Abstract
Shoreline changes on account of global climate change and sea level rise is one of the major problems along the coastlines in different continents of the world. This study was carried out along the coastlines of Andhra Pradesh state in India using multi-temporal satellite images from 1973 to 2015 period. Long-term coastal erosion and accretion rate over a period of 42 years has calculated using Digital Shoreline Analysis System and prepared shoreline maps by using GIS-Software. End point rate (EPR) statistical method is applied to estimate the shoreline change rate. Various coastal parameters like sea level rise, geomorphology, elevation and coastal slope were used to find out the interactive relationship between the physical parameters and shoreline changes in the area. The study revealed that the coastal changes are more dominant in Krishna and Godavari Deltaic plain. The average erosion and accretion rate observed in the Krishna Godavari delta was 10.63 and 17.29 m per year respectively. The study indicated that, climatic changes and fluvial process are playing key role in changing shoreline positions. The study exhibited that elevation and slope are played intense role in the shoreline positional change. The present study demonstrates that combined use of satellite imagery and EPR statistical method as an accurate and reliable method for shoreline change analysis.
Similar content being viewed by others
Avoid common mistakes on your manuscript.
Introduction
Tremendous population and developmental process have been building in the coastal regions for the last 40 years (Kumar et al. 2010). More than 50 percentages of the world’s population lives within the 60 km of a shoreline (United Nations Environment Programme (UNEP) 2007; Kumar et al. 2010). Urbanization and the fast developments of coastal cities exhibited dominant population trends over the last few decades, leading to the development of mega cities in all coastal regions across the world. In 1950 there were only two mega cities in the world (New York and London), whereas there were 20 M cities in 1990 (Nicholls 1995; Basheer Ahammed et al. 2016). The average population density in the coastal zone was 77/km2 in 1990 and 87/km2 in 2000, and a projected 99/km2 in 2010 (UNEP 2007; Chandrasekar et al. 2013).The scenario of people living in the coastal regions compared with available coastal lands further indicates that the people have tendency to live in coastal areas than inland.
The coastal environment is dynamic due to constantly changing types of interactions among the ocean, atmosphere, land and people (Nicholls et al. 2007). Coastlines are not static entities but fluctuate at a short-term seasonal level as well as a longer-term climatic-change level. However, much development in the littoral zone occurs with the intention of ‘stabilizing the shoreline’, creating a conflict between human use and the coastline’s natural processes. The dynamic systems found on the coast are under increasing pressure by anthropogenic development (Nicholls et al. 2007). Developmental pressures within the coastal zone are clearly set to increase as its population rises. It is estimated that by 2050, 91% of the world’s coast will be impacted by development (Nellemann and Hain 2008). Pressure is not only exerted from within the coastal zone but increasing at broader at more global scale (Swaney et al. 2012). Ultimately, with population and development growing at a rapid rate, the world’s coastal environments are under increasing stress. This pressure is exuberated by the growing threat of climate. Relative sea-levels are predicted to rise increasing the risk of erosion and inundation, coupled with an increased likelihood of extreme weather events and storm surges (Nicholls et al. 2007).
The eastern coast of India faces severe such threats due to rapid changes in coastal geomorphology by fluvial influences, sea-level change, tropical cyclones, and associated storm surges (Woodruff et al. 2013). Shores are influenced by the topography of the surrounding landscape, as well as by water induced erosion, such as waves. The geological composition of rock and soil dictates the type of shore which is created. The eastern coast of Indian subcontinent is more vulnerable to shoreline change (Rao et al. 2008; Mujabar and Chandrasekar 2013; Jayanthi et al. 2018) as compared to the western coast.
In this context, the need for effective management of eastern coast of India is paramount. Mainly, two approaches are used for shoreline and coastal land change analysis: field based methods, including topographic survey techniques (Thom and Hall 1991; Ruggiero et al. 2005; Yunus and Narayana 2015) and remote sensing interpretations using aerial photographs and temporal satellite images (Dolan et al. 1991; Fletcher et al. 2003; Ford 2013). The first technique provides high accuracy and detailed information, but it is time consuming, costly and also posses difficulties in assessment of historical shoreline movements. With the advent of space technology and the development of high-resolutions sensors, remote sensing-based applications for monitoring coastal and shoreline changes are widely used (Zhang et al. 2005; Ford 2011, 2013). The geospatial techniques are economical and require less time for processing as compared to on-site field investigation (Morton et al. 2004; Alemayehu et al. 2014; Tamassoki et al. 2014; Joesidawati and Suntoyo 2016). Due to its repetitive, multispectral and synoptic nature, satellite remote sensing has proved to be tremendously useful in providing information on various aspects of the coastal environment, viz. coastal landforms, shoreline changes, tidal levels, early tsunami warnings, weather forecasting, suspended sediment dynamics, coastal currents, vital coastal habitats, etc. However, long-term temporal analysis is to be done by remote sensing. Digital Shoreline Analysis System (DSAS) is an extension that enhances the normal functionality of ESRI ArcGIS software, and enables users to calculate shoreline rate of change statistics from a time series of multiple shoreline positions (Thieler et al. 2009) which are extracted from the time series satellite data.
Many worker attempted shoreline change analysis along the eastern coastline covering Andhra coast (Rao et al. 2008; Vivek 2015; Basheer Ahammed et al. 2016) and Tamilnadu coast (Mujabar and Chandrasekar 2013; Mageswaran et al. 2015; Natesan et al. 2015 and Jayanthi et al. 2018). Similar study along the western coast were attempted covering Kerala coast (Mohan and Jairaj 2014) Karnataka coast (ChenthamilSelvan et al. 2014; Jana and Hegde 2016) and Maharashtra coast (Vidya et al. 2015). All the studies described about the shoreline changes and its impacts on the coastline and habitats. However, there is little work carried out along the coastlines in Andhra Pradesh founding the shoreline change and controlling parameters. Assessment of shoreline changes help in understanding the dynamics of coastline and the expected impact on the natural and human resources. Positional change of shorelines will have a more adverse impact on low elevated coastal regions (Jayanthi et al. 2018). Therefore, regional impact studies in the vulnerable areas can provide baseline information required for coastal zone management and planning. Shoreline changes in different regions may get influenced interaction among different parameters such as coastal elevation, coastal slope, geomorphology and sea level rise. In this background, east coast of India, Andhra Pradesh was taken for the study as the region is well known for the vulnerability to extreme climatic events.
