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Coastal Vulnerability Mapping Using Geospatial Technologies Incuddalore-Pichavaram Coastal Tract, Tamil Nadu, India

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Coastal Vulnerability Mapping Using Geospatial Technologies Incuddalore-Pichavaram Coastal Tract, Tamil Nadu, India Available online at www.sciencedirect.com ScienceDirect Aquatic Procedia 4 ( 2015 ) 412 – 418 INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN ENGINEERING (ICWRCOE 2015) Coastal Vulnerability Mapping Using Geospatial Technologies InCuddalore-Pichavaram Coastal Tract, Tamil Nadu, India. T. Siva Sankaria*, AR. Chandramoulia, K. Gokula, S. S. Mangala Suryaa, J. Saravanavela aCentre for Remote Sensing, Bharathidasan University, Tiruchirapalli-620 023, Tamil Nadu, India. Abstract The Eustatic sea level rise due to global warming is predicted to be about 42 to 98cm by 2100 (IPCC 2013) which necessitates identification and protection of vulnerable sections of coasts. The current study area falling in the coastal zone of central Tamil Nadu from Cuddalore in the north and Pichavaram in the south along the southeast coast of India. The study aims in assessing the coastal vulnerability for the Cuddalore-Pichavaram coastal tract since the area is affected more due to 2004 Tsunami, 2008 Nisha and 2011 Thane cyclones using Remote Sensing and GIS tools. Six various terrain and physical parameters such as Geomorphology, Land use/Land cover, coastal slope, Offshore bathymetry, shoreline change (1970-2013), mean Tidal height has been considered to calculate the Coastal Vulnerability Index(CVI) based on USGS classification and they show significant variations all along the coastal tract. Based on the CVI values calculated for the study area the coast is classified as five classes of vulnerability viz., very low, low, moderate, high, very high. The present study can be used as tool for coastal disaster management for future development. © 20152015 The The Authors. Authors. Published Published by by Elsevier Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license (Peerhttp://creativecommons.org/licenses/by-nc-nd/4.0/-review under responsibility of organizing committee). of ICWRCOE 2015. Peer-review under responsibility of organizing committee of ICWRCOE 2015 Keywords:Cuddalore-Pichavaram coast; USGS classification; Remote Sensing; GIS tools; Coastal Vulnerability * Corresponding author. Tel.: +0-000-000-0000 ; fax: +0-000-000-0000 . E-mail address: [email protected] 2214-241X © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of organizing committee of ICWRCOE 2015 doi: 10.1016/j.aqpro.2015.02.055 T. Siva Sankari et al. / Aquatic Procedia 4 ( 2015 ) 412 – 418 413 1. Introduction Coastal areas are very important for human being since the beginning of time.In view of the disproportionate climate change, the coastal areas constitute the most productive, yet vulnerable ecosystems of the world. These coastal belts often prove to be the hot spots of severe impacts associated with permanent inundation of low-lying areas, increased flooding due to extreme weather events like storm surges, tsunami, greater erosion rates affecting beaches and cliffs and devastation due to calamities like cyclone (Nicholls and Cazenave, 2010; EC, 2005; EEA, 2006; Klein et al., 2003). Vulnerability can be defined as the degree which a person, community or a system is likely to experience harm due to an exposure to an external stress. Generically, vulnerability is a set of conditions and processes resulting from physical, social, economic and environmental factors that increase the susceptibility of a community to the impact of hazards. The present study, therefore, is an attempt to develop a coastal vulnerability index (CVI) for the Cuddalore - Pichavaram Coastal tract using six relative risk variables with the help of remote sensing and GIS. The erosion and accretion made in different parts of the study area have been measured and analyzed. The coastal vulnerability index (CVI) has been used to map the relative vulnerability of the study area and also characterize the vulnerability of the coast due to coastal processes and human activities. 2. Area of study The selected study area (fig.1.) is the part of Tamil Nadu state in the Bay of Bengal covering the coasts of CuddaloretoPichavaram. The geographical coordinates of the study area lies between latitude 11°15' to 11°45' N and longitudes 79°30' to 79°55' E. Two major rivers viz. Velar, Celeron, drain into the Bay of Bengal in this area. The areas between the two Rivers have brackish water with mangrove vegetation. The area is particularly selected because this area is highly affected during 2004 tsunami, and are highly vulnerable any natural disasters. Fig.1. Key map of our study area 3. Methodology The methodology used for present study is the methodology adopted by Theiler and Hammer close (2000).Parameters like geomorphology, land use/land cover, coastal slope, shoreline change, offshore bathymetry, 414 T. Siva Sankari et