Forest Parameter Extraction and Ecological Applications with UAV-Based Remote Sensing Techniques

Dear Colleagues,

Forest parameter extraction, particularly in the context of precision forestry and ecosystem management, has long been a crucial area of research. Traditional methods of forest inventory, such as field surveys, are labor-intensive and time-consuming, and sattelite-based remote sensing is often limited in spatial and temporal coverage. With the advancement of unmanned aerial vehicles (UAVs) and onboard sensors, there has been a paradigm shift in forest monitoring capabilities. The onboard sensors loaded on UAVs or drones can acquire high-precision data, including RGB, multispectral, hyerspectral, and point cloud data, and so on, enabling more detailed and accurate forest parameter extraction, ecological modelling, and applications.

This Special Issue aims to provide a platform for researchers and practitioners to showcase the latest advances in forest parameter extraction and ecological applications using UAV-acquired data, and works on the integration of deep learning techniques in processing UAV-acquired data are the most encouraged. The scope of this Special Issue includes, but not limited to:

• Automated extraction of individual and stand-level parameters from UAV-acquired data;
• Integration of optical and LiDAR data for improved forest parameter estimation;
• Methods for handling large-scale and complex UAV datasets;
• Assessment of the accuracy and uncertainty of deep learning-based forest parameter estimates;
• Applications of deep learning in precision forestry, forest management, and ecosystem services;
• Novel deep learning architectures for forest segmentation and classification.

This Special Issue seeks to showcase high-quality papers that present cutting-edge research in the field of forest parameter extraction using UAV-acquired data. The types of papers that are particularly encouraged include the following:

1. Methodological Developments: papers that propose novel deep learning algorithms or network architectures specifically designed for forest parameter extraction from UAV-derived data. These may include improvements in feature extraction, classification accuracy, or computational efficiency.
2. Case Studies: studies that demonstrate the application of deep learning techniques to real-world forest monitoring scenarios, highlighting their practicality, accuracy, and potential for operational use. Case studies should provide detailed descriptions of the data acquisition, preprocessing, and analysis steps, as well as an evaluation of the results.
3. Comparison Studies: papers that compare and contrast different deep learning methods or data sources (e.g., optical vs. LiDAR) for forest parameter extraction. These studies can provide valuable insights into the strengths and limitations of various approaches and data sources, and help guide future research directions.
4. Review Articles: comprehensive reviews of the current state-of-the-art in deep learning for forest parameter extraction and ecological applications using UAV-acquired data. These articles should synthesize existing research, identify key challenges and opportunities, and suggest future research directions.
5. Technical Reports: reports on technical advancements or best practices related to UAV data acquisition, preprocessing, or management that are essential for supporting deep learning-based forest parameter extraction.

In summary, this Special Issue aims to bring together researchers from diverse backgrounds, including remote sensing, forestry, computer vision, and machine learning, to share their latest findings and foster collaboration in the exciting field of forest parameter extraction and ecological application combining UAV-acquired data and deep learning techniques.

Prof. Dr. Jiayuan Lin
Dr. Haoming Xia
Dr. Hongxiang Wang
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.

• forest parameter extraction
• unmanned aerial vehicle (UAV) or drone
• remote sensing
• convolutional neural network (CNN)
• deep learning
• tree classification
• forest inventory
• precision forestry