Sensitivity assessment on continuous landscape variables to classify a discrete forest area

Abstract Forestmapping at the landscape scale for resource planning and monitoring usually applies a discrete forestdefinition based on the primary forestuse. Forestuses, however, can change in space and time. Therefore, the dependent forestdefinition may no longer be appropriate. We analysed how the Swiss National Forest Inventorypartitions the continuous landscape resources into discrete forest/non- forestclasses of the Swiss Jura mountain rangeand how such a binary classificationaffects the representation of forestresources in space. Additionally, we performed a sensitivity analysis to assess the effect of different forestclassification thresholds representing different forestdefinitions on the spatial estimates of the dependent forestarea. Relevant landscape variables for forestarea estimations were sampled from coloured aerial imagery in the Swiss Jura mountain rangeon a rectangular 500 m × 500 m spaced grid. Each sample plot of 50 m × 50 m dimension containing all measured landscape variables was then classified as forestor non- forestbased on the Swiss National Forest Inventorydefinition, as well as other selected thresholds along the landscape variables’ range. The resulting forestarea as defined by the Swiss National Forest Inventorycovered 45.2% of the Jura mountain rangecontaining 86.7% of the overall tree vegetation. In agreement with the original forestuse, tree vegetation on forestsample plots was taller, denser and stands were larger than on non- forest plots. However, we identified considerable amounts of tree vegetation located outside of the forestdispersed all over the landscape. 73.3% of all the sample plots contained some fractions of tree vegetation, while only 18.0% were fully covered by trees. Among the different continuous landscape variables tree canopycover had a large effect on forestarea estimates, while the effects of tree canopyheight and stand width were found to be moderate. This study confirmed that the applied forestdefinition of the Swiss National Forest Inventoryextracted the spatial domain of forestsappropriately if the original forestuse is of interest. However, our results also indicate that a change in forestuse and hence forestdefinition result in considerably different spatial pattern and forestarea estimation. In the presented approach forestbecomes a dependent variable, whereas the independent raw data is represented by continuous landscape variables. As a consequence, different forestdefinitions for different forestuses can be applied in a subsequent analysis step. By this, forestarea estimates can be adapted easily and consistently to a range of new forestuses as applied in the third Swiss National Forest Inventory.