Nat Hazards DOI 10.1007/s11069-016-2332-y ORIGINAL PAPER
Shoreline change and impacts of coastal protection structures on Puducherry, SE coast of India S. Chenthamil Selvan1 • R. S. Kankara1 • Vipin J. Markose1 B. Rajan1 • K. Prabhu1
•
Received: 8 October 2015 / Accepted: 22 March 2016 Ó Springer Science+Business Media Dordrecht 2016
Abstract This study examines the shoreline changes that have occurred in past 23 years along Puducherry region, SE coast of India. Satellite data sets such as Landsat, Cartosat-1, Resourcesat—1 and 2 of different periods—i.e., 1991, 2000, 2006, 2008, 2012, 2013 and 2014, were used for shoreline change analysis. Shoreline extracted from these data sets was used for estimation of shoreline change rate at every 20-m interval. The overall result found that 55.9 % shows erosion, 34.9 % falls under stable, and 9.2 % shows accretion in Puducherry coast. High accretion rate of 5.0 (±1.9) m/year was noticed along the Veerampattinam region located immediate south to the Puducherry port. Medium erosion of about -3.2 (±1.9) m/year was observed along the Thengaithittu region. Remaining coast was noticed with stable to low erosion. Severe erosion was observed in northern side of the breakwater during 1991–2000 periods, whereas southern side high accretion was noticed. Groins constructed at Thandrayankuppam and Nadukuppam region protected the coast from erosion. Due to this groin construction, erosion was shifted toward the northern portion (Chinna Mudaliar Chavadi, Periya Mudaliar Chavadi and to certain extend of Bommaiyarpalayam). This study demonstrates that combined use of satellite imagery after considering the uncertainties and statistical methods can be a reliable method for shoreline change analysis for any coastal conditions. Keywords Erosion Accretion Weighted linear regression DSAS GIS
& R. S. Kankara
[email protected] 1
Integrated Coastal and Marine Area Management Project Directorate (ICMAM-PD), Ministry of Earth Sciences, NIOT Campus, Velachery, Chennai 600100, India
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1 Introduction Coastal configuration is controlled by various oceanographic parameters such as waves, tides, currents, rainfall, storms and sea-level change (Albert and Jorger 1998; Morton and Miller 2005). Human activities along the coast such as port development, construction of dams in upstream part and sand mining in estuaries are often responsible for coastal erosion in many places. In India, shoreline change is one of the major environmental concerns for developmental activities in coastal zone. Remote sensing and GIS technology have widely used for quantification of shoreline changes on spatial and temporal scales because of its rapid, repetitive, synoptic and multispectral coverage (Howarth and Wickware 1981; Nayak 2002; Zuzek et al. 2002; Thieler et al. 2009). Multi-dated satellite images are widely used for shoreline change rate estimation of many areas in the world (Armenakis et al. 2003; Zimmerman and Bijker 2004; Shresta 2005; Chalabi et al. 2006; Xiodong et al. 2006). Maiti and Bhattacharya (2009) have analyzed shoreline change rate of Balasore coast using remote sensing and statistical approach. Wal et al. (2002) have estimated long-term morphological change of Ribble estuary, northwest England. Ghanavati et al. (2008) have used Landsat TM and ETM? data to monitor geomorphological changes of Hendijan River delta, southwestern Iran. Wu (2007) has analyzed the shoreline evolution of Nouakchott region (Mauritania) using remote sensing methods. Many algorithms were developed for extraction of shoreline from satellite data. In most of the studies, shorelines were extracted by automatic and manual digitization techniques (Ryu et al. 2002; Loos and Niemann 2002; Boak and Turner 2005; Yamano et al. 2006). Manual interpretation of features depends on individual skills of an interpreter (Anders and Byrnes 1991; Byrnes et al. 1991). The shoreline extracted from the satellite imagery has its own limitations such as rectification, spatial resolution, seasonal variation, pixel variation and tidal variation. Many statistical methods such as end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR) and least mean square (LMS) are widely used for shoreline change rate analysis (Maiti and Bhattacharya 2009; Kuleli et al. 2011; Natesan et al. 2013; Ozturk et al. 2015). Among these, WLR method is found to be more widely used method because it takes the account of all the uncertainties of the extracted shoreline position and it is statistically a robust quantitative method when a limited number of shorelines are available (Thieler and Danforth 1994; Selvan et al. 2014; Kankara et al. 2015). Understanding of shoreline changes on seasonal, annual, short- and long-term provides a foundation to develop a sustainable shoreline management plan. Moreover, they minimize potential random error and short-term variability (cyclic changes) through the use of statistical approach (Douglas and Crowell 2000). With growing population along the coast, artificial structures are constructed to protect the coastal community from the natural process. This led to severe hardening of coastlines and changes in sediment dynamics in many coastal settings (Airoldi et al. 2005). Alongshore structures