Continuous forest loss monitoring in a dynamic landscape of Central Portugal with Sentinel-2 data

Recent advances in satellite data availability, computing storage and processing power introduced a new land cover monitoring paradigm, settled on a continuous and timely identification of changes. The Continuous Change Detection and Classification (CCDC) algorithm has emerged as a powerful tool for continuous monitoring, being noteworthy for its ability to process high temporal frequency satellite data with components of seasonality, trend and break. Studies using CCDC were mostly limited to Landsat data, which offer lower spatial and temporal resolution in comparison to Sentinel-2 data. Therefore, our study aims to explore the potential of CCDC with Sentinel-2 data. For that purpose, an extensive reference dataset was developed for change detection accuracy assessment, comprising 290 sites of 200 m radius in a disturbance prone region in Central Portugal, ensuring an adequate representation of areas of vegetation loss. We focused on two specific forest species from this region, eucalyptus and maritime pine. Change date was determined through interpretation of orthophotos and satellite time series. We explored determinant aspects to CCDC performance, namely cloud and cloud shadow masking, algorithm parameterization, use of distinct vegetation indices and detection timeliness. Optimal accuracy was achieved with s2cloudless masking, lambda of 200, chi-square of 0.999, minYears of 1 and the Normalized Difference Vegetation Index. We computed the time lag vs omission error curve, showing comparable results (omission error rate close to 20 % was obtained with a time lag from 30 to 40 days) to methods designed to achieve near-real-time detection. Detections were spatially coherent, with patches of vegetation loss detected only with minor errors, mostly located in polygon borders. Disturbances in the first months resulted in poor model fitting, which undermined detection performance in some cases. Overall, results demonstrated how CCDC and Sentinel-2 data can be used to successfully monitor vegetation loss in a timely manner, especially as the satellite 's time series grows.