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Abstract: Aim: The aims of this study were to (1) estimate current rates of woody encroachment across African savannas; (2) identify relationships between change in woody cover and potential drivers, including water constraints, fire frequency and livestock density. The found relationships led us to pursue a third goal: (3) use temporal dynamics in woody cover to estimate potential woody cover. Location: Sub‐Saharan African savannas. Methods: The study used very high spatial resolution satellite imagery at sites with overlapping older (2002–2006) and newer (2011–2016) imagery to estimate change in woody cover. We sampled 596 sites in 38 separate areas across African savannas. Areas with high anthropogenic impact were avoided in order to more clearly identify the influence of environmental factors. Relationships between woody cover change and potential drivers were identified using linear regression and simultaneous autoregression, where the latter accounts for spatial autocorrelation. Results: The mean annual change in woody cover across our study areas was 0.25% per year. Although we cannot explain the general trend of encroachment based on our data, we found that change rates were positively correlated with the difference between potential woody cover and actual woody cover (a proxy for water availability; p < .001), and negatively correlated with fire frequency (p < .01). Using the relationship between rates of encroachment and initial cover, we estimated potential woody cover at different rainfall levels. Main conclusions: The results indicate that woody encroachment is ongoing and widespread across African savannas. The fact that the difference between potential and actual cover was the most significant predictor highlights the central role of water availability and tree–tree competition in controlling change in woody populations, both in water‐limited and mesic savannas. Our approach to derive potential woody cover from the woody cover change trajectories demonstrates that temporal dynamics in woody populations can be used to infer resource limitations. [ABSTRACT FROM AUTHOR]

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