The major objective of present research is to provide the systematic information about shoreline change and its controlling parameters along the east coast of India in part of Andhra Pradesh, using multi temporal satellite data and geospatial techniques. In the present study, End Point Rate (EPR) and Net Shoreline Movement (NSM) statistical methods is used for short term and long term shoreline change analysis respectively.
Study area
The state of Andhra Pradesh is located in the eastern coast of Indian sub continent. It is one of the 29 states of India. It lies between 120 35′ 23.67″ and 190 10′ 15.47” N latitude and 760 44′ 32.47”and 840 47′ 33.88″ E longitude. The state of Andhra Pradesh is shares its state boundaries with other states viz.; Odisha in the northeast, Telungana in the northwest, Karnataka in the west and Tamilnadu in the south. Bay of Bengal exist all the eastern boundary of the state (Fig. 1). It is the eighth largest state in the country covering an area of 160,205 km2 (Administrative and Geographical Profile 2015).This state has the second longest coastline of 972 Km among all the coastal states and playing very important role in the Indian economy and coastal bio diversity. Coastal Andhra Pradesh has large population concentrating on activities allied to aquatic and marine resources. The largest non-perennial rivers in India, the Krishna and the Godavari rivers meet with ocean in the same coast and those areas are cherished with extensive mangroves and wetlands. Rapid urbanization and development along the Andhra coast is leading to change prominent along the shorelines. Kakinada, Vijayawada, Vishakhapatnam, Nellore and Gundur are the major cities in the coastal regions of Andhra Pradesh.
: The location Map. (a Administrative boundary of India. b Administrative boundary of Andhra Pradesh State. c and d shows satellite image of the study area for the year 1973 and 2015 respectively
Materials and methods
Data used
Shoreline mapping was done with the help of geo-spatial techniques using Landsat satellite data (Table 1) acquired from earth explorer (www.earthexplorer.usgs.gov) pertaining to the period 1973, 1980, 1990, 2000, 2010 and 2015. Spatial resolution of the Landsat satellite data is varying in different sensors. Landsat 1–2 satellite provides Multi Spectral Sensor (MSS) data having 72.5 m spatial resolution; however USGS provides resembled 60 m spatial resolution data. The rest of the sensors viz. Thematic Mapper (TM), Enhanced Thematic Mapper (ETM+) and OLI) provide data with 30 m spatial resolution.
Mean sea level rise data is collected along the coastline of Andhra Pradesh from PSMSL- GLOSS (Global Sea Level Observing System) data depicting that most of the study area is under low sea level rise. Four tide gauge station (Port Blair, Paradip, Vishakhapatnam and Chennai) base data was used to estimate the regional sea level trend, there are. Tide gauge data for a period 74 years (1940 to 2014) was used to estimate sea level rise and changes.
Geomorphology map for entire Andhra Pradesh was prepared using Bhuvan thematic map, Landsat 8, ASTER GDEM and Google earth Images.
Coastal slopes was deduced along the coastline of Andhra Pradesh using GEBCO (General Bathymetric Chart of Oceans) data base which indicate that most of the area was covered by steep slope or gentle slope. GEBCO provides 30 Arc Sec Spatial resolutions.
Coastal regional elevation is derived along the coastline of Andhra Pradesh using ASTER (Advanced Space borne Thermal Emission and Reflection Radiometer) data depicting that most of the study area was below 3 m (MSL). ASTER GDEM V.2 provides 1 arc sec spatial resolution data which was further classified using ArcGIS into five classes.
Methodology
Shoreline change analysis
This study delineates the shoreline position change along the east coast of India, Andhra Pradesh. There are two major objectives addressed in the present research; the rate of changes of shoreline from 1973 to 2015 and physical parameters in the coastline controlling the shoreline positional changes. The End Point Rate (EPR) was used to estimate the shoreline change rate. Shoreline changes are calculated using Landsat satellite image. After the geometric correction and image enhancement, shoreline corresponding to individual period was generated using on-screen point mode digitization technique by using standard False Color Composite (FCC) using blue, green and near infra-red bands to separates land-water boundary distinctly. Further, the change in shoreline through processes of accretion and erosion was analyzed in a Geographic Information System (GIS) by measuring differences in shoreline locations using Digital Shoreline Analysis System (DSAS) (Thieler et al. 2009; DSAS 2015). Several statistical approaches are available within DSAS for both extracting shoreline positions and quantifying shoreline change. EPR are calculated by dividing the distance of shoreline movement by the time elapsed between the earliest and latest measurements i.e., the oldest and the youngest shorelines. Three components required to estimate shoreline changes include base line, shoreline and transect line. DSAS application uses a measurement baseline method (Clow and Leatherman 1984) to calculate rate-of-change statistics for a time series of shorelines. The baseline serves as the starting point for all transects cast by the DSAS application. Transects intersecting each shoreline at the measurement points are used to calculate shoreline-change rates (Fig. 2). The major advantage of the EPR is its ease of computation and minimal requirement for shoreline data as it requires only two shorelines data. The calculation of shoreline changes has been carried out as given in flow-chart (Fig. 3). Shoreline change rate pertaining to individual decades 70’s (1972–1980), 80’s (1980–1990), 90’s (1990–2000), 2000’s (2000–2010), and 2010’s (2010–2015) was calculated based on the data availability. The single overlaid map representing the end point rate for individual decades was prepared (Fig. 8). The negative and positive values in the result are depicting erosion and accretion respectively.
Figure shows the interaction between shoreline, baseline and transect (Left) interaction between transect line and measurement points (Right)
Figure shows the process of Shoreline change calculation with reference to the End Point Rate (EPR) Statistical method
Schematic diagram of the Methodology to shoreline assessment and controlling Parameters
The shoreline change rate was further categorized into five major classes representing values less than −10 m (high erosion), −10 to −2 m (medium erosion), −2 to +2 m (No changes/Stable), 2 to 10 m (medium accretion) and above 10 m (high accretion). Stable/ no change considered −2 to +2 m and these small changes that can also be considered as the technological/source errors (Kankara et al. 2015; Hegde and Akshaya 2015). Coastal geomorphology, elevation, coastal slope, and mean sea level rise comprises four physical parameters are acts as controlling factors in coastal dynamics. Further analysis of the shoreline change map overlaid with the physical parameters gave clear information about the dynamics in the coastal environment.
Factors controlling the shoreline change
In the present study we considered the prime factors controlling shoreline changes viz., sea level rise at the coast, geomorphology, coastal slope and elevation of the coastal region. These parameters were categorized in to five classes (very high to very low) on the basis of their influence to the shoreline change (Table 2). Shoreline changes and its distribution with physical parameters controlling the shoreline position change are discussed in Fig. 17. The schematic diagram of the overall methodology is shown in the Fig. 4.
