Remote Sensing Approach for Early Detection of Forest Disturbance
A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".
Deadline for manuscript submissions: 30 September 2025 | Viewed by 170
Special Issue Editors
2. College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
Interests: remote sensing; artificial intelligence; natural hazards; ecological environment
Special Issues, Collections and Topics in MDPI journals
Interests: radar remote sensing; machine learning and change detection; coastal wetlands mapping; GNSS; UAV LiDAR; SAR; multispectral and hyperspectral remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: environmental remote sensing; surface deformation; forest ecosystem research; ecological planning; watershed health indicators; water resources utilization
Special Issue Information
Dear Colleagues,
Forests are vital to the global ecological balance, yet they face numerous threats from natural and anthropogenic disturbances. The early detection of these disturbances is crucial for effective forest management, conservation, and restoration. Remote sensing technology offers a powerful tool for forest monitoring at various spatial and temporal scales and for the timely identification of changes in forest health, structure, and function.
This Special Issue invites original research articles, reviews, and case studies that explore innovative remote sensing approaches for the early detection of forest disturbances, as well as the ecological environment. Topics of interest include, but are not limited to, the use of satellite and airborne sensors, LiDAR, UAV-based imaging, and advanced image processing techniques such as machine learning and artificial intelligence. We are particularly interested in studies that addressing the challenges of detecting subtle or gradual changes in forests and ecological environment, distinguishing between different types of disturbances, and integrating multi-source data for comprehensive monitoring, forest species diversity, and ecological factors.
Contributions that demonstrate practical applications in forest management or that offer insights into the ecological impacts of forest disturbances are strongly encouraged. By bringing together cutting-edge research in this field, this Special Issue aims to advance our understanding of how remote sensing can be utilized for proactive forest disturbance detection and ultimately contribute to the sustainability and resilience of ecosystems worldwide.
Dr. Yaohui Liu
Dr. Peng Li
Guest Editors
Dr. Jin Wang
Dr. Pingjie Fu
Guest Editor Assistants
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. Forests is an international peer-reviewed open access monthly 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 2600 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
- forest disturbance
- forest monitoring
- ecological environment
- machine learning
- artificial intelligence
- high resolution
- LiDAR
- UAV
- recovery and management
- deep learning
- multispectral remote sensing
- infrared remote sensing
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