Using hyperspectral data to assess forest structure

Abstract

More accurate maps of forest composition will result from the new hyperspectral instruments, which read the abundance and distribution of woody biomass and standing litter by picking up slight variations in pigment, water content, and soil properties. Such information allows direct inferences about physical processes rather than requiring managers to make statistical associations. Although more difficult to analyze and interpret than multispectral images, these data will raise the level of accuracy for making forest management decisions.