The recovery phase following an earthquake event is essential for urban areas with a significant number of damaged buildings. A lot of changes can take place in such a landscape within the buildings’ footprints, such as total or partial collapses, debris removal and reconstruction. Remote sensing data and methodologies can considerably contribute to site monitoring. The main objective of this paper is the change detection of the building stock in the settlement of Vrissa on Lesvos Island during the recovery phase after the catastrophic earthquake of 12 June 2017, through the analysis and processing of UAV (unmanned aerial vehicle) images and the application of Artificial Neural Networks (ANNs). More specifically, change detection of the settlement’s building stock by applying an ANN on Gray-Level Co-occurrence Matrix (GLCM) texture features of orthophotomaps acquired by UAVs was performed. For the training of the ANN, a number of GLCM texture features were defined as the independent variable, while the existence or not of structural changes in the buildings were defined as the dependent variable, assigning, respectively, the values 1 or 0 (binary classification). The ANN was trained based on the Levenberg–Marquardt algorithm, and its ability to detect changes was evaluated on the basis of the buildings’ condition, as derived from the binary classification. In conclusion, the GLCM texture feature changes in conjunction with the ANN can provide satisfactory results in predicting the structural changes of buildings with an accuracy of almost 92%.

Groundwater nitrate contamination caused by the excessive use of nitrogen-based fertilizers has been widely recognized as an issue of significant concern in numerous rural areas worldwide. To mitigate nitrate contamination, corrective management practices, such as regulations on fertilizer usage, should be implemented. However, these measures often entail economic consequences that impact farmers’ income, and thus should be properly assessed. Within this context, an integrated framework combining the environmental and economic assessment of fertilization restrictions through multi-criteria decision analysis is presented in an effort to efficiently manage groundwater nitrate contamination in rural areas. For this task, various scenarios involving reductions (10%, 20%, 30%, 40% and 50%) in fertilizer application were investigated, evaluated and ranked in order to determine the most suitable option. The environmental assessment considered occurrences of nitrates in groundwater, with a specific emphasis on nitrate concentrations in water-supply wells, as obtained by a nitrate fate and transport model, while the economic analysis focused on the losses experienced by farmers due to the reduced fertilizer usage. Our case-study implementation showed that a 30% reduction in fertilization is the most appropriate option for the area being studied, highlighting the importance of adopting such an approach when confronted with conflicting outcomes among alternatives.

Shoreline configurations are a complex outcome of the dynamic interplay between natural forces and human actions. This interaction shapes unique coastal morphologies and affects sediment transport and erosion patterns along the coastline. Meanwhile, ephemeral river systems play a vital role in shaping coastlines and maintaining ecosystem sustainability, especially in island settings. In this context, the present study seeks to develop a holistic approach that views coast and watershed systems as a continuum, aiming to investigate their relationships in an island environment, while accounting for human interventions in the river regime. For this task, the empirical USLE method was employed to quantify sediment production and transport from the catchment area to the coast, while hydraulic simulations using HEC-RAS were conducted to assess sediment retention within flood-affected areas. Moreover, coastal vulnerability to erosion was evaluated by applying the InVEST CVI model in order to identify areas at risk from environmental threats. The coastal zone of Petra–Molyvos, Lesvos, Greece, was selected as the study area due to ongoing erosion issues, with particular emphasis on its interaction with the Petra stream as a result of significant human intervention at its mouth. According to the study’s findings, the examined coastal zone is highly vulnerable to combined erosion from wind and waves, while the river’s mouth receives only a small amount of sediment from water fluxes. Evidently, this leads to an increase in beach retreat phenomena, while highlighting the necessity for integrated coastal–watershed management.

This study applies the GALDIT index within a GIS environment to assess groundwater vulnerability to seawater intrusion (SWI) in the coastal aquifer of Katerini, Greece. In addition, a single-parameter sensitivity analysis (SPSA) is performed to evaluate the relative influence of the GALDIT parameters and to produce a modified vulnerability map based on SPSA-derived weights. The results indicate that, in both cases, most of the study area exhibits low and moderate vulnerability, with high vulnerability limited to small areas along the coastal zone. Minor differences were observed between the two approaches, with the modified GALDIT map showing a slightly larger proportion of moderate-vulnerability areas. The SPSA further revealed that L is the most influential parameter in the GALDIT index, whereas G and I have the least influence. Overall, the study findings may serve as a valuable basis for prioritizing groundwater monitoring and management in the study area, and for guiding targeted measures to help prevent SWI.