Big Earth Data in Support of the Sustainable Development Goals (2020): China 9782759826469

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Foreword

Foreword

T

he Sustainable Development Goals (SDGs) are at the core of Transforming Our World: the 2030 Agenda for

Sustainable Development, adopted by the 193 member states of the United Nations in September 2015. The SDGs represent a commitment to addressing the tri-dimensional social, economic, and environmental issues humankind faces in the most comprehensive way in history, with the aim of achieving sustainability. China is fully committed to the 2030 Agenda for Sustainable Development. As progress has been made across the board, there have been “early harvests” of multiple SDGs. The goal to end extreme poverty will be achieved within 2020, for example. China is also fully engaged in international cooperation in the SDGs, sharing knowledge and experience with and offering assistance, as much as we can, to other developing countries. The important role science and technology can play, already widely recognized, was reaffirmed in the Global Sustainable Development Report 2019 as vital to sustainable transformations as well as global development and change. The Chinese Academy of Sciences (CAS), as a member of the global scientific community, has worked vigorously through

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

the science-policy-society interface to provide solutions and support, domestically and internationally, to address the new demands and challenges that humanity must address before the lofty Sustainable Development Goals can be achieved. The SDGs are gigantic, complex, and diverse systems with dynamic interactions amongst themselves. Fundamental to their implementation are effective monitoring and evaluation, where PDQ\GLI¿FXOWLHVUHPDLQ7KHYROXQWDU\DQGQRQELQGLQJGlobal Indicator Framework for the Sustainable Development Goals was adopted in 2017 by the United Nations but must be further UH¿QHG:LWKRQO\WHQ\HDUVEHWZHHQQRZDQGWKHSURVSHFW of achieving the SDGs is not bright, not to mention the outbreak of COVID-19, which has brought unprecedented challenges to this effort. The CAS Big Earth Data Science Engineering Program (CASEarth) has studied, since 2018, six SDGs: 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), especially focusing on indicators of each of those SDGs where data and methods can be improved. CASEarth issues annual reports, Big Earth Data in Support of the Sustainable Development Goals, and leads CAS’ efforts to support the implementation of the SDGs. Big Earth Data in Support of the Sustainable Development

Foreword

Goals (2020): China discusses data integration, indicator development, and sustainability evaluation concerning the six SDGs through 42 case studies. Each case presents data products, methods, and models and how they can support policymaking, through five parts—the background, data used in the case, methods, results and analysis, and outlook—showcasing the value and prospects of applying Big Earth Data-enabled technologies and methods to the evaluation of the SDGs. In the Sustainable Development Goals Report 2019, United Nations Secretary-General António Guterres called for deeper, faster, and more ambitious responses to achieve the social and economic transformations required for the implementation of the SDGs. The report laid special emphasis on better use of data, a digital transformation while harnessing science, technology, and innovation, and more intelligent solutions. Technologies, especially data, will therefore have to play a more important role in achieving the SDGs. The United Nations “Technology Facilitation Mechanism” (TFM) is fully consistent with China’s strategy of driving development with innovation. China and many other developing countries face major challenges and pressure as we pursue the SDGs with limited capacity for data collection and processing, and for monitoring and evaluating SDG indicators, where Big Earth Data has a unique role to play. The year 2020 marks the 75 th anniversary of the United

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Nations and the start of the “Decade of Action” to deliver the Sustainable Development Goals by 2030. China’s science and technology community will continue to work with other countries to advance the 2030 Agenda and contribute to achieving the SDGs as scheduled. Chinese scientists hope to present this report as part of China’s contribution as we remain committed to collaboration and sharing with the rest of the world. Finally, I would like to express my respect and appreciation for the CASEarth team of scientists led by Academician Guo Huadong for the effort they have made toward the SDGs in the spirit of science and innovation.

Bai Chunli President, Chinese Academy of Sciences Head of the Leadership Group of CASEarth November 30, 2020

Preface

Preface

A

lmost five years after the adoption of Transforming Our World: the 2030 Agenda for Sustainable Development by

the United Nations, the lack of data on indicators is still holding EDFNWKHVFLHQWL¿FHYDOXDWLRQRISURJUHVVWRZDUGWKH6XVWDLQDEOH Development Goals (SDGs). The coronavirus pandemic since early 2020 has made the challenges countries face in realizing the SDGs all the more daunting. To support global implementation of the SDGs, the United Nations launched the “Technology Facilitation Mechanism”, encompassing three parts—the Interagency Task Team, including the 10-Member Group, the collaborative Multi-stakeholder Forum, and an online platform. One of the most important and pressing issues now is to achieve breakthroughs in data and methods for the monitoring of SDG indicators. Big Earth Data enables macroscopic, dynamic, and objective monitoring by making it possible to integrate and analyze data on the land, sea, atmosphere, and human activity to give a holistic understanding of a vast region. This technology can support policymaking by providing dynamic, multi-scale, and cyclical information on multiple SDG indicators closely related to Earth’s surface, environment, and resources.

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Our goal is to convert Big Earth Data to information relevant to the SDGs, construct and integrate such data to support SDG indicators, study the connections and couplings between the SDGs, and then serve as a tool for SDG-related policymaking. This year, we selected six SDGs to study, based on the advantages of Big Earth Data and features of SDG indicators. Big Earth Data can contribute to the evaluation of the six SDGs in three ways: by providing data products, new evaluation methods and models, and case studies to monitor progress and inform policymaking. In this report, 42 typical cases are presented to showcase the studies on and monitoring results of 23 SDG targets, including 41 data products, 21 methods, and 30 results that are of value to policymaking. The cases cover the topics of tracking the progress of land degradation neutrality, biodiversity conservation, urban sustainability assessment, spatial distribution of vegetated wetlands, analysis of the distribution and variation of marine debris and microplastics, offshore ecosystem health assessment in China, and assessing the processes and drivers of aeolian desertification in semi-arid areas of northern China. They all point to the great value of Big Earth Data and related technologies and methods as new analytical tools with which we will be able to more deeply understand and make better policies for the SDGs and related issues. This report could not have been completed without the

Preface

leadership and support from the Chinese Academy of Sciences, the Ministry of Foreign Affairs, the Ministry of Science and Technology, and other ministries. We are grateful for the valuable opinions shared by leaders and experts from the National Development and Reform Commission, the Ministry of Natural Resources, the Ministry of Ecology and Environment, the Ministry of Housing and Urban-Rural Development, the Ministry of Transport, the Ministry of Water Resources, the Ministry of Agriculture and Rural Affairs, the National Health Commission, the Ministry of Emergency Management, the National Bureau of Statistics, and the National Forestry and Grassland Administration. Finally, our utmost appreciation goes to every scientist on the team for their hard work.

Guo Huadong CAS Academician Chief Scientist of CASEarth Member of the UN 10-Member Group to support the TFM for SDGs November 30, 2020

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Executive Summary

Five years after the adoption of Transforming Our World: The 2030 Agenda for Sustainable Development (abbreviated to 2030 Agenda for Sustainable Development), the implementation of the Sustainable Development Goals (SDGs) is still constrained by the lack of data on progress, inadequate statistical methods, diverse issues concerning localization, and the multitude of indicators that are both intertwined and mutually restrictive. Big Earth Data is an innovative technology that can serve as a new key to unlocking Earth’s secrets and a new engine to drive discoveries. In the 2019 report Big Earth Data in Support of the Sustainable Development Goals, our research team prove the contributions that this technology can make to sustainable development. This year’s report focuses on the contributions of Big Earth Data toward monitoring and evaluating six SDGs, showing the contributions made by the Big Earth Data Science Engineering Project (CASEarth) in data products, methodologies, models, and policymaking support. 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).

With regard to SDG 2 (Zero Hunger), the 2020 report focuses on SDG 2.2.1 (prevalence of stunting among children under five years of age), SDG 2.4.1 (proportion of agricultural area under productive and sustainable agriculture), and SDG 2.C.1 (Indicator of food price anomalies). These indicators are closely related to the supply and demand of food and national actions. This report presents a creative technology that integrates multi-source Big Earth Data for producing scientific data and evaluating indicators. The report includes the following key findings and outcomes. First, a new model was developed to estimate the national crop yield gap. Second, it was found that China fulfilled SDG 2.2 in 2017 by reducing the rate of under-five stunting. Third, the national grain output was found to be able to meet the consumption estimates for 2030 LIWKHVSHFL¿HG\LHOGSRWHQWLDOFRXOGEHDFKLHYHGLQWKHODUJHVWZKHDWDQGULFHSURGXFWLRQ

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

areas. Lastly, the price of rice, wheat, and maize has been observed to fluctuate since 2015. The report provides recommendations for policymakers in key areas for the further UHGXFWLRQLQWKHUDWHRIXQGHU¿YHVWXQWLQJ0RUHRYHUWKHUHSRUWSURYLGHVDODQGDQGZDWHU UHVRXUFHGLVWULEXWLRQSODQIRULQFUHDVLQJSRWHQWLDOJUDLQDUHDVDQGLGHQWL¿HVNH\UHJLRQV for sustainable grain production with the reduced use of chemical fertilizers. The report also presents Big Earth Data’s potential for monitoring and evaluating SDG 2 and its advantages in supporting the Zero Hunger initiative.

Regarding SDG 6 (Clean Water and Sanitation), the report presents Big Earth Data-enabled methods for monitoring and evaluating SDG 6.3.2 (proportion of bodies of water with good ambient water quality), SDG 6.4.1 (water-use efficiency), SDG 6.4.2 (level of water stress), and SDG 6.6.1 (change in the extent of water-related ecosystems over time). It also includes a comprehensive index that focuses on water resources, the related environment and ecosystems, and case studies for a typical watershed in China. The following datasets are included: the water transparency in China’s lakes from 2000 to 2019; the aquatic vegetation and algae in China’s lakes in 2010, 2015, and 2019; the vegetated wetlands (2015); mangrove forests and 6SDUWLQDDOWHUQLÀRUD in China in 2015 DQGWKHZDWHUXVHHI¿FLHQF\ DQGVXVWDLQDEOHGHYHORSPHQWRIZDWHUVHFXULW\ in China’s provinces (autonomous regions and municipalities) (2015 and 2018); and the observed and predicted water stress for the Shule River basin in Western China for the 2000-2030 period. Most of the methods were developed for monitoring and evaluating water transparency and water stress in large lakes, and were coupled with a glacier module. The report concludes that the clarity of lakes and water-use efficiency in China has improved. Moreover, the level of water stress is increasing in China’s Shule River basin, the nation’s mangrove forests have been restored, and 6SDUWLQDDOWHUQLÀRUD, an invasive species, has been effectively controlled. These important achievements provide effective decision support for water environment monitoring and governance, the optimal allocation of water resources, and wetland protection actions in China and the world.

