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In the present study, we applied the Particle Swarming Optimization (PSO) procedure to parametrize two Local Maxima (LM)\udalgorithms and a pattern recognition model based on raster and point-cloud datasets in order to extract treetops of coniferous forests\udfrom high resolution LiDAR-data of different forest structures (monoplane, biplane and multi-layer) in the Alps region. The approach\udbased on the pattern recognition model uses the geomorphon algorithm applied to the DSM to detect the treetops.\ud \udThe geomorphon model gave good results in terms of matching rates (Rmat: 0.8) with intermediate values of commission and\udomission rates (Rcom: 0.22, Rom: 0.2). Therefore, it could be a valid alternative to the LM-algorithms when only raster products (DSM\ud– CHM) are available.\ud \udThe geomorphon pattern recognition model has been proved to be a powerful method in order to properly detect treetops of\udconiferous stands with complex forest structures. This model allows to obtain high detection rates and estimation accuracy of forest\udvolume, also in comparison to the most recent available literature data.\ud \udThe models are developed in Java under Free and Open Source license and are integrated in the JGrassTools library, which is now\udavailable as SpatialToolbox of the GIS gvSIG

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

APPLICATION OF A PATTERN RECOGNITION ALGORITHM FOR SINGLE TREE DETECTION FROM LiDAR DATA