# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest
## 4 Conclusions  
This study assessed the capability of different close-range remote sensing techniques to estimate DTM in a lowland deciduous forest. In general, the studied close-range remote sensing data sources were capable of providing complete and reasonably accurate DTM in a lowland deciduous forest. Four different data sources, PLS\({}_{\text{hh}}\), TLS, UAV\({}_{\text{image}}\) and ULS, were investigated, and all of them achieved accuracy better than 15 cm for DTM estimation. All datasets, except ULS, systematically overestimated the terrain height at 7-9 cm level. The TLS data are considered to be the highest quality LiDAR data, whereas compared to TLS, PLS\({}_{\text{hh}}\) has low geometrical accuracy and high mobility. Yet, PLS\({}_{\text{hh}}\) achieved results equivalent to TLS. Occlusions in TLS data can cause significant errors in DTM estimation, similar to SLAM misregistration effect in PLS\({}_{\text{hh}}\) data. The distances from the scanner or trajectory were found to have a small influence on the DTM estimation accuracy. The ULS and PLS\({}_{\text{hh}}\) used the same grade of LiDAR sensor, but achieved different results. ULS had increased dispersion due to increased ranging distance and scanning geometry (direct view of the ground, minimal obstruction by the low grass), compared to PLS\({}_{\text{hh}}\). UAV\({}_{\text{image}}\) posed the lowest accuracy of all close-range remote sensing datasets, which is not surprising and can be primarily attributed to photogrammetry and the presence of low grass during the data acquisition. Nevertheless, UAV\({}_{\text{image}}\) achieved relatively high accuracy and proved to be a suitable data source for DTM estimation in leaf-off forest conditions. The completeness of the point cloud was found to be of low relevance on the lowland terrain. Studies with high-quality reference data have been rare until now but are important for establishing fundamental findings, thus, future studies should focus on evaluating close-range remote sensing technology for DTM estimation in different forest conditions, primarily the effects of the slope and vegetation type. Conceptualization, L.J., M.G., X.L. and I.B. ; Funding acquisition, I.B. ; Methodology, L.J. ; Project administration, I.B. ; Software, L.J. and X.L. ; Supervision, X.L. and I.B. ; Validation, L.J. ; Writing--original draft, L.J. ; Writing--review & editing, M.G., X.L. and I.B. All authors have read and agreed to the published version of the manuscript. This research has been supported by projects \"Retrieval of Information from Different Optical 3D Remote Sensing Sources for Use in Forest Inventory (3D-FORINVENT)\" funded by the Croatian Science Foundation (HRZZ IP-2016-06-7686) and \"Mogucnost primjene tehnologie runog laserskog skeniranja za procjenu drvne zalibe sastojina u glavnom pribodu\" funded by the Scientific Research Program (ZIR) of Croatian Forests Ltd. The work of doctoral student [PERSON] has been supported in part by the \"Young researchers' career development project--training of doctoral students\" of the Croatian Science Foundation funded by the European Union from the European Social Fund. Not applicable. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistics** & **PLS\({}_{\text{hh}}\)** & **TLS** & **UAV\({}_{\text{image}}\)** & **ULS** \\ \hline RMSE (cm) & 9 & 10 & 14 & 9 \\ ME (cm) & 7 & 8 & 8 & 0 \\ SD (cm) & 6 & 7 & 12 & 9 \\ \hline NMAD (cm) & 5 & 6 & 9 & 9 \\ Median (cm) & 7 & 9 & 7 & 0 \\
68.3\% quantile (cm) & 9 & 12 & 14 & 10 \\
95\% quantile (cm) & 17 & 18 & 30 & 18 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Non-robust and robust statistics for close-range remote sensing datasets with excluded interpolated measurements. **Acknowledgments:** We would like to express our gratitude to the technicians of the Croatian Forest Research Institute who helped in setting-up and measurements of sampling plots, CADCOM d.o.o. for ULS data collection and GEOCENTAR d.o.o. for PLS\({}_{\text{hh}}\) and TLS data collection.