Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques Academic research paper on "Earth and related environmental sciences"

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Research Paper

Mapping of coastal south west coast of GIS techniques

S. Kaliraj^*, N. Chandrasekarb, K.K. Ramachandrana

a Central Geomatics Laboratory (CGL), ESSO - National Centre for Earth Science Studies (NCESS), Akkulam, Thiruvananthapuram 695011, Kerala State, India b Centre for GeoTechnology, Manonmaniam Sundaranar University, Tirunelveli 627012, Tamil Nadu, India

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landforms and volumetric change analysis in the Kanyakumari, South India using remote sensing and

ARTICLE INFO

ABSTRACT

Article history: Received 29 March 2016 Revised 26 October 2016 Accepted 26 December 2016 Available online xxxx

Keywords:

Geomorphic Change Detection DEM of Differencing GIS and remote sensing South-west coast of Kanyakumari

The coastal landforms along the south west coast of Kanyakumari have undergone remarkable change in terms of shape and disposition due to both natural and anthropogenic interference. An attempt is made here to map the coastal landforms along the coast using remote sensing and GIS techniques. Spatial data sources, such as, topographical map published by Survey of India, Landsat ETM+ (30 m) image, IKONOS image (0.82 m), SRTM and ASTER DEM datasets have been comprehensively analyzed for extracting coastal landforms. Change detection methods, such as, (i) topographical change detection, (ii) cross-shore profile analysis, (iii) Geomorphic Change Detection (GCD) using DEM of Difference (DoD) were adopted for assessment of volumetric changes of coastal landforms for the period between 2000 and 2011. The GCD analysis uses ASTER and SRTM DEM datasets by resampling them into common scale (pixel size) using pixel-by-pixel based Wavelet Transform and Pan-Sharpening techniques in ERDAS Imagine software. Volumetric changes of coastal landforms were validated with data derived from GPS-based field survey. Coastal landform units were mapped based on process of their evolution such as beach landforms including sandy beach, cusp, berm, scarp, beach terrace, upland, rockyshore, cliffs, wave-cut notches and wave-cut platforms; and the fluvial landforms. Comprising of alluvial plain, flood plains, and other shallow marshes in estuaries. The topographical change analysis reveals that the beach landforms have reduced their elevation ranging from 1 to 3 m probably due to sediment removal or flattening. Analysis of cross-shore profiles for twelve locations indicate varying degrees of loss or gain of coastal landforms. For example, the K3-K3' profile across the Kovalam coast has shown significant erosion (-0.26 to -0.76 m) of the sandy beaches resulting in the formation of beach cusps and beach scarps within a distance of 300 m from the shoreline. The volumetric change of sediment load estimated based on DoD model depict a loss of 241.69 m3/km2 for 62.82 km2 of the area and land gain of 6.96 m3/km2 for 202.80 km2 of the area during 2000-2011. However, an area of 26.38 km2 unchanged by maintaining equilibrium in sediment budgeting along the coastal stretch. The study apart from providing insight into the decadal change of coastal settings also supplements a database on the vulnerability of the coast, which would help the coastal managers in future.

© 2016 National Authority for Remote Sensing and Space Sciences. Production and hosting 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/).

1. Introduction

Geomorphic landforms of a coast is an expression of the characteristics of prevailing coastal processes over long-term scale. The

Peer review under responsibility of National Authority for Remote Sensing and Space Sciences.

* Corresponding author. E-mail addresses: s.kaliraj@ncess.gov.in (S. Kaliraj), profncsekar@gmail.com (N. Chandrasekar), raman.kk@ncess.gov.in (K.K. Ramachandran).

landforms of the coastal transition zone are sensitive to erosional and depositional processes due to actions of waves, littoral current, wind, sediment transport and certain anthropogenic activities (Carter, 1988; Carter and Woodroffe, 1994; Bird, 2000; Bauer, 2004; Pavlopoulos et al., 2009; Chandrasekar et al., 2012). Coastal landform configurations are dependent on the pre-existing coastal settings, geological structures and a variety of coastal processes. Therefore, mapping of landforms provides Insight into such morpho-hydrodynamic milieu. (Davies, 1972; Nordstrom, 2000; Woodroffe, 2002; Amos, 1995; Chandrasekar and Kaliraj, 2013).

http://dx.doi.org/10.1016/j.ejrs.2016.12.006

1110-9823/® 2016 National Authority for Remote Sensing and Space Sciences. Production and hosting 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/).

Along the Indian coast too, the tectonic and structural formations and continental shelves primarily responsible for shaping the land-forms which are acted upon subsequently by the prevailing hydro-dynamic settings characteristics (Nayak and Sahai, 1985; Chandrasekar and Rajamanickam, 1995; Sajeev et al., 1997; Sanil Kumar et al., 2006; Magesh et al., 2014).

Most of the landforms along southern coast of Tamil Nadu particularly on the south west coast of Kanyakumari district have undergone morphological deformation due to the effect of Tsu-namic occurred on December 26, 2004 (Chandrasekar et al., 2012). Artificial structures like groins, revetments, seawall and jetties those came up in the recent years have modified the coastal processes causing severe erosion on down-drift side in the coastal area (Kaliraj et al., 2013). Assessment of coastal landform changes can help in the analysis of coastal vulnerability (Nicholls et al., 2007; Kaliraj and Chandrasekar, 2012; James et al., 2012; Joevivek et al., 2013). Conventionally, mapping of coastal land-forms is performed using pre-existing maps, field observation and other collateral data sources compiled for different times and different scales which can lead to inaccurate information due to dynamic nature of coastal landforms (Desai et al., 1991; Embabi and Moawad, 2014). The mapping of coastal landforms using multi-temporal satellite images can provide robust information on shape, distribution, and morphological status during past and present (Butler and Walsh, 1998; Bocco et al., 2001; Smith et al., 2006; Bubenzer and Bolten, 2008; Abermann et al., 2010). Recent technological advancement in remote sensing and surveying techniques provides adequate information on spatial distribution of coastal landforms in GIS environment enabling us to prepare coastal geomorphologic map with higher granularity on a larger scalability (Chockalingam, 1993; Chandrasekar et al., 2000; Slaymaker, 2001; Nayak, 2002; Jayappa et al., 2006; Smith and Pain, 2009; Kaliraj and Chandrasekar, 2012). Coastal landforms of an area can be extracted using the Landsat ETM+ image with or without slope and topographical measurements onto a GIS based complementary platform (Mujabar and Chandrasekar, 2011). Moreover, recent advances in remote sensing and GIS play an important role on the development of numerical modelling of surface processes for quantitative assessment of morphological changes of landforms (Blanchard et al., 2010). GIS technique is an effective platform for mapping thematic features with corresponding attributes. Geo-computational algorithms facilitates automatic extraction of geomorphic landforms from the combination of datasets such as, satellite image, DEM and topographical map using numerical modelling, pixel-based classification and cellular automated techniques in GIS environment (Dawson and Smithers, 2010). High spatial resolution images of IKONOS, Quick Bird, and GeoEye incorporated with DEMs are progressively used for assessment of volumetric changes of coastal landforms (Bubenzer and Bolten, 2009; James et al., 2012). The mapping of geomorphic land-forms using remotely sensed images requires knowledge of basic interpretation elements such as tone, texture, shape, size and pattern, for unambiguous delineation of landforms. For example, the beaches and associated landforms have been identified based on linear shape and fine to medium coarse pattern (Rao, 2002). Land-forms are interpreted using multispectral images based on interpretation element keys to extract the information relatively accurate up to the post-field verification process (Maksud Kamal and Saburoh Midorikawa, 2004). According to Tomar and Singh, 2012, the coastal landforms are classified on the basis of topographical variations resulting from differential erosion and accretion processes, for example, the geomorphic units of alluvial plain, pediplain, structural hills and residual hills are mapped using DEM incorporated multispectral IRS-ID LISS-III images using visual interpretation technique along with field check. While digital analysis of landform extraction is faster, appropriate based on spectral

