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Remote Sensing
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Monitoring and Managing Environmental Sustainability Using Remote Sensing (Second Edition)
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Monitoring and Managing Environmental Sustainability Using Remote Sensing (Second Edition)

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

Deadline for manuscript submissions: 28 February 2026 | Viewed by 1294

Special Issue Editors

Prof. Dr. Dongmei Chen

E-Mail Website
Guest Editor
Department of Geography and Planning, Queen’s University, Kingston, ON K7L 3N6, Canada
Interests: remote sensing; GIS; change detection; feature extraction; environmental modeling
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yuhong He

E-Mail Website
Guest Editor
Department of Geography, University of Toronto Mississauga, 3359 Mississauga Road, Mississauga, ON L5L 1C6, Canada
Interests: remote sensing; geospatial analysis; ecosystem modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are excited to announce the second edition of the Special Issue “Monitoring and Managing Environmental Sustainability Using Remote Sensing” of the journal Remote Sensing.

Environmental sustainability is essential for maintaining the health and viability of our planet for current and future generations. As human activities are increasingly putting a strain on the environment, promoting sustainability is becoming crucial to mitigate the adverse impacts of climate change, deforestation, habitat loss, and more. Sustainable practices not only protect natural habitats but also enhance quality of life by ensuring clean air, water, and soil, which are fundamental to human health and well-being. Remote sensing technology plays a pivotal role in monitoring and managing environmental sustainability. By utilizing satellites and airborne sensors, remote sensing provides comprehensive and real-time data on various environmental parameters—such as land use changes, deforestation rates, natural disasters, and the health of ecosystems—with high precision and temporal resolution. The insights gained from remote sensing are invaluable for designing effective conservation strategies, implementing sustainable land management practices, and enforcing environmental regulations. As a result, remote sensing technology is indispensable for achieving and maintaining environmental sustainability in an era of rapid environmental change.

This Special Issue aims to compile a comprehensive collection of innovative research and insights into how remote sensing technology can be leveraged to monitor and manage environmental suitability, thus supporting sustainable development. 

Authors are invited to submit their original research articles and review papers on topics including, but not limited to, the following:

  • Land use and land cover change: remote sensing applications in detecting and analyzing changes in land use and land cover.
  • Climate change monitoring: using remote sensing data to assess and model the impacts of climate change on different ecosystems.
  • Biodiversity and habitat mapping: techniques for monitoring biodiversity and habitat health through remote sensing data.
  • Agricultural monitoring: remote sensing for assessing soil health, crop conditions, and agricultural sustainability.
  • Water resource management: utilizing remote sensing technology for monitoring water quality, distribution, and management.
  • Urban environment analysis: assessing urban heat islands, green spaces, and environmental quality in urban areas using remote sensing.
  • Disaster management: remote sensing in predicting, monitoring, and managing natural disasters such as floods, droughts, and wildfires.
  • Forest and vegetation monitoring: techniques for tracking forest health, deforestation, and reforestation efforts.
  • Environmental pollution monitoring: monitoring air quality, atmospheric composition, and pollution using remote sensing technologies.
  • Innovative remote sensing technologies for environmental monitoring: advances in sensors, platforms, and data processing techniques that enhance environmental monitoring capabilities.

Prof. Dr. Dongmei Chen
Prof. Dr. Yuhong He
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 250 words) can be sent to the Editorial Office for assessment.

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
  • environmental sustainability
  • land use/cover change
  • habitat health
  • climate change monitoring
  • satellite imagery
  • environmental monitoring
  • ecosystem assessment

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

Related Special Issue

Published Papers (2 papers)

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Research

24 pages, 4796 KB  
Article
Forest Height Estimation in Jiangsu: Integrating Dual-Polarimetric SAR, InSAR, and Optical Remote Sensing Features
by Fangyi Li, Yiheng Jiang, Yumei Long, Wenmei Li and Yuhong He
Remote Sens. 2025, 17(21), 3620; https://doi.org/10.3390/rs17213620 - 31 Oct 2025
Viewed by 554
Abstract
Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers [...] Read more.
Forest height is a key structural parameter for evaluating ecological functions, biodiversity, and carbon dynamics. While LiDAR and Synthetic Aperture Radar (SAR) provide vertical structure information, their large-scale use is restricted by sparse sampling (LiDAR) and temporal decorrelation (SAR). Optical remote sensing offers complementary spectral information but lacks direct height retrieval. To address these limitations, we developed a multi-modal framework integrating GEDI waveform LiDAR, Sentinel-1 SAR (InSAR and PolSAR), and Sentinel-2 multispectral data, combined with machine learning, to estimate forest canopy height across Jiangsu Province, China. GEDI L2A footprints were used as training labels, and a suite of structural and spectral features was extracted from SAR, GEDI, and Sentinel-2 data as input variables for canopy height estimation. The performance of two ensemble algorithms, Random Forest (RF) and Gradient Tree Boosting (GTB) for canopy height estimation, was evaluated through stratified five-fold cross-validation. RF consistently outperformed GTB, with the integration of SAR, GEDI, and optical features achieving the best accuracy (R2 = 0.708, RMSE = 2.564 m). The results demonstrate that InSAR features substantially enhance sensitivity to vertical heterogeneity, improving forest height estimation accuracy. These findings highlight the advantage of incorporating SAR, particularly InSAR with optical data, in enhancing sensitivity to vertical heterogeneity and improving the performance of RF and GTB in estimating forest height. The framework we proposed is scalable to other regions and has the potential to contribute to global sustainable forest monitoring initiatives. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing (Second Edition))
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24 pages, 8989 KB  
Article
Assessment of the Effectiveness of Spectral Indices Derived from EnMAP Hyperspectral Imageries Using Machine Learning and Deep Learning Models for Winter Wheat Yield Prediction
by László Mucsi, Dorottya Litkey-Kovács, Krisztián Bonus, Nizom Farmonov, Ali Elgendy, Lutfi Aji and Márkó Sóti
Remote Sens. 2025, 17(20), 3426; https://doi.org/10.3390/rs17203426 - 13 Oct 2025
Cited by 1 | Viewed by 580
Abstract
Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter wheat [...] Read more.
Accurate and timely crop yield estimation is essential for effective agricultural management and global food security, particularly for winter wheat. This study aimed to assess the effectiveness of EnMAP hyperspectral imagery in combination with machine learning and deep learning models for winter wheat yield prediction in Hungary. Using EnMAP images from February and May 2023, along with ground truth yield data from four fields, we derived 10 distinct vegetation indices. Random Forest, Gradient Boosting, and Multilayer Perceptron algorithms were employed, and model performance was evaluated using Mean Absolute Error (MAE) and Coefficient of Determination (R2) values. The results consistently demonstrated that integrating multi-temporal data significantly enhanced predictive accuracy, with the MLP model achieving an R2 of 0.79 and an MAE of 0.27, notably outperforming single-date predictions. Shortwave infrared (SWIR) indices were particularly critical for early-season yield estimations. This research highlights the substantial potential of hyperspectral data and advanced machine learning techniques in precision agriculture, emphasizing the promising role of future missions such as CHIME in further refining and expanding yield estimation capabilities. Full article
(This article belongs to the Special Issue Monitoring and Managing Environmental Sustainability Using Remote Sensing (Second Edition))
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