# Application of Terrestrial Laser Scanning to Tree Trunk Bark Structure Characteristics Evaluation and Analysis of Their Effect on the Flow Resistance Coefficient

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

^{−3}], ${A}_{d}$ is the vegetal area (${A}_{d}=h\xb7{d}_{p}$) (m

^{2}).

## 2. Materials and Methods

#### 2.1. Characterization of the Tree Trunks Used

#### 2.2. Tree Trunk Scanning

^{−1}, vertical range 10–25 degrees, horizontal range 0–60 degrees. The used scanner settings allowed point clouds with spatial resolution of 50 points/cm

^{2}to be obtained.

#### 2.3. Data Preprocessing

#### 2.4. Data Processing—Calculation of Tree Trunk Bark Characteristics

_{i}made by the sections with the OX axis were calculated.

_{i}calculated over the 0.05 m tree trunk segments.

#### 2.5. Evaluation of the Effect of Tree Trunk Bark Structure on Flow Resistance

^{−1}. The trough filling was controlled by a lid at one end of the trough. The measuring system was equipped with an electromagnetic flow meter and an electronic sensor of the water level. In the trough there was a dynamometric platform connected to a tensometric sensor capable of measuring tangential and stretching forces in the range up to 0.05 kN (Figure 3). Moreover, water temperature and speed of water reaching the tree trunk were measured using a micro ADV probe. Measurements were made for 11 different mean flow values from the range 0.1–0.7 m∙s

^{−1}for two variants of the experiment: for the natural trunks and for the trunks wrapped in foil. Each measurement was repeated twice.

## 3. Results

#### 3.1. Bark Characteristics of Tree Trunks

#### 3.2. Relation between Flow Resistance of Trees and Bark Structure

^{5}the values of the flow resistance coefficient increase for the natural trunk and that wrapped in foil to the values of 1.50 and 1.30, respectively. Mean value of ${C}_{d}$ for Oak I equals 1.35 and for Oak II 1.11. The bark structure of the natural oak tree trunk is responsible for the flow resistance coefficient increase by about 20% with respect to that for the trunk wrapped in foil. Similar results were obtained for Pine. The mean value of ${C}_{d}$ for Pine I equals 1.35 and for Pine II 1.13. In this case, the increase of BHF for natural tree trunk equals 20% (Table 4). The largest absolute differences between the values of ${C}_{d}$ occurred in the case of pine and amounted to 0.38. Table 4 also presents the percentage increase in ${C}_{d}$ for the natural stem related to foil wrapped.

## 4. Discussion

^{4}< ${R}_{e}$ < 10

^{5}(Figure 9 and Figure 10). The natural trunks, both oak and pine, achieved larger values of ${C}_{d}$. This shows that not taking into account the bark characteristics may lead to underestimating of ${C}_{d}$ and further on lowering the value of friction factor (Equation (1)). The figures also show that the conducted research covers only on certain range of ${R}_{e}$ values. However, it is particularly interesting from the point of view of average conditions occurring during floods when water may flow in areas covered with trees (velocities 0.1–0.5 m/s).

^{4}to 1.3∙10

^{5}. The drag coefficients obtained in research seem to be quite constant in the investigated range of Reynolds numbers. The drag coefficients of the fully coated cylinders are 15–25% higher than for the smooth cylinder. Obviously, the drag increases with increasing layer thickness. Values obtained in our research for natural stems were similar for BHF ranging from 3 to 10 mm. This confirmed the importance of this parameter on the coefficient of resistance to the flow of tree trunks.

## 5. Conclusions

^{2}, which allows for the development of highly detailed 3D models that can be used for analyzing tree trunk structure. The developed TTBSM model allows one to automate the process of calculating the bark characteristics of tree trunks based on the TLS data. The model can be used to analyze various species of trees of different ages.

