New Advances in High-Resolution Remote Sensing Applied for Land Use/Land Cover and Forest Succession Monitoring
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 1 January 2026 | Viewed by 719
Special Issue Editors
Interests: spatial analysis; forest succession mapping; reclaimed areas; geoinformation; geomatics; LIDAR; Sentinel-2; image classification
Special Issues, Collections and Topics in MDPI journals
Interests: GIS; land use; sustainable land development; spatial analysis; spatial statistics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing technology has advanced rapidly in recent years, especially in the field of high-resolution remote sensing, where researchers can easily access many different data options and combinations: high-spatial-resolution images, multimodal data (e.g., optical, hyperspectral data, SAR, airborne LiDAR), and time series data (e.g., Landsat Archive, Sentinel Archive). This technology provides a wide range of techniques and tools for monitoring land use/land cover (LULC) changes and forest succession, which is critical for understanding the dynamics of ecosystems and their responses to environmental changes. High-resolution remote sensing images combined with advanced image processing algorithms allow us to accurately track in detail the dynamic changes in specific land surfaces driven by seasonal and trend changes.
This Special Issue aims to solicit research on the application of high-resolution remote sensing in various aspects of LULC and forest evolution dynamics, including but not limited to regional to global scales. We welcome submissions that focus on research results on multi-platform remote sensing data integration for LULC mapping, classification algorithm development, accuracy assessment, and LULC change detection.
Potential topics include but are not limited to the following:
Land use/land cover change analysis;
Using hybrid data and methods to improve LULC accuracy;
Historical evaluation of LULC and forest evolution based on time series analysis;
Forest succession processes, including tree growth, species distribution, and forest health assessment;
Impacts of climate change on LULC/forests and future projections;
High-resolution remote sensing imagery for agricultural land use monitoring, including crop type, crop health, and yield estimation;
Environmental impacts of land use and forest succession changes.
Dr. Marta Szostak
Dr. Beata Calka
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
- high-resolution remote sensing
- land use/land cover (LULC)
- forest evolution
- image classification
- artificial intelligence
- spatiotemporal analysis
- environmental impact assessment
- data fusion and integration
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