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Despite the significant role of vegetation maps in understanding and monitoring patterns of rangeland ecosystems, limited work has been done in mapping rangeland vegetation especially in Africa. In this study, characterisation of vegetation composition and assessment of Landsat ETM+ and IKONOS spectral discrimination effectiveness for mapping rangeland physiognomic vegetation cover types using both maximum (ML) likelihood and fuzzy classifiers was done in Rakai and Kiruhura districts, South Western Uganda. Plot vegetation species growth form, cover and height data were collected from 450 sampling sites based on eight spectral strata generated using unsupervised image classification. Field data were grouped at four levels of seven, six, three and two vegetation physiognomic classes which were subjected to both ML and fuzzy classification using both Landsat ETM+ and IKONOS. Results of mapping accuracy assessment showed that IKONOS imagery classification was more accurate than Landsat ETM+. Fuzzy classification was associated with significantly higher mapping accuracy than ML (p<0.01). The highest overall accuracy with ML was 62.8% and 76.2% for Landsat ETM+ and IKONOS compared to 66.4% and 81% respectively when using fuzzy classification. Vegetation composition in the study area was shifting from woody to herbaceous dominated cover with predominance of stress resistance grass species. Improvement in mapping accuracy when using fuzzy classifier in this study provides useful insights in the limitations of maximum likelihood. There is need to investigate other classifiers in order to improve rangeland vegetation mapping and monitoring.

Classification and mapping of rangeland vegetation physiognomic composition using Landsat Enhanced Thematic Mapper and IKONOS imagery