Process-based approach to automated classification of forest structures using medium format digital aerial photos and ancillary GIS information

The methods of forest inventory data acquisition, based on the analysis of remotely sensed images have been well tested and implemented during the last decade. The predominant visual interpretation and pixel-based automated techniques are now being gradually replaced by the object-based image classification at multiple levels. This paper describes an experiment using medium-format digital aerial imagery for the purpose of automated updating of the existing GIS forest management database (LHPO). The method emphasises the pre-processing phase, where various image transforms and additional channels i.e. spectral ratios and vegetation indices (NDVI), low-pass filters, Sobel edge and GLCM (grey level co-occurrence matrix) texture measures are derived from the original dataset. The layer stack is then transferred into the object-oriented classification environment together with the existing thematic vector layer, and analysed on three hierarchical object levels. The classification involves the recognition of the successional stage of forest compartments and the estimation of tree species composition in terms of area coverage. In addition, age information on the GIS forestry management map can be updated and the spatial distribution of classes corrected using the multi-scale object relations of the former analysis. The advances of the automated procedure based on sequential processing of image objects are partially covered. Moreover, aspects of utilisation of the medium-format colour infra-red images (CIR) as an alternative to traditional aerial photos and very high resolution (VHR) satellite data, were considered.