Sea level change, a consequence of climate change, poses a global threat with escalating impacts on coastal regions. Since 1880, global mean sea level has risen by 8–9 inches (21–24 cm), reaching a record high in 2021. Projections by NOAA suggest an additional 10–12-inch increase by 2050. This paper explores research methodologies for studying sea level change, focusing on Geographic Information System (GIS) techniques. GIS has become a powerful tool in sea level change research, allowing the integration of spatial data, coastal process modeling, and impact assessment. This paper sets the link with sustainability and reviews key factors influencing sea level change, such as thermal expansion and ice-mass loss, and examines how GIS is applied. It also highlights the importance of using different scenarios, like Representative Concentration Pathways (RCP), for accurate predictions. The paper discusses data sources, index variables like the Coastal Vulnerability Index, and GIS solutions for modeling sea level rise impacts. By synthesizing findings from previous research, it contributes to a better understanding of GIS methodologies in sea level change studies. This knowledge aids policymakers and researchers in developing strategies to address sea level change challenges and enhance coastal resilience. Furthermore, global analysis highlights the pivotal roles of the United States and China in sea level change (SLC) and GIS research. In the Gulf Cooperation Council (GCC) region, rising temperatures have substantial impacts on local sea levels and extreme weather events, particularly affecting vulnerable coastal areas.

Forest fire is becoming an important research topic because of climate change and its impact on population. In this study, historical database about forest fire incidents was created for Gironde and Les Landes (1989 to 2022). To the knowledge of the authors, this database did not exist before. The database is essential in showing spatial and temporal distribution of forest fires and helps in decision-making. The database was created from newspapers and crosschecked with burned areas visible on Landsat and Sentinel-2 satellite images. Several factors were used to generate a forest fire hazard map such as vegetation water content utilizing Normalized Difference Moisture Index (NDMI), type of vegetation, topography, and barriers to the expansion of fire (proximity to water bodies and roads). All layers were converted to raster format and reclassified to a common scale. Weighted overlay in GIS was used to generate the final forest fire hazard map. Results showed that, temporally most forest fires occurred during March-April and July-August and majority are in Gironde. The hazard map highlights that most of Les Landes and Gironde departments are vulnerable to this hazard because of their extended forest areas and high dryness. The accuracy assessment showed that supervised classification had the best accuracy followed by the Burned Area Detection Index (BADI) and the manual digitization. Data from French institutions and higher-resolution satellite images could improve the accuracy of the historical forest fires database. The output from the study was made available to the public for further exploration (https://firemap.saro.app/).

In the context of global sea level rising, coasts are directly impacted. The retreat to coastlines and submersion of anthropic installations are among the major impacts. It is thus imperative to continuously monitor the coastlines status and devise the means and techniques to effectively assess their status. The United Arab Emirates (UAE) for example is a country which has a long sandy coastline. In this research, an algorithm was developed that makes use of remote sensing temporal data to assess the variability of the coastline in the UAE. The algorithm is used to automatically extract the whole coastline between 1991 and 2021 from Landsat 5 and 8 satellite images. They were selected for 1991, 2001, 2013 and 2021 because of the availability of data, and the significant changes that have been done in coastal areas due to urban development during this period.Only the Landsat spectral bands of green and near-infrared were utilized to calculate the spectral index of detection of the coast DDWI (Direct Difference Water Index). It is the first step of the algorithm developed. Then is used an automatic threshold Otsu to differentiate the land from water. The result is filled to remove the main artifacts and a canny edge detector is used to detect the coastline. At the end of the algorithm, the result is georeferenced because it lost it during the process. The georeferenced layer is polygonised so that the remaining artifacts are easier to remove. Then, a mask layer was created including boats, clouds, etc… and it is removed from the polygonised layer to get the final extracted coastline.The preliminary findings of this study show that the sandbanks have increased during the period of the study along the Arabian Gulf waters, suggesting that the coastline is retreating. The results showed a development of the sandbanks towards the Arabian Gulf in several places along the northern coastline but also their general retreat on the north-western one. This can be explained by the sediment settlement or the backfills that have been done to create new islands especially around Abu Dhabi city and Dubai. The creation of mangroves plantations or port infrastructures in the same place has completely changed the coastline layout of the UAE.On the other side of the UAE, along the sea of Oman, the sandbanks have retreated, suggesting either soil erosion by water currents or advancement of the coastline. The results show no significant change at all and no sandbanks. The only changes observed are linked to the anthropic modification of the coast. While the coastline did not change, the developed algorithm detected scattered sandbanks as the coastline. This confusion likely comes from the similar reflectance of sandbanks in shallow water with the sand of the coast. A further improvement for the developed algorithm will be pursued in the future to reduce such confusions.This methodology is applicable worldwide, but it is necessary to monitor the results for sandy areas such as the Middle East.