Abstract
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.
Highlights
Airborne Laser Scanning (ALS), often referred to as LiDAR, is used as a fast and accurate technique to collect topographic information
Before sorting the echoes into the different aggregation levels the height of each echo is normalized to the Z0 level, which is the height of the digital terrain model (DTM) at the XY-position of the laser point
Major differences are predominantly characterized by the neighboring Manning classes in the ALS derived data
Summary
Airborne Laser Scanning (ALS), often referred to as LiDAR, is used as a fast and accurate technique to collect topographic information. ALS is a time and cost-effective method to acquire large area topographic data with low amount of user interaction, high ground sampling density and height accuracy of less than 15 cm. It is used for area-wide 3D data acquisition to support a range of scientific disciplines like (geo)archeology, geology, geomorphology, hydrology and many more (Höfle and Rutzinger, 2011). Dense laser scanning point cloud data provide precise geometry and high vertical resolution allowing an improved 3D surface classification for hydraulic roughness map calculation. The use of FWF technology increases the ability to map vegetation in a more dense horizontal and vertical structure than with discrete echo recording systems (Doneus et al, 2010)
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