VEGETATION MODELLING BASED ON TLS DATA FOR ROUGHNESS COEFFICIENT ESTIMATION IN RIVER VALLEY

被引:0
|
作者
Tymkow, Przemyslaw [1 ]
Borkowski, Andrzej [1 ]
机构
[1] Wroclaw Univ Environm & Life Sci, Inst Geodesy & Geoinformat, PL-50375 Wroclaw, Poland
关键词
terrestrial laser scanning; 3D modelling; shrub modelling; convex hull; hydrodynamic modelling; point cloud;
D O I
暂无
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
Many environmental studies such as generation of hydrodynamic models, that are tools for risk management, require information about vegetation conditions. The description of vegetation from the hydraulic modelling point of view should include type, distribution and arrangement of existing plants. Geometric parameters of plants can be determined on the basis of laser scanning data. Terrestrial laser scanning (TLS) allows to determine precisely not only the external shape of the plant, but the geometry of individual branches as well. A method for macro and micro-structure estimation of a single shrub is presented in this paper. The data used in the research were measured with Leica ScanStation II. In the macro-structural approach, where the plant is considered as a compact solid, it is important to choose those measurement points that represent the surfaces of the plant. To achieve better matching to the non-convex parts of the hull the use of a multi-stage solid generation procedure is proposed. In this approach points are divided into segments with common edges. The method assumes that the plant is divided along the z axis into segments of a given width. First, points from one segment are projected onto the division plane. Then, 2D convex hull is generated for all the points. Finally, selected points (again in 3D space) are used for 3D convex hull generation. In order to define the geometry of vegetation the micro-structure procedure is supplemented by the segmentation algorithm to split points into groups, which form one branch. To verify the accuracy, the total surface area and the total shrub volume of branches calculated for individual variants were compared with the total surface area and volume derived from the direct measurements. Additionally, the qualitative analysis was also carried out.
引用
收藏
页码:309 / 313
页数:5
相关论文
共 50 条
  • [41] Estimation of surface roughness (z0) over a stabilizing coastal dune field based on vegetation and topography
    Levin, Noam
    Ben-Dor, Eyal
    Kidron, Giora J.
    Yaakov, Yaron
    EARTH SURFACE PROCESSES AND LANDFORMS, 2008, 33 (10) : 1520 - 1541
  • [42] Assessment of the soil and vegetation cover conditionof the river basin based on remote sensing data
    Shutov, P. S.
    Trifonova, T. A.
    Mishchenko, N. V.
    THEORETICAL AND APPLIED ECOLOGY, 2024, (01): : 73 - 81
  • [43] Complex vegetation cover classification study of the Yellow River Basin based on NDVI data
    Li, DF
    Li, CH
    Hao, FH
    Zheng, LF
    IGARSS 2004: IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM PROCEEDINGS, VOLS 1-7: SCIENCE FOR SOCIETY: EXPLORING AND MANAGING A CHANGING PLANET, 2004, : 3360 - 3363
  • [44] Dynamic Estimation for Vegetation Ecological Water Demand Based on Time Series Remote Sensing Data and GIS: a Case Study of Shule River in China
    Ye, Hongmei
    Shi, Yuezhen
    Hu, Guohua
    PROCEEDINGS OF THE 35TH IAHR WORLD CONGRESS, VOLS III AND IV, 2013,
  • [45] Road friction coefficient estimation based on multisensor data fusion for an AYC system
    Song, Jian
    Yang, Cai
    Li, Hongzhi
    Li, Liang
    Qinghua Daxue Xuebao/Journal of Tsinghua University, 2009, 49 (05): : 715 - 718
  • [46] Spatial modelling-based approach to phytogeographical regionalization using grassland vegetation data
    Hlasny, Tomas
    Turisova, Ingrid
    CENTRAL EUROPEAN JOURNAL OF BIOLOGY, 2012, 7 (02): : 318 - 326
  • [47] Estimation of greenhouse gas emission reductions based on vegetation changes after rewetting in Drentsche Aa brook valley
    Liu, W.
    Grootjans, A. P.
    Everts, H.
    Fritz, C.
    De Vries, N.
    MIRES AND PEAT, 2020, 26
  • [48] Turbulence statistics estimation across a step change in roughness via interpretable network-based modelling
    Iacobello, Giovanni
    Placidi, Marco
    Ding, Shan-Shan
    Carpentieri, Matteo
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2025, 36 (01)
  • [49] Artificial Intelligence-Based Surface Roughness Estimation Modelling for Milling of AA6061 Alloy
    Eser, Aykut
    Ayyildiz, Elmas Askar
    Ayyildiz, Mustafa
    Kara, Fuat
    ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2021, 2021
  • [50] Calibration of floodplain roughness and estimation of flood discharge based on tree-ring evidence and hydraulic modelling
    Ballesteros, J. A.
    Bodoque, J. M.
    Diez-Herrero, A.
    Sanchez-Silva, M.
    Stoffel, M.
    JOURNAL OF HYDROLOGY, 2011, 403 (1-2) : 103 - 115