signature, and pattern recognition of image properties using mathematical that would be able to detect, cluster and classify the features to represent the real world. Previous investigations have underlined advantages of using DEM and Lidar datasets for geo-morphic detection and volumetric change of sediment load along the coastal area (Shaikh et al., 1989; Anbarasu, 1994; Lillysand and Kiefer, 2000; Wright et al., 2006; Waldhoff et al., 2008; Smith and Pain, 2009; Blanchard et al., 2010). Assessment of topographical changes using DEMs provide insight on changes of sediment load due to erosion or deposition processes signifying past and present morphological structural response to coastal processes over time (Lane et al., 2003; Zhang et al., 2005; Wheaton et al., 2010; Schwendel et al., 2012). The DEM datasets acquired on two different times can preferably be used to measure vertical difference in sediment loads of the coastal landforms based on topological and morphometric rules (James et al., 2012). The DEM datasets such as SRTM and ASTER are being used for Geomorphic Change Detection analysis because of its mission specified accuracy, i.e. high vertical accuracies over terrain surface and bare soils and medium accuracies in terms of spatial resolutions (Cuartero et al., 2004). The topographical changes of the sediment load in the coastal landforms has been estimated from the temporal DEMs using the extracted cross-shore profile analysis that provide adequate information on geomorphic change of the various landforms in vertical scale (Gyasi-Agyei et al., 1995; Zandbergen, 2008; Dawson and Smithers, 2010; Hicks, 2012). The GIS-based Geomor-phic Change Detection (GCD) analysis provides volumetric change of coastal landforms from the DEMs acquired for different periods of interval (Lee, 1991; Wheaton et al., 2007; Siart et al., 2009; James et al., 2012). The GCD analysis is concerned with DEM of Difference (DoD) algorithm to estimate quantitative changes of land-forms, in a diverse set of environments, and at ranges of spatial scales and temporal frequencies (Wheaton et al., 2010; Hicks, 2012). The volumetric change of geomorphic features is estimated using two DEM data sets acquired for two different periods can result in estimating of land loss and land gain for a vast area appropriately validated through field surveys and measurements (Dawson and Smithers, 2010). Maksud Kamal and Saburoh Midor-ikawa (2004) have obtained the area and volume of geomorphic features that closely matched with field measurements. Stereo-pair of images are able to provide three-dimensional representations of the features through accurate derivation of digital elevation models (DEMs). The topographical changes of landforms estimated from these datasets have positive correlation with field measurements and hence useful for monitoring how landforms change over time due to subsidence or uplift of the coastal surface (Cuartero et al., 2004; Mith and Clark, 2005). Knight et al., 2011 have incorporated images and DEMs for rapid assessment of landform changes for large areas and have demonstrated that the remote sensing provides complete requirements if synergized with ground validation and measurements which can even be extended to geomorphological studies across all spatial scale. Mapping of landforms through field observation allows the most direct way to capture the landform characteristics and enable as a basis for terrain assessment and geomorphological analysis. The accuracy of field mapping is subjective and affected by the skills and experience of one who maps. The volumetric change of sediment load estimated using DEMs are capable of generating superior results on land loss and land gain that are relatively closer to the field-based measurements apart from providing spatial ensemble of coastal landforms with exceptional details (Smith and Pain, 2006). Many researchers have confirmed that the DEM derived results along with field data can produce relatively high accuracy in geomorphic change measurement for coastal area (Aniello, 2003; Nikolakopoulos et al., 2006; Zandbergen, 2008; Potts et al., 2008; Toutin, 2008; Blanchard et al., 2010). The primary aim of

the present study is to map coastal landforms and assess the volumetric change of sediment load over a decade along the south-west coast of Kanyakumari using integrated remote sensing and GIS techniques. The present study therefore used different change detection techniques such as (i) topographical change analysis, (ii) cross-shoreprofile change analysis, (iii) DEM of Difference (DoD) algorithm based Geomorphic Change Detection (GCD) analysis for estimating the volumetric changes (land loss or land gain) along the coastal stretch using the ArcGIS platform. This studya-part from assimilating decadal changes in landforms would also delineate various influencing factors that would form primary information source for coastal vulnerability management and would help in the preparation of developmental plans against any possible natural disasters that likely to affect the coastal region.

2. Study area

The study area is located along the south-west coast of Kanyakumari district, Tamil Nadu, India. The geographical coordinates extend from 77°9'49.20"E to 77°34'15.00"E longitude and 8°6'32.60"N to 8°14'15.30"N latitude. The coastal stretch is extended for a length of 58 km from Kanyakumari to Thengapatti-nam in southeast to northwest direction (Fig. 1). There are three major drainage networks such as Pazhayar, Valliyar and Thamira-barani along with their tributaries flowing in southerly direction from the Western Ghats. These are primary sources contributing discharge to maintain coastal landforms debouching their sedi-

ment load during both southwest and northeast monsoons (Chandrasekar and Mujabar, 2010). The coastal area is characterized by various landforms such as sandy beaches, coastal plains, beach terraces, sand dunes, rocky shore, estuaries and other fluvio-marine landforms (Kaliraj et al., 2013). The coastal upland in the Kanyakumari, Muttam and Colachel area are mainly associated with rocky-shores and offshore outcrops acting as natural barrier to wave actions and storm surges. Sandy beaches are formed on the Sanguthurai, Chothavilai, Pillaithoppu, Ganapathipuram, Rajakkamangalam, Colachel and Simonkudiyiruppu coastal stretches due to swashing of large amount of sediments resulting from waves (Hentry et al., 2010). However, the major parts of the coastal areas namely Kovalam, Pallam, Manavalakurichi, Mandai-kadu and Inayamputhenthurai are noticed with severe erosional activities due to backwashing of sediments by destructive wave actions. Onshore margin of the study area comprises Late Quaternary deposits composed of complex settings of granite-biotite-illuminate underlain sandstone interlined with sand, silt and clay partings and overlaid by sandy materials (Loveson, 1993). The coastal surface is generally sloping towards sea interspersed with settlements, coconut plantation, shallow water bodies like backwater and creeks (Jena et al., 2001; Magesh et al., 2014). Along the near shore area, the sand dunes are roughly parallel to shore though discontinuously distributed along the coast. The coastline along the Kanyakumari coast have experienced erosion due to high-energy wave action. The Teri sand dunes (reddish brown) are located along the coastal stretch from Kovalam to Manakudi with thickness increasing from 1.5 m in coastal headlands to a maximum of 7.0 m in the interior terrestrial area. The crystalline

77°10'0"E 77°20'0"E

Fig. 1. Geographical location of the study area.

rock types such as quaternary rocks, clay sand and sandy materials are predominantly found along the coast. The rocky boulders and sea cliffs are found in the Muttam, Kanyakumari and Cape Comorin coasts and sandstones are found along the study area are made up of igneous rock, and silt clay materials (Loveson, 1993). The alluvial sediments admixture with clay are found deposited at the mouth of the Thamirabarani estuary in Thengapattinam and Pazhayar estuary in Manakudi. Sandstone with clay intercalation structures is present along the eastern part of Thengapattinam coast. The study area is prevailing with a sub-tropical climate with the normal annual rainfall varying from 826 mm to 1456 mm and the annual mean minimum and maximum temperatures are 23.78 °C-33.95 °C respectively. The landforms along the coast frequently alter in morphological distribution due to both natural and anthropogenic factors and hence the present study is performed to understand coastal landforms and their changes.

