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30 October 2009 Discriminating tree species using hyperspectral reflectance data
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Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 74952X (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
With the widely application of Remote Sensing, the request for the accuracy of classification is getting higher and higher in each application fields. The aim of this paper is to test whether spectra reflectance of various tree leaves measured under ground-level conditions contain sufficient spectral information for discriminating tree species, and finds a way to discriminate tree species from their spectra reflectance. This study is one of the most important prerequisites to the future use of airborne and satellite hyper-spectral data. First, spectral reflectance of 8 tree species in Huazhong district including herbaceous, conifers and hardwoods which between 400nm and 900nm were recorded from canopy, using ASD hand-held Spectrometer. Next, the spectral were statistically tested using one-way ANOVA to see whether they significantly differ at every spectral location. Finally, the spectral separability between each tree species was quantified using the Jeffries-Matusita(J-M)distance measure. It turned out that the 8 species under study were statically different at most spectral locations, with a significant level of 0.01. Moreover, the J-M distance indices calculated for all species illustrated that the trees were spectrally separable.
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Yuanyong Dian and Shenghui Fang "Discriminating tree species using hyperspectral reflectance data", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 74952X (30 October 2009);

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