PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

e-ISSN 2231-8542
ISSN 1511-3701

Home / Regular Issue / JTAS Vol. 29 (1) Jan. 2021 / JST-2076-2020

 

Shoreline Change and its Impact on Land use Pattern and Vice Versa ─ A Critical Analysis in and Around Digha Area between 2000 and 2018 using Geospatial Techniques

Anindita Nath, Bappaditya Koley, Subhajit Saraswati, Basudeb Bhatta and Bidhan Chandra Ray

Pertanika Journal of Tropical Agricultural Science, Volume 29, Issue 1, January 2021

DOI: https://doi.org/10.47836/pjst.29.1.19

Keywords: Digital shoreline analysis system, end point rate, land use/land cover, littoral zone, linear regression rate, net shoreline movement

Published on: 22 January 2021

The shoreline is a very unpredictable, uncertain, and forever changing landscape for any coastal process. Due to erosional and accretional activities, the shoreline has continuously fluctuated with the continual process of waves and tides. Shore boundaries are determined by the shoreline at its furthest towards the sea (low tide) and extreme towards land (high tide). The present research aimed to identify the temporal alterations of shoreline and changes in land-cover between the areas of Rasulpur to Subarnarekha estuary, east coast of India with 70.04 km length of shoreline. An area amounting to 143sq.km had been selected for showing the land-cover changing and this area had witnessed the rapid growth of population and increasing industrial activities causing an unsurpassable impact on the environment. The present study used three multi dated imageries for land use/ land cover (LULC) map and seven multi-resolution satellite images were applied to estimate the long-term shoreline change rate by dividing the coastal area into three "littoral zones" (LZ). The Digital shoreline analysis system (DSAS) was applied to identify the shoreline change rate of the year 2000 to 2018. Several statistical methods, linear regression rate (LRR), net shoreline movement (NSM), End Point Rate (EPR) were used to find out the erosion and accretion rate. The result showed that maximum erosion had been found in LZ III, rate of -2.22 m/year. Maximum accretion had been identified in LZ I, at the rate of 35.5 m/year. The LULC showed that maximum vegetation area had been decreased in the year of 2010 (14.21sq.km) but 38.96sq.km vegetation area had increased in 2018. The prominent increase had been identified in built up and shallow water. Built up had been expanded from 25.59sq.km (2000) to 41.26sq.km (2018). Shallow water was increased from 5.53sq.km (2000) to 18.90sq.km (2018). Sand and soil showed a decreasing pattern from 2000 ─ 2018. The outcome acquired from the present study will play a significant role to estimate the shoreline migration rate and will be helpful for sustainable land use management. The shoreline change rate will be also useful for coastal planners to adopt mitigation measures.

  • Alesheikh, A. A., Ghorbanali, A., & Nouri, N. (2007). Coastline change detection using remote sensing. International Journal of Environmental Science and Technology, 4(1), 61-66. doi: https://doi.org/10.1007/BF03325962

  • Balachandar, D., Rutharvel M. K., Muruganandam, R., Sumathi, M., Sundararaj, P., &Kumaraswamy, K. (2011). Analysis of land use/ land covers using remote sensing techniques-A case study of Karur district, Tamil Nadu, India. International Journal of Current Research, 3(12), 226-229.

  • Biging, G. S., Colby, D. R., &Congalton, R. G. (1998). Sampling systems for change detection accuracy assessment, remote sensing change detection. In R. S. Lunetta & C. D. Elvidge (Eds.), Environmental monitoring methods and applications (pp. 281-308). Michigan, USA: Ann Arbor Press.

