Resolution Sensitivity of a Compound Terrain Derivative as Computed from LiDAR-Based Elevation Data

  • Ralph K. Straumann
  • Ross S. Purves
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


New technologies such as Light Detection And Ranging (LiDAR) provide high resolution digital elevation data. These data offer new possibilities in the field of terrain modelling and analysis. However, not very much is known about the effects when these data are used to compute broadly applied terrain derivatives. In this paper the sensitivity of the Topographic Wetness Index (TWI) and its two constituting components gradient and Specific Catchment Area (SCA) regarding the resolution of LiDAR-based elevation data is examined. For coarser resolutions a shift in the TWI distribution to higher values is noted. TWI distributions at different resolutions differ significantly from each other. These findings have an impact on aspatial and spatial modelling based on the TWI.


Terrain derivatives Topographic Wetness Index resolution sensitivity LiDAR 


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  1. [1]
    Kienzle S. W.: The effect of DEM raster resolution on first order, second orderand compound terrain derivatives. Transactions in GIS 8 (2004) 83–111CrossRefGoogle Scholar
  2. [2]
    Beven K. J.: Rainfall-Runoff Modelling. The Primer. John Wiley & Sons Ltd., Chichester (2001)Google Scholar
  3. [3]
    Beven K. J., Kirkby M. J.: A physically based, variable contributing area model ofbasin hydrology. Hydrological Sciences Bulletin 24 (1979) 43–69CrossRefGoogle Scholar
  4. [4]
    Wolock D. M., Price C. V.: Effects of digital elevation model map scale and dataresolution on a topography-based watershed model. Water Resources Research 30(1994)3041–3052CrossRefGoogle Scholar
  5. [5]
    Zhang W., Montgomery D. R.: Digital elevation model grid size, landscaperepresentation, and hydrologic simulations. Water Resources Research 30 (1994)imeresolutionoGoogle Scholar
  6. [6]
    Bruneau P., Gascuel-Odoux C, Robin P., Merot Ph., Beven K. J.: Sensitivity tospace and time resolution of a hydrological model using digital elevation data.Hydrological Processes 9 (1995) 69–81CrossRefGoogle Scholar
  7. [7]
    Franchini M., Wendling J., Obled C, Todini E.: Physical interpretation andsensitivity analysis of the TOPMODEL. Journal of Hydrology 175 (1996) 293–338CrossRefGoogle Scholar
  8. [8]
    Saulnier G.-M., Obled C, Beven K.: Analytical compensation between DTM gridresolution and effective values of saturated hydraulic conductivity within theTOPMODEL framework. Hydrological Processes 11 (1997) 1331–1346CrossRefGoogle Scholar
  9. [9]
    Brasington J., Richards K.: Interactions between model predictions, parametersand DTM scales for TOPMODEL. Computers & Geosciences 24 (1998) 299–314CrossRefGoogle Scholar
  10. [10]
    Higy C, Musy A.: Digital terrain analysis of the Haute-Mentue catchment andscale effect for hydrological modelling with TOPMODEL. Hydrology and EarthSystem Sciences 4 (2000) 225–237CrossRefGoogle Scholar
  11. [11]
    Lane S. N., Brookes C. J., Kirkby M. J., Holden J.: A network-index-basedversion of TOPMODEL for use with high-resolution digital topographic data.Hydrological Processes 18 (2004) 191–201CrossRefGoogle Scholar
  12. [12]
    Blyth E. M., Finch J., Robinson M., Rosier P.: Can soil moisture be mapped ontothe terrain? Hydrology and Earth System Sciences 8 (2004) 923–930CrossRefGoogle Scholar
  13. [13]
    Guntner A., Seibert J., Uhlenbrook S.: Modeling spatial patterns of saturatedareas: An evaluation of different terrain indices. Water Resources Research 40(2004) W05114Google Scholar
  14. [14]
    Blazkova S., Beven K., Tacheci P., Kulasova A.: Testing the distributed watertable predictions of TOPMODEL (allowing for uncertainty in model calibration).The death of TOPMODEL? Water Resources Research 38 (2002) 1257CrossRefGoogle Scholar
  15. [15]
    Xu Z. X., Li J. Y.: Estimating basin evapotranspiration using distributedhydrologic model. Journal of Hydrologic Engineering 8 (2003) 74–80CrossRefGoogle Scholar
  16. [16]
    Stieglitz M., Shaman J., McNamara J., Engel V., Shanley J., Kling G. W.: Anapproach to understanding hydrologic connectivity on the hillslope and theimplications for nutrient transport. Global Biogeochemical Cycles 17 (2003) 1105CrossRefGoogle Scholar
