Classifying the Canadian Boreal forest´s structure using multi-modal remote sensing

One of the most fundamental new technical challenges of a DESDynI-R space-borne mission is the fusion of the several sensor modalities, including the onboard SAR and external LiDAR and Optical sensors in order to accurately estimate desired 3D vegetation structures and biomass parameters in areas where the sensors overlap. The objective of this paper is to use measured datasets in conjunction with our sensor models to develop a classification algorithm that fuses multi-modal remote sensing technologies with a minimal amount of ground information and yields an accurate estimate of forest structure including dry biomass and canopy height.