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Forest structure reflects the forest disturbance regime and can provide important information about the rate of human impact. A better understanding of the structural variability and large-scale dynamics of natural forests is crucial for “close to nature” forest management planning. In this study, we developed a partly automated approach to assess the structure of potential primeval and managed beech forests in the Ukrainian Carpathians using WorldView-2 imagery. We analyzed the local (50 m × 50 m scale) canopy closure of these forests by extracting the canopy gaps and determined four forest structure types ranging from very closed to low density. The occurrence and frequencies of these structure types were significantly different in the primeval and managed beech forests. The four forest structure types were predicted and mapped using multinomial logistic regression based on the textural features derived from the original image bands and two vegetation indices. A 10-fold cross-validation resulted in an overall accuracy of 83% and a kappa coefficient of 75%, with the highest agreement for the very closed structure type (87%) and the lowest agreement for the medium density and low density structure types (79%). The forest structure type maps can be helpful for planning management activities in beech forests.