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Aquatic Procedia4 (2015) 151 - 156
INTERNATIONAL CONFERENCE ON WATER RESOURCES, COASTAL AND OCEAN
ENGINEERING (ICWRCOE 2015)
Shoreline Transformation Study of Karnataka Coast: Geospatial
Approach
A Vittal Hegdea, B J Akshayab*
abDepartment of Applied Mechanics and Hydraulics, NITK, Surathkal, Mangalore - 575025, India.
Abstract
Coastal erosion is a natural phenomenon affecting a large number of coastal areas. The coastal zone is an area with immense geological, geomorphological and ecological interest. Monitoring and mitigation of shoreline erosion along populated coastal areas is an important task and remains a difficult goal to achieve. The coast of Karnataka state extends from Talapadi in south to Sadashivgad in north covering a distance of about 280 km. In this study, an attempt has been made to investigate the shoreline transformation along the Karnataka coast. Fair weather satellite images of LAND SAT from 1991 to 2014 with an interval of eight years were used to delineate the shoreline. A Linear Regression Rate-of-change (LRR) and an End Point Rate (EPR) statistic was carried out using Digital Shoreline Analysis System (DSAS) computer software of United States Geological Survey (USGS). Highest EPR of about 15.96 m (1991-2014) was noticed in the Ankola taluk, whereas highest LRR was about 15.5 m in Karwar, both indicating accretion. Highest erosion was noticed in Honnavar with an LRR of 19.59 m respectively and EPR of 19.95 m.
© 2015TheAuthors.PublishedbyElsevier 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
Keywords: Shoreline Change, Karnataka coast, DSAS, LANDSAT
1. Introduction
Coast is a most important socio economic region supporting welfare of human species. But Coastal areas always under frequent threat from various natural and man induced threats including coastal erosion. Coastal erosion is
* Corresponding author. Tel.: +91-9986870588. E-mail address:akshjana@gmail. com
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.2015.02.021
recognized as permanent loss of habitual land along the shoreline resulting in transformation of coast. Tidal processes, sea-level fluctuations, sediment transport and deposition, and flooding also contribute in shift of shorelines resulting in to newer coastal land forms and also in their disappearance. So, in order to formulate development activities both in terms of infrastructure and ecology it is necessary to substantiate rate of change in shoreline.
Rate of change in coastal landforms and shoreline position is also important in advancement of setback planning, hazard zoning, erosion/accretion perspectives, sediment budgeting, and conceptual/ predictive modeling of coastal morphodynamics (Sherman and Bauer 1993; Al Bakri 1996; Zuzek et al. 2003). Shoreline change rate values imply the overall processes which have affected the coast through time in the form of historical shoreline position against time data (Fenster et al. 1993). Some of techniques like End Point Rate (EPR), Average of Rates (AOR), Linear Regression Rate (LRR) and jack-knifing are being extensively used to estimate and forecast the rate of change in shoreline. Calculation of accurate shoreline change rates are frequently employed to summarize historical shoreline movements and to predict the future shoreline positions through different modeling procedures (Li et al. 2001; Appeaning Addo et al. 2008).
Remote sensing provides a platform for rapid delineation of the coastlines at relatively low cost. Also, repeated observations over the time do allow a detailed quantification of shoreline change. In addition to that, coastal morphology may be quantified by coupling remotely sensed data with information on the historic coastline position from archived sources. Remotely sensed data can also provide valuable preliminary estimates of change and is a unique tool for research and monitoring coastal areas and deltaic environments (Ciavola et al. 1999; Yang et al. 1999).
The accuracy of shoreline change rate estimation reflects actual changes and prediction of future changes depends on several factors, such as the accuracy in shoreline position data, variability of the shoreline movement, number of measured shoreline data points (Kumar et al. 2010b), and total time span of the shoreline data acquisition (Douglas et al. 1998), temporal and spatial bias in the estimation of shoreline rate-of-change statistics (Eliot and Clarke 1989), and the method used to calculate the rate (Dolan et al. 1991). In addition, the causes of variation in the rate of change include geomorphic features such as inlets, wave energy, engineering changes, etc. (Douglas and Crowell 2000).
