Shoreline Change Analysis of Mangalore Coast and Morphometric Analysis of Netravathi-Gurupur and Mulky-pavanje Spits

Shetty, Ateeth; Jayappa, K. S.; Mitra, Debashis

doi:10.1016/j.aqpro.2015.02.025

Cited by 24 publications

(6 citation statements)

References 3 publications

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“…Digital photogrammetry with aerial and satellite imagery have been widely used for the monitoring of shorelines/coastlines, and detecting their temporal and spatial changes in several locations around the world [1][2][3][4][5][6][7][8][9][10][11][12][13]. Besides the optical imagery based approach, the synthetic aperture radar (SAR) based multi-polarization and multi-incidence data have also been used by many researchers for water monitoring and mapping purposes [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

et al. 2015

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Abstract:Monitoring of water cover and shorelines at a global scale is essential for better understanding climate change consequences and modern human disturbances. The level and turbidity of the surface water, and the background objects in which they interact with, vary significantly at a global scale. The existing water indices applicable to detection and extraction of water cover at local and regional scales cannot work efficiently everywhere in the globe. In this research, a new water index called Superfine Water Index (SWI) was developed for robust detection and discrimination of the surface water at a global scale using MODIS based multispectral data. The SWI was designed in such a way that it provides high contrast between the water and non-water areas. Achieving high contrast is vital for discriminating the surface water mixed with a variety of objects. The sensitivity analysis of the SWI demonstrated its high sensitivity to the surface water compared to the existing water indices. One single-layered global mosaic of a 90-percentile SWI image was used as a master image for global water cover mapping by reducing the large volume of MODIS data available between 2012 and 2014 globally. The random walker algorithm was applied in the SWI image with the support of reference training data for the extraction and mapping of water cover. This research produced an up-to-date global water cover map of the year 2013. OPEN ACCESSRemote Sens. 2015, 7 13808The performance of a new map was evaluated with a number of case studies and compared with existing maps. The supremacy of the SWI over the existing water indices, and high performance of the SWI based water map confirmed the reliability of the new water mapping methodology developed. We expect that this methodology can contribute to seasonal and annual change analysis of the global water cover as well.

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Section: Introductionmentioning

confidence: 99%

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

et al. 2015

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“…Correcting the atmosphere is important for minimizing the effect of the radiation dispersion, which is inversely proportional to the length of the wavelength. Therefore, shorter waves are more scattered than longer waves, and calculated ratio is closer to actual ratio [4,26,27]. A simple, powerful model was used to reduce the negative impacts of the atmosphere on the image by subtracting the dark object [28], where the clean and deep water of the Red Sea was used to the west of the study area.…”

Section: Radiometric Correctionmentioning

confidence: 99%

Sea level changes of Sharm Yanbu and its expected impacts on development processes using GIS and remote sensing

Nofal

Abboud

2019

Journal of Taibah University for Science

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The eastern coast of the Red Sea is characterised by many coastal entrances that penetrate the sea. Its origins can be traced to the estuaries of ancient wadis that influenced the evolution of the geological environment, marine structures, and sea factors. The objective of this study is to discover the geomorphological development of Sharm Yanbu and to determine the factors of its development and the most important changes that occurred on the coast between 1990-2016. The study also aims to identify areas that have suffered a decline due to erosion and marine factors that have threatened tourism and economic development in the region. The risk of flooding in marine areas and surrounding sea floods during periods of high tide and storm waves is also examined to determine coastal ranges. Two Landsat images are used over a 26years period to monitor changes in the coastline and identify the areas of decline through the satellite image analysis program. Radar data is also used to identify topography and surrounding areas, and to construct a pattern of occurrence of marine floods and identify areas likely to be covered by seawater in the future. The first factor is related to the geological formation and the structure that surrounds the sharm. The second factor is related to marine factors whose effects appear in the form of carving down and the dissolution of the limestone coastline to form grooves and trenches. The third factor relates to the topography of the low sloping coastal plain.

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“…The coastline is the meeting place of land and water, so it is one of the most vital features on the Earth's surface (Winarso and Budhiman, 2001). The coastlines have an abundance of natural resources and urban, for this about one-third of the humans are living close to it (Shetty et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Coastal Erosion Assessment of the Nile Delta Coast using Remote Sensing, GIS, and Modified Coastal Vulnerability Index

Abdelaty

2021

Alexandria Science Exchange Journal

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The coastal zones are very much dynamic and are changing very fast, so it is completely important to study them in a temporal domain, for which remote sensing and GIS are the best tools. The coastal zone along the Governorates of Kafr El Sheikh, Dakahlia, and Damietta in north Egypt are reported to be facing serious environmental challenges. This study evaluates the coastline erosion and accretion and coastal vulnerability index (CVI) along these three Governorates, with a total length of 153Km over the past 34 years are analyzed using satellite images of TM 1984, ETM+ 2001, and ASTER 2018. The results showed that the study area was exposed to remarkable and non-expected changes over the study periods. Erosion and accretion were found variable over time and place, ranging from 0.14 ha/year in Dakahlia governorate during 1984-2001 to 66.12 ha/year in Kafr El Sheikh Governorate during 2001-2018 for erosion, and from 3.15 ha/year in Dakahlia Governorate at 2001-2018 to 42.45 ha/year in Kafr El Sheikh Governorate at 1984-2001 for accretion. Total land loss due to erosion as high as about 2034.68 ha of shoreline, and land gain as high as about 1459.71 ha of coastline was estimated in the study area with a net of lost lands of about 574.97 ha. In general, Kafr El Sheikh Governorate was more affected by erosion. According to Modified CVI, about 9.66%, 11.03%, 33.10%, and 46.21% of the length of the coastline is classified as very high, high, moderate, and low vulnerability, respectively.

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Shoreline Change Analysis of Mangalore Coast and Morphometric Analysis of Netravathi-Gurupur and Mulky-pavanje Spits

Cited by 24 publications

References 3 publications

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Developing Superfine Water Index (SWI) for Global Water Cover Mapping Using MODIS Data

Sea level changes of Sharm Yanbu and its expected impacts on development processes using GIS and remote sensing

Coastal Erosion Assessment of the Nile Delta Coast using Remote Sensing, GIS, and Modified Coastal Vulnerability Index

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