Derivation of Forest Leaf Area Index from Multi - and Hyperspectral Remote Sensing Data

Abstract

This study evaluated systematically linear predictive models between vegetation indices (VIs) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of leaf area index (LAI) (n=40). Ratio-based and soil-line related broadband VIs were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VIs involved all possible types of 2-band combinations of RVI and PVI. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A perpendicular vegetation index (PVI) based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) (R2=0.67, RMSE=0.69m2m-2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R2=0.77, RMSE=0.54m2m-2 (17% of the mean)). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI with good accuracy. Best hyperspectral VIs in relation with LAI are typically based on wavebands related to prominent water absorption features. Such VIs measure the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.