Predicting priority management areas for land use/cover change in the transboundary Okavango basin based on machine learning.

Remote sensing and modelling of land use/land cover (LULC) change is useful to reveal the extent and spatial patterns of landscape changes at various environments and scales. Predicting susceptibility to LULC change is crucial for policy formulation and land management. However, the use of machine learning (ML) for modelling LULC change is limited. This study modelled LULC change susceptibility in the Okavango basin using ML techniques. Areas with high LULC change susceptibility are termed priority management areas (PMAs) in this study. Trajectories of LULC change between 1996 and 2020 are derived from existing LULC change maps of the Okavango basin. Overlay analysis is then used to detect patches of LULC change transitions. Three LULC transitional categories are adopted for modelling PMAs, namely 1) from natural to anthropogenic classes (Category A); 2) from anthropogenic to natural classes (Category B); and 3) from natural to another natural class (Category C). An ensemble of ML algorithms is calibrated with categories of LULC change and social-ecological drivers of change to produce maps showing the susceptibility of LULC change in the basin. Thereafter, thresholding is done on probability maps of susceptibility to LULC change based on the maximum sum of sensitivity and specificity (max SSS) to delineate PMAs. Results for trajectories of LULC change indicate that anthropogenic activities (croplands, built-up areas, and barelands) generally expanded, displacing natural areas (wetlands, woodlands, water, and shrubland) from 1996 to 2020. Regarding PMAs, anthropogenic-related PMAs (Category A ∼34 560 km