7KHFDVHXVHVWKH³SULRULW\FRQVHUYDWLRQLQGH[´DVWKHFRQVHUYDWLRQ SULRULW\PHDVXUHWRIDFLOLWDWHFRPSDULVRQEHWZHHQGLIIHUHQWUHJLRQV7KHFRQVHUYDWLRQSULRULW\LV GH¿QHGXVLQJWKHVWUHVVVWDWXVDQGIHHGEDFNLQGLFDWRUV

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Results and analysis 7KHWRSQDWLRQDOSDUNVKDYHDWRWDODUHDRIî6NP2DQGDFFRXQWIRURIWKHWRWDO JOREDOQDWLRQDOSDUNDUHD7KHVHQDWLRQDOSDUNVDUHORFDWHGLQFRXQWULHV$ERXWRIWKH QDWLRQDOSDUNVDUHLQ6RXWK$PHULFDDQG$IULFDDUHLQ$VLD2FHDQLDDQG1RUWK$PHULFD DQGDUHLQ(XURSH7KH7RSFRXQWULHVZLWKWKHODUJHVWQXPEHUDQGDUHDRIQDWLRQDOSDUNVDUH VKRZHGLQ7DEOH 7KHODQGVFDSHW\SHVZLWKLQWKHVHQDWLRQDOSDUNVLQFOXGHEURDGOHDYHGIRUHVWVî5NP2), FRQLIHURXVIRUHVWVî5NP2 PL[HGFRQLIHURXVDQGEURDGOHDYHGIRUHVWVî4NP2), shrubs î5NP2 JUDVVODQGVî5NP2 ZHWODQGVî4NP2 IDUPODQGVî4NP2), and other YHJHWDWLRQî5NP2 7KHODQGVFDSHDOVRLQFOXGHVZDWHUERGLHVî4NP2), permanent ice and VQRZFRYHUî4NP2 EDUHODQGVî5NP2 DQGFRQVWUXFWLRQODQGNP2  7KHWRSQDWLRQDOSDUNVZLWKWKHKLJKHVWFRQVHUYDWLRQSULRULW\FRYHUHGDQDUHDRIî5 NP2DQGDFFRXQWHGIRURIWKHWRWDODUHD$IULFDZDVREVHUYHGWRKDYHWKHPRVWKLJKSULRULW\ QDWLRQDOSDUNVUHSUHVHQWLQJRIWKHWRWDOQXPEHU&RQYHUVHO\1RUWK$PHULFDKDGWKHOHDVW DPRXQWRIKLJKSURWHFWLRQSULRULW\QDWLRQDOSDUNVDFFRXQWLQJIRURIWKHWRWDOQXPEHU

Chapter 7

SDG 15 Life on Land

Table 7-3 Top 20 countries with the largest number and area of national parks Country

Area of NPs (×104 km2)

1

Brazil

27.1

45

2

China*

22.4

Canada

35

3

Australia

22.1

4

Thailand

22

4

Canada

13.3

5

United States of America

21

5

Bolivarian Republic of Venezuela

11.2

6

Russian Federation

19

6

Democratic Republic of the Congo

9.1

7

Colombia

16

7

Mongolia

8.7

8

Bolivarian Republic of Venezuela

14

8

Chile

8.2

9

Mongolia

13

9

Russian Federation

7.9

10

Chile

11

10

Plurinational State of Bolivia

7.5

11

China*

10

11

Algeria

7.2

12

Plurinational State of Bolivia

10

12

Colombia

6.5

13

Zambia

10

13

Indonesia

6.4

14

Argentina

9

14

United States of America

6.2

15

Republic of Cameroon

9

15

Zambia

5.9

16

Indonesia

9

16

United Republic of Tanzania

5.1

17

India

9

17

Botswana

4.6

18

Democratic Republic of the Congo

8

18

Sweden

3.9

19

Ethiopia

8

19

Peru

3.6

20

United Republic of Tanzania

8

20

Central African Republic

3.2

Rank

Country

Number of NPs Rank

1

Australia

63

2

Brazil

3

Note: Calculated by Category Ĕ (National Park) of IUCN. * Chinese national park system pilots.

127

Global national parks ranked by conservation prioritization.

