Shoreline Change Detection Using Geo-Spatial Techniques- A case Study for Cuddalore Coast

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1 Shoreline Change Detection Using Geo-Spatial Techniques- A case Study for Cuddalore Coast Parthasarathy K.S.S. 1, Saravanan Subbarayan 2, Abijith D 3 1 Department of Applied Mechanics and Hydraulics, National Institute of Technology Surathkal 2 Department of Civil Engineering, National Institute of Technology Tiruchirappalli, 3 Department of Civil Engineering, Anna University Regional Campus, Tirunelveli, INDIA parthas1993@gmail.com Abstract: Coastal zones are constantly undergoing wide changes in shape and environment due to natural as well as human development activities. The shoreline change study has become a matter of great concern in the recent years. The measurement of shoreline is a key factor in coastal zone construction. In this study rate of shoreline changes is determined for 32 km stretch of Cuddalore coast between Gadilam and Vellar River. Shoreline digitization is done manually in ArcGIS software. End Point Rate (EPR) and Net Shoreline Movement (NSM) determination shows, northern part of Uppanar river mouth is under erosion (region A to C and E) and sediment deposition and maximum shoreline accretion rate is 7.6 m/yr from EPR and 114 m from NSM at transect 61 (region J to K). Maximum shoreline erosion rate is -3.8 m/yr from EPR and -57 m from NSM at transect 2. Keywords: Accretion, erosion, GIS, shoreline change, EPR, NSM. 1. INTRODUCTION The coastal area consists of the interface between land and sea. India has a long coastline extending over a distance of 6000 km and Tamil Nadu state constitutes a 750 km considerable length of it. Shoreline is one of most rapidly changing landforms of the coastal zone. Human civilization is growing rapidly along the coast worldwide due to abundant natural resources. Climate change and development of infrastructures put pressure on the coastal environment and this lead for various coastal hazards like coastal erosion, flooding, sea level rise, sea water intrusion, and bio-resource degradation (Kaliraj et al. 2013). The shoreline is one of the dynamic landforms in the coastal environment and it tends to change due to erosion and accretion processes induced by wave action, sea level rise, and sediment transportation. Therefore, coastal zone monitoring is an important task in sustainable development and environmental protection. The coastal areas are also the places where natural disasters experienced. The tsunami which occurred on December 26, 2004 was one of the most unexpected catastrophes to occur along Indian coast causes severe damage to life and property. In the Bay of Bengal, cyclonic disturbance and storm are quite frequent, the annual frequency of disturbance being 16 in an average and storm being six. Cyclonic disturbance being five to six are more frequent in Bay of Bengal. Further anthropogenic activities along the coast, changes in River catchment, offshore development such as ports, harbours etc.., also contribute to shoreline change. The application of geospatial technology is a promising tool by providing the synoptic coverage multi-temporal satellite images of the coastal area in different resolutions to estimate periodical shoreline changes. Detection, extraction and monitoring the shoreline are regarded as important tasks in safe navigation, coastal zone management and environmental protection, sustainable coastal development and planning (Gens 2010). A variety of data sources have been used to detect the shoreline, such as historical land-based photographs, coastal maps and charts, aerial photography, beach and GPS field surveys, and remote sensing imagery (Bouchahma & Yan 2012). Amongst such data, various remote sensing images have been used (Mujabar & Chandrasekar 2013), including multispectral and hyperspectral images (Feng et al. 2012), airborne light photography (Liu et al. 2007), microwave images (Ashar & Nobuhiro 2009), to name a few. Remotely sensed imagery from Landsat, SPOT, SAR, QuickBird, IKONOS and LiDAR has been widely used to map shorelines and detect shoreline changes (Feng & Han 2012). However, the selection of data for a study area is generally determined

