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This work presents a new four-tier hierarchical change-point algorithm designed to detect land-cover change from satellite data. We tested the algorithm using Global Inventory Modelling and Mapping Studies (GIMMS) data for eastern Africa. Using a unique sequence of four statistical change-point detection methods, we identified significant increases or decreases in normalized difference vegetation index (NDVI), estimated the approximate time of change, and characterized the likely forms of change (i.e. linear trend, abrupt mean and/or variability change, and hockey-stick shaped change). Our method allows not just the identification of the change point but also the manner of change, and it can provide considerable insights into land-cover trajectories. In that sense, our approach has a significant advantage over other types of change-detection methods commonly reported in the remote-sensing literature. Although we demonstrated our algorithm using annual averages for coarse resolution data, our method can be easily adapted to finer spatial or temporal scale data, assuming assumptions of normality and independence are met. Overall, the changes detected by the algorithm are consistent with changes observed by other authors for the East Africa study area. We have demonstrated a powerful new tool for the detection of land-cover change using multi-temporal satellite data.
