Bayesian detection of non-stand-replacing forest disturbances in satellite image time series

Non-stand-replacing forest disturbances are increasingly threatening Europe’s forests under climate change. Monitoring and mapping of these disturbances remain a challenge in remote sensing due to the small magnitude of change signals. We present a detection method for satellite image time series analysis based on the Kalman filter and the Neyman-Pearson lemma. The method (1) amplifies the spectral change signals from abrupt forest disturbances in time series data, and (2) compares the amplified change signal to a prior expectation. Through these improvements, detection performance is greatly improved, with initial results from six study areas across Finland showing an F₁-score of 0.7 for non-stand-replacing disturbances. Stand-replacing disturbances are detected by this method at an equal rate as the European Forest Disturbance Atlas and the Stochastic Continuous Change Detection methods. We demonstrate the theory behind this detection method along with initial results, sensitivity to different priors and potential for further improvement.