  • Boak, E., & Turner, I. (2005). Shoreline definition and detection: A review. Journal of Coastal Research, 21(4), 688-703. doi: https://doi.org/10.2112/03-0071.1

  • Boschetti, L., Flasse, S. P., & Brivio, P. A. (2004). Analysis of the conflict between omission and commission in low spatial resolution dichotomic thematic products: The pareto boundary. Remote Sensing of Environment, 91(3-4), 280-292. doi: https://doi.org/10.1016/j.rse.2004.02.015

  • Bradley, B. A. (2009). Accuracy assessments of mixed land cover using a GIS-designed sampling scheme. International Journal of Remote Sensing, 30(13), 3515-3529. doi: https://doi.org/10.1080/01431160802562263

  • Carlotto, M. J. (2009). Effect of errors in ground truth on classification accuracy. International Journal of Remote Sensing, 30(18), 4831-4849. doi: https://doi.org/10.1080/01431160802672864

  • Chand, P., & Acharya, P. (2010). Shoreline change and sea level rise along coast of Bhitarkanika wildlife sanctuary, Orissa: An analytical approach of remote sensing and statistical techniques. International Journal of Geomatics and Geosciences, 1(3), 436-455.

  • Chatterjee, R. K. (1995). A comparative study between East and West Indian Coast: A geographical account. Geographical Review of India, 12(1), 23-25.

  • Chavez, J. R. (1988). An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment, 24(3), 459-479. doi: https://doi.org/10.1016/0034-4257(88)90019-3

  • Congalton, R. G., & Green, K. (1999). Assessing the accuracy of remotely sensed data: Principles and practices. Boca Raton, Florida: CRC Press, Taylor & Francis Group.

  • Coppin, P. R., & Bauer, M. E. (1996). Digital change detection in forest ecosystems with remote sensing imagery. Remote Sensing Reviews, 13(3-4), 207-234. doi: https://doi.org/10.1080/02757259609532305

  • Dey, S., Ghosh, P., & Nayak, A. (2005). The influences of natural environment upon the evolution of sand dunes in tropical environments along Medinipore coastal area, India. Indonesian Journal of Geography, 37(1), 51-68.

  • Dolan, R., Fenster, M. S., & Holme, S. J. (1991). Temporal analysis of shoreline recession and accretion. Journal of Coastal Research, 7(3), 723-744.

  • Emran, A., Rob, M. A., Kabir, M. H., & Islam, M. N. (2016). Modeling spatio-temporal shoreline and areal dynamics of coastal island using geospatial technique. Modeling Earth Systems and Environment, 2(4), 1-11. doi: 10.1007/s40808-015-0060-z.

  • Everts, C. H., Battley Jr, J. P., & Gibson, P. N. (1983). Shoreline movements. Report 1. Cape Henry, Virginia, to Cape Hatteras, North Carolina, 1849-1980 (No. CERC-83-1-1). Technical Report. Coastal Engineering Research Center Vicksburg MS.

  • Garcia-Rubio, G., Huntley, D., Kingston, K., & Esteves, L. S. (2009). Shoreline identification using satellite images. Coastal Dynamics, 2009, 1-10. doi: 10.1142/9789814282475_0117

  • Goodbred Jr, S. L., & Kuehl, S. A. (2000). The significance of large sediment supply, active tectonism, andeustasy on margin sequence development: Late quaternary stratigraphy and evolution of the Ganges–Brahmaputra delta. Sedimentary Geology, 133(3-4), 227-248. doi: https://doi.org/10.1016/S0037-0738(00)00041-5

  • Guru, B., Neha, M., & Anubhooti, Y. (2014, December 9-12). Study the land use and land cover changes and CRZ in the coastal area of Ganjam District, Odisha. In International Society of Photogrammetry and Remote Sensing (ISPRS), Mid-Term Symposium of the Technical Commission VIII (pp. 1-5). Hyderabad, India.

  • Jana, A., Biswas, A., & Maiti, S. (2013). Shoreline changes in response to sea level rise along Digha Coast, Eastern India: An analytical approach of remote sensing, GIS and statistical techniques. Journal of Coastal Conservation, 18(3), 145-155. doi: https://doi.org/10.1007/s11852-013-0297-5

  • Khan, S. R., & Islam, B. (2008). Holocene stratigraphy of the lower Ganges-Brahmaputra river delta in Bangladesh. Frontiers of Earth Science in China, 2(4), 393-399. doi: https://doi.org/10.1007/s11707-008-0051-8

  • Koloa, C., & Samanta, S. (2013). Development impact assessment along Merkham River through remote sensing and GIS technology. International Journal Asian Academy Research Association, 5(1), 26-41.

