Modeling of Forest Structure with Remote Sensing Data

Dear Colleagues,

Forests, a vital component of the global terrestrial ecosystem, cover approximately one-third of the Earth's land surface and store about half of the terrestrial carbon pool. They play a key role in maintaining global carbon balance and ecological functions. Accurate monitoring and simulation of forest structural parameters, such as tree height, crown width, and canopy density, are crucial for understanding forest growth dynamics, assessing carbon sequestration capacity, and formulating scientific management strategies. In recent years, rapid advancements in remote sensing technology have provided multi-platform, multi-scale data support for forest structure research, including high-resolution optical imagery, synthetic aperture radar (SAR), and LiDAR, promoting the transition of forest modeling from two-dimensional and static approaches to three-dimensional and dynamic paradigms.

This Special Issue of Forests aims to focus on cutting-edge methods and applications of remote sensing technology in forest structure modeling. It seeks to explore how the integration of multi-source data fusion, mechanistic models, and artificial intelligence algorithms can enhance the quantitative characterization of forest vertical structure, biomass distribution, and species diversity. Contributions may address, but are not limited to, the following directions:

• Synergistic Inversion of Forest Parameters based on Multi-Source Remote Sensing Data: Combining optical, SAR, and LiDAR data to develop accurate retrieval algorithms for key forest parameters such as forest height, biomass, and leaf area index (LAI), with particular attention to model optimization in complex forest environments.
• Structure Modeling from Individual Tree to Landscape Scales: Utilizing UAV LiDAR or high-resolution satellite imagery to achieve individual tree segmentation, canopy morphology analysis, and 3D stand structure reconstruction, while exploring uncertainty constraints in scale extension.
• Coupling Forest Dynamics with Ecological Process Modeling: Using remote sensing-driven ecosystem models to reveal the response mechanisms of forest structural evolution to climate change and disturbance events, and the linkages between structure and function.
• Application of New Technologies in Forestry Practices: Case studies on digital twin platform construction, afforestation effectiveness evaluation, and desertification control monitoring, highlighting the decision-support role of remote sensing in forest management and ecological restoration.

We welcome the submission of original research and review articles from scholars worldwide to jointly advance progress in the theoretical innovation, technological breakthroughs, and practical applications of remote sensing-based forest modeling, providing scientific tools for sustainable forest management and global change response.

Prof. Dr. Xin Tian
Prof. Dr. Xiaoli Zhang
Prof. Dr. Yanqiu Xing
Guest Editors

Manuscript Submission Information

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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 3D structure information
• height
• diameter at breast height
• canopy density
• leaf area index
• forest stem volume/carbon storage/biomass
• LiDAR/SAR/photogrammetry remote sensing
• forest dynamic information