Mapping vegetation fractions in South Africa’s protected semi-arid savanna ecosystems: Assessing the impact of fractional cover proportions on the prediction accuracy

Abstract

Accurate estimation of vegetation composition is essential for effective conservation management strategies in protected savannas. In this study, we integrated annual temporal metrics from Sentinel-1 (S1) and Sentinel-2 (S2) time series at a 10 m resolution for 2017-2021 in Benfontein Nature Reserve (BNR). We trained a Random Forest Regression (RFR) model using training data derived from very high-resolution imagery (VHR) from Google Earth Pro^© and later used for results accuracy assessment and validation. Error metrics for predicting trees, shrubs and grasses were derived at each year. We selected the most important features for the most accurate models. Model error metrics were further derived by simplifying pixel fractions into broader fraction categories. Shrubs S1+S2 and S2 models produced the lowest mean absolute error (MAE) of 3.91%, while grass produced the highest MAE of 13.71% from S1 data. Trees produced the second-best accuracies obtained consistently with S1+S2 data. Trees achieved the highest R² values of up to 0.76% from S1+S2 data, followed by grass highest R² of 0.74% from S2 data. When predicting based on broader pixel categories, errors decreased significantly for near-pure pixels; with the largest decrease observed for pixels with 76% – 100% grass cover. Contrastingly, MAE for woody covers (shrubs and trees) increased as fraction in the pixel cover increased. Visible green and blue bands, VH and SAVI were the most important features for estimating trees. In addition to blue, VH and Coherence ranked high for shrubs, while blue and green ranked high for grass as well.
Conservation implications: The study is conducted in a nature reserve. Accurate information on composition and dynamics of vegetation is essential for parks management. Predicting change in woody cover is critical for monitoring woody encroachment that impacts herbivory and fires.