2.1. Coastal and oceanographic characteristics of the study area

Evolution of coastal landforms in various locations along the study area is mainly subject to ocean and coastal processes. Wave height is one of the main factors considered in setting up of hazard management system along the coastal region. Mean significant wave height along the coast is estimated around 1.4 m rendering higher energy along the coast causing erosion or subsidence of beaches (Hentry et al., 2012). The low-energy waves (with wave height <1 m) leads accretional processes due to entrainment of sediments carried by the swash (Murty and Varadachari, 1980; Jena et al., 2001; Mishra et al., 2011). Waves and currents prevailing in an area influences erosion and accretion of the beaches and headland features. It has been reported that along this coastal stretch, the mean annual wave energy ranges from 0.5 to 8.5 kJ/ km2. Nevertheless, the Kanyakumari coast is reportedly experienced with very high wave energy (6.5-8.5 kJ/km2) owing to the peculiar nature of the coastal configuration. Due to the unique geographical location of the coast at the southernmost tip of the Indian sub-continent, the waves and currents waves approaching from various directions while breaking at varying angle with the coast generate longshore currents in different directions and intensity. The coastal zones having lower energy (0.5-2.5 kJ/km2 and 1.53.5 kJ/km2) wave setup result in sandy beaches due to deposition of littoral sediments, consequent to which young beaches and other depositional landforms are formed. The southwest coast experiences three types of littoral current systems based on the wave direction and wind blow namely southwest monsoon current (June-September), north-east monsoon current (October-December) and summer current (March-May). The movement of seasonal currents varies in different parts causing shoreline changes due to deposition or scouring away of sediments by seasonal movement of longshore currents.

The average longshore current velocity along the coast is measured as 0.14 m/s, whereas the fastest flow of its velocity observed is 0.32 and 0.28 m/s in the Kanyakumari and Kovalam coasts during both SW and NE monsoons. This coastal stretch faces erosion due to littoral sediment movement towards north during the NE monsoon, while it is reversed towards southern direction during SW monsoon. The coast between Rajakkamangalam and Manakudi has been notified as low current velocity zones (0.14-0.22 m/s) and these areas have experienced accretion due to the presence of sea cliffs, eroded Teri sand dunes and very narrow sandy pocket beaches. The coastal configuration from Manavalakurichi to Then-gapattinam is towards southeast and north-west, where the summer and SW monsoon high velocity (0.28-0.30 m/s) currents flowing to south act on the headlands. This results in severe erosion along the coast scouring and removal of sediments by backwash and longshore current permanent and episodic change in

the low-lying areas of the coast. The tidal fluctuation is from 4.0 to 6.0 m along the estuaries causing significant changes in land-forms. Whereas, areas experiencing lower tidal range from 1.0 to 2.0 m show tendency of releasing suspended sediments to the coast.

The aeolian (wind) process is controlling formations and shape of the beaches and backshore landforms such as sand dune, barrier dune (foredune) depending on the seasonal wind velocity and directions. Beaches along the open coast are eroded due to abrasion or scouring through sandblasting of wind-borne action by trapping sediments on the backshore. Sand dunes in the area consists of dry sands that got piled or heaped-up by continuous eolian action over a long period of time. For example, the parabolic dune complexes have been evolved to the present elevation of 2-4 m due to accumulation of wind transported sediments from blowouts or open beaches along the various parts of the study area. The fore-dune complexes along the coastal stretches have also been formed due to sand blowing out from the incipient beaches and steadily growing to the seaward side. The recent development of coastal structures like groins, seawalls, revetments and jetties are intervening with the natural rhythm of the coastal process causing severe erosion on the down-drift side complementing accretion on the up-drift side. It is observed that the shape, size and distribution of landforms are frequently influenced by coastal and oceanographic factors and along certain stretches due to the influence of anthropogenic activities.

3. Materials and methods

Mapping of coastal landforms is primary to understanding of evolution of any coastal area. The south-west coast of Tamil Nadu comprises of various landforms that are experiencing morphody-namic changes in shape, size and distribution due to various coastal hydrodynamic factors including human interferences (Hentry et al., 2012). The depositional landforms like beaches and foredunes are maintaining stable morphological structures along coastal stretches prevailing with constructive wave action coastal stretches prevailing with constructive wave action (Kaliraj and Chandrasekar, 2012). Quantifying volumetric change of sediment load in a particular area provides insight into the ero-sional or depositional processes taking place over a period of time (Schwendel et al., 2012). The coastal landforms and vegetation cover along the coast has been significantly altered in terms of morphological settings has been significantly altered in terms of morphological settings after the Tsunami occurred on 26th, December 2004 (Chandrasekar et al., 2012). However, the coastal landforms have gradually disappeared in the down-drift side of the coastal structures such as groins, revetments, seawall and jetties due to interference to the littoral sediment flow along the coast (Kaliraj et al., 2013).

In some parts of the open coast, the high-energy waves and seasonal movement of littoral currents directly influence the sediment transport causing frequent changes of landforms and their morphological characteristics (Chandrasekar et al., 1996; Kaliraj et al., 2014). The morphodynamics of beach landforms based on beach profile analysis reveals that severe erosion in the Kovalam, Murungavilai, Mandaikadu, and Inayamputhenthurai coastal zones due to destructive wave actions and seasonal movement of littoral currents. Meanwhile, the constructive waves lead to processes of on the beaches of Sanguthurai, Chothavilai, and Midalam and up-drift side of Muttam coast (Cherian et al., 2012). The landforms of the coast are highly sensitive to marine and terrestrial forces to maintaining equilibrium and stability to the morphological structures, and hence analysis of the changes in coastal landforms using Remote sensing and GIS techniques indispensable inputs for

coastal zone management. Recent developments of geospatial technologies enable synergizing and analyzing spatial data sources such as maps, images and DEMs for extracting coastal landforms of an area in higher scalability (Siart et al., 2009).

The volumetric changes of sediment load can be estimated using DEMs acquired on temporal frequencies to derive vertical difference in elevation at a point of observation to assimilate changes with time (Farrell et al., 2012). The geomorphic change assessment using DEM of Difference (DoD) provide insight into volumetric change of landforms based on the numerical morpho-dynamics models (Wheaton et al., 2010). Earlier studies have demonstrated that the remote sensing and GIS approaches are

effective tools for analysis especially by incorporating time-line data to inquire into morphological change of the coastal landforms.

3.1. Mapping of coastal landforms

In the present study, the integrated remote sensing and GIS technique is employed for extracting the coastal geomorphological landforms at high resolution. The various types of spatial data sources such as topographical map (scale 1:25,000) published by Survey of India in the year 2000, Landsat ETM+ image (30 m) acquired for 2011, IKONOS multi-spectral high resolution image (3.2 m), ASTER and SRTM DEM datasets are used for mapping the

Fig. 2. Methodology flow-chart of coastal landform mapping and volumetric change analysis.

coastal landforms through a series of systematic geo-processing operations with ArcGIS 10.2 software. The Garmin ETREX 30 GPS was used for ground truth verification, pre and post field verification, training sets selection and checking the landform boundaries. The location point accuracy of Garmin ETREX 30 GPS has limitations in field survey using, which horizontal positioning deviations can be up to 4 m in open coastal region with proper sky view and in the worst scenario up to 10 m in areas with canopy cover, and considerable built-up where satellite visibility is much affected. Standard procedures were adopted for geomorphological mapping from multiple spatial data sources employing visual or digital interpretation/classification techniques (Zandbergen, 2008).

