Automatic identification of watercourses in flat and engineered landscapes by computing the skeleton of a LiDAR point cloud

Drainage networks play a crucial role in protecting land against floods. It is therefore important to have an accurate map of the watercourses that form the drainage network. Previous work on the automatic identification of watercourses was typically based on grids, focused on natural landscapes, and used mostly the slope and curvature of the terrain. We focus in this paper on areas that are characterised by low-lying, flat, and engineered landscapes; these are characteristic to the Netherlands for instance. We propose a new methodology to identify watercourses automatically from elevation data, it uses solely a raw classified LiDAR point cloud as input. We show that by computing twice a skeleton of the point cloud—once in 2D and once in 3D—and that by using the properties of the skeletons we can identify most of the watercourses. We have implemented our methodology and tested it for three different soil types around Utrecht, the Netherlands. We were able to detect 98% of the watercourses for one soil type, and around 75% for the worst case, when we compared to a reference dataset that was obtained semi-automatically.

[1]  Ravi Peters,et al.  Robust approximation of the Medial Axis Transform of LiDAR point clouds as a tool for visualisation , 2016, Comput. Geosci..

[2]  E. Foufoula‐Georgiou,et al.  Automatic geomorphic feature extraction from lidar in flat and engineered landscapes , 2011 .

[3]  Guillermo Sapiro,et al.  A geometric framework for channel network extraction from lidar: Nonlinear diffusion and geodesic paths , 2010 .

[4]  Philippe Lagacherie,et al.  Agrarian landscapes linear features detection from LiDAR: application to artificial drainage networks , 2008 .

[5]  Hyun-chong Cho,et al.  Morphology-based approaches for detecting stream channels from ALSM data , 2011 .

[6]  Jack Snoeyink,et al.  A One-Step Crust and Skeleton Extraction Algorithm , 2001, Algorithmica.

[7]  David G. Kirkpatrick,et al.  On the shape of a set of points in the plane , 1983, IEEE Trans. Inf. Theory.

[8]  Sang Won Bae,et al.  3D medial axis point approximation using nearest neighbors and the normal field , 2011, The Visual Computer.

[9]  Paola Passalacqua,et al.  GeoNet: An open source software for the automatic and objective extraction of channel heads, channel network, and channel morphology from high resolution topography data , 2016, Environ. Model. Softw..

[10]  Martin Held,et al.  VRONI: An engineering approach to the reliable and efficient computation of Voronoi diagrams of points and line segments , 2001, Comput. Geom..

[11]  P. Lohmann,et al.  Aspects of Lidar Processing in Coastal Areas , 2005 .

[12]  Geoffrey Edwards,et al.  The Voronoi spatial model : two-and three-dimensional applications in image analysis , 1999 .

[13]  P. Tarolli,et al.  Drainage network detection and assessment of network storage capacity in agrarian landscape , 2013 .

[14]  Uwe Soergel,et al.  Aspects of generating precise digital terrain models in the Wadden Sea from lidar–water classification and structure line extraction , 2008 .

[15]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[16]  Hector Malano,et al.  Management of Irrigation and Drainage Systems: A Service Approach , 2018 .

[17]  Philippe Lagacherie,et al.  A spatial stochastic algorithm to reconstruct artificial drainage networks from incomplete network delineations , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[18]  C. M. Cooper,et al.  Vegetative and structural characteristics of agricultural drainages in the Mississippi Delta landscapes. , 2004, Environmental pollution.

[19]  Venkat Devarajan,et al.  Auto hydro break line generation using lidar elevation and intensity data , 2014 .

[20]  T. Wilbanks,et al.  Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[21]  Christian Heipke,et al.  EVALUATION OF AUTOMATIC ROAD EXTRACTION , 2007 .

[22]  T. Rabbani,et al.  SEGMENTATION OF POINT CLOUDS USING SMOOTHNESS CONSTRAINT , 2006 .

[23]  Roderik Lindenbergh,et al.  Automatic detection of buried channel deposits from dense laser altimetry data , 2010 .

[24]  George Vosselman,et al.  Segmentation of point clouds using smoothness constraints , 2006 .

[25]  C. Gandolfi,et al.  INFLUENCE OF THE DRAINAGE NETWORK IDENTIFICATION METHOD ON GEOMORPHOLOGICAL PROPERTIES AND HYDROLOGICAL RESPONSE , 1997 .

[26]  Bharat Lohani,et al.  Application of airborne scanning laser altimetry to the study of tidal channel geomorphology , 2001 .

[27]  N. Pfeifer,et al.  SEGMENTATION BASED ROBUST INTERPOLATION - A NEW APPROACH TO LASER DATA FILTERING , 2005 .

[28]  Kaleem Siddiqi,et al.  Medial Representations: Mathematics, Algorithms and Applications , 2008 .

[29]  P. Fisher.  The pixel: A snare and a delusion , 1997 .

[30]  N. Pfeifer,et al.  Water surface mapping from airborne laser scanning using signal intensity and elevation data , 2009 .

[31]  A. Karnieli,et al.  Skeletonizing a DEM into a drainage network , 1995 .

[32]  Gabriel Taubin,et al.  The ball-pivoting algorithm for surface reconstruction , 1999, IEEE Transactions on Visualization and Computer Graphics.

[33]  W. Walthen-Dunn.  A Transformation for Extracting New De scriptors of Shape ' , in , 2017 .

[34]  M. Costa-Cabral,et al.  Digital Elevation Model Networks (DEMON): A model of flow over hillslopes for computation of contributing and dispersal areas , 1994 .

[35]  Sebastiano Trevisani,et al.  Semi-automatic derivation of channel network from a high-resolution DTM: the example of an Italian alpine region , 2013 .