Executive Summary

For SDG 11 (Sustainable Cities and Communities), eight indicators are covered within SDG 11, including: SDG 11.1.1 (informal settlements), SDG 11.2.1 (convenient access to public transport), SDG 11.3.1 (urbanization), SDG 11.4.1 (heritage protection), SDG 11.5.1/SDG 11.5.2 (disaster evaluation), SDG 11.6.2 (air pollution), and SDG 11.7.1 (open public space). The report includes multiple SDG 11 indicators at the provincial level and typical cities in China, which are enabled by Big Earth Data. The results include the production of datasets such as high-resolution gridded population data categorized by age and gender (2015 and 2018), an impervious surface dataset of 433 Chinese cities with a 30 m resolution (1990-2018), and an integrated assessment dataset with multiple indicators covering public transportation, urbanization, disasters, the environment and the urban open public space. Moreover, the results also include: the creation of a deep learning network model to identify shantytowns and disasters; the proposal of a new indicator, the ratio of Economic Growth Rate to Land Consumption Rate (EGRLCR); and the proposal of a human intervention index. The report concludes that urban open public space has continuously increased since 2015, and the population with easy access WRSXEOLFWUDQVSRUWDWLRQKDVJURZQE\0RUHRYHUWKHUHSRUW¿QGVWKDWFKDOOHQJHV VWLOOUHPDLQLQFRRUGLQDWLQJXUEDQL]DWLRQ$GGLWLRQDOO\WKHUHSRUW¿QGVWKDWSURWHFWLRQDQG development have continued to strengthen for UNESCO designated sites in China, and the nation has improved its capacity for disaster prevention and reduction. The losses caused E\QDWXUDOGLVDVWHUVKDYHGURSSHGVLJQL¿FDQWO\DQGWKHFRQFHQWUDWLRQRI30KDVVKRZQ a downward trend since 2013. The emergency response capacity for COVID-19 in Wuhan was evaluated using Big Earth Data based on SDG indicators. The results demonstrate China’s ability and experience in responding to major public health emergencies and urban safety governance. The results are based on the monitoring and comprehensive evaluation of SDG 11 indicators at the provincial level. They can be used as a basis for developing policies related to urban inclusion, security, land use, and environmental monitoring, and ¿QGLQJVROXWLRQVIRUJOREDOVXVWDLQDEOHXUEDQGHYHORSPHQW

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Regarding SDG 13 (Climate Action), this report focuses on SDG 13.1 (strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries) and SDG 13.2 (integrate climate change measures into national policies, strategies and planning). Furthermore, the report presents Big Earth Data applications to monitoring the progress of 6'*7KHIROORZLQJ¿QGLQJVDQGGHOLYHUDEOHVDUHLQFOXGHG,QWKHDUHDRIFOLPDWHUHODWHG hazards and natural disasters, datasets are generated based on the intensity and frequency of extreme high-temperature events and heatwaves in China, and the results reveal a marked upward trend in these events since the late 1990s. In the area of climate change response, the report provides projections on how climate change can impact crop phenology in China, and provides decision support for response to climate change in the interest of food security. This is accomplished by forecasting a forward shift in the anthesis and maturation of wheat and maize with a high degree of probability. An automatic glacial area extraction method was developed, and the reaction of glaciers to climate change was observed to be quite different in different areas. The glacial area in the Tarim River basin was observed to slightly increase, while the area in the Inner Plateau was stable. However, the glacial area in the Mekong basin was observed to be shrinking rapidly.

Regarding SDG 14 (Life Below Water), this report focuses on SDG SUHYHQWLQJDQGVLJQL¿FDQWO\UHGXFLQJPDULQHSROOXWLRQ 6'* (sustainably managing and protecting marine and coastal ecosystems), and SDG 14.3 (minimizing and addressing the impacts of ocean DFLGL¿FDWLRQ 7KHUHSRUWSUHVHQWVVL[FDVHVWXGLHVZKHUH%LJ(DUWK'DWD technology was applied to the dynamic monitoring and integrated assessment of various parameters nationally (China) and locally (e.g., typical marine areas) using spatiotemporal data fusion and model simulation among other methodologies. The case studies include: an assessment of the distribution and variation of marine debris and microplastics in China’s coastal waters; an assessment of the distribution of metal pollutants in China’s coastal areas using bivalves as bio-indicators; an ecosystem health assessment for typical bays; a risk assessment of harmful algal blooms in the Bohai Sea; an assessment of the changes in the scope of raft culture; and an assessment of eutrophication, hypoxia, and

Executive Summary

acidification in China’s coastal seas. The following findings were revealed: there was a GRZQZDUGWUHQGLQWKHDEXQGDQFHRIÀRDWLQJGHEULVLQ&KLQD¶VFRDVWDOZDWHUVIURP onward, and a continuous reduction in microplastics pollution between 2016 and 2019. 7KHFRQFHQWUDWLRQVRI&G&X=QDQG3ELQR\VWHUVZHUHVLJQL¿FDQWO\KLJKHUWKDQWKDW in other groups of bivalves. Moreover, the bivalves indicated that the Pearl River Delta exhibited the most serious metal pollution. The Jiaozhou Bay, Sishili Bay, and Daya Bay ecosystems were observed to be generally stable and had remained in a sound state of health since 2015. A risk assessment of harmful algal blooms in the Bohai Sea indicated that Bohai Bay, the coastal waters in Qinhuangdao, and Laizhou Bay had a relatively high risk compared with other regions. There was an increase in raft culture area in marine waters. Moreover, the areas near the offshore ecological conservation in Jiangsu and Fujian provinces remained stable between 2017 and 2020. An increase in the DIN (total inorganic nitrogen) and N/P ratio was observed in the Bohai Sea, as well as a decrease in the Si/N ratio, DO, and pH.

Regarding SDG 15 (Life on Land), this report focuses on four themes, including forests, land degradation, biodiversity conservation and invasive species. Furthermore, the report presents Big Earth Data-enabled SDG indicator evaluation models and methodologies, as well as pilot applications at the national and local levels. The deliverables include: a 2019 regional forest cover map with 30 m spatial resolution; a spatial dataset that depicts the state, scope of threats, and cumulative stress on China’s protected flora and fauna; an integrated spatial-temporal-spectral feature extraction model for forests; and a spatial abundance simulation methodology for threatened species. The case studies on SDG 15 also SUHVHQWWKHIROORZLQJ¿QGLQJV0DUNHGLPSURYHPHQWVZHUHREVHUYHGLQWKHVRLOORVVRQWKH /RHVV3ODWHDXDQGUHGXFWLRQLQDHROLDQGHVHUWL¿FDWLRQLQQRUWKHUQ&KLQDVLQFH7KUHH JOREDOFRQGLWLRQVVSHFL¿FWR&KLQDZHUHLQWURGXFHGIRUFDWHJRU\VSHFL¿FFRQVHUYDWLRQDQG sustainable use of biodiversity. Recommendations were also made on the strategies and solutions needed for ecosystem conservation and restoration in the context of the “National Projects for the Conservation and Restoration of Major Ecosystems”. These results can lend effective support for dynamically monitoring and evaluating SDG 15.

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Evaluation of water stress in the Shule River Basin in Northwest China

Water shortage assessment in the Shule River basin in China (2010-2030)

Water shortage assessment method based on comprehensive consideration of climate, the cryosphere, and socio-economic water demand

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:DWHUXVHHI¿FLHQF\DQDO\VLV of provincial administrative units in China (2015 and 2018)

:DWHUXVHHI¿FLHQF\DQG its change in China

SDG 6.3

SDG 6.4

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7KH¿UVWGDWDVHWRIWHPSRUDO and spatial distribution of surface water transparency in China (2000-2019, monthly, \1 km resolution)

Spatiotemporal patterns of water transparency in China’s lakes

Reference for optimal distribution of water resources in GGCAAs

5HGOLQHRIZDWHUXVHHI¿FLHQF\RI China’s water resources

Decision support for water environment management of Ministry of Ecology and Environment of the People’s Republic of China.

(DUO\ZDUQLQJRISULFHÀXFWXDWLRQV under different frequencies and precise control

Model approach to early warning of abnormal price movements, which presents a quantitative description of early warning triggers

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Patterns of potential yield increase and fertilizer reduction

A pathway to sustainable cropland LQWHQVL¿FDWLRQ

Variations in regional trends and identifying priority areas

Decision support

Remote sensing-based model for yield potential estimates

SDG 2.C

Spatial dataset of yield gap and reducible fertilizer of three major staple food crops (2015)

Monitoring and early warning of agri-product price volatility in China

Potential for improvement of the sustainability of crop production in China

Spatial dataset of multiple cropping index and its potential

Potential for sustainable FURSODQGLQWHQVL¿FDWLRQ in China

SDG 2.2

SDG 2.4

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Change in prevalence of stunting among children under ¿YH\HDUVRIDJHLQ&KLQD (2002, 2013 and 2017)

7KHWUHQGRIXQGHU¿YH stunting in China

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Methods and models

Data products

Cases

Targets

List of Cases on Big Earth Data for SDGs

List of Cases on Big Earth Data for SDGs xv

Population with easy access to public transportation, by age and gender, with 1 km resolution (2015 and 2018) Series of seven datasets of built-up areas of 433 cities in China (1990-2018)

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

Monitoring and assessing urbanization progress in China

SDG 11.1

SDG 11.2

SDG 11.3

Evaluation dataset for SDG 11.3.1 for 340 prefecture-level cities in China (2000-2010; 2010–2015; 2015-2018)

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Shantytown population ratio and vector boundaries in 27 cities in China (2019)

Estimates of population living in shantytowns as a percentage of main urban district residents in cities in China

Analysis of urban expansion and population growth in China

Semantic segmentation and transfer learning method for shantytown estimation

Sustainable development of water security in provinces (autonomous regions and municipalities) of China (2015)

Comprehensive analysis of the sustainable development of water security in China

SDG 6.1, 6.3, 6.4, 6.6

Extraction method of spatialization of regional population density in China based on machine learning

A new indicator — the ratio of economic growth rate to land consumption rate

Comprehensive analysis method of SDG 6 monitoring indicators

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7KH¿UVWQDWLRQDOVFDOHGDWDVHW of grass/algae distribution in lake ecosystems in China (2000-2010-2019, 30 m resolution)

SDG 6.6

HOHC method to map vegetated wetlands, mangrove forests, and Spartina DOWHUQLÀRUD

Methods and models

Spatial distribution and changes of aquatic vegetation and algae in typical lake ecosystems, China

Data products Datasets of China’s vegetated wetlands (2015), mangrove forests, and Spartina DOWHUQLÀRUD in China (2015 and 2018, 30 m resolution)

Cases

Changes in the spatiotemporal extent of China’s vegetated wetlands

Targets

Comprehensive evaluation of sustainable development of cities in China

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Solution for comprehensive utilization and systematic protection of water resources for similar developing countries around the world

Reference for the Ministry of Water Resources and the Ministry of Ecology and Environment of the People’s Republic of China

Informing policy on China’s implementation of the Ramsar Convention on Wetlands

Decision support

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xvi Big Earth Data in Support of the Sustainable Development Goals (2020): China

An SDG monitoring indicator is proposed for natural protected areas based on remote sensing data—the Human Intervention Degree

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Data products (2006 and 2017) of SDG 11.4.1 and an improved indicator of 51 demonstration sites in China (39 natural protected areas and 12 cultural heritage sites listed as UNESCO designated sites); Datasets of the Human Intervention Degree for four demonstration sites in China Data from monitoring indicators of disaster losses between 2013-2019; data from monitoring reconstruction and sustainability in quake-hit Yushu 4 km spatial resolution water depth data of Typhoon Nida in 2016 and Typhoon Mangkhut in 2018 Population-weighted PM2.5 dataset with a resolution of 1 km in China over 15 years from 2005 to 2019, providing DVFLHQWL¿FEDVLVIRUDVVHVVLQJ the health impact of pollution Social network check-in density in public spaces in China in 2018

Research on Sustainable Development Goals of UNESCO designated sites in China

Monitoring of disaster loss reduction and promoting sustainable development in vulnerable areas in China

Impact assessment of storm surge inundation in Shenzhen

Monitoring and DQDO\]LQJ¿QHSDUWLFXODWH matter (PM2.5) in cities in China

Assessment of urban public space in China

SDG 11.4

SDG 11.6

SDG 11.7

SDG 11.5

Methods and models

Data products

Cases

Targets

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5HÀHFWWKHWHPSRUDODQGVSDWLDO trends of urban-scale air pollution objectively and comprehensively; SURYLGHDVFLHQWL¿FEDVLVIRU evaluating health effects and formulating sustainable urban development strategies rationally

Digital-twin method to assess the impact of storm surges of different intensities on population and economy

Evidence of a large reduction in deaths, people affected, and direct economic loss as a result of disasters in China, effectively promoting sustainable development in disasterprone areas

Evaluation report on the applicability of SDG 11.4.1 indicators and improved indicators

Decision support

(continued)

List of Cases on Big Earth Data for SDGs xvii

SDG 13.2

SDG 13.1

SDG 11.2, SDG 11.3, SDG 11.5, SDG 11.6, SDG 11.7

SDG 11.7

Targets

Integrated assessment of multiple indicators of 340 prefecture-level cities in China Homogenized temperature series datasets Near-term probabilistic forecast datasets for the phenology of China’s major crops under climate change scenarios Glacial area changes on the Qinghai-Tibetan Plateau in the past 30 years

Integrated assessment of SDG 11 indicators at the provincial scale in China

Intensity and frequency of extreme hightemperature events and heatwaves in China

Predicting the impacts of future climate change on the phenology of major crops in China

Reaction of glaciers on the Qinghai-Tibetan Plateau to climate change

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Evaluation of the emergency response capacity for COVID-19 in Wuhan

Xuzhou: A typical case of resource-exhausted cities and regional transformation routes

Share of open public space area in cities in China (2015 and 2018)

Data products

Share of open public space area in cities in China

Cases

Automatic glacier extraction method on a cloud-based platform

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Combination of non-stationary models and parametric/nonparametric methods

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A three-level SDG indicator evaluation system for COVID-19 in Wuhan

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Methods and models

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Informing China’s policy on food production in response to climate change

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Sustainability assessment of major cities in China and a reference for integrated SDG assessments in other regions of China

A transformation path, including changes in concepts, institutional advantage, ecological space, science and technology support, industrial transformation, and integration of mining and local sectors

China’s plan for preventing and controlling COVID-19 presented to the international community.

