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Remote Sensing for Monitoring Land-Use/Land-Cover Change and Impacts on Ecosystem Service

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3437

Special Issue Editors

Dr. Shaohua Wang

E-Mail Website
Guest Editor
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: spatiotemporal big data analysis; GeoAI; remote sensing AI; deep learning
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liang Zhou

E-Mail Website
Guest Editor
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China
Interests: Remote Sensing of Urban Environment; Urban Simulation and Optimization; Evaluation of Human Settlement Environments
Special Issues, Collections and Topics in MDPI journals
Dr. Yeran Sun

E-Mail Website
Guest Editor
Department of Geography, University of Lincoln, Lincoln LN6 7TS, UK
Interests: remote sensing AI; GeoAI; quantitative human geography; sensing mobility and activity; geospatial big data analytics
Special Issues, Collections and Topics in MDPI journals
Dr. Haojian Liang

E-Mail Website
Guest Editor Assistant
Key Laboratory of Remote Sensing and Digital Earth Chinese Academy of Sciences, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, Beijing
Interests: remote sensing AI; GeoSpatial optimization; deep learning

Special Issue Information

Dear Colleagues,

The advent of remote sensing has revolutionized the manner in which we monitor and comprehend alterations in land use and land cover (LULC), furnishing crucial insights into their impact on ecosystem services. By employing sophisticated satellite and airborne sensors, remote sensing captures high-resolution data across vast spatial and temporal scales. This technology enables precise mapping and analysis of LULC changes, revealing patterns and trends essential for the management of natural resources and the assessment of environmental health.

Monitoring LULC changes is of critical importance, as these transformations significantly affect ecosystem services, including climate regulation, water purification, and biodiversity support. For instance, deforestation not only contributes to carbon emissions but also disrupts habitats and alters hydrological cycles. Accurate and timely remote sensing data are essential for policymakers and conservationists to implement sustainable land management practices, mitigate climate change, and enhance ecosystem resilience. In a world undergoing rapid change, the capacity to monitor and respond to LULC changes is of the utmost importance for the safeguarding of the environment and the achievement of sustainable development.

Articles may address, but are not limited to, the following topics:

  • Advanced Remote Sensing Technologies;
  • Environmental Health Remote Sensing;
  • Urban Expansion Monitoring;
  • Land-Cover Change;
  • Water Quality Assessment;
  • Climate Impact Studies;
  • Policy and Land Management;
  • Big Data and GIS Integration;
  • Land Use and Spatial Computing;
  • Land Use and GeoAI.

Dr. Shaohua Wang
Prof. Dr. Liang Zhou
Dr. Yeran Sun
Guest Editors

Dr. Haojian Liang
Guest Editor Assistant

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

  • remote sensing
  • land-use change
  • land-cover monitoring
  • urban environmental monitoring
  • urban infrastructure
  • sustainable land management
  • RS and GIS integration
  • deep learning applications