The remote sensing and GIS applications have proved effective in the delineation of coastal configuration and coastal landforms, detection of shoreline positions, estimation of shoreline and landform changes, extraction of shallow water bathymetry (Jantunen and Raitala 1984; Singh 1989; White and El Asmar 1999; Lafon et al. 2002; Ryu et al. 2002; Siddiqui and Maajid 2004; Yamano et al. 2006; Kumar and Jayappa 2009; Maiti and Bhattacharya 2009; Kumar et al. 2010a). Shoreline change study for Karnataka using geospatial techniques has been carried out by Selvan et al. (2014) for for a period of about 33 years (1989 to 2006) using EPR and Weighted Liner regression (WLR).
In this study an attempt has been made to determine the rate of transformation of Karnataka coast using remote sensing as well Geographical Information System (GIS) as tools. Present study focuses on the time period from 1991 to 2014 and LRR for analysis.
2. Study Area
The study area (Fig. 1) is the coast of Karnataka state between Longitude 74°5'22.09" E and 74°51'53.75" E and Latitude 14°53'36.53" N and 12°45'02" N, 13° N and 12°45'27.17" N with length of about 280 km and consists of coastal stretch of Udupi, Dakshina Kannada and Uttara Kannada districts. The coast is bordered by Arabian Sea on the west and Western Ghats in the east. The continental shelf of Karnataka has an average width of 80 km. Sand bars are seen in most of the estuaries (Kumar et al., 2012). The coast is exposed to the seasonally reversing monsoon winds, average rain fall per year being 4209 mm. Of the total rainfall, 80% is received during June to August. The temperature ranges from 210 C in December to 360 C in April.
The tides in the study area are mixed semidiurnal, the range of which raises towards the north of the state (Kumar et al., 2011a). During the monsoon along the west coast of India, significant wave height up to 6 m has been reported (Kumar et al., 2006), and is normally less than 1.5 m during rest of the period.
Fig. 1. Study Area
3. Methodology
The transformation of the coast in the study area was analyzed for a period of 24 years (1991 to 2014), which is regarded as medium term analysis (Crowell et al. 1993; Anfuso and Martinez Del Pozo 2009). Ortho-rectified satellite images of study area from the sensors Landsat MSS, ETM+ and OLI-TIRS in the years, 1991, 1998, 2006, and 2014 were downloaded from USGS Earth Explorer web tool. Additional information about the specifications of satellite data used in the study is given in Table 1. The tidal range along the study region is about 1.5 m and the submergence of the land associated with high tide period is less than 5-6 m (Bhat and Subrahmanya, 2000). Hence no additional corrections are undertaken for the delineation of shoreline other than approximately common acquisition time and period of the year.
Table 1: Details of images acquired for shoreline delineation
Acquisition date and time Sensor Type
10-02-1991, 4:53AM TM Geotiff
17-03-1998, 4:59AM TM Geotiff
11-02-2006, 5:13 AM ETM+ Geotiff
13-03-2014, 5:23 AM OLI-TIRS Geotiff
The most suitable band for the demarcation of the land-water boundary has been identified as the near infrared band (Maiti and Bhattacharya 2009), is used in the study to extract the shoreline from satellite. A binary image of was formed using near infrared band of each image by histogram splicing technique and were classified unsupervised to form image with complete separation between land and water classes. These classified images were used to extract the shorelines in the form of vector layer using ERDAS Imagine 9.2 and ArcMap 9.3. The digitized shorelines in the vector format of the years 1991, 1998, 2006 and 2014 were used as input in Digital Shoreline Analysis System (DSAS) extension of ArcGIS, for calculating shoreline change rate. DSAS computes rate-of change statistics from multiple historic shoreline positions residing in a GIS (Thieler et al. 2005).