Fig. 2 shows the geo-processing functional flow of coastal landform mapping and volumetric change analysis. Wherein, the multiple data sets were georeferenced to a common datum (WGS84) with projection onto UTM and systematic operations using geopro-cessing tools were executed for pre-processing and feature extraction in a GIS environment. The multispectral images are spectrally enhanced using majority filtering and mean filtering algorithm to obtain smooth spectral pattern of features in the image (Wood, 1996; Wright et al., 2006; Mujabar and Chandrasekar, 2011). Eventually, the DEM is processed using hydrological analysis tool in ArcGIS to remove sink pixels. Sub-pixel classification has been executed using the hybrid model composed of parallelepiped and maximum likelihood algorithms based on the concept of best possibilities for operator controlled pixel allocation method (Kaliraj and Chandrasekar, 2012). Extracting the coastal landforms like beach berms, beach cusps, beach ridge and beach terrace from the satellite image with medium resolution (30 m) is a complex

process owing to their shape and dimension on the earth surface. In order to overcome this complexity, the sub-pixel classification analysis is executed on 5 x 5 km grids (for nearshore area) for integrated images of high and medium resolution images. This analysis extracts one feature (i.e. beach berms) at a time and the repeated analysis produces small dimensional features. Hence, the multiple layers of different landforms are overlaid together and graphically represented them in 1: 10,000 scale. Encouragingly, the result of classified image shows an overall accuracy is 89.61% with a kappa coefficient statistics of 0.89 for 100 control points indicating the acceptable accuracy of the classified image. Based on the individual feature classes, the producer's accuracy is recorded as 74-100% and the user's accuracy is estimated as 60-100%, and it is mostly exceeded 90% in the final output. Additionally, the immanent pixels within a feature class are eliminated through majority-filtering technique (kernel window size 5 x 5) to achieve a stronger generalization for mapping the thematic classes in vector format (Potts et al., 2008). Furthermore, the resultant map is classified into various landforms by comparing the morphometric rule using visual image interpretation techniques. The SOI topographical map has been used to derive the basic geomorphic information and tectonic elements of the coastal area such as elevation (contour line), spot heights, benchmarks, high water line (HWL), and other natural and manmade landmarks and landscapes. Landsat ETM+ and IKO-NOS images were used as primary data source for mapping the coastal landforms such as coastal plain, beach landforms, flood plain, swale, water bodies, swamp or marsh lands, tidal plats, backwater creeks, and estuaries (Magesh et al., 2014). The combined datasets of DEM and image provide a vital clue for mapping the

77°lb'0"E 77°2b'0"E 77°зЬ'0"Е

Fig. 3. Coastal landforms map of the study area in 1: 10,000 scale extracted using Landsat ETM+ image, IKONOS image, ASTER DEM and Topographical map.

coastal landforms (Blanchard et al., 2010). The ASTER DEM (30 m) data has been incorporated with Landsat ETM+ image and IKONOS image for detecting the various geomorphologic units such as sand dunes, barrier dunes, coastal uplands, beach slope, cliffs, rocky shore, offshore rocky outcrops, mining area, sand spits and man-made structures like groins, seawalls, revetments, jetties. Finally, the combined datasets of ASTER DEM and topographical map have been to demarcate the topographical (relief) characteristics land-forms in the study area.

3.2. DEM of Differencing of volumetric change analysis

The GIS-based Geomorphic Change Detection (GCD) analysis provides volumetric change of sediment load in the landforms using DEM datasets acquired over periods of interval (Lee, 1991; Wheaton et al., 2010; James et al., 2012). The GCD analysis is concerned with DEM of Difference (DoD) algorithm for estimate the quantitative changes of landforms of the earth surface, in a diverse set of environments, and at a range of spatial scales and temporal frequencies (Hicks, 2012). In the present study, Geomorphic Change Detection of coastal landforms is estimated from SRTM and ASTER DEM datasets acquired for the years 2000 and 2011 respectively using DEM of Difference (DoD) method. The DoD is a mathematical algorithm for quantifying the volumetric change of the landforms using DEM datasets acquired on two different periods (Wheaton et al., 2010). The DEM datasets register the absolute ground elevation model of an area in the form of a raster data in which each grid cell (pixels) contains an elevation (height) values. Therefore, the DEM datasets are used worldwide for terrain visualization, hydrological modelling, orthorectification, and geomorphic change assessment (Fabris and Pesci, 2005; Nikolakopoulos et al., 2006; Siart et al., 2009; Blanchard et al., 2010; Marghany et al., 2010). Conversely, the DEM datasets such as SRTM (90 m) and ASTER (30 m) are constrained due to varying spatial and temporal resolutions whilst they are used directly for Geomorphic Change Detection analysis and they have limited experiences within the user community (James et al., 2012). Alternatively, to overcome this, DEM datasets were analyzed based on the most popular Data Fusion techniques such as pixel-by-pixel based Wavelet Transform and Pan-Sharpening techniques using ERDAS Imagine software to synergize DEM onto a common scale, i.e. the output DEMs are resampled to 10 x 10 m pixel (Wechsler, 2000; Wheaton, 2008; Blanchard et al., 2010; Magesh et al., 2014). The coastal landform features such as beach berms, beach terrace, beach cusps, beach ridges were demarcated from IKONOS images (3.42 m) due to their smaller spatial dimensions. The layer consisting of the coastal geo-morphological features are overlaid on the DEM for estimating vol-

umetric changes over time. The resultant of DEMs have preserved their pixel attributes (elevation), as it is an original image and the RMSE (root mean square error) values of DEMs have noted as 3.46 m and 6.05 m respectively. The DEM derived through the data fusion technique is found superior (with RMSE of 3.46 m) compared to the SRTM DEM Global scale (RMSE 16 m) or Local scale (6 m). Similarly, the RMSE of the reworked ASTER DEM is 6.05 compared to 25 m for Local scale (United States Geological Survey, 1997; Milan et al., 2011). Therefore, RMSE of both DEMs are reasonably good for Geomorphic Change Detection if not for the expected precision, a clear differentiation is possible which is indicative of geomorphic changes. The GCD analysis incorporates DoD algorithm processed using ArcGIS AddIn Tool namely ''Geomorphic Change Detection Tool" (GCD v6.0) developed by Wheaton et al. (2010) specifically to estimate the volumetric changes of coastal landforms during the year of 2000 and 2011. The DoD algorithm computes the differences by subtracting pixel values of two DEMs using the equation dE = Z2 - Zj, where dE is a output DEM showing changes in volumetric scale (m3); Z-i is a DEM of earlier period (i.e. SRTM DEM acquired on February 2000, and Z2 is a DEM of later period (i.e. ASTER DEM acquired on October 2011). Thus, the output DEM provides volumetric change of sediment load (dE) on various landforms due to erosion and deposition with time. In which, the negative and positive values represent the land lost (erosion) and land gain (deposition). However, the product of DoD may propagate and amplify certain uncertainties and therefore, it is essential to identify and minimize errors (Wood, 1996; Blanchard et al., 2010). These complexities are eliminated from the output DEM the equations ZActual = ZDEM ± dZ, where ZActual is the true elevation value obtained from the topographical map (scale 1:25,000) published by Survey of India in 2000 and dZ is the vertical error component of input datasets. Thus, the result of error analysis ensures the quality of output DoD map on the relative accuracy of volumetric change assessment (Siart et al., 2009; Marghany et al., 2010; Wheaton et al., 2010; James et al., 2012). Finally, the output map is converted into vector layer for preparation of geo-database of landform features with attributes including name, areal extent, and volumetric change rate using ESRI-ArcGIS 10.2 software.

4. Results and discussion

The various types of coastal landforms are extracted from a combination of datasets using remote sensing and GIS techniques for the study area. The landforms have undergone remarkable changes due to marine and terrestrial factors, which are responsible for the formation of erosional and depositional landforms

Table 1

Classification of the coastal landforms in the south-west coast of Kanyakumari.

Sl. no.

