Automated detection of countrywide forest cover and forest gaps using alpha shape

This work aims to develop a highly automated workflow for generating a forest cover map and detecting forest gaps at the countrywide level (i.e. Switzerland) using the alpha shape approach (Edelsbrunner et al. 1983).

Forest provides society with several functions. In Switzerland e.g., more than 50% of the forests have a protection function and mitigate or prevent the impact of a natural hazard. The accurate detection of forest gaps (openings in the forest canopy) is crucial for properly managing and planning protection forests. In addition, knowledge of the distribution of forest gaps is a useful indicator to assess forest structure and biodiversity. Although the required information is collected at the plot level within the framework of the National Forest Inventory (NFI), remote sensing allows us to derive spatially explicit and accurate products at the pixel level for the entire country.

The countrywide available 1 m spatial resolution Vegetation Height Model (VHM) (Ginzler & Hobi, 2015) serves as a basis to extract forest cover and forest gaps. The VHM was generated from image-based point clouds acquired between 2013 and 2021 for the full coverage of Switzerland. In the first step, a forest cover map was derived using the VHM. In a second step, a dense forest cover map was generated and forest gaps were delineated taking into account the Swiss NFI forest definition criteria comprising minimum tree height and width, crown coverage, and land use. In summary, the overall workflow consists of extracting the tree top points from the VHM (FINT software). Erroneous tree tops were removed using the probability forest mask derived from Sentinel-1/-2 data (Rüetschi et al. 2021). We then derived forest area and non-forest area polygons from the filtered tree top points using alpha shape (lasboundary, LAStools from rapidlasso) that computes a boundary polygon that encloses the points.

A dense forest cover map is calculated using a moving window approach and forest areas greater than 60% are extracted. The forest gaps detection within the dense forest cover map follows a similar approach adopted for the forest cover map, but the alpha shape polygons are extracted from the VHM which is converted to the las format. The entire workflow is developed in Python.

Accuracy assessments of forest cover boundary and forest gaps based on terrestrial and stereo image-interpreted NFI plots are promising and reveal an overall agreement of more than 95% over the entire country.

Reference

Edelsbrunner, H., Kirkpatrick, D.G., Seidel, R., 1983. On the shape of a set of points in the plane. IEEE Transactions on Information Theory, 29(4), pp.551-559.

Ginzler, C. and Hobi, M.L., 2015. Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss National Forest Inventory. Remote Sensing, 7(4), pp.4343-4370.

Rüetschi, M., Weber, D., Koch, T.L., Waser, L.T., Small, D. and Ginzler, C., 2021. Countrywide mapping of shrub forest using multi-sensor data and bias correction techniques. International Journal of Applied Earth Observation and Geoinformation, 105, 102613.