Research Article ISSN 2277–9051 International Journal of Remote Sensing and GIS, Volume 1, Issue 3, 2012, 175-182 © Copyright 2012, All rights reserved Research Publishing Group www.rpublishing.org
Chennai Coast Vulnerability Assessment Using Optical Satellite Data and GIS Techniques V.E. Nethaji Mariappan1* and R. Santhi Devi2 Scientist-D, Centre for Remote Sensing and Geoinformatics, Sathyabama University, Rajiv Gandhi Road, Jeppiaar Nagar, Chennai – 600 119 2 Lecturer, Bharathi Women’s College, Chennai *Corresponding author Email:
[email protected] Received 24 October 2012; received in revised form 2 January 2013; accepted 3 January 2013 1
ABSTRACT: The study area comprise Chennai coast covering a linear extent of 10 Km from Kovalam to Mamallapuram, Kancheepuram district. Geographically study area lies between 120 35’ to 120 50’ East Latitude and 800 12’ to 800 16’ North Longitude. Risk variables such as geomorphology, shoreline change, slope, wave height, tidal range, and bathymetry are used to derive coastal vulnerability index. Based on the nature of the theme vulnerability value was assigned a for each data variable, the coastal vulnerability index was calculated as the square root of the product of the ranked variables divided by the total number of variables. Result of the analysis showed that vulnerability of the coast was segmented as highly vulnerable and less vulnerable. The coast between Kovalam to Mamallapuram was accreted on the northern region and few areas of southern parts of the study. The shoreline change value used here, with positive numbers indicating accretion and negative numbers indicating erosion. Shoreline change rates on Chennai range from -0.1- 0.1 (0.16) m/yr of erosion (high vulnerability) and accretion to (0.45) m/yr (moderate vulnerability) were derived and 0.29m/y (low vulnerability). The study area was classified as the high vulnerable zone and therefore coastal protection measures are to be adopted in order to safeguard the Chennai coast. Key words: Chennai coast, thematic layers, Shore line displacement, Coastal Vulnerability Index (CVI)
1.
INTRODUCTION
Earth's resources are disturbed by increase in population, disturbing the environment for stabilizing sophistication that adds increased vulnerability of man and his infrastructure to the natural hazards. Population deployment in urban areas and their societal development is inevitable. Therefore, it is important to recognize that improved technology can be effectively used for the societal issues in disaster monitoring. The disaster monitoring system using space technology is indispensable for weather forecast, pollution monitoring, forest and grass fire, flood, tidal inundation, tsunami etc.,. Satellite monitoring by meteorological and earth observation involves pre and post assessment of natural disaster such as the damage incurred during the disaster Sankar Kumar Nath et.al, (2008); and also suggests escape routes, locations, temporary housing etc. and provide an early warning alert to the society. Post disaster; require mitigation efforts that can be structural or non-structural. Structural measures use technological solutions, like flood levees. Non-structural measures include legislation, land-use planning and insurance. Mitigation is the most cost-efficient method for reducing the impact of hazards. Risk assessment of various hazards like earthquakes, floods, cyclones and tsunami require meticulous prediction using geospatial tools and models. Such prediction should also specify the level of hazard at spatial scale enabling the policy planers to take up necessary steps to overcome the hazard. The vulnerability index is described by the USGS, the seven relative risk variables contained within this data base may be used to formulate a coastal vulnerability index. This index may be used to identify areas that are at risk to erosion and/or permanent or temporary inundation. Grid cells and/or line segments with high index values will tend to have low reliefs, erodible substrates, histories of subsidence and shoreline retreat, and high wave and tide energies (Gornitz et. al., 1991). The Coastal Vulnerability Index is one the indices used to assess coast at time of natural disaster was used by Thieler and Hammar-Klose (1999); Gornitz et al. (1994) and Shaw et. al. (1998). However, the Coastal Vulnerability Index (C.V.I.) developed by Gornitz et al. (1997) and Shaw et al. (1998) were used in this study.
