Land Cover Change Detection: Emerging Algorithms and Applications in Remote Sensing
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 4
Special Issue Editors
Interests: remote sensing of environment; land use and land cover change; precision agriculture; crop monitoring; time series analysis; fractional vegetation cover; food security
Special Issues, Collections and Topics in MDPI journals
Interests: mathematical modeling on remote sensing; radiative transfer of vegetation; inversion of fractional vegetation cover; monitoring of biological crusts; short-lived vegetation
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: thermal remote sensing; vegetation remote sensing; crop classification; smart agriculture
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Land cover change (LCC) detection is a central theme in remote sensing, with increasing importance for understanding global environmental dynamics and supporting sustainable development. Recent advances in algorithms—such as deep learning, spatio-temporal modeling, and multi-source data fusion (e.g., optical, SAR, LiDAR)—are reshaping how LCC is monitored across both global and regional scales. At the same time, diverse applications, including urban expansion, agricultural transitions, forest degradation, wetland and water dynamics, and desertification and land degradation, demonstrate the wide relevance of LCC studies to ecosystem management and policy. Moreover, linking LCC with climate change impacts, ecosystem services, and the UN Sustainable Development Goals (SDGs) highlights its broader significance.
We invite contributions on innovative algorithms and frameworks for LCC detection, validation, and interpretation, as well as studies showcasing real-world applications that provide insights into environmental change and management. Both methodological advances and application-oriented research are welcome, with a focus on integrating data, models, and policy-relevant outcomes. Papers are solicited in, but not limited to, the following topics:
- Novel algorithms for land cover change detection
- Multi-source and multi-scale data fusion
- Cropland change detection
- Urban expansion and land-use transitions
- Forest, wetland, and water body dynamics
- Desertification and land degradation monitoring
- Accuracy assessment and uncertainty analysis
- Linking land cover change to climate, ecosystem services, and SDGs
Dr. Lili Xu
Dr. Xu Ma
Dr. Na Chen
Dr. Ran Huang
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
- land cover change detection
- deep learning and machine learning
- spatio-temporal analysis
- multi-source data fusion (optical, SAR, LiDAR)
- urban expansion and agricultural transitions
- forest, wetland, and water dynamics
- desertification and land degradation
- climate change and ecosystem services
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