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

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dc.contributor.author Van Aardt, JAN
dc.contributor.author Wynne, RH
dc.date.accessioned 2007-07-02T10:04:17Z
dc.date.available 2007-07-02T10:04:17Z
dc.date.issued 2007-01
dc.identifier.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 en
dc.identifier.issn 0143-1161
dc.identifier.uri http://hdl.handle.net/10204/835
dc.description Copyright: 2007 Taylor and Francis Ltd en
dc.description.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. en
dc.language.iso en en
dc.publisher Taylor and Francis Ltd en
dc.subject AVIRIS en
dc.subject Airborne visible/infrared imaging spectrometer en
dc.subject Forestry en
dc.subject Hyperspectral data en
dc.title Examining pine spectral separability using hyperspectral data from an airborne sensor : an extension of field-based results en
dc.type Article en
dc.identifier.apacitation Van 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/10204/835 en_ZA
dc.identifier.chicagocitation Van 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/10204/835 en_ZA
dc.identifier.vancouvercitation Van 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. en_ZA
dc.identifier.ris 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 - en_ZA


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