Determining the optimal grid resolution for topographic analysis on an airborne lidar dataset

  • 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 estimatesDigital 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.show moreshow less

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Metadaten
Author:Taylor SmithORCiDGND, Aljoscha RheinwaltORCiDGND, Bodo BookhagenORCiDGND
URN:urn:nbn:de:kobv:517-opus4-430165
DOI:https://doi.org/10.25932/publishup-43016
ISSN:1866-8372
Parent Title (German):Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe
Series (Serial Number):Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe (725)
Document Type:Postprint
Language:English
Date of first Publication:2019/06/14
Year of Completion:2019
Publishing Institution:Universität Potsdam
Release Date:2019/06/14
Tag:Accuracy Asseessment; Digital Elevation Model; Error; Expression; Extraction; Landscape Response; Patterns; River Incision Model; Slope; Uncertainties
Issue:725
Pagenumber:15
First Page:475
Last Page:489
Source:Earth Surface Dynamics 7 (2019) S. 475–489 DOI: 10.5194/esurf-7-475-2019
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Geowissenschaften
Peer Review:Referiert
Publication Way:Open Access
Licence (German):License LogoCreative Commons - Namensnennung, 4.0 International
Notes extern:Bibliographieeintrag der Originalveröffentlichung/Quelle