Optical Indices as Bioindicators of Forest Condition from Hyperspectral CASI data

Abstract

This paper reports on progress made to link physiologically-based indicators to optical indices scaling-up from leaf level to the canopy through SAIL and Kuusk Canopy Reflectance Models (CR). Hyperspectral CASI data of 2m spatial resolution, 7.5 nm spectral resolution and 72 channels were collected in 1997 and 1998 deployments over twelve sites of Acer saccharum M. in the Algoma Region, Ontario (Canada). A field sampling campaign was carried out for biochemical analysis of leaf chlorophyll and carotenoid concentrations, and fluorescence along with leaf reflectance and transmittance. Leaf-level relationships obtained between optical indices and biochemical indicators were scaled-up to canopy level through CR models using input model parameters related to the canopy structure and viewing geometry at the time of data acquisition. The result is an algorithm which predicts leaf-level bioindicators from airborne hyperspectral imagery. A modeling study was carried out to determine the influence of CR parameters such as the Leaf Angle Distribution Function (LADF), LAI and the viewing geometry on the four types of optical indices used in this study: Visible Ratios, Visible/NIR Ratios, Red Edge Reflectance-Ratio Indices, and Spectral and Derivative Red Edge Indices. Nominal CR model parameters appear to be sufficient to allow accurate application of the optical index/bioindicator algorithm to airborne data.