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Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 31 August 2024 | Viewed by 13556

Special Issue Editors


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Guest Editor
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: digital earth; environmental remote sensing
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: global settlements mapping; global forest loss

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Guest Editor
The Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Interests: hydrological remote sensing; hydrological data assimilation

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Guest Editor
Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing Normal University, Nanjing 210023, China
Interests: geographic modeling and simulation; virtual geographic environments
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The 2030 Agenda for Sustainable Development, proposed by the United Nations, comprises 17 goals, 169 sub-goals and 230+ indicators. In response, China has not only released the "Chinese National Plan for Implementing the 2030 Agenda for Sustainable Development", but also issued the "Construction Plan for Chinese Implementation of the 2030 Agenda for Sustainable Development Innovation Demonstration Zone". The construction plan proposes constructing around 10 national sustainable development innovation demonstration zones (hereinafter referred to as "demonstration zones") to create a number of realistic models of sustainable development. This clearly requires that the construction of demonstration zones be based on the implementation of innovation-driven development strategies, focusing on solving the key bottlenecks of sustainable development, integrating various innovative resources, strengthening the transformation of scientific and technological achievements, exploring and improving institutional mechanism, and providing system solutions.

This Special Issue mainly focuses on methods and applications for the development of demonstration zones supported by big earth data, including SDG indicator localization, SDG multi-indicator collaborative research, etc., with a focus on sustainable development in innovation demonstration zones. Original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
  • The quantification methods of SDG indicators;
  • SDG multi-indicator collaborative analysis;
  • Case studies of SDG single-indicator evaluation;
  • Case studies of SDG multiple-indicator evaluation;
  • Comprehensive evaluation research on sustainable development of demonstration zones;
  • The sustainable development of a decision-making platform.

We look forward to receiving your contributions.

Dr. Lanwei Zhu
Dr. Lei Wang
Prof. Dr. Chunlin Huang
Prof. Dr. Min Chen
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big earth data
  • SDGs
  • multi-objective comprehensive evaluation
  • remote sensing
  • geospatial analysis

Published Papers (8 papers)

