Modeling Saturation of Spectral Reflectance and Radar Data and Developing Corresponding Methods for Biomass Estimation of Various Forests

Dear Colleagues,

Forests play a critical role in reducing carbon concentrations in the atmosphere and in the mitigation of global warming. Thus, accurately estimating and mapping forest biomass/carbon at regional, national and global scales is very important. Various remotely sensed data including optical images, LiDAR and radar data have been used for the purpose. However, saturation of spectral reflectance and radar data impedes the increase of estimation accuracy of forest biomass/carbon, and currently there have been only few reports that deal with examining the saturation and searching for corresponding solutions. LiDAR data provide a solution for the purpose, but, their applications are limited because of high cost for estimating and mapping forest biomass/carbon for large areas. Therefore, there is strong need of modeling the saturation of spectral reflectance and radar data for biomass/carbon estimation of various forests and developing corresponding methods.

This Special Issue, "Modeling Saturation of Spectral Reflectance and Radar Data and Developing Corresponding Methods for Biomass Estimation of Various Forests”, will call for papers that demonstrate the original research that can overcome current significant gaps in examining the saturation of spectral reflectance and radar data and develop corresponding solutions. Review articles are also welcome. The topics will include: 1) examining the saturation of spectral reflectance of optical images for estimating and mapping biomass/carbon of various forest ecosystems; 2) examining the saturation of radar data for estimating and mapping biomass/carbon of various forest ecosystems; and 3) developing new methods and algorithms for overcoming the saturations.

Prof. Dr. Guangxing Wang
Prof. Dr. Dengsheng Lu
Prof. Dr. Qi Chen
Prof. Dr. Markus Holopainen
Dr. Liyong Fu
Guest Editors

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