2 by the availability of data (Boak & Turner 2005). Saravanan et al (2015) studied an automatic shoreline extraction and coastal vulnerability study by using automatic water body extraction techniques for segment water and land regions for Chennai, East Coast of India. Many researchers are using the remote sensing techniques to study the coastal changes. Also there are many change detection techniques currently in use including image enhancement, multitemporal data classification density slice using single or multiple bands, and multi-spectral classification, both supervised and unsupervised, various thresholding based techniques have been used for automatic extraction of coastline from remotely sensed images. In India, shoreline change studies have successfully investigated using remote sensing and GIS by many researchers at different times (Alesheikh et al. 2007; Chandrasekar et al. 2011; Srinivasa Kumar et al. 2008; Kaliraj et al. 2013; Chenthamil Selvan 2014; Mageswaran et al. 2015). Arun & Kunte (2012) studied coastal vulnerable index (CVI) mapping for the Chennai coast to assess the coastal erosion impact. Aedla et al (2015) develop an automatic shoreline extraction method using clipped histogram equalization based contrast enhancement for enhancing coastal pixels and thresholding techniques for segment water and land regions for Netravati-Gurpur River mouth area, Mangalore Coast, West Coast of India. In this study, boundary between the land and the sea is demarcated by the visual interpretation on the satellite images. Base line and digitized shoreline data has been made use for the analysis of shoreline changes of Cuddalore coast from Gadilam River to Vellar River East Coast of India for the period of 2000 to STUDY AREA AND DATA PROCESSING The study area selected is coastal zone of Cuddalore city as shown in Figure 1, Tamil Nadu state, India. This analysis was carried out from Gadilam River to Vellar River for a stretch of 33 km distance. - - E. The coastline, which includes tourist resorts, ports, hotels, fishing villages, industries and towns, has experienced threat from many disasters such as storm, cyclone, flood, tsunami and erosion. This was one of the affected areas during 2004 Indian Ocean tsunami and various cyclone passed: Fanoos ( ), Nisha ( ), Jal ( ), Thane ( ), Nilam ( ), Madi ( ). Estimation of erosion and accretion along the Cuddalore coast was performed by using satellite image in ArcGIS using manual demarcation method. The spatial variability of shoreline changes are studied by using geometrically corrected and orthorectified satellite image of 2000, 2005, 2010, 2015 East Coast of India. These data are processed under GIS environment. Figure 1 Study Area Map of Cuddalore Coast, Tamil Nadu, India

3 3. METHODOLOGY Changes in shoreline through processes of accretion and erosion can be analysed in a geographic information system (GIS) by measuring differences in past and present shoreline locations. Several resources are available for both extracting shoreline positions and quantifying shoreline change. Shorelines are manually digitized from satellite image for the past years. Then, those shorelines are processed in ArcGIS software to calculate the shoreline change detection the detailed methodology is shown in Figure GIS and remote sensing techniques GIS and remote sensing is a great tool in capture, store, analyse and manage spatially referenced data. GIS have transformed the way spatial (geographic) data, relationships and patterns in the world are able to be interactively queried, processed, analysed, mapped, modelled, visualised, and displayed for an increasingly large range of users, for a multitude of purposes. Geographic information system (GIS) and remote sensing can play an important role in the management of coastal resources. Remote sensing data represents a powerful tool to understand the dynamics of the coastal process where the images allow a synoptic view of the area and establish relationship between coastal environment and vegetation on multi-temporal basis. In addition to an integrated database, a geographic information system combines different data sets and simultaneously, facilitates spatial and temporal analysis. It also permits the establishment of relationships between various coastal environments that allow for a more comprehensive, accurate and easier interpretation of coastal environmental features. Figure 2 Flow Chart of Methodology 3.2. Shoreline change analysis Multiple shoreline position along with a fictitious baseline are the basic requirement for analyzing the shoreline. Continuous shoreline position with regular time interval was demarcated in ArcGIS software for the following four years of 2000, 2005, 2010, Shoreline change analysis is done in order to calculate the shoreline rate of change from time series of multiple shoreline positions. A 33 km coastal stretch of Cuddalore coast from Gadilam River to Vellar River is taken into account for shoreline calculation. These shoreline delineation image for the four years are as follows in Figure 3.

