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Sustainability
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Application of Remote Sensing and GIS in Environmental Monitoring
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Application of Remote Sensing and GIS in Environmental Monitoring

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1100

Special Issue Editors

Prof. Dr. Jun Li

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Interests: remote sensing of ecology and environment; geospatial analysis; ecology and environment in mining areas; machine learning; spatiotemporal data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Chengye Zhang

E-Mail Website
Guest Editor
College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China
Interests: quantitative remote sensing; multi- and hyper-spectral remote sensing; remote sensing of vegetation; machine learning; radiative transfer model
Special Issues, Collections and Topics in MDPI journals
Dr. Yuanheng Sun

E-Mail Website
Guest Editor
Environmental Information Institute, Navigation College, Dalian Maritime University, Dalian 116026, China
Interests: quantitative remote sensing; optical and thermal remote sensing; vegetation variable retrieval; coastal wetland monitoring; google earth engine
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing (RS) and geographic information systems (GISs) have become indispensable tools for addressing pressing environmental challenges, offering scalable and cost-effective solutions to monitor dynamic ecosystems, assess human impacts, and guide sustainable decision making. As climate change, urbanization, and resource exploitation intensify, the integration of advanced RS technologies—such as multispectral imaging, LiDAR, and radar—with spatially explicit GIS analytics provides unprecedented opportunities to map, model, and mitigate environmental degradation. This Special Issue seeks to highlight innovative methodologies and applications of RS and GIS in environmental monitoring, emphasizing their role in advancing ecological resilience, resource management, and policy formulation.

We invite contributions that explore novel approaches for tracking environmental changes across terrestrial, aquatic, and atmospheric systems. Topics of interest include but are not limited to the following: the use of machine learning for analyzing satellite imagery; time-series analysis of ecosystem dynamics; real-time monitoring of natural disasters; and participatory GIS frameworks for community-driven conservation. By bridging technological advancements with practical environmental solutions, this Special Issue aims to foster interdisciplinary dialog and showcase cutting-edge research that supports global sustainability goals.

Both original research articles and comprehensive reviews are welcome. Submissions may address technical innovations, case studies, or critical evaluations of RS/GIS applications in diverse contexts, such as forests, wetlands, urban areas, and agricultural landscapes.

We look forward to receiving your contributions.

Prof. Dr. Jun Li
Dr. Chengye Zhang
Dr. Yuanheng Sun
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. Sustainability 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 2400 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

  • environmental impact assessments
  • vegetation dynamics
  • soil and water conservation
  • land degradation mapping
  • coastal wetland monitoring
  • water quality assessment
  • urban sprawl analysis
  • disaster risk management
  • machine learning in remote sensing
  • geospatial modeling

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

Published Papers (2 papers)

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Research

23 pages, 7574 KB  
Article
30-Year Dynamics of Vegetation Loss in China’s Surface Coal Mines: A Comparative Evaluation of CCDC and LandTrendr Algorithms
by Wanxi Liu, Yaling Xu, Huizhen Xie, Han Zhang, Li Guo, Jun Li and Chengye Zhang
Sustainability 2025, 17(20), 9011; https://doi.org/10.3390/su17209011 (registering DOI) - 11 Oct 2025
Viewed by 237
Abstract
Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap [...] Read more.
Large-scale vegetation loss induced by surface coal mining constitutes a critical driver of regional ecological degradation. However, the applicability of existing change detection methodologies based on remote sensing within complex mining areas under diverse climatic conditions remains systematically unverified. To address this gap and reveal nationwide disturbance patterns, this study systematically evaluates the performance of two algorithms—Continuous Change Detection and Classification (CCDC) and Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr)—in identifying vegetation loss across three major climatic zones of China (the humid, semi-humid, and semi-arid zones). Based on the optimal algorithm, the vegetation loss year and loss magnitude across all of China’s surface coal mining areas from 1990 to 2020 were accurately identified, enabling the reconstruction of the comprehensive, nationwide spatio-temporal pattern of mining-induced vegetation loss over the past 30 years. The results show that: (1) CCDC demonstrated superior stability and significantly higher accuracy (OA = 0.82) than LandTrendr (OA = 0.31) in identifying loss years across all zones. (2) The cumulative vegetation loss area reached 1429.68 km2, with semi-arid zones accounting for 86.76%. Temporal analysis revealed a continuous expansion of the loss area from 2003 to 2013, followed by a distinct inflection point and decline during 2014–2016 attributable to policy-driven regulations. (3) Further analysis revealed significant variations in the average magnitude of loss across different climatic zones, namely semi-arid (0.11), semi-humid (0.21), and humid (0.25). These findings underscore the imperative for region-specific restoration strategies to ensure effective conservation outcomes. This study provides a systematic quantification and analysis of long-term, nationwide evolution patterns and regional differentiation characteristics of vegetation loss induced by surface coal mining in China, offering critical support for sustainable development decision-making in balancing energy development and ecological conservation. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Monitoring)
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21 pages, 5676 KB  
Article
Surface Deformation Monitoring and Spatiotemporal Evolution Analysis of Open-Pit Mines Using Small-Baseline Subset and Distributed-Scatterer InSAR to Support Sustainable Mine Operations
by Zhouai Zhang, Yongfeng Li and Sihua Gao
Sustainability 2025, 17(19), 8834; https://doi.org/10.3390/su17198834 - 2 Oct 2025
Viewed by 335
Abstract
Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the [...] Read more.
Open-pit mining often induces geological hazards such as slope instability, surface subsidence, and ground fissures. To support sustainable mine operations and safety, high-resolution monitoring and mechanism-based interpretation are essential tools for early warning, risk management, and compliant reclamation. This study focuses on the Baorixile open-pit coal mine in Inner Mongolia, China, where 48 Sentinel-1 images acquired between 3 March 2017 and 23 April 2021 were processed using the Small-Baseline Subset and Distributed-Scatterer Interferometric Synthetic Aperture Radar (SBAS-DS-InSAR) technique to obtain dense and reliable time-series deformation. Furthermore, a Trend–Periodic–Residual Subspace-Constrained Regression (TPRSCR) method was developed to decompose the deformation signals into long-term trends, seasonal and annual components, and residual anomalies. By introducing Distributed-Scatterer (DS) phase optimization, the monitoring density in low-coherence regions increased from 1055 to 338,555 points (approximately 321-fold increase). Deformation measurements at common points showed high consistency (R2 = 0.97, regression slope = 0.88; mean rate difference = −0.093 mm/yr, standard deviation = 3.28 mm/yr), confirming the reliability of the results. Two major deformation zones were identified: one linked to ground compaction caused by transportation activities, and the other associated with minor subsidence from pre-mining site preparation. In addition, the deformation field exhibits a superimposed pattern of persistent subsidence and pronounced seasonality. TPRSCR results indicate that long-term trend rates range from −14.03 to 14.22 mm/yr, with a maximum periodic amplitude of 40 mm. Compared with the Seasonal-Trend decomposition using LOESS (STL), TPRSCR effectively suppressed “periodic leakage into trend” and reduced RMSEs of total, trend, and periodic components by 48.96%, 93.33%, and 89.71%, respectively. Correlation analysis with meteorological data revealed that periodic deformation is strongly controlled by precipitation and temperature, with an approximately 34-day lag relative to the temperature cycle. The proposed “monitoring–decomposition–interpretation” framework turns InSAR-derived deformation into sustainability indicators that enhance deformation characterization and guide early warning, targeted upkeep, climate-aware drainage, and reclamation. These metrics reduce downtime and resource-intensive repairs and inform integrated risk management in open-pit mining. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Environmental Monitoring)
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