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 45568 KiB  
Article
Detecting Long-Term Spatiotemporal Dynamics of Urban Green Spaces with Training Sample Migration Method
by Mengyao Wang, Pan Li, Chunyu Wang, Wei Chen, Zhongen Niu, Na Zeng, Xingxing Han and Xinchao Sun
Remote Sens. 2025, 17(8), 1426; https://doi.org/10.3390/rs17081426 - 17 Apr 2025
Viewed by 255
Abstract
Urban green spaces (UGSs) are critical for landscape, ecological, and climate studies. However, the generation of long-term annual UGSs maps is often constrained by the lack of sufficient, high-quality training samples for training classifiers. In this study, we introduce an automatic training sample [...] Read more.
Urban green spaces (UGSs) are critical for landscape, ecological, and climate studies. However, the generation of long-term annual UGSs maps is often constrained by the lack of sufficient, high-quality training samples for training classifiers. In this study, we introduce an automatic training sample migration method based on visually interpreted reference data and long-term Landsat imagery, implemented on the Google Earth Engine (GEE) platform, to produce annual UGSs maps for Tianjin from 1984 to 2022. Migrating training samples to each year significantly improved classification performance, especially for UGSs and water bodies. UGSs coverage in sample areas increased from 5% to 38%, resulting in more reliable trend detection. Our spatiotemporal analysis revealed that green coverage in the study area reached up to 40%, dominated by tree cover that is significantly underestimated in existing global and regional land cover products. Distinct temporal patterns emerged between the old built-up area (OBUA) and new built-up area (NBUA). Early UGS decline was largely driven by NBUAs, while post-2007 greening involved both OBUAs and NBUAs, as captured by classification maps and vegetation indices. Our study proposes a scalable and practical framework for long-term land cover mapping in rapidly urbanizing regions, with enhanced potential as higher-resolution data becomes increasingly accessible. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Land-Use/Land-Cover Change and Impacts on Ecosystem Service)
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31 pages, 10594 KiB  
Article
Research on Key Influencing Factors of Ecological Environment Quality in Barcelona Metropolitan Region Based on Remote Sensing
by Xu Zhang, Blanca Arellano Ramos and Josep Roca Cladera
Remote Sens. 2024, 16(24), 4735; https://doi.org/10.3390/rs16244735 - 18 Dec 2024
Cited by 1 | Viewed by 813
Abstract
With the rapid development of urbanization, the ecological environment is being degraded. Taking the Barcelona Metropolitan Region as an example, this paper developed an ecological environment quality-assessment system suitable for different times and regions, based on remote sensing, to evaluate the quality of [...] Read more.
With the rapid development of urbanization, the ecological environment is being degraded. Taking the Barcelona Metropolitan Region as an example, this paper developed an ecological environment quality-assessment system suitable for different times and regions, based on remote sensing, to evaluate the quality of the ecological environment from 2006 to 2018. We also built various ordinary least squares models to analyze multiple variables affecting the ecological environment. Finally, the characteristic triangular spatial structure was used to explain the interaction between the two key variables. The results showed that the ecological quality was unevenly distributed. The largest green space contributed the most benefits but was decreasing and becoming fragmented. NDVI (normalized difference vegetation index) was the most significant natural variable related to the distribution of green space. Precipitation was the most closely related climate factor to NDVI. There was a complex two-way interaction mechanism between the two, and its boundary value was getting higher and higher. In conclusion, the environmental quality of the BMR needs improvement. The characteristic triangle can effectively explain the interaction mechanism between precipitation and NDVI. This study deeply analyzes how various factors affect environmental quality from both the global and internal perspectives and provides a scientific basis for urban ecological management and sustainable development. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Land-Use/Land-Cover Change and Impacts on Ecosystem Service)
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21 pages, 11347 KiB  
Article
Analysis of Spatio-Temporal Relationship Between Ecosystem Services and Human Footprints Under Different Human Activity Gradients: A Case Study of Xiangjiang River Basin
by Songjia Chen, Junhua Yan, Yingping Wang, Zhongbin Chang, Guangcan Yu, Jie Li, Jun Jiang, Linhua Wang, Shuo Zhang, Yang Chen, Pingping Xu, Cong Wang, Xinyu Yan, Chunyi Liu, Sihui Qiu, Siyuan Fanrong and Mengxiao Yu
Remote Sens. 2024, 16(22), 4212; https://doi.org/10.3390/rs16224212 - 12 Nov 2024
Cited by 1 | Viewed by 1591
Abstract
Clarifying the relationship between human activities and the provision of ecosystem services has received significant interest in recent years because of a growing need for sustainable socio-ecological system development. Using multi-source remote sensing data, we assessed the spatial and temporal distribution of the [...] Read more.
Clarifying the relationship between human activities and the provision of ecosystem services has received significant interest in recent years because of a growing need for sustainable socio-ecological system development. Using multi-source remote sensing data, we assessed the spatial and temporal distribution of the human footprint index and five ecosystem services under four human activity gradients from 2010 to 2020 in the Xiangjiang River Basin. The five ecosystem services include water supply, soil conservation, food production, habitat quality, and carbon sequestration. The relationship between human footprint and ecosystem services was analyzed from quantitative and spatial perspectives. The results showed that over the past 10 years, water supply and habitat quality decreased by 4.59% and 16.49%, respectively. The other three services increased, and the upstream area of the basin had a higher level of ecosystem services provision. The human footprint index increased by 28.83% over the 10 years and was characterized by point and patchy clustering in the middle and lower reaches. In terms of quantitative characteristics, the relationship between human footprint and ecosystem services was primarily negative. The ecosystem services were sensitive to the human footprint index within the 0−0.4 range. In terms of spatial characteristics, the relationship was dominated by trade-offs. The risky “high–low” trade-offs were mainly distributed in the middle and lower reaches. As the gradients of human activity increased, the maximum fluctuation in ESs was 43%, and the maximum fluctuation in human footprint was 28%, making their relationship more complex. Our results identified response thresholds of ecosystem services to human activities, providing a guide for ecological management and sustainable development of basins. Full article
(This article belongs to the Special Issue Remote Sensing for Monitoring Land-Use/Land-Cover Change and Impacts on Ecosystem Service)
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