Deep Learning Techniques for Forests Parameter Retrieval and Accurate Tree Modeling from Remote Sensing Data—Volume Ⅱ

Dear Colleagues,

Due to the positive response from the researchers in the related domain regarding the Special Issue “Deep Learning Techniques for Forest Parameter Retrieval and Accurate Tree Modeling from Remote Sensing Data” belonging to the Journal of Forests, the succession of the second volume of this Special Issue with the same theme is set out herein to collect related manuscripts that convey the latest technologies applied in forestry. The original Special Issue of Volume I has been permanently closed.

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 can now make transformative advances, especially in 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 developments, significant issue remedies and forest scenario rendering, along with inspiring and heuristic concepts in the multidisciplinary field that promote implementing technologies into forestry.

Specific topics include, but are not limited to:

• The demonstration of deep learning methodologies for processing forest remote sensing data;
• Software approaches to forest visualization and modeling;
• A comparison between deep learning methods and other algorithms in a forest survey;
• Forest scenario reconstruction from LiDAR data or other remote sensing data;
• Virtual forest management based on virtual reality technology;
• Computer graphics or machine vision algorithms that enhance the fidelity of reproduced forest environments;
• The prediction of variations in forest growth properties based on deep learning frameworks from remote sensing data;
• The application of multi-remote sensing data in combination with deep learning frameworks for forestry carbon sink measurements;
• Processing terminal forest data acquired from various peripherals using deep learning approaches.

Prof. Dr. Ting Yun
Prof. Dr. Huaiqing Zhang
Prof. Dr. Ling Jiang
Dr. Eben Broadbent
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.

• remote sensing
• forest phenotypic traits
• tree modeling
• forest scenario rendering
• digital twin
• deep learning
• computer graphics
• machine vision