4 End Point Rate (EPR) The end point rate is calculated by dividing the distance of shoreline movement by the time elapsed between the oldest and the most recent shoreline (Figure 4). The major advantages of the EPR are the ease of computation and minimal requirement of only two shoreline dates. The major disadvantage is that in cases where more data are available, the additional information is ignored. Changes in sign (for example, accretion to erosion), magnitude, or cyclical trends may be missed. (Crowell et al. 1997) Net Shoreline Movement (NSM) The Net Shoreline Movement reports a distance, not a rate. The NSM is associated with the dates of only two shorelines. It reports the distance between the oldest and youngest shorelines for each transect. This represents the total distance between the oldest and youngest shorelines. NSM is if this distance is divided by the number of years elapsed between the two shoreline positions, the result is the EPR. NSM and EPR are essentially the same thing; however NSM gives us information on the absolute distance of shift as opposed to a rate. Figure 3 Shoreline Change Detection of Cuddalore Coast 4. RESULTS AND DISCUSSION The coastal region of Cuddalore is undergoing a rapid change due to the presence of River mouth in both north and southern part of the study area where two major Rivers present. These Rivers brings out to change the shoreline in a drastic manner. In this study, shoreline were demarcated from satellite images for 2000, 2005, 2010 and The changes were calculated by drawing the baseline and calculating the distance between the shorelines. Shoreline change rates were determined using NSM and EPR. The results of this study present that shoreline changes such as erosion and accretion have caused

5 the morphological changes to the Cuddalore coast. Maximum Accretion rate around Uppanar River was around 1.2 m/yr causing sedimentation of around 450 m below the Uppanar River mouth. The study region was divided into 5 segments according to the costal morphology. The average of erosion is around m/yr (Fig. 8). Accretion trend is around +2.5 m/yr. On the other hand the region of Northern Vellar River mouth was under an accretion of an average around 4.2 m/yr during the study period of It is noted that coastal erosion is in the Northern part of the study area. In the middle part of the study region is stable and accretion is in the southern part of the study (Figure 4). In the southern part of the study area presence of the Vellar River causes very high accretion in the northern part of the River mouth, due to the littoral drift movement. In figures 4 and 5 we can see rate of high erosion in the northern part of the study area i.e. at the Gaddilam River. This high erosion rates are due to the presence of groynes near the Uppanar River mouth, which restricts the movement of sediments towards north caused due to littoral drift. Thus the sediment load gets deposited only to the south of Uppanar River and not to the north of the Uppanar River In fact, as this is an area with high dynamics, the beach slope can change quickly causing a change in the shoreline position. Figure 4 Net Shoreline Movement in Comparison With the Study Area Figure 5 End Point Rate in Comparison with the Study Area

6 The maximum erosion is -3.8 m/yr South to Gaddilam River area and maximum accretion is +7.6 m/yr north side of Vellar River. The maximum erosion was observed between transect of 1 to 16 (Zone 1 and 2 shown in Figure 6) which is southern part of Cuddalore port because of Tsunami happened in the 2004 December. Then the Devanampatti to Sonakuppam region (Figure 6) has been under erosion along the study period. The net shoreline movement has -25 m eroded and accretion is 36.4 m. From the analysis it is observed that accretion has been took place during the last five years of the study ( ). The coastal erosion phenomenon occurring in Cuddalore port is attributed to the construction of man-made structures along upstream rivers which could trap sediment and diminish the amount of sand supplied by watersheds to the littoral system. Figure 6 Shoreline Change Detection of Cuddalore oast 5. CONCLUSION From the analysis of shoreline from the year 2000 to 2015 maximum places had eroded, due to severe consecutive cyclone like Fanoos ( ), Nisha ( ), Jal ( ), Thane ( ), Nilam ( ), Madi ( ) that haunted the coast. End Point Rate (EPR) and Net Shoreline Movement statistical methods are shown more substantial shoreline changes at Cuddalore coast especially, northern sector of Uppanar River mouth is under erosion and sediment deposition and maximum shoreline accretion rate is 7.6 m/yr from EPR and 114 m from NSM at transect 61. The Uppanar River (region C), has shown much change in shoreline and average shoreline change rate is -1.8 m/yr (EPR). The southern segment of Cuddalore and average shoreline accretion rate is 3.02 m/yr (EPR). Maximum shoreline accretion rate in region C is 7.6 m/yr (EPR) at transect REFERENCES Aedlaa, R., G.S. Dwarakish, D. Venkat Reddy, (2015), Automatic Shoreline Detection and Change Detection Analysis of Netravati-Gurpur River mouth Using Histogram Equalization and Adaptive Aquatic Procedia 4, Alesheikh, A.A., Ghorbanali, A., Nouri, N., (2007), Coastline change Int. J. Environ. Sci. Tech. 4 (1),