Origin process of landforms

Coastal landforms

Factors influencing landforms formation

Erosional features

Depositional features

Marine

Fluvio-Marine

Fluvial

Aeolian

Beach scarps, Rocky shore cliffs, Wave-cut platforms, Wave-cut notches, Cliff terraces, and Headlands Estuaries, Shoal and Swale

Buried Pediment, River terraces, Pediplain, Bajada, and Structural hill Teri sand (reddish) along the shore cliffs

Sandy beach, Sand bar, Sand spits, Beach ridge, Beach berm, Tidal flat, Mud flat, and Coastal plains Deltaic plain, Sand bar (at estuary)

Alluvial plain, Deltaic plain, Flood plain, Leeves

Sand dune (older) and Barrier dune

Erosion - is due to backwashing of sediments by waves, currents, placer mining and man-made structures

Accretion - is due to swashing of sediments by low wave energy and sediment deposition by longshore drift

Modification of landforms - due to tidal regime, divergent wave action

Formation of landforms - due to accumulation of river discharged

sediments by tidal and wave divergent action

Erosion of landform - due to runoff and overland flow

Deposition of landform - due to discharge of sediments by the river and

channels

Erosion - due to deflation of dune sand by wind from land and sea and high energy wave actions

Accretion - due to accumulation of sediments by sand blown towards inland from the beaches in front of them by onshore winds

(Saravanan and Chandrasekar, 2010). The volumetric change of landforms is quantified using DEM datasets in GIS software environment, and the results are presented detailing the location, distribution and volumetric change of sediment load for various landforms along the coastal area.

4.1. Mapping of coastal geomorphic features and its spatial distribution

The spatial distribution of the coastal landforms along the study area is depicted in the Fig. 3. Table 1 numerates the classification of coastal landforms of the study area based on their evaluation processes. Evolution of marine landforms is the result of cyclic coastal processes of ocean waves, winds, tides, and currents resulting in the formation of erosional and depositional landforms along the coast (Samsuddin et al., 1991). The coastal plain is a flat or gently sloping surface distributed along the backshore region. It comprises of sand, silt and clay particles manifested as geomorpholog-ical entities, such as, sand dunes, plantation, shrub vegetation, saltwater ponds, and backwater creeks resulting from the deposition of sediments over long periods. The total area of coastal plain covers 51.91 km2 resulting from the 17.78% of the total study area (Table 2). Out of these, the younger formation (28.59 km2) is distributed in the northeastern part of Kanyakumari coast and Manakudi-Pillaithoppu coastal tracks, while the older formation (23.32 km2) is in the northern part of Muttam coast and middle-western part of Chinnavilai, Mandaikadu, Keezhkulam and Mida-lam coastal areas. Beach landforms such as sandy beaches, cusps, ridges, berms, terraces, scarps, sandbars and sand spits are distributed along the nearshore region.

This is result of swashing or backwashing of littoral sediments due to action of waves, wind and littoral currents (Ahmad, 1972; Sanil Kumar et al., 2006; Kaliraj et al., 2014). The total expanse of this category is estimated around 6.38 km2 which is equal to 2.19% of the total area. Among them, the sandy beaches (1.16 km2) are extensively developed in the different parts of coastal stretches including Manakudi, Chothavilai, Sanguthurai, Periyakadu, Ganapathipuram, Muttam east, Kottilpadu, Midalam and Inayamputhenthurai coast. Beach terraces are gently sloping features developed due to sediment deposition, typically bounded by ridges and scarps on landward and seaward sides respectively. They are formed either from a pre-existing shoreline through marine abrasion or erosion or due to accumulations of sediments in the low wave energy zones by emerging slightly as marine-built terraces. Patches of beach terraces are distributed in various parts of the study area especially between Muttam and Rajakkaman-galam coastal stretches and the coverage of landform is restricted to 0.43 km2 (0.15%). However, enormous areas of the beaches were deformed and modified due to the Tsunami waves on 26th December 2004 (Chandrasekar et al., 2006). Beach ridges are formed as narrow and curve shaped features parallel to the shoreline between Manakudi and Pillaithoppu stretches due to swash-over-wash of sediments by the action of high-energy waves (Cherian et al., 2012). Similarly, the narrow and undulating berms and terraces are formed near Kasavanputhendurai (0.013 km2), Sanguthurai (0.03 km2) and Ganapathipuram (0.035 km2) coastal areas. These are often altering to multi-faced forms due to fluctuations in sediment accumulation through swash during monsoon (Kaliraj et al., 2014). Cusps are commonly distributed landforms in various parts of the study area for a length of 25.44 km due to action of breaking waves at the surf zone. Fig. 4 shows the sector-wise distribution of the coastal landforms along the study area.

The discontinuous cusps developed along the coastal segments of the western parts near Manavalakurichi-Colachel (4.6 km) and Keezhkulam-Inayamputhenthurai (1.2 km) are due to presence

of offshore rocky outcrops moderating the wave energy resulting in deposition of sediments along the coast. Scarps are wave-cut slope or miniature cliff on the seaward slope indicating erosional activities along the coast. Vertical expressions of scarps vary distinctly along the Rajakkamangalam coast (0.07 km2) and in the Chinnavilai-Manavalakurichi coastal stretches (0.13 km2) due to direct exposure of the coast to high-energy wave action resulting in wave-cuts along the beaches from several centimeters to a

Table 2

Spatial distribution and area extent of the coastal landforms in the south west coast of Kanyakumari.

Sl. no Coastal landforms Areal extent of landforms

Area Percentage of (km2) distribution (%)

i) Marine origin

1 Sandy beaches 1.16 0.40

2 Beach cusps 1.S9 0.54

3 Beach ridges 2.69 0.92

4 Beach berms 0.08 0.03

S Beach scarps 0.21 0.07

6 Beach terraces 0.43 0.15

7 Sandy spits 0.17 0.06

s Sand bars 0.05 0.02

9 Estuaries 0.76 0.26

10 Salt flats/salt pans 4.01 1.37

11 Marshy/swamp 9.90 3.39

12 Mud flats/tidal flats 3.10 1.06

13 Lagoon 0.03 0.01

14 Backwater creeks 0.96 0.33

1S Coastal plain (older) 23.32 7.99

16 Coastal plain (younger) 28.59 9.79

17 Coastal uplands 1.76 0.60

1s Rocky shore cliffs 1.13 0.39

19 Offshore rocky outcrops 0.14 0.05

20 Wave cut platforms 0.62 0.21

21 Wave cut notches 0.38 0.13

Total area of marine origin of 81.07 27.76

landforms

ii) Fluvio-marine origin

22 Shoal 0.24 0.08

23 Swale 0.92 0.32

24 Deltaic plain 60.99 20.89

Total area of fluvio-marine origin 62.15 21.28

of landforms

iii) Fluvial origin

2S Alluvial plain 45.93 15.73

26 Buried pediplain deep 9.S2 3.26

27 Buried pediplain shallow 7.01 2.40

2s Flood plain (older) 10.35 3.54

29 Flood plain (younger) 6.14 2.10

30 Pediment deep 2.98 1.02

31 Pediment (moderately 38.02 13.02

weathered)

32 Pediment shallow 4.18 1.43

33 Structural hill and Inselberg 1.54 0.53

34 Wetland shallow/waterlogged 1.31 0.45

Total area of fluvial origin of 126.98 43.49

landforms

iv) Aeolian origin

35 Sand dune 6.94 2.38

36 Barrier sand dune 9.08 3.11

37 Terisand (laterite) 5.42 1.86

Total area of aeolian origin of 21.44 7.34

landforms

v) Coastal structures

38 Groins 0.16 0.05

39 Revetments/sea wall 0.12 0.04

40 Jetties 0.08 0.03

Total area of coastal structures 0.36 0.12

Net total area of coastal landforms 292 100

distribution

few meters (Samsuddin et al., 1991). The sandbars (0.05 km2) are formed on the river mouth of the estuaries near Thengapattinam and Manakudi coastal areas, which is open to wave action and currents producing seasonal changes especially during monsoons. Shallow fluvio-marine landforms like salt marsh, tidal flat or mud flats are associated with estuaries near Thengapattinam coast (0.20 km2), Manakudi coast (2.61 km2) and Colachel coast (0.8 km2) due to deposition of fine muddy sediments from river discharge (Table 2). The backwater creeks (0.96 km2) are found in the Colachel, Manavalakurichi, Midalam and Rajakkaman-galamthurai coastal zones. Significantly, the coastal uplands in the Kanyakumari-Kovalam (0.22 km2), Muttam-Kadiyapattinam (0.69 km2) and Colachel-Kurumpanai (0.86 km2) coastal areas are made up of bedrocks overlaid with Terisand deposits which are exposed in the surrounding coastal landforms. In the Kanyakumari coast, the upland is characterized by thickly layered sandstone overlain by Teri sand deposits ranging in thickness from 8 to 56 m (Jayangondaperumal et al., 2012).

