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Abstract

This study was the first to use high-resolution IKONOS imagery to classify vegetation communities on
sub-Antarctic Heard Island. We focused on the use of texture measures, in addition to standard
multispectral information, to improve the classification of sub-Antarctic vegetation communities. Heard
Island’s pristine and rapidly changing environment makes it a relevant and exciting location to study the
regional effects of climate change. This study uses IKONOS imagery to provide automated, up-to-date,
and non-invasive means to map vegetation as an important indicator for environmental change. Three
classification techniques were compared:multispectral classification, texture based classification, and a
combination of both. Texture features were calculated using the Grey Level Co-occurrence Matrix
(GLCM). We investigated the effect of the texture window size on classification accuracy. The combined
approach produced a higher accuracy than using multispectral bands alone. It was also found that the
selection of GLCM texture features is critical. The highest accuracy (85%) was produced using all original
spectral bands and three uncorrelated texture features. Incorporating texture improved classification
accuracy by 6%.

Item Type:	Article
Authors/Creators:	Murray, H and Lucieer, A and Williams, R
Keywords:	Vegetation mapping Multispectral classification Grey level co-occurrence matrix (GLCM) Texture-based classification Sub-Antarctic Heard Island IKONOS imagery
Journal or Publication Title:	International Journal of Applied Earth Observation and Geoinformation
ISSN:	1569-8432
DOI / ID Number:	https://doi.org/10.1016/j.jag.2010.01.006
Additional Information:	The definitive version is available at http://www.sciencedirect.com
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