Monitoring of coastline change using Sentinel-2 MSI data. A case study in Thanh Hoa Province, Vietnam
DOI:
https://doi.org/10.12775/bgeo-2024-0006Keywords
coastline change, remote sensing, water index, Sentinel-2 MSI, Thanh Hoa Province, VietnamAbstract
Vietnam is a coastal country with a coastline of more than 3,260 km, stretching from north to south. Coastal change in Vietnam is complicated further by the effects of climate change, including erosion and accretion, causing great impacts on infrastructure and the environment. This article presents the results of assessing coastline changes in Thanh Hoa Province (North Central region of Vietnam) from Sentinel-2 MSI satellite image data for the period 2015–23. Three Sentinel-2 MSI images taken in December 2015, December 2020 and December 2023 were used to calculate the water indices, including Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI) Argumented Normalised Difference Water Index (ANDWI) and Automated Water Extraction Index (AWEIsh), then extract the shoreline using the thresholding method and select the water index with the highest accuracy through comparing the overall accuracy and Kappa coefficient. The coastlines of 2015, 2020 and 2023 years are overlaid to evaluate the coastal changes in the study area. The results received in the study provide objective and timely information, helping managers effectively monitor and respond to coastline changes.
References
ACHARYA T, SUBEDI A and LEE D, 2018, Evaluation of water indices for surface water extraction in a Landsat 8 scene of Nepal. Sensors 18(8): 2580. doi: https://doi.org/10.3390/s18082580.
ADHIKARI S, 2019, A comparison of water indices and binary thresholding techniques for water surface delineation for St. Croix watershed area. Yearbook of the Association of Pacific Coast Geographers 81: 182–204.
ASTITI S, OSAWA T and NUARSA I, 2019, Identification of shoreline changes using Sentinel-2 imagery data in Canggu coastal area. Journal of Environmental Science 13(2): 191. DOI: 10.24843/EJES.2019.v13.i02.p07.
ALESHEIKH A, GHORBANALI A and NOURI A, 2007, Coastline change detection using remote sensing. International Journal of Environmental Science and Technology 4(1): 61–66.
CASTILLO-CAMPO Y, MONTEYS X, BECH A and CAHANLANE C, 2023, Sentinel 2 derived waterlines for coastal monitoring applications: a new approach for quantifying vertical and horizontal accuracies. The International Archives of the Photogrammetry. Remote Sensing and Spatial Information Sciences XLVIII-1/W3-2023: 39–45.
CHANDRASEKAR K, SAI M, ROY P and DWEVEDI R, 2010, Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product. International Journal of Remote Sensing 31(15): 3987–4005. DOI: https://doi.org/10.1080/01431160802575653.
CUA CA. (2024). Available at: https://cau-ca.com/vn/thanh-hoa/hoang-hoa, Accessed: 10 February 2024.
Darwish K and Smith S, 2021, A comparison of Landsat-8 OLI, Sentinel-2 MSI and PlanetScope satellite imagery for assessing coastline change in El-Alamein, Egypt. Engineering Proceedings 10(1): 23. DOI: https://doi.org/10.3390/ecsa-8-11258.
DUONG TL, DANG VK, DAO NH, NGUYEN TD, DINH XV and WEBER C, 2021, Monitoring of coastline change using Sentinel-2A and Landsat 8 data, a case study of Cam Pha city – Quang Ninh province. Vietnam Journal of Earth Sciences 43(3): 249–272. DOI: https://doi.org/10.15625/2615-9783/16066.
FEYISA G, MEIBY H, FENSHOLT R and PROUD S, 2014, Automated water extraction index: A new technique for surface water mapping using Landsat imagery. Remote Sensing of Environment 140: 23–35.
FOODY G, 2002, Status of land cover classification accuracy assessment. Remote Sensing of Environment 80(1): 185–201.
GAO BC, 1996, NDWI – A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment 58: 257–266.
