Mapping of Coastal Landforms and Volumetric Change Analysis in the South West Coast of Kanyakumari, South India Using Remote Sensing and GIS Techniques ⇑ S

The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

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The Egyptian Journal of Remote Sensing and Space Sciences

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Research Paper Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques ⇑ S. Kaliraj a, , N. Chandrasekar b, K.K. Ramachandran a 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 article info abstract

Article history: The coastal landforms along the south west coast of Kanyakumari have undergone remarkable change in Received 29 March 2016 terms of shape and disposition due to both natural and anthropogenic interference. An attempt is made Revised 26 October 2016 here to map the coastal landforms along the coast using remote sensing and GIS techniques. Spatial data Accepted 26 December 2016 sources, such as, topographical map published by Survey of India, Landsat ETM+ (30 m) image, IKONOS Available online xxxx 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- Keywords: shore profile analysis, (iii) Geomorphic Change Detection (GCD) using DEM of Difference (DoD) were Geomorphic Change Detection adopted for assessment of volumetric changes of coastal landforms for the period between 2000 and DEM of Differencing GIS and remote sensing 2011. The GCD analysis uses ASTER and SRTM DEM datasets by resampling them into common scale South-west coast of Kanyakumari (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 flat- tening. Analysis of cross-shore profiles for twelve locations indicate varying degrees of loss or gain of coastal landforms. For example, the K3-K30 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. Ó 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 landforms of the coastal transition zone are sensitive to erosional and depositional processes due to actions of waves, littoral current, Geomorphic landforms of a coast is an expression of the charac- wind, sediment transport and certain anthropogenic activities teristics of prevailing coastal processes over long-term scale. The (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 Peer review under responsibility of National Authority for Remote Sensing and settings, geological structures and a variety of coastal processes. Space Sciences. ⇑ Therefore, mapping of landforms provides Insight into such Corresponding author. morpho-hydrodynamic milieu. (Davies, 1972; Nordstrom, 2000; E-mail addresses: [email protected] (S. Kaliraj), [email protected] (N. Chandrasekar), [email protected] (K.K. Ramachandran). Woodroffe, 2002; Amos, 1995; Chandrasekar and Kaliraj, 2013). http://dx.doi.org/10.1016/j.ejrs.2016.12.006 1110-9823/Ó 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/).

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 2 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

Along the Indian coast too, the tectonic and structural formations signature, and pattern recognition of image properties using math- and continental shelves primarily responsible for shaping the land- ematical that would be able to detect, cluster and classify the fea- forms which are acted upon subsequently by the prevailing hydro- tures to represent the real world. Previous investigations have dynamic settings characteristics (Nayak and Sahai, 1985; underlined advantages of using DEM and Lidar datasets for geo- Chandrasekar and Rajamanickam, 1995; Sajeev et al., 1997; Sanil morphic detection and volumetric change of sediment load along Kumar et al., 2006; Magesh et al., 2014). the coastal area (Shaikh et al., 1989; Anbarasu, 1994; Lillysand Most of the landforms along southern coast of Tamil Nadu par- and Kiefer, 2000; Wright et al., 2006; Waldhoff et al., 2008; ticularly on the south west coast of Kanyakumari district have Smith and Pain, 2009; Blanchard et al., 2010). Assessment of topo- undergone morphological deformation due to the effect of Tsu- graphical changes using DEMs provide insight on changes of sedi- namic occurred on December 26, 2004 (Chandrasekar et al., ment load due to erosion or deposition processes signifying past 2012). Artificial structures like groins, revetments, seawall and jet- and present morphological structural response to coastal processes ties those came up in the recent years have modified the coastal over time (Lane et al., 2003; Zhang et al., 2005; Wheaton et al., processes causing severe erosion on down-drift side in the coastal 2010; Schwendel et al., 2012). The DEM datasets acquired on two area (Kaliraj et al., 2013). Assessment of coastal landform changes different times can preferably be used to measure vertical differ- can help in the analysis of coastal vulnerability (Nicholls et al., ence in sediment loads of the coastal landforms based on topolog- 2007; Kaliraj and Chandrasekar, 2012; James et al., 2012; ical and morphometric rules (James et al., 2012). The DEM datasets Joevivek et al., 2013). Conventionally, mapping of coastal land- such as SRTM and ASTER are being used for Geomorphic Change forms is performed using pre-existing maps, field observation Detection analysis because of its mission specified accuracy, i.e. and other collateral data sources compiled for different times high vertical accuracies over terrain surface and bare soils and and different scales which can lead to inaccurate information due medium accuracies in terms of spatial resolutions (Cuartero to dynamic nature of coastal landforms (Desai et al., 1991; et al., 2004). The topographical changes of the sediment load in Embabi and Moawad, 2014). The mapping of coastal landforms the coastal landforms has been estimated from the temporal DEMs using multi-temporal satellite images can provide robust informa- using the extracted cross-shore profile analysis that provide ade- tion on shape, distribution, and morphological status during past quate information on geomorphic change of the various landforms and present (Butler and Walsh, 1998; Bocco et al., 2001; Smith in vertical scale (Gyasi-Agyei et al., 1995; Zandbergen, 2008; et al., 2006; Bubenzer and Bolten, 2008; Abermann et al., 2010). Dawson and Smithers, 2010; Hicks, 2012). The GIS-based Geomor- Recent technological advancement in remote sensing and survey- phic Change Detection (GCD) analysis provides volumetric change ing techniques provides adequate information on spatial distribu- of coastal landforms from the DEMs acquired for different periods tion of coastal landforms in GIS environment enabling us to of interval (Lee, 1991; Wheaton et al., 2007; Siart et al., 2009; prepare coastal geomorphologic map with higher granularity on James et al., 2012). The GCD analysis is concerned with DEM of Dif- a larger scalability (Chockalingam, 1993; Chandrasekar et al., ference (DoD) algorithm to estimate quantitative changes of land- 2000; Slaymaker, 2001; Nayak, 2002; Jayappa et al., 2006; Smith forms, in a diverse set of environments, and at ranges of spatial and Pain, 2009; Kaliraj and Chandrasekar, 2012). Coastal landforms scales and temporal frequencies (Wheaton et al., 2010; Hicks, of an area can be extracted using the Landsat ETM+ image with or 2012). The volumetric change of geomorphic features is estimated without slope and topographical measurements onto a GIS based using two DEM data sets acquired for two different periods can complementary platform (Mujabar and Chandrasekar, 2011). result in estimating of land loss and land gain for a vast area appro- Moreover, recent advances in remote sensing and GIS play an priately validated through field surveys and measurements important role on the development of numerical modelling of sur- (Dawson and Smithers, 2010). Maksud Kamal and Saburoh Midor- face processes for quantitative assessment of morphological ikawa (2004) have obtained the area and volume of geomorphic changes of landforms (Blanchard et al., 2010). GIS technique is an features that closely matched with field measurements. Stereo- effective platform for mapping thematic features with correspond- pair of images are able to provide three-dimensional representa- ing attributes. Geo-computational algorithms facilitates automatic tions of the features through accurate derivation of digital extraction of geomorphic landforms from the combination of data- elevation models (DEMs). The topographical changes of landforms sets such as, satellite image, DEM and topographical map using estimated from these datasets have positive correlation with field numerical modelling, pixel-based classification and cellular auto- measurements and hence useful for monitoring how landforms mated techniques in GIS environment (Dawson and Smithers, change over time due to subsidence or uplift of the coastal surface 2010). High spatial resolution images of IKONOS, Quick Bird, and (Cuartero et al., 2004; Mith and Clark, 2005). Knight et al., 2011 GeoEye incorporated with DEMs are progressively used for assess- have incorporated images and DEMs for rapid assessment of land- ment of volumetric changes of coastal landforms (Bubenzer and form changes for large areas and have demonstrated that the Bolten, 2009; James et al., 2012). The mapping of geomorphic land- remote sensing provides complete requirements if synergized with forms using remotely sensed images requires knowledge of basic ground validation and measurements which can even be extended interpretation elements such as tone, texture, shape, size and pat- to geomorphological studies across all spatial scale. Mapping of tern, for unambiguous delineation of landforms. For example, the landforms through field observation allows the most direct way beaches and associated landforms have been identified based on to capture the landform characteristics and enable as a basis for linear shape and fine to medium coarse pattern (Rao, 2002). Land- terrain assessment and geomorphological analysis. The accuracy forms are interpreted using multispectral images based on inter- of field mapping is subjective and affected by the skills and expe- pretation element keys to extract the information relatively rience of one who maps. The volumetric change of sediment load accurate up to the post-field verification process (Maksud Kamal estimated using DEMs are capable of generating superior results and Saburoh Midorikawa, 2004). According to Tomar and Singh, on land loss and land gain that are relatively closer to the field- 2012, the coastal landforms are classified on the basis of topo- based measurements apart from providing spatial ensemble of graphical variations resulting from differential erosion and accre- coastal landforms with exceptional details (Smith and Pain, tion processes, for example, the geomorphic units of alluvial 2006). Many researchers have confirmed that the DEM derived plain, pediplain, structural hills and residual hills are mapped using results along with field data can produce relatively high accuracy DEM incorporated multispectral IRS-ID LISS-III images using visual in geomorphic change measurement for coastal area (Aniello, interpretation technique along with field check. While digital anal- 2003; Nikolakopoulos et al., 2006; Zandbergen, 2008; Potts et al., ysis of landform extraction is faster, appropriate based on spectral 2008; Toutin, 2008; Blanchard et al., 2010). The primary aim of

