Determination of Shoreline Changes along Upstream of Se San River, Kontum Province based Multi-temporal Remote Sensing Data Analysis
Main Article Content
Abstract
Se San river upstream includes Poko tributary (on the right bank) and Dak Bla tributary (on the left bank), mostly located in Kon Tum province. The process of river sediment decline has dramatic shoreline changes in this area, which becomes a driving-force to modify the current socio-economic development as well as the impact on territorial planning in the future. This study aims to analyze the shoreline changes by extracting multi-temporal satellite imagery of Landsat in the period of 1990-2013 and identify its change effects on land-use. The results show that the strongest erosion rate was -2.96 m/year in Dak Bla tributary (in Kon Tum town). And in the Poko tributary, the average value of erosion rate is -1.31 m/year and the average accretion rate is 1.17 m/year. In the context of dramatic land-use change, this approach allows to support for territorial management and illustrates the accretion-erosion relationship in river basin evolution.
Keywords:
Se San river basin, shoreline change, multi-temporal remote sensing data.
References
[1] G. García-Rubio, D. Huntley, P. Russell, Evaluating shoreline identification using optical satellite images, Marine Geology 359 (2015) 96-106. https://doi.org/10.1016/j.margeo.2014.11.002.
[2] C.B. Boye, K. Appeaning Addo, G. Wiafe, K. Dzigbodi-Adjimah, Spatiotemporal analyses of shoreline change in the Western Region of Ghana, J. Coast. Conserv. Volume 22 Issues 4, 2018, pp. 769–776.
http://dx.doi.org/10.1007/s11852-018-0607-z.
[3] S.P. Leatherman, B.C. Douglas, J.L. LaBrecque, Sea level and coastal erosion require large-scale monitoring, EOS Trans. Volume 84 Issues 2, (2003) 13–20.
https://doi.org/10.1029/2003EO020001.
[4] G. Eman, M. Jehan, G. Douglass, M. Joanne, A. Mostafa, Nile Delta exhibited a spatial reversal in the rates of shoreline retreat on the Rosetta Promontory comparing pre-and post-beach protection, Geomorphology 228 (2015) 1–14. http://dx.doi.org/10.1016/j.geomorph.2014.08.021
[5] M.R. Muskananfola, Supriharyono, S. Febrianto, Spatio-temporal analysis of shoreline change along the coast of Sayung Demak, Indonesia using Digital Shoreline Analysis System, Regional Studies in Marine Science 34 (2020) 1-9. https://doi.org/10.1016/j.rsma.2020.101060.
[6] G. Anfuso, D. Bowman, C. Danese, E. Pranzini, Transect based analysis versus area based analysis to quantify shoreline displacement: spatial resolution issues, Environmental Monitoring and Assessment 188 (2016) 568.
https://doi.org/10.1007/s10661-016-5571-1.
[7] D.L. Strayer, S.E.G. Findlay, D. Miller, H.M. Malcom, D.T. Fischer, T. Coote, Biodiversity in Hudson River shore zones: influence of shoreline type and physical structure, Aquatic Sciences 74 (2012) 597–610. https://doi.org/10.1007/s00027-012-0252-9.
[8] M. Brabender, M. Weitere, C. Anlanger, M. Brauns, Secondary production and richness of native and non-native macroinvertebrates are driven by human-altered shoreline morphology in a large river, Hydrobiologia 776 (2016) 51-65. https://doi.org/10.1007/s10750-016-2734-6.
[9] Md.S. Newaz, R.W. Mackereth, A.U. Mallik, D. McCormick, How much boreal lake shoreline is burned by wildfire? Implications for emulating natural disturbance in riparian forest management, Forest Ecology and Management 473 (2020) 118-283.
https://doi.org/10.1016/j.foreco.2020.118283.
[10] X. Zhang, Z. Yang, Y. Zhang, Y. Ji, H. Wang, K. Lv, Z. Lu, Spatial and temporal shoreline changes of the southern Yellow River (Huanghe) Delta in 1976–2016, Marine Geology 395 (2018) 188-197. https://doi.org/10.1016/j.margeo.2017.10.006.
[11] B. Yang, C. Hwang, H.K. Cordell, Use of LiDAR shoreline extraction for analyzing revetment rock beach protection: A case study of Jekyll Island State Park, USA, Ocean & Coastal Management 69 (2012) 1-15.
https://doi.org/10.1016/j.ocecoaman.2012.06.007.
[12] J.B. Campbell, R.H. Wynne, Introduction to Remote Sensing, Fifth edition, Guildford Press, 2011, 607 pages.
[13] H. Burningham, J. French, Understanding coastal change using shoreline trend analysis supported by cluster-based segmentation, Geomorphology 282 (2017) 131-149.
https://doi.org/10.1016/j.geomorph.2016.12.029.
