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EXTRACTION AND CLASSIFICATION OF WETLAND FEATURES THROUGH FUSION OF REMOTE SENSING IMAGES IN THE OKAVANGO DELTA, BOTSWANA

The Okavango delta in northwestern Botswana is an extremely complex and dynamic wetland ecosystem. The spatial information on diverse wetland features of the delta is needed for hydrological modeling and water resources management. Due to large size and inaccessibility of the delta, satellite images provide the only viable means to reliably map and measure these features. For better identification and delineation of these features in the Okavango delta, efficient image analysis techniques are needed. The synergistic use of images from different sensors with varied spatial and spectral resolutions have the potential for better extraction and classification of features. This paper focuses on extraction and classification of landscape and land cover features through fusion of different resolution images acquired by Landsat 7 ETM+ and SPOT 5 HRG sensors over the Okavango delta. Both multispectral and panchromatic images from these two sensors are used. Different image fusion approaches are examined and used to increase reliability in feature interpretation. The effects of data fusion in recognition and extraction features are examined and illustrated. Thematic information extraction was carried out by means of supervised and unsupervised classification to produce landscape/land cover classes for different spatial resolution data set. The results indicate that as spatial resolution increases, high spatial frequency landscape/land cover features are extracted in increasing detail. However, spatial heterogeneity also increases with increasing spatial resolution.