Estimating boreal forest canopy height and above ground biomass using multi-modal remote sensing; a database driven approach

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. While the DESDynI missions have been reduced and renamed, their original goal to fuse several sensor modalities to achieve an understanding of the global carbon cycle is still valid. 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. We use existing datasets to develop and validate our fusion and extrapolation approach, which involves using our four sensor simulators, including our fractal-based tree geometry generator, in tandem with our in-house parameter estimation software which performs fusion and retrieval functions. We then use existing field and radarlidar-VNIR data for the Boreas southern study area to validate our simulators in this region and construct a large set of boreal trees for use in our fusion and extrapolation processes.