Tracking the behavior of an accidental oil spill and its impacts on the marine environment in the Eastern Mediterranean.

The eastern Mediterranean region is a vital hub for oil transportation and production because of its strategic location between Europe, Asia, and Africa. But its unique attributes, including narrow shipping routes, heavy marine traffic, and proximity to vulnerable ecosystems, render it particularly susceptible to accidental oil spills. In this research, an oil spill detection model, along with bathymetric and oceanographic parameters, was used to track oil spills that occurred at the Syrian Baniyas Station in the Eastern Mediterranean on August 23, 2021. Furthermore, the study employed a pairwise comparison matrix (PWCM) to assess the relative importance of wind speed and direction, water depth, and sea surface temperature (SST) in the dispersion of oil spills. Analysis of Sentinel-1 data obtained prior to, during, and after the incident revealed the accumulation of oil slicks along the Syrian coast from Baniyas to Latakia for up to twenty days. The spilled oil reached the coast of Cyprus six days after the incident. The study determined that wind speed and direction played a critical role in the dispersion of spilled oil, while water depth and SST were comparatively less significant factors in this process. The overall accuracy (OA) and Kappa coefficient (KC) for land, water, and oil slick classes derived from the random forest (RF) algorithm ranged from 90 % to 98 % and from 0.86 to 0.98, respectively. The spread of oil slicks at the incident location was revealed by the decorrelation stretch and band ratios of Sentinel-2 MultiSpectral Instrument (MSI) data. The accidental oil spill could have negative effects on the organic carbon cycle, chlorophyll production, and ecosystem productivity. It is essential to consider the vulnerability of specific regions in the Eastern Mediterranean to oil spills when developing adaptation strategies.

Monitoring of oil spill in the offshore zone of the Nile Delta using Sentinel data.

This study aims to monitor and map the oil spills which occurred from 2019 to 2021 along the northeastern portion of the Nile Delta using Sentinel-1 (SAR) and Sentinel-2 (MSI) data. The examination of VV polarized SAR-C images displayed the presence of the oil spills as dark spots of different sizes. These images were processed using the oil spills detection model in SNAP Toolbox. The oceanographic parameters that may influence the dispersal of oil spills were mapped using GIS technique. This study identified 29 oil spills during the study period in the research area. The largest spill was detected on February 23, 2019, and covered an area of about 10.5 km2. The band ratios and decorrelation stretch methods of available Sentinel-2 data confirmed the results of SAR-C data. The accuracy assessment of spills was achieved using Parallelepiped supervised classification model. The results demonstrated that the overall accuracy (OA) and Kappa coefficient (KC) for seawater, land, and oil spills classes were between 86% and 98% and 0.73% and 0.97%, respectively. The sensitivity zone of oil spills was higher in winter than in summer. This study proved the efficiency of VV polarized data of Sentinel-1 sensor for detection and mapping of oil spills. Several management strategies are needed in the offshore zone of the Nile Delta to limit oil pollution effects on the marine environment.

Dynamics of human-induced lakes and their impact on land surface temperature in Toshka Depression, Western Desert, Egypt.

Changes in land-use/land-cover (LULC) give rise to several environmental problems which modify the microclimate of a region. Therefore, land surface temperature (LST) is a significant environmental variable that is appreciably influenced by LULC changes. The main aim of this research was to quantify the impacts of LULC changes from the drying of Toshka Lakes on LST by remote sensing and geographic information system (GIS) techniques. Landsat series Thematic Mapper (TM) and Operational Land Imager (OLI) satellite images were used to estimate LST from 2001 to 2019. Automated Water Extraction Index (AWEI) was applied to the mosaicked Landsat images to extract water bodies from the research area. Optimized Soil-Adjusted Vegetation Index (OSAVI) was utilized to predict the reclaimed land in the Toshka region until 2019. The kappa coefficient had been involved in this research to assess the accuracy of water extraction. The results indicated a decrease in the lakes by about 1517.79 km2 with an average increase in LST by about 25.02 °C from 2001 to 2019. It was observed that the dried areas of the lakes were converted to bare soil and are covered by salt crusts. The results indicated that the land-use change was a significant driver for the increased LST. The mean annual LST increased considerably by 0.6 °C/year from 2009 to 2019. A strong negative correlation between LST and Toshka Lakes area (R-square = 0.98) estimated from regression analysis implied that Toshka Lakes drying considerably affected the microclimate of the study area. Severe drought conditions, soil degradation, and many environmental issues were predicted due to the rise of LST in the research area. The findings of this research focus on the benefits of satellite data for assessing human-induced changes and their impacts on microclimate. There is an urgent need to develop favorable strategies for sustainable environmental management in the Toshka region.

