Journal
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 28, Issue 1-2, Pages 431-436Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431160500444772
Keywords
AVIRIS; spectrometer; forestry; hyperspectral data; classification
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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 (400-2500nm) 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% (3x3pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air- or space-borne sensors.
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