Sediment grain size and its spatial distribution is a very important aspect for many applications and processes that occur in the coastal zone. One of these is coastal erosion which is strongly dependent on sediment distribution and transportation. To highlight this fact, surficial coastal sediments were collected from a densely populated coastal zone in Western Greece, which suffers extensive erosion, and grain size distribution was thoroughly analysed, to predict the spatial distribution of the median grain size diameter (D₅₀) and produce sediment distribution maps. Four different geostatistical interpolation techniques (Ordinary Kriging, Simple Kriging, Empirical Bayesian Kriging and Universal Kriging) and three deterministic (Radial Basis Function, Local Polynomial Interpolation, and Inverse Distance Weighting) were employed for the construction of the respective surficial sediment distribution maps with the use of GIS. Moreover, a comparative study between the deterministic and geostatistical approaches was applied and the performance of each interpolation method was evaluated using cross-validation and estimating the Pearson Corellation and the coefficient of determination (R²). The best interpolation technique for this research proved to be the Ordinary Kriging for the shoreline materials and the Empirical Bayesian Kriging (EBK) for the seabed materials since both had the lowest prediction errors and the highest R².

This study focuses on the role of topography in soil erosion modelling by examining the impact of topographic data from various sources on the calculation of the slope length and slope steepness factor (LS). For this purpose, the Pinios dam drainage basin in the Ilia Regional Unit, Western Greece, was selected as a pilot area of this study. Specifically, six Digital Elevation Models (DEM) from four different sources with various resolutions (5, 30, and 90 m) were compared with ground control point (GCP) values to assess their relative vertical accuracy. These DEM were acquired for the calculation of the LS factor by using two different equations. Then the calculated LS factors were implemented in the RUSLE model for the estimation of soil loss. The current study includes a comparative analysis of the elevation, the slopes, the LS factor, and the soil loss. The results showed that the 5 m resolution DEM had the best vertical accuracy, and thus it is considered to be the most suitable DEM for soil erosion modelling. Moreover, the comparison of the DEM elevation values showed high similarity, in contrast to the slope values. In addition, the comparative assessment of the LS and soil loss values calculated from each DEM with the two LS equations revealed a great divergence. It is noticeable that both LS and soil loss results presented higher values for slopes greater than 20°. It is concluded that the comparison of the LS values calculated with the two examined approaches and the use of different DEM with various resolutions and different sources does not change consistently with the increase of DEM grid size and accuracy. Thus, it is very significant in soil erosion modelling to use an LS equation that imports thresholds in its formula to avoid overestimation in soil loss calculations.

This study presents the synergy of multidisciplinary surveys for the monitoring of an active landslide in Western Greece. The aim of this paper is to highlight and validate a methodology based on multiple sensors data integration which can successfully be used to manage natural disasters or to improve the knowledge of a specific phenomenon in order to prevent and mitigate the risk. Photogrammetric and interferometric processing has been applied to a complex set of remote sensing data such as high resolution satellite images, digital airphotos, aerial photos acquired from an Unmanned Aerial Vehicle and radar data. Global Navigation Satellite System measurements and continuous inclinometer measurements are being performed. The multifunctional technology of Geographic Information Systems is used in order to collect, storage, manage, process, analyze and cartographically represent the previously described complex geoscientific information.

In recent decades, wildfires have become a serious threat worldwide, producing disasters in the natural and anthropogenic environment as well as serious economic losses. One of wildfire’s major impacts is soil erosion, as it may cause major problems in both the physical and anthropogenic environment and seriously affect the landscape. This study investigates the soil erosion rate changes in areas affected by wildfires and uses, as a pilot area, the drainage basin of the Pinios earth-filled dam located in the Ilia Regional Unit, western Greece, which has suffered serious erosion changes after a wildfire event. For this purpose, the Revised Universal Soil Loss Equation (RUSLE) is applied in GIS software, and the soil erosion rate changes in the selected investigation area are estimated at different time intervals. Specifically, soil erosion rate changes are calculated by importing the factors from the RUSLE equation in the GIS software and uses as a dependent variable the cover management factor C, which is strongly influenced by large destructive fires. The models that are produced are compared with each other by collating average annual soil erosion maps and rates before the fire, immediately after the fire and for the existing conditions occurring in the pilot area.

The aim of this specific study is to present a new weighted Coastal Vulnerability Index (CVI_WF), with an emphasis given to the geotechnical evaluation and shoreline evolution rate measured through high-resolution remote sensing, which seem to be the most interfering variables in CVI calculations. As a pilot area for the application of the new CVI_WF, the Gulf of Patras in Western Greece was selected, which is suffering erosion problems due to climate change, the sea level rising and human intervention. The new CVI_WF, which was applied in this research, includes the following innovations: (1) the use of geotechnical characterization instead of geological–geomorphological characterization, (2) the use of high-resolution remote sensing data for the detection of shoreline evolution rate and (3) the insertion of a specific weighted geotechnical factor in the CVI_WF formula. The results from the application of the unweighted CVI show that percentages of 20.13%, 20.47%, 24.56%, 29.39% and 5.45% of the gulf’s shoreline are under the regime of very low, low, moderate, high and very high vulnerability, respectively. On the other hand, the corresponding results from the application of the weighted CVI_WF show a percentage of 14.59%, 25.91%, 20.04, 36.48% and 2.98, respectively.