Shoreline change and sea level rise
Mean sea level is an average level for the surface of one or more of Earth’s oceans from which heights such as elevations may be measured. Sea level rise rate is calculated using tide gauge data and further these data (mean sea level rise for each stations) converted from non-spatial to spatial data format using ArcGIS, Sea level rise rate pertaining to the tide gauge stations were interpolated using krigging interpolation technique and the values along the coast were assigned the corresponding coastal segments using Arc GIS tool. Sea level rise rate is classified in to 5 classes. Below 1.08 mm/yr. considered as very low risk, flowingly, 1.081–2.5 considered low risk, 1.21–2.95 considered moderate risk 2.96–3.16 considered high risk and above 3.16 is considered very high risk. However, the study area is lying in very low risk zone according to sea level rise.
Shoreline change and coastal geomorphology
Coastal geomorphology helps to understand its influences in coastal process and express the relative erodability of different landforms. Geomorphology map for entire Andhra Pradesh with Level-II classification (RAO et al. 2015), was acquired from Bhuvan thematic map. The data was converted to vector format using Arc GIS and further overlaid with Google Earth and ASTER GDEM for understanding the relief variability of geomorphological units. The geomorphological units were classified in to the five classes for further analysis. Young coastal plains and sand beaches are having very highly influencing to get transform in to erosion or depositional landforms. The different risk classes for geomorphology are listed in the Table 5. Accordingly, there is no class has found along the study area lying in very low risk class (Table 2).
Shoreline change and costal slope
Slope is one of the important parameter for coastal dynamics and it is also controls the sea water inundation. Coastal Slope were generated using GEBCO Bathemetric Chart for the studya area (Gebco 2014) Risk classification for slope was divided in to five class, gentle slope (Flat/ nearly flat or Below 100) is considered as very high risk zone, the steep slope (Above 450) is considered as high risk zone; these steep slope areas in the coastline will be affected by the wave or wind forces and will result to the erosion. However, it is also considered as high risk. 100–200 degree considered as moderate risk, 200–300 as low and 300–450 is considered as very low risk zones. (Fig. 5) (Table 2).
Shoreline change and regional elevation
It is important to study the coastal regional elevation to identify and estimated extend of land area threatened by future sea-level rise (Kumar et al. 2010; Mahendra et al. 2011). On the basis of risk the DEM data was divided five classes, 0–3 m (very high vulnerable), 4–6 m (high), 7–12 m (medium) 12–18 m (low) and above 18 (very low) (Table 2). Regional elevation data for the Andhra coast depicting the maximum area under the very low elevated topography (less than 3 m).
Result
Shoreline change assessment using end point rate statistical method
The analysis emphasizing the shoreline change and its influencing factors revealed that the dynamic changes are more concentrated in the deltaic environments and young alluvial plains. In the case of shoreline change, not only erosion but sometimes accretion is also dangerous. Accretion can be a problem too because infilling of ports or formation of landward migrating dunes that can menace human structures. Assessment of shoreline changes pertaining to 42 years data during 1973–2015 revealed dynamic changes by coastal erosion and accretion presences along the 972 km long coastline in the Andhra Pradesh state (Figs. 7 and 8) (Table 3). There are the two techniques used to estimate the shoreline changes (End point rate and Net shoreline movement) are discussed below.
Shoreline changes during 1973–1980
During the period 1973–1980, erosional coast is more observed in comparison to depositional coast. Along the 972 km, around 257 km coastline was observed with high erosion. It is mainly observed in Krishna and Godavari river mouth, Kakinada, Pulikat, and some part of Vishakhapatnam. In contrast low erosion was observed along 172 km coastline and it was distributed in the small stretches along the coastline. Along the coastline, 206 km length was exhibited no change. In the case of accretion low accretion was observed in 210 km coastline and 124 km coastline was observed with high accretion. Vishakhapatnam, Sompeta, and some part of Krishna and Godavari rivers mouth observed high accretion. The highest erosions was recorded in the Krishna river mouth at Gangadipalem (Transect id 717) (Fig. 6) with the erosion rate of −332 m per year, and the highest accretion rate was recorded in the Godavari river mouth at Chollangi (Transect id 1157) (Fig. 6) with the accretion rate of 484 m per year (Figs. 7, 8 and 9) (Table 3).
Map showing the Coastal Slope of the Andhra Pradesh coast
Figure Shows the Transect lines with Transect ID. (a) Shows transect numbers for five districts viz., Nellore, Prakasam, Gundur, Krishna and West Godavari. (b) Shows transect numbers for East Godavari and Vishakhapatnam. (c) Shows transect numbers for Vizhinagaram and Srikakulam
Figure shows the decadal shoreline changes along the coastline for the decades 1973–1980, 1980–1990, 1990–2000, 2000–2010 and 2010–2015
Map of shoreline change (End Point Rate) pertaining to the period of 1973–1980, 1980–1990, 1990–2000, 2000–2010 and 2010–2015
Shoreline changes during 1980–1990
In compare to the previous decade, high accretion rate was observed in the Krishna Godavari deltaic plain. Especially, Kakinada, Narsapur, Lenkevanidibba, Pulicat coasts. High accretion is observed along 311 km of the coastline. The highest accretion rate was observed in the Krishna river mouth at Gangadipalem (Transect id 807) (Fig. 6) with the accretion rate of 325 m per year. Low accretion was observed only in 68 km of the coastline. Northern coast of Narsapur, Southern Krishna and some stretches of Pulicat coast were observed the low accretion rate. High and low erosion rate were observed in 155.52 km and 349.92 km respectively. The higher rate of erosion was recorded in the coast of Brahmasamedyam coast, southern part of Chollangi (Transect id 1191) (Fig. 6) with the rate of erosion 271 m per year. Srikakulam, Vizhinagar, Vishakhapatnam, Southern mouth of river Godavri, Ullipalem, Ramakrishnapuram, northeast coast of Orlagondi reserve forest and Dindi coast were observed as erosional coast. Along the 972 km coastline, 87.48 km coastline exhibited no changes during the decade. Northern and southern part of the study area were observed that low erosion and the central part was observed with high accretion (Figs. 7, 8 and 9) (Table 3).