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Decision support

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xviii Big Earth Data in Support of the Sustainable Development Goals (2020): China

SDG 14.3

SDG 14.2

SDG 14.1

Targets

Deep learning-enabled AI extraction of marine raft culture data

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Datasets from monitoring raft culture in key coastal provinces of China Time-series information on ecological factors such as temperature, salinity, nutrients, dissolved oxygen, etc. in key sea areas and typical transects.

Monitoring changes in raft culture in China’s coastal waters

Eutrophication, hypoxia DQGDFLGL¿FDWLRQLQ China’s coastal waters

Spatially gridded model for risk assessment of algal bloom disasters

Health assessment based on marine ecosystem structures and service functions

Long-term time-series dataset of harmful algal blooms in the Bohai Sea region

Ecosystem elements in Jiaozhou Bay, Daya Bay, and Sishili Bay

Ecosystem health assessment for typical bays in coastal China

Meta-analysis to integrate data and obtain combined effect values

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Methods and models

Risk assessment of harmful algal blooms in the Bohai Sea

Offshore metal enrichment in China

Distribution of marine debris and microplastics in China’s coastal waters

Distribution and variation of marine debris and microplastics in China’s coastal waters

Distribution of metal pollutants in coastal areas of China: using bivalves as bio-indicators

Data products

Cases

6FLHQWL¿FHYLGHQFHIRUWKHGLDJQRVLV and analysis of variability in China’s marine environment under multiple pressures, including human activity and global change

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Risk assessment report and visualization of algal bloom disasters in the Bohai Sea

Informing policymaking on how environmental factors in China’s typical coastal bays contribute to changes in the key elements of ecosystems, to keep the protection of coastal ecosystems anchored in science

Characteristics of metal pollution in China’s coastal zone and provides a reference for industrial development and offshore environmental protection

Regional distribution and patterns of change in pollution from marine debris and microplastics in China’s coastal waters for prevention and control

Decision support

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List of Cases on Big Earth Data for SDGs xix

SDG 15.3

SDG 15.1

Targets

Long time series data on the dynamics of aeolian GHVHUWL¿FDWLRQLQVHPLDULG and surrounding areas of northern China (1975-2015)

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Datasets of soil and water conservation in the Loess Plateau, including remote VHQVLQJDQG¿HOGREVHUYDWLRQ data

Large-scale vegetation restoration and soil & water conservation in the Loess Plateau of China in the past 20 years

Evaluating aeolian GHVHUWL¿FDWLRQSURFHVVHV and restoration in the semi-arid region and its surrounding areas of northern China

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Map of wetland distribution nationwide with 30 m spatial resolution from 2000 to 2015

Simulation method for the distribution of rare and endangered plants

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Suitability of no net loss as a wetland protection target

Datasets on the 3Cs for the conservation and sustainable use of biodiversity in China

Three conditions for biodiversity conservation and sustainable use in China

Time-series remotely sensed image synthesis based on multiple rules and an integrated spatial-temporalspectral framework for forest type feature extraction

Proportion of important sites of rare and endangered biodiversity covered by protected areas at the kilometer grid scale in China

Distribution of forest types in the Yangtze River basin for 2018 (10 m resolution)

Forest type distribution along the Yangtze River basin, China

Methods and models

Proportion of important sites for terrestrial, freshwater, and mountain biodiversity that are covered by protected areas

Data products

Cases

Assessing the processes and drivers RIDHROLDQGHVHUWL¿FDWLRQLQVHPLDULG and surrounding areas of northern China over last four decades, to VXSSRUWGHVHUWL¿FDWLRQFRQWURO

Informing soil erosion control by identifying spatial variations in the effectiveness of soil conservation on the Loess Plateau

6FLHQWL¿FEDVLVIRUWKHIRUPXODWLRQ and improvement of national wetland protection and restoration policies

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Localization of the three global conditions and strategies for ecosystem conservation and restoration in the context of the National Projects for the Conservation and Restoration of Major Ecosystems

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Decision support

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xx Big Earth Data in Support of the Sustainable Development Goals (2020): China

SDG 15.8

Plant diversity based on UHPRWHVHQVLQJLQ¿YH\HDU intervals from 2000 to 2017 in nature reserves China’s pinewood nematode GLVHDVHGLVWULEXWLRQVKDSH¿OH format) from 2015 to 2019

Spatiotemporal changes in plant diversity in four grassland nature reserves in northern China

Distribution and development of pinewood nematode disease in China

Risk distribution and conservation efforts of China’s plant diversity

China’s plant diversity: risks and conservation strategies

SDG 15.3

SDG 15.5

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30 m resolution map of the spatial change of soil salt content in Kenli County in the Yellow River Delta (2015 and 2019)

Salinization development and countermeasures in the Yellow River Delta

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A method for identifying the threats and stressors to biodiversity, GHYHORSHGDQGUH¿QHG

Methods and models

Data products

Cases

Targets

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Proactive conservation strategies and restoration solutions to protect the habitats of species by identifying gaps LQ&KLQD¶VÀRUDGLYHUVLW\FRQVHUYDWLRQ

Analysis of the temporal and spatial patterns, changes, and driving factors of salinization in Kenli County to propose prevention and control suggestions for key areas

Decision support

(continued)

List of Cases on Big Earth Data for SDGs xxi

Contents

i

Foreword

v

Preface

ix

Executive Summary

xv

List of Cases on Big Earth Data for SDGs

Chapter 1

Challenges to Implementing the SDGs / 2

Introduction

Big Earth Data / 3 Big Earth Data in Support of SDGs / 4

Background / 8 Main Contributions / 10 Case Study / 11 Chapter 2 SDG 2 Zero Hunger

7KHWUHQGRIXQGHUI௘LYHVWXQWLQJLQ&KLQD 3RWHQWLDOIRUVXVWDLQDEOHFURSODQGLQWHQVLI௘LFDWLRQLQ&KLQD Potential for improvement of the sustainability of crop production in China / 22 Monitoring and early warning of agri-product price volatility in China / 28 Summary / 34

Contents

Background / 38 Main Contributions / 39 Case Study / 41 Chapter 3 SDG 6 Clean Water and Sanitation

Spatiotemporal patterns of water transparency in China’s lakes / 41 :DWHUXVHHII௘LFLHQF\DQGLWVFKDQJHLQ&KLQD Evaluation of water stress in the Shule River Basin in Northwest China / 53 Changes in the spatiotemporal extent of China’s vegetated wetlands / 59 Spatial distribution and changes of aquatic vegetation and algae in typical lake ecosystems, China / 65 Comprehensive analysis of the sustainable development of water security in China / 71 Summary / 77

Background / 80 Main Contributions / 81 Case Study / 84 Chapter 4 SDG 11 Sustainable Cities and Communities

Estimates of population living in shantytowns as a percentage of main urban district residents in cities in China / 84 Proportion of the population with easy access to public transportation in China / 90 Monitoring and assessing urbanization progress in China / 96

xxiii

xxiv

Big Earth Data in Support of the Sustainable Development Goals (2020): China

Analysis of urban expansion and population growth in China / 104 Research on Sustainable Development Goals of UNESCO designated sites in China / 111 Monitoring of disaster loss reduction and promoting sustainable development in vulnerable areas in China / 119 Impact assessment of storm surge inundation in Shenzhen / 127 0RQLWRULQJDQGDQDO\]LQJI௘LQHSDUWLFXODWHPDWWHU302.5) in cities in China / 134 Assessment of urban public space in China / 139 Share of open public space area in cities in China / 148 Evaluation of the emergency response capacity for COVID-19 in Wuhan / 153 Xuzhou: A typical case of resource-exhausted cities and regional transformation routes / 167 Integrated assessment of SDG 11 indicators at the provincial scale in China / 180 Summary / 186

Background / 190 Main Contributions / 191 Case Study / 192 Chapter 5 SDG 13 Climate Action

Intensity and frequency of extreme high-temperature events and heatwaves in China / 192

Contents

Predicting the impacts of future climate change on the phenology of major crops in China / 198 Reaction of glaciers on the Qinghai-Tibetan Plateau to climate change / 203 Summary / 208

Background / 212 Main Contributions / 213 Case Study / 215 Chapter 6 SDG 14 Life below Water

Distribution and variation of marine debris and microplastics in China’s coastal waters / 215 Distribution of metal pollutants in coastal areas of China: using bivalves as bio-indicators / 223 Ecosystem health assessment for typical bays in coastal China / 229 Risk assessment of harmful algal blooms in the Bohai Sea / 235 Monitoring changes in raft culture in China’s coastal waters / 242 (XWURSKLFDWLRQK\SR[LDDQGDFLGLI௘LFDWLRQLQ China’s coastal waters / 247 Summary / 253

xxv

xxvi

Big Earth Data in Support of the Sustainable Development Goals (2020): China

Background / 256 Main Contributions / 258 Case Study / 260 Chapter 7 SDG 15 Life on Land

Forest type distribution along the Yangtze River basin, China / 260 Three conditions for biodiversity conservation and sustainable use in China / 265 Proportion of important sites for terrestrial, freshwater, and mountain biodiversity that are covered by protected areas / 272 Suitability of no net loss as a wetland protection target / 277 Large-scale vegetation restoration and soil & water conservation in the Loess Plateau of China in the past 20 years / 282 (YDOXDWLQJDHROLDQGHVHUWLI௘LFDWLRQSURFHVVHVDQGUHVWRUDWLRQLQWKH semi-arid region and its surrounding areas of northern China / 288 Salinization development and countermeasures in the Yellow River Delta / 295 China’s plant diversity: risks and conservation strategies / 300 Spatiotemporal changes in plant diversity in grassland nature reserves in northern China / 305 Distribution and development of pinewood nematode disease in China / 311 Summary / 317

Contents

Chapter 8

Summary and Prospects / 320

References / 324

Acronyms / 344

xxvii

2

Challenges to Implementing the SDGs

3

Big Earth Data

4

Big Earth Data in Support of SDGs

Chapter 1

Introduction

In 2015, the United Nations Sustainable Development Summit adopted the 2030 Agenda for Sustainable Development, which proposed 17 Sustainable Development Goals (SDGs) covering economic, social, and environmental aspects. These goals represent the direction of QDWLRQDOGHYHORSPHQWDQGLQWHUQDWLRQDOFRRSHUDWLRQ,QWKHDOPRVWI௘LYH\HDUVVLQFHWKHLUDGRSWLRQ PRQLWRULQJDQGHYDOXDWLRQRIWKH6'*VKDYHEHHQFRQVWUDLQHGE\DODFNRIGDWDYDU\LQJFDSDFLWLHV DQGLQGLFDWRUVWKDWDUHERWKLQWHUWZLQHGDQGPXWXDOO\UHVWULFWLYH7KHVROXWLRQVWRWKHVHSUHVVLQJ LVVXHVDUHHQWUXVWHGWRVFLHQWLI௘LFDQGWHFKQRORJLFDOLQQRYDWLRQ7KH&$6(DUWKGUDZLQJRQ%LJ (DUWK'DWD¶VVWUHQJWKVLQPXOWLVFDOHDQGQHDUUHDOWLPHSURFHVVLQJDQGV\VWHPLQWHJUDWLRQKDV UHOHDVHGDQQXDOVFLHQWLI௘LFHYLGHQFHEDVHGPRQLWRULQJUHVXOWVIRUVL[6'*VLQFOXGLQJ6'*=HUR +XQJHU 6'*&OHDQ:DWHUDQG6DQLWDWLRQ 6'*6XVWDLQDEOH&LWLHVDQG&RPPXQLWLHV  6'*&OLPDWH$FWLRQ 6'*/LIHEHORZ:DWHU DQG6'*/LIHRQ/DQG 7KHUHSRUWV represent a concrete contribution to the implementation of SDGs.