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx 3 the present study is to map coastal landforms and assess the volu- ment load during both southwest and northeast monsoons metric change of sediment load over a decade along the south-west (Chandrasekar and Mujabar, 2010). The coastal area is character- coast of Kanyakumari using integrated remote sensing and GIS ized by various landforms such as sandy beaches, coastal plains, techniques. The present study therefore used different change beach terraces, sand dunes, rocky shore, estuaries and other detection techniques such as (i) topographical change analysis, fluvio-marine landforms (Kaliraj et al., 2013). The coastal upland (ii) cross-shoreprofile change analysis, (iii) DEM of Difference in the Kanyakumari, Muttam and Colachel area are mainly associ- (DoD) algorithm based Geomorphic Change Detection (GCD) anal- ated with rocky-shores and offshore outcrops acting as natural bar- ysis for estimating the volumetric changes (land loss or land gain) rier to wave actions and storm surges. Sandy beaches are formed along the coastal stretch using the ArcGIS platform. This studya- on the Sanguthurai, Chothavilai, Pillaithoppu, Ganapathipuram, part from assimilating decadal changes in landforms would also Rajakkamangalam, Colachel and Simonkudiyiruppu coastal delineate various influencing factors that would form primary stretches due to swashing of large amount of sediments resulting information source for coastal vulnerability management and from waves (Hentry et al., 2010). However, the major parts of the would help in the preparation of developmental plans against coastal areas namely Kovalam, Pallam, Manavalakurichi, Mandai- any possible natural disasters that likely to affect the coastal kadu and Inayamputhenthurai are noticed with severe erosional region. activities due to backwashing of sediments by destructive wave actions. Onshore margin of the study area comprises Late Quater- nary deposits composed of complex settings of granite-biotite- 2. Study area illuminate underlain sandstone interlined with sand, silt and clay partings and overlaid by sandy materials (Loveson, 1993). The The study area is located along the south-west coast of coastal surface is generally sloping towards sea interspersed with Kanyakumari district, Tamil Nadu, India. The geographical coordi- settlements, coconut plantation, shallow water bodies like back- nates extend from 77°9049.2000Eto77°34015.0000E longitude and water and creeks (Jena et al., 2001; Magesh et al., 2014). Along 8°6032.6000Nto8°14015.3000N latitude. The coastal stretch is the near shore area, the sand dunes are roughly parallel to shore extended for a length of 58 km from Kanyakumari to Thengapatti- though discontinuously distributed along the coast. The coastline nam in southeast to northwest direction (Fig. 1). There are three along the Kanyakumari coast have experienced erosion due to major drainage networks such as Pazhayar, Valliyar and Thamira- high-energy wave action. The Teri sand dunes (reddish brown) barani along with their tributaries flowing in southerly direction are located along the coastal stretch from Kovalam to Manakudi from the Western Ghats. These are primary sources contributing with thickness increasing from 1.5 m in coastal headlands to a discharge to maintain coastal landforms debouching their sedi- maximum of 7.0 m in the interior terrestrial area. The crystalline

Fig. 1. Geographical location of the study area.

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 4 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx rock types such as quaternary rocks, clay sand and sandy materials the low-lying areas of the coast. The tidal fluctuation is from 4.0 are predominantly found along the coast. The rocky boulders and to 6.0 m along the estuaries causing significant changes in land- sea cliffs are found in the Muttam, Kanyakumari and Cape Comorin forms. Whereas, areas experiencing lower tidal range from 1.0 to coasts and sandstones are found along the study area are made up 2.0 m show tendency of releasing suspended sediments to the of igneous rock, and silt clay materials (Loveson, 1993). The alluvial coast. sediments admixture with clay are found deposited at the mouth The aeolian (wind) process is controlling formations and shape of the Thamirabarani estuary in Thengapattinam and Pazhayar of the beaches and backshore landforms such as sand dune, barrier estuary in Manakudi. Sandstone with clay intercalation structures dune (foredune) depending on the seasonal wind velocity and is present along the eastern part of Thengapattinam coast. The directions. Beaches along the open coast are eroded due to abra- study area is prevailing with a sub-tropical climate with the nor- sion or scouring through sandblasting of wind-borne action by mal annual rainfall varying from 826 mm to 1456 mm and the trapping sediments on the backshore. Sand dunes in the area con- annual mean minimum and maximum temperatures are sists of dry sands that got piled or heaped-up by continuous eolian 23.78 °C–33.95 °C respectively. The landforms along the coast fre- action over a long period of time. For example, the parabolic dune quently alter in morphological distribution due to both natural and complexes have been evolved to the present elevation of 2–4 m anthropogenic factors and hence the present study is performed to due to accumulation of wind transported sediments from blowouts understand coastal landforms and their changes. or open beaches along the various parts of the study area. The foredune complexes along the coastal stretches have also been formed 2.1. Coastal and oceanographic characteristics of the study area due to sand blowing out from the incipient beaches and steadily growing to the seaward side. The recent development of coastal Evolution of coastal landforms in various locations along the structures like groins, seawalls, revetments and jetties are inter- study area is mainly subject to ocean and coastal processes. Wave vening with the natural rhythm of the coastal process causing sev- height is one of the main factors considered in setting up of hazard ere erosion on the down-drift side complementing accretion on the management system along the coastal region. Mean significant up-drift side. It is observed that the shape, size and distribution of wave height along the coast is estimated around 1.4 m rendering landforms are frequently influenced by coastal and oceanographic higher energy along the coast causing erosion or subsidence of bea- factors and along certain stretches due to the influence of anthro- ches (Hentry et al., 2012). The low-energy waves (with wave pogenic activities. 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 prevail- 3. Materials and methods ing in an area influences erosion and accretion of the beaches and headland features. It has been reported that along this coastal Mapping of coastal landforms is primary to understanding of stretch, the mean annual wave energy ranges from 0.5 to 8.5 kJ/ evolution of any coastal area. The south-west coast of Tamil Nadu km2. Nevertheless, the Kanyakumari coast is reportedly experi- comprises of various landforms that are experiencing morphody- enced with very high wave energy (6.5–8.5 kJ/km2) owing to the namic changes in shape, size and distribution due to various peculiar nature of the coastal configuration. Due to the unique geo- coastal hydrodynamic factors including human interferences graphical location of the coast at the southernmost tip of the Indian (Hentry et al., 2012). The depositional landforms like beaches sub-continent, the waves and currents waves approaching from and foredunes are maintaining stable morphological structures various directions while breaking at varying angle with the coast along coastal stretches prevailing with constructive wave action generate longshore currents in different directions and intensity. coastal stretches prevailing with constructive wave action The coastal zones having lower energy (0.5–2.5 kJ/km2 and 1.5– (Kaliraj and Chandrasekar, 2012). Quantifying volumetric change 3.5 kJ/km2) wave setup result in sandy beaches due to deposition of sediment load in a particular area provides insight into the ero- of littoral sediments, consequent to which young beaches and sional or depositional processes taking place over a period of time other depositional landforms are formed. The southwest coast (Schwendel et al., 2012). The coastal landforms and vegetation experiences three types of littoral current systems based on the cover along the coast has been significantly altered in terms of wave direction and wind blow namely southwest monsoon current morphological settings has been significantly altered in terms of (June–September), north-east monsoon current (October–Decem- morphological settings after the Tsunami occurred on 26th, ber) and summer current (March–May). The movement of seasonal December 2004 (Chandrasekar et al., 2012). However, the coastal currents varies in different parts causing shoreline changes due to landforms have gradually disappeared in the down-drift side of deposition or scouring away of sediments by seasonal movement the coastal structures such as groins, revetments, seawall and jet- of longshore currents. ties due to interference to the littoral sediment flow along the coast The average longshore current velocity along the coast is mea- (Kaliraj et al., 2013). sured as 0.14 m/s, whereas the fastest flow of its velocity observed In some parts of the open coast, the high-energy waves and sea- is 0.32 and 0.28 m/s in the Kanyakumari and Kovalam coasts dur- sonal movement of littoral currents directly influence the sediment ing both SW and NE monsoons. This coastal stretch faces erosion transport causing frequent changes of landforms and their mor- due to littoral sediment movement towards north during the NE phological characteristics (Chandrasekar et al., 1996; Kaliraj monsoon, while it is reversed towards southern direction during et al., 2014). The morphodynamics of beach landforms based on SW monsoon. The coast between Rajakkamangalam and Manakudi beach profile analysis reveals that severe erosion in the Kovalam, has been notified as low current velocity zones (0.14–0.22 m/s) Murungavilai, Mandaikadu, and Inayamputhenthurai coastal zones and these areas have experienced accretion due to the presence due to destructive wave actions and seasonal movement of littoral of sea cliffs, eroded Teri sand dunes and very narrow sandy pocket currents. Meanwhile, the constructive waves lead to processes of beaches. The coastal configuration from Manavalakurichi to Then- on the beaches of Sanguthurai, Chothavilai, and Midalam and gapattinam is towards southeast and north-west, where the sum- up-drift side of Muttam coast (Cherian et al., 2012). The landforms mer and SW monsoon high velocity (0.28–0.30 m/s) currents of the coast are highly sensitive to marine and terrestrial forces to flowing to south act on the headlands. This results in severe ero- maintaining equilibrium and stability to the morphological struc- sion along the coast scouring and removal of sediments by back- tures, and hence analysis of the changes in coastal landforms using wash and longshore current permanent and episodic change in Remote sensing and GIS techniques indispensable inputs for