Shoreline changes during 1990–2000
In 1990–2000, a different scenario was observed in comparison to the previous decades. The Krishna Godavari delta region exhibited erosional trend in 1973–1980 and accretion trend in 1980–1990, whereas in this decade, there is accretion trend observed in the Godavari river mouth and erosional trend in the Krishna mouth. High and low accretion was observed along 107 and 233 km of coastline respectively. Tuni, Koringa, Kothapalem, Northern Pulicat and Nellore coastal region are observed accretion and the highest accretion rate have been observed in the northern coast of Koringa Wild life sanctuary, Chollangi with the accretion rate of 297 m per year (Transect id 1190) (Fig. 6). An increasing trend was observed in the stable class with 281 km coastline exhibited no changes from the previous decade’s shoreline positions. It was also observed that the southern and northern part of the study area was not much dynamic in nature as compare to the Krishna Godavari deltaic plain. The erosional coast was found along the coast of Srikakulam, Vizianagram, Vishakhapatnam, Krishna and small stretches of the Gundur and Prakasham coast. Around 145 km and 204 km coastline was observed with high erosion and low erosion respectively (Figs. 7, 8 and 9) (Table 3). The highest rate of erosion was observed in the southeast coast of Orlagondi reserve forest with the rate of 277 m per year (Transect id 788) (Fig. 6).
Shoreline changes during 2000–2010
During period 2000–2010, high erosion was recorded along the coast of Vishakhapatnam, Koringa, Southern Kakinada, Kothapalem, South east coast of Machilipatnam, Gangadipalem and Southeast coast of Krishna delta. Around 116 km coastline observed with high erosion and the highest rate of erosion was observed in Gangadipalem with the rate of erosion is 255 m per year (Transect id 967) (Fig. 6). Whereas low erosion has observed along the Northern Vishakhapatnam, some part of Koringa wildlife sanctuary, Gadimoga, Komaragiripatnam, Kummarapurugupalem, coast of Gangadipalem and northern Pulicat coastal region. Around 155 km of the coastline was observed with low erosion. The coastline with stable trend was found along 281 km of coastline and it was widely distributed along the coast of northern and southern part of the study area. High and low accretion was found along the coast of Kakinada, Vizhinagaram, Srikakulam, Nizampatnam, Patavala, Chirrayanam, Palletummalapalem, northeast coast of Orlagondi reserve forest, Varini and Northern Pulicat. Around 116 and 311 km coast was found with high and low accretion respectively. The highest accretion rate has recorded along the coast of Brahmasamedyam with rate of accretion of 295 m per year (Transect id 1344). In this decade we observed that the erosional rate was comparatively lesser than the previous decade (Figs. 7, 8 and 9) (Table 3).
Shoreline changes during 2010–2015
During the period from 2010 to 2015, dynamic change was observed in the coastline. Both high and low erosional coast are found along the coast of Vakalapudi, Kakinada, Chollangi, Brahmasamedyam, Pallamkurru, Antarvedi, Tavisipudi, Palletummalapalem, Ramakrishnapuram, northeast coast of Orlagondi reserve forest, southeast and northern coast of Gangadipalem, Koduru and Krishnapatnam, etc. it was also observed that around 243 and 223 km of coastline was found with high and low erosion respectively. The higher erosion rate was recorded along the northern coast of Gangadipalem with the rate of erosion is 306 m per year (Transect id 990). High and low accretion was observed along the 174 and 184 km coastline respectively. High accretion rate was observed along the Srikakulam, Vizhinagaram, Vishakhapatnam, Southern Brahmasamedyam, Surasaniyanam, Nimmakayala Kothapalle, Thurputallu, Pedayadara, Polatitippa, southern Orlagondi reserve forest, western Gangadipalem and Nellore coasts. The highest accretion rate was recorded in the southeast coast of Chollangi with the rate of erosion is 624 m per year (Transect id 1375) (Figs. 7, 8 and 9) (Table 3).
Net shoreline change analysis
Net Shoreline Movement reports the distance between the oldest and youngest shoreline features for each transect. The net shoreline movement is calculated for the period of 1973–2015. It was observed that more than half of the coastline experienced erosion and fewer part of coastline was observed with no change. High and low erosion was observed along the coastline was 104 and 479 km of coastline respectively. The highest erosion rate was observed along the Orlagondi reserve forest with rate of erosion is 2.79 km (Transect id 756). And the highest accretion was observed along the coast of Chollangi with the rate of accretion is 2.72 km. High and low accretion was observed along the coastline is around 68.27 and 306.97 km respectively, whereas only 13 km was found stable with no change (Figs. 10 and 11).
Figure shows the shoreline change scenario for the individual decades viz., 1973–1980, 1980–1990, 1990–2000, 2000–2010 and 2010–2015
Figure Shows the Net Shoreline Change rate for 1973–2015
The study observed, very high dynamic changes in the shoreline were observed along the deltaic environments of the Krishna and Godavari rivers. These changes were quite obvious in the deltaic environs of the major river system influenced by strong marine-fluvial processes. Apart from the deltaic environment, part of the eroding coastline along the Andhra state was protected by the sea wall. The most of these protecting barriers were more closely seen in and around the harbors. These regions exhibit characteristics of artificial coasts. However, the environmental sensitive areas were still under risk and these areas frequently exhibit changes by cyclone and other climate impact in the protected coast also. The decadal changes in the shoreline revealed high accretion and medium erosion during 1980–1990 by forming the new spit in the Godavari river mouth near Narsapur Coast (Fig. 12). Similar trend continued till the year 2000. Fewer changes in the shoreline position were observed after 2000 by recording medium accretion and no change/ stable classes revealed comparatively less coastal dynamics. In contrast, during 2010–2015 again increase in erosion compared to previous decade was recorded probably reflecting increased human interference and effect of extreme weather conditions. The northern and the southern part of the study area were mostly stable. However select small stretches with erosion and accretion in these regions can be protect by proper management plan and policies like constructing of sea wall and plantation of mangroves, etc. During the period 2000–2010 Vishakhapatnam and surrounding district recorded both accretion and high erosion. Accretional changes along the coast were influenced by the developmental activities like Vishakhapatnam port and the erosional features may be developed by the Cyclonic Storm Laila occurred in 21 May 2010.