Challenges to Implementing the SDGs 7KH*OREDO,QGLFDWRU)UDPHZRUNIRUWKH6XVWDLQDEOH'HYHORSPHQW*RDOVDQG7DUJHWVRI the 2030 Agenda for Sustainable DevelopmentZDVDGRSWHGE\WKH8QLWHG1DWLRQV81  LQ7KLVIUDPHZRUNVHUYHVDVDYROXQWDU\SUHOLPLQDU\V\VWHPIRUWKH0HPEHU6WDWHV WRPRQLWRUSURJUHVVWRZDUGVWKHLPSOHPHQWDWLRQRIWKH6'*V7KHIUDPHZRUNLVVXEMHFWWR UHJXODUUHI௘LQHPHQWDQGXSGDWHVDQGIDFHVWKHIROORZLQJLVVXHV  /DFNRIGDWD7KHSUHYLRXVVLWXDWLRQUHODWHGWRWKHDEVHQFHRIHYDOXDWLRQPHWKRGV DQGGDWDKDVLPSURYHGIRUDOOLQGLFDWRUVRYHUWKHSDVWI௘LYH\HDUV+RZHYHUGHVSLWHWKH DYDLODELOLW\RIPHWKRGVWKHUHLVVWLOODODFNRIGDWDIRURIDOOLQGLFDWRUV,QUHJDUGWR LQGLFDWRUVZKHUHERWKPHWKRGVDQGGDWDDUHDYDLODEOHWKHUHVXOWVDUHODUJHO\PHDVXUHGYLD VWDWLVWLFVZLWKRXWWKHVXSSRUWRIVSDWLDOGLVWULEXWLRQLQIRUPDWLRQ7KHUHIRUHLWLVQHFHVVDU\ WRDFTXLUHVSDWLDOGDWDWKDWLVREMHFWLYHDQGDFFXUDWHZKLFKLQFOXGLQJYDULRXVVSDWLDOVFDOHV 6SHFLI௘LFDOO\WKHGDWDWKDWLVFROOHFWHGIRUVFLHQWLI௘LFPHDQVFDQEHXVHGWRUHJXODUO\DQG TXDQWLWDWLYHO\DVVHVVFKDQJHVLQWKHQDWXUDOHQYLURQPHQWDFFXUDWHO\LGHQWLI\WKHVSDWLDO

Chapter 1

Introduction

position of disasters, and predict their future trends. This refers to monitoring cases such as H[WUHPHKLJKWHPSHUDWXUHVDQGKHDWZDYHVWKHKLJKIUHTXHQF\RII௘LUHVRFHDQDFLGLI௘LFDWLRQ LQFUHDVHGHXWURSKLFDWLRQFRQWLQXHGODQGGHJUDGDWLRQUHGXFHGELRGLYHUVLW\DQGLQFUHDVHG environmental impact on agricultural production.  ,PEDODQFHLQFDSDFLWLHV'HYHORSLQJFRXQWULHVDUHFRQVWUDLQHGLQWKHLUDELOLW\WR UHJXODUO\DQGTXDQWLWDWLYHO\FROOHFWDQGDQDO\]HGDWDE\WKHLUOHYHORIHFRQRPLFJURZWKDQG WKHFDUU\LQJFDSDFLW\RIUHVRXUFHVDQGWKHHQYLURQPHQW7KHODFNRIGDWDKDVREIXVFDWHG VHULRXVLVVXHVVXFKDVWKHKLJKUDWLRRIVWXQWLQJWKHLQDGHTXDF\RIXUEDQKRXVLQJDQG SXEOLFVSDFHZHDNGLVDVWHUUHVLOLHQFHWKHODFNRIDFFHVVWRVDIHGULQNLQJZDWHUDQGWKH RYHUXVHRIIRUHVWV%LJ(DUWK'DWDFDQEHXVHGWRFROOHFWREMHFWLYHGDWDDWJOREDOUHJLRQDO DQG ORFDO VFDOHV LQ D WLPHO\ DFFXUDWH DQG FRPSUHKHQVLYH ZD\7KLV FDQ LPSURYH WKH FRPSDWLELOLW\DQGFRPSDUDELOLW\RIGDWDVRWKDW³QRRQHLVOHIWEHKLQG´LQUHJDUGWRWKH information that is essential for SDG achievement.  7KHLQGLFDWRUVDUHLQWHUWZLQHGEXWPXWXDOO\UHVWULFWLYH6'*LQGLFDWRUVDUHZLGH UDQJLQJ ORQJWHUP DQG LQWHUWZLQHG7KHVH GLYHUVH DQG FRPSOH[LQGLFDWRUV FRQVLVW RI multiple tiers that come together to form a coherent, feasible whole. There is an urgent need to create methods and models for effective monitoring and evaluation based on FRPSDWLEOHDQGTXDQWLI௘LDEOHGDWD

Big Earth Data 7KH8QLWHG1DWLRQVODXQFKHGWKH7HFKQRORJ\)DFLOLWDWLRQ0HFKDQLVP7)0 WRDGGUHVV WKHDERYHPHQWLRQHGLVVXHVDQGFKDOOHQJHVWKURXJK6FLHQFH7HFKQRORJ\DQG,QQRYDWLRQ 67, 7KH LQLWLDWLYH FRPELQHV WKH FROOHFWLYH ZLVGRP RI WKH VFLHQWLI௘LF DQG EXVLQHVV FRPPXQLWLHVDQGVWDNHKROGHUV %LJ(DUWK'DWDUHIHUVWRODUJHGDWDVHWVLQWKH(DUWKVFLHQFHVWKDWIHDWXUHVSDWLDODWWULEXWHV HVSHFLDOO\WKHPDVVLYH(DUWKREVHUYDWLRQGDWDJHQHUDWHGE\VSDFHWHFKQRORJ\*XRHWDO  6XFKGDWDLVPDLQO\SURGXFHGDWODUJHVSDWLDOVFDOHVE\VFLHQWLI௘LFGHYLFHVGHWHFWLRQ HTXLSPHQWVHQVRUVVRFLRHFRQRPLFREVHUYDWLRQVDQGFRPSXWHUVLPXODWLRQSURFHVVHV /LNHRWKHUW\SHVRIELJGDWD %LJ(DUWK'DWDLVPDVVLYH PXOWLVRXUFHKHWHURJHQHRXV PXOWLWHPSRUDO PXOWLVFDOH DQG QRQVWDWLRQDU\ 0RUHRYHU %LJ(DUWK 'DWD KDV VWURQJ VSDWLRWHPSRUDODQGSK\VLFDOFRUUHODWLRQVDQGWKHGDWDJHQHUDWLRQPHWKRGVDQGVRXUFHVDUH FRQWUROODEOH%LJ(DUWK'DWDVFLHQFHLVLQWHUGLVFLSOLQDU\HQFRPSDVVLQJQDWXUDOVFLHQFHV VRFLDOVFLHQFHVDQGHQJLQHHULQJ,WV\VWHPDWLFDOO\VWXGLHVWKHFRUUHODWLRQDQGFRXSOLQJRI

3

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

WKH(DUWKV\VWHPEDVHGRQGDWDDQDO\VLV(DUWKLVREVHUYHGDQGVWXGLHGE\VLPXOWDQHRXVO\ HPSOR\LQJ ELJ GDWD DUWLI௘LFLDO LQWHOOLJHQFH DQG FORXG FRPSXWLQJ WR XQGHUVWDQG WKH FRPSOH[LQWHUDFWLRQVDQGGHYHORSPHQWSURFHVVHVEHWZHHQ(DUWK¶VQDWXUDOV\VWHPDQGWKH KXPDQVRFLDOV\VWHP2YHUDOO%LJ(DUWK'DWDFDQPDNHDQLPSRUWDQWFRQWULEXWLRQWRWKH UHDOL]DWLRQRI6'*V ,Q  &$6 ODXQFKHG WKH &$6(DUWK ZKLFK UHSUHVHQWV DQ HQGHDYRU WR DFFHOHUDWH WKHWUDQVIRUPDWLRQIURP(DUWKGDWDV\VWHPVDQGWR'LJLWDO(DUWK,WSURPRWHVWKHVKDULQJ RIGDWDNQRZOHGJHDQGH[SHULHQFHDFURVVWKHZRUOGDQGVXSSRUWVVFLHQWLI௘LFGLVFRYHU\ WHFKQRORJLFDOLQQRYDWLRQDQGSROLF\PDNLQJ%LJ(DUWK'DWDVFLHQFHLVDVROXWLRQIRUFURVV VHFWRUDODQGPXOWLGLVFLSOLQDU\FROODERUDWLRQ*XRHWDOD ,WLVDQLQQRYDWLRQXQGHU WKH7)0WKDWFDQVXSSRUWWKHDFKLHYHPHQWRI6'*V*XRHWDOE 

Big Earth Data in Support of SDGs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ÀRSIRUKLJKSHUIRUPDQFHFRPSXWLQJDQGELJGDWDSURFHVVLQJLQWKH FORXG7KH%LJ(DUWK'DWDV\VWHP¶VIXOOFDSDELOLWLHVUDQJLQJIURPGDWDWRLQIRUPDWLRQ YLVXDOL]DWLRQWRQXPHULFDOVLPXODWLRQFDQVXSSRUWG\QDPLFPRQLWRULQJDQGPDFUROHYHO SROLF\PDNLQJIRU6'*V

Chapter 1

Introduction

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5

8

Background

10 Main Contributions 11

Case Study

34 Summary

Chapter 2

SDG 2 Zero Hunger

8

Big Earth Data in Support of the Sustainable Development Goals (2020): China

Background 6'*  =HUR +XQJHU  DLPV WR HQG KXQJHU DFKLHYH IRRG VHFXULW\ LPSURYH QXWULWLRQ DQG promote sustainable agriculture. It is the foundation of global sustainable development. Global IRRGVHFXULW\KDVPDGHUHPDUNDEOHSURJUHVV²IRRGSURGXFWLRQKDVPRUHWKDQGRXEOHGVLQFHWKH PLGGOHRIWKHODVWFHQWXU\)$2D DQGWKHSURSRUWLRQRIXQGHUQRXULVKHGSHRSOHKDVGURSSHG IURPLQWRLQ81D )LJXUHD @$PRQJWKH WKUHHELYDOYHJURXSVWKHSURSRUWLRQRIR\VWHUVI௘LWWLQJLQWRORZDQGWHUULEOHTXDOLW\VWDQGDUGVZDVDV KLJKDVZKLFKZDVVLJQLI௘LFDQWO\ODUJHUWKDQWKHRWKHUWZRJURXSVRIELYDOYHV>)LJXUHE @ Further analysis also showed obviously higher concentrations of Cd, Cu, Zn, and Pb in oysters

Figure 6.6

N

N

Cd High Medium Low Terrible

Pb High Medium Low Terrible

N

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Cu High Medium Low Terrible

Zn High Medium Low Terrible

Distribution of metal pollution in coastal areas of China with oysters as a bio-indicator

Chapter 6

SDG 14 Life below Water

WKDQWKRVHLQWKHRWKHUWZRELYDOYHV>)LJXUHF @ZKLFKPD\EHGXHWRWKHLUUHODWLYHO\ODUJH VL]HVWURQJI௘LOWUDWLRQFDSDFLW\DQGKLJKEDFNJURXQGFRQFHQWUDWLRQV7KHVWURQJHUELRDFFXPXODWLRQ FDSDFLW\RIR\VWHUVIRU&XDQG=QZDVDOVRFRQI௘LUPHGE\WKH0XVVHO:DWFK3URJUDPFRQGXFWHGE\ NOAA (Kimbrough et al., 2008). With oysters, Cd accumulation was mainly assigned to the medium-quality standard, and the Pearl River Delta, with its developed industry, was the typical area of Cd pollution. Pb accumulation was generally slight and mainly belonged to the medium-quality standard; Cu DQG =Q FRQFHQWUDWLRQV SULPDULO\ I௘LW LQWR WKH ORZ DQG WHUULEOH TXDOLW\ VWDQGDUGV 6HULRXV &X pollution areas primarily occurred in the Yangtze River Delta and the Pearl River Delta, while severe Zn contamination was mainly centered in the Pearl River Delta. As a gathering place for the light industry, the Pearl River Delta has many enterprises engaged in hardware, chemical, and electroplating; therefore, serious metal pollution might result from the lack of standardized treatment of waste metals (Figure 6.6).