[14] D.E. Reeve, J. Horrillo-Caraballo, H. Karunarathna, S. Pan, A new perspective on meso-scale shoreline dynamics through data-driven analysis, Geomorphology 341 (2019) 169-191.
https://doi.org/10.1016/j.geomorph.2019.04.033.
[15] J. Almonacid-Caballer, E. Sánchez-García, J.E. Pardo-Pascual, A.A. Balaguer-Beser, J. Palomar-Vázqueza, Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator, Marine Geology 372 (2016) 79-88.
https://doi.org/10.1016/j.margeo.2015.12.015.
[16] E. Sánchez-Garcíaa, J.M. Palomar-Vázquez, J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, L. Gómez-Pujol, An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery, Coastal Engineering 160 (2020) 103732. https://doi.org/10.1016/j.coastaleng.2020.103732.
[17] J.E. Pardo-Pascual, J. Almonacid-Caballer, L.A. Ruiz, J. Palomar-Vázquez, Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision Remote Sensing of Environment 123 (2012) 1-11. https://doi.org/10.1016/j.rse.2012.02.024.
[18] R. Aedla, G.S. Dwarakish, D.V. Reddy, Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques, Aquatic Procedia 4 (2015) 563-570. https://doi.org/10.1016/j.aqpro.2015.02.073.
[19] Md.A. Kabir, Md. Salauddin, K.T. Hossain, I.A. Tanim, Md.M.H. Saddam, A.U. Ahmad, Assessing the shoreline dynamics of Hatiya Island of Meghna estuary in Bangladesh using multiband satellite imageries and hydro-meteorological data. Regional Studies in Marine Science 35 (2020) 101167. https://doi.org/10.1016/j.rsma.2020.101167.
[20] J. Moussaid, A.A. Fora, B. Zourarah, M. Maanan, M. Maanan, Using automatic computation to analyze the rate of shoreline change on the Kenitra coast, Morocco, Ocean Engineering 102 (2015) pp. 71-77.
https://doi.org/10.1016/j.oceaneng.2015.04.044.
[21] E.H. Boak, I.L. Turner, Shoreline Definition and Detection: A Review, Journal of Coastal Research 214 (2005) 688-703.
https://doi.org/10.2112/03-0071.1.
[22] Nguyen Quang Tuan, Hoang Cong Tin, Luong Quang Doc, Tran Anh Tuan, Historical Monitoring of Shoreline Changes in the Cua Dai Estuary, Central Vietnam Using Multi-Temporal Remote Sensing Data, Geosciences 7 (3) (2017) 72. https://doi.org/10.3390/geosciences7030072.
[23] Tran Ha Phuong, Hoang Phi Phung, Nguyen Thanh Hung, Hoang Cong Tri, Assessing the shoreline changes in Tra Vinh province using multi-temporal remote sensing data, Vietnam Journal of Science and Technology 56 (5) (2018) 612-624.
https://doi.org/10.15625/2525-2518/56/5/10944.
[24] N. Raj, B. Gurugnanam, V. Sudhakar, P.G. Francis, Estuarine shoreline change analysis along The Ennore river mouth, south east coast of India, using digital shoreline analysis system, Geodesy and Geodynamics 10, Issue 3 (2019) 205-212. https://doi.org/10.1016/j.geog.2019.04.002.
[25] J.T. Kelly, S. McSweeney, J. Shulmeister, A.M. Gontz, Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia, Marine Geology 415 (2019) 105971.
https://doi.org/10.1016/j.margeo.2019.105971.
[26] R.R. Goforth, S.M. Carman, Multiscale Relationships between Great Lakes Nearshore Fish Communities and Anthropogenic Shoreline Factors, Journal of Great Lakes Research 35 (2) (2009) 215-223.
https://doi.org/10.1016/j.jglr.2009.02.001.
[27] S. Buckman, M.A. de Alarcon, J. Maigret, Tracing shoreline flooding: Using visualization approaches to inform resilience planning for small Great Lakes communities, Applied Geography 113 (2019) 102097.
https://doi.org/10.1016/j.apgeog.2019.102097.
[28] W.G. Rees, Physical Principles of Remote Sensing, Third edition, Cambridge University Press, 2013, 492 pages.
[29] G. Winasor, S. Budhiman, The Potential Application of Remote Sensing Data for Coastal Study, Proc. 22nd Asian Conference on Remote Sensing, Singapore, 2001.
http://www.crisp.nus.edu.sg/~acrs2001/pdf/084Winar.pdf.
[30] J. B. MacQueen, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281-297.
https://projecteuclid.org/download/pdf_1/euclid.bsmsp/1200512992.