Using DSAS transects were cast perpendicular to the baseline at a 100 m interval all along the shore. Interactions of these transects with shoreline along the baseline is then used to calculate the rate-of-change statistics. Linear Regression Rate (LRR) method of shoreline change rate estimation was used in this study. LRR uses all the available
data to find a line, which has the overall minimum of the squared distance to the known shoreline and is an established method for computing long-term rates of shoreline change (Crowell and Leatherman, 1999).
In addition to the LRR, End point rate (EPR) for the shoreline was also computed for the shorelines making use
Fig. 2. Rate of Shoreline Change along different Talukas
of the same transects. EPR is calculated by dividing the distance of total shoreline movement by the time elapsed
between the earliest and latest measurements at each transect. Rate of change calculated in this study is particular to total Karnataka coast as a whole. Study does not involve specific analysis at different morphological features. Figure 2 repesents the LRR and EPR along each of talukas.
4. Results and Discussions
Overall an average of 1.1 m/yr accretion and about 1.0 m/yr erosion was noticed along the Karnataka coast. Table 2 provides the details of LRR and EPR for eight talukas with an uncertainty of +4.4 m, a default setting of DSAS. The average rate in the table is the average of LRR values for the number of transects cast for a taluk. Negative values in the table indicate the erosion where as positive values represent accretion. Highest accretion rate of 15.5 m per year was noticed in Karwar where as highest erosion rate was in Honnavar with a value of about 19.59 m. But average rate is highest in the Bhatkal taluk indicating that coast of Bhatkal is subjected most to erosion. Also, the coast of Mangalore is found subjected to accretion, average rate being 0.31 m. Coast of Kumta has least mean erosion of about 0.02 m per year. Highest standard deviation was noticed in Ankola and least in Kundapur.
The value presented here are generalised valves for each talukas. Since no specific analysis has been carried for morphological features, the change rate estimated may found be inaccurate in specific deltaic regions.
Table 2: Details of LRR and EPR for each talukas
Taluk Karwar Ankola Kumta Honnavar Bhatkal Kundapur Udupi Mangalore
No. of transects 452 188 390 254 299 434 527 357
LRR Mean rate -0.05 -0.2 -0.02 -0.6 -0.64 -0.49 -0.13 0.31
(m/yr) Standard deviation 1.68 3.03 1.89 1.97 1.45 0.76 1.33 2.35
Highest accretion 15.5 13.67 8.73 3.98 12.27 2.35 10.72 6.59
Highest erosion -6.65 -18.66 -9.85 -19.59 -5.53 -2.45 -5.38 -7.33
EPR Mean rate -0.11 -0.1 -0.05 -0.65 -0.74 -0.65 -0.38 0.1
(m/yr) Standard deviation 1.54 3.47 1.95 2.17 1.36 0.81 1.36 2.41
Highest accretion 15.23 15.96 9.67 5.8 14.12 2.36 10.77 7.85
Highest erosion -7.55 -18.26 -8.61 -19.95 -4.82 -2.87 -4.14 -8.45
It is evident from the table that the though mean rate of change is highest in Bhatkal, the deviation from the mean is highest in Ankola, wherein we also notice highest erosion valve .In this regard the coast of Ankola is subjected to high erosion.
5. Conclusion
The study focuses on the medium term change analysis of entire Karnataka coast using combination of remote sensing and GIS techniques for the detection of coastline movement that changes over time in response to economic, social, and environmental forces, since know how about the changes can to facilitate suitable planning, management, and regulation of coastal zones. Overall average accretion was 1.1 m/yr and erosion was 1.0 m/yr along the Karnataka coast. Highest erosion is observed for Ankola and highest accretion was noticed in Mangalore. The present study suggests that multi-dated satellite data along with statistical techniques can be effectively used for prediction of shoreline changes.
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