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Research

24 pages, 11458 KiB  
Article
Monitoring Net Primary Productivity of Vegetation and Analyzing Its Drivers in Support of SDG Indicator 15.3.1: A Case Study of Northeast China
by Yue Qiu, Xuesheng Zhao, Deqin Fan, Zhoutao Zheng, Yuhan Zhang and Jinyu Zhang
Remote Sens. 2024, 16(13), 2455; https://doi.org/10.3390/rs16132455 - 4 Jul 2024
Viewed by 509
Abstract
Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land [...] Read more.
Assessing Sustainable Development Goal (SDG) indicator 15.3.1, which refers to the proportion of degraded land to total land area, and analysing its status and drivers is essential for the development of policies to promote the early achievement of SDG target 15.3 of Land Degradation Neutrality (LDN). In this study, Northeast China was selected as the study area, and the progress of indicator 15.3.1 was assessed based on the perspective of Net Primary Productivity (NPP) calculated by the CASA model. WorldPop population spatial distribution data were used as a proxy for human activities, combined with climate data to calculate the effects of changes in temperature, precipitation and population spatial distribution on vegetation NPP based on the partial correlation coefficient method and the Geodetector method. The results showed that 92.81% of the areas that passed the test of significance showed an increasing trend in vegetation NPP from 2000 to 2020. The vegetation NPP was affected by a combination of temperature, precipitation and population. The effects of temperature and precipitation on spatial differences in NPP for various vegetation types were significantly greater than those of population, but in high-density population zones, the effects of population on spatial differences in NPP were generally greater than those of temperature and precipitation. Precipitation was the main driver for spatial variation in NPP in deciduous broad-leaved forests, cultivated vegetation and thickets, while temperature was the main driver for spatial variation in NPP in evergreen coniferous forests. Generally, the warming and wetting trend in Northeast China contributed to the accumulation of NPP in cultivated vegetation, thickets, steppes and grasslands. The sensitivity of NPP to temperature and precipitation in deciduous broad-leaved and deciduous coniferous forests varied according to geographical location. Full article
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20 pages, 4084 KiB  
Article
Satellite-Based Water Quality Assessment of the Beijing Section of the Grand Canal: Implications for SDG11.4 Evaluation
by Ya Xie, Qing Zhou, Xiao Xiao, Fulong Chen, Yingchun Huang, Jinlong Kang, Shenglei Wang, Fangfang Zhang, Min Gao, Yichen Du, Wei Shen and Junsheng Li
Remote Sens. 2024, 16(5), 909; https://doi.org/10.3390/rs16050909 - 4 Mar 2024
Cited by 1 | Viewed by 912
Abstract
The Beijing-Hangzhou Grand Canal in China became a World Cultural Heritage Site in 2014, and the water quality of this ancient man-made canal has increasingly attracted societal attention. This study focuses on monitoring the water quality of the Beijing section of the Grand [...] Read more.
The Beijing-Hangzhou Grand Canal in China became a World Cultural Heritage Site in 2014, and the water quality of this ancient man-made canal has increasingly attracted societal attention. This study focuses on monitoring the water quality of the Beijing section of the Grand Canal (BGC) using remote sensing technology. Analysis of the comprehensive trophic level index (TLI) indicates that the water in the Canal was predominantly light eutrophic from 2016 to 2022. The annual average results of the TLI reveal that the water quality in the Kunming Lake and North Canal of BGC is generally good, characterized by some mesotrophic waters, and others are in light eutrophication. The TLI for the entire BGC water body decreased from 64.7 in 2016 to 60.3 in 2022, indicating an improvement trend in water quality. Notably, the proportion of good water with TLI less than 60 increased from 50% in 2016 to 83% in 2022. This improvement of water quality may be attributed to the reduced use of fertilizers and pesticides and the implementation of various environmental policies by Beijing Municipal government. These findings offer valuable insights for the management and protection of the water resources of the BGC, and further contribute to the evaluation of the United Nations Sustainable Development Goal (SDG) 11.4. Full article
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25 pages, 6689 KiB  
Article
Analysis of Vegetation Cover Change in the Geomorphic Zoning of the Han River Basin Based on Sustainable Development
by Yuqing Xiong, Zizheng Zhang, Meichen Fu, Li Wang, Sijia Li, Cankun Wei and Lei Wang
Remote Sens. 2023, 15(20), 4916; https://doi.org/10.3390/rs15204916 - 11 Oct 2023
Cited by 5 | Viewed by 1245
Abstract
The Han River Basin, a critical water conservation and ecological barrier in Hubei Province, is intricately associated with the United Nations Sustainable Development Goals (SDGs). Research results show that vegetation cover changes are affected by multiple factors, and understanding the influences of climate [...] Read more.
