VERTICAL VEGETATION STRUCTURE ANALYSIS AND HYDRAULIC ROUGHNESS DETERMINATION USING DENSE ALS POINT CLOUD DATA - A VOXEL BASED APPROACH

Abstract. In this contribution the complexity of the vertical vegetation structure, based on dense airborne laser scanning (ALS) point cloud data (25 echoes/m2 ), is analyzed to calculate vegetation roughness for hydraulic applications. Using the original 3D ALS point cloud, three levels of abstractions are derived (cells, voxels and connections) to analyze ALS data based on a 1×1 m2 raster over the whole data set. A voxel structure is used to count the echoes in predefined detrended height levels within each cell. In general, it is assumed that the number of voxels containing echoes is an indicator for elevated objects and consequently for increased roughness. Neighboring voxels containing at least one data point are merged together to connections. An additional height threshold is applied to connect vertical neighboring voxels with a certain distance in between. Thus, the connections indicate continuous vegetation structures. The height of the surface near or lowest connection is an indicator for hydrodynamic roughness coefficients. For cells, voxels and connections the laser echoes are counted within the structure and various statistical measures are calculated. Based on these derived statistical parameters a rule-based classification is developed by applying a decision tree to assess vegetation types. Roughness coefficient values such as Manning's n are estimated, which are used as input for 2D hydrodynamic-numerical modeling. The estimated Manning’s values from the ALS point cloud are compared with a traditional Manning's map. Finally, the effect of these two different Manning's n maps as input on the 2D hydraulics are quantified by calculating a height difference model of the inundated depth maps. The results show the large potential of using the entire vertical vegetation structure for hydraulic roughness estimation.

[1]  Martin J. Baptist,et al.  Floodplain roughness parameterization using airborne laser scanning and spectral remote sensing , 2008 .

[2]  Christian Briese,et al.  ANALYSIS OF FULL-WAVEFORM ALS DATA BY SIMULTANEOUSLY ACQUIRED TLS DATA: TOWARDS AN ADVANCED DTM GENERATION IN WOODED AREAS , 2010 .

[3]  M. Hollaus,et al.  URBAN VEGETATION DETECTION USING HIGH DENSITY FULL-WAVEFORM AIRBORNE LIDAR DATA-COMBINATION OF OBJECT-BASED IMAGE AND POINT CLOUD ANALYSIS , 2010 .

[4]  E. Næsset,et al.  Laser scanning of forest resources: the nordic experience , 2004 .

[5]  Markus Hollaus,et al.  VERTICAL ROUGHNESS MAPPING - ALS BASED CLASSIFICATION OF THE VERTICAL VEGETATION STRUCTURE IN FORESTED AREAS , 2010 .

[6]  K. Tansey,et al.  Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas , 2010 .

[7]  Markus Hollaus,et al.  Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification , 2008, Sensors.

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

[9]  S. Lane,et al.  A method for parameterising roughness and topographic sub-grid scale effects in hydraulic modelling from LiDAR data , 2010 .

[10]  Gregory Asner,et al.  Three-dimensional woody vegetation structure across different land-use types and -land-use intensities in a semi-arid savanna , 2009, 2009 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Barbara Koch,et al.  Status and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment , 2010 .

[12]  V. R. Schneider,et al.  GUIDE FOR SELECTING MANNING'S ROUGHNESS COEFFICIENTS FOR NATURAL CHANNELS AND FLOOD PLAINS , 1989 .

[13]  P. Litkey,et al.  Algorithms and methods of airborne laser-scanning for forest measurements , 2004 .

[14]  B. Höfle,et al.  Topographic airborne LiDAR in geomorphology: A technological perspective , 2011 .

[15]  D. Milan Terrestrial Laser Scanning of grain roughness in a gravel-bed river , 2009 .

[16]  W. Marcus,et al.  Remote Sensing of the Hydraulic Environment in Gravel‐Bed Rivers , 2012 .

[17]  M. Hollaus,et al.  Estimation of aboveground biomass using airborne LiDAR data , 2010 .

[18]  Martin Rutzinger,et al.  Water classification using 3D airborne laser scanning point clouds , 2009 .

[19]  Norbert Pfeifer,et al.  Optimisation of LiDAR derived terrain models for river flow modelling , 2008 .

[20]  Eduard Naudascher Hydraulik der Gerinne und Gerinnebauwerke , 1987 .

[21]  Gottfried Mandlburger,et al.  Estimating changes of riverine landscapes and riverbeds by using airborne LIDAR data and river cross - sections , 2011 .

[22]  Martin Pfennigbauer,et al.  Improving quality of laser scanning data acquisition through calibrated amplitude and pulse deviation measurement , 2010, Defense + Commercial Sensing.

[23]  K. Kraus,et al.  Determination of terrain models in wooded areas with airborne laser scanner data , 1998 .

[24]  Christoph Aubrecht,et al.  Roughness Mapping on Various Vertical Scales Based on Full-Waveform Airborne Laser Scanning Data , 2011, Remote. Sens..

[25]  M. Hollaus,et al.  TERRAIN ROUGHNESS PARAMETERS FROM FULL-WAVEFORM AIRBORNE LIDAR DATA , 2010 .