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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/835

Title: Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results
Authors: Van Aardt, JAN
Wynne, RH
Keywords: AVIRIS
Airborne visible/infrared imaging spectrometer
Forestry
Hyperspectral data
Issue Date: Jan-2007
Publisher: Taylor and Francis Ltd
Citation: Van Aardt, JAN and Wynne, RH. 2007. Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results. International Journal of remote sensing. Vol. 28(1-2), pp 431-436
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 spectro-radiometer (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% (3 times 3 pixel radiance data), indicating that these species have strong potential to be classified accurately using hyperspectral data from air- or space-borne sensors.
Description: Copyright: 2007 Taylor and Francis Ltd
URI: http://hdl.handle.net/10204/835
ISSN: 0143-1161
Appears in Collections:Ecosystems processes & dynamics
General science, engineering & technology

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