An adaptive approach to monitor the Shoreline changes in ICZM framework: A case study of Chennai coast

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1 Indian Journal of Geo-Marine Sciences Vol. 43 (7), July 2014, pp An adaptive approach to monitor the Shoreline changes in ICZM framework: A case study of Chennai coast R. S. Kankara *, S. Chenthamil Selvan, B. Rajan & S. Arockiaraj Ministry of Earth Sciences, ICMAM - Project Directorate, NIOT Campus, Pallikaranai, Chennai , India *[ kankara@icmam.gov.in] Received 20 August 2013; revised 24 October 2013 Shoreline change study was carried out for 25 km long stretch of Chennai coast. Landsat TM (1990), Landsat ETM+ (2000), CARTOSAT-1 (2006), Resourcesat-1 (2008) and Resourcesat-2 (2012, 2013) satellite images were used as input dataset. Field survey was also carried out using Arc-Pad GPS instrument for Three methods i.e. End Point Rate, Linear regression Rate and Weighted Linear Regression were employed to calculate shoreline change rate for While, EPR method is used to calculate the short-term analysis for and Study area was divided into four distinct zones. Totally 412 transects were generated with 50 m spacing and the length of each transects was 200 m. From the long term analysis, the high erosion was noticed on the northern side of the Thiruvottiyur region with a rate of more than 5 m/yr. Royapuram fishing harbour is noticed with an erosion rate of 4m/yr during the period of ( ). 2 to 4 m/yr accretion was seen all along the marina beach. Whereas, south of marina beach, the places like Foreshore estate, Elliot beach and Thiruvanmiyur regions shows low erosion rate. From the analysis it clearly shows that the northern portion of the Chennai port is eroding and the southern portion of the port is accreting. Combined use of satellite imagery and statistical methods proves to be a reliable method for shoreline change analysis. Of all the statistical methods, WLR statistical method is found to be more reliable as it takes uncertainties and quality of datasets into account to calculate the rates of shoreline change. [Keywords: Erosion, Accretion, Satellite imagery, Weighted Linear Regression Rate and End Point Rate] Introduction The shoreline is defined as the line of contact between land and sea. It is constantly influenced by sea level variations, climate and ecosystems that occur over a wide range of time-scales, from geological time to short-lived, extreme events such as storms. Wind, waves and currents are natural driving forces that easily move the unconsolidated sand and soils in the coastal area, resulting in rapid changes in the position of the shoreline. Further anthropogenic activities along the coast, changes in river catchments, off shore developments such as land reclamation in coastal areas, port and harbour construction, river damming, diversion of rivers, sand mining, dredging etc., also contribute to shoreline changes 1. The combination of natural and manmade activities often exacerbates the shoreline change and increases the risk factors to coastal community 2. About 23% of shoreline along the Indian main land was affected by various degree of erosion varying from minor, moderate to severe. Shoreline change is one of the three identified environmental concerns for considering the developmental activities such as ports, harbour, fishing jetties, embankment facilities etc. Assessment and monitoring of the Shoreline changes are primary requirement for setting up a long-term programme to provide the systematic information on erosion in coastal zone management perspective. The documentation and understanding of shoreline changes on seasonal, annual, short-term and long-term provides a foundation to develop a sustainable shoreline management plan. There are several techniques for characterizing and quantifying the shoreline changes. Remote Sensing technology and GIS has been recognized as one of the most dominant tool for quantifying the shoreline changes on temporal scales as it provides the information in digital form 3,4. Remote Sensing data has rapid, repetitive, synoptic and multispectral coverage of the satellites is found to be useful in environmental monitoring programmes where the objective is to monitor the changes in surface phenomena over time Howarth P J and Wickware 5 for digital spatial data analysis and mapping, remote sensing and Geographic Information Systems (GIS) are widely applied in environmental and natural resources monitoring 6,7. Therefore, the application of GIS and remote sensing it is very vital to understand the shoreline changes at national scale. The shoreline is being mapped by various researchers and