## Author Contributions

## Funding

## Conflicts of Interest

## References

- Baptist, M.J.; Babovic, V.; Rodríguez Uthurburu, J.; Keijzer, M.; Uittenbogaard, R.E.; Mynett, A.; Verwey, A. On inducing equations for vegetation resistance. J. Hydraul. Res.
**2007**, 45, 435–450. [Google Scholar] [CrossRef] - Horritt, M.S. A methodology for the validation of uncertain flood inundation models. J. Hydrol.
**2006**, 326, 153–165. [Google Scholar] [CrossRef] - Klopstra, D.; Barneveld, H.J.; van Noortwijk, J.; van Velzen, E. Analytical model for hydraulic roughness of submerged vegetation. In Proceedings of the 27th IAHR Conference, San Francisco, CA, USA, 10–15 August 1997; American Society of Civil Engineers (ASCE): New York, NY, USA, 1997; pp. 775–780. [Google Scholar]
- Wu, Y.; Falconer, R.A.; Struve, J. Mathematical modelling of tidal currents in mangrove forests. Environ. Model. Softw.
**2001**, 16, 19–29. [Google Scholar] [CrossRef] - Yang, W.; Choi, S.U. A two-layer approach for depth-limited open-channel flows with submerged vegetation. J. Hydraul. Res.
**2010**, 48, 466–475. [Google Scholar] [CrossRef] - Crosato, A.; Saleh, M.S. Numerical study on the effects of floodplain vegetation on river planform style. Earth Surf. Proc. Landf.
**2011**, 36, 711–720. [Google Scholar] [CrossRef] - Perona, P.; Camporeale, C.; Perucca, E.; Savina, M.; Molnar, P.; Burlando, P.; Ridolfi, L. Modelling river and riparian vegetation interactions and related importance for sustainable ecosystem management. Aquat. Sci.
**2009**, 71, 266. [Google Scholar] [CrossRef] - Velasco, D.; Bateman, A.; Medina, V. A new integrated, hydro-mechanical model applied to flexible vegetation in riverbeds. J. Hydraul. Res.
**2008**, 46, 579–597. [Google Scholar] [CrossRef] - Meijer, D.G.; Van Velzen, E.H. Prototype-scale flume experiments on hydraulic roughness of submerged vegetation. In Proceedings of the 28th International IAHR Conference, Graz, Austria, 22–27 August 1999; Technical University Graz, Institute for Hydraulics and Hydrology: Graz, Austria, 1999. [Google Scholar]
- Murphy, E.; Ghisalberti, M.; Nepf, H. Model and laboratory study of dispersion in flows with submerged vegetation. Water Resour. Res.
**2007**, 43. [Google Scholar] [CrossRef] [Green Version] - Tal, M.; Paola, C. Effects of vegetation on channel morphodynamics: Results and insights from laboratory experiments. Earth Surf. Proc. Landf.
**2010**, 35, 1014–1028. [Google Scholar] [CrossRef] - Straatsma, M. 3D float tracking: In situ floodplain roughness estimation. Hydrol. Process.
**2009**, 23, 201–212. [Google Scholar] [CrossRef] - Walczak, Z.; Sojka, M.; Wróżyński, R.; Laks, I. Estimation of Polder Retention Capacity Based on ASTER, SRTM and LIDAR DEMs: The Case of Majdany Polder (West Poland). Water
**2016**, 8, 230. [Google Scholar] [CrossRef] - Laks, I.; Sojka, M.; Walczak, Z.; Wróżyński, R. Possibilities of Using Low Quality Digital Elevation Models of Floodplains in Hydraulic Numerical Models. Water
**2017**, 9, 283. [Google Scholar] [CrossRef] - Forzieri, G.; Degetto, M.; Righetti, M.; Castelli, F.; Preti, F. Satellite multispectral data for improved floodplain roughness modelling. J. Hydrol.
**2011**, 407, 41–57. [Google Scholar] [CrossRef] - Antonarakis, A.S.; Richards, K.S.; Brasington, J.; Muller, E. Determining leaf area index and leafy tree roughness using terrestrial laser scanning. Water Resour. Res.
**2010**, 46. [Google Scholar] [CrossRef] [Green Version] - Petryk, S.; Bosmajian, G., III. Analysis of flow through vegetation. J. Hydraul. Div.
**1975**, 101, 871–884. [Google Scholar] - Arcement, G.J.; Schneider, V.R. Guide for Selecting Manning’s Roughness Coefficients for Natural Channels and Flood Plains; U.S. Geological Survey: Denver, CO, USA, 1989.
- Tanaka, T.; Yamaguchi, J.; Takeda, Y. Measurement of forest canopy structure with a laser plane range-finding method—Development of a measurement system and applications to real forests. Agric. For. Meteorol.
