Deep Learning Techniques for Forest Parameter Retrieval and Accurate Tree Modeling from 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: closed (20 June 2023) | Viewed by 48126
Special Issue Editors
Interests: artificial intelligence for forestry; forest digital twin; LiDAR data; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; forest ecology; conservation biology; airborne sensors; GatorEye; landscape simulation models; ecosystem and canopy ecology
Special Issues, Collections and Topics in MDPI journals
Interests: forest digital twin; virtual reality; artificial intelligence for forestry
Special Issues, Collections and Topics in MDPI journals
Interests: Internet of Things in forestry; multispectral remote sensing; intelligence systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Deep learning and digital twin technologies have the potential to retrieve forest parameters and simulate the forest life cycle, which is beneficial for forest silvicultural management and tree phenotypic trait characterization. While the foundations of these technologies have been laid through proof-of-concept studies, we are now in a position to make transformative advances, especially in the forest studies.
In this issue, we welcome all studies which deploy deep learning technologies and digital twin techniques in forestry applications. We intend to cover some aspects including various remote sensing data analysis, deep learning method development, key issue remedy and forest scenario rendering, along with affording inspiration and heuristic concepts in the multidisciplinary field for promoting the implementation of the technologies in forestry.
Specific topics include, but are not limited to:
- Demonstration of deep learning methodologies for processing forest remote sensing data
- Software approaches to forest visualization and modeling
- Comparison between deep learning methods and other algorithms in forest survey
- Forest scenario reconstruction from LiDAR data or other remote sensing data
- Virtual forest management based on the virtual reality technology
- Computer graphics or machine vision algorithms to enhance the fidelity of the reproduced forest environment
- Prediction of the variations in forest growth properties based on deep learning frameworks from remote sensing data
- Application of multi-remote sensing data in combination with deep learning frameworks for forestry carbon sink measurement
- Processing terminal forest data acquired from various peripherals using deep learning approaches
Prof. Dr. Ting Yun
Dr. Eben Broadbent
Prof. Dr. Huaiqing Zhang
Prof. Dr. Ling Jiang
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
- remote sensing
- forest phenotypic traits
- tree modelling
- forest scenario rendering
- digital twin
- deep learning
- computer graphics
- machine vision
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.