2 KANKARA et al.: AN ADAPTIVE APPROACH TO MONITOR THE SHORELINE CHANGES 1267 institutions in India. The mainland shoreline changes were mapped by Space application Centre (SAC) Ahmadabad for using two time period data sets. NCSCM has prepared the shoreline change map for few states using the similar approach. ICMAM-PD has initiated a national project to monitor the shoreline changes along Indian coast to provide the systemic information on shoreline changes for entire coast considering the need for more reliable and upto-date information. In this paper, shoreline change for the Chennai coast have been studied from 1990 to 2013 using different satellite images and various methods. The aim of this study was to assess the performance of available methods and estimation of inherent uncertainty in data sets to adopt a suitable approach for implementation. Materials and Methods Table 1 Data used List of Image Pixel Size (m) Date Source Landsat 5 TM 30 01/01/1990 USGS IRS 1C (LISS III) /01/1998 NRSC IRS 1C (PAN) /03/1999 NRSC IRS P5 (Cartosat-1) /07/2006 NRSC IRS P6 (Resourcesat-1) - (LISS-III) /05/2008 NRSC Resourcesat-2- (LISS- IV) /03/2012 NRSC IRS P6 (Resourcesat-1) - (LISS-III) /03/2013 NRSC Study area The study was carried out along the Chennai coast from Thiruvottiyur to Thiruvanmiyur located between to E and N to N. The study area covers around 25km as shown in Fig. 1. The north stretch includes Thiruvottiyur to Royapuram fishing harbour and the southern part includes various tourist beaches i.e., Marina, Foreshore, Besant Nagar and Thiruvanmiyur etc. In between them Chennai port is located. Marina beach, it is the second largest beach of the world. The area is bounded by Buckingham canal flows from northern to southern direction, Coovum and Adyar are the two Rivers flows in to the Bay of Bengal. Due to the construction of the breakwaters for the formation of harbour of Chennai port, the north Chennai coast is being subjected to erosion due to the predominant northerly drifting of net annual sediment transport. Data products Estimation of the rates of erosion and accretion along Chennai coast was performed for the studied periods. Two approaches were applied: a long-term analysis using seven shorelines (1990, 1998, 1999, 2006, 2008, 2012 and 2013) and a short-term analysis using two periods i.e and A comparison of three statistical methods was used to check the output rates (Linear Regression [LRR], Weighted Linear Regression [WLR] and End Point Rate [EPR]). In terms of distances they were reported as Net Shoreline Movement (NSM) which represents the total distance between the oldest and youngest shorelines Approach adopted Fig. 1 Study area Definition of Shoreline Key issue in monitoring the coastal changes is the selection of an adequate feature that can serve as a shoreline indicator or proxy, so that it properly reflects the real shoreline position and evolution 11,12. Defining any shoreline change, the most crucial problem is interpretation or defining the shoreline position. This is because, the coasts are dynamic in nature and hence defining a shoreline position is normally a difficult task. In the present work, High-Water Lines (HWL) were used as shoreline

3 1268 INDIAN J MAR SCI, VOL. 43, NO. 7, JULY 2014 proxies. The HWL is usually considered as equivalent to the last high tide mark or the wet/dry line identifiable on beach sand on the image. Despite its limitations regarding the short-term variability, it is deemed as a valid indicator of shoreline position, and so it was used in this study. Remote Sensing Techniques The georeferenced satellite images were verified and corrected with the aid of GPS coordinates recorded from the selected locations with Global Positioning System (GPS) data and corrected wherever necessary.the shorelines were identified and delineated by processing the NIR bands of Landsat TM using Gray Level Thresholding and by Edge Enhancement Technique 13. In the present study, the exact land-water boundary was obtained by using a nonlinear edge-enhancement technique with Sobel operator (3 3 kernel matrix). These operations were applied to image data to produce an enhanced image output for subsequent visual interpretations. The enhancement techniques improve the feature exhibition and increases visual distinctions between features contained in a scene. This technique gives a clear demarcation of the land-water boundary. In case of IRS 1C data, Convolution filtering technique with (3 3 kernel) edge detection is applied to enhance the feature characteristics for interpretation. High resolution Pan Data are manually digitized using ArcGIS software. Shoreline Change Analysis: Multiple shoreline positions along with a fictitious baseline are the basic requirement for analyzing the shoreline. Continuous shoreline positions was digitized manually as per 1:25,000 scale requirement for seven different periods i.e. 1990, 1998, 1999, 2006, 2008, 2012 and 2013 with 5 attribute fields i.e. ObjectID (a unique number assigned to each transect), shape, shape length, ID, date (original survey year) and uncertainty values for further analysis. All different shoreline features were then merged within a single line on the attribute table, which enabled the multiple coastline files to be appended together into a single shape file for further analysis. Digital Shoreline Analysis System (DSAS), an extension of ESRI ArcGIS software was used to calculate the shoreline rate-of-change statistics from a time series of multiple shoreline positions. 