such as seawalls, ripraps and revetments are widely used coastal protection measures to reduce the erosion. Thereby, it alters the beach width and migrate the erosion process to adjacent regions (Hall and Pilkey 1991; Griggs 2005a, b). Artificial structures are also responsible for erosions which arrest the natural process. In most of the developed countries, these hardened structures were constructed to reduce erosion, coastal flooding, coastal developments and for infrastructure development (Charlier et al. 2005). Due to coastal structures, wave reflections become more dominant along the coast (e.g., Hall and Pilkey 1991; Miles et al. 2001; Griggs 2005a, b). Above-mentioned studies clearly show that coastal protection structures play a major role in shoreline configuration. Therefore, it
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is necessary to study how the coastal protection structures will affect the coastline changes. Keeping this view, the present research work focuses on shoreline changes occurred in past 23 years and adverse impact of artificial structures on shoreline configuration of Puducherry coast, which is a major tourist destination of southern India. The study examines the long- and short-term shoreline changes with reference to coastal structures using remote sensing data. The results obtained from the study are very useful to authorities for spatial planning and remedial measures for effective coastal zone management.
2 Study area Puducherry coast is about 25.5-km-long stretch starting from Veerampattinam on south to Kanaga Chettikulam on north (Fig. 1). The terrain is gently undulating with prominent high grounds varying from 30 to 45 m above mean sea level (MSL) toward interior northwest and northeastern parts of region. The coastline appears to be almost straight, and it is a part of a larger concave coast. Gingee and Ponnaiyar (Penniyar) are the two major river systems draining into the Bay of Bengal along Puducherry coast. The region is
Fig. 1 Map showing location of the study area with three different cells
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experiencing both northeast and southwest monsoon seasons annually. The area consists of various landforms like sand dunes, beaches, tidal flats and estuaries. Beaches are comparatively broader, and sand accumulation was taking place in south of the southern breakwater. Sand dunes are found in northern region of the coast. Estuarine mouths are prominent at southern region where both Gingee and Ponnaiyar rivers join the Bay of Bengal. Puducherry coast is protected with different artificial structures such as seawalls, groins and breakwaters. The details of these structures are shown in Table 1. The study area is divided into three different cells (A, B and C) to understand the change in detailed manner. Cell A is an open coast covered with sandy beaches where there is no coastal protection structure. Cell B covers Puducherry port, and it is fully protected coast where the impact of erosion is high. It also covers some part of Tamil Nadu coast. Cell C which is again an open coast is down to the Gingee River.
3 Materials and methods The multi-dated satellite data such as Landsat 5, 7, Cartosat-1, Resourcesat 1, 2 acquired at different periods were used for shoreline extraction and shoreline change rate calculation. The details of these data are shown in Table 2. Field survey was conducted all along the coast to collect the ground control points (GCPs) using Global Positioning System (GPS). These GCPs were evenly distributed all over the image to give the best coverage for calculating the transformation. Using ERDAS IMAGINE 2013 software, all images were rectified (projection: UTM, datum: WGS-84) with second-order polynomial transformation method. All images, root-mean-square error (RMSE), were maintained within a pixel of the image.
3.1 Shoreline extraction Before analyzing the shoreline change rate, it is necessary to define the shoreline indicator that will act as a proxy for the land–water interface. Advantages and limitations of the Table 1 Description of artificial structures constructed in the study area Sl. no
Artificial structures
Description
1
Concrete seawall
Puducherry city is protected by a seawall which stretches 2 km along its coastline. This seawall was built by the French Government in 1735. The seawall reaches a height of 27 feet above sea level. Again this seawall is protected from the direct onslaught of waves by rows of boulders which are reinforced every year to stop erosion
2
Ripraps
About 6 km of the coast is protected with ripraps as on 2014. The big boulders are placed parallel to the coast and protect the village from direct contact with the waves
3
Groins
Nine groin structures were constructed all along the study area. Two groins each at Thandrayankuppam, Kottakuppam, Sadhanaikuppam, Kuruchikuppam and one groin at Puducherry township. Each groin field varies in length
4
Breakwater
Breakwater was constructed during 1990 for port protection as the main purpose of the breakwater is to control the flow of long-shore current and sediment to maintain the channels
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List of image
Pixel size (m)
Date
Source
Landsat 5 TM
30
21/08/1991
USGS
Landsat 7 ETM?
