Optimisation of LiDAR derived terrain models for river flow modelling
Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m<sup>2</sup> and height accuracy of better than &plusmn;15 cm. For all these reasons LiDAR is particularly suitable f...
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2009-08-01
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doaj-db9a9740085a4e7cb1cca9cdd5c59f6d2020-11-24T21:09:46ZengCopernicus PublicationsHydrology and Earth System Sciences1027-56061607-79382009-08-0113814531466Optimisation of LiDAR derived terrain models for river flow modellingG. MandlburgerC. HauerB. HöfleH. HabersackN. PfeiferAirborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m<sup>2</sup> and height accuracy of better than &plusmn;15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance &plusmn;20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes is gaining importance in river flow modelling. http://www.hydrol-earth-syst-sci.net/13/1453/2009/hess-13-1453-2009.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
G. Mandlburger C. Hauer B. Höfle H. Habersack N. Pfeifer |
spellingShingle |
G. Mandlburger C. Hauer B. Höfle H. Habersack N. Pfeifer Optimisation of LiDAR derived terrain models for river flow modelling Hydrology and Earth System Sciences |
author_facet |
G. Mandlburger C. Hauer B. Höfle H. Habersack N. Pfeifer |
author_sort |
G. Mandlburger |
title |
Optimisation of LiDAR derived terrain models for river flow modelling |
title_short |
Optimisation of LiDAR derived terrain models for river flow modelling |
title_full |
Optimisation of LiDAR derived terrain models for river flow modelling |
title_fullStr |
Optimisation of LiDAR derived terrain models for river flow modelling |
title_full_unstemmed |
Optimisation of LiDAR derived terrain models for river flow modelling |
title_sort |
optimisation of lidar derived terrain models for river flow modelling |
publisher |
Copernicus Publications |
series |
Hydrology and Earth System Sciences |
issn |
1027-5606 1607-7938 |
publishDate |
2009-08-01 |
description |
Airborne LiDAR (Light Detection And Ranging) combines cost efficiency, high degree of automation, high point density of typically 1–10 points per m<sup>2</sup> and height accuracy of better than &plusmn;15 cm. For all these reasons LiDAR is particularly suitable for deriving precise Digital Terrain Models (DTM) as geometric basis for hydrodynamic-numerical (HN) simulations. The application of LiDAR for river flow modelling requires a series of preprocessing steps. Terrain points have to be filtered and merged with river bed data, e.g. from echo sounding. Then, a smooth Digital Terrain Model of the Watercourse (DTM-W) needs to be derived, preferably considering the random measurement error during surface interpolation. In a subsequent step, a hydraulic computation mesh has to be constructed. Hydraulic simulation software is often restricted to a limited number of nodes and elements, thus, data reduction and data conditioning of the high resolution LiDAR DTM-W becomes necessary. We will present a DTM thinning approach based on adaptive TIN refinement which allows a very effective compression of the point data (more than 95% in flood plains and up to 90% in steep areas) while preserving the most relevant topographic features (height tolerance &plusmn;20 cm). Traditional hydraulic mesh generators focus primarily on physical aspects of the computation grid like aspect ratio, expansion ratio and angle criterion. They often neglect the detailed shape of the topography as provided by LiDAR data. In contrast, our approach considers both the high geometric resolution of the LiDAR data and additional mesh quality parameters. It will be shown that the modelling results (flood extents, flow velocities, etc.) can vary remarkably by the availability of surface details. Thus, the inclusion of such geometric details in the hydraulic computation meshes is gaining importance in river flow modelling. |
url |
http://www.hydrol-earth-syst-sci.net/13/1453/2009/hess-13-1453-2009.pdf |
work_keys_str_mv |
AT gmandlburger optimisationoflidarderivedterrainmodelsforriverflowmodelling AT chauer optimisationoflidarderivedterrainmodelsforriverflowmodelling AT bhofle optimisationoflidarderivedterrainmodelsforriverflowmodelling AT hhabersack optimisationoflidarderivedterrainmodelsforriverflowmodelling AT npfeifer optimisationoflidarderivedterrainmodelsforriverflowmodelling |
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1716757381254217728 |