Forest Remote Sensing with Machine Learning Methods
A Special Issue of Remote Sensing (ISSN 2072-4292) belonging to the section "Forest Remote Sensing".
Deadline for manuscript submissions: 30 March 2027 | Viewed by 336
Editors
Interests: artificial intelligence; smart forestry; dual carbon research; smart education; big data applications
Special Issue Information
Dear Colleagues,
Forest remote sensing integrates multiple sources of observational data such as optical, synthetic aperture radar (SAR), laser radar (LiDAR), and hyperspectral data to characterize the structure, composition, and dynamic changes in forests. It serves as an important data foundation for biomass and carbon storage estimation, as well as forest monitoring. Meanwhile, machine learning and deep learning methods have been widely applied to tasks such as tree species classification, point cloud interpretation, and forest disturbance recognition, significantly improving the efficiency and accuracy of extracting forest remote sensing information. These advancements also provide new research topics for developing robust, transferable, and interpretable forest remote sensing machine learning methods.
This Special Issue focuses on the theoretical progress, methodological innovation and application research of machine learning methods in forest remote sensing. It particularly emphasizes multi-source data fusion, time-series modeling, and transfer learning, as well as model validation and interpretability for practical applications in forest remote sensing.
The topics for this Special Issue include, but are not limited to: multi-modal remote sensing data fusion and representation learning, such as optical-SAR, LiDAR-optical, and hyperspectral-LiDAR fusion; monitoring of forest disturbance and recovery based on Sentinel-1/2 time-series data; mapping of tree species and functional types, single-tree crown segmentation, and 3D point cloud analysis; forest remote sensing methods theory; forest remote sensing equipment and applications; and forest remote sensing applications, such as biomass estimation, bias correction, and uncertainty quantification, etc. Original research papers, method and data papers, review papers, and opinion articles are all welcome for submission.
Dr. Chao Mou
Dr. Hao Lu
Guest Editors
Manuscript Submission Information
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Keywords
- forest remote sensing and its applications
- machine learning and deep learning
- multi-source data fusion
- LiDAR
- synthetic aperture radar (SAR)
- hyperspectral remote sensing
- time-series remote sensing
- biomass/carbon stock estimation
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