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

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

Deadline for manuscript submissions: 15 February 2024 | Viewed by 4428

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Special Issue Editors

Dr. Lanwei Zhu
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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

Dr. Lei Wang

E-Mail Website
Guest Editor

Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: global settlements mapping; global forest loss

Prof. Dr. Chunlin Huang

E-Mail Website
Guest Editor

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

Prof. Dr. Min Chen

E-Mail Website
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
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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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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 (4 papers)

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Research

Open AccessArticle

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

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Remote Sens. 2023, 15(15), 3885; https://doi.org/10.3390/rs15153885 - 05 Aug 2023

Viewed by 348

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 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

(This article belongs to the Special Issue Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation)

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Open AccessArticle

Spatial Population Distribution Data Disaggregation Based on SDGSAT-1 Nighttime Light and Land Use Data Using Guilin, China, as an Example

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Remote Sens. 2023, 15(11), 2926; https://doi.org/10.3390/rs15112926 - 03 Jun 2023

Viewed by 1000

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 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

(This article belongs to the Special Issue Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation)

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Open AccessArticle

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

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Remote Sens. 2023, 15(6), 1668; https://doi.org/10.3390/rs15061668 - 20 Mar 2023

Viewed by 1019

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 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

(This article belongs to the Special Issue Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation)

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Open AccessArticle

Recent Response of Vegetation Water Use Efficiency to Climate Change in Central Asia

and

Patient Mindje Kayumba

Remote Sens. 2022, 14(23), 5999; https://doi.org/10.3390/rs14235999 - 26 Nov 2022

Cited by 1 | Viewed by 1074

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 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 ε_Pre 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 ε_Tem 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

(This article belongs to the Special Issue Big Earth Data and Sustainable Development Goals (SDGs) Multi-Objectives Comprehensive Evaluation)

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