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Potential and limits of airborne remote sensing data for extraction of fragctional canopy cover and forest stands and detection of tree species

This study presents a methodology for derivation of fractional canopy cover, detection of main tree species, and extraction of forest stands using logistic regression, airborne remote sensing data and field samples. In a first step, canopy height models (CHMs) are generated using medium point density LiDAR DSM and DTM and a high-quality matching DSM. Then, fractional canopy covers are calculated using logistic regression models and explanatory variables from LiDAR and matching CHM, whereas the latter produced better results due to higher quality and was therefore further used in this study. Based on this fractional canopy cover, main tree species and forest stands are modelled using logistic regression and airborne digital sensor data ADS40 and CIR aerial image data as input variables. Good accuracy for the extraction of canopy cover, distinction between coniferous and deciduous trees and classification of five main tree species (kappa = 0.7 to 0.9) were obtained but classification of additional three deciduous tree species was less accurate. The extraction of forest stands produced visually satisfactory results but this method suffers from some limitations and further research is needed. The present study reveals that the extracted forest attributes may be helpful to support stereo-image interpretation and field surveys in the frame of the Swiss National Forest Inventory (NFI) and may also be useful for updating existing forest masks and forest management and protection tasks.