**1998**, 91, 149–160. [Google Scholar] [CrossRef] - Lindner, K. Der Strömungswiderstand von Pflanzenbeständen; Mitteilungen; Heft 75; Technische Universität Braunschweig; Leichtweiss-InstitutfürWasserbau: Braunschweig, Germany, 1982. [Google Scholar]
- Nakayama, A. Introduction to Fluid Mechanics; Butterworth-Heinemann: Oxford, UK, 1999. [Google Scholar]
- Aberle, J.; Järvelä, J. Flow resistance of emergent rigid and flexible floodplain vegetation. J. Hydraul. Res.
**2013**, 51, 33–45. [Google Scholar] [CrossRef] - Straatsma, M.W.; Baptist, M.J. Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing. Remote Sens. Environ.
**2008**, 112, 1062–1080. [Google Scholar] [CrossRef] - Casas, A.; Lane, S.N.; Yu, D.; Benito, G. A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data. Hydrol. Earth Syst. Sci.
**2010**, 14, 1567–1579. [Google Scholar] [CrossRef] [Green Version] - Shendryk, I.; Broich, M.; Tulbure, M.G.; Alexandrov, S.V. Bottom-up delineation of individual trees from full-waveform airborne laser scans in a structurally complex eucalypt forest. Remote Sens. Environ.
**2016**, 173, 69–83. [Google Scholar] [CrossRef] - Antonarakis, A.S.; Richards, K.S.; Brasington, J.; Bithell, M. Leafless roughness of complex tree morphology using terrestrial lidar. Water Resour. Res.
**2009**, 45. [Google Scholar] [CrossRef] [Green Version] - Brolly, G.; Király, G. Algorithms for stem mapping by means of terrestrial laser scanning. Acta Silvatica et Lignaria Hungarica
**2009**, 5, 119–130. [Google Scholar] - Antonarakis, A.S.; Richards, K.S.; Brasington, J.; Bithell, M.; Muller, E. Retrieval of vegetative fluid resistance terms for rigid stems using airborne lidar. J. Geophys. Res. Biogeosci.
**2008**, 113. [Google Scholar] [CrossRef] [Green Version] - Lovell, J.L.; Jupp, D.L.B.; Newnham, G.J.; Culvenor, D.S. Measuring tree stem diameters using intensity profiles from ground-based scanning lidar from a fixed viewpoint. ISPRS J. Photogramm. Remote Sens.
**2011**, 66, 46–55. [Google Scholar] [CrossRef] - Othmani, A.; Piboule, A.; Dalmau, O.; Lomenie, N.; Mokrani, S.; Voon, Y. Tree species classification based on 3D bark texture analysis. In Proceedings of the Pacific-Rim Symposium on Image and Video Technology; Springer: Berlin, Germany, 2013; pp. 279–289. [Google Scholar]
- Bienert, A.; Maas, H.G.; Scheller, S. Analysis of the information content of terrestrial laser scanner point clouds for the automatic determination of forest inventory parameters. In Proceedings of the Workshop on 3D Remote Sensing in Forestry, Vienna, Austria, 14–15 February 2006; Volume 14, pp. 1–7. [Google Scholar]
- Yang, X.; Strahler, A.H.; Schaaf, C.B.; Jupp, D.L.; Yao, T.; Zhao, F.; Dubayah, R.O. Three-dimensional forest reconstruction and structural parameter retrievals using a terrestrial full-waveform lidar instrument (Echidna
^{®}). Remote Sens. Environ.**2013**, 135, 36–51. [Google Scholar] [CrossRef] - Wang, D.; Hollaus, M.; Puttonen, E.; Pfeifer, N. Fast and robust stem reconstruction in complex environments using terrestrial laser scanning. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.