Similarly, the Muttam-Kadiyapattinam coastal tract has presence of sedimentary rocks in the upland varying in thickness from 4 to 73 m. Colachel upland has notable outcrops of sandstone composed of sand and boulder derived from the Teri sand materials with a thickness of 6-34 m. Rocky outcrops are also seen in the offshore of the Kanyakumari, Muttam, Colachel and Inayamputhen-thurai coastal areas at the distance of 0.5-5 km from the coast. These are considered as remnants of headlands detached earlier due to wave erosion and tectonic activities (Jayappa et al., 2006). Chandrasekar and Mujabar, 2010 have demarcated wave-cut

notches and attributed their presence to sea level changes and local and regional tectonic activities. They have surmised that the notches represents the past sea level stands varying from 12 to 25 m above the MSL. Notches of the southern coast equated to past stand of sea level hint at slow long-term uplift along the coastal tracts of southeast coast of Tamil Nadu. In the study area, the seaward slope of the rocky shore comprises of wave-cut notches (0.38 km2) and wave-cut platforms (0.62 km2) increasing in their shape and size in the Kanyakumari, Cape Comorin, Kovalam, Mut-tam and Colachel coasts mainly due to the slumping of the rocky shore towards the sea by the undercutting action of the waves. Sand dunes are formed as parabolic dune complexes with a height of 2-4 m along the Kanyakumari-Kovalam (3.27 km2), Manakudi-Periyakadu (2.38 km2) and Manavalakurichi-Colachel (1.30 km2) coastal areas. It is observed that the evolution of the landform reflects the prevailing coastal processes sculpturing their morphology and distribution. Sector wise distribution of the coastal land-forms is shown in Fig. 3 and the areal extents of the landforms are given in Table 2.

4.2. Topographical change detection and assessment

'The topographical change indicates vertical difference of sediment load in the area providing insight into the morphological expressions to coastal processes over time (Gyasi-Agyei et al., 1995; Pavlopoulos et al., 2009; James et al., 2012). As explained earlier, topographical change analysis In this study is carried out using SRTM DEM and ASTER DEM estimating the vertical difference

Fig. 4. Sector wise coastal landforms of the study area.

of sediment load for various landforms during the periods of2000-2011. Fig. 5 shows the topographical characteristics of the coastal landforms using SRTM DEM for the year 2000, and the ASTER DEM represents the same for the year 2011. The elevation of land-forms represented SRTM DEM is 0-157 m AMSL (Fig. 3A). Whereas, the elevation range in the ASTER DEM depicts a reduction to 0138 m AMSL (Fig. 3B). Morphodynamics of beaches through beach profiling shows that the coastal zone prevailing with low wave energy gets accreted with mass balance of sediment load up to 1-2 m in pre-monsoon and decreases to 0.5-1.0 m during rest of periods (Hentry et al., 2012). High wave energy zones experience severe erosion causing landward movement of shoreline due to erosion, beach subsidence and wave run-up. Moreover, artificial structures along the coast such as groins, revetments, and jetties pose hindrance to the natural hydrodynamic processes of waves and currents causing severe erosion on the down-drift side (Jayappa et al., 2006; Kaliraj et al., 2013). The low-laying landforms such as creeks, salt marshes and backwater that are above/below MSL in their topographical setting render an elevation of -7 to +6 m with respect to MSL in 2000; however this has been modified subsequently to 0-9 m during 2011. This could be due siltation from the adjacent landforms by marine and fluvial activities.

The beach landforms such as berms, cusps, ridges, scarps, terraces and sandy beaches have undergone relief reduction in terms of elevation from 1-3 m to 1-2 m due to erosional processes of wave, current and wind causing loss of sediments from the coast

(Cherian et al., 2012). Moreover, in the backshore region, relative heights of the sand dunes and foredunes have been reduced from 9-13 m to 6-11 m due to removal of surface dune layer by wind winnowing during monsoons. Significant removal of sediments have taken place from the dune complexes due to the effect of the Tsunami occurred on 26th, December 2004 (Chandrasekar et al., 2012). In the inland area, even the fluvial landforms such as alluvial plains and flood plains have undergone reduction in elevation from 34-55 in 2000 to 34-38 m in 2011. Severe erosion due to river and surface runoff during monsoon is considered responsible the lost of landforms.

4.3. Cross-shore profile change detection and assessment

The extracted cross-shore profile analysis using the DEM datasets provide information on geomorphic change of the various landforms in vertical scale (Zandbergen, 2008; Dawson and Smithers, 2010; Hicks, 2012). In the present study, the twelve cross-shore profile sets (3 profiles for each sector) were separately extracted using SRTM and ASTER DEM datasets for the four sectors namely Kanyakumari, Rajakkamangalam, Muttam and Thengapat-tinam at 5 km of interval. In this analysis, the total areal extend (292 km2) of the DEM datasets is gridded into 60 segments with a grid size of 5 x 5 km to extract the twelve cross-shore profile based on the concept of one profile flow across the five grids for estimation of vertical changes on different types of landforms.

Fig. 5. Topographical change (vertical difference of elevation) detection analysis using SRTM and ASTER DEM datasets for the periods of 2000-2011.

Thus, the results of vertical differences between these cross-shore profiles denotes land lost or land gain of sediment load in the coastal landforms along the four sectors during 2000-2011. In the Kanyakumari sector, the profile K1-K1' extracted on northeastern part depicts increases of beach elevation as 1.33 m within a distance 100 m from the shoreline. Whereas, the K2-K2' profile across the Cape Comorin coast indicates -1.45 to -6.25 m of cliff erosion along the rockyshore due to high energy waves actions. Similarly, the K3-K3' profile across the Kovalam coast shows severe erosion (-0.26 to -0.76 m) on the sandy beaches leading to formation of beach cusps and beach scarps within a distance 300 m from the shoreline. In the northeastern part, the coastal plain within a distance of 100-500 m illustrates reduction in elevation up to -0.68 to -0.93 m, besides, the sand dunes within a distance of 500-1000 m in the Cape Comorin coast also decreased in its elevation from -0.25 to -1.38 m. The deltaic plain within a distance 1100 and 2400 m in the northeastern part (profile K1-K1'), also depicts land loss (-0.26 to -1.80 m) in the upper parts and land gain (0.63-3.80 m) in the downslope region (Fig. 6).

In the Rajakkamangalam sector, the R1-R1' profile depicts certain modifications of landforms, such as, beach cusps, beach ridges and foredunes either due to erosion (-0.08 to -0.31 m) or accretion (0.06-0.15 m) within a distance of 600 m from the shoreline. However, the R2-R2' profile exhibit buildup of beach berms (0.41-0.63 m) in proximity to the coast within a distance of 200 m due to trapping of littoral sediments and erosion from fore-dune sand during SW and NE monsoons respectively. The fore-dunes within a distance of 200-500 have been significantly affected due to reduction of sediment load at a rate of -0.48 to -1.03 m and facilitates formation of berms along the coast. The R3-R3' profile depicts decrease of sediment loads at a rate of

-1.55 m within a distance of 100 m from the shoreline. The coastal plain within a distance of 600-1500 m illustrates minor reduction in sediment load (-0.25 to -0.45 m) in the upper slope surface as the erosion processes helped them to accumulate in the lower downslope surface recording a sediment load of up to 0.240.34 m (Fig. 7).

