What question did this study set out to answer?

This research aims to identify high conservation value (HCV) forest areas using density analysis and GIS methods.

April 11, 2026Open Access

Delineating high conservation value forest areas using density analysis: It is not clear‐cut

Key Points

This research aims to identify high conservation value (HCV) forest areas using density analysis and GIS methods.
Used multiple official spatial datasets to identify HCV forests at a 10 m resolution.
Calculated HCV-forest density within moving windows of varying sizes around each grid cell.
Delineated HCV areas based on different density thresholds involving stakeholder engagement.
Findings show that smaller search windows yield greater precision and smaller HCV areas.
Larger search windows find larger HCV areas but could miss small or irregular shapes.
Low density thresholds identify small HCV areas, increasing non-HCV land, while high thresholds do the opposite.

Abstract

Abstract High conservation value (HCV) areas include natural habitats with high ecological, biological, social or cultural values. Using spatial analysis and Geographic Information Systems (GIS) to identify HCV areas is more cost‐efficient and less time‐consuming than conducting field surveys. GIS‐based approaches can also enable the identification of HCV areas in heterogeneous landscapes where, for example, HCV forests are scattered in a production landscape. This study explores the use of density analysis—computing the proportion of HCV‐forest cells in the neighborhood of each grid cell in a landscape—to delineate HCV forest areas in the county of Norrbotten, Sweden (99,000 km 2 ). First, multiple official spatial datasets were used to identify the existence of HCV forest with a resolution of 10 m. Second, the share of HCV forest in relation to total forest area (i.e., HCV‐forest density) within moving windows of varying sizes around each 10 m cell in the county was calculated. Finally, HCV areas were delineated using different HCV‐forest density thresholds. Stakeholders were involved at every step. Results are highly dependent on both the size of the moving window and the density threshold. Using a smaller search window results in greater precision and smaller HCV areas, while a larger search window identifies larger areas but fails to identify small or irregularly shaped HCV areas. Similarly, a low density threshold can be used to identify small and irregularly shaped HCV areas but includes more non‐HCV land. The opposite can be observed for a large density threshold. The use of density analysis for the purpose of delineating HCV areas in mixed forest landscapes can be effective for rationalizing the inventory of HCV areas, but method selection is critical and manual evaluation and adjustments are necessary. The potential for further method development, considering other relevant aspects, for example, ecological connectivity, is notable.

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