**2016**, 41, 411–417. [Google Scholar] [CrossRef] - Kankare, V.; Holopainen, M.; Vastaranta, M.; Puttonen, E.; Yu, X.; Hyyppä, J.; Alho, P. Individual tree biomass estimation using terrestrial laser scanning. ISPRS J. Photogramm. Remote Sens.
**2013**, 75, 64–75. [Google Scholar] [CrossRef] - Hyyti, H.; Visala, A. Feature Based modeling and mapping of tree trunks and natural terrain using 3D laser scanner measurement system. IFAC Proc. Vol.
**2013**, 46, 248–255. [Google Scholar] [CrossRef] - Othmani, A.A.; Jiang, C.; Lomenie, N.; Favreau, J.M.; Piboule, A.; Voon, Y. A novel computer-aided tree species identification method based on burst wind segmentation of 3D bark textures. Mach. Vis. Appl.
**2016**, 27, 751–766. [Google Scholar] [CrossRef] - Lin, Y.; Herold, M. Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data. Agric. For. Meteorol.
**2016**, 216, 105–114. [Google Scholar] [CrossRef] - Hosoi, F.; Omasa, K. Voxel-based 3-D modeling of individual trees for estimating leaf area density using high-resolution portable scanning lidar. IEEE Trans. Geosci. Remote Sens.
**2006**, 44, 3610–3618. [Google Scholar] [CrossRef] - Calders, K.; Armston, J.; Newnham, G.; Herold, M.; Goodwin, N. Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDAR. Agric. For. Meteorol.
**2014**, 194, 104–117. [Google Scholar] [CrossRef] - Calders, K.; Newnham, G.; Burt, A.; Murphy, S.; Raumonen, P.; Herold, M.; Kaasalainen, M. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol. Evol.
**2015**, 6, 198–208. [Google Scholar] [CrossRef] - Sioma, A.; Socha, J.; Klamerus-Iwan, A. A New Method for Characterizing Bark Microrelief Using 3D Vision Systems. Forests
**2018**, 9, 30. [Google Scholar] [CrossRef] - Westoby, M.J.; Brasington, J.; Glasser, N.F.; Hambrey, M.J.; Reynolds, J.M. ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology
**2012**, 179, 300–314. [Google Scholar] [CrossRef] [Green Version] - Wróżyński, R.; Pyszny, K.; Sojka, M.; Przybyła, C.; Murat-Błażejewska, S. Ground volume assessment using ‘Structure from Motion’ photogrammetry with a smartphone and a compact camera. Open Geosci.
**2017**, 9, 281–294. [Google Scholar] [CrossRef] - Tanaka, N.; Takenaka, H.; Yagisawa, J.; Morinaga, T. Estimation of drag coefficient of a real tree considering the vertical stand structure of trunk, branches, and leaves. Int. J. River Basin Manag.
**2011**, 9, 221–230. [Google Scholar] [CrossRef] - Dassot, M.; Colin, A.; Santenoise, P.; Fournier, M.; Constant, T. Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput. Electron. Agric.
**2012**, 89, 86–93. [Google Scholar] [CrossRef] - Kükenbrink, D.; Schneider, F.D.; Leiterer, R.; Schaepman, M.E.; Morsdorf, F. Quantification of hidden canopy volume of airborne laser scanning data using a voxel traversal algorithm. Remote Sens. Environ.
**2017**, 194, 424–436. [Google Scholar] [CrossRef] - ASME. Surface Texture (Surface Roughness, Waviness, and Lay): An American Standard; ASME B46.1-1995 (Revision of ANSI/ASME B46.1-1985); ASME: New York, NY, USA, 1996. [Google Scholar]
- Walczak, Z.; Sojka, M.; Laks, I. Assessment of mapping of embankments and control structure on digital elevation model based up on Majdany polder. Rocznik Ochrona Srodowiska
**2013**, 15, 2711–2724. [Google Scholar] - Schlichting, H.; Gersten, K. Grenzschicht-Theorie; Springer: Berlin/Heidelberg, Germany, 2006. [Google Scholar]
- Ruck, B.; Klausmann, K.; Wacker, T. Drag reduction of cylinders by partial porous coating. In Proceedings of the 9th International Conference on Heat Transfer, Fluid Mechanics and Thermodynamics, Malta, 16–18 July 2012. [Google Scholar]