Big Earth Data in Support of the Sustainable Development Goals (2019)

Figure 7-2

128

Chapter 7

SDG 15 Life on Land

Highlights  7  KHWRSQDWLRQDOSDUNVDFFRXQWHGIRURIWKHZRUOG·VWRWDOQDWLRQDO park area, and 70.5% of these parks had a high conservation priority. Although the construction of national parks started late, China has paid more attention to top-level design, standardized construction, and implemented the strictest protection. There is an urgent need to use the Big Earth Data methodology to strengthen the monitoring, evaluation, and management of global national parks.

Outlook Globally, protected areas have experienced downgrading, downsizing, and degazettement. The establishment, monitoring, evaluation, and management standards of national parks are not consistent due to different political systems, cultures, economic and social development, and the VFLHQWL¿FDQGWHFKQRORJLFDOGHYHORSPHQWRIGLIIHUHQWFRXQWULHV7KHPHWKRGRORJ\SUHVHQWHGLQWKLV case can aid in simplifying the process. The Big Earth Data methodology can be applied to monitor, evaluate, and manage national parks around the world. The Big Earth Data information and assessment indicators will be strengthened in the future. Furthermore, the application of Earth observation technology for the assessment of SDG 15.1.2 will be improved to serve global sustainable development.

129

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Study on forest protection proportion indicators Scale: National Study area: China

Forest ecosystems have an important role in maintaining biodiversity, ecological processes, and ecological function of natural ecosystems in addition to providing essential resources for human survival. The establishment of nature reserves is one of the most important ways to protect forest ecosystems. However, the status of forest ecosystem protection in existing nature reserves is unclear due to the lack of high-resolution distribution maps for various types of forest ecosystems. This report employs big data engineering to identify the spatial distribution of different types of forest ecosystems and their key protection areas and analyzes their protection in nature reserves.

Target 15.1: By 2020, ensure conservation, restoration, and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains, and drylands, in line with obligations under international agreements.

Indicator 15.1.2: Proportion of important sites and ecosystems for terrestrial and freshwater biodiversity that are covered by protected areas.

Method Object-oriented multi-scale segmentation and decision tree classification was performed using data from HJ-1A/B and Landsat OLI (30-meter resolution) images to identify ecosystems. This method was supported using sample databases constructed from ground survey sites. The forest ecosystems were divided into four secondary subclasses consisting of coniferous forests, broadleaved forests, mixed coniferous and broadleaved forests, and sparse forests. This data also included six tertiary subclasses of evergreen broadleaved forests, deciduous broadleaved forests, evergreen coniferous forests, mixed coniferous and broadleaved forests, and sparse forests. However, these IRUHVWFODVVL¿FDWLRQVZHUHXQDEOHWRPHHWWKHQHHGVRIHFRORJLFDOSURWHFWLRQ7KXVDPRUHGHWDLOHG FODVVL¿FDWLRQRIWKHHFRV\VWHPZDVSHUIRUPHGXVLQJWKHDERYHUHVXOWVLQFRPELQDWLRQZLWK&KLQD¶V vegetation map, in which China’s forest ecosystems were divided into 343 subclasses.

Chapter 7

SDG 15 Life on Land

$ VHULHV RI LQGLFDWRUV ZDV VHOHFWHG WR LGHQWLI\ NH\ SURWHFWHG DUHDV LQ WKH IRUHVW HFRV\VWHPV and their protection status. These indicators included endangerment, peculiarity, typicality, and integrity. These were then used to construct an ecosystem evaluation index system. The key HFRV\VWHPVZHUHIXUWKHUGLYLGHGLQWRWKUHHOHYHOVLQFOXGLQJH[WUHPHO\LPSRUWDQWLPSRUWDQWDQG JHQHUDOO\LPSRUWDQW)RUHVWHFRV\VWHPVDQGWKHNH\SURWHFWHGDUHDVZHUHVXSHULPSRVHGZLWKQDWXUH UHVHUYHVZKLFKSURYLGHGWKHVWDWXVRIIRUHVWHFRV\VWHPSURWHFWLRQLQ&KLQD/DVWO\DUHDVZLWKQR SURWHFWLRQZHUHLGHQWL¿HGLQWKHIRUHVWHFRV\VWHP

Data used in the case The data included environmental and disaster monitoring and forecasting using small satellite FRQVWHOODWLRQ +-$%  DQG /DQGVDW 2/, PHWHU UHVROXWLRQ  LPDJHU\$  VFDOH vegetation map was included, as well as spatial distribution data for the nature reserves.