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

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 6 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx coastal landforms through a series of systematic geo-processing process owing to their shape and dimension on the earth surface. operations with ArcGIS 10.2 software. The Garmin ETREX 30 GPS In order to overcome this complexity, the sub-pixel classification was used for ground truth verification, pre and post field verifica- analysis is executed on 5 5 km grids (for nearshore area) for inte- tion, training sets selection and checking the landform boundaries. grated images of high and medium resolution images. This analysis The location point accuracy of Garmin ETREX 30 GPS has limita- extracts one feature (i.e. beach berms) at a time and the repeated tions in field survey using, which horizontal positioning deviations analysis produces small dimensional features. Hence, the multiple can be up to 4 m in open coastal region with proper sky view and in layers of different landforms are overlaid together and graphically the worst scenario up to 10 m in areas with canopy cover, and con- represented them in 1: 10,000 scale. Encouragingly, the result of siderable built-up where satellite visibility is much affected. Stan- classified image shows an overall accuracy is 89.61% with a kappa dard procedures were adopted for geomorphological mapping coefficient statistics of 0.89 for 100 control points indicating the from multiple spatial data sources employing visual or digital acceptable accuracy of the classified image. Based on the individual interpretation/classification techniques (Zandbergen, 2008). feature classes, the producer’s accuracy is recorded as 74–100% and Fig. 2 shows the geo-processing functional flow of coastal land- the user’s accuracy is estimated as 60–100%, and it is mostly form mapping and volumetric change analysis. Wherein, the mul- exceeded 90% in the final output. Additionally, the immanent pix- tiple data sets were georeferenced to a common datum (WGS84) els within a feature class are eliminated through majority-filtering with projection onto UTM and systematic operations using geopro- technique (kernel window size 5 5) to achieve a stronger gener- cessing tools were executed for pre-processing and feature extrac- alization for mapping the thematic classes in vector format (Potts tion in a GIS environment. The multispectral images are spectrally et al., 2008). Furthermore, the resultant map is classified into var- enhanced using majority filtering and mean filtering algorithm to ious landforms by comparing the morphometric rule using visual obtain smooth spectral pattern of features in the image (Wood, image interpretation techniques. The SOI topographical map has 1996; Wright et al., 2006; Mujabar and Chandrasekar, 2011). Even- been used to derive the basic geomorphic information and tectonic tually, the DEM is processed using hydrological analysis tool in elements of the coastal area such as elevation (contour line), spot ArcGIS to remove sink pixels. Sub-pixel classification has been exe- heights, benchmarks, high water line (HWL), and other natural cuted using the hybrid model composed of parallelepiped and and manmade landmarks and landscapes. Landsat ETM+ and IKO- maximum likelihood algorithms based on the concept of best pos- NOS images were used as primary data source for mapping the sibilities for operator controlled pixel allocation method (Kaliraj coastal landforms such as coastal plain, beach landforms, flood and Chandrasekar, 2012). Extracting the coastal landforms like plain, swale, water bodies, swamp or marsh lands, tidal plats, back- beach berms, beach cusps, beach ridge and beach terrace from water creeks, and estuaries (Magesh et al., 2014). The combined the satellite image with medium resolution (30 m) is a complex datasets of DEM and image provide a vital clue for mapping the

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.

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx 7 coastal landforms (Blanchard et al., 2010). The ASTER DEM (30 m) umetric changes over time. The resultant of DEMs have preserved data has been incorporated with Landsat ETM+ image and IKONOS their pixel attributes (elevation), as it is an original image and the image for detecting the various geomorphologic units such as sand RMSE (root mean square error) values of DEMs have noted as dunes, barrier dunes, coastal uplands, beach slope, cliffs, rocky 3.46 m and 6.05 m respectively. The DEM derived through the data shore, offshore rocky outcrops, mining area, sand spits and man- fusion technique is found superior (with RMSE of 3.46 m) com- made structures like groins, seawalls, revetments, jetties. Finally, pared to the SRTM DEM Global scale (RMSE 16 m) or Local scale the combined datasets of ASTER DEM and topographical map have (6 m). Similarly, the RMSE of the reworked ASTER DEM is 6.05 been to demarcate the topographical (relief) characteristics land- compared to 25 m for Local scale (United States Geological forms in the study area. 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 3.2. DEM of Differencing of volumetric change analysis indicative of geomorphic changes. The GCD analysis incorporates DoD algorithm processed using ArcGIS AddIn Tool namely ‘‘Geo- The GIS-based Geomorphic Change Detection (GCD) analysis morphic Change Detection Tool” (GCD v6.0) developed by provides volumetric change of sediment load in the landforms Wheaton et al. (2010) speciﬁcally to estimate the volumetric using DEM datasets acquired over periods of interval (Lee, 1991; changes of coastal landforms during the year of 2000 and 2011. Wheaton et al., 2010; James et al., 2012). The GCD analysis is con- The DoD algorithm computes the differences by subtracting pixel cerned with DEM of Difference (DoD) algorithm for estimate the values of two DEMs using the equation d = Z Z , where d is a quantitative changes of landforms of the earth surface, in a diverse E 2 1 E output DEM showing changes in volumetric scale (m3); Z is a set of environments, and at a range of spatial scales and temporal 1 DEM of earlier period (i.e. SRTM DEM acquired on February 2000, frequencies (Hicks, 2012). In the present study, Geomorphic and Z is a DEM of later period (i.e. ASTER DEM acquired on Octo- Change Detection of coastal landforms is estimated from SRTM 2 ber 2011). Thus, the output DEM provides volumetric change of and ASTER DEM datasets acquired for the years 2000 and 2011 sediment load (d ) on various landforms due to erosion and depo- respectively using DEM of Difference (DoD) method. The DoD is a E sition with time. In which, the negative and positive values repre- mathematical algorithm for quantifying the volumetric change of sent the land lost (erosion) and land gain (deposition). However, the landforms using DEM datasets acquired on two different peri- the product of DoD may propagate and amplify certain uncertain- ods (Wheaton et al., 2010). The DEM datasets register the absolute ties and therefore, it is essential to identify and minimize errors ground elevation model of an area in the form of a raster data in (Wood, 1996; Blanchard et al., 2010). These complexities are elim- which each grid cell (pixels) contains an elevation (height) values. inated from the output DEM the equations Z = Z ± d , where Therefore, the DEM datasets are used worldwide for terrain visual- Actual DEM Z Z is the true elevation value obtained from the topographical ization, hydrological modelling, orthorectiﬁcation, and geomorphic Actual map (scale 1:25,000) published by Survey of India in 2000 and d change assessment (Fabris and Pesci, 2005; Nikolakopoulos et al., Z is the vertical error component of input datasets. Thus, the result 2006; Siart et al., 2009; Blanchard et al., 2010; Marghany et al., of error analysis ensures the quality of output DoD map on the rel- 2010). Conversely, the DEM datasets such as SRTM (90 m) and ative accuracy of volumetric change assessment (Siart et al., 2009; ASTER (30 m) are constrained due to varying spatial and temporal Marghany et al., 2010; Wheaton et al., 2010; James et al., 2012). resolutions whilst they are used directly for Geomorphic Change Finally, the output map is converted into vector layer for prepara- Detection analysis and they have limited experiences within the tion of geo-database of landform features with attributes including user community (James et al., 2012). Alternatively, to overcome name, areal extent, and volumetric change rate using ESRI-ArcGIS this, DEM datasets were analyzed based on the most popular Data 10.2 software. 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 4. Results and discussion resampled to 10 10 m pixel (Wechsler, 2000; Wheaton, 2008; Blanchard et al., 2010; Magesh et al., 2014). The coastal landform The various types of coastal landforms are extracted from a features such as beach berms, beach terrace, beach cusps, beach combination of datasets using remote sensing and GIS techniques ridges were demarcated from IKONOS images (3.42 m) due to their for the study area. The landforms have undergone remarkable smaller spatial dimensions. The layer consisting of the coastal geo- changes due to marine and terrestrial factors, which are responsi- morphological features are overlaid on the DEM for estimating vol- ble 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 Coastal landforms Factors influencing landforms formation of landforms Erosional features Depositional features 1 Marine Beach scarps, Rocky shore Sandy beach, Sand bar, Sand Erosion – is due to backwashing of sediments by waves, currents, placer cliffs, Wave-cut platforms, spits, Beach ridge, Beach mining and man-made structures Wave-cut notches, Cliff berm, Tidal flat, Mud flat, Accretion – is due to swashing of sediments by low wave energy and terraces, and Headlands and Coastal plains sediment deposition by longshore drift 2 Fluvio-Marine Estuaries, Shoal and Swale Deltaic plain, Sand bar (at Modification of landforms – due to tidal regime, divergent wave action estuary) Formation of landforms – due to accumulation of river discharged sediments by tidal and wave divergent action 3 Fluvial Buried Pediment, River Alluvial plain, Deltaic plain, Erosion of landform – due to runoff and overland flow terraces, Pediplain, Bajada, Flood plain, Leeves Deposition of landform – due to discharge of sediments by the river and and Structural hill channels 4 Aeolian Teri sand (reddish) along Sand dune (older) and Erosion – due to deflation of dune sand by wind from land and sea and high the shore cliffs Barrier dune energy wave actions Accretion - due to accumulation of sediments by sand blown towards inland from the beaches in front of them by onshore winds

