Sedimentation mapping in shallow shoreline of arid environments using active remote sensing data

Abstract

The applications of remote sensing in monitoring land cover features are an essential tool of natural resources management schemes. The sedimentation mapping of shallow shorelines is insufficient using passive remote sensing images because of the image corrections and weather implications that need to be considered, while active remote sensing data can overcome the difficulties of the weather interference and reach to more reliable results. The current research work took place in the shoreline on Umluj city, west of Saudi Arabia, representing one of the most sensitive wetland habitats within the country. Sentinel-1 images were downloaded and analyzed to delineate the sedimentation process from the European Space Agency. The archive image was acquired in August 2018, while the crisis emerged was acquired in March 2019 after an unusual rainfall event that took place in the vicinity of the study area. Remote sensing techniques of sedimentation mapping end change detection were implemented in the study area to estimate the sedimentation process and its influences on the wetlands. The wetland habitats were decreased by nearly 87% throughout the period of flash floods between November 2018 and March 2019. Meanwhile, sediment deposits along the shoreline of the study area increased by nearly 171%. Therefore, monitoring of the shorelines sedimentation and the wetland habitats using temporal remote sensing data are decision-making priorities to conserve the natural resources within similar arid environments.

The diachronic analysis of island dynamics along the Vedea-Oltenița Danube river sector using SAR imagery

“…They have shown their usefulness and effectiveness in: the detection of soil surface moisture in arid and semi-arid zones (Troufleau et al 1994); monitoring deforestation, monitoring ice melt, lithology in the polar environment (Budkewitsch et al 1996a, b); the study and detection of mesoscale phenomena in oceanography (Laborde and Deveaux 1996); the pattern recognition, spatial planning (Rudant et al 1996); the classification of cultures (Bruniquel and Lopes 1994;Lopes and Sery 1997); mobility analysis and classification of Brakhanes dynamics (Hachemi and Thomas 2013;Hachemi et al 2020); the geological and structural mapping (Singhroy and Saint-Jean 1999;Wade et al 2001); the detection and characterization of oil spills in seas and oceans (Mercier et al 2004); the mapping of urban damage due to natural or industrial disasters and changes in coastal boundaries (Ba et al 2007); the flood extension mapping (Brivio et al 2002); mapping the evolution of the coastal features and the extension of cities (Hachemi et al 2014(Hachemi et al , 2015; the impact assessment and monitoring of the hydrological phenomena (floods, floods, etc.) in urban areas (Mcmillan et al 2006); the detection, analysis and interpretation of the landscape changes in very complex regions of the Carpathians (Hachemi et al 2009(Hachemi et al , 2010; the sedimentation mapping in shallow shoreline of the arid environments (Elhag and Bahrawi 2019); the flood inundation mapping and monitoring and its impact on Ramganga River in Ganga basin (Agnihotri et al 2019).…”

Volumetric Quantification of Flash Flood Using Microwave Data on a Watershed Scale in Arid Environments, Saudi Arabia

“…Combine the images and create an RGB composite to distinguish the flood area and non-flood area. The RGB composite image should be used as a guide to identify the flood area [48,49]. The red color represents the flood near the permanent water body, and the blue color represents the flood in the non-permanent water body (e.g., seasonal streams).…”