Use of high resolution lidar and hyperspectral data to evaluate the sensitivity of net ecosystem exchange to stand structural and plant chemical properties

Abstract

We use a fused high resolution Lidar and hyperspectral dataset to produce gridded parameter maps that are used as input into land surface models (LSMs). The maps contain stand structural information (e.g. leaf area index) derived from lidar data and information derived from reflectance data (e.g. chlorophyll concentration). Ground based leaf level measurements (e.g. maximum rate of carboxilation and the potential rate of electron transport) are upscaled to the area with the remotely sensed data. Using both LSM output and flux data analysis we demonstrate that the spatial variability of these parameters has a major impact on the resulting net ecosystem exchange. To account for this spatial variability we propose to use the parameter maps derived from the remotely sensed data to replace fixed parameters in the LSM.