DIGITALNA ARHIVA ŠUMARSKOG LISTA
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ŠUMARSKI LIST 11-12/2018 str. 12     <-- 12 -->        PDF

points∙m–2, respectively. Characteristics of LiDAR sensor, data processing, and the accuracy of DTMLiD are presented in Table 1.
Method for an automatic detection of elevation errors in DTMPHMMetoda za automatsku detekciju visinskih pogrešaka u DTMPHM
An automatic method for elevation errors detection in DTMPHM for the lowland forest was developed using Grass GIS software (Figure 3). The recent study of Balenović et al. (2018) revealed that the gross errors (outliers) in DTMPHM were caused by errors in the photogrammetric source data, primarily by the point data (mass and height points) used to generate DTMPHM. Therefore, the presented method in this study focused exclusively on point data, while line data were not analyzed. Line objects representing embankment edges, forest roads, and river basins were excluded from the raster DTMPHM by creating a 25-m buffer area around each feature, which is 50% less than the average distance of measured points for DTM. The slope analysis, performed on the raster DTMPHM, distinguished areas with high slope inclination angles (S) that included both potential error points as well as error-free points of their neighborhood (Figure 4). To extract the error points from DTMPHM, the method was complemented with the tangential curvature analysis (T) (Mitášová and Hofierka, 1993), where the tangential curvature represents the curvature orthogonal to the line of the steepest gradient (Alkhasawneh et al., 2013). The