30
28/10/2000
USGS
Cartosat-1 (Pan)
2.5
01/07/2006
NRSC
Resourcesat-1—(LISS-III)
23.5
29/10/2008
NRSC
Resourcesat-2—(LISS-IV)
5.8
18/03/2012
NRSC
Resourcesat-2—(LISS-IV)
5.8
04/06/2013
NRSC
Resourcesat-2—LISS-IV)
5.8
12/06/2014
NRSC
coast have to be understood before defining the shoreline position within the available data source. The technique for identifying the shoreline position and defining the shoreline has the potential to induce error when estimating a shoreline position (Stockdon et al. 2002). It is very difficult to extract the shoreline position with fixing a single feature as shoreline position. Determination of a shoreline position in satellite data is very subjective due to different ranges of tide-induced variability, variations in meteorological conditions, inequalities in data resolution, seasonal setup and scaling of remote sensing data during different periods of data acquisition. Many coastal researchers have used different proxies to represent the shoreline position. Common shoreline proxies include the high water line (HWL) (Shalowitz 1964); line of vegetation (Hwang 1981); the toe or crest of the sand dune (Moore and Griggs 2002); low water line (Fletcher et al. 2003); mean high water (MHW) (Morton et al. 2004); wet–dry line (Moore et al. 2006); and cliff base or top (Hapke and Reid 2007). Therefore, after careful consideration of varying field conditions, coastal features variability and limitations of remote sensing data, this study considered the HWL mark from each image as shoreline position which is equivalent to the last high tide mark or the wet–dry line identifiable on beach sand on the image (Kankara et al. 2014, 2015). In case of narrow beach protected with seawalls, the boulder line is considered as shoreline position. The previous HWL can be clearly identified from imageries. Accordingly, the shoreline positions were extracted using ArcGIS 9.3 software for different period. There are several approaches to calculate the rates of shoreline change such as conventional surveys, numerical models and remote sensing technique. Many studies (e.g., Braud and Feng 1998; Mas 1999; Frazier and Page 2000; Ryu et al. 2002) have explained the importance of various techniques such as image enhancement, density slice, supervised and unsupervised classification technique like ISODATA, PCA and Tasseled Cap. In addition, Liu and Jezek (2004) have explained several image processing algorithms such as pre-segmentation, segmentation and post-segmentation for automatic shoreline extraction. Chalabi et al. (2006) found that multi-scale data can be used for change analysis and mapping. Shoreline change rate was generated in GIS environment with the Digital Shoreline Analysis System (DSAS) (Thieler et al. 2009). A baseline (onshore) was created parallel to the shoreline, and a total of 1182 transects were generated at 20-m interval using DSAS (Fig. 2a). Two separate methods were adopted to calculate the rate of change. Even though both are statistical methods, it differs in its mode of function. Short-term analysis was carried out with end point rate (EPR) method. This method requires two different dated shoreline positions to calculate the rate in m/year. The overall change between the shoreline is measured with net shoreline movement (NSM).
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Fig. 2 a Close view of shoreline positions of different periods along with baseline and transects; b shoreline positions plotted with respect to time. The shoreline measurement points are shown with positional uncertainty values, and the slope of the regression line is the rate
EPR ðm/year) ¼
Distance ðA BÞ in m Time between youngest and oldest shoreline
ð1Þ
Long-term shoreline change rates were calculated for each transects using WLR method by compiling all shoreline positions from the oldest (1991) to the most recent (2014). WLR method is similar to that of linear regression method as rate-of-change statistic can be determined by fitting a least-squares regression line to all shoreline points for a particular transect. In addition to that, a weightage value is also added to shoreline data obtained from the measurement and positional uncertainties involved in each shoreline position. Highresolution/high-quality data sets are given greater emphasis or weightage toward determining a best-fit line in comparison with unreliable data sets, i.e., the regression line can be placed in such a way so that the sum of the squared residuals is minimized. The weight (w) is defined as a function of the variance in the uncertainty of the measurement (e) (Genz et al. 2007): w¼
1 ðeÞ2
ð2Þ
where e = shoreline uncertainty value.
