Spaceborne LiDAR for Forest Disturbance Assessment
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".
Deadline for manuscript submissions: closed (30 November 2024) | Viewed by 521
Special Issue Editors
Interests: forest disturbances; forest modeling; land cover classification; LiDAR; machine and deep learning
Interests: data science; ecosystem demography; forest disturbances; LiDAR; machine and deep learning
Interests: lidar remote sensing (ALS, TLS, UAV-lidar, GEDI); tropical forest structure and ecology; industrial forest plantations, algorithms and tools development; data integration and change detection
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Understanding and managing forest disturbances are critical for maintaining ecosystem health, biodiversity, and the services forests provide, including carbon sequestration and habitat provision. Disturbances such as wildfires, hurricanes, insect outbreaks, and deforestation have profound effects on forest structure, function, and the global carbon cycle. In addressing these challenges, spaceborne Light Detection and Ranging (LiDAR) has proven to be an important tool, offering three-dimensional representations of forest canopies, essential for assessing forest structure and biomass. Products generated from spaceborne missions, like NASA’s GEDI and ICESat-2, offer unprecedented opportunities to advance our understanding of forest dynamics. Moreover, their integration with wall-to-wall remote sensing data presents an opportunity for the large-scale mapping and assessment of forest disturbances. This capability also enables more accurate estimates of carbon stock variations and aids in evaluating ecosystem recovery and resilience.
This Special Issue on “Spaceborne LiDAR for Forest Disturbance Assessment” aims to underscore the importance in understanding the dynamics of forest disturbances and to bring together state-of-the-art LiDAR applications at large scales.
We invite contributions that address the following topics:
- Novel approaches in processing spaceborne LiDAR data for forest disturbance assessment, with an emphasis on machine learning and deep learning techniques.
- Strategies for upscaling LiDAR measurements to model forest disturbances across regional and global scales.
- Integration of spaceborne LiDAR with other remote sensing data and algorithms to enhance the detection, quantification, and monitoring of forest disturbances.
- Methodologies that leverage LiDAR data for improved biomass and carbon stock estimation post disturbance.
- Reviews and perspectives on the future of spaceborne LiDAR technology in the context of the mapping and monitoring of forest attributes.
Dr. Inacio Bueno
Dr. Caio Hamamura
Dr. Carlos Alberto Silva
Guest Editors
Manuscript Submission Information
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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
- forest disturbance
- forest attributes
- spaceborne LiDAR
- ecosystem dynamics
- canopy height
- canopy cover
- forest aboveground biomass
- upscaling
- carbon dynamics
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