Highlights This case analyzed a dataset of 978 metal concentrations, involving 231 sampling sites along Chinese coastal areas, three groups of marine bivalves (oysters, mussels, and clams)DQGIɧLYHFRPPRQWUDFHPHWDOVFDGPLXP(Cd), lead (Pb), mercury (Hg), copper (Cu) and zinc (Zn). A meta-analytic approach was employed for data integration and calculation of the combined effect size. The metal pollution status of coastal areas of China was evaluated following the Marine Biological Quality Standard of China. The results showed that the concentrations of Cd, Cu, Zn, and Pb LQ R\VWHUV ZHUH VLJQLIɧLFDQWO\ KLJKHU WKDQ RWKHU JURXSV RI ELYDOYHV$ ODUJH SURSRUWLRQRI&XDQG=QDFFXPXODWLRQVZHUHRI´ORZµDQG´WHUULEOHµTXDOLWLHV ZKLOHPRVW&G3EDQG+JDFFXPXODWLRQVIɧLWLQWRKLJKDQGPHGLXPTXDOLW\ VWDQGDUGV7KH3HDUO5LYHU'HOWDH[KLELWHGWKHPRVWVHULRXVPHWDOSROOXWLRQ indicated by bivalves. This case revealed the distribution characteristics of metal pollution in Chinese coastal areas and provided the basis for decision-makers to form strategies and policies for mariculture layout, elimination of obsolete metal production capacity, environmental monitoring, and pollutant emissions. In addition, this case also provided a reference for national marine management departments to formulate relevant standards.

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Outlook Based on the published literature, this case generated datasets of metal concentrations and biological quality of bivalves in the coastal areas of China, revealing the metal pollution status and its distribution characteristics. This case provided data to support the government to perform a rational layout of bivalve farming and make policies for environmental monitoring and pollutant emission. The current Marine Biological Quality (GB18421—2001) that selects marine bivalves as bioindicators was proposed and interpreted by the State Oceanic Administration of China in 2001. This standard together with another seawater quality standard was concurrently executed for marine environmental assessment in this study. Considering that oysters have a strong capacity of metal bioaccumulation in the natural environment, the threshold value of low-quality oysters ZDVLPSURYHG+RZHYHUWKLVDQDO\VLVLQGLFDWHGWKDWWKHSURSRUWLRQRIR\VWHUVI௘LWWLQJLQWRORZDQG terrible quality standards (45%) was still higher than that of two other groups of bivalves. Previous VWXGLHVKDYHFRQI௘LUPHGWKDWWKHUHZHUHREYLRXVGLIIHUHQFHVLQPHWDODFFXPXODWLRQDPRQJGLIIHUHQW groups of bivalves at the same sampling site (Tang et al., 2009; Lu et al., 2017; Lu and Wang,  7KHUHIRUHFODVVLI௘LFDWLRQEDVHGRQWKHFXUUHQW0DULQH%LRORJLFDO4XDOLW\VHHPVELDVHG because the evaluation might be species dependent. Overall, this case will provide a reference for WKHVHOHFWLRQRIHQYLURQPHQWDOPRQLWRULQJRUJDQLVPVDQGVHWWLQJWKHOLPLWVRITXDOLW\FODVVLI௘LFDWLRQ

Chapter 6

SDG 14 Life below Water

Ecosystem health assessment for typical bays in coastal China Target SDG 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems WRDYRLGVLJQLIɧLFDQWDGYHUVHLPSDFWVLQFOXGLQJE\VWUHQJWKHQLQJWKHLUUHVLOLHQFHDQG take action for their restoration in order to achieve healthy and productive oceans

Scale Representative bays of China

Background The ocean covers nearly three-quarters of our planet’s surface and plays a vital role, acting as the blue lung of the planet. Healthy oceans are vital for our societies and the future of our planet, though industry, agriculture, aquaculture, tourism, and other human activities bring compounding pressures on global coastal ecosystems, and result in various environmental problems including coastal ecosystem degradation, increasing ecological disasters and decline of ecosystem services. SDG 14.2 aims to “By 2020, sustainably manage and protect marine and coastal ecosystems WRDYRLGVLJQLI௘LFDQWDGYHUVHLPSDFWVLQFOXGLQJE\VWUHQJWKHQLQJWKHLUUHVLOLHQFHDQGWDNHDFWLRQ for their restoration in order to achieve healthy and productive oceans”. Ecosystem-based coastal management could provide effective solutions for the protection of marine assets and resources and sustainable uses, and therefore represents the future trend in marine management (Rapport, 1995; Polland, 1998). Marine ecosystem health assessments are instrumental in ocean governance and the development DQGXVHRIRFHDQVDQGVHDVSURYLGHDQLPSRUWDQWVFLHQWLI௘LFEDVLVIRUWKHSURWHFWLRQRIPDULQH ecosystems and environment and ecological management, and help us move forward on a range of issues related to the marine environment and resource protection. In 2019, targeting both the sustainable management of marine and coastal ecosystems and achieving marine ecosystem health, the CASEarth took Jiaozhou Bay as a case study and developed a primary marine ecosystem health assessment framework. In 2020, we improved and repeated our ecosystem health assessment methodology for various coastal areas including Sishili Bay and Daya Bay. Joint health assessments were performed in order to explore potential management solutions for SDG 14.2.

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Data used in the case Datasets on hydrological, chemical, planktonic, benthic, microbiological and other environmental/ ecological factors in representative bays available on the National Ecosystem Research Network of China (CNERN) for the period of January 2007 to December 2019.

Method /LQNHG WR 6'*  WKLV VWXG\ DLPHG WR UHI௘LQH WKH PHWKRGRORJ\ RI DVVHVVPHQW LQ OLJKW RI the characteristics of bay ecosystems, analyze the current state and variation trends of different ecosystem elements, and adjust and select parameters against the SDG indicators. The study conducted machine learning-enabled data mining to introduce additional threshold criteria or improve existing ones for assessment. Finally, a report card assessment model was developed in support of policymaking on coastal ecosystem management (Logan et al., 2020).

Results and analysis For the purposes of this study, Sishili Bay, Jiaozhou Bay, and Daya Bay were chosen as representative samples of different ecological environments and human activity. Sishili Bay is located in the northern part of the Yellow Sea, and the bay itself and its surrounding areas are home to agriculture, industry, aquaculture, port services, and so on, with a high incidence of red tides in the bay. Jiaozhou Bay is located in the southern part of the Yellow Sea and is subjected to a range of anthropogenic stressors, including port services, aquaculture, and a long bridge arching over the bay. Daya Bay is located in the South China Sea, home to the Daya Bay Nuclear Power Base with WKUHHXQLWVLQRSHUDWLRQ-RLQWKHDOWKDVVHVVPHQWVZHUHXVHGWRSURYLGHVFLHQWLI௘LFLQIRUPDWLRQIRU the protection and management of different marine areas. 1. A general mapping of the health of bay ecosystems 7KHI௘LQGLQJVLQGLFDWHWKDWWKHHFRV\VWHPVLQ6LVKLOL%D\-LDR]KRX%D\DQG'D\D%D\DUHLQJRRG health at present (Grade “B”). From 2007 to 2018 the health of the Jiaozhou Bay ecosystem was stable with moderate improvement. The overall ecosystem health of Sishili Bay from 2015 to 2019 and of Daya Bay from 2013 to 2018 was stable. Since 2016, there was a moderate improvement in WKHHFRV\VWHPKHDOWKRI'D\D%D\7KHDVVHVVPHQWI௘LQGLQJVDUHVKRZQLQ)LJXUH

Chapter 6

SDG 14 Life below Water

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Figure 6.7

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Ecosystem health mapping results in selected bays, 2007-2019

2. Assessment of individual key elements of typical bay ecosystems The assessment of individual elements indicates the health condition of an ecosystem at different levels. For instance, the sea surface Chlorophyll a (Chl a) concentration and the Bacillariophyta Pyrrophyta Ratio (BP ratio) can respectively represent the primary productivity level and

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

phytoplankton community structure in a given area to some extent and are therefore important indicators of its ecosystem health. We assessed the health of the three bays by measuring those two values and the results are as follows. The health of the phytoplankton community structure in Sishili Bay, as indicated by the BP ratio, showed a trend of sustained improvement and its primary productivity remained good and stable. The phytoplankton community structure of Daya Bay improved from 2014, but its primary productivity was rated the lowest among the three bays, which might be attributable to its nutrient level. Jiaozhou Bay was rated slightly lower than the other two in phytoplankton community structure health while its primary productivity health remained good. See Figure 6.8 for details. 1.0

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Year (b) Chlorophyll a Study Areas Daya Bay

Jiaozhou Bay

Sishili Bay

Figure 6.8 Assessment results of key elements of typical bays (grades and levels as shown in Figure 6.7)

3. Analysis of the contribution of environmental factors to changes in key elements of ecosystems Machine learning was used to study inner relationships between coastal ecological factors and to study how key environmental factors contributed to the variations in environmental elements using data mining techniques (De’Ath, 2007). Higher contribution indicates a greater role in

Chapter 6

SDG 14 Life below Water

environmental element variations. Taking the Bacillariophyta (dominant species of phytoplankton) as an exemple, we analyzed relative contribution rates of environmental factors to Bacillariophyta abundance in the bays (Figure 6.9). The results indicate that the greatest contribution to Bacillariophyta abundance variations came from nutrients in Jiaozhou Bay and Sishili Bay while water temperature was the number one contributing factor to the Bacillariophyta abundance variations in Daya Bay They are consistent with the characteristics of the three bays studied. In both Jiaozhou Bay and Sishili Bay, the ecological elements were closely correlated with nutrients EHFDXVHRIWKHKLJKHUFRQFHQWUDWLRQRIQXWULHQWVDVDUHVXOWRIVLJQLI௘LFDQWLPSDFWVIURPKXPDQ activity such as industry, agriculture, and aquaculture. On the other hand, the water temperature variations in Daya Bay contributed the most to changes in Bacillariophyta abundance, probably as a direct consequence of the heated water discharged from the nuclear power plants located around Daya Bay.

DIN

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5HODWLYHLQÀXHQFH

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(a) Jiaozhou Bay

(b) Sishili Bay

(c) Daya Bay

Figure 6.9

Contributions of environmental factors to the abundance of Bacillariophyta, a dominant species of phytoplankton in

Highlights The ecosystems of Jiaozhou Bay, Sishili Bay, and Daya Bay are in relatively good health at present. For more than a decade from 2007 to 2019, the overall health of the Jiaozhou Bay ecosystem was stable with some upticks, while that of Sishili Bay and Daya Bay remained stable. The health condition of Daya Bay showed a slight improvement from 2016 onward.

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Outlook This case study focused on SDG 14.2 and improved the coastal ecosystem health assessment methodology of the CASEarth by studying combined traits of the coastal ecosystems such as structure, services, and functions. We performed a number of assessments jointly conducted in three representative coastal bays in China, analyzing the current status and variation trends of these coastal ecosystems, comparing their key health elements, and investigating major contributing factors to changes in the state of marine ecosystems. The case study provided FRDVWDOHQYLURQPHQWDOGDWDVHWVDWWKHQDWLRQDOFRDVWDOVFDOHDQGSURYLGHGDVFLHQWLI௘LFEDVLVIRUWKH protection of marine ecosystem management by translating monitoring, observation, and research results into information that can be understood easily by policymakers. This research will be further improved through a combination of an expert diagnostic model that would be able to make the diagnosis more objective by analyzing the stress factors affecting ecosystem health. Customized decision support systems that include analytical tools catering to different users and use scenarios will also be developed, in support of coastal environmental protection and management.