Map showing the net shoreline movement (NSM)for the period 1973–2015 along the eastern coast of Andhra Pradesh
Trend of shoreline changes
The trend of shoreline changes during 1973–2015, (42 years) (Fig. 13) (Tables 4), indicated that the coastline was more witnessed to erosional activity. It was observed that the erosional rate was exhibiting decreasing trend whereas the accretion rate shown increasing trend. During the period 1973 to1990, the highest amount of erosional activity along the coast were observed, whereas, after the 1990 the rate of erosion was decreasing till 2010. In 2010 again rate of erosion started to increase. High accretion rate exhibited decrease in beginning but from 1990 the area effecting high accretion increased. Stable coast are exhibited decrease and it was continuous till the end. The average shoreline change along the coastline was observed as −3.04 m per year and it indicated that the overall coastal changes were falling in negative trend. It is remark that after 50 years period, around 150 m coastline may shift towards the landward direction according to the current status of the shoreline position change (Fig. 14). Figure 14 was prepared using the average shoreline changes for the periods 1973, 1980,1990,2000,2010 and 2010.
Figure shows temporal satellite Image combined with shoreline pertaining to Narsapur coast shows the coastal process during 1980 to 1990
Figure shows the long term shoreline trend calculated using Net Shoreline Movement (NSM) for the period 1973 to 2015
Factors controlling to the shoreline change
Shoreline change and sea level rise
Sea level rise is a long term process and is not only directly influencing the coastal process but also related to shoreline changes. However, small amount of sea level rise will influence the shoreline movement in the coastal region having nearly-flat and very low elevated regions. The lowest sea level rise is observed in Chennai Station (0.33 mm/y) and the highest sea level rise was observed in Port Blair station(2.05 mm/y) (Fig. 15). The highest sea level change observed in the study area was 1.03 mm/year. According to USGS classification, sea level rise below 1.8 mm per year is considered as low risk to the coastline (Thieler and Hammar-Klose 1999). Therefore the entire coast is falling under the low risk category. (Fig. 18Abcde) (Table 5).
Figure shows the average shoreline Position along the Andhra Pradesh coast
Shoreline change and geomorphology
The coastal geomorphology is an important parameter which controls coastal process and reflect the impact of these process. Various types of coastal geomorphological features are soft and dynamic in nature, thus frequent marine and fluvial processes influence high vulnerability to the shoreline change (Del Río et al. 2013; Fletcher et al. 2011; Barnhardt 2009). The geomorphology expresses the chance of erosion of different landform types. This geomorphological map further reclassified in to five different classes on the basis of the influencing capability to shoreline positional change. Coastal origin land forms such as alluvial plains, flood plains and deltas are very dynamic in nature and easy to get changes, but denudational and structural origin landforms are comparatively hard in nature with rocky structure, thereby making them comparatively less dynamic and it will take time to get changes over them. The most of the coastal region are formed by coastal origin. However, some regions are also formed by denudational origin especially in Vishakhapatnam region (Figs. 16). The present study revealed 550 km coastline exhibited high erosion. Within 550 km length 232 km coastline found in the young alluvial and deltaic coast (very high risk zone), and 80 km in the alluvial coast (high risk zone). Moderate and low risk zone recorded 71 and 23 km length of coastline respectively (Fig. 18a). Around 33 km coastline was observed with low erosion, and among them 3.58 and 19.77 km coastline was falling under very high and high risk classes respectively. In the moderate risk class 6.27 km and 1.34 km coastline with low erosion was observed in the low risk zone (Fig. 18b).
Figure shows the mean sea level trend for the Stations, Port Blair, Paradip, Vishakhapatnam and Chennai
Map showing the common geomorphologicl feature of the state of Andhra Pradesh (RAO et al. 2015)
Map showing the coastal regional elevation of the Andhra Pradesh coast
Figure shows the correlation with controlling parameters and shoreline change (Fig.18a: Interaction of controlling parameters with high erosion coast. Fig 18b: Interaction of controlling parameters with low erosion coast. 18c: Interaction of controlling parameters with high accretion coast. 18d: Interaction of controlling parameters with low accretion coast. 18e: Interaction of controlling parameters with stable coast)
Stable coast were mostly found along the high and moderate risk zone with 4.03 and 4.06 km coastline observed in the high and moderate risk zones. However, few coastlines were also observed in very high and low risk zones, which are contributing to 0.90 and 0.05 km coastlines respectively (Fig. 18e). High accretion was recorded during 1973 to 2015 was 349 km and among them, 232 km coastline was falling under the very high risk zone, whereas, 80 km falls under the high risk zone. In addition 71 km and 23 km coastline was observed in the moderate and low risk zones respectively (Fig. 18c). There was only 25.54 km coastline observed in low accretion. Among the 25.54 km coastline 1.79 km length falling under very high risk zone and 12.98 km length falling under high risk zone, whereas, 5.74 and 3.13 km length falls under the moderate and low risk zones respectively (Fig. 18d) (Table 5).
These results show that the erosion and accretion was found more in the very high and high risk zones, i.e. deltaic environment and the alluvial plains, however, the small area also observed in the estuaries and lagoons.
Shoreline change and costal slope
Slope always play a key role in the shoreline changes. Flat and nearly flat coasts in the low elevated coast were observed with both erosion and accretion and mainly accretion is more in the flat regions. The gentle slope may cause high dynamics of coastal regions in comparison to steep slopes. The flat area in the low laying areas will be affected by coastal inundation during the cyclonic surge or the high tide time. The depositional landforms are created by the fluvial sediments deposited in the surrounding regions where Krishna and Godavari rivers meet with Bay of Bengal. High erosion in the slope classes were observed that, around 77 km length contributing to the very high risk zone according to the slope variation, whereas 126 km length eroded in the high risk zone which is the steep slope area. The steep slope always chance to get erosion by the wind as well as the wave action. High erosion also observed in the moderate risk zones with the contribution of 51 km coastlines. Low and very low risk zones recorded high erosion with 69 and 82 km coastline respectively (Fig. 18a). Low erosion was observed along the 33 km, among this 2 km length recoded in the very high risk zone and 5.88 km recorded in the high risk zone. Around 4 km length coastline was recorded in the moderate risk zones, whereas, 8.55 and 9.54 km coastline were recorded in the low and very low risk zones respectively (Fig. 18b). Most of the stable coastlines were found in the low and very low risk zones. Among the 13 km of stable coastline 3.27 km length recorded in the very low risk zone and 1.45 and 2.50 km were recorded in the low and moderate risk zoned respectively. Stable coasts were also recorded in high and very high risk zones with the contributions of 1.34 and 0.47 km respectively (Fig. 18e). High accretion was observed in 349.23 km along the coastline. The most of the high accretion were recorded in the flat/ nearly flat regions, i.e. very high risk zone. Around 126 km coastlines were observed with high accretion in the very high risk zone and also observed that the 77.01 km coastline falling under the high risk zone. High accretion was not specifically concentrated on any particular risk classes but also found along the all risk zones. 82.87 km coastlines were found along the moderate risk zone. Low and very low risk zone also found the high accretion, it was around observed along the 69.88 and 51.94 km along the coastline (Fig. 18c). Low accretion was observed in 25.54 km along the coastline. Among the 25.54 km it was observed that, 4.03 and 3.11 km coastlines recorded in very high and high risk zones and 2.64 km coastline observed in the moderate risk zone, whereas, 9.45 km coastline has observed in the very low risk zone and 4.42 km recorded low risk zones(Fig. 18d) (Table 5).
