COMPARISONS AMONG VEGETATION INDEXES AND BANDWISE REGRESSION IN A HIGHLY DISTURBED, HETEROGENEOUS LANDSCAPE - MOUNT ST. HELENS, WASHINGTON

Spectral vegetation Indices have been used extensively to predict ecol ogical variables, such as percent vegetation cover, above-ground bioma ss, and leaf-area index. We examined the use of various vegetation ind ices and multiple linear regression using raw spectral bands for predi cting vegetation cover in a landscape characterized by high variabilit y in vegetation cover and soil properties. We were able to improve the explanatory value of several vegetation indices by using regression f itting techniques including log transformations and polynomial regress ions. We expected soil-adjusted indices to perform better than nonadju sted indices. However, soil-adjusted vegetation indices based on a rat io of red and near-infrared bands explained 55-65% of the variability in vegetation cover, while two nonadjusted indices each explained 70%. An index using six spectral bands explained 40%. The best multiple re gression model used the red and near-infrared bands and explained 75% of the variability in vegetation cover. Among the soil-adjusted indice s, an index which used a computed soil line performed best. Ratio-base d vegetation indices were less sensitive to shadow influences, but thi s influence was outweighed by the advantages of multiple regression ag ainst original bands. (C) Elsevier Science Inc., 1998.