In remote sensing-based forest inventories 3D point cloud data, such as acquired from airbornelaser scanning, are well suited for estimating the volume of growing stock and stand height, buttree species recognition often requires additional optical imagery. A combination of 3D data andoptical imagery can be acquired based on aerial imaging only, by using stereo photogrammetric3D canopy modeling. The use of aerial imagery is well suited for large-area forest inventories,due to low costs, good area coverage and temporally rapid cycle of data acquisition. Stereophotogrammetric canopy modeling can also be applied to previously acquired imagery, such asfor aerial ortho-mosaic production, assuming that the imagery has sufficient stereo overlap. In thisstudy we compared two stereo-photogrammetric canopy models combined with contemporarysatellite imagery in forest inventory. One canopy model was based on standard archived imageryacquired primarily for ortho-mosaic production, and another was based on aerial imagery whoseacquisition parameters were better oriented for stereo-photogrammetric canopy modeling, including higher imaging resolution and greater stereo-coverage. Aerial and satellite data were tested inthe estimation of growing stock volume, volumes of main tree species, basal area and diameterand height. Despite the better quality of the latter canopy model, the difference of the accuracyof the forest estimates based on the two different data sets was relatively small for most variables(differences in RMSEs were 0–20%, depending on variable). However, the estimates based onstereo-photogrammetrically oriented aerial data retained better the original variation of the forestvariables present in the study area.
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