Shoreline change and regional elevation
According to the elevation, shoreline changes were observed only between very high to moderate risk zones. Very low elevated topography was recorded in southern parts of the region, especially Pulicat, Koduru, Krishna and Godavari deltaic plain. Lowest elevation was found along the Godavari river mouth recording 3 m below mean sea level. Moderate and high elevated topography were also spread along the northern coast. Very high elevation was recorded in Vishakhapatnam coasts because of the presents of the Eastern Ghat. The highest elevation was recorded in Vishakhapatnam with elevation of 12 m along the coast (Fig. 17). Around fifty percentage of the high erosion was observed in the very high risk zone, it is comes around 266.95 km of the total coastline. High erosion was observed along the 104.92 km of the coastline; whereas 36.12 km coastline was observe in the moderate risk zone (Fig. 18a). Low erosion with the elevation was also observed that around 23.65 km coastline was coming under the very high risk zone in addition 4.73 and 2.58 km coastline was recorded in the high and moderate risk zones respectively (Fig. 18b). Stable coast are more dominated in the moderate risk zone, 4.35 out of 13 km length was recorded in the moderate risk zone. However, it was also observed that the few of the stable coast are also found along the high and very high risk zone. Around 2.15 and 2.54 km of the coastline are recorded in the very high and high risk zones (Fig. 18e). Among the 25.54 km low accretion coast observed that, 16.77 km coastlines recorded in very high risk zones. High and moderate risk zones also recorded low accretion with 4.73 And 2.15 km coastlines respectively (Fig. 18c). High accretion also more dominated in the low elevated topographic regions. Most of the high accretion was concentrated in high and very high risk zone. Around 266.95 and 104.92 km coastlines are observed in very high and high risk zones, whereas, 2.15 km coastline observed in the moderate risk zone (Fig. 18d) (Table 5).
Discussion
Data and techniques used in this study provide useful insight in assessing the shoreline and morphological changes in the coastal environs of the Andhra Pradesh coastline. The inundation vulnerability in the coastlines by flood or SLR over different periods depending not only the environmental factors like coastal regional elevation, geomorphology, coastal slope, historical shoreline change and sea level rise, but also on the socioeconomic vulnerability to adopt protective and mitigative measures (Cooper and Pilkey 2004). Physical parameters along the coast played an important role in position changes of shoreline. Very high positional changes observed in the locations where the low elevated topography and alluvial plains. The analysis revealed that more than ninety percentage of the total coastal stretch had recorded erosion or accretion. The coastline witnessed coastal changes by two main processes which are river system influenced by strong marine-fluvial processes and extreme events like cyclone, storm surge, etc. The northern part of the study area shows low changes due to elevated topography of the Eastern Ghats and geomorphology. Time series shorelines exhibit dynamic changes in the Krishna and Godavari deltaic region. This area is continually affected by cyclone and storm surge as well as fluvial process in the deltaic environment. Sea level rise is not a big challenge in the particular study area, however, Sea level is increasing frequently (Bindoff et al. 2007) and in near future it will be big threaten to human activities and biodiversity where the areas having low elevation. Since 40 years Andhra Pradesh coast affected 62 cyclones including depression, cyclone surge, and severe cyclone surges (Rao et al. 2009; Rao et al. 2012). Among this cyclone, there are 32 cyclones affected in Krishna Godavari region, including four districts East Godavari, West Godavari, Krishna, and Guntur respectively. Apart from that, the result shows a high shoreline change recorded in Vishakhapatnam during the period 2000–2010 due to Cyclone Laila (Kanase and Salvekar 2014a, b). The study envisages that the areas recorded high shoreline changes and may get damaged by extreme events and strong fluvial process.
Among the 972 km shoreline we noticed that average erosion rate observed in the Krishna Godavari delta was 10.63 m per year, in contrast average accretion rate observed as 17.29 m per year. However, considering to the whole study area, the average erosion and accretion rate was calculated and observed to be 5.15 and 9.34 m per year respectively. The Krishna and Godavari delta region is very sensitive in nature which is rich in Mangrove forest and young alluvial plain. Human intervention is also one of the major issues in this region and it also drive to increase shoreline changes.
The controlling parameters influence to the shoreline position change were calculated and identified that the high erosion and accretion were placed more in the very high and high risk zones for most of the parameters. Sea level rise is not a big challenge in the study area; however small changes in sea level will create huge impact on the coastal zones where low lying and hence would be exposed to faster erosion and shoreline change (Pye and Blott 2006). The increase in sea level will continue for several decades even in the absence of future variations in atmospheric composition because of the changes that have already occurred (Wigley 2005). East Asia and the Pacific (EAP) region face the maximum potential economic loss due to climate change impacts on wetlands (Blankespoor et al. 2012).
Conclusion
The study finally observed that the both erosion and accretion would affected to the coastal environment, thus proper management policies reduce the shoreline changes along the Andhra coast especially in the deltaic environment may be developed. Protection of mangroves and reconstruction of degraded mangrove forests can help to protect both shoreline and coastal eco-system. Remote sensing data and techniques were the most relabeled methods to estimate historical shoreline change. Advance remote sensing techniques and satellite data can be used for asset mapping and vulnerability assessment. Results of this study may provide useful inputs for the coastal zone management and planning. Temporal coastline Maps produced may aid in the coastal disaster management and mitigation. These maps further can be used for the construction of barriers along the coast, assessment and management of ecological sensitive zones, etc. The study can be further improved by using the high-resolution satellite data along with detail field observations.
