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Hierarchical spatio-temporal classification of time features derived from Sentinel-2A+B time series (Level-2A data) using a Random Forest (RF) classifier with 200 trees. The selected time window ranges from 01-03-2018 to 31-10-2018 and covers the whole reference year 2018. In total, 59 statistical time features have been calculated using more than 138,000 samples, automatically collected from the LUCAS 2018 database and various CLMS products plus additional manual sampling. First, a binary Tree Cover Mask (TCM) with 2 classes (all non-tree covered areas / tree cover) has been generated. Various post-processing steps have been applied in order to improve the quality of the mask (e.g. correction of omission errors caused by the topographic overcorrection within the native Sentinel-2 Level-2A input data). Subsequently, the DLT has been seperately classified within the confines of the Tree Cover Mask. The final product will be accompanied by a series of quality layers (Confidence Layer, Data Score, Layer, Parent Scene Identification Layer).

Quality assurance follows the ISO 9001:2015 standards for Quality Management and comprises of dedicated procedures of quality checks (QA breakpoints) during implementation of the production chain, in order to keep persistent control over the various stages of production, assure fitness-for-purpose of the end-products and that all quality requirements are fulfilled. Priority has been given to the target thematic accuracy to be achieved by each product, as well as to the issues of product consistency (spatial, thematic, temporal) and homogeneity. Quality Assessment: The quality assessment has been performed according to INSPIRE Data Specifications. The data quality elements considered are: (i) Completeness, (ii) Logical Consistency, (iii) positional accuracy, (iv) Thematic Accuracy, (v) Temporal quality and (vi) Usability.

Geometric accuracy (positioning scale): Less than one pixel (10m) according to ortho-rectified satellite image base (Sentinel-2 Level-2A) delivered by ESA.

Thematic target accuracy: 90% producer and user accuracy.

Validation results DLT: 96.52% overall accuracy with a 95% confidence level applied (broadleved: 95.62% producer accuracy and 90.21% user accuracy; coniferous: 93.39% producer accuracy and 96.64% user accuracy).

Thematic accuracy has been assessed using a stratified random sampling approach with 9,695 points (area weighted), visually interpreted using VHR_IMAGE_2018 data and Sentinel-2 time series data, complemented by additional data sources like virtual globes (e.g. Google Earth Pro).

More information about this product can be found in the HRL Forest 2018 product specifications here: https://land.copernicus.eu/user-corner/technical-library/forest-2018-user-manual-v1-0.pdf