Chapter 6

SDG 14 Life below Water

Risk assessment of harmful algal blooms in the Bohai Sea Target SDG 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems WRDYRLGVLJQLIɧLFDQWDGYHUVHLPSDFWVLQFOXGLQJE\VWUHQJWKHQLQJWKHLUUHVLOLHQFHDQG take action for their restoration in order to achieve healthy and productive oceans.

Scale Bohai Sea, China

Background Harmful algal blooms are proliferations of certain noxious and/or toxic microalgae, macroalgae, and cyanobacteria, regardless of their concentration, with negative impacts on aquatic ecosystems and human health and wellbeing (GlobalHAB, 2017). Blooms of cyanobacteria, such as Microcystis, are typical harmful algal blooms in freshwater. In marine environments, “red tides” and “brown tides” formed by microalgae, as well as “green tides” and “golden tides” formed by macroalgae, are common harmful algal blooms (Anderson et al., 2012; Smetacek and Zingone, 2013). Harmful algal blooms in the sea can have negative impacts on human health, ecosystem health, and socio-economic development through a variety of mechanisms like producing toxins, affecting marine life, damaging marine ecosystems, and degrading the properties of seawater (Anderson et al., 2015). Therefore, harmful algal blooms are often considered a type of marine ecological disaster. The number of harmful algal blooms recorded in the coastal waters of China has increased notably in the last several decades. According to data in the Bulletin of Marine Environmental Quality and other references, only a small number of harmful algal blooms were recorded in China before the 1990s, and their scale was generally less than several hundred square kilometers. After 2000, however, their frequency increased and the affected area also expanded. About 50-80 events have been recorded each year, and the total area affected amounted to thousands of square kilometers. A single bloom could last for more than one month. The Bohai Sea, a semi-enclosed LQODQGVHDZLWKRQO\WKH%RKDL6WUDLWFRQQHFWLQJWRWKH2,000

Distribution of harmful algal blooms (1952-2017) in the Bohai Sea

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

FDXVDWLYHVSHFLHVRIKDUPIXODOJDOEORRPVH[KLELWHGWUHQGVRIGLYHUVLI௘LFDWLRQDQGPLQLDWXUL]DWLRQ as a whole. An increasing number of toxic/noxious microalgae led to the formation of harmful algal blooms. Haptophyte Phaeocystis globosa and pelagophyte Aureococcus anophagefferens, for example, became major bloom-forming microalgae and led to serious damage to aquaculture LQGXVWU\ *,6 ZDV XVHG WR DQDO\]H WKH VSDWLDO SDWWHUQ DQG KRWVSRWV I௘LQGLQJ WKDW EORRPV ZHUH mainly recorded in the three bays of the Bohai Sea, i.e., Bohai Bay, Laizhou Bay, and Liaodong Bay, as well as in the Bohai Strait (Figure 6.10). Figure 6.11 combines the frequency and scale of the blooms, diversity of bloom-forming microalgae, and the number of toxic bloom-forming microalgae to illustrate the hazards of harmful algal blooms in the Bohai Sea.

N

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Figure 6.11

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Hazards of harmful algal blooms in different areas of the Bohai Sea

Chapter 6

SDG 14 Life below Water

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Hebei

Tianjin

Hebei Vulnerability Low 0 15 30

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90

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Figure 6.12 Vulnerability assessment based on mariculture development in the regions surrounding the Bohai Sea

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Tianjin

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90

Figure 6.13

120 km

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Risk degree Low Moderate low Moderate Moderate high High

Risk assessment results of harmful algal blooms in the Bohai Sea

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Based on the statistical data of seafood production, mariculture production, coastal tourism, and major industrial facilities in the three provinces and one municipality around the Bohai Sea, the vulnerability of the different regions was analyzed and categorized. The results of the assessment based on mariculture are illustrated in Figure 6.12. The risk of harmful algal blooms in the Bohai Sea was assessed (Figure 6.13) based on the results of hazard and vulnerability assessments of the different regions around the sea. The HYDOXDWLRQUHVXOWVLQGLFDWHGWKDWWKHVLWXDWLRQLPSURYHGVLJQLI௘LFDQWO\LQUHFHQW\HDUV7KHULVNVLQ Bohai Bay, the coastal waters of Qinhuangdao, and Laizhou Bay were higher compared to the other regions. The risks in the coastal waters of Qinhuangdao and Bohai Bay were mainly due to the hazards of harmful algal blooms. The coastal waters of Qinhuangdao, in particular, were affected by the brown tides of Aureococcus anophagefferens. In contrast, the risk in Laizhou Bay was mainly due to the development of the mariculture industry, which is vulnerable to harmful algal blooms. The hotspots moved from Bohai Bay to the coastal waters of Qinhuangdao.

Highlights A dataset was compiled of harmful algal blooms (mainly red tides) recorded from 1952 to 2017 in the Bohai Sea. Analysis of the dataset indicated that the causative species of harmful algal blooms in the Bohai Sea exhibited DSSDUHQWWUHQGVRIGLYHUVLIɧLFDWLRQDQGPLQLDWXUL]DWLRQDORQJZLWKLQFUHDVLQJ negative impacts. The hotspots of harmful algal blooms moved from Bohai Bay to the coastal waters of Qinhuangdao recently. A risk assessment method of harmful algal blooms was established, considering both hazards of harmful algal blooms and vulnerability of disaster-bearing bodies. The hazards were assessed and categorized using frequency, scale, diversity of bloom-forming microalgae, and the number of toxic microalgal species. The vulnerability of each disaster-bearing body was evaluated and categorized considering seafood production, mariculture SURGXFWLRQFRDVWDOWRXULVPDQGPDMRULQGXVWULDOIDFLOLWLHV The results of risk assessment of harmful algal blooms in the Bohai Sea indicated that Bohai Bay, coastal waters of Qinhuangdao, and Laizhou Bay had higher risk compared to the other regions. The results could support management and mitigation measures against harmful algal blooms in the Bohai Sea, and provide a protocol for risk assessment in other regions.

Chapter 6

SDG 14 Life below Water

Outlook As an inland sea of China, the Bohai Sea has a comprehensive monitoring system for harmful algal blooms and there are relatively complete datasets to perform risk assessments, the results of which in this study indicated that the situation of harmful algal blooms has improved. The FDXVDWLYHVSHFLHVKRZHYHUVWLOOH[KLELWHGWKHWUHQGVRIGLYHUVLI௘LFDWLRQDQGPLQLDWXUL]DWLRQZLWK increasing negative impacts. Bohai Bay and the coastal waters near Qinhuangdao had relatively high risks, which require more intensive monitoring efforts. The results of this assessment are expected to provide the basis and guidance for the prevention and control of harmful algal blooms in the Bohai Sea in the future. The risk assessment methods in this case study not only take into account the hazards of harmful algal blooms, but also the vulnerability of water bodies. Therefore, the method can comprehensively reflect the risks, which will provide ideas and methods for risk assessment and management in other areas. The case study is expected to contribute to the achievement of SDG 14.2 for sustainable management and protection of marine and coastal ecosystems from the perspective of preventing and controlling harmful algal blooms.

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Monitoring changes in raft culture in China’s coastal waters Target SDG 14.2: By 2020, sustainably manage and protect marine and coastal ecosystems WRDYRLGVLJQLIɧLFDQWDGYHUVHLPSDFWVLQFOXGLQJE\VWUHQJWKHQLQJWKHLUUHVLOLHQFHDQG take action for their restoration in order to achieve healthy and productive oceans.

Scale Coastal waters of China

Background &KLQDKDVWKHPRVWH[WHQVLYHO\GHYHORSHGPDULFXOWXUHLQWKHZRUOGUDQNLQJI௘LUVWLQWHUPVRI aquaculture area (1,579,000 ha in 2008) and total output. Raft culture density is too high due to blind construction of rafts and the erection of suspended rafts on the sea far beyond the capacity of the PDULQHHFRV\VWHP$ODUJHQXPEHURIUHVLGXDOEDLWGHEULVI௘LVKDQGVKULPSIHFHVDQGGHFRPSRVLQJ waste from workers has led to the eutrophication of aquaculture waters, which provides a suitable ecological environment for organisms that are the root cause of red tide. Unplanned mariculture is becoming an important source of pollution in coastal waters, even surpassing land-based pollution. The marine raft culture is an important form of marine aquaculture. It has three subsets, namely, ÀRDWLQJUDIWFXOWXUHFDJHFXOWXUHDQGORQJOLQHFXOWXUH&RPSDUHGWRQHDUVKRUHSRQGVDQGPXGÀDW DTXDFXOWXUHUDIWFXOWXUHLVPRUHGLII௘LFXOWWRPDQDJHEHFDXVHLWLVIXUWKHUDZD\IURPWKHFRDVW Because raft culture tends to be scattered around a large area, traditional monitoring methods, such as onsite measurement using navigation and positioning systems, are labor-intensive, timeconsuming, and unproductive when accurate results of a large area are required. As an important part of Big Earth Data, satellite imagery can help overcome the inadequacies RII௘LHOGVXUYH\VDQGLVDUHOLDEOHVWDWHRIWKHDUWWHFKQRORJ\IRUVDIHDQGHIIHFWLYHPXOWLWHPSRUDO dynamic monitoring of marine raft culture across large areas. Some domestic scholars and scholars abroad (Geng, 2016; Lee et al., 2006; Choe, 2012) have carried out research on raft culture extraction using SAR images. However, due to the low signalWRQRLVHUDWLRVLQJOHIHDWXUHVDQGJUD\VFDOHMXPSLQKRPRJHQHRXVUHJLRQVLWLVGLII௘LFXOWWRH[WUDFW UDIWFXOWXUHDUHDVHII௘LFLHQWO\DQGDXWRPDWLFDOO\)XUWKHUPRUHPRVWRIWKHZRUNLQWKLVDUHDLVLQWKH research and experimental stage, and has not achieved large regional and business applications. Our work is based on the actual needs of marine ecological protection, using remote sensing and innovative technical methods to quickly grasp the status of and changes in marine aquaculture, combined with other business data, such as ecological protection red line data and marine

Chapter 6

SDG 14 Life below Water

eutrophication data. We should assess the development and utilization of marine resources and support the formulation of macro-control policies.

Data used in the case GF-3 and Sentinel-1/2 satellite imagery of China’s coastal waters for 2017, 2019, and 2020, of which the 2017 and 2019 images were for the period April to September of each year and the 2020 images were from April to June. 9DOLGDWHGI௘LHOGVXUYH\GDWDRIPDULQHUDIWFXOWXUHIRU Policy data, including data on red line planning for the protection of marine ecosystems, obtained from ecological protection red line data published by provinces (autonomous regions and municipalities) and countries to provide decision support for the strict monitoring of breeding areas within the ecological protection red line.

Method This study used an AI-enabled remote sensing approach that supports monitoring large areas and FRPSOH[VFHQDULRVRIPDULQHUDIWFXOWXUHIDUPV7KHVSHFLI௘LFSURFHVVLVVKRZQLQ)LJXUH The method of this case study includes three parts: sample database construction, deep learning model optimization, and data analysis. Due to the characteristics of the raft culture area in SAR remote sensing images, such as unclear edges, single image features, and regular geometric arrangement, the U-Net deep learning network was improved by using a multi-core convolution layer, adding texture features, and using a strip pooling layer and attention mechanism, so as to form a deep learning model suitable for raft culture area monitoring. There are large differences in aquaculture in Sentinel-1 images Image perprocessing Orthophoto

Sample labeling

Feature extraction (texture, intensity, etc)

Sample analysis and enhancement Sample library

U-Net

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Spatiotemporal analysis, big data analysis

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

China’s coastal areas, so a large number of representative samples were used to label samples from south to north. The data analysis mainly included the aquaculture area in different time periods, the distribution and change in the aquaculture area, and ecological protection red line data.

