Network-based flow accumulation for point clouds
- Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.
| Verfasserangaben: | Aljoscha RheinwaltORCiDGND, Bodo BookhagenORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1117/12.2318424 |
| ISBN: | 978-1-5106-2150-3 |
| ISSN: | 0277-786X |
| ISSN: | 1996-756X |
| Titel des übergeordneten Werks (Englisch): | Remote Sensing for Agriculture, Ecosystems, and Hydrology XX |
| Untertitel (Englisch): | Facet-Flow Networks (FFN) |
| Verlag: | SPIE-INT Society of Photo-Optical Instrumentation Engineers |
| Verlagsort: | Bellingham |
| Publikationstyp: | Sonstiges |
| Sprache: | Englisch |
| Datum der Erstveröffentlichung: | 10.10.2018 |
| Erscheinungsjahr: | 2018 |
| Datum der Freischaltung: | 11.03.2022 |
| Freies Schlagwort / Tag: | DEM; TIN; drainage networks; flow accumulation; lidar; point clouds; stochastic filtering; uncertainty quantification |
| Band: | 10783 |
| Seitenanzahl: | 12 |
| Organisationseinheiten: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
| DDC-Klassifikation: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
