This study evaluates the accuracy of bathymetric maps generated from multispectral satellite datasets acquired from different multispectral sensors, namely the Worldview 2, PlanetScope, and the Sentinel 2, in the bay of Elounda in Crete. Image pre-processing steps were implemented before the use of the three empirical methods for estimating bathymetry. A dedicated correction and median filter have been applied to minimize noise from the sun glint and the sea waves. Due to the spectral complexity of the selected study area, statistical correlation with different numbers of bands was applied. The analysis indicated that blue and green bands obtained the best results with higher accuracy. Then, three empirical models, namely the Single Band Linear Algorithm, the Multiband Linear Algorithm, and the Ratio Transform Algorithm, were applied to the three multispectral images. Bathymetric and error distribution maps were created and used for the error assessment of results. The accuracy of the bathymetric maps estimated from different empirical models is compared with on-site Single beam Echo Sounder measurements. The most accurate bathymetric maps were obtained using the WorldView 2 and the empirical model of the Ratio Transform algorithm, with the RMSE reaching 1.01 m.

During the last years, various methods and techniques have been used to estimate the seafloor topography with high accuracy. The topography is considered an important component for marine applications since several activities and infrastructures are being carried out at the bottom, column and surface of the sea. In this, study, freely and open distributed optical satellite images were used to obtain bathymetric data for shallow waters, (until 30 m below sea level) based on timeseries analysis of multispectral Sentinel-2 datasets. The ratio transform algorithm was implemented for twelve (12) monthly images covering thus a whole year. The different generated Digital Terrain Models (DTMs) were compared with high resolution LIDAR measurements. The results showed that bathymetry can be obtained from satellite data within a Root Mean Square Error (RMSE) ranging from 1.39 up to 2.56 m while more accurate results were generated during the summer period.

This review discusses the evolution and integration of open-access remote sensing technology in shoreline detection and coastal erosion monitoring through the use of Geographic Information Systems (GIS), Artificial Intelligence (AI), Unmanned Aerial Vehicles (UAVs), and Ground Truth Data (GTD). The Sentinel-2 and Landsat 8/9 missions are highlighted as the primary core datasets due to their open-access policy, worldwide coverage, and demonstrated applicability in long-term coastal monitoring. Landsat data have allowed the detection of multi-decadal trends in erosion since 1972, and Sentinel-2 has provided enhanced spatial and temporal resolutions since 2015. Through integration with GIS programs such as the Digital Shoreline Analysis System (DSAS), AI-based processes such as sophisticated models including WaterNet, U-Net, and Convolutional Neural Networks (CNNs) are highly accurate in shoreline segmentation. UAVs supply complementary high-resolution data for localized validation, and ground truthing based on GNSS increases the precision of the produced map results. The fusion of UAV imagery, satellite data, and machine learning aids a multi-resolution approach to real-time shoreline monitoring and early warnings. Despite the developments seen with these tools, issues relating to atmosphere such as cloud cover, data fusion, and model generalizability in different coastal environments continue to require resolutions to be addressed by future studies in terms of enhanced sensors and adaptive learning approaches with the rise of AI technology the recent years.

Coastal risk assessment is crucial for coastal management and decision making, especially in areas already experiencing the negative impacts of climate change. This study aims to investigate the coastal vulnerability due to climate change and human activities in an area west of the Limassol district’s coastline, in Cyprus, on which there have been limited studies. Furthermore, an analysis is conducted utilising the Coastal Vulnerability Index (CVI) by exploiting eight key parameters: land cover, coastal slope, shoreline erosion rates, tidal range, significant wave height, coastal elevation, sea-level rise, and coastal geomorphology. These parameters were assessed utilising remote sensing (RS) data and Geographical Information Systems (GISs) along a 36.1 km stretch of coastline. The results exhibited varying risk levels of coastal vulnerability, mainly highlighting a coastal area where the Kouris River estuary is highly vulnerable. The study underscores the need for targeted coastal management strategies to address the risks associated with coastal erosion. Additionally, the CVI developed in this study can be exploited as a tool for decision makers, empowering them to prioritise areas for intervention and bolster the resilience of coastal areas in the face of environmental changes.

Global forest degradation and deforestation present urgent environmental challenges demanding efficient strategies for ecological restoration to maximize the impacts and minimize the costs. This study aims to develop a spatial decision support tool to prioritize post-fire restoration actions in Mediterranean ecosystems, with a focus on Cyprus. At the core of this study is the GRESTO Index (GreenHIT-RESTORATION Index), a novel geospatial tool designed to guide reforestation efforts in fire-affected areas. GRESTO integrates geospatial data and ecological criteria through a multi-criteria decision-making approach based on the Analytic Hierarchy Process (AHP). The model incorporates nine key indicators, including fire severity, tree density, land cover, fire history, slope, elevation, aspect, precipitation, and temperature, and classifies restoration priority zones into low, medium, and high categories. When applied to the Solea fire event in Cyprus, the model identified 24% of the area as high priority, 66% as medium and 10% as low. The validation against previous restoration actions implemented in the study area demonstrated reliable agreement, with an overall accuracy of 80.9%, a recall of 0.70 for high priority areas, and an AUC of 0.79, indicating very good separability. Moreover, sensitivity analysis further confirmed the robustness of the model under varying parameter weights. These findings highlight the GRESTO model’s potential to support data-driven, cost-effective restoration planning aligned with national and international environmental goals.