25 km of coastal stretch of Chennai coast from Thiruvottiyur to Thiruvanmiyur is taken in to account for shoreline calculation. Totally 412 transects were generated with 50 m spacing and the length of transects was 1000 m. DSAS generates transects that are cast perpendicular to the baseline at a user-specified spacing alongshore as shown in Fig. 2. The transect intersect along this shorelines are then used to calculate the rate-of-change. Shoreline rate-of-change was determined by fitting a least squares regression line to all shoreline points for a particular transect. Three statistical approaches such as End Point Rate (EPR), Linear Regression Rate (LRR) and Weighted Linear Regression (WLR) were employed to compute the change rates. The EPR is simple approach and calculate the change rates by dividing the distance of shoreline movement by the time period between the oldest and the youngest shoreline. While the LRR is determined by fitting least-squares regression line to all shoreline points for a particular transects. It does not take uncertainty value in calculating the shoreline change rate. WLR method is adopted for shoreline change rate calculation. The method used to calculate shoreline rates of change is based on measured differences between shoreline positions through time. The reported rates are expressed as meters of change along transects per year. In a weighted linear regression, more reliable data are given greater emphasis or weight towards determining a best-fit line. In the computation of rateof-change statistics for shorelines, greater emphasis is placed on data points for which the position uncertainty is smaller. The WLR statistical method is more reliable because it takes into account the uncertainty field to calculate the long-term rates of shoreline change as shown in Fig. 3. The weight (w) is defined as a function of the variance in the uncertainty of the measurement (e) 14 : Fig. 2 showing the shoreline position, baseline and transect

4 KANKARA et al.: AN ADAPTIVE APPROACH TO MONITOR THE SHORELINE CHANGES 1269 w = 1/ (e 2 ) Where, e = shoreline uncertainty value To analysis the Weighted Linear Regression Rate (WLR), it is necessary to accurately estimate the errors and uncertainties associated with each shoreline. Several sources of error impact the accuracy of historical shoreline. There are 7 different sources of error in identifying the shoreline positions on aerial photographs and T-sheets (3 positional and 4 measurement errors) The 7 different sources of errors are summed in quadrature (the square root of the sum of the squares) to get a total positional uncertainty (Ut). Overall uncertainty value, was estimated for each shoreline by accounting for both positional and measurement uncertainties as given equation below: error of the estimate (WSE), the standard error of the slope with user-selected confidence interval (WCI), and the R-squared value (WR2) are reported as shown in Fig. 4. Results and Discussion Long-term rates of shoreline change were calculated for entire study area at each transect for 23 years i.e to 2013 using EPR, LRR and WLR methods considering 7 datasets. The study area was divided into four zones for handling the data and visualization of results at appropriate level. The zone-wise analysis of accretion and erosion pattern obtained from EPR, LRR and WLR is presented in Et = ± E s + E td + E c + E d + E p + E r + E ts The uncertainity for each data sets were worked out considering the data product with due weightage of the quality of each data. The uncertainty field of the shoreline feature class is used to calculate the weight. In conjunction with the weighted linear regression rate, the standard Fig. 3 The (WLR) rate was determined by plotting the shoreline positions with respect to time Fig. 5 Length of shoreline in different classes along the Chennai coast Fig. 4 Calculating shoreline change rate using the single-transect using WLR method. The slope of the line is the annual shoreline change rate. R 2 indicates regression coefficient Fig. 6 Shoreline change over time, for zone1 and 2 between and 1999 to 2006

