Drone Based Information Fusion for Forestry Application and Conservation
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 (31 July 2025) | Viewed by 283
Special Issue Editors
2. Key Laboratory of Biodiversity Conservation of National Forestry and Grassland Administration, Beijing 100091, China
Interests: forest monitoring; landscape dynamics; remote sensing; ecosystem service
Interests: LiDAR remote sensing; 3D point cloud analysis; forest inventory
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Forest resource surveys play a crucial role in sustainable forest management and ecological environment protection. They provide essential data support, aiding in the formulation and implementation of effective forestry policies, biodiversity conservation, and climate change response. Traditional methods of forest resource surveys require a substantial work force and material resources. The advent of drone technology offers unprecedented opportunities for advancing forestry data collection, especially in areas with challenging geographical conditions that surveyors cannot easily access. Drone remote sensing technology can quickly obtain high-precision spatial remote sensing information of the required areas, enabling precise forest plot delineation at low cost, a high efficiency, and high timeliness.
This Special Issue, "Drone Based Information Fusion for Forestry Application and Conservation", aims to explore the innovative integration of drone-based data with other sources of information to enhance forest management and conservation strategies. Potential topics include, but are not limited to, the following: (i) filtering methods for UAV-LiDAR data in forested landscapes; (ii) forest monitoring, vegetated surface classification and tree species identification; (iii) biomass modeling, carbon sink, wildfire detection and illegal logging detection; (iv) resource survey, biodiversity assessment and ecological restoration; (v) forestry conservation and management for urban areas, forest parks, natural protected areas or national parks. By sharing the research findings and practical experiences, this Special Issue aims to showcase the latest research and developments in drone technology, fostering a deeper understanding of its potential to support sustainable forestry practices and conservation initiatives worldwide.
Dr. Sisi Yu
Dr. Zhenyang Hui
Dr. Xiao 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.
Keywords
- drone technology
- filtering methods
- multiply sources data fusion
- spatiotemporal dynamic
- forestry conservation
- forestry management
- natural protected area
- remote sensing
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