Model-based estimation of large area forest canopy height and biomass using radar and optical remote sensing with limited lidar data

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 role in numerous planetary systems including the carbon cycle. The objective of this study 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 large regions spanning many square kilometers. We present a method to combine measured datasets with our sensor models to develop a feature estimation 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 in a region spanning over 60 km
²
.