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More information can be found in the Satelligence Resource Centre.

Satelligence's deforestation analysis methodology integrates a robust forest baseline creation process with advanced change detection algorithms to identify forest loss with high accuracy.

The forest baseline is generated using a hybrid approach that merges curated and harmonized open datasets (e.g., JRC Tropical Moist Forest Layers, UMD primary forest maps) with Satelligence's proprietary data based on forest classification models. These models apply FAO and national forest definitions, categorizing forests into primary, disturbed, regrowth, dry forest, and native vegetation classes.

Open data layers are quality-checked, standardized, and adjusted to remove areas deforested before defined cutoff dates (e.g., 31 Dec 2020). Proprietary commodity plot data, based on mapped EUDR commodities and commodities like sugar cane & coconut, are created using Landsat, Sentinel-1, and Sentinel-2 data, and are added as an additional layer to eliminate false positives, especially in agricultural production landscapes.

Satelligence uses two change detection algorithms — one for optical data and one for radar data.

Satelligence's forest detection methodology ensures temporal consistency, minimizes false positives and negatives, and supports multiple reporting frameworks (EUDR, NDPE, RSPO) for near real-time deforestation monitoring.

Besides using and developing commodity maps (used as a layer to distinguish operational/production areas from forests), Satelligence uses the following data sources to develop their forest baseline: