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Bush encroachment (BE) describes a global problem severely affecting savanna ecosystems in Africa. Invasive species and woody vegetation spread out in areas where they are not naturally occurring and suppress endemic vegetation, mainly grasses. Livestock is directly affected by decreasing grasslands and inedible invasive species which are a result of the process of BE. For many small scale farmers in developing countries livestock represents a type of insurance particular in times of crop failure and droughts. Among that, BE is also becoming an increasing problem for crop production. Studies on the mapping of BE have so far only focused on smaller regions using high-resolution data and aerial photography. But they rarely provide information that goes beyond the local or national level. In our project, we aimed at a continental-wide assessment of BE. For this, we developed a process chain using a multi-scale approach to detect woody vegetation for the African continent. The resulted map was calibrated with field data provided by field surveys and experts in Southern and Eastern Africa. Supervised classification linked field data of woody vegetation, known as BE, to the respective pixel of multi-scale remote sensing data. The regression technique was based on random forests, a machine learning classification and regression approach programmed in R. Hotpots of woody vegetation were further overlaid with significant increasing Normalized Difference Vegetation Index (NDVI) trends which can refer to BE. Secondly, the probability of BE occurrence based on possible identified causes such as fire occurrence, mean annual precipitation rates, soil moisture, cattle density and CO2 emissions was analyzed. By this, possible areas for BE occurrence based on their pre-conditions and risk factors were identified. This approach includes multiple datasets derived from earth observation data to detect BE – a severe and ongoing global problem – at the continental level. Within the study´s duration of seven months, a method to upscale field data to a larger level could be developed. Nevertheless, improvement is needed to provide a reliable continental map on BE. Especially the integration of more field data will be needed which is currently under consideration. The identification of woody vegetation and the probability of its occurrence can help to prevent further ecosystem degradation. Moreover, sustainable land management strategies in these areas can be focused to support pastoralists and their livelihoods in rural areas.


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