The Han River Basin, a critical water conservation and ecological barrier in Hubei Province, is intricately associated with the United Nations Sustainable Development Goals (SDGs). Research results show that vegetation cover changes are affected by multiple factors, and understanding the influences of climate change and human activities on vegetation is imperative for achieving sustainable development in the basin. Through quantitative assessment of vegetation changes in diverse landform regions, implementing adaptive ecological construction and environmental protection will foster the sustainable development of ecological civilization in the Han River Basin. This study utilizes MODIS13Q1 data and employs diverse analytical methods to investigate the characteristics of vegetation change and the interrelationships between climate change, meteorological factors, and vegetation cover in various geomorphological areas of the Han River Basin from 2000 to 2020. The results showed that (1) throughout the entire study period, the NDVI of the six types of geomorphological divisions in the Han River Basin exhibited a fluctuating upward trend, with the changes in the low-altitude hilly geomorphic regions being particularly noteworthy. (2) Within the study area, approximately 92.67% of vegetation coverage displayed an increasing trend, while 7.33% showed degradation, predominantly in plains and platforms. Notably, the area of continuous improvement (31.16%) outweighed the area of continuous degradation (3.05%), with low and middle-relief mountain areas demonstrating the most robust growth and sustainability. (3) Human agriculture activities and urbanization processes have emerged as the primary driving force behind vegetation changes in the Han River Basin. The responses of vegetation to climate change and human activities exhibited significant variations across diverse geomorphological regions. In areas characterized by vegetation improvement, the contribution rate of human activities to NDVI changes in different vegetation types surpassed 70%, with plain areas displaying the highest contribution rate at a remarkable 90%. In contrast, the plain and platform regions of the vegetation degradation area were significantly influenced by climate change. In future watershed ecological environment management, it is essential to not only recognize the dominant role of human activities in promoting the growth of mountain vegetation NDVI but also address the impact of climate change on the degradation of vegetation NDVI in plains and platforms. A comprehensive understanding of these factors is crucial for devising effective strategies to ensure sustainable development and ecological balance in the Han River Basin. Full article
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18 pages, 5899 KiB  
Article
Comprehensive Assessment of Sustainable Development Goal 11 at the Sub-City Scale: A Case Study of Guilin City
by Yao Chang, Xiaoying Ouyang, Xianyun Fei, Zhongchang Sun, Sijia Li, Huiping Jiang and Hongwei Li
Remote Sens. 2023, 15(19), 4722; https://doi.org/10.3390/rs15194722 - 27 Sep 2023
Cited by 1 | Viewed by 1402
Abstract
Quantifying the progress and interactions of the 11 indicators of Sustainable Development Goal 11 plays a crucial role in improving urban living and promoting urban prosperity. SDG 11, focused on sustainable cities and communities, employs forward-thinking strategies to address challenges arising from urban [...] Read more.
Quantifying the progress and interactions of the 11 indicators of Sustainable Development Goal 11 plays a crucial role in improving urban living and promoting urban prosperity. SDG 11, focused on sustainable cities and communities, employs forward-thinking strategies to address challenges arising from urban prosperity and development, such as land scarcity and resource shortages. This paper positions the indicators of SDG 11, analyzing the patterns, trends, dynamics, and issues of urbanization development in Guilin using a combination of geospatial satellite resource data and categorical statistical data. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale, exploring the current state, spatial aggregation, synergies, and trade-offs in the development of Guilin City. The study introduces a framework and positioning method for assessing sustainable development at the city–county scale. Utilizing a localized evaluation system, it explores the developmental status of Guilin City. The application of Moran’s Index observes spatial aggregation among entities. By investigating Spearman’s rank correlation coefficient, it delves into the interplay of synergies and trade-offs within the studied region. Ultimately, it reveals significant disparities in the developmental landscape of the evaluated area, with a comprehensive spatial distribution indicating higher levels of development in the central and western regions and lower levels in the southeastern part. Strengthened cross-leverage and coordination are imperative to address the interconnections and harmonization of the developmental trends of the six synergistic indicators and nine trade-off indicators during the developmental process. The sustainable development of Guilin lays the groundwork for urban planning, construction, conservation, and management, positioning it as a potential model for successful sustainable development practices. Full article
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20 pages, 5167 KiB  
Article
Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data
by Junxia Miao, Xiaoyu Song, Fanglei Zhong and Chunlin Huang
Remote Sens. 2023, 15(15), 3885; https://doi.org/10.3390/rs15153885 - 5 Aug 2023
Cited by 3 | Viewed by 1396
Abstract
Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions [...] Read more.
Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding. Full article
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18 pages, 8141 KiB  
Article
Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example
by Can Liu, Yu Chen, Yongming Wei and Fang Chen
Remote Sens. 2023, 15(11), 2926; https://doi.org/10.3390/rs15112926 - 3 Jun 2023
Cited by 3 | Viewed by 2508
Abstract
A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are [...] Read more.
