Spectral Mixture Analysis and Geometric-Optical Reflectance Modeling of Boreal Forest Biophysical Structure

Abstract

Abstract Biophysical structural information such as biomass, LAI, and NPP are important inputs to regional scale models of ecosystem processes and photosynthetic activity within boreal forests. However, traditional methods such as NDVI for deriving these variables from remotely sensed data have been inconsistent and unsatisfactory due to factors such as the confounding influence of background reflectance and canopy geometry on the overall pixel signal. To address this problem, we present new results which use spectral mixture analysis to determine areal fractions of sunlit canopy, sunlit background, and shadow at subpixel scales for predicting these biophysical variables. Geometric-optical reflectance models are used to estimate sunlit canopy component reflectance for input to the analysis together with field measures of background and shadow reflectance. In this article, we compare cylinder, cone, and spheroid models of canopy geometry and evaluate the importance of solar zenith angle variations in reflectance estimates for mixture fractions. These are computed from helicopter MMR radiometer data for 31 stands of black spruce along a gradient of stand densities near the southern fringe of the North American boreal forest. Component fractions are evaluated against ground data derived from dense-grid point analyses of coincident high resolution color photography and also for predicting biophysical variables. In general, the Li–Strahler spheroid model was better than the cone and cylinder models and the importance of correcting for solar zenith angle (SZA) was illustrated, with significant improvements noted for higher SZA as a result of corrections for canopy mutual shadowing. The best overall results were obtained from the shadow fraction using a spheroid model of canopy geometry at SZA 45°. Linear regression analyses showed biomass could be estimated with r2 values of 0.83 and a standard error (S.E.) of 1.7 kg/m2; LAI: r2=0.82, S.E.=0.46; and NPP: r2=0.86, S.E.=0.05 kg/m2/yr. These results were significantly higher than with NDVI for estimating biomass (r2=0.44), LAI (r2=0.60), and NPP (r2=0.56). Current and future areas of research are outlined towards improving our understanding of carbon cycling in large forested ecosystems as a function of variability in the physical climate system and environmental change.