Most change detection algorithms are designed to detect specific forest disturbances, which may not effectively capture diverse events. These algorithms typically model seasonal changes using dense observations to reduce phenological noise, making them unsuitable for regions with frequent cloud cover. In this study, we propose a change detection algorithm for diverse forest disturbances of varying magnitudes using sparse Landsat time series. The Normalized Difference Moisture Index (NDMI) was spatially normalized (SNDMI) to remove forest seasonality, reducing the need for dense observations. Residuals obtained from SNDMI fitting using the spatial error model were input into the Exponentially Weighted Moving Average t (EWMA-t) chart, designed for sparse data and sensitive to low-magnitude disturbances. An adaptive strategy to the EWMA-t chart (AEWMA-t) that adjusts the weights of historical chart values and current residual statistics is introduced. Low-magnitude disturbances exceed control limits with small values, while high-magnitude disturbances prioritize current residuals for rapid detection. Disturbances are identified when chart values consecutively exceed control limits. Applied to a cloudy subtropical forest with diverse disturbances, the proposed algorithm achieved 84.6% and 89.3% accuracy in spatial and temporal domains, offering a reliable approach for detecting diverse disturbances in low-data regions.