A high-resolution population distribution map is crucial for numerous applications such as urban planning, disaster management, public health, and resource allocation, and it plays a pivotal role in evaluating and making decisions to achieve the UN Sustainable Development Goals (SDGs). Although there are many population products derived from remote sensing nighttime light (NTL) and other auxiliary data, they are limited by the coarse spatial resolution of NTL data. As a result, the outcomes’ spatial resolution is restricted, and it cannot meet the requirements of some applications. To address this limitation, this study employs the nighttime light data provided by the SDGSAT-1 satellite, which has a spatial resolution of 10 m, and land use data as auxiliary data to disaggregate the population distribution data from WorldPop data (100 m resolution) to a high resolution of 10 m. The case study conducted in Guilin, China, using the multi-class weighted dasymetric mapping method shows that the total error during the disaggregation is 0.63%, and the accuracy of 146 towns in the study area is represented by an R2 of 0.99. In comparison to the WorldPop data, the result’s information entropy and spatial frequency increases by 345% and 1142%, respectively, which demonstrates the effectiveness of this approach in studying population distributions with high spatial resolution. Full article
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19 pages, 6675 KiB  
Article
Assessing Progress and Interactions toward SDG 11 Indicators Based on Geospatial Big Data at Prefecture-Level Cities in the Yellow River Basin between 2015 and 2020
by Yaya Feng, Chunlin Huang, Xiaoyu Song and Juan Gu
Remote Sens. 2023, 15(6), 1668; https://doi.org/10.3390/rs15061668 - 20 Mar 2023
Cited by 2 | Viewed by 2055
Abstract
Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable [...] Read more.
Rapid urbanization brings a series of dilemmas to the development of human society. To address urban sustainability, Sustainable Development Goal 11 (SDG 11) is formulated by the United Nations (UN). Quantifying progress and interactions toward SDG 11 indicators is essential to achieving Sustainable Development Goals (SDGs). However, it is limited by a lack of data in many countries, particularly at small scales. To address the gap, this study used systematic methods to calculate the integrated index of SDG 11 at prefecture-level cities with different economic groups in the Yellow River Basin based on Big Earth Data and statistical data, analyzed its spatial aggregation characteristics using spatial statistical analysis methods, and quantified synergies and trade-offs among indicators under SDG 11. We found the following results: (1) except for SDG 11.1.1, the performance of the integrated index and seven indicators improved from 2015 to 2020. (2) In GDP and disposable income groups, the top 10 cities had higher values, whereas the bottom 10 cities experienced greater growth rates in the integrated index. However, the indicators’ values and growth rates varied between the two groups. (3) There were four pairs of indicators with trade-offs that were required to overcome and eight pairs with synergies that were crucial to be reinforced and cross-leveraged in the future within SDG 11 at a 0.05 significance level. Our study identified indicators that urgently paid attention to the urban development of the Yellow River Basin and laid the foundation for local decision-makers to more effectively implement the 2030 Agenda for Sustainable Development (the 2030 Agenda). Full article
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22 pages, 6756 KiB  
Article
Recent Response of Vegetation Water Use Efficiency to Climate Change in Central Asia
by Haichao Hao, Xingming Hao, Jianhua Xu, Yaning Chen, Hongfang Zhao, Zhi Li and Patient Mindje Kayumba
Remote Sens. 2022, 14(23), 5999; https://doi.org/10.3390/rs14235999 - 26 Nov 2022
Cited by 4 | Viewed by 1922
Abstract
Quantifying the coupled cycles of carbon and water is essential for exploring the response mechanisms of arid zone terrestrial ecosystems and for formulating a sustainable and practical solution to issues caused by climate change. Water use efficiency (WUE), one of the comprehensive indicators [...] Read more.
Quantifying the coupled cycles of carbon and water is essential for exploring the response mechanisms of arid zone terrestrial ecosystems and for formulating a sustainable and practical solution to issues caused by climate change. Water use efficiency (WUE), one of the comprehensive indicators for assessing plant growth suitability, can accurately reflect vegetation’s dynamic response to changing climate patterns. This study assesses the spatio-temporal changes in WUE (ecosystem water use efficiency, soil water use efficiency, and precipitation water use efficiency) from 2000 to 2018 and quantifies their relationship with meteorological elements (precipitation, temperature, drought) and the vegetation index (NDVI). The study finds that the sensitivity of NDVI to WUE is highly consistent with the spatial law of precipitation. The εPre threshold range of different types of WUE is about 200 mm or 1600 mm (low-value valley point) and 300 mm or 1500 mm (high-value peak point), and the εTem threshold value is 3~6 °C (high-value peak point) and 9~12 °C (low-value valley point). The degree to which vegetation WUE is influenced by precipitation is positively correlated with its time lag, whereas the degree to which temperature influences vegetation is negatively correlated. The WUE time lag is very long in hilly regions and is less impacted by drought; it is quite short in plains and deserts, where it is substantially affected by drought. These findings may be of great significance in responding to the severe situation of increasingly scarce water resources and the deterioration of the ecological environment across Central Asia. Full article
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