Examining pine spectral separability using hyperspectral data from an airborne sensor: An extension of field‐based results

Abstract

Three southern USA forestry species, loblolly pine (Pinus taeda), Virginia pine (Pinus virginiana), and shortleaf pine (Pinus echinata), were previously shown to be spectrally separable (83% accuracy) using data from a full‐range spectroradiometer (400–2500 nm) acquired above tree canopies. This study focused on whether these same species are also separable using hyperspectral data acquired using the airborne visible/infrared imaging spectrometer (AVIRIS). Stepwise discriminant techniques were used to reduce data dimensionality to a maximum of 10 spectral bands, followed by discriminant techniques to measure separability. Discriminatory variables were largely located in the visible and near‐infrared regions of the spectrum. Cross‐validation accuracies ranged from 65% (1 pixel radiance data) to as high as 85% (3×3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air‐ or space‐borne sensors.

Keywords:

• AVIRIS
• spectrometer
• forestry
• hyperspectral data
• classification

Acknowledgments

This research was made possible by funding and data from NASA (grant no. NG65‐10548; NG613‐03019), the McIntire‐Stennis Research Program (grant no. VA‐136589), Science Applications International Corporation, and the Forestry Department at Virginia Polytechnic Institute and State University.