Gully erosion is an environmental problem recognized as one of the worst land degradation processes worldwide. Insight into regional gully perturbations is required to combat the serious on‐ and off‐site impacts of gullying on a catchment management scale. In response, we intersect different perspectives on gully erosion‐specific views in South Africa (SA), a country that exhibits various physiographic properties and spans 1.22 million km2. While the debate surrounding gully origin continues, there is consensus that anthropogenic activities are a major contemporary driver. The anthropogenic impact caused gullying to transcend climatic, geomorphic, and land‐use boundaries, although it becomes more prominent in central to eastern SA. Soil erodibility plays a crucial role in what extent of gully erosion severity is attained from human impact, contributing to the east–west imbalance of erosion in SA. Soil erosion rates from gullying and badlands are limited but suggest that it ranges between 30 and 123 t ha−1 yr−1 in the more prominent areas. These soil loss rates are comparable to global rates where gullying is concerned; moreover, they are up to four orders of magnitude higher than the estimated baseline erosion rate. On a national scale, the complexity of gullying is evident from the different temporal timings of (re)activation or stabilizing and different evolution rates. Continued efforts are required to understand the intricate interplay of human activities, climate, and preconditions determining soil erodibility. In SA, more medium‐ to long‐term studies are required to understand better how changing control factors affect gully evolution. More research is needed to implement and appraise mitigation measures, especially using indigenous knowledge. Establishing (semi)‐automated mapping procedures would aid in gully monitoring and assessing the effectiveness of implemented mitigation measures. More urgently, the expected changes in climate and land‐use necessitate further research on how environmental change affects short‐term gully erosion dynamics.

<p>Gully erosion affects land and water resources, resulting in serious environmental and socio-economic consequences. To aid mitigation and rehabilitation efforts, gully susceptibility mapping of broader gully-prone regions should be augmented by the rapid detection of existing gully features. Numerous works have been published on (semi-)automated approaches to detect gully erosion, most recently incorporating machine learning. However, upscaling and transferability capabilities of these approaches are rarely investigated. Establishing algorithms that are scalable and transferrable will constrain uncertainties when conducting quantitative analysis, allowing comparable results at different landscape scales and/or geo-environmental settings. Here, we aim to develop and apply a semi-automated approach based on Object-Based Image Analysis (OBIA) with low data needs, at different scales and geo-environmental regions. The segmentation process is underpinned by two gully morphological properties: 1) Height Above Nearest Drainage (HAND) and normalised slope, calculated from a Digital Elevation Model (DEM) with a spatial resolution of 2 m, with 93% coverage of South Africa’s 1.22 million km<sup>2</sup> expanse. HAND is a terrain model that normalises topography according to local relative heights above a drainage channel (herein, a gully channel). While this has been implemented in flood mapping studies for river systems, it remains unused in gully detection algorithms. Slope, which is often used as a gully predictor variable, is used to confine HAND and implemented here as a normalised slope input, calculated by subtracting a convolved mean slope value with a designated filter size from the DEM-derived slope. Detected gully features are refined using expert knowledge, merging, and pixel-based growing and shrinking. Preliminary development at a local gully scale suggests good performance, with an overall accuracy of 82.3% (includes a user accuracy of 65.5% of gully and 99.0% for non-gullied areas, and a producer accuracy of 98.5% for gully and 74.2% for non-gullied areas) and a kappa index of 0.65. We also discuss the broader performance of our approach when upscaling and implemented in other geo-environmental settings covered by the 2 m-DEM. </p>

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