Table 3 Total uncertainty value for different data sets
Data and year
Et (m)
Landsat—1991
15.00
Landsat—2000
15.00
Cartosat-1—2006
123
1.07
Resourcesat-1 LISS-III-2008
17.77
Resourcesat-2 LISS-IV-2012
9.1
Resourcesat-2 LISS-IV-2013
9.1
Resourcesat-2 LISS-IV-2014
9.1
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3.2 Uncertainty in shoreline position The accuracy of final result obtained from the analysis is affected by many factors acting on shoreline movement. Therefore, it is necessary to accurately estimate the errors and uncertainties associated with each shoreline position. There are several sources of errors which affect the accuracy of historical shoreline positions (Morton et al. 2004). In the present study, two positional and three measurement errors have been identified in the given input. qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 2 ¼ E2 þ E2 þ E2 ð3Þ Et ¼ Es2 þ Etd P r d The total positional uncertainty (Ut) for each images separately is shown in Table 3. The uncertainty values thus obtained are incorporated into the shoreline change result using WLR method (Thieler et al. 2009). In WLR method additional weightage valve is added along with the uncertainty to obtain the final result. Figure 2b shows that the shoreline position of transect number 670 is plotted with respect to time. The error bar in shoreline measurement point is obtained after adding the weighted values to each shoreline positions.
4 Results and discussion Short-term changes were analyzed for three different periods, i.e., 1991–2000, 2000–2006 and 2006–2014, and long-term change was analyzed separately by appending all available shorelines.
4.1 Short-term changes 4.1.1 Cell A Cell A is an open coast with almost covered with sandy beaches. The zone is *8.5 km long starting from north of Thandrayankuppam to Kanaga Chettikulam. During 1991–2000, 65 % of the coast was observed accretion with few pockets of erosion between Pillaichavady and Chinnakalapet (Fig. 3a). Similar trend was observed during 2000–2006 with 80 % of coast under accreting condition (Table 4), whereas during 2006–2014 periods, the result shows that the region between northern Thandrayankuppam and Bommaiyarpalayam is eroded in faster way. Net shoreline change of *79 m along these regions was eroded. Erosion toward northern part is mainly due to the adverse effect of northern groin at Thandrayankuppam which was constructed during 2006–2007 periods. Griggs (2005a, b) has observed that coastal structures will reduce the natural landward migration of the shoreline in one side, whereas in other side, it leads to loss of beach area and width. In order to prevent erosion, seawall was constructed at Chinna Mudaliar Chavadi after 2008. Due to scouring, seawall was collapsed and the boulders got sinked down. Again 2013 another layer of seawall was constructed along Chinna Mudaliar Chavadi (Fig. 5a). Due to this seawall, erosion was shifted toward north and the region such as Periya Mudaliar Chavadi and Auroville was affected with erosion. Auroville beach was eroded mainly due to the impacts of coastal structures (Fig. 5c, d).
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Fig. 3 Short-term shoreline change rate with respect to time for three different periods (1991–2000, 2000–2006 and 2006–2014) for a cell A, b cell B, c cell C
4.1.2 Cell B Cell B covers about 8.3-km-long stretch starting from Puducherry township to Thandrayankuppam. Puducherry port falls between Veerampattinam and Thengaithittu region. In 1986 construction of harbor started in the Ariyankuppam estuary by constructing breakwater on either side of the river mouth. During 1991–2000, net shoreline movement of *55 m was eroded toward north of northern breakwater. Due to obstruction of breakwater, accretion was noticed in southern side. In 1991–2000 periods, *150 m length was accreted along southern side of the breakwater (Fig. 3b). These breakwaters trap the sediments along up coast region and erode the down coast side. On open coasts, groin, seawalls, revetments, jetties, geotextile tubes and other engineered structures alter the wave regime and modify processes that deposit and retain mobile sediments on exposed sandy beaches (e.g., Miles et al. 2001). Figure 4a shows the field photograph taken during 2013 at Veerampattinam region where sand accumulation was noticed. Most of the region is protected with seawall along this zone (Fig. 5b). Low erosion to stable condition was
