Separating measurement from definition: an object-based workflow for national forest mapping
Year: 2026
Type: preprint
Abstract: Forest is defined rather than observed, and definitions are written to serve different management objectives. In Norway, four authoritative datasets place forest between 35 and 43% of the mainland because each encodes a different rule. Categorical forest maps inherit this problem, since each serves only the definition it was built on. We present a documented object-based workflow that separates measurement from definition. Very high-resolution imagery, Sentinel-2, and a laser-derived canopy height model are combined with the existing national masks in a spatial database, and the result is segmented in two steps into tree crowns, tree points derived from them, and forest-stand segments carrying height, crown cover, species, and site index. A rule-based expert system then classifies the tree points. Applied to mainland Norway, the workflow produced 2.72 billion tree crowns and 57.8 million forest-stand segments covering 144,755 km2. Because structure is stored as attributed objects rather than a single class, thresholding one dataset reproduces forest areas from 15.8 to 44.7% and brackets all four national estimates. Independent interpretation of 702 sampled points gave a commission error of 0.6% for the accepted tree points. The resulting dataset is called Skogmaske.
DOI: 10.31223/x5vj7b
Institution: Norwegian Institute of Bioeconomy Research
Language: English
Subfield: Forestry
Domain: Life Sciences
SDG: Life on land
Open Access status: green
Awards: G1323989911, G1349645249
OpenAlex ID: W7220404821