Forest mapping at the landscape scale for resource planning and monitoring usually applies a discrete forest definition based on the primary forest use. Forest uses, however, can change in space and time. Therefore, the dependent forest definition may no longer be appropriate. We analysed how the Swiss National Forest Inventory partitions the continuous landscape resources into discrete forest/non-forest classes of the Swiss Jura mountain range and how such a binary classification affects the representation of forest resources in space. Additionally, we performed a sensitivity analysis to assess the effect of different forest classification thresholds representing different forest definitions on the spatial estimates of the dependent forest area.
Relevant landscape variables for forest area estimations were sampled from coloured aerial imagery in the Swiss Jura mountain range on a rectangular 500 m x 500 m spaced grid. Each sample plot of 50 m x 50 m dimension containing all measured landscape variables was then classified as forest or non-forest based on the Swiss National Forest Inventory definition, as well as other selected thresholds along the landscape variables' range. The resulting forest area as defined by the Swiss National Forest Inventory covered 45.2% of the Jura mountain range containing 86.7% of the overall tree vegetation. In agreement with the original forest use, tree vegetation on forest sample 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 forest dispersed 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 canopy cover had a large effect on forest area estimates, while the effects,of tree canopy height and stand width were found to be moderate.
This study confirmed that the applied forest definition of the Swiss National Forest Inventory extracted the spatial domain of forests appropriately if the original forest use is of interest. However,our results also indicate that a change in forest use and hence forest definition result in considerably different spatial pattern and forest area estimation. In the presented approach forest becomes a dependent variable, whereas the independent raw data is represented by continuous landscape variables. As a consequence, different forest definitions for different forest uses can be applied in a subsequent analysis step. By this, forest area estimates can be adapted easily and consistently to a range of new forest uses as applied in the third Swiss National Forest Inventory.