# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 3 Results and Discussion
It is well known that LiDAR technology outperforms photogrammetry in dense forest areas, but it is still not clear how photogrammetry performs in leaf-off conditions. Only a few studies [24; 25] have investigated the potential of UAV photogrammetry to provide a high accuracy DTM in leaf-off conditions. Photogrammetry is known to slightly overestimate terrain height in forest areas [1; 19; 24], and our results confirm such findings in deciduous forests. The presence of low grass in the study area during the time of acquisition certainly contributed to the overestimation, but similar results could be expected during most of the leaf-off period, namely, the ground is covered in a thick layer of dry leaves at the beginning of the leaf-off season, followed by snow. Snow compresses the leaves slightly and, after melting, the terrain surface is closest to the true ground and, soon, before the end of the leaf-off season, a low grass arises. Hence, the accuracy of the photogrammetric DTM in a broadleaved forest area can be improved only to a certain degree by aiming at a specific time period to collect the data, and further improvements have to be based on data collection and processing methodologies. [PERSON] et al. [24] assessed the accuracy of UAV\({}_{\text{image}}\)- and ALS-derived DTM on 796 GNSS/RTK checkpoints in a steppe, aquatic vegetation area and forest area. In the forest area, RMSE values of 12 and 15 cm, NMAD values of 11 and 11 cm and ME values of 4 and 6 cm were reported for airborne LiDAR and UAV\({}_{\text{image}}\), respectively. Another study [1] assessed the accuracy of UAV\({}_{\text{image}}\) and ULS derived DTM in a river levee. In total, 60 GNSS/RTK checkpoints were used to evaluate DTM in the area with the presence of low vegetation (below 20 cm), achieving ME values of 6 and 0 cm, and RMSE values of 14 and 11 cm, for UAV\({}_{\text{image}}\) and ULS, respectively. The results of both studies are in agreement with our results, despite the use of different sensors and differences in the study areas. An international TLS benchmark study [27] investigated TLS based DTM estimation in different difficult conditions of the boreal forest. The DTM estimation method, which was also used in this study, achieved the best results (RMSE = 5 cm) for the plots with similar conditions. Each evaluated data collection method has its advantages in forest area data collection; PLS\({}_{\text{hh}}\) has great potential to deliver highly accurate DTM primarily due to its mobility, TLS provides highly accurate data with a narrow beam capable of penetrating dense vegetation during leaf-off conditions, UAV\({}_{\text{image}}\) has a clear view of the ground from above and ULS has the capability of capturing both the crowns of the trees and ground below the trees. One of the influences that might cause errors in PLS\({}_{\text{hh}}\) and TLS data is the distance from an object, which translates to the return angle of the laser beam reflecting from the object, i.e., the more distant the object, the narrower the return angle. All checkpoints in our dataset were located within 20 m from the nearest TLS station and within 14 m from the nearest PLS\({}_{\text{hh}}\) trajectory. Figure 3 displays the distance of checkpoints from the TLS station or PLS\({}_{\text{hh}}\) trajectory with respect to deviations from the reference data. PLS\({}_{\text{hh}}\) achieved consistent discrepancies through the whole dataset, with a slightly positive trend and with the majority lying in the range from 0 to 20 cm, with a few exceptions. A dense group of points in Figure 3 indicates that the mobility of PLS\({}_{\text{hh}}\) is highly beneficial when DTM is produced from the data. Manual inspection of a few unexpected high discrepancies in the vicinity of the PLS\({}_{\text{hh}}\) trajectory revealed that they were related to SLAM misregistration. TLS also displays a slightly positive trend and consistent dispersion.