Results and analysis The study found that the overall protection proportion in nature reserves for China’s forest ecosystems was 11%, with a large variation among various ecosystem types. Under the six forestHFRV\VWHPFODVVL¿FDWLRQV\VWHPV)LJXUH WKHSURWHFWHGSURSRUWLRQZDVLQDVFHQGLQJRUGHU  evergreen broadleaved forest, evergreen coniferous forest, mixed coniferous and broadleaved forest, deciduous broadleaved forest, deciduous coniferous forest, and sparse forest. The protection proportion for evergreen broadleaved forests was less than 10%, while sparse forests had the KLJKHVWYDOXHV )LJXUH 6RPHRIWKHIRUHVWVXEW\SHVZHUHWRWDOO\SURWHFWHG while others were unprotected. There was high variability in the proportion of forest ecosystem protection for different types of ecosystems. The results suggest the forests with lower protection proportions require additional protection. The coverage rates are the most important indicators for protecting the key areas in forest ecosystems. The ratios for forests that were observed to have greater importance in nature reserves ZHUHDQGUHVSHFWLYHO\7KLVLQGLFDWHVWKDWIRUHVWVWKDWDUHPRUHYDOXHGE\ society are better protected. However, there were still many conservation gaps in the northwestern SDUWRI30 m) are currently being developed that uses higher resolution datasets. The dynamics of land productivity are greatly affected by climatic factors. Thus, it is necessary to develop corresponding correction methods and a land productivity assessment method that VHSDUDWHVWKHHIIHFWVRIFOLPDWHÀXFWXDWLRQV

Chapter 7

SDG 15 Life on Land

The proportion of degraded land to the total land area in Central Asia 6FDOH5HJLRQDO 6WXG\DUHD&HQWUDO$VLD

Central Asia mostly consists of hot, dry land characterized by a fragile ecological environment. 7KHULVNRIODQGGHJUDGDWLRQLQWKHUHJLRQLVLQFUHDVLQJGXHWRWKHLQWHQVL¿FDWLRQRIJOREDOFOLPDWH change and human activities (e.g., water and soil development, mineral resources development, transportation and energy corridor construction, and urban expansion). The prevention and control of land degradation is an important component for guaranteeing ecological security and sustainable socio-economic development in the region.

Target %\FRPEDWGHVHUWLÀFDWLRQDQGUHVWRUHGHJUDGHGODQGDQGVRLOVLQFOXGLQJ ODQGDIIHFWHGE\GHVHUWLILFDWLRQGURXJKWDQGIORRGVDQGVWULYHWRDFKLHYHDODQG GHJUDGDWLRQQHXWUDOZRUOG

Indicator 3URSRUWLRQRIGHJUDGHGODQGWRWKHWRWDOODQGDUHD

Method Land degradation was assessed by calculating a composite index. A land degradation index (LDI) was calculated using the geometric mean of indicators to monitor land degradation. Land degradation was identified using three measures of change derived from LDI time series data, including: trajectory, performance, and states suggested by the UNCCD.

Data used in the case The datasets included MODIS data products, a CCI-LC dataset, and soil taxonomy units developed by the United States Department of Agriculture. The indicator datasets included: NDVI, albedo, LST, the Temperature-Vegetation Dryness Index (TVDI), and the Modified Soil-Adjusted Vegetation Index (MSAVI). The NDVI, albedo, and LST were derived from MOD13A1, MCD43A3, and MOD11A2, respectively. The MSAVI and TVDI datasets were provided by CASEarth.

143

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Results and analysis Most of the land degradation was observed in the western part of Central Asia, whereas most of the land improvement occurred in eastern Central Asia. Land degradation around the Aral Sea was more serious than in other regions. In Central Asia, 4.97% of total land area exhibited land degradation, whereas 5.32% of total land area was categorized as land improvement. The proportions of land degradation in Kazakhstan and Kyrgyzstan were 5.35% and 6.92%, respectively, which were greater than improved land areas. In contrast, the land improvement areas in Uzbekistan, Tajikistan, and Turkmenistan accounted for 8.75%, 11.68%, and 5.75% of the total area, respectively, which were greater than the amount of land degradation areas. Tajikistan and Kyrgyzstan had the highest proportion of land degradation compared to the RWKHU&HQWUDO$VLDQFRXQWULHV7KHORZHVWSURSRUWLRQ RIODQGGHJUDGDWLRQZDVLGHQWL¿HG in Turkmenistan. Higher percentages of land improvement were found in Turkmenistan and Uzbekistan than in other countries. The lowest proportion (4.09%) of land improvement was observed in Kyrgyzstan.

