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Fusion of multi-resolution surface (terrestrial laser scanning) and subsurface geodata (ERT, SRT) for karst landform investigation and geomorphometric quantification


Correspondence to: C. Siart, Heidelberg University, Institute of Geography, Laboratory for Geomorphology and Geoecology, Im Neuenheimer Feld 348, Heidelberg 69120, Germany. Email:


A multi-method research design based on terrestrial laser scanning, GIS, geophysical prospecting (electrical resistivity tomography, refraction seismics) and sedimentology is applied for the first time to investigate enclosed karst depressions in an integrated way. Fusing multi-resolution surface and subsurface geodata provides profound insights into the formation, geometry and geomorphologic processes of dolines. The studied landforms, which are located in the Dikti Mountains of East Crete, are shown to be filled by loose sediments of thicknesses of up to 30 m that mainly consist of fine-grained material overlying solid bedrock at depths below 35 to 45 m. By combining subsurface observations with geomorphometric calculations, local doline genesis can be traced back to initial collapse of fractured bedrock followed by subsequent infilling with colluvials. In order to define crucial methodological requirements and guidelines for data fusion, both the impact of different elevation models and the influence of data resolution are assessed. Surface volumes of depressions derived by the digital surface model are 7–21% higher than the results obtained from the terrain model due to vegetation. Similarly, estimates of infill volume calculated on the basis of geophysical outcomes and elevation data differ by up to 13%. Calculations of the landforms' current volumes (i.e. total surface and subsurface volume), however, are fairly insensitive to raster resolution. Hence, the distinct geomorphologic properties of landforms (e.g. shape, terrain roughness, slope inclination) substantially determine the geomorphometric analysis of both surface and subsurface data. As shown by the findings, data fusion to integrate digital terrain, geophysical and sedimentological datasets of varied resolutions benefits geomorphologic studies and helps provide a comprehensive image of landforms. Copyright © 2013 John Wiley & Sons, Ltd.

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