Remote-Sensing Data in Estimating Inputs to Ecosystem Models

摘要

To better understand carbon cycling in forest ecosystems, several process-based ecosystem models have been developed. The forest leaf-area index (LAI) and other variables describing plant biomass are necessary to run these models. The only feasible means of estimating these forest variables for spatial extents of tens of meters and larger depend on remote-sensing instruments. In the past, reflectance index measurements based on optical remote-sensing data have been used to estimate LAI. This work involves developing algorithms for combining remote-sensing data in the microwave and optical regions of the electromagnetic spectrum (1) to increase the accuracy of LA1 estimates, as well as the estimates of other variables; and (2) to extend the range of validity of LA1 estimates beyond that achieved from optical data alone.