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.

Earth observation (EO) techniques have significantly evolved over time, covering a wide range of applications in different domains. The scope of this study is to review the research conducted on EO in the Eastern Mediterranean, Middle East, and North Africa (EMMENA) region and to identify the main knowledge gaps. We searched through the Web of Science database for papers published between 2018 and 2022 for EO studies in the EMMENA. We categorized the papers in the following thematic areas: atmosphere, water, agriculture, land, disaster risk reduction (DRR), cultural heritage, energy, marine safety and security (MSS), and big Earth data (BED); 6647 papers were found with the highest number of publications in the thematic areas of BED (27%) and land (22%). Most of the EMMENA countries are surrounded by sea, yet there was a very small number of studies on MSS (0.9% of total number of papers). This study detected a gap in fundamental research in the BED thematic area. Other future needs identified by this study are the limited availability of very high-resolution and near-real-time remote sensing data, the lack of harmonized methodologies and the need for further development of models, algorithms, early warning systems, and services.

Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

Coastal areas are highly dynamic environments, vulnerable to natural processes and human interventions. This study presents the first application of the Integrated Coastal Vulnerability Index (ICVI) in Cyprus, focusing on two major tourism-dependent beaches, Fig Tree Bay and Vrysi Beach, located along the Protaras coastline. Despite their economic significance, these coastal areas face increasing vulnerability due to intensive tourism-driven modifications and natural coastal dynamics, necessitating a structured assessment framework. This research addresses this gap by integrating the ICVI with geographical information system (GIS) and analytic hierarchy process (AHP) methodologies to evaluate the coastal risks in this tourism-dependent environment, providing a replicable approach for similar Mediterranean coastal settings. Ten key parameters were analysed, including coastal slope, rate of coastline erosion, geomorphology, elevation, tidal range, wave height, relative sea level rise, land cover, population density, and road network. The results revealed spatial variations in vulnerability, with 16% of the coastline classified as having very high vulnerability and another 16% as having high vulnerability. Fig Tree Bay, which is part of this coastline, emerged as a critical hotspot due to its geomorphological instability, low elevation, and intensive human interventions, including seasonal beach modifications and infrastructure development. This study underscores the need for sustainable coastal management practices, including dune preservation, controlled development, and the integration of the ICVI into planning frameworks to balance economic growth and environmental conservation.

This study explores the potential of Digital Earth Africa (DE Africa) coastlines products for assessing the Coastal Vulnerability Index (CVI) along the Casablanca coastline, Morocco. The analysis integrates remotely sensed shoreline data with elevation, slope, and geomorphological information from ASTER GDEM and geological maps within a GIS environment. Shoreline change metrics, including Shoreline Change Envelope (SCE), Net Shoreline Movement (NSM), Linear Regression Rate (LRR), and End Point Rate (EPR), were used to evaluate erosion trends from 2000 to 2023. Results show that sandy beach areas, particularly those below 12 m elevation, are highly exposed to erosion (up to 1.5 m/yr) and vulnerable to coastal hazards. Approximately 44% and 23% of the study area were classified as having very high and high vulnerability, respectively. The results indicate that remotely sensed data and GIS techniques are valuable and cost-effective tools for multi-scale geo-hazard coastal assessment studies. The study demonstrates that DE Africa products, combined with local landscape data, provide a valuable tool for coastal vulnerability assessment and monitoring in Africa.

Recent reports stress the vulnerability of forest ecosystems in the European Union (EU), especially in the south. Cyprus is an island in the south of EU and the eastern of the Mediterranean Sea. While Cyprus’ vulnerability is stressed, Cyprus was included in the worst-performing countries regarding EU carbon emission’s targets of 2020. For mitigating climate change, Cyprus could benefit for tailored education and improved policy making. This study analyses the perceptions of the Cypriot residents about climate change and forest degradation aiming (1) to gain a better understanding of whether Cypriot residents understand its importance, (2) to understand if the general public is able to observe the changes noted in the literature, (3) to understand how perceptions are differentiated across different demographic categories, and (4) to derive correlations between demographic data and perceptions. This is a quantitative study; a questionnaire was used as a tool and the responses received were 416. It was highlighted that 65.62% of the participants stated that they noticed moderate to very much degradation of Cypriot coniferous forests. A potential degradation reason was written down by 150 people, of whom 31.33% referred to tree die-back, while many stated decreased soil moisture and difficulty in regeneration. All these reasons of degradation were either stated or suspected in the literature. Additionally, the demographic analysis showed that there may be an association between employability and beliefs/observations about climate change. The results of the research could be used for tailored education, further research, and promoting environmentally friendly policies. This will support Cyprus and other countries in reaching their Green Deal targets and, consequently, mitigate the severe effects of climate change.