Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure aboveground biomass and LAI to develop and validate hyperspectral-based models for estimating these measures remotely. Derivatives of individual hyperspectral bands improved the correlations between imaged data and actual on-site measurements. Six derivative-based normalized difference vegetation indices (DNDVI) were developed; three of them were superior to the commonly used normalized difference vegetation index (NDVI) in estimating aboveground biomass of YST, but did not improve estimates of LAI. The locally integrated derivatives-based vegetation indices (LDVI) from adjacent bands within three different spectral regions (the blue, red, and green reflectance ranges) were used to enhance absorption characteristics. Three LDVIs outperformed NDVI in estimating LAI, but not biomass. Multiple regression models were developed to improve the estimation of LAI and aboveground biomass of YST, and explained 75% and 53% of the variance in biomass and LAI, respectively, based on validation assessments with actual ground measurements.

Nomenclature: Yellow starthistle, Centaurea solstitialis L