Application of Artificial Intelligence for Monitoring Shoreline Changes in the Central Coast of Vietnam
Main Article Content
Abstract
The identification and monitoring of coastline and shoreline plays an important role in coastal erosion assessment. The deep learning models can be a potential tool to detect the coastlines and shorelines in Vietnam using ultra-high resolution satellite images. The aims of the study are:
i) To propose a set of indicators to determine the coastlines and shoreline; ii) To build deep machine learning models that automatically interpret the coastlines and shorelines on ultra-high resolution remote sensing images; and iii) To apply developed deep learning (DL) models to monitor coastal erosion in central Vietnam. Eight DL models were implemented based on four artificial intelligence network structures, including U-Net, U2-Net, U-Net3+, and DexiNed. Satellite images collected through Google Earth Pro software were used as input for all models. As a result, the U-Net model has been effectively applied to coasts in Cu De, Lai Giang, and Bien Lo estuaries,. The output results were used to calculate the rate of erosion/accretion in these areas. Additionally, the study indicated that coastline is a suitable criterion in assessing coastal erosion under the impact of sea level rise during storms. On the other hand, shoreline is a suitable criterion in assessing tidal fluctuations or instantaneous movements of wave currents during the year.
Keywords:
Shoreline, Coastline, Deep learning, Optimization, Erosion, Accretion.
References
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B. Pradhan, Coastal Erosion Vulnerability Assessment Along the Eastern Coast of Bangladesh Using Geospatial Techniques, Ocean and Coastal Management, Vol. 199, 2021,
pp. 105408, https://doi.org/10.1016/j.ocecoaman.2020.105408.
[3] M. E. Hanley, S. P. G. G. Hoggart, D. J. Simmonds, A. Bichot, M. A. Colangelo, F. Bozzeda,
H. Heurtefeux, B. Ondiviela, R. Ostrowski,
M. Recio, R. Trude, E. Z. Kahlau, R. C. Thompson, Shifting Sands? Coastal Protection by Sand Banks, Beaches and Dunes, Coastal Engineering, Vol. 87, 2014, pp. 136-146, https://doi.org/10.1016/j.coastaleng.2013.10.020.
[4] G. Scardino, F. Sabatier, G. Scicchitano,
A. Piscitelli, M. Milella, A. Vecchio, M. Anzidei, G. Mastronuzzi, Sea-Level Rise and Shoreline Changes Along An Open Sandy Coast: Case Study of Gulf of Taranto, Italy, Water (Switzerland),
Vol. 12, 2020, pp. 13-20, https://doi.org/10.3390/w12051414.
[5] Z. T. Zhu, F. Cai, S. L. Chen, D. Q. Gu, A. P. Feng, C. Cao, H. S. Qi, G. Lei, Coastal Vulnerability to Erosion Using A Multi-Criteria Index: A Case Study of the Xiamen Coast, Sustainability (Switzerland), Vol. 11, 2018, https://doi.org/10.3390/su11010093.
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A. S. Arcilla, I. Trumbic, The Role of Coastal Setbacks in The Context of Coastal Erosion and Climate Change, Ocean and Coastal Management, Vol. 54, 2011, pp. 943-950, https://doi.org/10.1016/j.ocecoaman.2011.06.008.
[7] J. G. Titus, C. Richman, Maps of Lands Vulnerble to Sea Level Rise: Modeled Elevations Along the US Atlantic and Gulf Coasts, Climate Research, Vol. 18, 2001, pp. 205-228, https://doi.org/10.3354/cr018205.
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[9] N. M. Hai, S. Ouillon, V. D. Vinh, Sea-level Rise in Hai Phong Coastal Area (Vietnam) and its Response To Enso - evidence from Tide Gauge Measurement of 1960-2020, Vietnam Journal of Earth Science, Vol. 44, 2022, pp. 109-126, https://doi.org/10.15625/2615-9783/16961.
[10] X. X. Zhu, D. Tuia, L. Mou, G. S. Xia, L. Zhang, F. Xu, F. Fraundorfer, Deep Learning in Remote Sensing: A Review, 2017, https://doi.org/10.1109/MGRS.2017.2762307.
[11] V. V. Tac, Variability of Sea Surface Chlorophyll_A Concentration in the South Vietnam Coastal Waters Related to Enso Phenomenon, Vietnam Journal of Earth Sciences, Vol. 42, 2020, pp. 67-74, https://doi.org/10.15625/0866-7187 /42/1/14759.
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[14] V. L. Ngo, V. B. