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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4017

Title: Spectral variability within species and its effects on savanna tree species discrimination
Authors: Cho, MA
Debba, P
Mathieu, R
Van Aardt, J
Asner, G
Naidoo, L
Main, R
Keywords: Savanna tree species
Spectral variability
Geoscience
Spectral angle mapper
Kruger National Park
Phenology
Intraspecies spectral variability
Remote sensing
Issue Date: Jul-2009
Publisher: IEEE
Citation: Cho, MA, Debba, P, Mathieu, R 2009. Spectral variability within species and its effects on savanna tree species discrimination. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009, pp 191-193
Abstract: Differences in within-species phenology and structure driven by factors including topography, edaphic properties, and climatic variables present important challenges for species differentiation with remote sensing in the Kruger National Park, South Africa. The objective of this study was to examine probable factors including intraspecies spectral variability and the spectral sample size that could affect remote sensing of Savanna tree species across a and-use gradient in the Kruger National park. Eighteen species were examined: Acacia gerradii, Acacia nigrescens, Combretum apiculatum, Combretum collinum, Combretum hereroense, Combretum imberbe, Combretum zeyheri, Dichrostachys cinerea, Euclea sp (E. divinurum and E. natalensis, Gymnosporia sp (G. buxifolia and G. senegalensis), Lonchocarpus capassa, Peltoforum africanum, Piliostigma thonningii, Pterocarpus rotundifolia, Sclerocarya birrea, Strychnos sp (S. madagascariensis, S. usambarensis), Terminalia sericea and Ziziphus mucronata. Discriminating species using the K-nearest neighbour (K = 1) classifier with spectral angle mapper (SAM) yielded a higher classification accuracy (48% overall accuracy) compared to 16% for the classification involving the mean spectra for each species as the training spectral set. Within-species spectral variability and the training sample size were identified as important factors affecting classification accuracy of the tree species. The authors recommend a nonparametric classifier such as K-nearest neighbour classifier for classifying and mapping tree species in a highly complex environment such as the savanna system of the Kruger National Park.
Description: Copyright: 2009 IEEE, International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, 12-17 July 2009
URI: http://hdl.handle.net/10204/4017
ISBN: 978-1-4244-3395-7
Appears in Collections:Earth observation
General science, engineering & technology

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