**Figure 1.**Scan of the trunk (P) with the four reference balls (K); photogrammetric image of point cloud intensity.

**Figure 5.**(

**A**) transformed point cloud; (

**B**) triangulated irregular network; (

**C**) Bark height factor (BHF) of Oak I and Oak II samples.

**Figure 6.**Changes in the slope (

**A**); standard deviation of slope (

**B**); 2D to 3D area ratio (

**C**) and BHF (

**D**) along the trunks of oak and pine trees.

**Figure 9.**Flow resistance coefficient ${C}_{d}$ versus Reynolds number ${R}_{e}$ for oak against the ${C}_{d}$ = f (${R}_{e}$) [49].

**Figure 10.**Flow resistance coefficient ${C}_{d}$ versus Reynolds number Re for pine against the ${C}_{d}$ = f (${R}_{e}$) [49].

Parameters | Units | Tree Species | ||
---|---|---|---|---|

Pine | Oak | |||

Height | (m) | 0.5 | 0.5 | |

Weight | (kg) | 9.3 | 11.2 | |

Circumference | ||||

bottom | (m) | 0.60 | 0.67 | |

mid | (m) | 0.62 | 0.68 | |

top | (m) | 0.56 | 0.64 | |

Diameter | ||||

bottom | (m) | 0.190 | 0.205 | |

top | (m) | 0.185 | 0.210 |

Ranging Unit | |

Range | 0.6–120 m |

Measurement speed | 122,000–976,000 points s^{−1} |

Ranging error | ±2 mm at 10 m |

Laser (Optical Transmitter) | |

Laser power (cw Ø) | 20 mW |

Wavelength | 905 nm |

Beam divergence | 0.19 mrad (0.011°) |

Beam diameter at exit | 3.0 mm, circular |

Deflection Unit | |

Vertical field of view | 305° |

Horizontal field of view | 360° |

Vertical step size | 0.009° (40,960 3D pixel on 360°) |

Horizontal step size | 0.009° (40,960 3D pixel on 360°) |

Max. vertical scan speed | 5820 rpm or 97 Hz |

Slope (°) | Standard Deviation of Slope (°) | Area 2D/3D (-) | BHF (cm) | ||||||
---|---|---|---|---|---|---|---|---|---|

