Steadily increasing of technological possibilities in the field of digital photogrammetry and airborne laser scanning and data availability with high spatial resolution leads to new opportunities in the mapping. Automatic image analysis becames a key concept to better use of spatial, spectral and contextual information. At present, we can get a unique outputs also useful for mapping forest areas by linking quality input data of remote sensing data with effective ways of image processing. The work deals with the use of various range of input data of digital photogrammetry and airborne laser scanning for automatic identification of forest by object-oriented classification. The aim of this work was to determine the best possible input materials for object-oriented classification of forests and to introduce the effective procedures for automatic classification of forests fulfilling criteria of the forest according to the National inventory control and monitoring of forests of the Slovak republic. For the analysis we used set of orthophotomosaics from vegetation and non vegetation periods with different geometric resolution and spectral properties. We investigated the effect of various parameters on the process and the results of the analysis itself. Results showed a significant effect of geometric resolution. It is appropriate to use lower geometric resolution in larger areas considering difficulty of processing. In terms of spectral properties it is more appropriate to use infrared images. Input materials in a further step for classifications represented generated normalized digital surface models (nDSM) and combination of orthophotomosaics and the nDSM. We get the best results just with the combination of infrared orthophotomosaics and nDSM. These results were used in further identification of forest stand boundaries. Forest stand boundaries were identified at 3 location in the territory of University Enterprise. Identified boundaries of forests were in further part of thesis used to identify and quantify white areas located in the locations concerned. In the last part we have used automated analysis to identify the stand groups located in the segmented stand on one of the locations.