Airborne and Terrestrial Laser Scanning in Forests
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 June 2024 | Viewed by 1060
Special Issue Editors
Interests: remote sensing image understanding; deep learning; high-performance computing
Interests: artificial intelligence in forest ecosystem monitoring; multi-scale sensor fusion for natural resource mapping wildland fuel and fire connectivity; tree morphology assessment and simulation
Interests: satellite; satellite image processing; geospatial science; image matching
Special Issue Information
Dear Colleagues,
Nowadays, airborne and terrestrial laser scanning are widely utilized to collect point cloud data for hectare- or larger-scale forest resource surveys, aiding in the estimation of tree and stand attributes, detection and delineation of tree crowns, analysis of forest borders, changes and its ecosystem and biodiversity, etc.
In this Special Issue, we welcome new research progress and contributions for forest-related research using airborne and terrestrial laser scanning (or combining other earth observation data). We look forward to novel datasets, new algorithm design or broader application domains in forest resource surveys.
Traditional pipelines for extracting different forest structural attributes from point cloud data include pulse-based or voxel-based gap probability methods (used for the estimation of plant or leaf area indexes of forest stands) and geometrical modeling (used for explicit reconstruction of individual tree structure). In addition, radiative transfer models (RTMs) also improve the accuracy of the retrieval of forest biophysical properties when coupled with earth observation data.
Over the recent years, artificial intelligence, especially the machine learning and deep learning model, has had a significant effect on processing point cloud data and shown great potential in forest resource surveys. Moreover, fusing other multi-modal data (such as optical remote sensing data or GIS data) with point cloud data (collected from UAV, airborne and spaceborne) has become a popular way to improve the performance in regional and large-scale forest resource surveys.
We are looking for papers that focus on forest-related surveys using point cloud data from airborne and terrestrial laser scanning (or combining other kinds of remote sensing data or GIS data), including forest parameter estimation (such as diameter at breast height, tree height, aboveground biomass, leaf area index, etc.), tree detection (such as location, delineation, counting, species classification, etc.) and large-scale forest surveys (such as forest borders, change detection, forest ecosystem, biodiversity, etc.)
Dr. Juepeng Zheng
Dr. Zhouxin Xi
Dr. Zhen Ye
Dr. Shangshu Cai
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. 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
- airborne and terrestrial laser scanning
- forest parameter estimation
- individual tree detection and delineation
- point cloud data processing
- data fusing
- radiative transfer models
- machine learning
- deep learning