Results and analysis In this study, Jiangsu Province and Fujian Province were chosen to be monitored for changes N

Jiangsu Province

N

N

Jiangsu Province

Jiangsu Province

(a) Distribution of offshore raft culture in Jiangsu province in 2017

(b) Distribution of offshore raft culture in Jiangsu province in 2019

N

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244

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1,500

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Figure 6.15

(e) Variation of offshore raft culture in Jiangsu province 2019-2020

New raft culture area

Reduced raft culture area

2017

2019 Year

2020

(f) Statistics of the area of offshore raft culture in Jiangsu province 0 20 40

Observed distribution and change of raft culture in Jiangsu Province

80

120 km

Chapter 6

SDG 14 Life below Water

in their raft culture farms (Figure 6.15 and Figure 6.16). It was found that most raft culture farms were located close to shore. From 2017 to 2020, the total area of raft culture in the two provinces was in the growth phase with a slowdown in the rate of growth. In addition, through the joint analysis of the changes in marine culture and the ecological protection red line, the results show that after the implementation of ecological red line planning, most of the marine aquaculture within the ecological protection red line had basically no change.

N

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Fujian Province

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(a) Distribution of offshore raft culture in Fujian province in 2017 N

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(b) Distribution of offshore raft culture in Fujian province in 2019

(c) Distribution of offshore raft culture in Fujian province in 2020

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800 600 400 200 0

(d) Variation of offshore raft culture in Fujian province 2017-2019 Offshore raft culture area

Figure 6.16

(e) Variation of offshore raft culture in Fujian province 2019-2020

New raft culture area

Reduced raft culture area

2017

2019 Year

2020

(f) Statistics of the area of offshore raft culture in Fujian province 0 20 50

Observed distribution and change of raft culture in Fujian Province

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Highlights Dynamic monitoring datasets (at 30 m spatial resolution) of the distribution of raft culture in key coastal provinces (autonomous regions and municipalities)RI&KLQDLQDQGZHUHJHQHUDWHGIRUWKHIɧLUVW time, aided by deep learning. Findings indicate that the area of marine raft culture in China is still in the growth phase, while the area of raft culture within the boundaries of the coastal ecological conservation red line has remained stable overall.

Outlook Marine aquaculture is not only an important means whereby marine resources are used for human purposes, but also an important factor that has a bearing on the health of marine ecosystems. While monitoring large areas using traditional methods for aquaculture surveys is very challenging, the increasing availability of satellite remote sensing data and other Big Earth Data provides a new way to overcome this problem. Going forward, we shall extend the application of Big Earth Data to relevant domains, determine the optimal remote sensing time slots for different raft culture farms in various marine areas, and improve the accuracy and timeliness of information obtained from marine aquaculture monitoring in terms of type, scale, period, and any change in the aquaculture farm environment.

Chapter 6

SDG 14 Life below Water

(XWURSKLFDWLRQK\SR[LDDQGDFLGLI௘LFDWLRQLQ China’s coastal waters Target 6'*0LQLPL]HDQGDGGUHVVWKHLPSDFWVRIRFHDQDFLGLIɧLFDWLRQLQFOXGLQJWKURXJK HQKDQFHGVFLHQWLIɧLFFRRSHUDWLRQDWDOOOHYHOV

Scale Coastal waters of China

Background Eutrophication is a process in which increasing nutrient charges and changing compositions lead to elevated productivity in aquatic ecosystems. In recent decades, coastal eutrophication has been exacerbated, resulting in frequent occurrences of harmful algal blooms, loss of underwater RUJDQLVPVDQGEHQWKRVVLJQLI௘LFDQWO\LQÀXHQFLQJWKHZDWHUTXDOLW\DQGHFRORJLFDOHQYLURQPHQWRI aquatic ecosystems. Dissolved oxygen (DO) is an essential element for aerobic life, and when the DO content in seawater is reduced to a lethal or sublethal threshold, the marine ecosystem will be WKUHDWHQHG7KHUHIRUHWKHK\SR[LFDUHDLVDOVRFDOOHGWKH³GHDG]RQH´:LWKWKHLQWHQVLI௘LFDWLRQRI human activity, hypoxia in shelf seas is getting more and more serious. Meanwhile, the increase in carbon dioxide (CO2) emissions since the industrial revolution has also led to global warming, and caused decreases in DO content (i.e., deoxygenation) (Breitburg et al., 2018) and pH value LHDFLGLI௘LFDWLRQ )HHO\HWDO 2FHDQGHR[\JHQDWLRQDQGDFLGLI௘LFDWLRQKDYHEHFRPHQHZ FKDOOHQJHVVLJQLI௘LFDQWO\LQÀXHQFLQJDQGWKUHDWHQLQJPDULQHRUJDQLVPVDQGHFRV\VWHPVDQGHYHQ the development of human society and economic systems (Tang et al., 2013; Breitburg et al., 2018). As revealed by previous studies, DO content and pH value in seawater have greatly changed from the open ocean to shelf seas (Feely et al., 2009; Tang et al., 2013; Breitburg et al.,   1RWDEO\ GHR[\JHQDWLRQ DQG DFLGLI௘LFDWLRQ DUH DOVR UHJXODWHG E\ UHJLRQDO SK\VLFDO DQG biogeochemical processes, such as eutrophication and upwelling. Under eutrophication, the GHJUHHRIK\SR[LDDQGDFLGLI௘LFDWLRQZLOOEHLQWHQVLI௘LHG0RUHRYHUK\SR[LFZDWHUVDUHJHQHUDOO\ characterized by a low-pH value and weak buffer capacity, and are thus more vulnerable to ocean DFLGLI௘LFDWLRQ&DLHWDO 7KHUHIRUHWKHFRDVWDOHXWURSKLFDWLRQDUHDVDUHDOVRKRWVSRWVRI RFHDQDFLGLI௘LFDWLRQDQGK\SR[LD $WSUHVHQWHXWURSKLFDWLRQK\SR[LDGHR[\JHQDWLRQDQGDFLGLI௘LFDWLRQDUHQRWRQO\WKHFRQFHUQRI natural science, but also closely related to the sustainable development of human society. China’s

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

coastal waters constitute important support for the national economy and society. Thereby it is of JUHDWVFLHQWLI௘LFVLJQLI௘LFDQFHIRUXVWRH[SORUHWKHHYROXWLRQRIWKHPDULQHHQYLURQPHQWZKLFKLV helpful for the sustainable development and utilization of marginal ecosystems.

Data used in the case Data primarily originate from environmental monitoring conducted by the State Oceanic Administration (SOA) of China obtained each year during the period 1976-2006 along the section of the Yellow River estuary to Liaodong Bay in the Bohai Sea, the section of Chengshanjiao to Dalian in the northern Yellow Sea, and the section of 36°N in the southern Yellow Sea. The datasets mainly include temperature, salinity, nutrients, DO, and pH. Some additional data in several years after 2010 were also collected and compiled.

Method First, based on time-series observational data, long-term variations in nutrients, DO, and S+ZHUHLGHQWLI௘LHGYLDOLQHDUUHJUHVVLRQDQDO\VHVRIUHJLRQDOPHDQYDOXHVRIHDFKSDUDPHWHU investigating the temporal trends of related physicochemical parameters. Thus, the rates of DO decline and deoxygenation were obtained. Then, the coexistence and coupling of eutrophication, K\SR[LDDQGDFLGLI௘LFDWLRQZHUHH[SORUHGE\H[DPLQLQJWKHOLQNDJHVDPRQJGLIIHUHQWSDUDPHWHUV Finally, the related results were expanded.

Results and analysis In this section, we primarily take the Bohai Sea, which is influenced by multiple stressors, as an example, aiming to examine the changing marine environment in a typical marginal sea. Overall, DIN (dissolved inorganic nitrogen) concentration and N/P ratio both showed an increasing WUHQG LQ VXPPHU VXUIDFH ZDWHUV >)LJXUHV D  DQG E @ ZKLOH WKH 6L1 UDWLR JUDGXDOO\ GHFUHDVHG>)LJXUHF @,QVXPPHUERWWRPZDWHUV'2DQGS+ERWKGLVSOD\HGDGHFUHDVLQJ WUHQGZLWKDUDWHRIȝPROGP3āD DQGDUHVSHFWLYHO\>)LJXUHVG DQGH @ Statistical analysis shows that DO content was negatively linearly correlated with pH values in VXPPHUERWWRPZDWHUV>)LJXUHI @0RUHRYHUWKHUHZDVDQLQFUHDVHLQWKHDUHDRIWKHERWWRP K\SR[LF]RQHLQWKH%RKDL6HDGXULQJVXPPHU>)LJXUHVJ L @:HLHWDOD=KDL et al., 2019; Zhang et al., 2016). These results indicate that the decrease in pH was synchronous with the declining DO in summer bottom waters in the Bohai Sea. Moreover, they were both closely related to the increasing DIN and changing nutrient structure. In fact, the deposition and decomposition of organic debris caused by the growth and reproduction of phytoplankton under eutrophication are the main causes of driving DO and pH to decrease continuously. Therefore, the

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Chapter 6

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regional eutrophication process plays an important role in regulating the decrease in DO and pH in summer bottom waters, resulting in the coexistence and coupling of eutrophication, hypoxia, and DFLGLI௘LFDWLRQ Remarkably, the DIN and N/P ratio both slowed down and decreased after 2010 according to the long-term variations in annual mean values, although they showed a general increasing trend (Figure 6.18). This is consistent with the long-term trend of remote sensing chlorophyll a concentration(Sidman et al., 2020). On the whole, the increasing trend of chlorophyll a content before 2010 was basically in accordance with the change in DIN; after that, with the decrease in DIN, chlorophyll a content began to decline accordingly. These new phenomena and patterns

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

indicate that the continuous deteriorating trend of eutrophication may has been alleviated in the past ten years, especially since the 18th National Congress of the Communist Party of China transformed the economic development mode and implemented eco-friendly marine construction.

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(b) relationships among eutrophication, hypoxia/ GHR[\JHQDWLRQDQGDFLGLI௘LFDWLRQ

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Chapter 6

SDG 14 Life below Water

In addition to the Bohai Sea, the Changjiang (Yangtze) River and Pearl River estuaries in China are also among the most serious eutrophic areas, with frequent occurrence of hypoxia DQGDFLGLI௘LFDWLRQ:HLHWDO=KDRHWDO 0HDQZKLOHVHDVRQDODFLGLI௘LFDWLRQ=KDL 2018) and long-term deoxygenation (Wei et al., 2019b) exist in the central Yellow Sea as well. &RPSUHKHQVLYHO\WKHKRWVSRWVRIK\SR[LDDQGDFLGLI௘LFDWLRQLQ&KLQD¶VFRDVWDOZDWHUVDUHVKRZQ in Figure 6.19, and the intrinsic relationships among eutrophication, hypoxia/deoxygenation and DFLGLI௘LFDWLRQDUHDOVRLQGLFDWHG&KLQD¶VFRDVWDOZDWHUVFRQVWLWXWHLPSRUWDQWVXSSRUWIRUQDWLRQDO economic and social development, among which the Bohai Sea, Changjiang River Delta, and Pearl River Delta have become the most developed regions in China, accounting for more than 40% of China’s GDP. However, these environmental issues, such as eutrophication, hypoxia/ GHR[\JHQDWLRQDQGDFLGLI௘LFDWLRQKDYHSRWHQWLDOO\LQÀXHQFHGWKHVXVWDLQDEOHGHYHORSPHQWRIWKH marine ecosystem and the social economy. Therefore, it is urgent to develop adaptive management countermeasures on the basis of environmental diagnosis and analysis.

Highlights DIN (dissolved inorganic nitrogen) concentration and the N/P ratio are increasing in the Bohai Sea, while theSi/N ratio, DO (dissolved oxygen), and S+DUHJUDGXDOO\GHFUHDVLQJ7KHUDWHVRIGHR[\JHQDWLRQDQGDFLGLIɧLFDWLRQ LQWKHVXPPHUERWWRPZDWHUVFDQUHDFKѥPRO(dm3·a) and 0.0027/a, respectively. Thus, the coexistence and coupling of eutrophication, GHR[\JHQDWLRQDQGDFLGLIɧLFDWLRQZHUHH[SORUHG The continuous deteriorating trend of eutrophication may has been alleviated in the pastten years, especially since the 18th National Congress of the Communist Party of China and subsequent transformation of the economic development mode and the implementation of eco-friendly marine construction.  7  KHKRWSRWVRIHXWURSKLFDWLRQK\SR[LDDQGDFLGLIɧLFDWLRQLQ&KLQD·VFRDVWDO ZDWHUVZHUHIXUWKHULGHQWLIɧLHGOD\LQJDQLPSRUWDQWIRXQGDWLRQIRUGLDJQRVLQJ the changes in the ecological environment and proposing adaptive management countermeasures.