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Fig. 4 Field photograph showing artificial beach. a Veerampattinam which is south of southern breakwater where huge amount of sands are trapped making it along sandy beach. b Thandrayankuppam which is protected with two groins on either side of the village. Due to groins *30-m beach was formed
sparsely distributed all along this zone. The regions such as Solai Nagar, Vaithikuppam, Kurusukuppam were noticed with low erosion. Stable to low accretion was noticed during 2000–2006 (Fig. 3b). Tamil Nadu Govt. has initiated the protection measures by constructing seawall at a length of 500 m in Nadukuppam during 2003/2004, 920 m in Sadhanaikuppam during 2005/2006 and 820 m in Kottakuppam during 2006/2007 and also at Chinna Mudaliar Chavadi. During 2000–2006, northern side of the breakwater was noticed with low accretion to stable along Puducherry port region. During 2001–2003 periods, beach nourishment was carried out which results the accretion in northern part of the coast (Fig. 3b). The maximum net shoreline movement was about 80 m down to the southern breakwater and 28 m on northern side of breakwater. Overall scenario indicates that the sand depletion was reduced at Thengaithittu region. Shoreline change for 2006–2014 periods clearly indicates accretion at Thandrayankuppam and Nadukuppam region. Sand accumulation was noticed between the groins, thereby making it as artificial accreting
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coast. Maximum net shoreline movement of 36 m was noticed between two groin fields at Thandrayankuppam (Fig. 4b). It is quite evident from the analysis that the northern side of the breakwater is almost stable to low eroding condition. Even though the erosion is a major concern along the coast, the zone is well protected by seawalls. Due to this, the coast seems to be stable in condition. Overall analysis of shoreline change between 2006 and 2014 shows that 89 % of the coast is either stable to low eroding condition and remaining 11 % coast is accreting nature (Table 4) . Two layers of seawalls were constructed along Kurusukuppam region to reduce the erosion (Fig. 3b).
4.1.3 Cell C For three different periods, 8.5 km of coastal length was analyzed. It covers the region from Pudukuppam in south to Gingee River toward north. The overall trends of shoreline change for three different periods are shown in Fig. 3c. Analysis of 1991–2000 indicates that 76 % of the coast is accreting nature. There is no major erosion threat along the cell during this period. Accretion was increased to 89 % from 2000 to 2006 periods (Table 4). Pudukuppam, Nallavadu, Pannithithu regions were noticed with moderate
Fig. 5 Field photographs showing two seawall protective layers. a Chinna Mudaliar Chavadi village with two seawalls constructed during 2010 and 2013, b showing two seawall layers at Kurusukuppam village near to Puducherry town. The older layer is scoured at the toe by wave action. c Erosion at Auroville beach taken during 2013, d same location at Auroville beach taken during 2014. Tree roots were seen due to the wave action
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accretion during 2000–2006 periods. After 2006, same region was fully eroded. High erosion was observed at the southern side of the Gingee River. Erosion occurred during 2006–2014 periods may be due to the impacts of cyclone (Thane—Dec 28, 2011, and Nilam—Oct 31, 2012).
4.2 Long-term changes Weighted linear regression was adopted for shoreline change rate for past 23 (1991–2014) years using seven different resolution data sets, in which four are high-resolution data with pixel size of \5.8 m. The spatial resolution of data is very vital for shoreline delineation and change analysis. The shoreline change rate depends on the position of the shorelines as well as the uncertainty associated with it. Shoreline positions with higher uncertainty will have less influence on the trend line than data points with smaller due to assigned weightage. Long-term changes of cell A analysis indicate that 88 % of coast was under stable to low eroding in condition (Fig. 6a). In cell B 65 % of coast was in eroding condition (Fig. 6b). Even though the zone falls in port region, the coast is almost protected with structures, thereby making the coast to low eroding in nature. In cell C, 97 % of coast is seen with erosion (Fig. 6c). High accretion of 5 ± 1.9 m/year was noticed along the Veerampattinam region, whereas maximum erosion of -3.2 ± 1.9 m/year was noticed at Thengaithittu region. Ramesh et al. (2011) also found the high erosion in this region. Remaining coast was noticed with stable to low erosion. The WLR rate thus obtained from the analysis with error (±WCI-85) value as shown in Table 5. This suggests that the data (including shoreline position, uncertainty and selected confidence interval) reporting with 85 % confidence show steady erosional trend on the northern side of breakwater and steady accretional trend on southern side. Based on shoreline change rate values, the entire area has been classified into five categories (Fig. 7). Boundaries of each class are demarcated as Table 4 Details of short-term (1991–2000, 2000–2006 and 2006–2014) shoreline change rate for three different cells Details
