TY - JOUR A1 - Schwanghart, Wolfgang A1 - Groom, Geoff A1 - Kuhn, Nikolaus J. A1 - Heckrath, Goswin T1 - Flow network derivation from a high resolution DEM in a low relief, agrarian landscape JF - Earth surface processes and landforms : the journal of the British Geomorphological Research Group N2 - Digital flow networks derived from digital elevation models (DEMs) sensitively react to errors due to measurement, data processing and data representation. Since high-resolution DEMs are increasingly used in geomorphological and hydrological research, automated and semi-automated procedures to reduce the impact of such errors on flow networks are required. One such technique is stream-carving, a hydrological conditioning technique to ensure drainage connectivity in DEMs towards the DEM edges. Here we test and modify a state-of-the-art carving algorithm for flow network derivation in a low-relief, agricultural landscape characterized by a large number of spurious, topographic depressions. Our results show that the investigated algorithm reconstructs a benchmark network insufficiently in terms of carving energy, distance and a topological network measure. The modification to the algorithm that performed best, combines the least-cost auxiliary topography (LCAT) carving with a constrained breaching algorithm that explicitly takes automatically identified channel locations into account. We applied our methods to a low relief landscape, but the results can be transferred to flow network derivation of DEMs in moderate to mountainous relief in situations where the valley bottom is broad and flat and precise derivations of the flow networks are needed. KW - digital terrain analysis KW - digital elevation model KW - hydrological conditioning KW - drainage networks Y1 - 2013 U6 - https://doi.org/10.1002/esp.3452 SN - 0197-9337 SN - 1096-9837 VL - 38 IS - 13 SP - 1576 EP - 1586 PB - Wiley-Blackwell CY - Hoboken ER - TY - GEN A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Network-based flow accumulation for point clouds BT - Facet-Flow Networks (FFN) T2 - Remote Sensing for Agriculture, Ecosystems, and Hydrology XX N2 - 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. KW - lidar KW - point clouds KW - stochastic filtering KW - flow accumulation KW - drainage networks KW - uncertainty quantification KW - TIN KW - DEM Y1 - 2018 SN - 978-1-5106-2150-3 U6 - https://doi.org/10.1117/12.2318424 SN - 0277-786X SN - 1996-756X VL - 10783 PB - SPIE-INT Society of Photo-Optical Instrumentation Engineers CY - Bellingham ER - TY - JOUR A1 - Rheinwahlt, Aljoscha A1 - Goswami, Bedartha A1 - Bookhagen, Bodo T1 - A network-based flow accumulation algorithm for point clouds BT - Facet-Flow Networks (FFNs) JF - Journal of geophysical research : Earth surface N2 - Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds. KW - point clouds KW - drainage networks KW - lidar KW - tin KW - surface runoff KW - spatial sampling Y1 - 2019 U6 - https://doi.org/10.1029/2018JF004827 SN - 2169-9003 SN - 2169-9011 VL - 124 IS - 7 SP - 2013 EP - 2033 PB - American Geophysical Union CY - Washington ER -