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(Saravanan and Chandrasekar, 2010). The volumetric change of of offshore rocky outcrops moderating the wave energy resulting landforms is quantiﬁed using DEM datasets in GIS software envi- in deposition of sediments along the coast. Scarps are wave-cut ronment, and the results are presented detailing the location, dis- slope or miniature cliff on the seaward slope indicating erosional tribution and volumetric change of sediment load for various activities along the coast. Vertical expressions of scarps vary dis- landforms along the coastal area. tinctly along the Rajakkamangalam coast (0.07 km2) and in the Chinnavilai-Manavalakurichi coastal stretches (0.13 km2) due to 4.1. Mapping of coastal geomorphic features and its spatial direct exposure of the coast to high-energy wave action resulting distribution in wave-cuts along the beaches from several centimeters to a

The spatial distribution of the coastal landforms along the study area is depicted in the Fig. 3. Table 1 numerates the classification of Table 2 coastal landforms of the study area based on their evaluation pro- Spatial distribution and area extent of the coastal landforms in the south west coast of cesses. Evolution of marine landforms is the result of cyclic coastal Kanyakumari. processes of ocean waves, winds, tides, and currents resulting in Sl. no Coastal landforms Areal extent of landforms the formation of erosional and depositional landforms along the Area Percentage of coast (Samsuddin et al., 1991). The coastal plain is a flat or gently (km2) distribution (%) sloping surface distributed along the backshore region. It com- i) Marine origin prises of sand, silt and clay particles manifested as geomorpholog- 1 Sandy beaches 1.16 0.40 ical entities, such as, sand dunes, plantation, shrub vegetation, 2 Beach cusps 1.59 0.54 saltwater ponds, and backwater creeks resulting from the deposi- 3 Beach ridges 2.69 0.92 4 Beach berms 0.08 0.03 tion of sediments over long periods. The total area of coastal plain 5 Beach scarps 0.21 0.07 2 covers 51.91 km resulting from the 17.78% of the total study area 6 Beach terraces 0.43 0.15 (Table 2). Out of these, the younger formation (28.59 km2) is dis- 7 Sandy spits 0.17 0.06 tributed in the northeastern part of Kanyakumari coast and 8 Sand bars 0.05 0.02 Manakudi-Pillaithoppu coastal tracks, while the older formation 9 Estuaries 0.76 0.26 2 10 Salt flats/salt pans 4.01 1.37 (23.32 km ) is in the northern part of Muttam coast and middle- 11 Marshy/swamp 9.90 3.39 western part of Chinnavilai, Mandaikadu, Keezhkulam and Mida- 12 Mud flats/tidal flats 3.10 1.06 lam coastal areas. Beach landforms such as sandy beaches, cusps, 13 Lagoon 0.03 0.01 ridges, berms, terraces, scarps, sandbars and sand spits are dis- 14 Backwater creeks 0.96 0.33 15 Coastal plain (older) 23.32 7.99 tributed along the nearshore region. 16 Coastal plain (younger) 28.59 9.79 This is result of swashing or backwashing of littoral sediments 17 Coastal uplands 1.76 0.60 due to action of waves, wind and littoral currents (Ahmad, 1972; 18 Rocky shore cliffs 1.13 0.39 Sanil Kumar et al., 2006; Kaliraj et al., 2014). The total expanse of 19 Offshore rocky outcrops 0.14 0.05 this category is estimated around 6.38 km2 which is equal to 20 Wave cut platforms 0.62 0.21 21 Wave cut notches 0.38 0.13 2.19% of the total area. Among them, the sandy beaches Total area of marine origin of 81.07 27.76 2 (1.16 km ) are extensively developed in the different parts of landforms coastal stretches including Manakudi, Chothavilai, Sanguthurai, ii) Fluvio-marine origin Periyakadu, Ganapathipuram, Muttam east, Kottilpadu, Midalam 22 Shoal 0.24 0.08 and Inayamputhenthurai coast. Beach terraces are gently sloping 23 Swale 0.92 0.32 features developed due to sediment deposition, typically bounded 24 Deltaic plain 60.99 20.89 Total area of fluvio-marine origin 62.15 21.28 by ridges and scarps on landward and seaward sides respectively. of landforms They are formed either from a pre-existing shoreline through mar- iii) Fluvial origin ine abrasion or erosion or due to accumulations of sediments in the 25 Alluvial plain 45.93 15.73 low wave energy zones by emerging slightly as marine-built ter- 26 Buried pediplain deep 9.52 3.26 races. Patches of beach terraces are distributed in various parts 27 Buried pediplain shallow 7.01 2.40 of the study area especially between Muttam and Rajakkaman- 28 Flood plain (older) 10.35 3.54 galam coastal stretches and the coverage of landform is restricted 29 Flood plain (younger) 6.14 2.10 2 30 Pediment deep 2.98 1.02 to 0.43 km (0.15%). However, enormous areas of the beaches were 31 Pediment (moderately 38.02 13.02 deformed and modified due to the Tsunami waves on 26th Decem- weathered) ber 2004 (Chandrasekar et al., 2006). Beach ridges are formed as 32 Pediment shallow 4.18 1.43 narrow and curve shaped features parallel to the shoreline 33 Structural hill and Inselberg 1.54 0.53 34 Wetland shallow/waterlogged 1.31 0.45 between Manakudi and Pillaithoppu stretches due to swash- area over-wash of sediments by the action of high-energy waves Total area of fluvial origin of 126.98 43.49 (Cherian et al., 2012). Similarly, the narrow and undulating berms landforms 2 and terraces are formed near Kasavanputhendurai (0.013 km ), iv) Aeolian origin Sanguthurai (0.03 km2) and Ganapathipuram (0.035 km2) coastal 35 Sand dune 6.94 2.38 areas. These are often altering to multi-faced forms due to fluctu- 36 Barrier sand dune 9.08 3.11 ations in sediment accumulation through swash during monsoon 37 Terisand (laterite) 5.42 1.86 Total area of aeolian origin of 21.44 7.34 (Kaliraj et al., 2014). Cusps are commonly distributed landforms landforms in various parts of the study area for a length of 25.44 km due to v) Coastal structures action of breaking waves at the surf zone. Fig. 4 shows the 38 Groins 0.16 0.05 sector-wise distribution of the coastal landforms along the study 39 Revetments/sea wall 0.12 0.04 area. 40 Jetties 0.08 0.03 The discontinuous cusps developed along the coastal segments Total area of coastal structures 0.36 0.12 Net total area of coastal landforms 292 100 of the western parts near Manavalakurichi–Colachel (4.6 km) and distribution Keezhkulam–Inayamputhenthurai (1.2 km) are due to presence

