Advanced Remote Sensing Technology for Precision Forestry and Carbon Sink Assessment

Dear Colleagues,

The urgent need for sustainable forest management and effective climate change mitigation has propelled precision forestry and accurate carbon sink assessment to the forefront of research. Rapid advancements in remote sensing technologies—spanning high-resolution satellite imagery, unmanned aerial vehicles (UAVs), LiDAR, hyperspectral sensing, and synthetic aperture radar (SAR)—combined with artificial intelligence (AI) and big data analytics, are revolutionizing forest monitoring. These technologies enable a paradigm shift from traditional, labor-intensive field surveys to real-time, high-precision, and large-scale observations. Accurate quantification of forest structure, biomass, and carbon stocks is now feasible, supporting global efforts toward carbon neutrality and ecosystem conservation.

This Special Issue focuses on cutting-edge remote sensing technologies for precision forestry and carbon sink assessment. It highlights innovations in 3D structure modeling (LiDAR, SfM, and 3D point clouds), multi-source data fusion (optical/SAR/UAV), and dynamic monitoring (forest structure/function changes). Aligned with Remote Sensing’s mission to advance technological innovation and interdisciplinary applications, it provides a platform to share methods and case studies that bridge remote sensing with sustainable forest management and carbon neutrality.

We welcome original research, reviews, and technical communications on the following topics:

• Multi-source remote sensing data fusion (e.g., optical, SAR, LiDAR, and hyperspectral) for forest structure and biomass estimation;
• UAV and satellite-based high-spatiotemporal-resolution monitoring of forest dynamics;
• AI and deep learning for forest classification, change detection, and carbon stock mapping;
• 3D forest structure modeling using LiDAR and photogrammetric point clouds;
• Remote sensing of forest carbon cycles and carbon sink assessment;
• Hyperspectral and thermal remote sensing for forest health and stress monitoring;
• Integration of remote sensing with IoT and ground-based observations;
• Cloud computing and open-source platforms for large-scale forest monitoring;
• Applications in forest fire, pest, and disease detection;
• Digital twin development and intelligent decision support systems in forestry.

Prof. Dr. Jincheng Liu
Prof. Dr. Jia Wang
Prof. Dr. Tao Liu
Prof. Dr. Weiheng Xu
Dr. Zhichao Wang
Guest Editors

Manuscript Submission Information

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