# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 3 Results and Discussion
The trend is identical in both PLS\({}_{\text{hh}}\) and TLS datasets and has a relatively low influence on terrain overestimation, i.e., overestimation starts at \(\sim\)5 cm, and increases to 11 cm at a distance of 10 m, reaching 19 cm at a distance of 20 m. Manual examination of large discrepancies in the TLS data showed that the majority of checkpoints with large dispersion were occluded to most TLS stations and had a sparse point cloud nearby. Scan misregistration detected in PLS\({}_{\text{hh}}\) data, which caused sporadic large discrepancies of the points near the trajectory, was not present in the TLS dataset, since TLS scans were registered with high accuracy using spheres. These results indicate the equivalent performance of PLS\({}_{\text{hh}}\) and TLS for DTM estimation. The lower geometrical accuracy of the PLS\({}_{\text{hh}}\) is compensated for with mobility, thus the reduced ranging distance to the ground. In PLS, all the distances between trajectory and checkpoints were less than 14 m, where 90% of distances were less than 10 m and 50% were less than 3 m. In TLS, all distances were less than 20 m, where 90% of distances were less than 15 m and 50% of distances were less than 6 m. It is important to note that due to data acquisition in different time periods, TLS conditions were slightly more difficult than PLS\({}_{\text{hh}}\), thus TLS would probably achieve slightly better results if data were collected in the same period as PLS\({}_{\text{hh}}\). Both TLS and PLS displayed good performances on the plot level. However, it can be assumed that PLS\({}_{\text{hh}}\) would be more suitable for larger area scanning, especially when the trajectory is well planned in advance to sufficiently cover the area of interest. TLS, PLS\({}_{\text{hh}}\) and UAV\({}_{\text{image}}\) are typically associated with high-density point clouds. It is important to note that a significant part of TLS, PLS\({}_{\text{hh}}\) and ULS point clouds were not related to the ground, but vegetation as compared with UAV\({}_{\text{image}}\) point clouds that lacked tree structure. All datasets, besides TLS, were collected from moving platforms, of which a typical benefit is the reduction of occlusions and gaps in the data, however, the significance of such a benefit is unclear. To make an assessment, we analyzed classified ground points of each dataset and observed how many checkpoints had classified ground points in the vicinity (<30 cm), i.e., if the DTM was interpolated at that specific point based on nearby data, or not. In total, 7, 3, 1 and 33 points with no classified ground points in the nearby area (<30 cm) were found in PLS\({}_{\text{hh}}\), TLS, UAV\({}_{\text{image}}\) and ULS datasets, respectively. After excluding those points from the datasets, statistical results did show a slight improvement (Table 3) compared to statistics based on all points (Table 2), but the difference was negligible, which was expected for PLS\({}_{\text{hh}}\), TLS and UAV\({}_{\text{image}}\) considering the small number of excluded points. However, ULS did not show a significant change after excluding \(\sim\)20% of all checkpoints. These results indicate that linear interpolation is a safe technique for DTM interpolation in areas with no data, in a lowland deciduous forest, and it should also be noted that a high completeness of the data, for example, UAV\({}_{\text{image}}\), does not guarantee high accuracy. The low number of excluded points for the PLS\({}_{\text{hh}}\) and TLS dataset prove that despite the static platform, when planned correctly, multi-scan TLS data can provide high ground data completeness at the plot level. Figure 3: Height discrepancies with respect to distance from (a) PLS\({}_{\text{hh}}\) trajectory and (b) TLS stations.