(a)

(b)

(d) (c)

Figure 7-10 Spatial distribution and trends in LDI from 2000 to 2018. (a) Spatial distribution of the mean LDI. E $QQXDOFKDQJHLQ/',F /DQGFRYHUFKDQJHFODVVL¿HGLQWRWKUHHFODVVHVLQFOXGLQJODQGGHJUDGDWLRQVWDEOH DUHDVDQGODQGLPSURYHPHQWG 3HUFHQWFKDQJHLQ',IRUGLIIHUHQWFRXQWULHV1RWH&$UHIHUVWRWKH¿YH&HQWUDO Asian countries, where KAZ: Kazakhstan, UZB: Uzbekistan, KGZ: Kyrgyzstan, TJK: Tajikistan, and TKM: Turkmenistan. Both (d) and (c) have the same legend.

Chapter 7

SDG 15 Life on Land

Figure 7-11 This area was once the Aral Sea.

Highlights  )  URP  WR  ODQG GHJUDGDWLRQ LQ &HQWUDO$VLD PDLQO\ RFFXUUHG LQ WKHZHVWZKLOHODQGLPSURYHPHQWZDVFRQFHQWUDWHGLQWKHHDVW7KHPRVW serious case of land degradation occurred near the Aral Sea.  /  DQG GHJUDGDWLRQ LQ &HQWUDO$VLD DFFRXQWHG IRU  RI WKH WRWDO DUHD Both Kazakhstan and Kyrgyzstan had high rates of land degradation with YDOXHVRIDQGUHVSHFWLYHO\

Outlook The land degradation evaluation index system can provide a new data source for the SDG 15.3.1 indicator in inland arid areas. In the future, the identification of land degradation areas should be the main spatial targets for the deployment of restoration plans. This can provide a decision-making reference for the implementation of the land degradation neutrality initiative for regional governments. The major drivers of the land degradation process will be investigated in future studies, especially in the area surrounding the Aral Sea.

145

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Identifying land degradation area and risk control countermeasures in Mongolia and along the China-Mongolia railway Scale: National Study area: Mongolia

Mongolia is currently experiencing severe land degradation challenges, which have a direct impact on the ecosystem of the Mongolian Plateau and in adjacent areas. The environmental changes resulting from the land degradation process have also begun to affect local infrastructure, including the multinational transportation artery connecting China, Mongolia, and Russia, and the China-Mongolia railway. This effect increases risks to passengers and goods moving along the region’s economic corridor. The development of solutions to the issue require an understanding the dynamics of land degradation in the region.

Target 15.3: By 2030, combat desertification, restore degraded land and soil, including land affected by desertification, drought and floods, and strive to achieve a land degradation-neutral world.

Indicator 15.3.1: Proportion of degraded land to the total land area.

Method 5HPRWHVHQVLQJODQGFRYHUSURGXFWVZLWKPHWHUUHVROXWLRQZHUHXVHGWRWUDFNGHVHUWL¿FDWLRQ in Mongolia during 1990, 2010, and 2015. The three phases of land cover data for 1990, 2010, and 2015 were superimposed to obtain maps of land use change in Mongolia from 1990 to 2010 and 1990 to 2015. The distribution of desertified land cover was then calculated for the time period. Buffers with sizes of 5 km, 10 km, 30 km, 50 km, 100 km, and 200 km were created and FRPELQHGZLWKWKHGLVWULEXWLRQRIGHVHUWL¿HGODQGFRYHUDORQJWKH&KLQD0RQJROLDUDLOZD\DQGWKH land degradation changes in 1990-2010 and 1990-2015. This data was used to analyze the risk to LQIUDVWUXFWXUHIURPWKHGHVHUWL¿FDWLRQSURFHVVLQWKHUHJLRQ

Chapter 7

SDG 15 Life on Land

Data used in the case Remote sensing data included Landsat TM and OLI images. The imaging time ranged from June to September in 1990, 2010, and 2015. The auxiliary data included DEMs and slope data for Mongolia, Mongolian administrative district data, average annual temperature data, average annual precipitation data and vector data for buffer zones on both sides of the China-Mongolia railway. The data also included statistics for agricultural land, population, and livestock.

