DIGITALNA ARHIVA UMARSKOG LISTA

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UMARSKI LIST 11-12/2018 str. 14 <-- 14 --> PDF |

and DTM _{LiD} with a spatial resolution of 0.5 m were created using the Global Mapper software. The normality test of vertical errors distribution between DTM _{PHM} and DTM_{LiD} based on histograms and normal Q-Q plots revealed the non-normal distribution of vertical errors (Figure 5). Therefore, in addition to standard accuracy measures, robust accuracy measures, suggested by Höhle and Höhle (2009), were used for vertical accuracy assessment of DTM_{PHM }and_{ }DTM_{PHMc}. The standard accuracy measures included the maximum positive error (max), maximum negative error (min), mean error (ME), standard deviation (SD) and root mean square error (RMSE), whereas the robust measures included the median or 50% quantile (Q_{50}), normalized median absolute deviation (NMAD), 68.3% quantile (Q_{68.3}) and 95% quantile (Q_{95}). The equations for all the measures can be found in Höhle and Höhle (2009). To evaluate the method efficiency in more detail, the accuracy assessment was carried out for the entire study area, as well as for the three smaller rectangular subset areas (700 m × 565 m) (Figure 1). Values of all pixels from the difference raster within the entire area and three subset areas (with the exclusion of pixels within a 25-m buffer around line objects) were used to calculate accuracy measures. All statistical analyses were performed using the R programming language (ver. 3.3.3, R Core Team, Vienna, Austria). RESULTS WITH DISCUSSION REZULTATI S DISKUSIJOM For the entire study area, the method automatically detects 91 error points (outliers) or 3.2% of the total number of source points used to generate DTM _{PHM }(Table 2). This means that, on average, one outlier occurs in the digital terrain source data within each 22.04 ha of the research area (0.05 outliers·ha^{-1}). Using the previously described manual method, Balenovię et al. (2018) detected a total of 21 outliers at the same but the somewhat smaller area (991.50 ha). This means that, on average, one outlier was detected within each 47.21 ha (0.02 outliers·ha^{-1}). The greater number of outliers detected and eliminated by the automatic method leads to a considerably greater improvement of the DTM_{PHM} vertical accuracy compared to the one obtained by the manual method, which is especially evident in subset areas 2 and 3 (Figure 1) according to several accuracy measures (Q_{95}, max, SD, RMSE). Furthermore, the considerable decrease of Q_{95 }and max values, as well as unchanged min values after removing the outliers indicate that only positive error points occur in DTM_{PHM} when compared to reference DTM_{LiD}. The improvements in accuracy are also evident in Figure 6 and Figure 7. Namely, Figure 6a-c and Figure 7a,b show no change because error points are not detected in subset area 1. Conversely, Figure 6d-e and Figure 6g-i, as well as Figure 7c-f show the improvement in accuracy of DTM _{PHMc} compared to DTM_{PHM} for subset areas 2 and 3. Detected points are very noticeable in the difference raster in Figure 6d and Figure 6g while the justification for their removal is confirmed by vertical profile through exemplary areas (Figure 6f and Figure 6i). Furthermore, the elimination of outliers consequently leads to an improved coefficient of correlation (r) between DTM_{PHMc} and DTM_{LiD} elevation values compared to r obtained between DTM_{PHM} and DTM_{LiD} elevation values (Figure 7). |