Remote Sensing Application in Forest Biomass and Carbon Cycle

Dear Colleagues,

Forests are the dominant terrestrial ecosystem of the earth, and they contain about 606 gigatonnes of living biomass and account for 80% of the earth's total plant biomass. As the main body of the terrestrial ecosystem, the change of forest biomass and carbon storage reflects the influence of forest succession, human activities, natural disturbance, and climate change, which is of great significance in the study of the global terrestrial ecosystem carbon cycle and climate change. Over the past few years, significant progress has been made in the remote sensing monitoring of forest above-ground biomass and the carbon cycle. Multi-resource remote sensing including airborne/spaceborne multi-and hyperspectral, LiDAR (e.g., the new spaceborne GEDI and ICESat-2), interferometric SAR, and polarimetric interferometric SAR (PolInSAR) can generate regional to global maps of forest above-ground biomass. Meanwhile, novel approaches have improved the accuracy of forest biomass estimation, such as a combination of lidar data and mechanistic models, fusion of multispectral and lidar, and the application of machine learning and deep learning.

This Special Issue focuses on the application of remote sensing in forest above-ground biomass and carbon cycles, all original research findings and perspectives relative to forest biomass estimation are welcomed

Dr. Qisheng He
Dr. Wenmei Li
Guest Editors

Manuscript Submission Information

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