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

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 10 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx of sediment load for various landforms during the periods of 2000– (Cherian et al., 2012). Moreover, in the backshore region, relative 2011. Fig. 5 shows the topographical characteristics of the coastal heights of the sand dunes and foredunes have been reduced from landforms using SRTM DEM for the year 2000, and the ASTER 9–13 m to 6–11 m due to removal of surface dune layer by wind DEM represents the same for the year 2011. The elevation of land- winnowing during monsoons. Significant removal of sediments forms represented SRTM DEM is 0–157 m AMSL (Fig. 3A). Whereas, have taken place from the dune complexes due to the effect of the elevation range in the ASTER DEM depicts a reduction to 0– the Tsunami occurred on 26th, December 2004 (Chandrasekar 138 m AMSL (Fig. 3B). Morphodynamics of beaches through beach et al., 2012). In the inland area, even the fluvial landforms such profiling shows that the coastal zone prevailing with low wave as alluvial plains and flood plains have undergone reduction in ele- energy gets accreted with mass balance of sediment load up to vation from 34–55 in 2000 to 34–38 m in 2011. Severe erosion due 1–2 m in pre-monsoon and decreases to 0.5–1.0 m during rest of to river and surface runoff during monsoon is considered responsi- periods (Hentry et al., 2012). High wave energy zones experience ble the lost of landforms. severe erosion causing landward movement of shoreline due to erosion, beach subsidence and wave run-up. Moreover, artificial 4.3. Cross-shore profile change detection and assessment structures along the coast such as groins, revetments, and jetties pose hindrance to the natural hydrodynamic processes of waves The extracted cross-shore profile analysis using the DEM data- and currents causing severe erosion on the down-drift side sets provide information on geomorphic change of the various (Jayappa et al., 2006; Kaliraj et al., 2013). The low-laying landforms landforms in vertical scale (Zandbergen, 2008; Dawson and such as creeks, salt marshes and backwater that are above/below Smithers, 2010; Hicks, 2012). In the present study, the twelve MSL in their topographical setting render an elevation of 7to cross-shore profile sets (3 profiles for each sector) were separately +6 m with respect to MSL in 2000; however this has been modified extracted using SRTM and ASTER DEM datasets for the four sectors subsequently to 0–9 m during 2011. This could be due siltation namely Kanyakumari, Rajakkamangalam, Muttam and Thengapat- from the adjacent landforms by marine and fluvial activities. tinam at 5 km of interval. In this analysis, the total areal extend The beach landforms such as berms, cusps, ridges, scarps, ter- (292 km2) of the DEM datasets is gridded into 60 segments with races and sandy beaches have undergone relief reduction in terms a grid size of 5 5 km to extract the twelve cross-shore profile of elevation from 1–3 m to 1–2 m due to erosional processes of based on the concept of one profile flow across the five grids for wave, current and wind causing loss of sediments from the coast 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 1.55 m within a distance of 100 m from the shoreline. The coastal profiles denotes land lost or land gain of sediment load in the plain within a distance of 600–1500 m illustrates minor reduction coastal landforms along the four sectors during 2000–2011. In in sediment load (0.25 to 0.45 m) in the upper slope surface as the Kanyakumari sector, the profile K1-K10 extracted on northeast- the erosion processes helped them to accumulate in the lower ern part depicts increases of beach elevation as 1.33 m within a downslope surface recording a sediment load of up to 0.24– distance 100 m from the shoreline. Whereas, the K2-K20 profile 0.34 m (Fig. 7). across the Cape Comorin coast indicates 1.45 to 6.25 m of cliff In the Muttam sector, the M1-M10 profile drawn across the erosion along the rockyshore due to high energy waves actions. Murungavilai coast records a little land loss from the beach land- Similarly, the K3-K30 profile across the Kovalam coast shows severe forms (0.18 to 0.96 m) at a distance of 800 m from the shore- erosion (0.26 to 0.76 m) on the sandy beaches leading to forma- line. Swashing of littoral sediments maintains stability of sandy tion of beach cusps and beach scarps within a distance 300 m from beaches during SW and NE monsoons due to the action of low the shoreline. In the northeastern part, the coastal plain within a energy waves. The M2-M20 profile at the Muttam coast points to distance of 100–500 m illustrates reduction in elevation up to the development of narrow sandy beaches (0.73–1.57 m) along 0.68 to 0.93 m, besides, the sand dunes within a distance of the bottom of rockyshore within distance 480 m from shoreline 500–1000 m in the Cape Comorin coast also decreased in its eleva- due to the specific coastal configuration settings. Similarly, the tion from 0.25 to 1.38 m. The deltaic plain within a distance M3-M30 profile across the Kottilpadu coast exhibits accumulation 1100 and 2400 m in the northeastern part (profile K1-K10), also of sediment loads (0.2–0.99 m) on the beach landforms by rework- depicts land loss (0.26 to 1.80 m) in the upper parts and land ing of waves and currents on the sediments deposited from river gain (0.63–3.80 m) in the downslope region (Fig. 6). discharge leading to the formation of berms, ridges and foredunes In the Rajakkamangalam sector, the R1-R10 profile depicts cer- coinciding with the SW and NE monsoons (Hentry et al., 2010; tain modifications of landforms, such as, beach cusps, beach ridges Magesh et al., 2014). However, the coastal plains between 800 and foredunes either due to erosion (0.08 to 0.31 m) or accre- and 1500 m have experienced accumulation at a rate of 0.90– tion (0.06–0.15 m) within a distance of 600 m from the shoreline. 1.09 m due to constant deposition of substantial quantity of wind However, the R2-R20 profile exhibit buildup of beach berms transported sediments from seaward and landward sources. The (0.41–0.63 m) in proximity to the coast within a distance of alluvial plain across M3-M30 profile records accumulation at a fas- 200 m due to trapping of littoral sediments and erosion from fore- ter rate of 0.98–3.49 m sourcing from the weathered pediment dune sand during SW and NE monsoons respectively. The fore- materials transported and deposited through the river and surface dunes within a distance of 200–500 have been significantly runoff process (Fig. 8). affected due to reduction of sediment load at a rate of 0.48 to In the Thengapattinam sector, the fluviomarine processes 1.03 m and facilitates formation of berms along the coast. The mainly influence the morphological changes of landforms (Kunte R3-R30 profile depicts decrease of sediment loads at a rate of and Wagle, 2001). The profile T1-T10 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.

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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 depicts that the changes of sediment loads in each segments study a distance of 290 m from the shoreline. TheT2-T20 profile across the area at temporal frequencies. Midalam coast Also shows significant changes of sediment loads on beach landforms that include ridges, berms, cusps and sandy bea- 4.4. Volumetric change assessment of coastal landforms ches 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 Fig. 10 shows volumetric change of sediment load in each geo- 0 to destructive waves and littoral currents. However, the T3-T3 morphic units for specific sites of the study area. Table 3 shows the profile across the Thengapattinam estuary reflects development volumetric changes of sediment loads in various landforms, in of sandy beaches on the up-drift side with distinct ridges and which the negative and positive values represent the rate of land berms at an accumulation rate of 0.60 m whereas, loss of sediment lost (erosion) and land gain (deposition) of sediment load load (1.17 m) on the down-drift side resulted in the formation of respectively. beach cusps within the extent of 280 m from the shoreline. The Fig. 11 shows the net volumetric changes of sediment load in mudflats and shallow marshy landscapes within a distance of the different coastal landforms estimated as land lost 280–960 m are significantly deflated due to erosion at a rate of (241.69 m3/km2) for 62.82 km2and land gain (196.96 m3/km2) for 0.86 to 2.25 m and 1.94 to 3.90 respectively. The coastal the area of 202.80 km2 during 2000–2011. However, an area of upland at the Colachel has suffered a land loss (1.31 to 26.38 km2 remain unchanged either being stable or by maintaining 4.44 m) within a distance of 295–680 m due to the removal of a balance in the processes of accretion or erosion irrespective of the the surface layer for construction and urban development activi- seasons. Significant volume of land lost (113.82 m3/km2) and land ties. The fluvial landforms such as buried pediment deep, buried gain (105.78 m3/km2) for the area of 17.18 km2 and 53.70 km2 pediplain shallow and buried pediplain deep have undergone relief respectively. Wherein, the erosional landforms such as cusps and reduction at the rate of 0.58 to 4.39 m in various parts of the scarps have significantly increased in the area compared to their area with a distance of 1280–1470 m, 2235–2960 m and 3288– pre-morphological structures due to sediment accumulation at 3890 m. Here, the flood plain (younger) within a distance of the rate of 5.04–6.21 m3/km2 and 2.67–6.12 m3/km2 respectively. 0 1107–1920 m across T3-T3 profile is recurrently altered due to Estimated sediment loss for these areas indicates severe erosion the high rate of erosion (2.42 to 3.61 m) and the rate of accre- due to high-energy waves and rip currents (Kaliraj et al., 2013). tion ranging from 1.46 to 3.20 m during 2000–2011 (Fig. 9). It is Hentry et al. (2012) has estimated morphodynamics of beach land- observed that the morphological changes of various landforms forms along the southern coast through beach profiling reporting a are triggered by wave refraction and diffraction, littoral current profile elevation reduction up to 1–2 m during post-monsoon due movement, beach placer mining, encroachment and other anthro- to removal sediments by littoral currents and reduction sediment pogenic activities. Obviously, the rate of change in sediment load discharge from rivers. Similarly, sea cliffs have enlarged their shape (difference of DN values) estimated using cross-shore profile 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.

