Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy

Measurements of forest structure are important for wildlife habitat management. An optimal strategy for mapping forest structure would include detailed measurements of the vertical dimension, which are traditionally provided by field sampling, together with the broad spatial coverage afforded by remote sensing. While no single sensor is capable of delivering this at the present time, it should be possible to combine information from multiple sensors to achieve a reasonable approximation. In this study, we compare estimates of forest structural metrics derived from remote sensing to measurements obtained in the field (large tree maximum canopy height, mean canopy height, standard deviation canopy height, and biomass). We then statistically combine structural information from LiDAR, RaDAR, and passive optical sensors in an attempt to improve accuracy of our estimates. The results of this study indicate that LiDAR is the best single sensor for estimating canopy height and biomass. The addition of ETM+ metrics significantly improved LiDAR estimates of large tree structure, while Quickbird and InSAR/SAR improved estimates either marginally or not at all. The combination of all sensors was more accurate than LiDAR alone, but only marginally better than the combination of LiDAR and ETM+. Structure metrics from LiDAR and RaDAR are essentially redundant, as are ETM+ and Quickbird.