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The study area, Chennai coast lie along the Bay of Bengal is one of the five cyclones prone areas of the world is likely vulnerable to Natural Hazards. Occurrence of Tsunami in 2004, frequent floods due to cyclone especially during North East Monsoon lead to lot of structural changes along the shores of the city. Changing Climate scenario and resultant sea level rise in Chennai are also reported to be one of the factors for Coastal vulnerability hazard. Therefore a study has been taken up to assess coastal vulnerability using remote sensing and GIS. The objective of this study is to identify vulnerable areas of Chennai coast with special reference to various coastal hazards using advanced Remote Sensing and Geographical Information System (GIS). 2. METHODOLOGY 2.1. Study Area Chennai coast covering an extent of 10 Km from Kovalam to Mamallapuram, was taken up for coastal vulnerability assessment to natural disaster. The study area lies between 120 35’ to 120 50’ North Latitude and 800 12’ to 800 16’ East Longitude. Tsunami has struck Chennai coast on 26th December, 2004 causing maximum devastation to inland settlement. The extent of wave penetration during Tsunami was 1.0 – 1.5 km from seashore. Hence a width of 2 km from High Tide Line was used for this study (fig.1). The total area for Vulnerability mapping in Chennai coast was 20 sq. km. Chennai city has been a major kind of attraction due to rapid development and establishment of major ports, a fishing harbor and industries covering thermal power plants, refineries, pharmaceuticals, fertilizers and amusement parks. A lot of factors such as dense population, encroachment are responsible for degradation of coastal and inland ecosystem. Hence, there is an urgent need for vulnerability assessment and vulnerability mapping for entire Tamil Nadu coast and specifically Chennai coast.
Tamil Nadu
Fig.1 Base map of the study area 2.2 Satellite Data Acquisition Landsat ETM data of path 142 and row 051 of 1:50000 scale corresponding the date of acquisition 07th February, 2006 comprising band 2, 3 and 4 with a spatial resolution of 30 m were downloaded as GeoTIFF format as individual bands from the (Global Land Cover Facility) GLCF website http://glcfapp.glcf.umd.edu:8080/esdi/index.jsp. These bands were subjected to geometric corrections to minimize the geometric distortions introduced by extraneous factors. Individual bands were layer stacked and mosaicked in order to get entire study area in Universal Traverse Mercator (UTM) projection in Leica's Erdas Imagine 8.7 software. A vector dataset in shape file format of Chennai coast was derived from Survey of India 1972 topo sheet of same scale f satellite data and overlaid above the composite image, thus study area boundary was subset from the entire scene was used for thematic map generation.
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2.3 Spatial thematic generation Thematic maps such as slope, geomorphology, shore line displacement, tidal ranges, wave heights and bathymetry was generated and integrated with historical data of the coastal hazards in GIS environment. GIS layers such as slope, shore line displacement, geomorphology as polygons, bathymetry, wave heights and tidal ranges as line layers were derived from the satellite data. Ranks and weightages were assigned to thematic layers (Table 1) according to their vulnerability nature of the variable prone to hazards. Coastal Vulnerability Index was arrived by as the square root of the product of the ranked variables divided by the total number of variables and further classified as high, medium and low. Table.1 Criterion for Vulnerability Ranking of Variables on the Chennai Coast
Sl No 1
Variables
Low
Geomorphology (area in sq.km)
Alluvial shallow Alluvial shallow >+1.0
2
plain and plain
Moderate
High
Coastal Plain older and Coastal plain young
Beach, salt pan and flood basin
Shoreline Erosion(-) -0.1 – 1.0 1.06 5 Tide Range (m) > 0.5 0.1-0.5 < 0.1 6 Bathymetry Value (m) 36-50 21-35 5-20 The identified area having high coastal vulnerability index values will tend to have low reliefs, erodible substrates, histories of subsidence and shoreline retreat, and high wave and tide energies. 2.4 Calculating the Coastal Vulnerability Index The CVI allows the six variables to be related in a quantifiable manner that expresses the relative vulnerability of the coast to geological and physical changes due to future sea-level rise. This method yields numerical data that cannot be equated directly with particular physical effects. It does, however, highlight areas where the various effects of sea-level rise may be the greatest. Once, each section of coastline is assigned a vulnerability value for each specific data variable, the coastal vulnerability index is calculated as the square root of the product of the ranked variables divided by the total number of variables (USGS Open-File Report pp. 2004). CVI=
(a*b*c*d*e*f) 6
where, a = geomorphology, b = shoreline erosion/accretion rate, c = coastal slope, d = bathymetry, e = wave height, and f = tide range. The calculated CVI value is then divided by the total n umber of variables to highlight different vulnerabilities within the coast. The numeric CVI values that correspond to a specific vulnerability index (low–medium-high) are unique to Chennai coast. This approach best describes and highlights the vulnerability specific areas. The index allows the six physical variables to be related in a quantifiable manner that expresses the relative vulnerability of the coast to physical changes due to natural disaster. 3.
