# Assessment of Close-Range Remote Sensing Methods for DTM Estimation in a Lowland Deciduous Forest  
[PERSON]  
1 Division for Forest Management and Forestry Economics, Croatian Forest Research Institute,  
Tmjanska cesta 35, HR-10000 Zagreb, Croatia; [EMAIL_ADDRESS] (I.J. ); [EMAIL_ADDRESS] (I.B.) [PERSON]  
2 Chiar of Photogrammetry and Remote Sensing, Faculty of Geodesy, University of Zagreb, Kaciceva 26,  
HR-10000 Zagreb, Croatia; [EMAIL_ADDRESS] 2  
[PERSON]  
3 The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430070, China 3*Correspondence:[PERSON]@hotmail.com3  
[PERSON]  
1 Division for Forest Management and Forestry Economics, Croatian Forest Research Institute,  
Tmjanska cesta 35, HR-10000 Zagreb, Croatia; [EMAIL_ADDRESS] (I.J. ); [EMAIL_ADDRESS] (I.B.) Footnote 1: [[https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing)]([https://www.mdpi.com/journal/renotesensing](https://www.mdpi.com/journal/renotesensing))  
Footnote 2: [[https://www.mdpi.com/j](https://www.mdpi.com/j)]([https://www.mdpi.com/j](https://www.mdpi.com/j))  
Global Navigation Satellite System (GNSS) and total station measurements, to acquire reliable geospatial data. However, topographic surveys are time-consuming and provide data only for a limited number of discrete points. Additionally, topographic surveys in forest areas are cumbersome and time-consuming since trees and low vegetation obstruct the line-of-sight and tree crowns obstruct GNSS signals and may cause numerous errors. In addition, low coverage of the global system for mobile communication signal in forest areas hinders the use of continuously operating reference stations for real-time kinematic (RTK) positioning. Thus, GNSS positioning in forest areas is, in general, difficult [11]. Currently, a precise DTM derived for large areas relies primarily on remote sensing methods, in the first place on airborne laser scanning (ALS) based on LiDAR technology or, secondarily, on digital aerial photogrammetry (DAP). They all perform well in open areas; however, ALS outperforms DAP in areas covered with vegetation [12]. ALS is an active technology that can penetrate vegetation, record multiple returns and reach the ground. In contrast, DAP requires an intersection of rays from multiple views to generate a 3D point, which is difficult to meet under the forest canopy. Thus, reconstructing the ground in a forest area using DAP is, in general, very challenging. The accuracy of a DTM in a forest area is largely dependent on the used technology and the vegetation type [13; 14]. A DTM produced by both ALS and DAP has been assessed in different forest areas [15; 16; 17; 18]; the results varied significantly and primarily depended on the forest structure (species, stem density, understory, etc.). For example, [PERSON] et al. [19] compared ALS and the official national DTM in a lowland mixed deciduous forest and detected a relatively large number of gross errors in the official national DTM ground points dataset derived by the means of manual stereophotogrammetry (dataset contained 91 erroneous points, which is 3.2% of the whole dataset), highlighting the issues related to DAP in vegetated areas. [PERSON] and [PERSON] [16] evaluated spring and summer season ALS based DTM and found that summer DTM has a significantly higher variation than spring DTM, as well as that seasonal influence primarily depended on stand structure complexity, i.e., errors were more stable in even-aged single layer stands than in multi-layer stands. While remote sensing data sources based on an aerial platform have been widely studied, close-range remote sensing data sources are still being developed and require research. The development of unmanned aerial vehicles (UAVs) brought UAV aerial photogrammetry (UAV\({}_{\text{image}}\)) and UAV laser scanning (ULS) close to Earth's surface, allowing for the acquisition of highly detailed and accurate data [1].