Adaptive smoothing of valleys in DEMs using TIN interpolation from ridgeline elevations: An application to morphotectonic aspect analysis
2007 (English)In: Computers & Geosciences, ISSN 0098-3004, E-ISSN 1873-7803, Vol. 33, no 4, 573-585 p.Article in journal (Refereed) Published
This paper presents a smoothing method that eliminates valleys of various Strahler-order drainage lines from a digital elevation model (DEM), thus enabling the recovery of local and regional trends in a terrain. A novel method for automated extraction of high-density channel network is developed to identify ridgelines defined as the watershed boundaries of channel segments. A DEM using TIN interpolation is calculated based on elevations of digitally extracted ridgelines. This removes first-order watersheds from the DEM. Higher levels of DEM smoothing can be achieved by the application of the method to ridgelines of higher-order channels. The advantage of the proposed smoothing method over traditional smoothing methods of moving kernel, trend and spectral methods is that it does not require pre-definition of smoothing parameters, such as kernel or trend parameters, and thus it follows topography in an adaptive way. Another advantage is that smoothing is controlled by the physical-hydrological properties of the terrain, as opposed to mathematical filters. Level of smoothing depends on ridgeline geometry and density, and the applied user-defined channel order. The method requires digital extraction of a high-density channel and ridgeline network. The advantage of the smoothing method over traditional methods is demonstrated through a case study of the Kali Basin test site in Hungary. The smoothing method is used in this study for aspect generalisation for morphotectonic investigations in a small watershed.
Place, publisher, year, edition, pages
2007. Vol. 33, no 4, 573-585 p.
aspect analysis, digital elevation model, digital drainage analysis, digital terrain modelling, rose diagram, smoothing, tectonic geomorphology
Earth and Related Environmental Sciences
IdentifiersURN: urn:nbn:se:uu:diva-144951DOI: 10.1016/j.cageo.2006.08.010ISI: 000245938000010OAI: oai:DiVA.org:uu-144951DiVA: diva2:395087