TY - GEN A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 725 KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-430165 SN - 1866-8372 IS - 725 SP - 475 EP - 489 ER - TY - JOUR A1 - Smith, Taylor A1 - Rheinwalt, Aljoscha A1 - Bookhagen, Bodo T1 - Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset JF - Earth Surface Dynamics N2 - Digital elevation models (DEMs) are a gridded representation of the surface of the Earth and typically contain uncertainties due to data collection and processing. Slope and aspect estimates on a DEM contain errors and uncertainties inherited from the representation of a continuous surface as a grid (referred to as truncation error; TE) and from any DEM uncertainty. We analyze in detail the impacts of TE and propagated elevation uncertainty (PEU) on slope and aspect. Using synthetic data as a control, we define functions to quantify both TE and PEU for arbitrary grids. We then develop a quality metric which captures the combined impact of both TE and PEU on the calculation of topographic metrics. Our quality metric allows us to examine the spatial patterns of error and uncertainty in topographic metrics and to compare calculations on DEMs of different sizes and accuracies. Using lidar data with point density of ∼10 pts m−2 covering Santa Cruz Island in southern California, we are able to generate DEMs and uncertainty estimates at several grid resolutions. Slope (aspect) errors on the 1 m dataset are on average 0.3∘ (0.9∘) from TE and 5.5∘ (14.5∘) from PEU. We calculate an optimal DEM resolution for our SCI lidar dataset of 4 m that minimizes the error bounds on topographic metric calculations due to the combined influence of TE and PEU for both slope and aspect calculations over the entire SCI. Average slope (aspect) errors from the 4 m DEM are 0.25∘ (0.75∘) from TE and 5∘ (12.5∘) from PEU. While the smallest grid resolution possible from the high-density SCI lidar is not necessarily optimal for calculating topographic metrics, high point-density data are essential for measuring DEM uncertainty across a range of resolutions. KW - Digital Elevation Model KW - River Incision Model KW - Accuracy Asseessment KW - Landscape Response KW - Error KW - Slope KW - Uncertainties KW - Extraction KW - Expression KW - Patterns Y1 - 2019 U6 - https://doi.org/10.5194/esurf-7-475-2019 SN - 2196-6311 SN - 2196-632X VL - 7 SP - 475 EP - 489 PB - Copernicus Publ. CY - Göttingen ER - TY - JOUR A1 - Loos, Martin A1 - Elsenbeer, Helmut T1 - Topographic controls on overland flow generation in a forest - An ensemble tree approach JF - Journal of hydrology N2 - Overland flow is an important hydrological pathway in many forests of the humid tropics. Its generation is subject to topographic controls at differing spatial scales. Our objective was to identify such controls on the occurrence of overland flow in a lowland tropical rainforest. To this end, we installed 95 overland flow detectors (OFDs) in four nested subcatchments of the Lutzito catchment on Barro Colorado Island, Panama, and monitored the frequency of overland flow occurrence during 18 rainfall events at each OFD location temporal frequency. For each such location, we derived three non-digital terrain attributes and 17 digital ones, of which 15 were based on Digital Elevation Models (DEMs) of three different resolutions. These attributes then served as input into a Random Forest ensemble tree model to elucidate the importance and partial and joint dependencies of topographic controls for overland flow occurrence. Lutzito features a high median temporal frequency in overland flow occurrence of 0.421 among OFD locations. However, spatial temporal frequencies of overland flow occurrence vary strongly among these locations and the subcatchments of Lutzito catchment. This variability is best explained by (1) microtopography, (2) coarse terrain sloping and (3) various measures of distance-to-channel, with the contribution of all other terrain attributes being small. Microtopographic features such as concentrated flowlines and wash areas produce highest temporal frequencies, whereas the occurrence of overland flow drops sharply for flow distances and terrain sloping beyond certain threshold values. Our study contributes to understanding both the spatial controls on overland flow generation and the limitations of terrain attributes for the spatially explicit prediction of overland flow frequencies. KW - Overland Bow KW - Tropical rainforest KW - Random Forest KW - Spatial scale KW - Digital Elevation Model KW - Topography Y1 - 2011 U6 - https://doi.org/10.1016/j.jhydrol.2011.08.002 SN - 0022-1694 VL - 409 IS - 1-2 SP - 94 EP - 103 PB - Elsevier CY - Amsterdam ER -