# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 3 Results and Discussion  
The height discrepancies of the close-range remote sensing data can be seen in Figure 2. It can be seen in Figure 2a,b that PLS\({}_{\text{hh}}\), TLS and UAV\({}_{\text{image}}\) tend to overestimate ground, as well as that UAV\({}_{\text{image}}\) and ULS have greater dispersion than PLS\({}_{\text{hh}}\) and TLS. In addition, a few higher discrepancies indicate that outliers might be present in some datasets. After calculating non-robust and robust statistics, outliers were filtered out based on three times the RMSE value [29]. There were zero (0.00%), one (0.00%), four (0.02%) and four (0.02%) detected outliers in PLS\({}_{\text{hh}}\), TLS, UAV\({}_{\text{image}}\) and ULS data, respectively. After removing the detected outliers from the dataset, non-robust statistics were calculated once again. In general, the accuracy measures are similar for all datasets (Table 2), especially for PLS\({}_{\text{hh}}\) and TLS. Due to a small number of outliers, removal did not influence the statistics significantly. For outlier free datasets, the RMSE values range from 10 cm to 14 cm, and the ME values range from 0 cm to 8 cm. The PLS\({}_{\text{hh}}\) and TLS have RMSE values of 10 and 11 cm, ME values of 7 and 8 cm and SD values of 6 and 7 cm, respectively; while the UAV\({}_{\text{image}}\) and ULS have RMSE values of 14 and 9 cm, ME values of 8 and 0 cm and SD values of 12 and 9 cm, respectively. Concerning robust statistics, NMAD values of 5, 6, 9 and 10 cm, as well as median values of 7, 9, 7 and 0 cm were achieved for PLS\({}_{\text{hh}}\), TLS, UAV\({}_{\text{image}}\) and ULS, respectively. The similar results obtained with PLS\({}_{\text{hh}}\) and TLS are probably due to shared geometry, i.e., a terrestrial platform, whereas the slightly different results obtained with UAV\({}_{\text{image}}\) and ULS, although both shared an airborne platform, are due to a different technology, i.e., photogrammetry and LiDAR. The difference in the performances of PLS\({}_{\text{hh}}\) and TLS is negligible, which is unexpected considering that multi-scan TLS data is generally understood to be the best LiDAR data available and is typically used as reference data [25]. On the other hand, SLAM-based handheld laser scanners typically use low-cost sensors, resulting in data of a lower quality and increased noise. SLAM-based devices are also prone to drift due to a lack of features in sparse forests [21], but this did not occur in our case, which was confirmed by the high accuracy of the georeferencing data (RMSE \(\leq\) 2.4 cm). The PLS\({}_{\text{hh}}\) has an advantage in mobility, allowing it to record a larger area and reduce occlusions, which might have contributed to its accuracy. In contrast, TLS has high geometrical accuracy but visibility was hindered by low grass that was present during data acquisition, and it could be assumed that in the same conditions as in the PLS\({}_{\text{hh}}\) dataset (February, no low vegetation), TLS would achieve slightly better results than those presented in this study. \begin{table}
\begin{tabular}{c c c c c} \hline
**Statistics.** & **PLS\({}_{\text{hh}}\)** & **TLS** & **UAV\({}_{\text{image}}\)** & **ULS** \\ \hline RMSE (cm) & 10 & 11 & 16 & 10 \\ ME (cm) & 7 & 9 & 7 & 1 \\ SD (cm) & 6 & 7 & 14 & 10 \\ \hline RMSE * (cm) & 10 & 11 & 14 & 9 \\ ME * (cm) & 7 & 8 & 8 & 0 \\ SD * (cm) & 6 & 7 & 12 & 9 \\ \hline NMAD (cm) & 5 & 6 & 9 & 10 \\ Median (cm) & 7 & 9 & 7 & 0 \\
68.3\% quantile (cm) & 10 & 12 & 15 & 9 \\
95\% quantile (cm) & 18 & 19 & 3 & 20 \\ \hline \end{tabular} \({}^{\text{*}}\) After removal of outliers.  
\end{table} 
Table 2: Non-robust and robust statistics for close-range remote sensing datasets. Figure 2: Height discrepancies from reference data. (**a**) For PLS\({}_{\text{hh}}\) and TLS; (**b**) For UAV\({}_{\text{image}}\) and ULS. The results of this study indicate that LiDAR technology, both terrestrial and UAV-based, performs better than UAV photogrammetry in forest areas.