In the Muttam sector, the M1-M1' profile drawn across the Murungavilai coast records a little land loss from the beach land-forms (-0.18 to -0.96 m) at a distance of 800 m from the shoreline. Swashing of littoral sediments maintains stability of sandy beaches during SW and NE monsoons due to the action of low energy waves. The M2-M2' profile at the Muttam coast points to the development of narrow sandy beaches (0.73-1.57 m) along the bottom of rockyshore within distance 480 m from shoreline due to the specific coastal configuration settings. Similarly, the M3-M3' profile across the Kottilpadu coast exhibits accumulation of sediment loads (0.2-0.99 m) on the beach landforms by reworking of waves and currents on the sediments deposited from river discharge leading to the formation of berms, ridges and foredunes coinciding with the SW and NE monsoons (Hentry et al., 2010; Magesh et al., 2014). However, the coastal plains between 800 and 1500 m have experienced accumulation at a rate of 0.901.09 m due to constant deposition of substantial quantity of wind transported sediments from seaward and landward sources. The alluvial plain across M3-M3' profile records accumulation at a faster rate of 0.98-3.49 m sourcing from the weathered pediment materials transported and deposited through the river and surface runoff process (Fig. 8).

In the Thengapattinam sector, the fluviomarine processes mainly influence the morphological changes of landforms (Kunte and Wagle, 2001). The profile T1-T1' across the Colachel coast

Fig. 6. SRTM and ASTER DEMs extracted cross-shore profile change assessment of sediment load across the shoreline to 5 km distance of inland in the Kanyakumari sector.

Fig. 7. SRTM and ASTER DEMs extracted cross-shore profile change assessment of sediment load across the Rajakkamangalam sector.

shows a cliff erosion (-2.72 to -3.94 m) of the rocky shore within a distance of 290 m from the shoreline. TheT2-T2' profile across the Midalam coast Also shows significant changes of sediment loads on beach landforms that include ridges, berms, cusps and sandy beaches Those beach landscapes have also undergone erosion at a rate of -0.24 to -1.27 m within a distance of 300 m from shoreline due to destructive waves and littoral currents. However, the T3-T3' profile across the Thengapattinam estuary reflects development of sandy beaches on the up-drift side with distinct ridges and berms at an accumulation rate of 0.60 m whereas, loss of sediment load (-1.17 m) on the down-drift side resulted in the formation of beach cusps within the extent of 280 m from the shoreline. The mudflats and shallow marshy landscapes within a distance of 280-960 m are significantly deflated due to erosion at a rate of -0.86 to -2.25 m and -1.94 to -3.90 respectively. The coastal upland at the Colachel has suffered a land loss (-1.31 to -4.44 m) within a distance of 295-680 m due to the removal of the surface layer for construction and urban development activities. The fluvial landforms such as buried pediment deep, buried pediplain shallow and buried pediplain deep have undergone relief reduction at the rate of -0.58 to -4.39 m in various parts of the area with a distance of 1280-1470 m, 2235-2960 m and 32883890 m. Here, the flood plain (younger) within a distance of 1107-1920 m across T3-T3' profile is recurrently altered due to the high rate of erosion (-2.42 to -3.61 m) and the rate of accretion ranging from 1.46 to 3.20 m during 2000-2011 (Fig. 9). It is observed that the morphological changes of various landforms are triggered by wave refraction and diffraction, littoral current movement, beach placer mining, encroachment and other anthropogenic activities. Obviously, the rate of change in sediment load (difference of DN values) estimated using cross-shore profile

depicts that the changes of sediment loads in each segments study area at temporal frequencies.

4.4. Volumetric change assessment of coastal landforms

Fig. 10 shows volumetric change of sediment load in each geo-morphic units for specific sites of the study area. Table 3 shows the volumetric changes of sediment loads in various landforms, in which the negative and positive values represent the rate of land lost (erosion) and land gain (deposition) of sediment load respectively.

Fig. 11 shows the net volumetric changes of sediment load in the different coastal landforms estimated as land lost (241.69 m3/km2) for 62.82 km2and land gain (196.96 m3/km2) for the area of 202.80 km2 during 2000-2011. However, an area of 26.38 km2 remain unchanged either being stable or by maintaining a balance in the processes of accretion or erosion irrespective of the seasons. Significant volume of land lost (113.82 m3/km2) and land gain (105.78 m3/km2) for the area of 17.18 km2 and 53.70 km2 respectively. Wherein, the erosional landforms such as cusps and scarps have significantly increased in the area compared to their pre-morphological structures due to sediment accumulation at the rate of 5.04-6.21 m3/km2 and 2.67-6.12 m3/km2 respectively. Estimated sediment loss for these areas indicates severe erosion due to high-energy waves and rip currents (Kaliraj et al., 2013). Hentry et al. (2012) has estimated morphodynamics of beach land-forms along the southern coast through beach profiling reporting a profile elevation reduction up to 1-2 m during post-monsoon due to removal sediments by littoral currents and reduction sediment discharge from rivers. Similarly, sea cliffs have enlarged their shape and size at the rate of 3.28-6.52 m3/km2 along the rocky coast of

Fig. 8. SRTM and ASTER DEMs extracted cross-shore profile change assessment of sediment load across the Muttam sector.

Fig. 9. SRTM and ASTER DEMs extracted cross-shore profile change assessment of sediment load across the Thengapattinam sector.

77°10'0"E 77°20'0"E 77'30'0"E

Fig. 10. Volumetric change of sediment load of the coastal geomorphic landform in the study area using DEM of Differencing method for the periods of 2000-2011.

Kanyakumari-Kovalam, Muttam and Colachel coastal areas. Whereas, the high-energy waves attacking the rockyshore results formation of wave-cut notches (4.68 m3/km2) and wave-cut platforms (6.15m3/km2) by removing sediments from the weak weathered horizons of rockyshore.

Sandy beaches have experienced land lost at the rate of 6.153.24 m3/km2 probably due to backwashing of sediments carried offshore by frequent incidences of rip currents (Hentry et al., 2010). The berms and terraces have also undergone considerable changes in their morphology due to removal of sediment at the rate of 6.72-6.46 m3/km2 and 3.89-5.25 m3/km2 respectively. Changes in the breaker angle with the shoreline is frequently altering the morphology of these landscapes as it affects the littoral sediment transport along the coast (Reddy et al., 1984; Sajeev et al., 1997; Saravanan and Chandrasekar, 2010). Sandbars endure seasonal fluctuations as the sediment accumulation ranges from 5.28 to 2.09 m3/km2 in the shallow depth of nearshore at the mouth of the estuaries near Thengapattinam and Manakudi coasts (Sanil Kumar et al., 2006). The coastal plains have experienced significant reduction in sediment load with a rate of 7.87-5.12 m3/ km2 due to erosion of surface layer by aeolian processes and surface runoff. Eventually, the deltaic plains have suffered relief reduction at the rate of 7.13-6.94 m3/km2 due to reduction of sub-aqueous and sub-aerial sediment materials supplied from river discharges and surface runoff (Samsuddin et al., 1991). The coastal landforms have been either washed out or modified due to effect of Tsunami on 26th December 2004 and some parts are recovering to their pre-Tsunami morphologies (Cherian et al., 2012).

In the inland area, the fluvial landforms have undergone significant volumetric changes at the rate of land lost (88.91 m3/km2) for the areal extent of 27.90 km2 and land gain (57.58 m3/km2) for the areal extent of 89.61 km2 due to fluvial process and anthropogenic activities. The alluvial plain has volumetrically decreased in their sediment load from 7.92 to 5.99 m3/km2 due to removal of sand and silt materials by fluvial action (Chandrasekar et al., 2012). The sand dune complexes formed from aeolian action have shown extensive reduction in sediment volume at the rate of 6.25-4.41, 6.68-6.36 and 9.42-5.52 m3/km2 respectively. It is observed that the volumetric change of beach landforms is mainly influenced by the constructive or destructive action of waves and littoral currents, while the inland landforms are mainly subject to fluvial action, flooding, surface runoff and other anthropogenic activities.

