Exploring the Canadian boreal forest using airsar, LandSAT5, and virtual lidar

Data synergy or fusion is a mechanism whereby discrete types of data are used together to achieve a better understanding than was possible with each individually. Spanning over 30% of the Earth's landmass, the global forest plays a significant role in numerous planetary systems; the carbon cycle included. The objective of this paper is to couple simulated forest stands with measured datasets from various instruments to estimate a forest's mean canopy height and aboveground dry-biomass in regions where lidar measurements are sparse; we propose to create a virtual lidar instrument based on other readily available sensor measurements. We present a method to combine measured datasets 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. We show the performance of our proposed method in regions lidar measurements as well as in regions lacking these measurements. Finally, we present our method using virtual lidar and show that there is minimal degradation in our estimation.