Advances in Remote Sensing Monitoring of Post-Disturbance Forest Recovery

Dear Colleagues,

Forests are facing unprecedented levels of both natural and anthropogenic disturbances; however, our understanding of their recovery from these disturbances remains limited. Monitoring post-disturbance forest recovery is crucial for reflecting forest resilience, determining landscape dynamics, and identifying sustainable forest management practices. Forest recovery can be measured in different dimensions such as structure (e.g., canopy cover, shape & width; tree age, diameter and height), composition (e.g., tree species and biodiversity), and function (e.g., productivity, biomass, carbon flux, and other services). Many factors such as climate, disturbance severity, disturbance regimes (e.g., fire, windthrow, extreme climate, land conversion, logging, insect & diseases), forest species, soil condition, and management can affect the post-disturbance forest recovery patterns.  Remote sensing has been suggested as a complementary tool for studying post-disturbance recovery, overcoming some limitations of field-based approaches. Forest disturbance occurrence, severity, location, and extent have been widely explored using various spaceborne and airborne remote sensors and change detection algorithms on various platforms such as ENVI, GEE and other GIS & RS software. Beyond these, fewer studies have devoted in post-disturbance forest recovery research. It is necessary to summarize the current progress in remote sensing indicators (metrics), methodology and platforms, and put forward new ideas to improve forest recovery monitoring. 

This Special Issue aims at studies relevant to new remote sensing technologies, new sensors, data collections, and processing methodologies that can be successfully applied in disturbance regime mapping, forest recovery monitoring, and post-disturbance management.

We welcome submissions that cover but are not limited to:

• Mapping forest disturbance recovery patterns at local and regional scales using the remote sensing approach;
• Improved or new methods or techniques for detecting time-series post-disturbance forest recovery;
• New metrics or methods for reflecting post-disturbance forest recovery in structure, composition and function (services);
• Forest disturbance regimes evaluation and monitoring with big data and artificial intelligence classification;
• Remote sensing-based assessments for the impacts of climate, forest type, disturbance severity, disturbance regimes and management on the post-disturbance forest recovery patterns;
• Remote sensing assessment for the impacts of forest management practices on forest recovery;
• Application of 3D mapping by photogrammetry, LiDAR, and SAR in post-disturbance studies.

Prof. Dr. Guangsheng Chen
Prof. Dr. Zoltan Szantoi
Dr. Santonu Goswami
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.

• forest disturbance
• forest recovery
• forest resilience
• forest regrowth
• canopy structure
• forest composition
• forest service
• productivity and biomass
• recovery trajectory
• disturbance regime
• disturbance severity
• big data and artificial intelligence
• remote sensing application
• forest management