Landsat TM image segmentation for delineating geological zone correlated vegetation stratification in the Kruger National Park,South Africa |
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| Institution: | 1. Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, NT 0909, Australia;2. Department of Biogeochemical Processes, Max Planck Institute for Biogeochemistry, 07745 Jena, Germany;3. Darwin Centre for Bushfires Research, Charles Darwin University, Darwin, NT 0909, Australia;4. CSIRO Tropical Ecosystems Research Centre, Australia;1. University of Trier, Department of Environmental Remote Sensing and Geoinformatics, Behringstr. 21, 54286 Trier, Germany;2. Freie Universität Berlin, Institute of Geographical Sciences, Remote Sensing and Geoinformatics, Malteserstr. 74-100, 12249 Berlin, Germany;3. Justus Liebig University of Giessen, Institute of Agricultural Policy and Market Research, Senckenbergstraße 3, 35390 Giessen, Germany;1. Department of Geography and Geoinformation Science, George Mason University, Fairfax, VA, United States;2. Department of Geographical Sciences, University of Maryland College Park, College Park, MD, United States;3. School of Geosciences, University of Edinburgh, Edinburgh, United Kingdom;4. International Rice Research Institute, South Asia Research Center, Varanasi, India;5. Precision Agriculture Group, Advanced Agriculture and Food Cluster, Council for Scientific and Industrial Research, Pretoria, South Africa;7. Faculty of Natural and Agricultural Sciences, University of Pretoria, Pretoria, South Africa;8. Global Change Institute, University of Witwatersrand, Johannesburg, South Africa;1. Department of Wildlife Ecology and Conservation, University of Florida, 110 Newins-Ziegler Hall, Gainesville, FL 32611, USA;2. Department of Biological Sciences, University of Swaziland, Kwaluseni, Manzini, Swaziland;3. Biometric Research, Albany, Western Australia, Australia |
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| Abstract: | Image classification approaches are widely used in mapping vegetation on remotely sensed images. Vegetation assemblages are equivalent to habitats. Whereas sub-pixel classification approaches potentially can produce more realistic, homogenous habitat maps, pixel-based hard classifier approaches often result in non-homogenous habitat zones. This salt-and-pepper habitat mapping is particularly a challenge on images of savannas, given the characteristic patchy texture of scattered trees and grass. Image segmentation techniques offer possibilities for homogenous habitat classification. This study aimed at establishing the extent to which established, field surveyed and geology-related vegetation types in South Africa’s Kruger National Park (KNP) can be reproduced using image segmentation. Rain season Landsat TM images were used, selected to coincide with the peak in vegetation productivity, which was deemed the time of year when discrimination between key habitats in KNP is most likely to be successful. The multiresolution segmentation mode in eCognition 5.0 was employed, object classification accomplished using the nearest neighbour (NN) classifier, using object texture and training area mean values in the NN feature space.Compared to delineations of the vegetation types of KNP on a digital map of the vegetation zones that was tested, image segmentation successfully mapped the zones (overall accuracy 85.3%, K^ = 82.7%) despite slight shifts in the location of vegetation zone boundaries. Maximum likelihood classification (MLC) of the same images was only 37% accurate (K^ = 24.2%). Whereas the vegetation zones resulting from MLC were non-homogenous, with considerable spectral confusion among the vegetation zones, image segmentation produced more homogenous vegetation zones, comparably more useful for conservation management, because realistic and meaningful habitat maps are important in biodiversity conservation as input data upon which to base management decisions. Image segmentation appears to be a useful approach in mapping savanna vegetation. |
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