Oskar Englund 

High conservation value (HCV) areas include natural habitats with high ecological, biological, social or cultural values. The use of spatial analysis using Geographic Information Systems (GIS) to identify HCV areas is more cost-efficient and less time consuming that field surveys. GIS-based approaches can also be necessary for identifying HCV areas in heterogeneous landscapes where, e.g., HCV forests are scattered across a production landscape.

This study explores the use of density analysis to identify and delineate HCV forest areas in the county of Norrbotten, Sweden (99,000 km2). 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 a moving window of varying size around each 10 m cell in the county was calculated. Finally, HCV areas were delineated using different thresholds for HCV forest density. Stakeholders were involved in every step.

The results show that outcomes are highly dependent both on the size of the moving window and the density threshold. The use of 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 results in inaccurate delineation of larger homogenous HCV areas. 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, e.g., ecological connectivity, is notable.