Forest Parameter Detection and Modeling Using Remote Sensing Data
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: 28 October 2024 | Viewed by 54
Special Issue Editors
Interests: forest management; orbital imagery; Lidar; mapping; digital photogrammetry; image analysis and processing; GIS; machine learning
Interests: remote sensing; forest ecology; synthetic aperture; Radar; forest management; radar; earth observation; environment science
Interests: classification; machine learning; forests; deep learning; Lidar; remote sensing; geoscience; data fusion; feature selection; multispectral image analysis; soil carbon content
Special Issue Information
Dear Colleagues,
Measuring forest parameters is crucial for forest inventorying, management, and conservation and remote sensing data derived from both active and passive sensors provide valuable information to estimate these parameters. With the rapid development of artificial intelligence and technologies such as machine learning, research into forest resource inventorying as applied to forest management has gained significant importance.
The aim of this Special Issue is to present the latest developments and applications of deep learning techniques for extracting and modeling forest parameters from remote sensing data. Topics include feature engineering, data augmentation, network architecture adaptation, model interpretation, and uncertainty quantification.
This Special Issue will feature original research articles demonstrating the benefits and challenges of deep learning methods in solving forest parameter measurement problems. Applications include deep learning for tree detection and diameter estimation, forest inventorying and planning, and structural and forest health estimation.
We welcome contributions introducing novel and innovative deep learning approaches to forest parameter detection and tree modeling and reviewing the current state and future prospects of deep learning for forestry. Papers addressing the practical issues and limitations of deep learning for forestry applications are also highly encouraged.
Prof. Dr. Marcos Benedito Schimalski
Dr. Vasco M. Mantas
Dr. Camile Sothe
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
- deep learning
- forest parameters
- remote sensing
- UAV
- digital photogrammetry