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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. In the inland area, the fluvial landforms have undergone signif- Whereas, the high-energy waves attacking the rockyshore results icant volumetric changes at the rate of land lost (88.91 m3/km2) for formation of wave-cut notches (4.68 m3/km2) and wave-cut plat- the areal extent of 27.90 km2 and land gain (57.58 m3/km2) for the forms (6.15 m3/km2) by removing sediments from the weak areal extent of 89.61 km2 due to fluvial process and anthropogenic weathered horizons of rockyshore. activities. The alluvial plain has volumetrically decreased in their Sandy beaches have experienced land lost at the rate of 6.15– sediment load from 7.92 to 5.99 m3/km2 due to removal of sand 3.24 m3/km2 probably due to backwashing of sediments carried and silt materials by fluvial action (Chandrasekar et al., 2012). offshore by frequent incidences of rip currents (Hentry et al., The sand dune complexes formed from aeolian action have shown 2010). The berms and terraces have also undergone considerable extensive reduction in sediment volume at the rate of 6.25–4.41, changes in their morphology due to removal of sediment at the 6.68–6.36 and 9.42–5.52 m3/km2 respectively. It is observed that rate of 6.72–6.46 m3/km2 and 3.89–5.25 m3/km2 respectively. the volumetric change of beach landforms is mainly influenced Changes in the breaker angle with the shoreline is frequently alter- by the constructive or destructive action of waves and littoral cur- ing the morphology of these landscapes as it affects the littoral sed- rents, while the inland landforms are mainly subject to fluvial iment transport along the coast (Reddy et al., 1984; Sajeev et al., action, flooding, surface runoff and other anthropogenic activities. 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 5. Field verification and volumetric change validation mouth of the estuaries near Thengapattinam and Manakudi coasts (Sanil Kumar et al., 2006). The coastal plains have experienced sig- The result of the Geomorphic Change Detection has been cross- nificant reduction in sediment load with a rate of 7.87–5.12 m3/ verified using GPS based field surveyed on the landforms at specific km2 due to erosion of surface layer by aeolian processes and sur- ground reference points for the year 2011. The handheld Garmin face runoff. Eventually, the deltaic plains have suffered relief ETREX 30 GPS instrument was used for demarcating the beach reduction at the rate of 7.13–6.94 m3/km2 due to reduction of landforms at selected locations with reference to derived output sub-aqueous and sub-aerial sediment materials supplied from of geomorphic maps. The post-field verification using GPS has river discharges and surface runoff (Samsuddin et al., 1991). The got the limitation of accuracy up to 4 m in open coastal area and coastal landforms have been either washed out or modified due 10 m in build-up and canopy covers. However, the vertical mea- to effect of Tsunami on 26th December 2004 and some parts are surement derived from GPS and the DEMs are unlikely to be exact recovering to their pre-Tsunami morphologies (Cherian et al., representations of the earth surface due to uncertainties including 2012). 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). There- Zandbergen, 2008; Wheaton et al., 2010). Field validation exercise fore, the result of measurement validation using GPS field survey proved a general acceptability of measurement of volumetric are indicative in nature. The control points of grid cell (pixels) with changes at selected control points on various geomorphological elevation (height) values were obtained from the SRTM and ASTER landforms. However, accuracy of the volumetric estimate of mor- DEM datasets at a particular coordinate for validation analysis phological change is limited to the extent of the earth surface com- (Siart et al., 2009; Yanyan et al., 2013). It is observed that the mean plexities, environmental setting and spatial resolution of DEMs difference of change in measurement is 2.1 between the control introducing bias on the successive volumetric measurement of points in DEM versus the collateral GPS observed points. Confor- the coastal landform changes. mity 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 6. Conclusions 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 Mapping of coastal geomorphology in large scale (1:10,000) is a 1.4 m. Difference in elevation observed are not substantial over first attempt for the study area. Coastal processes involving wave, the 10 control points of the inland area from both DEM and GPS current wind and anthropogenic activities mainly influence the measurements. This s indicative of the acceptability of accuracy characteristics of landforms. Seasonal changes of these factors of geomorphic change assessment (Wood and Hine, 2007; are expressed in the volumetric changes in their morphology due

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 16 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

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 sed- work. The corresponding author is thankful to DST-INSPIRE Divi- iment) on the landforms. The abnormal changes of various land- sion, Department of Science & Technology, Government of India forms like sandy beaches, dune complexes, and estuaries along for the award of INSPIRE Fellowship - SRF (DST/INSPIRE/ 2011/ various parts of the coast could be due to the effect of the Tsunami IF110366) for pursuing Ph.D. Degree in Remote sensing- during December 2004. The DoD analysis of geomorphic change GeoTechnology at Manonmaniam Sundaranar University, Tirunel- assessment reveals changes in morphologies due to erosion or veli – 627012. The authors would like to acknowledge Wheaton deposition processes. The spatial variation of sediment load sug- et al. (for GCD software) and USGS-GLCF, USA. Authors thank the gests morphologies of the landforms are closely related to the mar- anonymous referees for their critical and constructive contribution ine and terrestrial processes. The premises of this DoD analysis is to the paper. 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 References the specific control points on various landforms reveals minor variations between the measurements indicate the limitation, coher- Abermann, J., Fischer, A., Lambrecht, A., Geist, T., 2010. On the potential of very high ence and promising nature of the study. The present study resolution repeat DEMs in glacial and periglacial environments. Cryosphere 4, 53–65. provides primary information for coastal vulnerability assessment Ahmad, E., 1972. Coastal Geomorphology of India. Orient Longman, New Delhi, pp. and monitoring at a regional level using available data sets for 1–222. screening an area for detailed studies. Amos, C.L., 1995. Siliciclastic tidal flats. In: Perillo, G.M. (Ed.), Geomorphology and Sedimentology of Estuaries. Elsevier, Amsterdam, pp. 273–306. Anbarasu, K., 1994. Geomorphology of the Northern Tamilnadu Coast Using Remote Conflict of interest Sensing Techniques (Unpubli. Ph.D. thesis). Bharathidasan University, Tiruchirapalli, pp. 184–190. Aniello, P., 2003. Using ASTER DEMs to produce IKONOS orthophotos. In: ASPRS None. Annual Conference Proceedings, Anchorage, Alaska, pp. 122-128. Bauer, B.O., 2004. Geomorphology. In: Goudie, A.S. (Ed.), Encyclopedia of Geomorphology, vol. 1. Routledge, London, pp. 428–435. Acknowledgment Bird, E.C.F., 2000. Coastal Geomorphology: An Introduction. Wiley, Chichester, pp. 332–342. Blanchard, S.D., Rogan, J., Woodcock, D.W., 2010. Geomorphic change analysis using Authors are thankful to the Director, NCESS for provining sup- ASTER and SRTM digital elevation models in central Massachusetts, USA. GISci. port and continuous encouragement to carried out this research Remote Sens. 47 (1), 1–24.

