Van Aardt, JANWynne, RH2007-07-022007-07-022007-01Van 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 431-4360143-1161http://hdl.handle.net/10204/835Copyright: 2007 Taylor and Francis LtdThree 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.enAVIRISAirborne visible/infrared imaging spectrometerForestryHyperspectral dataArticleVan Aardt, J., & Wynne, R. (2007). Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results. http://hdl.handle.net/10204/835Van Aardt, JAN, and RH Wynne "Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results." (2007) http://hdl.handle.net/10204/835Van Aardt J, Wynne R. Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results. 2007; http://hdl.handle.net/10204/835.TY - Article AU - Van Aardt, JAN AU - Wynne, RH AB - 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. DA - 2007-01 DB - ResearchSpace DP - CSIR KW - AVIRIS KW - Airborne visible/infrared imaging spectrometer KW - Forestry KW - Hyperspectral data LK - https://researchspace.csir.co.za PY - 2007 SM - 0143-1161 T1 - Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results TI - Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results UR - http://hdl.handle.net/10204/835 ER -