1991–2000
2000–2006
2006–2014
Cell A
Cell B
Cell C
Cell A
Cell B
Cell C
Cell A
Cell B
Cell C
Total number of transects
347
414
423
347
414
423
347
414
423
Shoreline length (km)
8.7
8.3
8.5
8.7
8.3
8.5
8.7
8.3
8.5
Mean shoreline change rate (m/ year)
0.3
0.9
-3.2
1.1
2.6
-1.9
1.2
2.3
-4.0
Minimum shoreline change rate (m/year)
-2.7
-3.7
-10.0
-6.0
5.1
-9.4
-1.7
-2.6
-8.7
Maximum shoreline change rate (m/year)
3.0
4.8
–
15.0
14.0
5.4
5.7
6.6
–
Total transects record erosion
121
243
98
105
81
44
347
357
423
Total transects record accretion
226
171
325
242
333
379
–
57
–
% of total transects record erosion
34.9
58.7
23.2
30.3
19.6
10.4
100
86.2
100
% of total transects record accretion
65.1
41.3
76.8
69.7
80.4
89.6
–
13.8
–
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Fig. 6 Long-term shoreline change rate with respect to time for 23 years (1991–2014). Rates are depicted with different color code based on classification scheme. a Shoreline change rate for cell A, b shoreline change rate for cell B, c shoreline change rate for cell C
Table 5 Details of long-term (1991–2014) shoreline change rate for three different cells Sl. no
Regions
Mean
Accretion (m/year) Max
Erosion (m/year) Max
1
Cell A
-0.9 (±1.1)
0.4 (±0.9)
-3.2 (±1.9)
2
Cell B
0.4 (±1.3)
5.0 (±1.9)
-2.1 (±1.1)
3
Cell C
-0.7 (±1.5)
0.2 (±1.9)
-2.2 (±2.6)
per the work carried out by Kankara et al. (2014). The overall results found that 55.9 % of Puducherry coast shows erosion, 34.9 % is under stable, and 9.2 % of coastline falls under accretion category.
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Fig. 7 Long-term (1991–2014) shoreline change rate map with different categories
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5 Conclusion The main objective of the work is to understand the shoreline change and impacts of artificial structures such as breakwater, groins and seawall on shoreline change using satellite imagery as a primary data source. The results found that the coast along Puducherry and Villupuram district of Tamil Nadu has been experiencing low to moderate erosion for the past 23 years. Short-term shoreline change results reveal that erosion was high in all three cells during 2006–2014 periods which may be due to the impacts of cyclone (Thane—December 28, 2011, and Nilam—October 31, 2012). Overall, it indicates that during 1991–2000 and 2000–2006 periods stable to low erosion was dominant with few pockets of accretion all along the coast. But, after 2006, erosion rate has increased. It suggests that the coast is not experiencing even change. Sand accumulation was noticed between the groins, and maximum net shoreline movement of 36 m was noticed between two groin fields at Thandrayankuppam region. The results found that high accretion of 5 (?1.9) m/year was noticed along the Veerampattinam region, whereas maximum erosion of 3.2 (?1.9) m/year was noticed at Thengaithittu region. Remaining coast was noticed with stable to low erosion. It is quite evident from the analysis that the northern side of the breakwater is almost stable to low eroding in condition and southern side shows accretion pattern. The coast is always under threat of natural events like cyclones which is a major controlling factor for short-term changes. Causes of shoreline change are not only due to natural processes, but also artificial structures will impact the coast. Even though the coast is protected with coastal structures, careful study has to be carried out to understand the impacts of these artificial structures on the coast. Continuous monitoring of the coast is necessary to protect the coast. The study demonstrates that combined use of satellite imagery after considering the uncertainties and statistical methods can be a reliable method for shoreline change analysis for any coastal conditions. Acknowledgments Authors wish to thank the Secretary, Ministry of Earth Sciences, Government of India and Project Director, ICMAM, for their keen interest and encouragement for this work. The authors are thankful to both anonymous reviewers for giving valuable suggestions which improve the quality of the manuscript.