al. / Aquatic Procedia 4 ( 2015 ) 412 – 418 mean tidal range are taken. Each parameter is given individual rank and weight age and by integrating above parameters using GIS, the coastal vulnerability index is calculated. The overall methodology of the present study is given below: Fig.2. The overall methodology of the present study Table 1. Source data used for different parameters Parameters Data used Shoreline change Land sat 8, TM, ETM [(1990 – 2013)(30 m resolution)] Geomorphology Land sat 8 [(Feb 2013)(30 m resolution)] LU/LC Land sat 8 (Feb 2013)(30 m resolution) Slope topographical information SRTM [(90 m resolution)] Offshore bathymetry GEBCO Bathymetry data chart Tidal and Wave height data Naval Hydro graphic Chart 4. Thematic Layers 4.1. Geomorphology Geomorphology is defined as the study of landforms and landscapes, including the description, classification, origin, development and history of planetary surfaces. Geomorphology seeks to identify the regularities among landforms and what processes lead to patterns. In the present study, Land sat pan sharpened FCC(band 4,3 and 2 combination) image of 2013 has been used to generate geomorphology map (Fig.3.a). The various coastal geomorphic features identified in the study area are beach ridge, creek, deep pediment, deltaic plain, dissected uplands, estuarine, flood plain, inter tidal flat, mangrove swamp, mud flat, pediplain, river, salt flat, Supratidal flat, swale, undissected uplands, valley, tanks. 4.2. Land use/ land cover A Land-use/land-cover map is essential to understand the changes in the land use/ land-cover classes in a particular region and how it helps in increasing or diminishing the vulnerability of an area. The land use land cover map (Fig.3.b) has been generated using Land sat pan sharpened FCC image of 2013. The LU/LC features identified T. Siva Sankari et al. / Aquatic Procedia 4 ( 2015 ) 412 – 418 415 are build-up land, cropland, fallow land, plantation, scrub forest, Mangroves, degraded mangrove forest, barren land, barren land with scrubs, swampy land, salt affected area, sandy area, agricultural area, wetland, river, tank. a) b) Fig.3.a) Geomorphology map; b) Land use/ Land cover map 4.3. Offshore Bathymetry The bathymetry shows the depth from the coast towards the open ocean, it is the underwater equivalent of contour lines on the land. For the present study the GEBCO bathymetry data chart has been used to generate the bathymetry map (Fig.4.a). The depth contour has been developed using ArcGIS 10.1 after geo-referencing with Universal Transverse Mercator (UTM) projection system with WGS-84 datum. The bathymetry is calculated using the formula, Bathymetry = (Distance between Shoreline and Contour/Contour Interval)*100 4.4. Coastal slope Slope is used to describe the measurement of the steepness, incline, gradient, or grade of a straight line. For the present study the slope map (Fig.4.b) has been generated using SRTM data of 90m resolution. The Coastal slope is calculated using the formula, Slope = (Distance between Shoreline and Contour/ Contour Interval)*100 a)b) Fig.4.a) Bathymetry map; b) Slope map 416 T. Siva Sankari et al. / Aquatic Procedia 4 ( 2015 ) 412 – 418 4.5. Shoreline change Ortho-rectified Land sat TM and ETM images covering the study area for the years 1991, 2000, 2006, 2010 and 2013 were used. The data have been projected to the Universal Transverse Mercator (UTM) projection system with WGS-84 datum. The shorelines (Fig.5.a)of different years were digitized using Arc Map 10.1. The near infrared band that is most suitable for the demarcation of the land–water boundary has been used to extract the shoreline. The digitized shoreline for the years 1991, 2000, 2006, 2010 and 2013 in the vector format were used as the input to the Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change. The inputs required for this tool are shoreline in the vector format, date of each vector layer, and transect distance. The rate of shoreline change is calculated for the entire study area, and risk ratings are assigned. 4.6. Mean tidal range Tidal range is the vertical difference between the highest high tide and the lowest low tide. For the current study, coastal areas with high tidal range are considered as high vulnerable and low tidal range as low vulnerable. In the current study, predicted tide data (Fig.5.a) from WX Tide software for the year 2002 is taken as the base data, and the maximum amplitudes of the tide in a year for the Indian coastal locations are calculated, and risk rates are assigned. a)b) Fig.5.a) Shoreline change sfrom1991 to 2013; b) Mean tidal Range data collected from WX Tide software 5. Results and Discussion To generate the vulnerability indexes for each parameter (Fig.6.) 500m grids were buffered out from the shoreline for our study area. Each grid is assigned different variables based on the various parametric features present within the grids. Vulnerability Index (CVI) is calculated based on USGS classification.
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