JIANG W, NI Y, PANG Z, LI X, JU H, HE G, YANG K, FU J and QIN X, 2021, An effective water body extraction method with new water index for Sentinel-2 imagery. Water 13(12): 1647. DOI: https://doi.org/10.3390/w13121647.
LAONAMSAI J, JULPHUNTHONG P, SAPRATHET T, KIMMANY B, GANCHANASURAGIT T, CHOMCHEAWCHAN P & TOMUN N, 2023. Utilizing NDWI, MNDWI, SAVI, WRI, and AWEI for estimating erosion and accretion in Ping River in Thailand. Hydrology 10(3): 70. DOI: https://doi.org/10.3390/hydrology10030070.
LI J and WANG S, 2015, An automatic method for mapping inland surface waterbodies with Radarsat 2 imagery. International Journal of Remote Sensing 36(5): 1367–1384. DOI: http://dx.doi.org/10.1080/01431161.2015.1009653.
LIU H, HU H, LIU X, JIANG H, LIU W and YIN X, 2022, A comparison of different water indices and band downscaling methods for water bodies mapping from Sentinel-2 imagery at 10m resolution. Water 14: 2696. DOI: https://doi.org/10.3390/w14172696.
McFEETERS SK, 1996, The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7): 1425–1432. doi: https://doi.org/10.1080/01431169608948714.
MUSTAFA TM, HASSOON KI, HUSSAIN HM and ABD MH, 2017, Using water indices (NDWI, MNDWI, WRI and AWEI) to detect physical and chemical parameters by apply remote sensing and GIS techniques. International Journal of Research – Granthalayah 5(10): 117–128.
RAD AM, KREITLER J and SADEGH M, 2021, Augmented Normalized Difference Water Index for improved surface water monitoring. Environmental Modelling and Software 140: 105030. DOI: https://doi.org/10.1016/j.envsoft.2021.105030.
SERBAN C, MAFTEI C and DOBRICA G, 2022, Surface water change detection via water indices and predictive modeling using remote sensing imagery: A case study of Nuntasi-Tuzla Lake, Romania. Water 14: 556. DOI: https://doi.org/10.3390/w14040556.
SHEN L and LI C, 2010, Water Body Extraction from Landsat ETM+ imagery using adaboost algorithm. Proceedings of the 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010: 1–4.
TANG W, ZHAO C, LIN J, JIAO C, ZHENG G, ZHU J, PAN X and HAN X, 2022, Improved spectral water index combined with Otsu algorithm to extract muddy coastline data. Water 14(6): 885. DOI: https://doi.org/10.3390/w14060855.
TAUFIK M, NUGRAINI L, PRATOMO D, KURRNIAWAN A and UTAMA W, 2019, Detection of Arosbaya coastline changes using Sentinel-2A (study year of 2015–2018). IOP Conference Series: Earth and Environmental Science 328: 012045.
TRINH LH, LE TG, KIEU VH, TRAN TML and NGUYEN TTN, 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). ES2004: 1–15. doi: 10.2205/2020ES000686.
TRUONG DD, DAT TT, HANG ND and HUAN LH, 2022, Vulnerability assessment of climate change in Vietnam: a case study of Binh Chanh district, Hochiminh city. Frontiers in Environmental Science 10: 880254. doi: https://doi.org/10.3389/fenvs.2022.880254.
XU H, 2006, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing 27(14): 3025–3033.
WANG X, XIE S, ZHANG X, CHEN C, GUO H, DU J and DUAN Z, 2018, A robust multi-band water index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery. International Journal of Applied Earth Observation and Geoinformation 68: 73–91.
YAN P, ZHANG YJ and ZHANG Y, 2007, A study on Information extraction of water system in semi-arid regions with the Enhanced Water Index (EWI) and GIS based noise remove techniques. Remote Sensing Information 6: 62–67.
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Copyright (c) 2024 Le Hung Trinh, Thi Giang Le, Xuan Bien Tran, Quoc Vinh Tran, Van Phu Le, Thi Phuong To
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