References Airoldi L, Abbiati M, Beck MW, Hawkins SJ, Jonsson PR, Martin D, Moschella PS, Sundelof A, Thompson RC, Aberg P (2005) An ecological perspective on the deployment and design of low-crested and other hard coastal defence structures. Coast Eng 52:1073–1087 Albert P, Jorge G (1998) Coastal changes in the Ebro delta: natural and human factors. J Coast Conserv 4:17–26 Anders FJ, Byrnes MR (1991) Accuracy of shoreline change rates as determined from maps and aerial photographs. Shore Beach 59:17–26 Armenakis C, Leduc F, Cyr I, Savopol F, Cavayas F (2003) A comparative analysis of scanned maps and imagery for mapping applications. ISPRS J Photogramm Remote Sens 57:304–314 Boak E, Turner I (2005) Shoreline definition and detection: a review. J Coast Res 21:688–703 Braud DH, Feng W (1998) Semi-automated construction of the Luisiana coastline digital land/water boundary using landsat thematic mapper satellite imagery. Technical report 97-002, Department of Geography and Anthropology, Luisina State University. Luisiana Applied Oil Spill Research and Development Program, OSRAPD, 1998 Byrnes MR, McBride RA, Penland S, Hiland MW, Westphal KA (1991) Historical changes in shoreline position along the Mississippi Sound barrier islands. Coastal depositional systems in the Gulf of Mexico: quaternary framework and environmental issues. Gulf Coast SSEPM Foundation, pp 43–55
123
Nat Hazards Chalabi A, Mohd-Lokman H, Mohd-Suffian I, Karamali K, Karthigeyan V, Masita M (2006) Monitoring shoreline change using Ikonos image and aerial photographs: a case study of Kuala Terengganu area, Malaysia. In: Proceedings of the ISPRS Mid-term symposium proceeding. Enschede, Netherlands Charlier RH, Chaineux MCP, Morcos S (2005) Panorama of the history of coastal protection. J Coast Res 21(1):79–111 Douglas BC, Crowell M (2000) Long-term shoreline position prediction and error propagation. J Coast Res 16:145–152 Fletcher CH, Rooney JJB, Barbee M, Lim SC, Richmond BM (2003) Mapping shoreline change using digital ortho photogrammetry on Maui, Hawaii. J Coast Res Spec Issue 38:106–124 Frazier PS, Page KJ (2000) Water body detection and delineation with Landsat TM data. Photogramm Eng Remote Sens 66(12):147–167 Genz AS, Fletcher CH, Dunn RA, Frazer LN, Rooney JJ (2007) The predictive accuracy of shoreline change rate methods and alongshore beach variation on Maui, Hawaii. J Coast Res 23:87–105 Ghanavati E, Firouzabadi PZ, Jangi AA, Khosravi S (2008) Monitoring geomorphologic changes using Landsat TM and ETM ? data in the Hendijan River delta, Southwest Iran. Int J Remote Sens 29:945–959 Griggs GB (2005a) California’s retreating coastline: where do we go from here? In: Santa Barbara CA, Magoon OT, Converse H, Baird B, Miller-Henson M. (ed) California and the World Ocean, conference proceedings, October 2002. American Society of Civil Engineers, Reston, VA, pp 121–125 Griggs GB (2005b) The impacts of coastal armoring. Shore Beach 73(1):13–22 Hall MJ, Pilkey OH (1991) Effects of hard stabilization on dry beach widths for New Jersey. J Coast Res 7(3):771–785 Hapke CJ, Reid D (2007) National assessment of shoreline change, part 4: historical coastal cliff retreat along the California coast. US geological survey, Reston, VA, USA, Open-file report 2007-1133 Howarth PJ, Wickware GM (1981) Procedures for change detection using Landsat digital data. Int J Remote Sens 2:277–291 Hwang DJ (1981) Beach changes on Oahu as revealed by aerial photographs. Hawaii Office of State Planning, Coastal Zone Management Program, Honolulu Kankara RS, Selvan CS, Rajan B, Arockiaraj S (2014) An adaptive approach to monitor the Shoreline changes in ICZM framework: a case study of Chennai coast. Indian J Mar Sci 43(7):1271–1279 Kankara RS, Selvan CS, Markose VJ, Rajan B, Arockiaraj S (2015) Estimation of long and short term shoreline changes along Andhra Pradesh coast using remote sensing and GIS techniques. Proced Eng 116:855–862 Kuleli T, Guneroglu A, Karsli F, Dihkan M (2011) Automatic detection of shoreline change on coastal Ramsar Wetlands of Turkey. Ocean Eng 38:1141–1149 Liu H, Jezek KC (2004) Automated extraction of coastline from satellite imagery by integrating canny edge detection and locally adaptive thresholding