Results and analysis Results revealed that the recent trends in land degradation featured a strong zonal characteristic. Degraded land was mainly distributed in northwestern, central, and northeastern Mongolia, and spanned IURPQRUWKHDVWWRVRXWKZHVWDQGIURPQRUWKWRVRXWK%\WKHDUHDRIGHVHUWL¿HGODQGFRYHUDORQJWKH China-Mongolia railway (Mongolian section) accounted for about 47.49% of the total area (Figure 7-12). The combined effects of natural and socioeconomic factors have resulted in land degradation along WKH0RQJROLDQVHFWLRQRIWKH&KLQD0RQJROLDUDLOZD\/DUJHWHPSHUDWXUHÀXFWXDWLRQVDQGUHGXFHG rainfall were observed as the natural factors. The land degradation process was also accelerated by over grazing, population migration, infrastructure construction, mineral exploitation, land reclamation for agriculture, and rapid urbanization. The following suggestions are offered to combat the issue. (1) Future planning should focus on farmland reclamation and controlling the growth rate of agricultural land in northern Mongolia. Future plans should also be developed to (2) control urban construction, and to (3) improve mining technology, and regulate the mining industry to discourage harmful practices. (4) Additionally, plans are also required to regulate livestock populations and to ensure reasonable collocation of livestock species. (5) Lastly, the goal should be to improve the region’s ability to respond to climate change and a focus on ecological risk prevention and control. This is necessary to promote sustainable development in the China-Mongolia-Russia economic corridor.

Highlights Over the past 25 years, the degree of land degradation in Mongolia has gradually increased from northeast to southwest and from north to south. These new land degradation regions have a strong zonal nature and are mainly distributed in northwestern, central, and northeastern Mongolia. Land degradation areas are observed to be expanding northward along the Mongolian section of the China-Mongolia railway. The combined effects of natural and socioeconomic factors have resulted in accelerated land degradation in this region.

147

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Big Earth Data in Support of the Sustainable Development Goals (2019)

(a) Mongolin

(b) China-Mongolia railway (Mongolian section)

Figure 7-12

Land degradation distribution for 1990 to 2015.

Chapter 7

SDG 15 Life on Land

Outlook Feature space models were constructed using Albedo-NDVI, Albedo-MSAVI (Modified Soil Adjusted Vegetation Index) and Albedo-TGSI (Topsoil Grain Size Index) for three geographical regions. These regions included the central province and its northern region, the Eastern Mongolian plateau, and the southern Gobi region. These datasets were then used to extract desertification information along the Mongolian section of the China-Mongolia railway. Updated data products for land degradation will be released in the future to provide data support for the prevention and control of land degradation in Mongolia. Future work will be aimed towards GHYHORSLQJODQGGHJUDGDWLRQULVNSUHYHQWLRQDQGFRQWUROVWUDWHJLHVIRUDUHDVDORQJWKHPDLQWUDI¿F URXWHVRIWKH&KLQD0RQJROLD5XVVLDHFRQRPLFFRUULGRUDQGIRUGHVHUWL¿FDWLRQVHQVLWLYHDUHDVLQ Mongolia.

149

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Big Earth Data in Support of the Sustainable Development Goals (2019)

Applications of the Mountain Green Cover Index in countries and regions along the Belt and Road Scale: Regional Study area: Belt and Road Region

