Characterising mountain forest structure using landscape metrics on LiDAR-based canopy surface models

Forest structure is a key element to determine the capacity of mountain forests to protect people and their assets against natural hazards. LiDAR (Light Detection And Ranging) offers new ways for describing forest structure in 3D. However, mountain forest structures are complex and creative methods are therefore needed to extract reliable structural information from LiDAR. The objective of this study was to investigate if the application of landscape metrics to a normalised canopy model (nCM) allows an automatic characterisation of forest structure. We used a generic automated approach that created height class patches based on a segmented nCM. Two multi-resolution segmentations were carried out: level 1 objects represented tree crowns and collectives of tree crowns, level 2 objects represented forest stands. Level 1 objects were classified into four height classes and subsequently overlaid with level 2 stands in order to calculate the metrics 1) canopy density and vertical layering of the forest, 2) forest gap distribution and 3) canopy roughness using the Division Index (DIVI). Canopy density values of each height class allowed the classification of the vertical layering. Distinguishing between single- and multi-layered stands, 82% of all the sample plots were correctly classified. The DIVI calculated on gaps proved to be sufficient to describe the spatial arrangement of patches and distinguish between stands with many small gaps from stands with only a few but larger gaps. Canopy roughness could not satisfactorily be described using the DIVI based on the validation plots. With the approach presented, resource and natural hazard managers can assess the structure of forest stands and as such more easily take into account the protective effect of forests.

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