Remote sensing of forested environments: The effects of a radiometrically porous and structurally complex surface

This study used foliar chemistry samples as calibration data to address the use of high spatial and spectral resolution hyperspectral and LiDAR data to model and predict foliar chlorophyll. We used linear multiple regression models to derive three relationships: total plot reflectance only, total plot integrated with LiDAR structure, and top of canopy reflectance. Results of the modeling suggest that nonfoliar reflectors degrade the results of the modeling and that the use of LiDAR-defined structural descriptors do little to help resolve this. The top of the canopy with the highest S/N yielded the best results. Preliminary analysis of LiDAR-related canopy structure yields some clues into the relationships with reflectance.