According to the UNEP World Conservation Monitoring Centre, mountains make up 24% of the world’s land area. Mountain areas have a concentrated and rich bioclimatic vertical zone spectrum, which provides important ecological services for maintaining biological diversity. These land features also regulate regional climates and conserve water resources. They are also a source of multiple geological resources and act as an important ecological barrier for social development. 7KH0RXQWDLQ*UHHQ&RYHU,QGH[LVGH¿QHGDVWKHUDWLREHWZHHQJUHHQSODQWVORFDWHGRQPRXQWDLQV (e.g., forests, shrubs, woodlands, pastures, farmland) with the total mountain area. This index is used by the International Mountain Science Committee as an important indicator for reflecting the state of environmental protection in mountain areas. The index is also used in the SDG 15 indicator system. Currently, it is generally agreed that there is a direct link between the Mountain Green Cover Index and the health and function of mountain ecosystems. The conservation capacity and health status of mountain ecosystems can be diagnosed by monitoring the index over time. The index provides information for forest, woodland, and green vegetation management. Exponential changes over time reflect changes in the health of vegetation in mountain areas. For example, a decline in the index can reveal excessive vegetation grazing and destruction, urbanization, GHIRUHVWDWLRQORJJLQJIXHOZRRGH[WUDFWLRQDQGIRUHVW¿UHV&RUUHVSRQGLQJO\DQLQFUHDVHLQWKH index can be attributed to the positive restoration of vegetation, such as effective soil and water conservation, afforestation, and forest restoration. The Belt and Road Initiative involves many mountainous countries within six economic corridors. For example, the China-Pakistan Economic Corridor crosses the Karakoram Mountains, Hindu Kush Mountains, the Pamirs, and the western end of the Himalayas. The terrain is complex and the ecological environment is highly fragile in these areas. Remote sensing technology is therefore essential for calculating the Mountain Green Cover Index. Big Earth Data methodologies can be used to monitor and evaluate the Mountain Green Cover Index for regions, countries, and corridors along the Belt and Road. This has significant importance for eco-environmental protection and green sustainable development. The Big Earth Data-based index calculation method can also provide technical, methodological, and decision-making support for the achievement of the 2030 Agenda for Sustainable Development.

Chapter 7

SDG 15 Life on Land

Target 15.4: By 2030, ensure the conservation of mountain ecosystems and biodiversity to enhance their capacity for providing benefits that are essential for sustainable development.

Indicator 15.4.2: Mountain Green Cover Index.

Method 7KHGDWDXVHGLQWKLVFDVHLQFOXGHVVXUIDFHUHÀHFWDQFHDQG1'9,HVWLPDWHVIURPPXOWLVRXUFH VDWHOOLWHLPDJHU\3UHSURFHVVHGUHPRWHVHQVLQJLPDJHU\DQGHFRV\VWHPGDWDZDVREWDLQHGIURP WKH IROORZLQJ VRXUFHV 02',6 /DQGVDW WKH 81(3:RUOG &RQVHUYDWLRQ 0RQLWRULQJ &HQWHU &KLQD'LJLWDO0RXQWDLQPDSDQG$67(5*OREDO'LJLWDO(OHYDWLRQ0RGHO*'(0 7KHPHWKRGV DQGPRGHOVLQFOXGHGLPDJHVHJPHQWDWLRQDPXOWLFODVVL¿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

Data used in the case 7KHGDWDXVHGLQWKLVFDVHLQFOXGHGPHWHUVXUIDFHUHÀHFWDQFHDQG1'9,GDWDREWDLQHGIURP 02',6DQGPHWHUVXUIDFHUHÀHFWDQFH1'9,GDWDWKDWZDVFDOFXODWHGXVLQJ/DQGVDW70DQG 2/,7KHHFRV\VWHPGDWDIRUHFRQRPLFFRUULGRUVDQG&HQWUDO$VLDZDVREWDLQHGIURP&$6(DUWK $67(5*'(0&KLQD'LJLWDO0RXQWDLQ0DSDQG81(3:RUOG&RQVHUYDWLRQ0RQLWRULQJ&HQWUH

Results and analysis )LJXUHGLVSOD\VWKHGHWDLOHGVSDWLDOSDWWHUQVIRUWKHJULGGHG0RXQWDLQ*UHHQ&RYHU,QGH[DW WKHHFRQRPLFFRUULGRUDQGFRXQWU\VFDOH7KHVWDWLVWLFVIRUWKHDYHUDJHPRXQWDLQDUHDUDWLRDQGWKH LQGH[DUHVKRZQLQ7DEOH7KHSURSRUWLRQRIPRXQWDLQDUHDVZDVUDQNHGKLJKHVWWRORZHVWDV

151

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Big Earth Data in Support of the Sustainable Development Goals (2019)

(a)

(b)

(c)

Figure 7-13 The Mountain Green Cover Index for the Belt and Road Initiative case study area (2015, 1 km). (a) The six economic corridors. (b) China. (c) Kyrgyzstan and Tajikistan. The economic corridor region in (a) ZDVGHWHUPLQHGE\EXIIHULQJNPRIWKHNH\QRGHFLWLHVDQGWUDI¿FOLQHVZKLOHPDLQWDLQLQJWKHLQWHJULW\RI geographic units. The enlarged details in (a) illuminate the Landsat-8 OLI images and Mountain Green Cover Index for the Hindu-Kush Mountains in the CPEC region. The bar plot in (a) reveals the statistics for the Mountain Green Cover Index at different elevations in the economic corridors.

