This research deals with the characterization of areas associated with flash floods and erosion caused by severe rainfall storm and sediment transport and accumulation using topographic attributes and profiles, spectral indices (SI), and principal component analysis (PCA). To achieve our objectives, topographic attributes and profiles were retrieved from ASTER-V2 DEM. PCA and nine SI were derived from two Landsat-OLI images acquired before and after the flood-storm. The images data were atmospherically corrected, sensor radiometric drift calibrated, and geometric and topographic distortions rectified. For validation purposes, the acquired photos during the flood-storm, lithological and geological maps were used. The analysis of approximately 100 colour composite combinations in the RGB system permitted the selection of two combinations due to their potential for characterizing soil erosion classes and sediment accumulation. The first considers the "Intensity, NDWI and NMDI", while the second associates form index (FI), brightness index (BI) and NDWI. These two combinations provide very good separating power between different levels of soil erosion and degradation. Moreover, the derived erosion risk and sediment accumulation map based on the selected spectral indices segmentation and topographic attributes and profiles illustrated the tendency of water accumulation in the landscape, and highlighted areas prone to both fast moving and pooling water. In addition, it demonstrated that the rainfall, the topographic morphology and the lithology are the major contributing factors for flash flooding, catastrophic inundation, and erosion risk in the study area. The runoff-water power delivers vulnerable topsoil and contributes strongly to the erosion process, and then transports soil material and sediment to the plain areas through waterpower and gravity.
Sound navigating and ranging (SONAR) detection systems can provide valuable information for navigation and security, especially in shallow coastal areas. The last few years have seen an important increase in the volume of bathymetric data produced by Multi-Beam Echo-sounder Systems (MBES). Recently, the General Bathymetric Chart of the Oceans (GEBCO) released these MBES dataset preprocessed and processed with Computer Aided Resource Information System (CARIS) for public domain use. For the first time, this research focuses on the validation of these released MBES-CARIS dataset performance and robustness for bathymetric mapping of shallow water at the regional scale in the Kingdom of Bahrain (Arabian Gulf). The data were imported, converted and processed in a GIS environment. Only area that covers the Bahrain national water boundary was extracted, avoiding the land surfaces. As the released dataset were stored in a node-grid points uniformly spaced with approximately 923 m and 834 m in north and west directions, respectively, simple kriging was used for densification and bathymetric continuous surface map derivation with a 30 by 30 m pixel size. In addition to dataset cross-validation, 1200 bathymetric points representing different water depths between 0 and −30 m were selected randomly and extracted from a medium scale (1:100,000) nautical map, and they were used for validation purposes. The cross-validation results showed that the modeled semi-variogram was adjusted appropriately assuring satisfactory results. Moreover, the validation results by reference to the nautical map showed that when we consider the total validation points with different water depths, linear statistical regression analysis at a 95% confidence level (p < 0.05) provide a good coefficient of correlation (R 2 = 0.95), a good index of agreement (D = 0.82), and a root mean square error (RMSE) of 1.34 m. However, when we consider only the validation points (~800) with depth lower than −10 m, both R 2 and D decreased to 0.79 and 0.52, respectively, while the RMSE increased to 1.92 m. Otherwise, when we consider exclusively shallow water points (~400) with a depth higher than −10 m, the results showed a very significant R 2 (0.97), a good D (0.84) and a low RMSE (0.51 m). Certainly, the released MBES-CARIS data are more appropriate for shallow water bathymetric mapping. However, for the relatively deeper areas the obtained results are relatively less accurate because probably the MBSE did not cover the bottom in several deeper pockmarks as the rapid change in depth. Possibly the steep slopes and the rough seafloor affect the integrity of the acquired raw data. Moreover, the interpolation of the missed areas' values between MBSE acquisition data points may not reflect the true depths of these areas. It is possible also that the nautical map used for validation was not established with a good accuracy in the deeper regions.
This research compares the potential of SRTM-V4.1 and ASTER-V2.1 with 30-m pixel size to derive topographic attributes (elevation, slopes, aspects, and flow accumulation) and hydrologic indices such as STI (sediment transport index), CTI (compound topographic index) and SPI (stream power index) to detect areas associated with flash floods caused by rainfall storms and sediment accumulation. The study area is Guelmim city in Morocco, which has been flooded several times over the past 50 years, and which was declared a "disaster area" in December 2014 after violent rainfall storms killed 46 people and caused significant damage to the infrastructure. The obtained results indicate that the SRTM DEM performs better than ASTER in terms of micro-topography, hydrologic-network and structural information characterization. In addition, with reference to a topographic contours map (1:50000), the derived global height surfaces accuracies are ±3.15 m and ±9.17 m for SRTM and ASTER, respectively. These accuracies are significantly influenced by topography; errors are larger (SRTM = 11.34 m, ASTER = 19.20 m) for high altitude terrain with strong slopes, while they are smaller (SRTM = 1.92 m, ASTER = 3.76 m) in the low to medium-relief areas with indulgent slopes. Moreover, all the considered hydrological indices are significantly characterized with SRTM compared to ASTER. They demonstrated that the rainfall and the topographic morphology are the major contributing factors in flash flooding and catastrophic inundation in this area. The runoff waterpower delivers vulnerable topsoil and contributes strongly to the erosion and transport of soil material and sediment to the plain areas through waterpower and gravity. Likewise, the role of the lithology associated with the terrain morphology is decisive in the erosion risk and land degradation in this region.
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