Bocco, G., Mendoza, M., Velàzquez, A., 2001. Remote sensing and GIS-based regional Joevivek, V., Chandrasekar, N., Saravanan, S., 2013. Coastal vulnerability and geomorphological mapping – a tool for land use planning in developing shoreline changes for southern tip of India-Remote sensing and GIS approach. countries. Geomorphology 39, 211–219. Earth Sci. Clim. Change. http://dx.doi.org/10.4172/2157-761711000144. Bubenzer, O., Bolten, A., 2008. The use of new elevation data (SRTM/ASTER) for the Kaliraj, S., Chandrasekar, N., 2012. Geo-processing model on coastal vulnerability detection and morphometric quantification of pleistocene megadunes (draa) in index to explore risk zone along the South West Coast of Tamilnadu, India. Int. J. the eastern sahara and the southern namib. Geomorphology 102 (2), 221–231. Earth Sci. Eng. 5 (5), 1138–1147. Bubenzer, O., Bolten, A., 2009. The use of new elevation data (SRTM/ASTER) for the Kaliraj, S., Chandrasekar, N., Magesh, N.S., 2013. Evaluation of coastal erosion and detection and morphometric quantification of pleistocene megadunes (draa) in accretion process along the South-West coast of Kanyakumari, Tamil Nadu the Eastern Sahara and the Southern Namib. Geomorphology 102 (2), 221–231. using geospatial techniques. Arab. J. Geosci. http://dx.doi.org/10.1007/s12517- Butler, D., Walsh, S., 1998. The application of remote sensing and geographic 013-1216-7. information systems in the study of geomorphology: an introduction. Kaliraj, S., Chandrasekar, N., Magesh, N.S., 2014. Multispectral image analysis of Geomorphology 21, 179–181. suspended sediment concentration along the Southern coast of Kanyakumari, Carter, R.W.G., 1988. Coastal Environments. Academic Press, pp. 617–622. Tamil Nadu, India. J. Coastal Sci. 1 (1), 63–71. Carter, R.W.G., Woodroffe, C.D., 1994. Coastal Evolution: Late Quaternary Shoreline Knight, J., Mitchell, W., Rose J., 2011. Geomorphological Field Mapping. Smith, M. J., Morphodynamics. Cambridge University Press, Cambridge, pp. 517–527. Paron, P., Griffiths J (Eds.), Geomorphological Mapping: methods and Chandrasekar, N., Kaliraj, S., 2013. Coastal erosion and accretion assessment and applications. Elsevier: London, pp. 151–188. inundation risk zone mapping along SW coast of Kanyakumari, Tamil Nadu, Kunte, Pravin D., Wagle, B.G., 2001. Littoral transport studies along west coast of India. In: conference on Geological Engineering Hosted by University of Malaya, India – a review. Indian J. Mar. Sci. 30, 57–64. Kuala Lampur, Malaysia. AUN-SEED Group, Malaysia, pp. 75–91. Lane, S.N., Westaway, R.M., Hicks, D.M., 2003. Estimation of erosion and deposition Chandrasekar, N., Rajamanickam, G.V., 1995. Role of geomorphology in the volumes in a large, gravel-bed, braided river using synoptic remote sensing. distribution of heavy minerals along the Central Tamilnadu Coast. Earth Surf. Proc. Land. 28, 249–271. Geomorphol. Soc. 20, 44–47. Lee, J., 1991. Comparison of existing methods for building triangular irregular Chandrasekar, N., Cherian, A., Rajamanickam, M., Rajamanickam, G.V., 2000. Coastal network models of terrain from grid digital elevation models. Int. J. Geogr. Inf. landform mapping between Tuticorin and Vaippar using IRS-IC data. Indian J. Syst. 5, 267–285. Geomorphol. 5 (1&2), 115–122. Lillysand, M.T., Kiefer, W.R., 2000. Remote Sensing and Image Interpretation. John Chandrasekar, N., Loveson, V.J., Rajamanickam, G.V., 1996. Longshore sediment Wiley & Sons Inc. transport along the southern coast. Tamil Nadu. Indian Geological Congress, 60– Loveson, V.J., 1993. Geological and Geomorphological Investigation Related to Sea- 63. Level Variation and Heavy Mineral Accumulation Along the Southern Chandrasekar, N., Mujabar, P.S., 2010. Computer application on evaluating beach Tamilnadu Beaches, India (Unpublished Ph.D. thesis). Madurai Kamaraj sediment erosion and accretion from profile survey data. Computat. Geosci., University, Madurai, pp. 212–223. 503–508 Magesh, N.S., Chandrasekar, N., Kaliraj, S., 2014. Mapping the coastal Chandrasekar, N., Saravanan, S., Loveson, J., Rajamanickam, J., Rajamanickam, G.V., geomorphological landforms through Aster DEM and Landsat data – a case 2006. Classification of Tsunami Hazard along the Southern coast of India: an study between Tuticorin – Vembar coastal stretch, southeast coast of India. J. initiative to safeguard the coastal environment from similar debacle. Sci. Coastal Sci. 1 (1), 01–05. Tsunami Hazard. 24 (1), 3–23. Maksud Kamal, A.S.M., Midorikawa, S., 2004. GIS-based geomorphological mapping Chandrasekar, N., Saravanan, S., Rajamanickam, M., Hentry, C., Rajamanickam, G.V., using remote sensing data and supplementary geoinformation: A case study of 2012. Correlation between coastal geomorphology and Tsunami inundation the Dhaka city area, Bangladesh. Int. J. Appl. Earth Obs. Geoinf. 6 (2), 111–125. along the coast of Kanyakumari, India. J. Ocean Univ. China, 1–6. Marghany, M., Sabu, Z., Hashim, M., 2010. Mapping coastal geomorphology changes Cherian, A., Chandrasekar, N., Gujar, A.R., Rajamanickam, G.V., 2012. Coastal erosion using synthetic aperture radar data. Int. J. Phys. Sci. 5 (2), 1890–1896. assessment along the southern Tamilnadu coast, India. I. J. Earth Sci. Eng. 5 (2), Milan, D.J., Heritage, G.L., Large, A.R.G., Fuller, I.C., 2011. Filtering spatial error from 352–357. DEMs: implications for morphological change estimation. Geomorphology 125, Chockalingam, M., 1993. Coastal Geomorphological Studies of the Region 160–171. Subtended between Mandapam and Devipattinam, Tamilnadu (Unpublished Mith, M.J., Clark, C.D., 2005. Methods for the visualization of digital elevation Ph.D. thesis). Tamil University, Thanjavur, pp. 117–127. models for landform mapping. Earth. Surf. Proc. Land. 30, 885–900. Cuartero, A., Felicisimo, A.M., Ariza, R.J., 2004. Accuracy of DEM generation from Mishra, P., Patra, S.K., Ramana Murthy, M.V., Mohanty, P.K., Panda, U.S., 2011. Terra-ASTER Stereo data. Int. Arch. Photogramm. Remote Sens. 35, 559–563. Interaction of monsoonal wave, current and tide near Gopalpur, east coast of Davies, J.L., 1972. Geographical Variation in Coastal Development. Oliver and Boyd, India, and their impact on beach profile: a case study. Nat. Hazards. 59, 1145– Edinburgh, pp. 204–212. 1159. Dawson, J.L., Smithers, S.G., 2010. Shoreline and beach volume change between Mujabar, S.P., Chandrasekar, N., 2011. Landform dynamics along the southern 1967 and 2007 at Raine Island, Great Barrier Reef, Australia. Global Planet. coastal Tamil Nadu of India by using remote sensing and GIS. Geo-spat. Inf. Sci. Change 72, 141–154. 14 (4), 282–293. Desai, P.S., Narain, A., Nayak, S.R., Manikiam, B., Adiga, S., Nath, A.N., 1991. IRS 1A Murty, C.S., Varadachari, V.V.R., 1980. Topographic changes of the beach at applications for coastal and marine resources. Curr. Sci. 61 (3 & 4), 204–208. Valiyathura. Kerala. Indian. J. Mar. Sci. 9, 31–34. Embabi, N.S., Moawad, M.B., 2014. A semi-automated approach for mapping Nayak, S., 2002. Use of satellite data in coastal mapping. Indian Cartographer 22, geomorphology of El Bardawil Lake, Northern Sinai, Egypt, using integrated 147–156. remote sensing and GIS techniques. Egypt. J. Remote Sens. Space Sci. 17 (1), 41– Nayak, S., Sahai, B., 1985. Coastal morphology: a case study in the Gulf of Khambhat 60. (Cambay). Int. J. Remote Sens. 6 (3&4), 559–568. Fabris, M., Pesci, A., 2005. Automated DEM extraction in digital aerial Nicholls, R.J., Klein, R.J.T., Tol, R.S.J., 2007. Managing coastal vulnerability and photogrammetry: precision and validation for mass movement monitoring. climate change: a nation to global perspective. In: McFadden, L., Nicholls, R.J., Ann. Geophys. 48, 973–988. Pennington-Rowsell, E.C. (Eds.), Managing Coastal Vulnerability: An Integrated Farrell, E., Sherman, D., Ellis, J., Li, B., 2012. Vertical distribution of grain size for Approach. Elsevier, Amsterdam, pp. 221–240. windblown sand. Aeolian Res. 7, 51–61. Nikolakopoulos, K.G., Kamaratakis, E.K., Chrysoulakis, N., 2006. SRTM vs ASTER Gyasi-Agyei, Y., Willgoose, G., De Troch, F., 1995. Effects of vertical resolution and elevation products. Comparing for two regions in Crete, Greece. Int. J. Remote map scale of Digital Elevation Models on geomorphological parameters used in Sens. 27 (21), 4819–4838. hydrology. Hydrol. Process. 9, 363–382. Nordstrom, K.F., 2000. Beaches and Dunes of Developed Coasts. Cambridge Hentry, C., Chandrasekar, N., Saravanan, S., Dajkumar Sahayam, J., 2010. Influence of University Press, Cambridge, pp. 330–338. geomorphology and bathymetry on the effects of the 2004 tsunami at Colachel, Pavlopoulos, K., Evelpidou, N., Vassilopoulos, A., 2009. Mapping Geomorphological South India. Bull. Eng. Geol. Environ. 69, 431–442. Environments. Springer, Dordrecht Heidelberg, London. http://dx.doi.org/ Hentry, C., Chandrasekar, N., Saravanan, S., 2012. Beach dynamics of Colachel open 10.1007/978-3-642-01950-0. coast, Kanyakumari District (SW India). Z. Geomorphol. http://dx.doi.org/ Potts, L.V., Akyilmaz, O., Braun, A., Shum, C.K., 2008. Multi-resolution dune 10.1127/0372-8854/2012/0077. morphology using Shuttle Radar Topography Mission (SRTM) and dune Hicks, D.M., 2012. Remotely sensed topographic change in gravel riverbeds with mobility from fuzzy inference systems using SRTM and altimetric data. Int. J. flowing channels. In: Gravel-Bed Rivers. Wiley, pp. 303–314. Remote Sens. 29 (2), 819–901. James, L.A., Hodgson, M.E., Ghoshal, S., Latiolais, M.M., 2012. Geomorphic change Rao, D., 2002. Remote sensing application in geomorphology. Trop. Ecol. 43, 49–59. detection using historic maps and DEM differencing: the temporal dimension of Reddy, B.S.R., Sarma, K.G.S., Kumar, K.H., 1984. Beach changes during normal and geospatial analysis. Geomorphology 137, 181–198. cyclonic periods along the Visakhapatnam coast. Indian J. Mar. Sci. 14, 210–216. Jayangondaperumal, R., Murari, M.K., Sivasubramanian, P., Chandrasekar, N., Sajeev, P., Chandamohan, P., Josanto, V., Sanakaranarayanan, V.N., 1997. Studies on Singhvi, A.K., 2012. Luminescence dating of fluvial and coastal red sediments sediment transport along Kerala coast, south west coast of India. Indian J. Mar. in the SE coast, India, and implications for paleoenvironmental changes and Sci. 26, 11–15. dune reddening. Quatern. Res. 77, 468–481. Samsuddin, M., Ramachandran, K.K., Suchindan, G.K., 1991. Sediment Jayappa, K.S., Mitra, D., Mishra, A.K., 2006. Coastal geomorphological and land-use characteristics, processes and stability of the Beaches in the northern Kerala and land-cover study of Sagar Island, Bay of Bengal (India) using remotely coast, India. J. Geol. Soc. India 38, 82–95. sensed data. Int. J. Remote Sens. 27, 3671–3682. Sanil Kumar, V., Pathakm, K.C., Pednekar, P., Raju, N.S.N., Gowthaman, R., 2006. Jena, B.K., Chandramohan, P., Sanil Kumar, V., 2001. Longshore transport based on Coastal Processes along the Indian Coastline. Curr. Sci. 91 (4), 530–536. directional waves along North Tamilnadu coast, India. J. Coastal Res. 7, 322– Saravanan, S., Chandrasekar, N., 2010. Distribution and assemblages of heavy 327. mineral suites, SE India. Int. J. Oceans Oceanogr. 4 (1), 1–15.