Chapter 7

SDG 15 Life on Land

the China, Central Asia, and West Asia Economic Corridor (CCWAEC, 51.44%), China-Pakistan Economic Corridor (CPEC, 46.39%), China-Indochina Peninsula Economic Corridor (CIPEC, 41.89%), Bangladesh-China-India-Myanmar Economic Corridor (BCIMEC, 36.75%), New Eurasia Land Bridge Economic Corridor (NELBEC, 19.85%), and the China-Mongolia-Russia Economic Corridor (CMREC, 15.59%). The Mountain Green Cover Index for BCIMEC, CIPEC, and CMREC was observed to be higher than 96%. The Mountain Green Cover Index for NELBEC (64.43%) and CCWAEC (58.47%) was lower than the above three corridors, while the index for CPEC (33.58%) was the lowest among the six corridors. These results suggest that it is therefore necessary to focus on the protection of mountain biodiversity in the BCIMEC, CIPEC, and CMREC regions, and on ecoenvironmental protection and vegetation restoration in the CPEC, NELBEC, and CCWAEC regions. According to the China Digital Mountain Map, mountainous areas in China accounted for 64.89% of the total land area. Moreover, the Mountain Green Cover Index accounted for 89.48% of the total mountain area. The index was observed to follow a heterogeneous distribution pattern. The index values were highest in humid regions, high in semi-arid regions, and lowest in arid regions. The

Table 7-4

Statistics for mountain areas and the Mountain Green Cover Index in the Belt and Road Initiative case areas.

Economic Corridors along the Belt and Road

Case Countries along the Belt and Road

Region

Mountain area ratio*

Average Mountain Green Cover Index

China-Central Asia-West Asia Economic Corridor (CCWAEC)

51.44%

58.47%

China-Pakistan Economic Corridor (CPEC)

46.39%

33.58%

China-Indochina Peninsula Economic Corridor (CIPEC)

41.89%

99.67%

Bangladesh-China-India-Myanmar Economic Corridor (BCIMEC)

36.75%

99.77%

New Eurasia Land Bridge Economic Corridor (NELBEC)

19.85%

64.43%

China-Mongolia-Russia Economic Corridor (CMREC)

15.59%

96.78%

China

64.89%

89.48%

Tajikistan

93.19%

43.66%

Kyrgyzstan

89.20%

60.04%

Kazakhstan

15.20%

79.89%

Uzbekistan

13.82%

65.87%

Turkmenistan

5.11%

49.94%

1RWH7KHGH¿QLWLRQRIPRXQWDLQVDQGFDOFXODWLRQRIWKH0RXQWDLQ*UHHQ&RYHU,QGH[IRU&KLQDLVEDVHGRQWKH&KLQD'LJLWDO0RXQWDLQ map, while the other regions are based on UNEP- World Conservation Monitoring Centre mountain data.

153

154

Big Earth Data in Support of the Sustainable Development Goals (2019)

mountain areas in the five Central Asian countries accounted for 15.20% of the total land area in the region. The mountain area ratios for Tajikistan and Kyrgyzstan were 93.19% and 89.20%, with a mountainous green cover index of 43.66% and 60.04%, respectively. Mountains are known as WKH³OLIHOLQH´IRUWKHHFRORJ\DQGGHYHORSPHQWRIDULGUHJLRQV7KHFOLPDWHRIWKH¿YH&HQWUDO$VLD countries is dry and the mountain ecosystem is fragile. It is therefore necessary to monitor, evaluate, and protect mountain ecosystem health in the region.

