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A Network‐Based Flow Accumulation Algorithm for Point Clouds: Facet‐Flow Networks (FFNs)
Author(s) -
Rheinwalt Aljoscha,
Goswami Bedartha,
Bookhagen Bodo
Publication year - 2019
Publication title -
journal of geophysical research: earth surface
Language(s) - English
Resource type - Journals
eISSN - 2169-9011
pISSN - 2169-9003
DOI - 10.1029/2018jf004827
Subject(s) - triangulated irregular network , sampling (signal processing) , point cloud , algorithm , flow (mathematics) , computer science , tracing , geology , remote sensing , mathematics , geometry , digital elevation model , artificial intelligence , computer vision , filter (signal processing) , operating system
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 ( S C A ) 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 ∞ 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.