Oceanographic factors of oil pollution dispersion offshore the Nile Delta (Egypt) using GIS.

Oil pollution is a worldwide concern due to the increasing rate of offshore oil production and transport. As the Nile Delta coast is adjacent to the main route of oil transport through the Suez Canal, accidental oil spills along the offshore region may have a tremendous impact on the coastal waters. If the oil slicks reach the coast, the impact should also be significant. This study aimed to map the oceanographic parameters that may affect oil pollution dispersion along the offshore zone of the Nile Delta using geographic information system (GIS) techniques. Oceanographic data (wave patterns, bathymetry data, sea surface temperature (SST), chlorophyll content, phytoplankton concentration, and organic carbon concentration) were used to analyze the characteristics of water at the onset of oil pollution to interpret the dispersion of the oil slick once discharged into the seawater. The results showed that SSTs and the depth of the coastal zone primarily influence the distribution of the other parameters.

The development of an overlay model to predict soil salinity risks by using remote sensing and GIS techniques: a case study in soils around Idku Lake, Egypt.

Soil salinization is one of the major environmental problems facing agricultural lands in arid and semiarid areas of the world because of its detrimental impacts on agricultural production and on the sustainable development of land resources. Hence, predicting soil salinity is essential to avoiding further soil degradation. The present study is intended to develop a model for predicting soil salinity in soils around Idku Lake by using remote sensing and geographic information system techniques. This lake is a shallow brackish basin located in the western part of the Nile Delta. For this purpose, Landsat 8-OLI images and shuttle radar topography mission 1Arc-Second Digital Elevation Model data were utilized in this research. A total of 91 surface samples were collected across the study area at a depth between 0 and 30 cm and were analyzed via traditional laboratory analysis methods. Five environmental parameters were used in the design of the soil salinity model. A pairwise comparison matrix was used to calculate the factor weight value for each of the layers. A linear regression model was used to plot the relationship between the EC value and raster value of the salinity map derived from the overlay model. According to the results obtained from a pairwise comparison of the factor layers, water table level was the greatest influential factor of soil salinity, followed by landforms. The validation of the model demonstrated a high degree of correlation (R2 = 0.72) between the measured EC values and the salinity values derived from the model. Furthermore, this model could be a useful tool for predicting soil salinity with a suitable validation.

The use of cartographic modeling to assess the impacts of coastal flooding: a case study of Port Said Governorate, Egypt.

Low-set coastal areas are expected to aggravate inundation on account of sea level rise (SLR). The present study is planned to appraise the impacts of coastal flooding in Port Said city, Egypt by using remote sensing, GIS, and cartographic modeling techniques. To accomplish this scope, Landsat 8-OLI image dated 2016 and SRTM 1Arc-Second Digital Elevation Model (DEM) data were used. Landsat image was classified into seven land use and land cover (LULC) classes by using remote sensing and GIS's software. Different inundation scenarios 1.0, 2.0, and 3.0-m coastal elevation were used to figure the influence of SLR on the study area. Estimation of potential losses under SLR was made by overlaying the expected scenarios on land use. The inundation areas under the expected SLR scenarios of 1.0, 2.0, and 3.0 m were estimated at 827.49, 1072.67, and 1179.41 km2, respectively. In conclusion, this study demonstrated that expected coastal flooding scenarios will lead up to serious impacts on LULC classes and coastal features in the study area.