[31] E.R. Thieler, E.A. Himmelstoss, J.L. Zichichi, A. Ergul, The Digital Shoreline Analysis System (DSAS) Version 4.0-an ArcGIS Extension for Calculating Shoreline Change, US Geological Survey Open-File Report, 2009.
https://cmgds.marine.usgs.gov/publications/DSAS/of2008-1278.
[2] C.B. Boye, K. Appeaning Addo, G. Wiafe, K. Dzigbodi-Adjimah, Spatiotemporal analyses of shoreline change in the Western Region of Ghana, J. Coast. Conserv. Volume 22 Issues 4, 2018, pp. 769–776.
http://dx.doi.org/10.1007/s11852-018-0607-z.
[3] S.P. Leatherman, B.C. Douglas, J.L. LaBrecque, Sea level and coastal erosion require large-scale monitoring, EOS Trans. Volume 84 Issues 2, (2003) 13–20.
https://doi.org/10.1029/2003EO020001.
[4] G. Eman, M. Jehan, G. Douglass, M. Joanne, A. Mostafa, Nile Delta exhibited a spatial reversal in the rates of shoreline retreat on the Rosetta Promontory comparing pre-and post-beach protection, Geomorphology 228 (2015) 1–14. http://dx.doi.org/10.1016/j.geomorph.2014.08.021
[5] M.R. Muskananfola, Supriharyono, S. Febrianto, Spatio-temporal analysis of shoreline change along the coast of Sayung Demak, Indonesia using Digital Shoreline Analysis System, Regional Studies in Marine Science 34 (2020) 1-9. https://doi.org/10.1016/j.rsma.2020.101060.
[6] G. Anfuso, D. Bowman, C. Danese, E. Pranzini, Transect based analysis versus area based analysis to quantify shoreline displacement: spatial resolution issues, Environmental Monitoring and Assessment 188 (2016) 568.
https://doi.org/10.1007/s10661-016-5571-1.
[7] D.L. Strayer, S.E.G. Findlay, D. Miller, H.M. Malcom, D.T. Fischer, T. Coote, Biodiversity in Hudson River shore zones: influence of shoreline type and physical structure, Aquatic Sciences 74 (2012) 597–610. https://doi.org/10.1007/s00027-012-0252-9.
[8] M. Brabender, M. Weitere, C. Anlanger, M. Brauns, Secondary production and richness of native and non-native macroinvertebrates are driven by human-altered shoreline morphology in a large river, Hydrobiologia 776 (2016) 51-65. https://doi.org/10.1007/s10750-016-2734-6.
[9] Md.S. Newaz, R.W. Mackereth, A.U. Mallik, D. McCormick, How much boreal lake shoreline is burned by wildfire? Implications for emulating natural disturbance in riparian forest management, Forest Ecology and Management 473 (2020) 118-283.
https://doi.org/10.1016/j.foreco.2020.118283.
[10] X. Zhang, Z. Yang, Y. Zhang, Y. Ji, H. Wang, K. Lv, Z. Lu, Spatial and temporal shoreline changes of the southern Yellow River (Huanghe) Delta in 1976–2016, Marine Geology 395 (2018) 188-197. https://doi.org/10.1016/j.margeo.2017.10.006.
[11] B. Yang, C. Hwang, H.K. Cordell, Use of LiDAR shoreline extraction for analyzing revetment rock beach protection: A case study of Jekyll Island State Park, USA, Ocean & Coastal Management 69 (2012) 1-15.
https://doi.org/10.1016/j.ocecoaman.2012.06.007.
[12] J.B. Campbell, R.H. Wynne, Introduction to Remote Sensing, Fifth edition, Guildford Press, 2011, 607 pages.
[13] H. Burningham, J. French, Understanding coastal change using shoreline trend analysis supported by cluster-based segmentation, Geomorphology 282 (2017) 131-149.
https://doi.org/10.1016/j.geomorph.2016.12.029.
[14] D.E. Reeve, J. Horrillo-Caraballo, H. Karunarathna, S. Pan, A new perspective on meso-scale shoreline dynamics through data-driven analysis, Geomorphology 341 (2019) 169-191.
https://doi.org/10.1016/j.geomorph.2019.04.033.
[15] J. Almonacid-Caballer, E. Sánchez-García, J.E. Pardo-Pascual, A.A. Balaguer-Beser, J. Palomar-Vázqueza, Evaluation of annual mean shoreline position deduced from Landsat imagery as a mid-term coastal evolution indicator, Marine Geology 372 (2016) 79-88.
https://doi.org/10.1016/j.margeo.2015.12.015.
