Estimating Forest Canopy Characteristics as Inputs for Models of Forest Carbon Exchange by High Spectral Resolution Remote Sensing

Abstract

NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) was used to derive input parameters for a model of forest ecosystem carbon (C) balance. These parameters include canopy nitrogen (N) concentration and foliar biomass predicted with multiple linear regression equations using selected spectral bands, and species composition determined by means of a supervised image classification. The model predicted total net photosynthesis for the study area (mixed hardwood forest with conifer stands at the Harvard Forest in Petersham, Massachusetts, USA) with a spatial resolution of 20 m. The model was simulated five times using the input variables of species, foliar N concentration and foliar biomass derived from either field sampling or spectral data. Although the mean value for annual net photosynthesis over the 400-ha study site was similar when derived from both existing field data and remotely sensed data (656 g C m-2 and 630 g C m-2, respectively), the latter provided information on the spatial variability of photosynthesis throughout the study area that was not evident when coarse-scale field data were used.