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The Effect of DEM Raster Resolution on First Order, Second Order and Compound Terrain Derivatives
Abstract
It is well known that the grid cell size of a raster digital elevation model has significant effects on derived terrain variables such as slope, aspect, plan and profile curvature or the wetness index. In this paper the quality of DEMs derived from the interpolation of photogrammetrically derived elevation points in Alberta, Canada, is tested. DEMs with grid cell sizes ranging from 100 to 5 m were interpolated from 100 m regularly spaced elevation points and numerous surface‐specific point elevations using the ANUDEM interpolation method. In order to identify the grid resolution that matches the information content of the source data, three approaches were applied: density analysis of point elevations, an analysis of cumulative frequency distributions using the Kolmogorov‐Smirnov test and the root mean square slope measure. Results reveal that the optimum grid cell size is between 5 and 20 m, depending on terrain com‐plexity and terrain derivative. Terrain variables based on 100 m regularly sampled elevation points are compared to an independent high‐resolution DEM used as a benchmark. Subsequent correlation analysis reveals that only elevation and local slope have a strong positive relationship while all other terrain derivatives are not represented realistically when derived from a coarse DEM. Calculations of root mean square errors and relative root mean square errors further quantify the quality of terrain derivatives.
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Cites
BEVEN, K. J., & KIRKBY, M. J. (1979) · Hydrological Sciences Bulletin · doi:10.1080/02626667909491834 · Cited by 6,670
Cited by
Uncertainties Associated with Digital Elevation Models for Hydrologic Applications: A Review
Wechsler, S. P. (2007) · Hydrology and Earth System Sciences · doi:10.5194/hess-11-1481-2007 · Cited by 280
Accuracy Assessment of the TanDEM-X 90 Digital Elevation Model for Selected Floodplain Sites
Hawker, L., Neal, J., & Bates, P. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111319 · Cited by 151
Land-surface parameters for spatial predictive mapping and modeling
Maxwell, A. E., & Shobe, C. M. (2022) · Earth-Science Reviews · doi:10.1016/j.earscirev.2022.103944 · Cited by 105
Thomas, J., Joseph, S., Thrivikramji, K. P., & Arunkumar, K. S. (2014) · Geoscience Frontiers · doi:10.1016/j.gsf.2013.12.008 · Cited by 67
Becker, D., De Andrés-Herrero, M., Willmes, C., Weniger, G.-C., & Bareth, G. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6020036 · Cited by 51
Metadata
- Authors
- Kienzle, S.
- Venue
- Transactions in GIS
- Publisher
- Wiley
- Published
- 2003-12-10
- Volume
- 8
- Issue
- 1
- Pages
- 83-111
- ISSN
- 1361-1682 (ISSN-L)
- 1467-9671
- OpenAlex
- W2139156616
- Cited by
- 292
- References
- 1
- FWCI
- 11.85
- Type
- journal article
- Language
- en
- Open access
- closed
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