Dimension Expansion-based Spatiotemporal Land Cover Change Detection: A Study Case Using Sentinel-2 Satellite Time Series

Abstract. Satellite time series data enable continuous land cover change detection, classification, and monitoring across large geographical areas. Time series-based statistical methods for abrupt change detection remain widely used in understanding and monitoring environmental dynamics but face limitations, including sensitivity to noise, challenges in differentiating change classes and causes, detecting change in near real-time, and incomplete uncertainty quantification. These challenges are obvious in cultivated lands, where the seasonality and cultivated areas often alter in different years. On the other hand, change detection and classification in space-time is difficult due to the nonstationary presented in data. In this study we used dimension expansion-based approach that projects data to higher dimensionality for stationarity and understand the change of spatial stationarity over time. Our case study focuses on vegetation dynamics in a cultivated and managed terrestrial area in the Takamanda National Park in Cameroon, a protected area of significant ecological value, using Sentinel-2 satellite time series data. The results imply the possibility of new spatiotemporal approach that is robustness against noise and enables near-real-time monitoring.