5. Field verification and volumetric change validation

The result of the Geomorphic Change Detection has been cross-verified using GPS based field surveyed on the landforms at specific ground reference points for the year 2011. The handheld Garmin ETREX 30 GPS instrument was used for demarcating the beach landforms at selected locations with reference to derived output of geomorphic maps. The post-field verification using GPS has got the limitation of accuracy up to 4 m in open coastal area and 10 m in build-up and canopy covers. However, the vertical measurement derived from GPS and the DEMs are unlikely to be exact representations of the earth surface due to uncertainties including sensor motion and inclination, topographic distortion, geodetic

Table 3

Volumetric changes of coastal landforms in the south-west coast of Kanyakumari.

Sl. No. Geomorphological landforms Volumetric changes of coastal landforms subject to their area of erosion and

deposition

Net land loss Area Net land gain Area Unchanged area

(m3/km2) (km2) (m3/km2) (km2) (km2)

i) Marine origin

1 Sandy beaches 6.15 0.04 3.24 0.23 0.86

2 Beach cusps 5.04 0.02 6.21 0.07 1.51

3 Beach ridges 6.17 0.12 4.57 1.01 1.58

4 Beach berms 6.46 0.08 6.72 0.41 0.03

5 Beach scarps 2.67 0.02 6.12 0.04 0.16

6 Beach terraces 3.89 0.12 5.25 0.22 0.11

7 Sandy spits 6.42 0.07 3.86 0.06 0.04

8 Sand bars 5.28 0.03 2.09 0.01 0.01

9 Estuaries 6.57 0.22 4.89 0.41 0.16

10 Salt flat/salt pan 5.73 2.41 8.39 1.31 0.31

11 Salt marshy/swamps 6.01 2.14 5.49 6.88 0.91

12 Mud flat/tidal flat 6.06 1.21 5.39 1.76 0.15

13 Lagoon 7.01 0.01 3.57 0.01 0.01

14 Backwater creeks 5.82 0.41 5.27 0.47 0.11

15 Coastal plain (older) 7.87 4.13 5.12 17.62 1.59

16 Coastal plain (younger) 6.66 5.13 5.47 20.65 2.82

17 Coastal uplands 6.52 0.31 4.37 1.39 0.11

18 Rocky shore cliffs 3.28 0.09 6.52 0.87 1.18

19 Offshore rocky outcrops 5.63 0.08 2.41 0.02 0.04

20 Wave cut platforms 2.16 0.32 6.15 0.22 0.08

21 Wave cut notches 2.42 0.22 4.68 0.04 0.12

Total volumetric changes of marine origin of landforms 113.82 17.18 105.78 53.70 11.89

ii) Fluvio-marine origin

20 Shoal 5.24 0.06 5.02 0.17 0.02

21 Swale 4.24 0.21 5.35 0.64 0.08

22 Deltaic plain 7.13 14.11 6.94 42.15 4.75

Total volumetric changes of fluvio-marine origin of landforms 16.61 14.38 17.31 42.96 4.85

iii) Fluvial origin

25 Alluvial plain 7.92 8.01 5.99 34.36 2.87

26 Buried pediplain deep 7.43 2.21 5.03 6.44 0.53

27 Buried pediplain shallow 8.28 1.22 5.18 4.74 0.41

28 Flood plain (older) 7.99 2.36 6.47 7.39 0.61

29 Flood plain (younger) 8.33 1.23 7.63 4.41 0.51

30 Pediment deep 7.51 0.69 5.35 2.13 0.17

31 Pediment moderate 8.68 9.23 7.06 26.0 2.34

32 Pediment shallow 6.61 1.21 5.11 2.57 0.41

33 Structural hill/inselberg 17.98 0.11 4.71 1.34 0.08

34 Wetland shallow/waterlogged area 8.18 0.43 5.05 0.23 0.13

Total volumetric changes of fluvial origin of landforms 88.91 27.90 57.58 89.61 8.06

iv) Aeolian origin

35 Sand dune 6.25 0.86 4.41 5.31 0.77

36 Barrier sand dune 6.68 1.54 6.36 6.74 0.57

37 Terisand (laterite) 9.42 0.71 5.52 4.48 0.24

Total volumetric changes of aeolian origin of landforms 22.35 3.36 16.29 16.53 1.58

Total volumetric changes of coastal landforms 241.69 62.82 196.96 202.80 26.38

Note: The total volume of changes of landforms in cubic meter (m3) can be calculated by multiplying net erosion or net deposition values with their corresponding area in m2 (1 km2 = 1,000,000 m2).

control, and processing methods (Schwendel et al., 2012). Therefore, the result of measurement validation using GPS field survey are indicative in nature. The control points of grid cell (pixels) with elevation (height) values were obtained from the SRTM and ASTER DEM datasets at a particular coordinate for validation analysis (Siart et al., 2009; Yanyan et al., 2013). It is observed that the mean difference of change in measurement is 2.1 between the control points in DEM versus the collateral GPS observed points. Conformity of values are therefore reasonable for accepting the Geomor-phic Change Detection measurement. The mean values of vertical difference over the 10 control points in the sandy beaches have estimated in the range of 2.0-2.6 m in the DEMs and 1.6-3.2 m in the GPS observed points and the minimum mean difference is 1.4 m. Difference in elevation observed are not substantial over the 10 control points of the inland area from both DEM and GPS measurements. This s indicative of the acceptability of accuracy of geomorphic change assessment (Wood and Hine, 2007;

Zandbergen, 2008; Wheaton et al., 2010). Field validation exercise proved a general acceptability of measurement of volumetric changes at selected control points on various geomorphological landforms. However, accuracy of the volumetric estimate of morphological change is limited to the extent of the earth surface complexities, environmental setting and spatial resolution of DEMs introducing bias on the successive volumetric measurement of the coastal landform changes.

6. Conclusions

Mapping of coastal geomorphology in large scale (1:10,000) is a first attempt for the study area. Coastal processes involving wave, current wind and anthropogenic activities mainly influence the characteristics of landforms. Seasonal changes of these factors are expressed in the volumetric changes in their morphology due

77°10'0"E 77°20'0"E 77*30'0"E

Fig. 11. Net volumetric change of sediment load of the coastal geomorphic landform in the study area using Cut and Fill method for the periods of 2000-2011.

to land lost (erosion of sediment) and land gain (deposition of sediment) on the landforms. The abnormal changes of various land-forms like sandy beaches, dune complexes, and estuaries along various parts of the coast could be due to the effect of the Tsunami during December 2004. The DoD analysis of geomorphic change assessment reveals changes in morphologies due to erosion or deposition processes. The spatial variation of sediment load suggests morphologies of the landforms are closely related to the marine and terrestrial processes. The premises of this DoD analysis is meaningful in evaluating low magnitude geomorphic changes through minimum criteria of change detection analyses of DEM surface. Comparing DoD results with GPS field surveyed data at the specific control points on various landforms reveals minor variations between the measurements indicate the limitation, coherence and promising nature of the study. The present study provides primary information for coastal vulnerability assessment and monitoring at a regional level using available data sets for screening an area for detailed studies.

Conflict of interest

None. Acknowledgment

Authors are thankful to the Director, NCESS for provining support and continuous encouragement to carried out this research

work. The corresponding author is thankful to DST-INSPIRE Division, Department of Science & Technology, Government of India for the award of INSPIRE Fellowship - SRF (DST/INSPIRE/ 2011/ IF110366) for pursuing Ph.D. Degree in Remote sensing-GeoTechnology at Manonmaniam Sundaranar University, Tirunel-veli - 627012. The authors would like to acknowledge Wheaton et al. (for GCD software) and USGS-GLCF, USA. Authors thank the anonymous referees for their critical and constructive contribution to the paper.

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