[16] E. Sánchez-Garcíaa, J.M. Palomar-Vázquez, J.E. Pardo-Pascual, J. Almonacid-Caballer, C. Cabezas-Rabadán, L. Gómez-Pujol, An efficient protocol for accurate and massive shoreline definition from mid-resolution satellite imagery, Coastal Engineering 160 (2020) 103732. https://doi.org/10.1016/j.coastaleng.2020.103732.
[17] J.E. Pardo-Pascual, J. Almonacid-Caballer, L.A. Ruiz, J. Palomar-Vázquez, Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision Remote Sensing of Environment 123 (2012) 1-11. https://doi.org/10.1016/j.rse.2012.02.024.
[18] R. Aedla, G.S. Dwarakish, D.V. Reddy, Automatic Shoreline Detection and Change Detection Analysis of Netravati-GurpurRivermouth Using Histogram Equalization and Adaptive Thresholding Techniques, Aquatic Procedia 4 (2015) 563-570. https://doi.org/10.1016/j.aqpro.2015.02.073.
[19] Md.A. Kabir, Md. Salauddin, K.T. Hossain, I.A. Tanim, Md.M.H. Saddam, A.U. Ahmad, Assessing the shoreline dynamics of Hatiya Island of Meghna estuary in Bangladesh using multiband satellite imageries and hydro-meteorological data. Regional Studies in Marine Science 35 (2020) 101167. https://doi.org/10.1016/j.rsma.2020.101167.
[20] J. Moussaid, A.A. Fora, B. Zourarah, M. Maanan, M. Maanan, Using automatic computation to analyze the rate of shoreline change on the Kenitra coast, Morocco, Ocean Engineering 102 (2015) pp. 71-77.
https://doi.org/10.1016/j.oceaneng.2015.04.044.
[21] E.H. Boak, I.L. Turner, Shoreline Definition and Detection: A Review, Journal of Coastal Research 214 (2005) 688-703.
https://doi.org/10.2112/03-0071.1.
[22] Nguyen Quang Tuan, Hoang Cong Tin, Luong Quang Doc, Tran Anh Tuan, Historical Monitoring of Shoreline Changes in the Cua Dai Estuary, Central Vietnam Using Multi-Temporal Remote Sensing Data, Geosciences 7 (3) (2017) 72. https://doi.org/10.3390/geosciences7030072.
[23] Tran Ha Phuong, Hoang Phi Phung, Nguyen Thanh Hung, Hoang Cong Tri, Assessing the shoreline changes in Tra Vinh province using multi-temporal remote sensing data, Vietnam Journal of Science and Technology 56 (5) (2018) 612-624.
https://doi.org/10.15625/2525-2518/56/5/10944.
[24] N. Raj, B. Gurugnanam, V. Sudhakar, P.G. Francis, Estuarine shoreline change analysis along The Ennore river mouth, south east coast of India, using digital shoreline analysis system, Geodesy and Geodynamics 10, Issue 3 (2019) 205-212. https://doi.org/10.1016/j.geog.2019.04.002.
[25] J.T. Kelly, S. McSweeney, J. Shulmeister, A.M. Gontz, Bimodal climate control of shoreline change influenced by Interdecadal Pacific Oscillation variability along the Cooloola Sand Mass, Queensland, Australia, Marine Geology 415 (2019) 105971.
https://doi.org/10.1016/j.margeo.2019.105971.
[26] R.R. Goforth, S.M. Carman, Multiscale Relationships between Great Lakes Nearshore Fish Communities and Anthropogenic Shoreline Factors, Journal of Great Lakes Research 35 (2) (2009) 215-223.
https://doi.org/10.1016/j.jglr.2009.02.001.
[27] S. Buckman, M.A. de Alarcon, J. Maigret, Tracing shoreline flooding: Using visualization approaches to inform resilience planning for small Great Lakes communities, Applied Geography 113 (2019) 102097.
https://doi.org/10.1016/j.apgeog.2019.102097.
[28] W.G. Rees, Physical Principles of Remote Sensing, Third edition, Cambridge University Press, 2013, 492 pages.
[29] G. Winasor, S. Budhiman, The Potential Application of Remote Sensing Data for Coastal Study, Proc. 22nd Asian Conference on Remote Sensing, Singapore, 2001.
http://www.crisp.nus.edu.sg/~acrs2001/pdf/084Winar.pdf.
[30] J. B. MacQueen, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, 1967, pp. 281-297.
https://projecteuclid.org/download/pdf_1/euclid.bsmsp/1200512992.
[31] E.R. Thieler, E.A. Himmelstoss, J.L. Zichichi, A. Ergul, The Digital Shoreline Analysis System (DSAS) Version 4.0-an ArcGIS Extension for Calculating Shoreline Change, US Geological Survey Open-File Report, 2009.
https://cmgds.marine.usgs.gov/publications/DSAS/of2008-1278.