Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral Imagery

Abstract

This research examined the utility of Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for estimating the biomass of three forest crops-sycamore, sweetgum, and loblolly pine-planted in experimental plots with a range of fertilization and irrigation treatments on the Savannah River Site near Aiken, South Carolina. Both vegetation index (VI) and red-edge positioning (REP) approaches were investigated to estimate the biomass associated with 12 treatment conditions. The optimum band pairs using the VI approach for biomass estimation were located mainly in the visible, NIR, and/or water absorption region around 970 nm, depending on the treatment conditions. Both the selected hyperspectral variables (i.e., VI and REP) resulted in good performance for biomass estimation for a range of treatment conditions except for those associated with loblolly pine. The hyperspectral variables were also examined to determine if they were able to identify the optimum fertilization treatment level. For the fertilization treatment conditions with good biomass estimation (R-2>0.9), their optimum treatment levels were successfully identified.