As a result of climate change, many lands are under risk due to the rising sea levels (RSL). Studies show that the mean sea level will likely rise by 0.16 to 0.63 metres before 2050, and 0.2 to 2.5 metres by 2100. Lower-lying islands are more endangered from RSL. One of such islands is Failaka, a small island in Kuwait lying at the entrance of Kuwait Bay, which is located on the north-western side of the Arabian Gulf (Also called the Persian Gulf). Most of Failaka Island is lower than three meters. The Governmental plans are to develop and populate the island. SLR should be considered in such planning. This study focuses particularly on detecting the areas of Failaka Island which are under high threat from the SLR. To detect these areas, spatial analysis of the Digital elevation model (DEM) are used. DEM is estimated for three SLR scenarios (1, 2 and 3 metres). It is expected that 31% of the island will be under sea level height for the SLR of 1 m; 54% for the SLR of 2 metres; and 87% for the SLR of 3 m. Coastal Vulnerability Index (CVI) is estimated as well. The CVI shows that the eastern coast is the most susceptible with regard to the SLR. The model was validated through using ground elevation points (n = 40), and a positive correlation was found with  of 0.8019. Geographic Information System (GIS) and Remote sensing (RS) are confirmed to be effective tools for estimating spatial influence of the SLR.

Mapping bathymetry is essential for many fields, including science, engineering, and the military, among others. Bathymetry is extremely important in the scientific field because it is linked to many physical and environmental issues such as coastal erosion, sea-level rise, and water quality. Traditionally, conventional methods, such as pre-measured cable passage, were used to estimate depths. Lately, echo-sounder assessments were used on hydrograph ships. This method is effective, but it is very costly in both economic and time terms. Remote sensing technology provides modern methods for mapping bathymetry, such as the use of active and passive remote sensing. Many satellite sensors cover multispectral bands. Some are commercial, such as IKONOS and WorldView, while others are freely available, such as Landsat 8 and Sentinel-2. In this study, Landsat 8 (15 meters spatial resolution) was used to estimate the depths of the waters of Kuwait, an Arabian Gulf country located on the Northwestern side of the gulf. Ground truthing points (GTPs) were used to build a bathymetric model of Kuwaiti territorial water (KTW) using the ratio transform algorithm (RTA) applied on Landsat 8 data. The results showed a good ability of Landsat 8 and RTA to estimate the depths of Kuwait’s waters, where the relationship between the derived model from Landsat 8 and the GTPs was positive (r2 = 0.9634). Meanwhile, the accuracy of the derived bathymetric model was evaluated by computing the Root Mean Square Error (RMSE = ± 1.66 meters) and Mean Absolute Error (MAE ± = 1.29).

Studying physical oceanography is one of the important fields of remote sensing applications. Previously, the thermal mapping of seas and oceans relied on primitive methods, such as the use of sensors installed on buoys, extracting contour lines, and deriving the values from the confluence of contour lines. Today's remote sensing provides more advanced methods for extracting sea surface temperature (SST) values for all bodies of water as a continuous raster model, through thermal sensors installed on satellites designated to monitor and observe the Earth. The Landsat program has facilitated a quantum leap by providing its data free for the public. What has become increasingly important is the inclusion, in Landsat 8, of a thermal band on the TIRS sensor through which SST can be extracted with a spatial resolution of 100 m2. In this article, the accuracy of the two thermal bands (band 10 and 11) of Landsat 8 was validated in estimating the SST of Kuwaiti and Northwest Arabian Gulf waters, through the use of 62 thermal images and 66 ground-truthing points (GTPs) taken from the field in the period from July 2013 to March 2020. This was achieved through a function provided by the ENVI 5.3 software - “brightness temperature” - to derive the surface temperature. The accuracy of Landsat 8 to monitor the SST of Kuwait and north-west Arabian Gulf waters was validated by calculating the root mean square error (RMSE) and the mean absolute percentage error (MAPE). The accuracy of the thermal band 10 was ± 2.03 degrees (7.9%), while the accuracy of the thermal band 11 was ± 3.13 degrees (13.7%). Therefore, this study demonstrated that the thermal band 10 of Landsat 8 is more accurate than the thermal band 11 in monitoring the SST of Kuwaiti and north-west Arabian Gulf waters, with a difference of ± 1.1 degrees (5.8%).

Researchers need to delineate the shoreline for different applications with no access to costly resources such as topographic maps and high-resolution satellite images. With the increase of open source data, this study aims to present a methodology to use open source data in the best possible way to map the shoreline. Several methods have been tested using open source remote sensing data (Landsat and Aster), such as supervised classification, unsupervised classification, manual mapping, and by applying some spectral indices, among others. The accuracy of the extracted shoreline data was verified using high-resolution open database images (such as Google Earth basemap). The results showed that the manually mapped shoreline through applying spectral index (green- near infrared/green+ near infrared) is the most accurate, although it remains important to modify it using high-resolution images of open databases. Open-source data showed acceptable accuracy in mapping the shoreline.

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