Dense labelled time-series for mapping European forest disturbance agents

Abstract. Attributing disturbance agents to canopy mortality in European forests remains difficult due to sparse, heterogeneous, and often single-agent reference data. We present DISFOR (Viehweger et al., 2026), a uniformly re-interpreted ground-truth dataset of forest disturbance agents, designed to serve as training data for multi-temporal classification and analysis of disturbance agents with Sentinel-2 time-series. The dataset comprises 3,822 unique sample points, each defined at the 10×10 m pixel level, labelled by disturbance event and agent and fully temporally segmented into consecutive events and forest states for the years 2015–2024. Labels follow a three-level hierarchical scheme that supports analyses from broad "alive vs. disturbed" partitions to specific agents such as bark beetle, windthrow, wildfire, and salvage logging. Samples were drawn from multi-source ancillary data (e.g. EFFIS, FORWIND, Copernicus EMS, and regional forestry datasets) and consistently re-interpreted using an open-source interface and generally available primary data like Sentinel-2 and very high resolution imagery. For each sample we provide interpreter confidence, cluster identifiers to capture spatio-temporal autocorrelation, and additional metadata. Alongside interpreted samples, we release two Sentinel-2 data products tailored to complementary use cases: (i) a tabular single pixel reflectance time series between 2015–2024 and (ii) georeferenced 32×32-pixel image chips centred on the sampled points suitable for computer vision applications, with Python utilities for reproducible data loading and filtering. The dataset is suited for training and calibration of both change detection and agent attribution algorithms at sub-annual resolution, it supports training on single timestamps as well as on time-series, and facilitates studies that integrate spectral dynamics with spatial context. By harmonizing multiple first-level disturbance agent products and providing dense, temporally explicit labels, this resource lowers a major barrier to developing European forest disturbance and recovery monitoring.