Landslides or rockfalls in mountainous areas in Western Greece are a continuous hazard and thus, observation either in situ or through satellite data is of great necessity. In order to study landslides or rockfalls a very accurate representation of the relief is a prerequisite. In now days diverse remote sensing satellites provides digital surface elevation models (DSMs) with global coverage. The specific data sets present the advantages of covering large areas in a very low cost in comparison with the surveys in the field. In addition, high resolution DSMs enable the analysis of topography with high levels of detail and consequently geomorphometric approach becomes more accurate for studying landlsides or rockfall events. Information which can be extracted from geological and topographic maps in GIS format such as the slope angle distribution, deduced from high resolution DSMs, are substantial in rockfalls survey. In the present work, the accuracy of freely available DSMs are under control for landslides areas in Achaia prefecture in NW Peloponnese. The accuracy of the DSMs in the broader area is investigated using reference points of certified elevation while for the landslide area ground control collected with differential GNSS receiver are used. More specifically, free available DSMs as TanDEMX, ASTER GDEM, SRTM DEM with 30m and 90m spatial resolution, ALOS AW3D30 DEM, DSM from photogrammetric airphoto processing and DSM produced by Interferometric processing of radar images, are under examination in the current study. Furthermore, diverse statistical parameters such as the 2D Root Mean Square Error or the percentile value are computed and presented. The purpose of the aforementioned procedure is to detect the most accurate DSMs which are appropriate for landslide of rockfall monitoring.
Mass movements and therefore rockfalls are common natural hazards that require the development of new methodologies and techniques for an effective research, which could help governments, communities or policy makers in the adoption of the most appropriate practices. In that context different remote sensing data have been used and several methodologies have been tested. In the present study, we initially map a rockfall occurred on the settlement Myloi, which is located near the village of Andritsaina in Western Greece, while later we estimated the volume of rock fragments. The data sets consist of repeated GNSS measurements, laser scanning surveys and UAV campaigns over the study area. The precise mapping of the rockfall was carried out through the processing of GNSS measurements. However, mapping was also performed using orthophotos derived from UAV data and 3D images of laser scanning campaigns. Regarding the volume estimation, three methodologies were applied -two of them were photogrammetric and one was geophysical- using ArcGIS, Cloud Compare and Oasis Montaj from Geosoft respectively. The selection of different types of data and processing methodologies took place within the framework of the comparison of their results in terms of accuracy as well as the achievement of their synergy in the direction of a more detailed research.
The aim of this work is to detect and qualify natural karst depressions in the Aitoloakarnania Prefecture, Western Greece, using remote sensing data in conjunction with the Geographical Information Systems - GIS. The study area is a part of the Ionian geotectonic zone, and its geological background consists of the Triassic Evaporates. The Triassic carbonate breccias where formed as a result of the tectonic and orogenetic setting of the external Hellenides and the diaper phenomena of the Triassic Evaporates. The landscape characterized by exokarst features closed depressions in the Triassic carbonate breccias. At the threshold of this study, an in situ observation was performed in order to identify dolines and swallow holes. The creation of sinkholes, in general, is based on the collapse of the surface layer due to chemical dissolution of carbonate rocks. In the current study airphotos stereopairs, DSMs and GIS were combined in order to detect and map the karst features. Thirty seven airphotos were imported in Leica Photogrammetry Suite and a stereo model of the study area was created. Then in 3D view possible karst features were detected and digitized. Those sites were verified during the in situ survey. ASTER GDEM, SRTM DEM, high resolution airphoto DSM created from the Greek Cadastral and a DEM from digitized contours from the 1/50,000 topographic were also evaluated in GIS environment for the automatic detection of the karst depressions. The results are presented in this study.