Dear Colleagues, 

Forests have an essential function in the global climate system, in large part through their uptake and storage of carbon, which slows the build-up of anthropogenic greenhouse gases in the atmosphere. Managing forests to enhance carbon stores as a strategy for mitigating future climate change requires accurate and reliable information for monitoring, reporting, and verification at the spatial and temporal scales that policy and decisions are made. The availability of new, state-of-the-art remote sensing systems is revolutionizing the way in which forests are measured and studied—from individual trees to continental extents. The current challenge is to integrate these multi-modal, multi-resolution, and spatio-temporally variable remote sensing data sets within inventory and modeling frameworks for the diagnosis of forest carbon stocks, attribution of their dynamics, and prediction of their future functioning.

This Special Issue invites papers highlighting cutting-edge research in the remote sensing of forest carbon, including advances in the data and methodologies used to measure the key biophysical parameters required for its estimation. For example, optical and hyperspectral data distinguish forest tree species composition, ranging technologies such as radar and lidar make detailed measurements of three-dimensional forest structure, and thermal sensing provides insight into forest ecosystem function. Remote sensors are collecting these data from the full range of terrestrial, UAS, airborne, and spaceborne platforms. We look to include papers in this Special Issue that describe emerging methods such as machine learning in the estimation and scaling of forest carbon attributes, as well as time-series algorithms leveraging the historical satellite record to provide perspectives on forest carbon dynamics. Studies demonstrating the remote sensing retrieval and integration of key forest parameters for initializing, calibrating, and/or validating mechanistic carbon cycle and land surface models are also encouraged. 

Dr. Daniel Hayes
Dr. Chad Babcock
Guest Editors

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