Special Issue "3D Point Clouds in Forest Remote Sensing"
Deadline for manuscript submissions: 30 November 2020.
Interests: vegetation structure mapping; vegetation dynamics; RPAS data analysis; multispectral remote sensing; biodiversity monitoring
Interests: ALS; Photogrammetry clouds; Forest inventory; Forest fires; Forest monitoring; Forest modeling
3D point clouds have become a well stablished data source for characterizing and monitoring forest structure. Particularly, the use of such data from active sensors, like airborne LiDAR, has confirmed its interest in forest studies from its early development in the 1970’s and 1980’s, to the establishment of robust and cost-efficient systems from the 1990’s onwards, due to the improvement of global positioning and inertial units (GNSS/IMU). Even though airborne LiDAR has been the prevalent technology in forest 3D point cloud acquisition, other alternative or complementary technologies has been also present in forest studies at different scales in the last decades, namely airborne/shuttle/satellite radar, terrain laser scanning or photogrammetry from either photogrammetric or consumer grade cameras. Regarding the latter, the fast evolving of the Remotely Piloted Aircraft Systems (RPAS), along with the streamlining of consumer grade cameras data processing by computer vision software, has popularized the use of ultra-high resolution 3D point clouds at an unprecedent cost-efficiency and spatial-temporal flexibility for local scale studies.
This Special Issue aims at studies covering different uses of 3D point clouds acquired by different sensors and platforms in forest sciences. Topics may cover anything from the classical estimation of forest variables at a tree or stand level, to more comprehensive aims and scales. Hence, multisource data integration (e.g., multispectral, hyperspectral, and thermal), multiscale approaches or studies focused on forest ecosystem services monitoring, among other issues, are welcome. Articles may address, but are not limited, to the following topics:
- Tree and stand variables inventory
- Forest land cover mapping and pattern analysis
- Forest planning and management
- Forest ecology
- Forest change
- Biodiversity and wildlife
- Forest fuel and fire studies
- Biotic and abiotic forest damage
- Forest plants functional traits
- Carbon cycle/sequestration
- Terrain analysis
Dr. Ramón Alberto Díaz Varela
Dr. Eduardo Manuel Gonzalez Ferreiro
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 papers will be 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 2200 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 inventory
- Forest structure and function
- Forest dynamics
- Structure from motion
- Airborne laser scanning
- Terrain laser scanning
- 3D point cloud analysis
- Spectral and structural data fusion