Methods. Int J Remote Sens 25(5):937–958 Loos EA, Niemann KO (2002) Shoreline feature extraction from remotely sensed imagery. Geoscience and remote sensing symposium, 2002.IGARSS’02. IEEE Int 6:3417–3419 Maiti S, Bhattacharya AK (2009) Shoreline change analysis and its application to prediction: a remote sensing and statistics based approach. Mar Geol 257(1–4):11–23 Mas JF (1999) Monitoring land-cover changes: a comparison of change detection techniques. Int J Remote Sens 20:139–152 Miles JR, Russell PE, Huntley DA (2001) Field measurements of sediment dynamics in front of a seawall. J Coastal Res 17(1):195–206 Moore LJ, Griggs GB (2002) Long-term cliff retreat and erosion hotspots along the central shores of the Monterey Bay National Marine Sanctuary. Mar Geol 181(1–3):265–284 Moore LJ, Ruggiero P, List JH (2006) Comparing mean high water and high water shorelines: should proxydatum offsets be incorporated into shoreline change analysis? J Coast Res 22(4):894–905 Morton RA, Miller TL (2005) National Assessment of shoreline Change: part 2: historical shoreline change and associated land loss along the US South East Atlantic Coast: US Geological survey open-file report 2005-1401 Morton RA, Miller TL, Moore LJ (2004) National assessment of shoreline change: part 1, historical shoreline changes and associated coastal land loss along the US Gulf of Mexico, US Geological Survey open file report 2004-1043 Natesan U, Thulasiraman N, Deepthi K, Kathiravan K (2013) Shoreline change analysis of Vedaranyam coast, Tamil Nadu, India. Environ Monit Assess 185(6):5099–5109 Nayak SR (2002) Use of satellite data in coastal mapping. Indian Cartogr 5:147–157 Ozturk D, Beyazit I, Kilic F (2015) Spatiotemporal analysis of shoreline changes of the Kizilirmak Delta. J Coast Res. doi:10.2112/JCOASTRES-D-14-00159.1
123
Nat Hazards Ramesh R, Purvaja R, Senthil Vel A (2011) National assessment of shoreline change: Puducherry coast, NCSCM/MoEF report 2011-01, Chennai, India, p 57 Ryu JH, Won JS, Min KD (2002) Waterline extraction from Landsat TM data in a tidal flat: a case study in Gosmo Bay. Korea Remote Sens Environ 83:442–456 Selvan CS, Kankara RS, Rajan B (2014) Assessment of shoreline changes along Karnataka coast, India using GIS & Remote sensing techniques. Ind J Mar Sci 43(7):1293–1298 Shalowitz AL (1964). Shore and sea boundaries, U.S. Department of Commerce, National Oceanic and Atmospheric Administration, National Ocean Service Shresta HL (2005) Comparative evaluation of different spatial resolution data for timer volume estimation. ISPRS workshop proceeding, May 2005, Hannover Stockdon HF, Sallenger AH, List JH, Holman RA (2002) Estimation of shoreline position and change using airborne topographic Lidar data. J Coast Res 18(3):502–513 Thieler ER, Danforth WW (1994) Historical shoreline mapping (1). Improving techniques and reducing positioning errors. J Coast Res 10:549–563 Thieler ER, Himmelstoss EA, Zichichi JL, Ayhan E (2009) Digital shoreline analysis system (DSAS) version 4.0. An ArcGIS extension for calculating shoreline change. U.S. Geological Survey open-file report 2008-1278.79. http://pubs.usgs.gov/of/2008/1278/ Wal D, Pye K, Neal A (2002) Long-term morphological change in the Ribble Estuary, northwest England. Mar Geol 189(3–4):249–266 Wu W (2007) Coastline evolution monitoring and estimation-a case study in the region of Nouakchott, Mauritania. Int J Remote Sens 28(24):5461–5484 Xiodong Z, Ya GJ, Darren L (2006) A strategy of change detection based on remotely sensed imagery and GIS data. ISPRS mid-term symposium proceeding, May 2006, Enschede Yamano H, Shimazaki H, Matsunaga T, Ishoda A, McClennen C, Yokoki H, Fujita K, Osawa Y, Kayanne H (2006) Evaluation of various satellite sensors for waterline extraction in a coral reef environment: Majuro Atoll, Marshall Islands. Geomorphology 82(3–4):398–411 Zimmerman G, Bijker W (2004) Monitoring the Amazon with different spatial and temporal resolution. The XXISPRS congress proceedings, July 2004, Istanbul Zuzek PJ, Nairn RB, Thieme SJ (2002) Spatial and temporal considerations for calculating shoreline change rates in the Great Lakes basin. J Coast Res 38:125–146
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