The assessment of flood disasters is considered an essential factor in land use management, being necessary to understand and define the magnitude of past events. In this regard, several flood diagnoses have been developed using Sentinel-2 multispectral imagery, especially in large water bodies. However, one of the main challenges is still related to floods, where water surfaces have sizes similar to the spatial resolution of the analyzed satellite images, being difficult to detect and map. Therefore, the present study developed a combined methodology for flood mapping in small-sized water bodies using Sentinel-2 MSI imagery. The method consisted of evaluating the effectiveness of the application and combination of (a) a super-resolution algorithm to improve image resolution, (b) a set of seven spectral indices for highlighting water-covered areas, such as AWE indices, and (c) two methods for flood mapping, including a machine learning method based on unsupervised classification (EM cluster) and 14 thresholding methods for automatic determination. The processes were evaluated in the Carrión River, Palencia, Spain. It was determined that the approach with the best results in flood mapping was the one that combined AWE spectral indices with methods such as Huang and Wang, Li and Tam, Otsu, moment preservation, and EM cluster classification, showing global accuracy and Kappa coefficient values higher than 0.88 and 0.75, respectively, when applying the quantitative accuracy index.
The sustainable management of fluvial systems requires reliable knowledge of the mechanisms that control the basins and their drainages, which in turn must be prioritized for the application of measures for flood-risk reduction. Thus, given the need to develop methodological frameworks capable of integrating remote sensing technologies at different scales, as well as traditional metrics and anthropic variables, in this study, a multiscale method is proposed for the characterization and prioritization of river stretches for fluvial risk management. This methodology involves the study of drivers at the watershed level, and a detailed morphometric and hydrogeomorphological analysis of the main channel for fluvial landscape classification, segmentation, and aggregation into units, considering also anthropic variables. Therefore, it includes the use of LiDAR data and exploration GIS tools, whose results are corroborated through fieldwork, where ephemeral and topographic evidence of fluvial dynamics are collected. The procedure is validated in the Carrión river basin, Palencia, Spain, where a high degree of maturity and geomorphological development are determined. Hence, the main channel can be classified into eight geomorphic units and divided into homogeneous segments, which, according to categorical elements such as urban interventions, are prioritized, obtaining, as a result, six stretches of main interest for river risk management.
<p>Floods are one of the most common and catastrophic natural events worldwide, making studies on the magnitude, severity and frequency of past events essential for risk management. On this wise, remote sensing techniques have been widely used in flooding diagnoses, where Sentinel-2 images are one of the main resources employed in surface water mapping. These studies have developed single band, spectral indexes and machine learning-based methods, which have typically been applied to large water bodies. However, one of the issues in identifying water surfaces remains their size. When water surfaces have sizes close to the spatial resolution of satellite images, they are difficult to detect and map. To improve remotely sensed images' spatial resolution, an algorithm for super-resolving imagery has been developed, giving good results, especially in areas covered by agricultural land with large uniform surfaces. Although this method has proved effective on Sentinel-2 images, it has not been tested for enhancing flood mapping. Thus, flood mapping is still considered an open research topic, as no suitable method has been found for all datasets and all conditions. Consequently, the present study has developed a methodology for flood delineation in small-sized water bodies. The method leverages the advantages of Sentinel-2 MSI data, image preprocessing techniques, thresholding algorithms, spectral indexes and an unsupervised classification method. Thus, this framework includes a) the generation of super-resolved Sentinel-2 images, b) the application of seven spectral indexes to highlight flood surfaces and evaluation of their effectiveness, c) the application of fifteen methods for flood extent mapping, including fourteen thresholding algorithms and one unsupervised classification method and, d) the evaluation and comparison of the performance of flood mapping methods. The technique was applied in the Carri&#243;n River, located in the Duero basin, province of Palencia, Spain. This river is classified as a narrow water body, which presents recurrent flood events due to its morphometric characteristics, fluvial dynamics, and land uses. The results obtained show optimal performances when highlighting flood zones by combining AWE spectral indices with methods such as those of Huang and Wang, Li and Tam, Otsu, and momentum-preserving thresholding algorithms and EM cluster classification.</p>
The quantification of soil loss are studies driven by the importance of soil as a resource and are mainly due to risks of laminar and/or runoff water erosion. These problems directly affect the daily life of the population and serve as predictors of environmental effects. In this work, the quantification and calculation of the sheet water erosion caused mainly by rainfall has been carried out in a study area located in the municipality of Larrodrigo (Salamanca, Spain), based on the simultaneous application of the RUSLE model with GIS techniques. Thematic cartographies have been generated to determine soil loss in Tm/Ha/year and mm/year based on the use of parameters of the physical environment (lithology, rainfall, slopes…) where the erosive risk is quantified and its applicability to the study area by spatio-temporal extrapolation techniques. Simultaneously, the use of the A-DInSAR technique was implemented to calculate average ground deformation velocities in mm/year associated with water erosion. Two sectors with greater vulnerability to water erosion have been detected within the area of interest: one of them called main, which corresponds to the slopes near the Larrodrigo stream, with soil losses showing values of 0.3- > 12 mm/year, and a secondary sector belonging to the tributaries or channels derived from the mainstream with values of 0.3- > 12 mm/year. This type of study makes it possible to manage and organise human support practicesin order to subsequently establish measures that can prevent, mitigate and/or correct those areas with the greatest damage.
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