251

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Big Earth Data in Support of the Sustainable Development Goals (2020): China

Outlook In this case study, the Bohai Sea was taken as an example to comprehensively explore the FRXSOLQJRIHXWURSKLFDWLRQK\SR[LDDQGDFLGLI௘LFDWLRQLQ&KLQD¶VFRDVWDOZDWHUVUHYHDOLQJWKH intrinsic relationships among them. Generally, DIN concentration and the N/P ratio presented an increasing trend in the Bohai Sea, while the Si/N ratio, DO, and pH gradually decreased. The rates RIGHR[\JHQDWLRQDQGDFLGLI௘LFDWLRQLQWKHERWWRPZDWHUVUHDFKHGȝPROGP3·a) and 0.0027/a, UHVSHFWLYHO\7KXVHXWURSKLFDWLRQK\SR[LDDQGDFLGLI௘LFDWLRQDSSHDUWREHFRH[LVWLQJ7KHUHIRUHLW LVRIJUHDWVFLHQWLI௘LFVLJQLI௘LFDQFHIRUXVWRH[SORUHWKHHYROXWLRQRIWKHPDULQHHQYLURQPHQWZKLFK is helpful for maintaining the sustainable development and utilization of marginal ecosystems. The results lay an important foundation for diagnosing the changes in the ecological environment under increasing natural and anthropogenic perturbations. With rapid economic development, accelerating urbanization processes, and increases in energy consumption, eutrophication may continue in the future, which will result in further decreases in DO content and pH values. Though policies may have alleviated the deteriorating trend of eutrophication, there may be a lag for the response of the coastal ecosystem to changes in external stressors, and the accumulated nutrients in marine environments need a long period of selfSXULI௘LFDWLRQWRLPSURYHVRLWLVGLII௘LFXOWWRDFKLHYHVWDEOHRUUHYHUVHHXWURSKLFDWLRQLQWKHVKRUW term. Meanwhile, due to global warming, deoxygenation also exists during winter (Wei et al., 2019b), although the water column is vertically well-mixed. Given that China’s coastal waters (especially bays and estuaries) are spawning grounds DQG QXUVHU\ JURXQGV IRU PXFK LPSRUWDQW HFRQRPLF I௘LVK DQG PDULQH RUJDQLVPV WKH SURFHVVHV RI HXWURSKLFDWLRQ K\SR[LDGHR[\JHQDWLRQ DQG DFLGLI௘LFDWLRQ PD\ SRVH D WKUHDW WR WKH PDULQH HFRV\VWHPWKXVDIIHFWLQJI௘LVKHU\UHVRXUFHVZKLFKQHHGVWREHSDLGPRUHDWWHQWLRQ&RQVHTXHQWO\ further multidisciplinary efforts should be dedicated to combining biogeochemistry, biology, DQGSK\VLFVWRPRQLWRURQJRLQJHXWURSKLFDWLRQGHR[\JHQDWLRQDFLGLI௘LFDWLRQDQGLQYHVWLJDWHWKHLU implications on marine habitats, which is necessary for modeling the changing ecosystems and for ecosystem management in the future.

Chapter 6

SDG 14 Life below Water

Summary This chapter focused on the sustainable development of the regional seas in China, and provided data, methods, and technical support for SDG 14 indicators. For the SDG 14.1 target of prevention DQGVLJQLI௘LFDQWUHGXFWLRQRIPDULQHSROOXWLRQ&DVHDQDO\]HGWKHVSDWLDODQGWHPSRUDOFKDQJHV RIPLFURSODVWLFVLQGLIIHUHQWVHDDUHDVDQGWKHPDLQIDFWRUVLQÀXHQFLQJWKHVSDWLDOGLVWULEXWLRQRI marine litter and microplastics. It provided an objective grasp of the distribution characteristics and changing trends of marine litter and microplastics in different sea areas of China. Case 2 collected and organized a dataset of 978 metal enrichments off the coast of China to reveal the distribution characteristics of metal pollution in the Chinese coastal zone. For the SDG 14.2 target of sustainable management and protection of marine and coastal ecosystems, Case 3 carried out a joint assessment of ecosystem health in several typical coastal water of China (Jiaozhou Bay, Daya Bay, and Sishili Bay ). Case 4 established a risk assessment system for harmful algal EORRPVLQWKH%RKDL6HDUHJLRQDQG&DVHXVHGDUWLI௘LFLDOLQWHOOLJHQFHWRH[WUDFWLQIRUPDWLRQRQ the scale and changes of marine aquaculture in China’s coastal waters. For the SDG 14.3 target of PLQLPL]HDQGDGGUHVVWKHLPSDFWVRIRFHDQDFLGLI௘LFDWLRQ&DVHDQDO\]HGWKHVSDWLDODQGWHPSRUDO distribution pattern and evolution of hypoxia/deoxygenation zones in a typical sea area to clarify WKHUDWHVRIGHR[\JHQDWLRQDFLGLI௘LFDWLRQDQGWKHFRQWULEXWLRQRIHXWURSKLFDWLRQDQGDFLGL¿FDWLRQ to deoxygenation. These cases provided valuable information and decision support for coastal zone protection, integrated management of marine ecological health, and prevention of marine pollution in China (including at the regional level). Going forward, we shall further strengthen the application of airborne and spaceborne remote sensing data, AI, and other technologies in the monitoring and analysis of SDG 14 indicators, in the hope of extending research from China’s coastal waters to distant waters on a larger scale and in greater depth, to develop a more complete system of data, techniques, and methods for monitoring and evaluating the sustainable development of marine ecosystems.

253

256

Background

258 Main Contributions 260 Case Study 317 Summary

Chapter 7

SDG 15 Life on Land

256

Big Earth Data in Support of the Sustainable Development Goals (2020): China

Background &KLQDKDVDOZD\VLQVLVWHGRQSXWWLQJSURWHFWLRQDQGUHVWRUDWLRQI௘LUVWDQGLQWKHSDVWGHFDGHLWKDV PDGHSRVLWLYHDFKLHYHPHQWVLQUHDOL]LQJ6'*³3URWHFWUHVWRUHDQGSURPRWHVXVWDLQDEOHXVHRI WHUUHVWULDOHFRV\VWHPVVXVWDLQDEO\PDQDJHIRUHVWVFRPEDWGHVHUWLI௘LFDWLRQDQGKDOWDQGUHYHUVHODQG GHJUDGDWLRQDQGKDOWELRGLYHUVLW\ORVV´)RUH[DPSOHWKHFRXQWU\KDVYLJRURXVO\SURPRWHGSURMHFWV UHWXUQLQJIDUPODQGWRIRUHVWDQGJUDVVODQGFRQVWUXFWLQJQDWXUDOUHVHUYHVIRUSURWHFWLQJHQGDQJHUHG VSHFLHVDQGPDQDJLQJVRLOHURVLRQGHVHUWLI௘LFDWLRQDQGURFN\GHVHUWLI௘LFDWLRQ+RZHYHUWKHTXDOLW\ RI &KLQD¶V HFRV\VWHPV LV VWLOO UHODWLYHO\ ORZ7KH XUJHQF\ RI HFRV\VWHP SURWHFWLRQ REYLRXVO\ FRQÀLFWVZLWKWKHUHTXLUHPHQWVRIHFRQRPLFGHYHORSPHQWDQGDFKLHYLQJ6'*LQWKLVFRQWH[W VWLOOIDFHVJUHDWFKDOOHQJHV 7KHWRSSULRULW\LVWRHQKDQFHWKHVFLHQWLI௘LFDQGWHFKQRORJLFDOFRQWHQWRIWKHSURWHFWLRQDQG UHVWRUDWLRQVWUDWHJ\DQGWRSURPRWHWKHPD[LPXPEHQHI௘LWVRIHFRORJLFDOSURWHFWLRQDQGUHVWRUDWLRQ ZLWKWHFKQRORJLFDOLQQRYDWLRQDVWKHGULYLQJIRUFH&DUU\LQJRXWDODUJHVFDOHVSDWLDOO\DZDUH DVVHVVPHQWRIWKHVWDWXVRIWKHLQGLFDWRUVRI6'*DQGFODULI\LQJWKHFRUUHVSRQGLQJFRQWULEXWLRQV RIFOLPDWHFKDQJHDQGKXPDQDFWLYLW\DUHRIJUHDWVLJQLI௘LFDQFHIRUJXLGLQJWKHIRUPXODWLRQRI VFLHQWLI௘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

Chapter 7

Table 7.1

SDG 15 Life on Land

SDG 15 indicators in focus

Targets

Indicators

Tier FODVVLI௘LFDWLRQV

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SDG 15.1.1)RUHVWDUHDDVD proportion of total land area

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SDG 15.1%\HQVXUHWKHFRQVHUYDWLRQ UHVWRUDWLRQDQGVXVWDLQDEOHXVHRIWHUUHVWULDODQG LQODQGIUHVKZDWHUHFRV\VWHPVDQGWKHLUVHUYLFHV LQSDUWLFXODUIRUHVWVZHWODQGVPRXQWDLQVDQG GU\ODQGVLQOLQHZLWKREOLJDWLRQVXQGHULQWHUQDWLRQDO agreements

SDG 15.1.23URSRUWLRQRI important sites for terrestrial and IUHVKZDWHUELRGLYHUVLW\WKDWDUH FRYHUHGE\SURWHFWHGDUHDVE\ ecosystem type

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SDG 15.3%\FRPEDWGHVHUWLI௘LFDWLRQUHVWRUH GHJUDGHGODQGDQGVRLOLQFOXGLQJODQGDIIHFWHGE\ GHVHUWLI௘LFDWLRQGURXJKWDQGÀRRGVDQGVWULYHWR DFKLHYHDODQGGHJUDGDWLRQQHXWUDOZRUOG

SDG 15.3.13URSRUWLRQRIODQG that is degraded over total land area

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SDG 15.4%\HQVXUHWKHFRQVHUYDWLRQRI PRXQWDLQHFRV\VWHPVLQFOXGLQJWKHLUELRGLYHUVLW\LQ RUGHUWRHQKDQFHWKHLUFDSDFLW\WRSURYLGHEHQHI௘LWV WKDWDUHHVVHQWLDOIRUVXVWDLQDEOHGHYHORSPHQW

SDG 15.4.1&RYHUDJHE\ protected areas of important sites IRUPRXQWDLQELRGLYHUVLW\

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SDG 15.57DNHXUJHQWDQGVLJQLI௘LFDQWDFWLRQWR UHGXFHWKHGHJUDGDWLRQRIQDWXUDOKDELWDWVKDOW WKHORVVRIELRGLYHUVLW\DQGE\SURWHFWDQG SUHYHQWWKHH[WLQFWLRQRIWKUHDWHQHGVSHFLHV

SDG 15.5.15HG/LVW,QGH[

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SDG 15.8%\LQWURGXFHPHDVXUHVWRSUHYHQW WKHLQWURGXFWLRQDQGVLJQLI௘LFDQWO\UHGXFHWKH impact of invasive alien species on land and water ecosystems and control or eradicate the priority species

—

—

257

258

Big Earth Data in Support of the Sustainable Development Goals (2020): China

Main Contributions Table 7.2 Targets/Indicators

SDG 15.1.1)RUHVWDUHDDV a proportion of total land area

SDG 15.1.23URSRUWLRQ of important sites for terrestrial and freshwater ELRGLYHUVLW\WKDWDUH FRYHUHGE\SURWHFWHG DUHDVE\HFRV\VWHPW\SH

SDG 15.3.13URSRUWLRQRI land that is degraded over total land area

Cases and their main contributions

Cases

Contributions

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Data product:'LVWULEXWLRQRIIRUHVWW\SHVLQWKH