  • Kuleli, T. (2009). Quantitative analysis of shoreline changes at the Mediterranean Coast in Turkey. Remote Sensing of Environment, 167(1-4), 387-397. doi: https://doi.org/10.1007/s10661-009-1057-8

  • Kumar, A., & Jayappa, K. S. (2009). Long and short-term shoreline changes along Mangalore Coast, India. International Journal Environmental Research, 3(2), 177-188.

  • Li, B., & Zhou, Q. (2009). Accuracy assessment on multi‐temporal land‐cover change detection using a trajectory error matrix. International Journal of Remote Sensing, 30(5), 1283-1296. doi: https://doi.org/10.1080/01431160802474022

  • Lu, D., &Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870. doi: https://doi.org/10.1080/01431160600746456

  • Mageswaran, T., Mohan, V. R., Selvan, S. C., Arumugam, T., Usha, T., & Kankara, R. S. (2015). Assessment of shoreline changes along Nagapattinam coast using geospatial techniques. International Journal of Geomatics and Geosciences, 5(4), 555-563.

  • Maiti, S., & Bhattacharya, A. K. (2009). Shoreline change analysis and its application to prediction: A remote sensing and statistics based approach. Marine Geology, 257(1-4), 11-23. doi: https://doi.org/10.1016/j.margeo.2008.10.006

  • Mujabar, P. S., & Chandrasekar, N. (2011). Coastal erosion hazard and vulnerability assessment for southern coastal Tamil Nadu of India by using remote sensing and GIS. Natural Hazards, 69(3), 1295-1314. doi: https://doi.org/10.1007/s11069-011-9962-x

  • Mujabar, P. S., & Chandrasekar, N. (2013). Shoreline change analysis along the coast between Kanyakumari and Tuticorin of India using remote sensing and GIS. Arabian Journal of Geosciences, 6(3), 647-664. doi: https://doi.org/10.1007/s12517-011-0394-4

  • Mukhopadhyay, A., Hornby, D. D., Hutton, C. W., Lázár, A. N., Johnson, F. A., & Ghosh, T. (2018). Land cover and land use analysis in coastal Bangladesh. In Ecosystem Services for Well-Being in Deltas (pp. 367-381). Cham, Switzerland: Palgrave Macmillan.

  • Mukhopadhyay, A., Mukherjee, S., Garg, R. D., & Ghosh, T. (2013). Spatio-temporal analysis of land use - land cover changes in Delhi using remote sensing and GIS techniques. International Journal of Geomatics and Geosciences, 4(1), 212-223.

  • Mukhopadhyay, A., Mukherjee, S., Hazra, S., & Mitra, D. (2011). Sea level rise and shoreline changes: A geo-informatics appraisal of Chandipur coast, Orissa. International Journal of Geology, Earth and Environmental Sciences, 1(1), 9-17.

  • Mukhopadhyay, A., Mukherjee, S., Mukherjee, S., Gosh, S., Hazra, S., & Mitra, D. (2012). Automatic shoreline detection and future prediction: A case study on Puri Coast, Bay of Bengal, India. European Journal of Remote Sensing, 45(1), 201-213. doi: https://doi.org/10.5721/EuJRS20124519

  • Munday, J. C., & Alfoldi, T. T. (1979). LANDSAT test of diffuse reflectance models for aquatic suspended solids measurement. Remote Sensing Environment, 8(2), 169-183. doi: https://doi.org/10.1016/0034-4257(79)90015-4

  • Murali, R. M., & Kumar, P. D. (2015). Implications of sea level rise scenarios on land use/land cover classes of the coastal zones of Cochin, India. Journal of Environmental Management, 148, 124-133. doi: https://doi.org/10.1016/j.jenvman.2014.06.010

  • Nguyen, H. H., Pullar, D., Duke, N., McAlpine, C., Nguyen, H. T., & Johansen, K. (2010, November 1-5). Historic shoreline changes: An indicator of coastal vulnerability for human landuse and development in Kien Giang, Vietnam. In 31st Asian Conference on Remote Sensing (pp. 1835-1843). Hanoi, Vietnam.