5 1270 INDIAN J MAR SCI, VOL. 43, NO. 7, JULY 2014 Tables 2, 3 and 4 respectively. EPR has inherent limitations for long term analysis as it considers only 2 datasets, but it is useful for short term analysis. Short-term rates of shoreline change were calculated at each transect for two different periods ( and ). The rate of erosion was quite severe about 5 m/yr in zone 1 & 2 during 1990 to But the trend of shoreline changes was quite moderate during 1999 to 2006 as shown in Fig. 6. This could be attributed due to coastal processes prevailed during the period in this region and/or the implementation of coastal protection work during 2004 onwards. The LRR results are compactable for linear changes of coastline in terms of space and time. WLR considers the uncertainties with weightage of the data quality; therefore results are systematic for all regions. Uncertainties values in linear and weighted linear regression for the long-term rates (± values in Tables 3 and 4) are also computed with 85% confidence interval for the slope of the regression line. This means with 85% statistical confidence that the true rate of shoreline change falls within the range defined by the reported value plus or minus the error value. The Zone 1 i.e. northern most portion of study area from south of Ennore creek to Palagaitottikuppam covers 100 transects. All three methods reflected the eroding nature of coast with varying rates. Erosion is more than 7 m/yr between Transect 26 to 38, which is evident from past records too. The average rate of change was -3.8±1.6 m/yr. Zone 2 has 105 transects and 67% of coast is under erosion with the maximum rate is -5.5±2.4 m/yr. Port breakwater is one of the major cause for coastal change. Zone 3 is the region where Marina is located and covers totally of 142 transects. The northern portion of the Coovum Table 2 Long-term shoreline change from EPR for 23 years using two shorelines i.e to 2013 Zones No Number of transects Mean rate (m/yr) % Erosion Erosion Rate (m/yr) Accretion Rate (m/yr) % Accretion Max Mean Max Mean Zones No Table 3 Long-term shoreline change from LRR for 23 years using seven shorelines from 1990 to 2013 Number of transects Mean rate (m/yr) % Erosion Erosion Rate (m/yr) Accretion Rate (m/yr) % Accretion Max Mean Max Mean ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±0.6 Table 4 Long-term shoreline change from WLR for 23 years using seven shorelines from 1990 to Zones No Number of transects Mean rate (m/yr) % Erosion Erosion Rate (m/yr) Accretion Rate (m/yr) % Accretion Max Mean Max Mean ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±0.6

6 KANKARA et al.: AN ADAPTIVE APPROACH TO MONITOR THE SHORELINE CHANGES 1271 river covers 28 transects. This is the region where Indian Coast Guard and Navy is located. Whereas, the remaining transects falls below Coovum river. The analysis indicates that the northern transect present above the Coovum river shows moderate erosion rate. Whereas, Marina region is seen with accretion with an average rate 1.4 ±1.0 m/yr. A small pocket of region is noticed with low to moderate erosion where Dummingkuppam is located. Zone 4 with 65 transects starts from south of Adyar river to Thiruvanmiyur. Transects between 348 to 364 is noticed with low erosion. The remaining portion shows low accretion. Elliot beach falls under this zone and is noticed with minor accretion rate. Overall the average rate of change is -0.4 ±0.8 m/yr. Maximum amount of accretion along the zone is 2.7 ±2.0 m/yr. Long-term rates of shoreline change were determined at each transect by taking the slope of the regression line applied to all seven shoreline positions. The resulting rate is reported in units of m/yr as shown in Fig. 5 as a plan view. Conclusions Shoreline Mapping, inventory and monitoring is very important information for characterization and management of coastal systems which needs to be documented. In this paper we have analysed the shoreline changes rates using EPR, LRR and WLR methods to provide consistent and reliable information in complex coastal systems across different time scales. Shoreline changes along the Chennai coast over the last 23 years were studied. The high erosion was noticed on the northern side of the Thiruvottiyur region with a rate of more than 7 m/yr, whereas, high accretion was noticed along the southern part of Coovum river mouth with a rate of 5 m/yr. While comparing the three methods, the results of the WLR method seem to be more systematic and reliable. The method considers entire set of the data with additional options of weightage to quality of data at each intersecting transects individually. WLR approach is most ideal to compute the shoreline change rates using fine resolution satellite data of 2.5 m and even 1m. Further the definition of shoreline position is normally a difficult task. The shoreline may be defined using a pragmatic approach. Therefore, the shoreline was fixed as HWL considering equivalent to the last high tide mark or the wet/dry line which is clearly identifiable on beach sand on the image and on the site while field monitoring. Acknowledgements Authors would like to thank Dr. Shailesh Nayak, Secretary, Ministry of Earth Sciences, Government of India for his keen interest and encouragement. References 1 Ordu. S and Demir. 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