Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic and random processes of change. The magnitude and prevailing signals of land use transitions are assessed taking into account net change and swap change. Moreover, spatiotemporal patterns and the relationship of precipitation and the Normalized Difference Vegetation Index (NDVI) are explored to evaluate landscape degradation. The assessment showed that 44% of net change and about 54% of total change occurred during the study period, with the latter being due to swap change. The conversion of over 39% of woodland to cropland accounts for the existence of the highest loss of valuable ecosystem of the region. The spatial relationship of NDVI and precipitation also showed R2 of below 0.5 over 55% of the landscape with no significant changes in the precipitation trend, thus representing an indicative symptom of land degradation. This in-depth analysis of random and systematic landscape change is crucial for designing policy intervention to halt woodland degradation in this fragile environment.

The objective of this study was to analyze patterns, dynamics and processes of land-use/cover changes in the transboundary Mara River Basin in East Africa. We specifically focused on deforestation and expansion of agriculture in the watershed. The intensity analysis approach was used to analyze data from satellite imagery-derived land-use/cover maps. Results indicate that swap change accounted for more than 50% of the overall change, which shows a very dynamic landscape transformation. Transition from closed forest to open forest was found to be a dominant landscape change, as opposed to a random change. Similarly, transition from open forest to small-scale agriculture was also found to be a dominant transition. This suggests a trend (pathway) of deforestation from closed forest to small-scale agriculture, with open forest as a transitional land cover. The observed deforestation may be attributed to continuous encroachment and a series of excisions of the forest reserve. Transition from rangeland to mechanized agriculture was found to be a dominant land-use change, which was attributed to change in land tenure. These findings are crucial for designing strategies and integrated watershed management policies to arrest further deforestation in the forest reserves as well as to sustainably control expansion of agriculture.

The objective of this study was to analyze patterns, dynamics and processes of land-use/cover changes in the transboundary Mara River Basin in East Africa. We specifically focused on deforestation and expansion of agriculture in the watershed. The intensity analysis approach was used to analyze data from satellite imagery-derived land-use/cover maps. Results indicate that swap change accounted for more than 50% of the overall change, which shows a very dynamic landscape transformation. Transition from closed forest to open forest was found to be a dominant landscape change, as opposed to a random change. Similarly, transition from open forest to small-scale agriculture was also found to be a dominant transition. This suggests a trend (pathway) of deforestation from closed forest to small-scale agriculture, with open forest as a transitional land cover. The observed deforestation may be attributed to continuous encroachment and a series of excisions of the forest reserve. Transition from rangeland to mechanized agriculture was found to be a dominant land-use change, which was attributed to change in land tenure. These findings are crucial for designing strategies and integrated watershed management policies to arrest further deforestation in the forest reserves as well as to sustainably control expansion of agriculture.

Land use transition in dryland ecosystems is one of the major driving forces to landscape change that directly impacts the welfare of humans. In this study, the support vector machine (SVM) classification algorithm and cross tabulation matrix analysis are used to identify systematic and random processes of change. The magnitude and prevailing signals of land use transitions are assessed taking into account net change and swap change. Moreover, spatiotemporal patterns and the relationship of precipitation and the Normalized Difference Vegetation Index (NDVI) are explored to evaluate landscape degradation. The assessment showed that 44% of net change and about 54% of total change occurred during the study period, with the latter being due to swap change. The conversion of over 39% of woodland to cropland accounts for the existence of the highest loss of valuable ecosystem of the region. The spatial relationship of NDVI and precipitation also showed R2 of below 0.5 over 55% of the landscape with no significant changes in the precipitation trend, thus representing an indicative symptom of land degradation. This in-depth analysis of random and systematic landscape change is crucial for designing policy intervention to halt woodland degradation in this fragile environment.