References
Administrative and Geographical Profile (2015) http://www.ap.gov.in/APStateStatisticalAbstractMay2014/1ADMINISTRATIVEANDGEOGRAPHICALPROFILE.pdf
Alemayehu F, Richard O, Kinyanjui MJ, Oliverv W (2014) Assessment of shoreline changes in the period 51 page 12 of 14 environ Monit assess (2018) 190:51 1969–2010 in Watamuarea, Kenya. Global Journal of Science Frontier Research 14(6):19–31
ASTER Data: NASA and Japan ASTER Program (2011) ASTER scene, ASTGDEMV2.NASA EOSDIS Land Processes DAAC, USGS Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota (https://lpdaac.usgs.gov). Accessed 03 12, 2015, at http://www.earthexplore.usgs.gov.
Barnhardt, W. A. (2009). Coastal change along the shore of northeastern South Carolina--the South Carolina coastal Erosion study
Basheer Ahammed KK, Mahendra RS, Pandey AC (2016) Coastal vulnerability assessment for eastern coast of India, Andhra Pradesh by UsingGeo-spatial technique. Geoinfor Geostat: An Overview 4:3. https://doi.org/10.4172/2327-4581.1000146
Blankespoor, B., Dasgupta, S., & Laplante, B. (2012). Sea-level rise and coastal wetlands: impacts and costs
Bindoff NL, Willebrand J, Artale V, Cazenave A, Gregory JM, Gulev S, Shum CK (2007) Observations: oceanic climate change and sea level
Chandrasekar N, Viviek VJ, Saravanan S (2013) Coastal vulnerability and shoreline changes for southern tip of India-remote sensing and GIS approach. J Earth SciClim Change 04:4–144. https://doi.org/10.4172/2157-7617.1000144
ChenthamilSelvan S, Kankara RS, Rajan B (2014) Assessment of shoreline changes along Karnataka coast, India using GIS & Remote sensing techniques. Indian Journal of Marine Sciences 43(7)
Clow JB, Leatherman SP (1984) Metric mapping: An automated technique of shoreline mapping. In 44th American Congress on Surveying and Mapping (pp. 309–318). Falls Church, VA: The American Congress on Surveying and Mapping
Cooper JAG, Pilkey OH (2004) Sea-level rise and shoreline retreat: time to abandon the Bruun rule. Glob Planet Chang 43(3–4):157–171
Del Río L, Gracia FJ, Benavente J (2013) Shoreline change patterns in sandy coasts. A case study in SW Spain. Geomorphology 196:252–266
Digital Shoreline Analysis System. (2015) http://woodshole.er.usgs.gov/project-pages/dsas/, http://marine.usgs.gov/, http://woodshole.er.usgs.govProgramming
Dolan R, Hayden B, Rea C, Heywood J (1979) Shoreline erosion rates along the middle Atlantic coast of the United States. Geology 7(12):602–606
Dolan R, Fenster MS, Holme SJ (1991) Temporal analysis of shoreline recession and accretion. J Coast Res 7:723–744
Fletcher C, Rooney J, Barbee M, Lim S, Richmond B (2003) Mapping shoreline change using digital orthophotogrammetry on Maui, Hawaii. J Coast Res 38:106–124
Fletcher, C. H., Romine, B. M., Genz, A. S., Barbee, M. M., Dyer, M., Anderson, T. R., ... & Richmond, B. M. (2011). National assessment of shoreline change: Historical shoreline change in the Hawaiian Islands
Ford M (2011) Shoreline changes on an urban atoll in the Central Pacific Ocean: Majuro atoll, Marshall Islands. J Coast Res 28(1):11–22
Ford M (2013) Shoreline changes interpreted from multi-temporal aerial photographs and high resolution satellite images: Wotje atoll, Marshall Islands. Remote Sens Environ 135:130–140
Gebco Data: IOC, IHO, BODC (2014) Centenary edition of the GEBCO digital atlas, published on CD-ROM on behalf of the intergovernmental oceanographic commission and the international hydrographic organization as part of the general bathymetric chart of the oceans. British Oceanographic Data Centre, Liverpool, U.K
Hegde AV, Akshaya BJ (2015) Shoreline transformation study of Karnataka coast: geospatial approach. Aquatic Procedia 4:151–156
Jana AB, Hegde AV (2016) GIS based approach for vulnerability assessment of the Karnataka coast. India Hindawi Publishing Corporation Advances in Civil Engineering 2016:1–10
Jayanthi M, Thirumurthy S, Samynathan M, Duraisamy M, Muralidhar M, Ashokkumar J, Vijayan KK (2018) Shoreline change and potential sea level rise impacts in a climate hazardous location in southeast coast of India. Environ Monit Assess 190(1):51
Joesidawati MI, Suntoyo S (2016) Shoreline change in Tuban district, East Java using geospatial and digital shoreline analysis system (DSAS) techniques. International Journal of Oceans and Oceanography 10(2):235–246
Kanase R, Salvekar P (2014a) Study of weak intensity cyclones over bay of Bengal using WRF model. Atmospheric and Climate Sciences 4:534–548. https://doi.org/10.4236/acs.2014.44049
Kanase RD, Salvekar PS (2014b) Study of weak intensity cyclones over bay of Bengal using WRF model. Atmospheric and Climate Sciences 4(04):534
Kankara RS, Selvan SC, Markose VJ, Rajan B, Arockiaraj S (2015) Estimation of long and short term shoreline changes along Andhra Pradesh coast using remote sensing and GIS techniques. Procedia Engineering 116:855–862
Kannan R, Ramanamurthy MV, Kanungo A (2016) Shoreline change monitoring in Nellore coast at East Coast Andhra Pradesh District using remote sensing and GIS. J Fisheries Livest Prod 4:161
Kumar, T. S., Mahendra, R. S., Nayak, S., Radhakrishnan, K., Sahu, K. C., (2010). Coastal vulnerability assessment for Orissa state, east coast of India. J Coast Res, 26(3), 523–534. ISSN 0749-0208
Leatherman SP, Clow B (1983) UND shoreline mapping project. Geoscience and Remote Sensing Society Newsletter, IEEE 22:5–8
Mahendra, RS, Mohanty PC, Bisoyi H, Kumar TS, Nayak S (2011) Assessment and management of coastal multi-hazard vulnerability along the Cuddalore–Villupuram, east coast of India using geospatial techniques. Ocean & Coastal Management 54(4):302–311
Mageswaran T, Mohan VR, Selvan SC, Arumugam T, Usha T, Kankara RS (2015) Assessment of shoreline changes along Nagapattinam coast using geospatial techniques. International Journal of Geomatics and Geosciences 5(4):555–563
Mohan GS, Jairaj PG (2014) Coastal vulnerability assessment along Kerala coast using remote sensing and GIS. International Journal of Scientific & Engineering Research 5(7):228–234
