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.
| Author details: | Aljoscha RheinwaltORCiDGND, Bodo BookhagenORCiDGND |
|---|---|
| DOI: | https://doi.org/10.1117/12.2318424 |
| ISBN: | 978-1-5106-2150-3 |
| ISSN: | 0277-786X |
| ISSN: | 1996-756X |
| Title of parent work (English): | Remote Sensing for Agriculture, Ecosystems, and Hydrology XX |
| Subtitle (English): | Facet-Flow Networks (FFN) |
| Publisher: | SPIE-INT Society of Photo-Optical Instrumentation Engineers |
| Place of publishing: | Bellingham |
| Publication type: | Other |
| Language: | English |
| Date of first publication: | 2018/10/10 |
| Publication year: | 2018 |
| Release date: | 2022/03/11 |
| Tag: | DEM; TIN; drainage networks; flow accumulation; lidar; point clouds; stochastic filtering; uncertainty quantification |
| Volume: | 10783 |
| Number of pages: | 12 |
| Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften |
| DDC classification: | 5 Naturwissenschaften und Mathematik / 55 Geowissenschaften, Geologie / 550 Geowissenschaften |
