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  -