Published in Scientific Papers. Series "Journal of Young Scientist", Vol. 5
Written by Mihnea CĂŢEANU
Remote sensing enables the recording of accurate geomorphological data with the capability to efficiently cover large areas. However, the presence of vegetation makes the use of remote methods for terrain mapping difficult. LiDAR can be a solution for forestry projects, as the laser pulses can cross the entire forest canopy and reach the soil underneath. LiDAR data is stored as 3D point clouds containing the pulse returns from the ground or various objects above it (such as power lines, buildings or vegetation). In order to interpolate an accurate Digital Terrain Model (DTM), the points coresponding to the ground returns have to be extracted from the initial point cloud. This process is called ground-filtering or simply filtering. This paper aims to provide a performance analysis of multiple algorithms for LiDAR data classification. Algorithm performance is reviewed for the case of mountainous terrain, characterised by moderate and steep slopes and forest vegetation of a generally high consistency. Our findings suggest that the Lasground-new algorithm implemented in the Lastools software package provides the most accurate results, with a Root Mean Square Error of elevation values for the study site of 0.34 metres (with over 80 percent of the area having an elevation error of less than 0.20 metres) and an average RMSE for the field plots of 0.66 metres. Free algorithms such as Maximum Local Slope or gLidar provide relatively similar results in terms of RMSE. taking into account the difficult test conditions (topographically complex surface with dense canopy cover) we consider LiDAR data to be a possible solution for collecting geomorphological data for forestry applications, as long as a sampling of elevation at finer scales is not required.
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