  • Oumer, H. A. (2009). Land use and land cover change, drivers and its impact: A comparative study from Kuhar Michael and LencheDima of Blue Nile and Awash Basins of Ethiopia (Unpublished thesis). Cornell University, NY, USA.

  • Pal, B., Samanta, S., & Pal, D. K. (2012). Morphometric and hydrological analysis and mapping for Watut watershed using Remote Sensing and GIS techniques. International Journal of Advances in Engineering and Technology, 2(1), 357-368.

  • Pandian, P. K., Ramesh, S., Murthy, M. V. R., Ramachandran, S., & Thayumanavan, S. (2004). Shoreline changes and near shore processes along Ennore coast, east coast of South India. Journal of Coastal Research, 20(203), 828-845. doi: https://doi.org/10.2112/1551-5036(2004)20[828:SCANSP]2.0.CO;2

  • River Research Institute. (2009). Report on the beach profile survey at Digha form West Bengal-Orissa Border to Mandermoni. Report [Hard Copy].

  • Rossiter, D. G. (2014). Statistical methods for accuracy assessment of classified thematic maps. Technical Note. International Institute for Geo-information Science and Earth Observation (ITC).

  • Saha, A. K., Arora, M. K., Csaplovics, N. E., & Gupta, R. P. (2005). Land covers classification using IRS LISS III Image and DEM in a Rugged Terrain: A case study in Himalayas. Geocarto International, 20(2), 33-40. doi: https://doi.org/10.1080/10106040508542343

  • Samanta, S., & Paul, S. (2016). Geospatial analysis of shoreline and land use/land covers changes through remote sensing and GIS techniques. Modeling Earth Systems and Environment, 2(3), 1-8. doi: https://doi.org/10.1007/s40808-016-0180-0

  • SCGE. (2011). Supervised/unsupervised land use land cover classification using ERDAS imagine. Summer course computational geoecology. Retrieved May 30, 2015, from http://horizon.science.uva

  • Scott, A. J., & Symons, M. J. (1971). Clustering methods based on likelihood ratio criteria. Biometrics, 27(2), 387-397.

  • Selvan, S. C., Kankara, R. S., & Rajan, B. (2014). Assessment of shoreline changes along Karnataka coast, India using GIS and Remote sensing techniques. Indian Journal of Marine Sciences, 43(7), 1286-1291

  • Story, M., & Congalton, R. (1986). Accuracy assessment: A user’s perspective. Photogrammetric Engineering and Remote Sensing, 52(3), 397-399.

  • Thieler, E. R., Himmelstoss, E. A., Zichichi, J. L., & Ayhan, E. (2009). Digital shoreline analysis system (DSAS) version 4.3. An Arc GIS extension for calculating shoreline change: U.S. Geological Survey Open-File Report 2008-1278. Retrieved November 5, 2018, from http://pubs.usgs.gov/of/2008/1278.

  • Trinh, L. H., Le, T. G., Kieu, V. H., Tran, T. M. L., & Nguyen, T. T. N. (2020). Application of remote sensing technique for shoreline change detection in Ninh Binh and Nam Dinh provinces (Vietnam) during the period 1988 to 2018 based on water indices. Russian Journal of Earth Sciences, 20(2), 1-36. doi: 10.2205/2020ES000686.

  • Umitsu, M., & Sen, B. (1987). Late quaternary sedimentary environment and landform evolution in the Bengal low land. Geographical Review of Japan, Series B., 60(2), 164-178. doi: https://doi.org/10.4157/grj1984b.60.164

  • Van, T. T., & Binh, T. T. (2008, December 4-6). Shoreline change detection to serve sustainable management of coastal zone in Cu Long Estuary. In Proceedings of the International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences (pp. 1-6). Hanoi, Vietnam.

  • Zhang, S., Zhang, S., & Zhang, J. (2000). A study on wetland classification model of remote sensing in the Sangjiang plain. Chinese Geographical Science, 10(1), 68-73. doi: https://doi.org/10.1007/s11769-000-0038-1

ISSN 1511-3701

e-ISSN 2231-8542

Article ID

JST-2076-2020

Download Full Article PDF

Share this article

Recent Articles