RESULTS AND DISCUSSION
3.1 Geomorphology Visual interpretation of satellite data and geomorphology map of Chennai were used for generation of geomorphology layer of Chennai coast. The study area were classified into eight categories viz., buried pediplain, alluvial plain, Shallow alluvial, coastal plain older, coastal plain younger, flood plain, saltpan and beach. Based on the formation they were regrouped into three categories viz., Buried Pediplain, Mud flat and young coastal plain in the order of high vulnerability to low in table 2. Random field checks were made within the area to verify the geomorphologic classifications.
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Table 2. Geomorphology categories derived from satellite data Geomorphology Area in Area in (%) sq.km. Buried Pediplain 13.12 31.81 Mud Flat 17.02 41.26 Young Coastal Plain 11.11 26.93 Total Area 41.25 100 The different geomorphic categories considered to be highly vulnerable were beach, salt pan with flood basin; moderate vulnerability zone are older coastal plain and younger coastal plain and the low vulnerable like alluvial plain deep and alluvial plain shallow. Among high vulnerable category beach was considered to be most vulnerable in addition anthropogenic activities like building construction, dredging of coastal areas and fishing activities are most common in those areas. 3.2 Coastal slope Elevation data were obtained from the total station at an interval of 250 meters. Those data were incorporated in to the ArcGIS software as point values were then spatially interpolated to polygon data. The determination of regional coastal slope identifies the relative vulnerability of inundation and the potential rapidity of shoreline retreat similarly low-sloping coastal regions should retreat faster than steeper regions (Pilkey and Davis, 1987); (U.S. Department of the Interior San Francisco Bay Area Network Inventory and Monitoring Program Resource Brief June 2010). A major part of the study area falls under the high vulnerability region (33.69sq.km; 82.21 per cent). (Table. 3). Table 3. Slope ranking and its area of Chennai coast Ranking 0-1 1-2 2-3 Total
Area(sq.km.) 33.69 6.99 0.52 41.2
Area in % 82.21 16.53 1.26 100
3.3 Shoreline Displacement Shoreline displacement represents the horizontal movements of a shoreline with reference to benchmark shoreline. Landsat ETM data of path 142 and row 051 of 1:50000 scale corresponding the date of acquisition 07th February, 2006 has been compared with the topographical maps (as reference line) on 1:50,000 corresponding to the year 1972 and thus shoreline displacement delineated was provided in the figure 2. Shoreline erosion map prepared from toposheet of the year 1972, Landsat ETM data (2006) data was used for Shoreline rates of change (m/yr) were calculated at 200 m intervals (transects) along the study area. Rate of shoreline displacement has been calculated as follows Value Rate = ((year1- year2) + 1)
where year 1 represent reference shoreline SOI toposheet (1972) and year 2 as by the of the IRS P6 data for the year 2005. The shoreline change value with positive numbers indicates accretion and negative numbers indicate erosion Gutierrez et al. (2009). Shoreline change rates on Chennai ranking range from -0.1- 0.1 (0.16) m/yr of erosion (moderate vulnerability) and accretion to (0.45) m/yr (moderate vulnerability) were derived (Table. 4 and Figure. 2).
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Table No. 4. Shoreline Displacement Ranking Shoreline category Area in (sq.km) >+1.0 Erosion and Accretion 0.61 -0.1 – 1.0 Erosion 0.16