7 Arun & Kunte, (2012), Coastal vulnerability assessment for Chennai, east coast of India using geospatial techniques Nat Hazards 64, Ashar, M.L, & Nobuhiro, I., (2009), Shoreline changes and vertical displacement of the 2 April 2007 Solomon Islands earthquake Mw 8.1 revealed by ALOS PALSAR images Physics and Chemistry of the Earth 34(6), Boak Elizabeth, H., & Ian, L. Turner, (2005), Shoreline Definition and Detection: A Review, Journal of Coastal Research 21(4), Bouchahma, M., & Yan, W., (2012), Automatic Measurement of Shoreline Change on Djeba Island of Tunisia Comput. Inf. Sci. 5(5), Chandrasekar, N, Joevivek, V, John Prince, Soundaranayagam, & Divya, C., (2011), Geospatial analysis of Coastal Geomorphological Vulnerability GeoSpatial World Forum, Chenthamil Selvan, S., Kankara, R.S., and Rajan, B., (2014), An adaptive approach to monitor the Shoreline changes in ICZM framework: A case study of Chennai coast Indian Journal of Marine Sciences 43(7). Crowell, M., & S.P. Leatherman, M.K. Buckle, (1991), Historical shoreline change error analysis and mapping accuracy J. Coast. Res. 7(3), Feng, Y., & Han, Z., (2012), Cellular automata approach to extract shoreline from remote sensing J Image Graph 17(3), Gens, R., 2010, Remote sensing of coastlines: detec Journal of Remote Sensing 31(7), pp International Kaliraj, S., Chandrasekar, N., Magesh, N.S., (2013), Impacts of wave energy and littoral currents on shoreline erosion/accretion along the south-west coast of Kanyakumari, Tamil Nadu using DSAS and geospatial technology, Environ Earth Science 71(10), Liu, H., D. Sherman, and S. Gu, (2007), Automated extraction of shorelines from airborne light detection and ranging data and accuracy assessment based on Monte Carlo simulation, Journal of Coastal Research 23, Mageswaran, T., RamMohan, V., ChenthamilSelvan, S., Arumugam, T., Tune Usha, and Kankara, R.S., (2015), Assessment of shoreline changes along Nagapattinam coast using geospatial techniques, International journal of geomatics and geosciences 5(4), Mujabar. P.S, & Chandrasekar N., (2013), Shoreline change analysis along the coast between Kanyakumari and Tuticorin of Ind, Arabian Journal of Geosciences 6(3), Saravanan. S., Parthasarathy K.S.S., Kumaresan.P.R., Vishnu prasath.s.r., Vasanth Kumar.T. (2015), Shoreline Change Detection for Chennai Coast Using Geospatial Techniques. Srinivasa Kumar T, Mahendra R.S, Nayak S, Radhakrishnan K, Sahu K.C. (2008), Coastal vulnerability assessment for Orissa state, east coast of India J Coast Res 26(3),