Musasa, Tatenda2026-06-152026-06-152026https://hdl.handle.net/10566/24440This study aimed to evaluate the distribution and development of gully erosion in Zimbabwe using spatial-temporal analysis to capture changes that traditional field-based methods often miss. In order to achieve this, the study set four key objectives that formed the basis to enhance the multi-scale assessment of gully erosion. Firstly, the study assessed the potential of remotely sensed data (Landsat 8 and Sentinel 2 MSI) combined with in-situ data in soil erosion assessment and monitoring. The second objective was to model soil erosion risk in rural subcatchments of Zimbabwe using RUSLE, remote sensing and machine learning approaches. These methods were adopted since they are unique and offer novel insights in addressing soil erosion problems. The study third objective was to assess soil susceptibility in discrete rural dominated landscapes located in semi-arid environments. In addition, an integral chapter which tested the potential of the Weight of Evidence (WoE) geospatial modelling framework for gully erosion hazard assessment across different land management systems and ecological zones was considered. This will enable wide scale detailed application of the study findings in Zimbabwe to see how well the findings resonate to the country’s efforts in addressing land degradation problems. The study adopted machine learning and multi-source data to enhance the multiscale assessment and monitoring of gully erosion development in Zimbabwe. The methodology was more robust as it adopted cloud computing techniques that is GEE, combined with geospatial modelling to assess soil erosion dynamics as well as make a detailed assessment of how environmental factors influence gully erosion in Zimbabwe. The study methodology also applied Revised Universal Soil Loss Equation (RUSLE) model to understand the drivers of soil loss in the sub-catchments. The study findings demonstrate the unique strengths of the GEE cloud computing platform and its advanced image-processing techniques for soil erosion monitoring, assessment and soil loss risk estimation. It is clear that remotely sensed Landsat 8 and Sentinel 2 datasets can accurately map eroded areas. This shows that, machine learning and multi-source data combined with in situ data contribute towards accurate and near-real time assessment and monitoring of soil erosion in resource-constrained communities at catchment scale. The study findings show that, the Shashe sub-catchment had mean soil losses of 15.75, 45.25, and 23.51t ha−1 year−1 for 2016, 2020, and 2023, respectively. In the TugwiZibagwe sub-catchment, the mean soil losses were 11.62, 18.45, and 37.34 t ha−1 year−1 for the same years. The results also show that LULC changes were one of the major drivers to soil loss in the predominantly rural sub-catchments. The findings have also shown rather than applying the RUSLE to understand the soil erosion dynamics, the integration of several spectral matrices in GEE can also enhance the quality of soil erosion susceptibility mapping which can enhance the country’s efforts in reversing land degradation hence fulfilment of the Sustainable Development Goals. Derived gully erosion thematic maps showed that high erosion hazard risk areas were dominant in area close to the rivers. The predictive potential of the weight of evidence model applied in this study suggests that the various environmental factors applied, are useful in modelling gully erosion. The study integrated machine learning approaches with multi-source data in cloud computing to capture spatial-temporal changes that traditional methods often miss. This will provide baseline information for decision making in soil erosion monitoring and assessment in rural dominated landscapes located in data-scarce environments especially using the novel approaches such as Vegetation Indices which have not been fully exploited in soil erosion susceptibility in Zimbabwe yet they provide essential information.engully erosionZimbabweEarth ObservationLand Use Land ChangeSustainable Development GoalsThesis