Background and Theoretical Foundations: The southern coastal strip of Iran has various facilities and installations due to its strategic and political location. Therefore, for the development of these facilities and the increase of infrastructure, information and mapping of the bed material of these areas is of particular importance. On the other hand, information on the bed material of the coast is very important and practical not only for the development of infrastructure and safe maritime navigation but also for management and monitoring. For this reason, the study of the bed material of the coasts using remote sensing techniques plays an important role in the monitoring, management and optimal exploitation of these areas and conducting studies on coastal processes. Therefore, in the present study, the bed material mapping of the southern Iranian coastal strip (Persian Gulf and Sea of Oman) was carried out. In general, the bed material mapping process in this study was carried out with the aim of obtaining more information about the material and compositions constituting the coast using Sentinel2 images and the Google Earth Engine (GEE) web-based system in these areas.
Methodology: In this study, Sentinel2 multispectral (MSI) images were used as the best available and free multispectral dataset to extract bed material maps of coastal areas. In addition to these multispectral images, soil texture images classified according to the USDA system standard were also used (OpenLandMap.org). Soil classes in this standard include soil classes at different depths (including 0, 10, 30, 60, 100, and 200 cm) and a resolution of 250 meters, which were mapped in this study at the 10x10 meter pixel level of Sentinel2 images. Also, spectral indices (Normalized Difference Index of Water Areas (NDWI), Normalized Difference Index of Vegetation (NDVI), Normalized Difference Index of Soil (NDSI), and finally Soil Adjustment Vegetation Index (SAVI)) were used to accurately distinguish and separate water, soil, and vegetation zones in the region. Finally, in order to validate the results with actual observations, a comparison was made with the information sources of the ICZM plan.
Findings: As the results showed, the studied beach bed type consists of four classes: loam (clay bed), sand, clay, and sand-loam, with the largest area being allocated to the sand class. In this regard, the results obtained, considering the ability of Sentinel2 satellite images to identify beach bed type and its high correspondence compared to existing field data, indicate the efficiency of this method for identifying different types of beach bed type in other target areas.
Conclusion: In general, the mapping process using satellite images has provided a broad perspective in coastal areas due to its high efficiency and low cost. On the other hand, atmospheric reflectance corrections and the use of spectral indices are important processes that make coastal area mapping more reliable. |
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