Please cite this article in press as: Kaliraj, S., et al.. Egypt. J. Remote Sensing Space Sci. (2017), http://dx.doi.org/10.1016/j.ejrs.2016.12.006 18 S. Kaliraj et al. / The Egyptian Journal of Remote Sensing and Space Sciences xxx (2017) xxx–xxx

Schwendel, A.C., Fuller, I.C., Death, R.G., 2012. Assessing DEM interpolation methods Wechsler, S.P., 2000. Effect of DEM Uncertainty on Topographic Parameters, DEM for effective representation of upland stream morphology for rapid appraisal of Scale and Terrain Evaluation. State University of New York, Syracuse, New York, bed stability. River Res. Appl. 28, 567–584. pp. 380–389. Shaikh, M.G., Nayak, S., Shah, P.N., Jambusaria, B.B., 1989. Coastal land form Wheaton, J. M., 2008. Uncertainty in morphological sediment budgeting of rivers. mapping around the Gulf of Khambhat using LANDSAT TM data. J. Ind. Soc. (Ph.D. thesis). University of Southampton, Southampton, 412–420. Remote Sens. 17 (1), 41–48. Wheaton, J.M., Brasington, J., Brewer, P. A., Darby, S., Pasternack, G.B., Sear, D., Siart, C., Bubenzer, O., Eitel, B., 2009. Combining digital elevation data (ASTER/ Vericat, D., Williams, R., 2007. Improved fluvial geomorphic interpretation from SRTM), high resolution satellite imagery (Quickbird) and GIS for DEM differencing. In: Eos Trans. AGU. 88(52): Fall Meet. Suppl., Abstract H43E- geomorphological mapping: a multi-component case study on Mediterranean 1672. karst in Central Crete. Geomorphology 112, 106–121. Wheaton, J.M., Brasington, J., Darby, S.E., Sear, D.A., 2010. Accounting for Slaymaker, O., 2001. The role of remote sensing in geomorphology and terrain uncertainty in DEMs from repeat topographic surveys: improved sediment analysis in the Canadian Cordillera. Int. J. Appl. Earth Obs. Geoinf. 3, 11–17. budgets. Earth Surf. Proc. Land. 35, 136–156. Smith, M., Pain, C., 2006. Applications of remote sensing in geomorphology. Prog. Wood, J., 1996. The Geomorphological Characterization of Digital Elevation Models Phys. Geog. 33, 568–582. (Ph.D. thesis). Department of Geography, University of Leicester, Leicester, UK, Smith, M.J., Pain, C.F., 2009. Applications of remote sensing in geomorphology. Prog. pp. 60–72. Phys. Geogr. 33 (4), 568–582. Wood, N., Hine, A.C., 2007. Spatial trends in marsh sediment deposition within a Smith, M.J., Rose, J., Booth, S., 2006. Geomorphological mapping of glacial landforms microtidal creek system, Waccasassa Bay, Florida. J. Coastal Res. 23 (4), 823–833. from remotely sensed data: an evaluation of the principal data sources and an Woodroffe, C.D., 2002. Coasts: Form, Process and Evolution. Cambridge University assessment of their quality. Geomorphology 76, 148–165. Press, Cambridge, pp. 623–632. Tomar, A.S., Singh, U.C., 2012. Geomorphological mapping using remote sensing and Wright, R., Garbeil, H., Baloga, S., Mouginis-Mark, P.J., 2006. An assessment of GIS A tool for land use planning around shivpuri city, M.P., India. IOSR-JCE 5 (1), shuttle radar topography mission digital elevation data for studies of volcano 28–30. morphology. Remote Sens. Environ. 105, 41–53. Toutin, T., 2008. ASTER DEMs for geomatic and geoscientific applications: a review. Yanyan, K., Fei, X., Xianrong, D., Changkuan, Z., Ligang, C., Xiaoping, G., Jennifer, G., Int. J. Remote Sens. 29 (7), 1855–1875. 2013. Coastal evolution of Yancheng, northern Jiangsu, China since the mid- United States Geological Survey., 1997. Standards for Digital Elevation Models. Holocene based on the Landsat MSS imagery. J. Geogr. Sci. 23 (5), 915–931. Department of the Interior, Washington DC, pp. 60–70. Zandbergen, P., 2008. Applications of shuttle radar topography mission elevation Waldhoff, G., Bubenzer, O., Bolten, A., Koppe, W., Bareth, G., 2008. Spectral analysis data. Geogr. Compass 2 (5), 1404–1431. of ASTER, hyperion, and Quickbird data for geomorphological and geological Zhang, K., Whitman, D., Leatherman, S., Robertson, W., 2005. Quantification of beach research in Egypt (Dakhla Oasis, Western Desert). Int. Arch. Photogramm. changes caused by hurricane Floyd along Florida’s Atlantic Coast using airborne Remote Sens. Spat. Inf. Sci. 37 (1), 201–206. laser surveys. J. Coastal Res. 21, 44–134.

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