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Multi-Scale Remote Sensing for Wetland Landscape Change Monitoring and Ecological Resilience

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

Deadline for manuscript submissions: 31 December 2025 | Viewed by 758

Special Issue Editors


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Guest Editor
College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China
Interests: remote sensing for wetlands; time series remote sensing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: urban wetlands and ecohydrology monitoring; remote sensing big data and artificial intelligence
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Aerospace Information Research Institute, Beijing 100094, China
Interests: wetland remote sensing; wetland protection evaluation; wetland and global change
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Interests: remote sensing for coastal wetlands; big earth data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wetlands are among the most dynamic and ecologically vital ecosystems on Earth, providing critical services such as carbon storage, water purification, flood regulation, and biodiversity support. However, they are also highly sensitive to anthropogenic disturbances and climate change. Monitoring wetland landscape change and assessing their ecological resilience is essential for effective conservation and sustainable management. Advances in remote sensing technologies—including satellites, UAVs, and ground-based platforms—have enabled multi-scale, high-resolution observations of wetland environments, offering new opportunities to detect spatiotemporal changes, quantify ecosystem dynamics, and model resilience under varying environmental pressures.

This Special Issue of Remote Sensing aims to highlight innovative research on the use of multi-scale remote sensing techniques for wetland monitoring and resilience assessment. It aligns with the journal’s scope by showcasing innovative applications of remote sensing methods to address pressing environmental and ecological challenges.

Topics of interest include, but are not limited to, the following research areas:

  • Multi-sensor and multi-resolution data fusion for wetland mapping;
  • Quantitative monitoring of wetland extent, structure, and function;
  • Time-series analysis of wetland dynamics;
  • Remote sensing-based indicators of wetland resilience;
  • AI and machine learning approaches for wetland classification and trend analysis;
  • Applications of hyperspectral, LiDAR, SAR, and/or UAV data;
  • Integration of remote sensing with ecological modeling or field observations.

Prof. Dr. Yinghai Ke
Prof. Dr. Weiguo Jiang
Prof. Dr. Zhenguo Niu
Prof. Dr. Huaiqing Zhang
Dr. Mingming Jia
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

  • multi-scale analysis
  • wetland ecological resilience
  • wetland structure and function
  • multi-sensor applications
  • ecosystem dynamics
  • remote sensing indicators
  • time-series remote sensing

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Published Papers (1 paper)

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Research

18 pages, 3444 KB  
Article
Enhancing Wildfire Monitoring with SDGSAT-1: A Performance Analysis
by Xinkun Zhu, Guojiang Zhang, Bo Xiang, Jiangxia Ye, Lei Kong, Wenlong Yang, Mingshan Wu, Song Yang, Wenquan Wang, Weili Kou, Qiuhua Wang and Zhichao Huang
Remote Sens. 2025, 17(19), 3339; https://doi.org/10.3390/rs17193339 - 30 Sep 2025
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Abstract
Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a [...] Read more.
Advancements in remote sensing technology have enabled the acquisition of high spatial and radiometric resolution imagery, offering abundant and reliable data sources for forest fire monitoring. In order to explore the ability of Sustainable Development Science Satellite 1 (SDGSAT-1) in wildfire monitoring, a systematic and comprehensive study was proposed on smoke detection during the wildfire early warning phase, fire point identification during the fire occurrence, and burned area delineation after the wildfire. The smoke detection effect of SDGSAT-1 was analyzed by machine learning and the discriminating potential of SDGSAT-1 burned area was discussed by Mid-Infrared Burn Index (MIRBI) and Normalized Burn Ratio 2 (NBR2). In addition, compared with Sentinel-2, the fixed-threshold method and the two-channel fixed-threshold plus contextual approach are further used to demonstrate the performance of SDGSAT-1 in fire point identification. The results show that the average accuracy of SDGSAT-1 fire burned area recognition is 90.21%, and a clear fire boundary can be obtained. The average smoke detection precision is 81.72%, while the fire point accuracy is 97.40%, and the minimum identified fire area is 0.0009 km2, which implies SDGSAT-1 offers significant advantages in the early detection and identification of small-scale fires, which is significant in fire emergency and disposal. The performance of fire point detection is superior to that of Sentinel-2 and Landsat 8. SDGSAT-1 demonstrates great potential in monitoring the entire process of wildfire occurrence, development, and evolution. With its higher-resolution satellite imagery, it has become an important data source for monitoring in the field of remote sensing. Full article
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