  17. [17]
    Irvin B. J., Ventura S. J., Slater B. K.: Fuzzy and isodata classification oflandform elements from digital terrain data in Pleasant Valley, Wisconsin.Geoderma 77 (1997) 137–154CrossRefGoogle Scholar
  18. [18]
    MacMillan R. A., Pettapiece W. W., Nolan S. C, Goddard T. W.: A genericprocedure for automatically segmenting landforms into landform elements usingDEMs, heuristic rules and fuzzy logic. Fuzzy Sets and Systems 113 (2000) 81–109CrossRefGoogle Scholar
  19. [19]
    Burrough P. A., van Gaansa P. F. M., MacMillan R. A.: High-resolutionlandform classification using fuzzy k-means. Fuzzy Sets and Systems 113 (2000)37–52CrossRefGoogle Scholar
  20. [20]
    Li Z., Qing Z., Gold, C: Digital Terrain Modeling. Principles and Methodology. CRC Press, Boca Rotan (2005)Google Scholar
  21. [21]
    Wilson J.P., Gallant J. (eds.): Terrain analysis: Principle and Applications. JohnWiley & Sons, Singapore (2000)Google Scholar
  22. [22]
    Vieux, B.E.: DEM aggregation and smoothing effects on surface runoffmodeling. Journal of Computing in Civil Engineering 7 (1993) 310–338CrossRefGoogle Scholar
  23. [23]
    Gao, J.: Resolution and accuracy of terrain representation by grid DEMs at amicro-scale. International Journal of Geographical Information Science 11 (1997)199–212CrossRefGoogle Scholar
  24. [24]
    Thompson J. A., Bell J. C, Butler C. A.: Digital elevation model resolution:effects on terrain attribute calculation and quantitative soil-landscape modeling.Geoderma 100 (2001) 67–89CrossRefGoogle Scholar
  25. [25]
    Claessens L., Heuvelink G. B. M., Schoorl J. M., Veldkamp A.: DEM resolutioneffects on shallow landslide hazard and soil redistribution modelling. EarthSurface Processes and Landforms 30 (2005) 461–477CrossRefGoogle Scholar
  26. [26]
    Wilson J. P., Repetto P. L., Snyder R. D.: Effect of data source, grid resolutionand flow-routing method on computed topographic attributes. In: Wilson J. P., Gallant J. C. (eds.): Terrain Analysis. Principles and Applications. John Wiley &Sons, New York (2000) 133–161Google Scholar
  27. [27]
    Garbrecht, J., Martz, L.: Grid size dependency of parameters extracted fromDigital Elevation Models. Computers & Geosciences 20 (1994) 85–87CrossRefGoogle Scholar
  28. [28]
  29. [29]
    SAGA GIS (2006): A System for an Automated Geographical Analysis,
  30. [30]
    Quinn P., Beven K. J., Chevallier P., Planchon O.: The prediction of hillslopeflow paths for distributed hydrological modelling using digital terrain models.Hydrological Processes 5 (1991) 59–79CrossRefGoogle Scholar
  31. [31]
    Quinn P. F., Beven K. J., Lamb R.: The ln(A/tan beta) index-How to calculateit and how to use it within the TOPMODEL framework. Hydrological Processes9(1995) 161–182CrossRefGoogle Scholar
  32. [32]
    Evans I. S.: General geomorphometry, derivatives of altitude, and descriptivestatistics. In: Chorley R. J. (ed.): Spatial Analysis in Geomorphology. Methuen &Co. Ltd., London (1972) 17–90Google Scholar
  33. [33]
    Duke G. D., Kienzle S. W., Johnson D. L., Byrne J. M.: Improving overland flowrouting by incorporating ancillary road data into digital elevation models. Journalof Spatial Hydrology 3 (2003)Google Scholar
  34. [34]
    Duke G. D., Kienzle S. W., Johnson D. L., Byrne J. M.: Incorporating ancillarydata to refine anthropogenically modified overland flow paths. HydrologicalProcesses 20 (2006) 1827–1843Google Scholar
  35. [35]
    Holko L., Lepisto A.: Modelling the hydrological behaviour of a mountaincatchment using TOPMODEL. Journal of Hydrology 196 (1997) 361–377CrossRefGoogle Scholar
  36. [36]
    Wood J. D.: The geomorphological characterisation of digital elevation models.PhD thesis at the University of Leicester, UK (1996)Google Scholar
  37. [37]
    Raaflaub L. D.: The effect of error in gridded digital elevation models ontopographic analysis and on the distributed hydrological model TOPMODEL.MSc thesis at the University of Calgary, Alberta (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ralph K. Straumann
    • 1
  • Ross S. Purves
    • 1
  1. 1.Department of GeographyUniversity of Zurich - IrchelZurichSwitzerland

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