Potential and limits of extraction of forest attributes by fusion of medium point density LiDAR data with ADS40 and RC30 images.

This study presents an approach for semi-automated derivation of forest attributes (area, composition, stands) by fusion of medium point density LiDAR data with ADS40 and RC30 images to support tasks of the National Forest Inventory (NFI). In a first step, two different canopy height models (CHMs) are generated using a LiDAR DTM with two DSMs derived from the LiDAR data and RC30 images. In a second step, forest area was obtained using a logistic regression approach and explanatory variables from both CHMs. Based on the forest area, tree composition and main tree species are modelled again using logistic regression models and explanatory variables derived from both the ADS40 and RC30 aerial images. In a third step, forest stands are extracted by combining homogenous parts of the CHM with tree species information. Generally, results based on LiDAR CHM produced less satisfactory results due to lower quality. High accuracy for the extraction of forest area, main tree species (kappa = 0.7 to 0.9) is obtained. Further research is needed for the extraction of forest stands. The present study reveals the potential and limits to derive forest attributes and highlights possibilities of their usage for tasks of the Swiss National Forest Inventory.

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