Morton, R. A. (2008). National assessment of shoreline change: part 1: historical shoreline changes and associated coastal land loss along the US Gulf of Mexico. Diane Publishing
Morton, R. A., Miller, T. L., & Moore, L. J. (2004). National Assessment of Shoreline Change: Part1 Historical shoreline changes and associated coastal land loss along the U.S Gulf of Mexico: U.S geological survey Open-file report 2004–1043, 45p
Mujabar PS, Chandrasekar N (2013) Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arab J Geosci 6(3):647–664
Natesan U, Parthasarathy A, Vishnunath R, Jeba Kumar GE, Ferrer VA (2015) Monitoring long term shoreline changes along Tamil Nadu, India using geospatial techniques. Aquatic Procedia 4:325–332
Nellemann, C., & Hain, S. (2008). In dead water: merging of climate change with pollution, over-harvest, and infestations in the world's fishing grounds. UNEP/Earthprint
Nicholls RJ (1995) Coastal megacities and climate change. Geojournal 37(3):369–379
Nicholls, R.J., P.P. Wong, V.R. Burkett, J.O. Codignotto, J.E. Hay, R.F. McLean, S. Ragoonaden and C.D. Woodroffe (2007). Coastal systems and low-lying areas. Climate change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson, Eds., Cambridge University Press, Cambridge, UK, 315–356
Ouma YO, Tateishi R (2006) A water index for rapid mapping of shoreline changes of five east African Rift Valley lakes: an empirical analysis using Landsat TM and ETM+ data. Int J Remote Sens 27(15):3153–3181
Pye K, Blott SJ (2006) Coastal processes and morphological change in the Dunwich-Sizewell area, Suffolk, UK. J Coast Res 223:453–473
Q. Qin, L. Zhu, A. Ghulam and Z. Li and P. Nan. (2008) Satellite monitoring of spatio-temporal dynamics of China’s coastal zone eco-environments: preliminary analysis on the relationship between the environment, climate change and human behavior. Environmental Geology
Rao KN, Subraelu P, Rao TV, Malini BH, Ratheesh R, Bhattacharya S, Rajawat AS, Ajai (2008) Sea level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. J Coast Conserv 12(4):195–207
Rao KN, Subraelu P, Venkateswara Rao T, Malini BH, Ratheesh R, Bhattacharya S, Rajawat AS (2009) Sea-level rise and coastal vulnerability: an assessment of Andhra Pradesh coast, India through remote sensing and GIS. J Coast Conserv 12:195–207. https://doi.org/10.1007/s11852-009-0042-2
Rao AD, Murty PLN, Jain I, Kankara RS, Dube SK, Murty TS (2012) Simulation of water levels and extent of coastal inundation due to a cyclonic storm along the east coast of India. Nat Hazards 66:1431–1441. https://doi.org/10.1007/s11069-012-0193-6
RAO M, Ramamurthy VS, RAJ B (2015) Standards, spatial framework and technologies for national GIS. National Institute of Advanced Studies, Bangalore
Ruggiero P, Kaminsky GM, Gelfenbaum G, Voigt B (2005) Seasonal to interannualmorphodynamics along a high-energy dissipative littoral cell. J Coast Res 21(3):553–578
Swaney DP, Hong B, Ti C, Howarth RW, Humborg C (2012) Net anthropogenic nitrogen inputs to watersheds and riverine N export to coastal waters: a brief overview. Curr Opin Environ Sustain 4:203–211
Tamassoki, E., H. Amiri, and Z. Soleymani. (2014) Monitoring of shoreline changes using remote sensing (case study: coastal city of Bandar Abbas). IOP conference series: earth and environmental science. Vol. 20. No. 1. IOP publishing
Thieler, E. R., & Hammar-Klose, E. S. (1999). National assessment of coastal vulnerability to sea-level rise; US Atlantic Coast (No. 99–593)
Thieler, E.R., Himmelstoss, E.A., Zichichi, J.L., and Ergul, Ayhan, (2009). Digital Shoreline Analysis System (DSAS) version 4.0 — An ArcGIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008–1278. *current version 4.3
Thom B, Hall W (1991) Behaviour of beach profiles during accretion and erosion dominated periods. Earth Surf Process Landf 16(2):113–127
To DV, Thao PTP (2008) A shoreline analysis using DSAS in Nam Dinh coastal area. International Journal of Geoinformatics 4(1):37–42
United Nations Environment Programme (1992), The world environment 1972–1992: two decades of challenge. Chapman & Hall, New York, NY (USA), 884
United Nations Environment Programme (UNEP) (2007) Physical Alteration and Destruction of Habitats. http://www.gpa.unep.org/content.html (accessed May 27, 2015)
Vidya R, Biradar RS, Inamda AB, Srivastava S, Pikle M (2015) Assessment of shoreline changes of Alibag coast (Maharashtra, India) using remote sensing and GIS. Journal of Marine Biological Association of India 57(2):83–89
Vivek, G. (2015). Shoreline change analysis for north east coast of Andhra Pradesh, India. ISPRS WG VIII/1 Workshop on geospatial technology for disaster risk reduction. Vol. Commission VIII, WG VIII. 1https://doi.org/10.13140/RG.2.2.19763.73767
Wigley TML (2005) The climate change commitment. Science 307(5716):1766–1769
Woodruff JD, Irish JL, Camargo SJ (2013) Coastal flooding by tropical cyclones and sea-level rise. Nature 504(7478):44–52
Yunus AP, Narayana AC (2015) Short-term morphological and shoreline changes at Trinkat Island, Andaman and Nicobar, India, after the 2004 tsunami. Mar Geod 38(1):26–39
Zhang K, Whitman D, Leatherman S, Robertson W (2005) Quantification of beach changes caused by hurricane Floyd along Florida’s Atlantic coast using airborne laser surveys. J Coast Res 21(1):123–134
Acknowledgements
The authors would like to thank US Geological Survey, for the Landsat data, Global Sea Level Observing System (GLOSS) for the sea level data and USGS for the making available the Digital Shoreline Analysis Software (DSAS) on their website. Also the authors thank to the anonymous reviewers for their valuable comments and suggestions, which helped to improve the quality of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Basheer Ahammed, K.K., Pandey, A.C. Shoreline morphology changes along the Eastern Coast of India, Andhra Pradesh by using geospatial technology. J Coast Conserv 23, 331–353 (2019). https://doi.org/10.1007/s11852-018-0662-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11852-018-0662-5