Min | Mean | Min | Mean | Min | Mean | Min | Mean | ||

Max | Median | Max | Median | Max | Median | Max | Median | ||

Oak | I | 31.6 | 32.2 | 17.5 | 18.1 | 0.7 | 0.71 | 0.35 | 0.37 |

32.8 | 32.1 | 18.6 | 18.1 | 0.72 | 0.7 | 0.38 | 0.37 | ||

II | 26.1 | 26.7 | 15.3 | 15.9 | 0.8 | 0.81 | 0.24 | 0.25 | |

27.5 | 26.6 | 16.2 | 16 | 0.82 | 0.81 | 0.26 | 0.25 | ||

Pine | I | 31.7 | 32.6 | 17.6 | 18 | 0.68 | 0.7 | 0.38 | 0.41 |

34.5 | 32.2 | 18.5 | 18 | 0.71 | 0.7 | 0.46 | 0.39 | ||

II | 25.7 | 26.4 | 14.7 | 15.7 | 0.79 | 0.81 | 0.26 | 0.27 | |

27.1 | 26.4 | 16.5 | 15.7 | 0.83 | 0.81 | 0.28 | 0.27 |

Oak | Pine | ||||||||
---|---|---|---|---|---|---|---|---|---|

${\mathit{R}}_{\mathit{e}}$ | ${\mathit{C}}_{\mathit{d}\mathit{I}}$ | ${\mathit{C}}_{\mathit{d}\mathit{I}\mathit{I}}$ | $\Delta {\mathit{C}}_{\mathit{d}}$ | % | ${\mathit{R}}_{\mathit{e}}$ | ${\mathit{C}}_{\mathit{d}\mathit{I}}$ | ${\mathit{C}}_{\mathit{d}\mathit{I}\mathit{I}}$ | $\Delta {\mathit{C}}_{\mathit{d}}$ | % |

3.19 × 10^{4} | 1.32 | 1.10 | 0.22 | 20 | 1.71 × 10^{4} | 1.44 | 1.21 | 0.24 | 20 |

4.42 × 10^{4} | 1.35 | 1.10 | 0.25 | 23 | 2.84 × 10^{4} | 1.30 | 1.29 | 0.01 | 1 |

6.17 × 10^{4} | 1.16 | 1.07 | 0.09 | 8 | 4.08 × 10^{4} | 1.30 | 1.14 | 0.16 | 14 |

7.34 × 10^{4} | 1.21 | 0.95 | 0.26 | 28 | 5.47 × 10^{4} | 1.42 | 1.05 | 0.37 | 35 |

8.64 × 10^{4} | 1.33 | 1.03 | 0.31 | 30 | 6.48 × 10^{4} | 1.30 | 1.07 | 0.23 | 21 |

9.71 × 10^{4} | 1.30 | 1.04 | 0.26 | 25 | 7.80 × 10^{4} | 1.26 | 1.00 | 0.26 | 26 |

1.08 × 10^{5} | 1.41 | 1.13 | 0.28 | 25 | 8.65 × 10^{4} | 1.26 | 1.01 | 0.25 | 25 |

1.20 × 10^{5} | 1.52 | 1.25 | 0.27 | 22 | 9.66 × 10^{4} | 1.31 | 1.17 | 0.15 | 12 |

1.37 × 10^{5} | 1.52 | 1.30 | 0.22 | 17 | 1.02 × 10^{5} | 1.44 | 1.06 | 0.38 | 35 |

1.29 × 10^{5} | 1.51 | 1.29 | 0.22 | 17 | 1.11 × 10^{5} | 1.45 | 1.29 | 0.16 | 12 |

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**MDPI and ACS Style**

Kałuża, T.; Sojka, M.; Strzeliński, P.; Wróżyński, R.
Application of Terrestrial Laser Scanning to Tree Trunk Bark Structure Characteristics Evaluation and Analysis of Their Effect on the Flow Resistance Coefficient. *Water* **2018**, *10*, 753.
https://doi.org/10.3390/w10060753

**AMA Style**

Kałuża T, Sojka M, Strzeliński P, Wróżyński R.
Application of Terrestrial Laser Scanning to Tree Trunk Bark Structure Characteristics Evaluation and Analysis of Their Effect on the Flow Resistance Coefficient. *Water*. 2018; 10(6):753.
https://doi.org/10.3390/w10060753

**Chicago/Turabian Style**

Kałuża, Tomasz, Mariusz Sojka, Paweł Strzeliński, and Rafał Wróżyński.
2018. "Application of Terrestrial Laser Scanning to Tree Trunk Bark Structure Characteristics Evaluation and Analysis of Their Effect on the Flow Resistance Coefficient" *Water* 10, no. 6: 753.
https://doi.org/10.3390/w10060753