Highlights The grid-based Mountain Green Cover Index calculation model was GHYHORSHGDQGUHÁHFWVWKHHQYLURQPHQWDOJUDGLHQWDQGKLJKVSDWLRWHPSRUDO heterogeneity characteristics of mountains areas. Big Earth Data was used to map the Mountain Green Cover Index for six economic corridors and mountains countries along the Belt and Road in 2015.

Outlook Big Earth Data is an innovative methodology that can achieve high temporal resolution (e.g., daily, monthly, yearly) and high spatial resolution (-@Bulletin of the Chinses Academy of Sciences, 33(8): 768-773. Guo, H. D. (2018). Steps to the digital Silk Road[J]. Nature, 554: 25-27. Guo, H. D. (2018). Using Big Earth Data in support of sustanabile development. People’s Daily, 30 July 2018. http://paper.people.com.cn/rmrb/html/2018-07/30/nw. D110000renmrb20180730_2-07. htm. Guo, H. D., Liu, Z., Jian,g H., et al. (2017). Big Earth Data: a new challenge and opportunity for Digital Earth’s development[J]. International Journal of Digital Earth, 10(1): 1-12. Guo, H. D., Liu, J., Qiu, Y. B., et al. (2018). The Digital Belt and Road program in support of regional sustainability[J]. International Journal of Digital Earth, 11(7): 657-669. Halpern, B. S., Walbridge, S., Selkoe, K. A., et al. (2008). A global map of human impact on marine ecosystems[J]. Science, 319 (5865): 948-952.

173

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Halpern, B. S., Longo, C., Hardy, D., et al $QLQGH[WRDVVHVVWKHKHDOWKDQGEHQH¿WVRIWKH global ocean[J]. Nature, 488 (7413): 615-620. He, L. F. (2017). National Report on New Urbanization[M]. Beijing: China Planning Press. He, Z., Zeng, Z. C., Lei, L., et al. (2017). A data-driven assessment of biosphere-atmosphere LQWHUDFWLRQ LPSDFW RQ VHDVRQDO F\FOH SDWWHUQV RI ;&2 2 using GOSAT and MODIS observations[J]. Remote Sensing, 9(3): 251. Hsu, N. C., Tsay, S. C., King, M. D., et al. (2006). Deep blue retrievals of asian aerosol properties during ACE-Asia[J]. IEEE Transactions on Geoscience and Remote Sensing, 44(11): 31803195. ,8&1 $*OREDO6WDQGDUGIRUWKH,GHQWL¿FDWLRQRI.H\%LRGLYHUVLW\$UHDV9HUVLRQ First edition. Gland, Swizerland: IUCN. Jiang, L. L., Jiapaer, G., Bao, A. M., et al 0RQLWRULQJWKHORQJWHUPGHVHUWL¿FDWLRQSURFHVV and assessing the relative roles of its drivers in Central Asia[J]. Ecological Indicators, 104: 195208. Johnson, S., Logan, M., Fox, D., et al. (2016). Environmental decision-making using bayesian networks: Creating an environmental report card[J]. Applied Stochastic Models in Business and Industry, 33(4): 335-347. Kaufman, Y. J., Tanré, D., Remer, L. A., et al. (1997). Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer[J]. Journal of Geophysical Research-Atmospheres, 102(D14): 17051-17067. Klein, I., Gessner, U., Dietz, A. J., et al. (2017). Global WaterPack – A 250 m resolution dataset revealing the daily dynamics of global inland water bodies[J]. Remote Sensing of Environment, 198: 345-362. .RVPDV&.LUNE\0DQG*HHVRQ1 0DQXDORQNH\LQGLFDWRUVRIGHVHUWL¿FDWLRQDQG PDSSLQJHQYLURQPHQWDOO\VHQVLWLYHDUHDVWRGHVHUWL¿FDWLRQEuropean Commission, 87. Kroner, R.E.G., Qin, S. Y., Cook, C. N., et al. (2019). The uncertain future of protected lands and waters[J]. Science, 364(6443): 881. Levy, R. C., Remer, L. A., Kleidman, R. G., et al. (2010). Global evaluation of the Collection 5 MODIS dark-target aerosol products over land[J]. Atmospheric Chemistry and Physics, 10(21): 10399-10420. Liao, J. J. and Zhen, J. N. (2019).Landsat data of mangrove forest changes in Hainan Island during 1987-2017[J/OL]. Science Data Bank, 4(2): 12-19.

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