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Time-Series Mapping and Analysis of Land Surface Parameters and Changes Using Remote Sensing Data

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

Deadline for manuscript submissions: 15 June 2025 | Viewed by 2925

Special Issue Editors

School of Geographical Sciences, Fujian Normal University, Fuzhou 0591, China
Interests: vegetation remote sensing and carbon neutrality; remote sensing big data; dynamic monitoring of land surface changes
State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: remote sensing of vegetation ecosystem structure and functioning; land cover dynamics; ecoinformatics; ecosystem restoration and conservation
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Guest Editor
1. School of Resource and Environmental Sciences, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
2. Department of Geography and Planning, University of Toronto, No. 100 St. George St., Toronto, ON M5S 3G3, Canada
Interests: remote sensing; urban vegetation; vegetation index; spatial-temporal reconstruction; ecosystem carbon cycle; climate change
Special Issues, Collections and Topics in MDPI journals
College of Environmental and Resource Science, Zhejiang University, Hangzhou, China
Interests: land disturbance; agricultural remote sensing; time series analysis; near real-time monitoring
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430000, China
Interests: forest disturbances; multi-source remote sensing; artificial intelligence

Special Issue Information

Dear Colleagues,

In this era of rapid global change, it is of utmost importance to understand the dynamics of the Earth’s surface. Land surface parameters, encompassing land cover and land use types, vegetation structure, biochemical parameters, and soil parameters, serve as indispensable indicators of environmental well-being, providing crucial insights into multi-scale environmental changes, including climate change, urbanization, and deforestation. The advent of multi-source remote sensing, particularly satellite remote sensing, has revolutionized our ability to monitor land surface parameters at unprecedented spatial and temporal scales. As a result, time-series mapping and analysis have emerged as powerful tools for tracking and predicting land surface changes over time. Advanced research into the applications of time-series remote sensing for mapping and analyzing land surface parameters and their associated dynamic changes will empower people to enhance global land use management and monitoring.

This Special Issue of Remote Sensing, entitled “Time-series Mapping and Analysis of Land Surface Parameters and Changes”, aims to showcase cutting-edge research that harnesses the potential of time-series data to understand the dynamics of land surface parameters. We invite contributions that:

  • Develop data pre-processing algorithms for time-series mapping and analysis.
  • Develop novel methodologies for retrieving, estimating, and mapping land surface parameters.
  • Develop novel approaches for time-series mapping of land surface changes (including land cover and land use changes, land or forest disturbances, forest transitions, etc.).
  • Employ remote sensing data to study long-term trends, cyclic patterns, or abrupt changes in land surface parameters.
  • Integrate multi-source data, including ground observations, to validate and enhance the accuracy of remote sensing-derived time-series datasets.
  • Explore the implications of land surface changes on ecosystems, climate, and human societies.

By bringing together a collection of high-quality research articles, this Special Issue seeks to foster a deeper understanding of the Earth’s changing landscapes and to promote the development of innovative solutions for sustainable land management. Potential authors are encouraged to contribute their expertise and research insights in order to enrich the global discourse on the importance of time-series mapping and analysis in the realm of remote sensing.

Dr. Rong Shang
Dr. Wang Li
Dr. Xiaobin Guan
Dr. Su Ye
Dr. Feng Zhao
Dr. Naoto Yokoya
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

  • time-series mapping
  • time-series analysis
  • change detection
  • dynamic monitoring
  • data pre-processing
  • data fusion
  • land surface parameters
  • vegetation structure parameters
  • vegetation biochemical parameters
  • land cover mapping
  • land disturbance
  • forest disturbance
  • remote sensing

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Published Papers (2 papers)

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Research

17 pages, 6276 KiB  
Article
Tracking the Dynamics of Salt Marsh Including Mixed-Vegetation Zones Employing Sentinel-1 and Sentinel-2 Time-Series Images
by Yujun Yi, Kebing Chen, Jiaxin Xu and Qiyong Luo
Remote Sens. 2025, 17(1), 56; https://doi.org/10.3390/rs17010056 - 27 Dec 2024
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Abstract
Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate [...] Read more.
Salt marshes, as one of the most productive ecosystems on earth, have experienced fragmentation, degradation, and losses due to the impacts of climate change and human overexploitation. Accurate monitoring of vegetation distribution and composition is crucial for salt marsh protection. However, achieving accurate mapping has posed a challenge. Leveraging the high spatiotemporal resolution of the Sentinel series data, this study developed a method for high-accuracy mapping based on monthly changes across the vegetation life cycle, utilizing the random forest algorithm. This method was applied to identify Phragmites australis, Suaeda salsa, Spartina alterniflora, and the mixed-vegetation zones of Tamarix chinensis in the Yellow River Delta, and to analyze the key features of the model. The results indicate that: (1) integrating Sentinel-1 and Sentinel-2 satellite data achieved superior mapping accuracy (OA = 90.7%) compared to using either satellite individually; (2) the inclusion of SAR data significantly enhanced the classification accuracy within the mixed-vegetation zone, with “SARdivi” in July emerging as the pivotal distinguishing feature; and (3) the overall extent of salt marsh vegetation in the Yellow River Delta remained relatively stable from 2018 to 2022, with the largest area recorded in 2020 (265.69 km2). These results demonstrate the robustness of integrating Sentinel-1 and Sentinel-2 features for mapping salt marsh, particularly in complex mixed-vegetation zones. Such insights offer valuable guidance for the conservation and management of salt marsh ecosystems. Full article
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18 pages, 6748 KiB  
Article
FD-Net: A Single-Stage Fire Detection Framework for Remote Sensing in Complex Environments
by Jianye Yuan, Haofei Wang, Minghao Li, Xiaohan Wang, Weiwei Song, Song Li and Wei Gong
Remote Sens. 2024, 16(18), 3382; https://doi.org/10.3390/rs16183382 - 11 Sep 2024
Viewed by 1353
Abstract
Fire detection is crucial due to the exorbitant annual toll on both human lives and the economy resulting from fire-related incidents. To enhance forest fire detection in complex environments, we propose a new algorithm called FD-Net for various environments. Firstly, to improve detection [...] Read more.
Fire detection is crucial due to the exorbitant annual toll on both human lives and the economy resulting from fire-related incidents. To enhance forest fire detection in complex environments, we propose a new algorithm called FD-Net for various environments. Firstly, to improve detection performance, we introduce a Fire Attention (FA) mechanism that utilizes the position information from feature maps. Secondly, to prevent geometric distortion during image cropping, we propose a Three-Scale Pooling (TSP) module. Lastly, we fine-tune the YOLOv5 network and incorporate a new Fire Fusion (FF) module to enhance the network’s precision in identifying fire targets. Through qualitative and quantitative comparisons, we found that FD-Net outperforms current state-of-the-art algorithms in performance on both fire and fire-and-smoke datasets. This further demonstrates FD-Net’s effectiveness for application in fire detection. Full article
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