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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 15, issue 8 | Copyright
Hydrol. Earth Syst. Sci., 15, 2665-2678, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 25 Aug 2011

Research article | 25 Aug 2011

Derivation of groundwater flow-paths based on semi-automatic extraction of lineaments from remote sensing data

U. Mallast1,2, R. Gloaguen2, S. Geyer1, T. Rödiger1, and C. Siebert1 U. Mallast et al.
  • 1Department Catchment Hydrology, Helmholtz-Centre for Environmental Research (UFZ), 06120 Halle, Germany
  • 2Remote Sensing Group, Institute of Geology, Freiberg University of Mining and Technology, 09599 Freiberg, Germany

Abstract. In this paper we present a semi-automatic method to infer groundwater flow-paths based on the extraction of lineaments from digital elevation models. This method is especially adequate in remote and inaccessible areas where in-situ data are scarce. The combined method of linear filtering and object-based classification provides a lineament map with a high degree of accuracy. Subsequently, lineaments are differentiated into geological and morphological lineaments using auxiliary information and finally evaluated in terms of hydro-geological significance. Using the example of the western catchment of the Dead Sea (Israel/Palestine), the orientation and location of the differentiated lineaments are compared to characteristics of known structural features. We demonstrate that a strong correlation between lineaments and structural features exists. Using Euclidean distances between lineaments and wells provides an assessment criterion to evaluate the hydraulic significance of detected lineaments. Based on this analysis, we suggest that the statistical analysis of lineaments allows a delineation of flow-paths and thus significant information on groundwater movements. To validate the flow-paths we compare them to existing results of groundwater models that are based on well data.

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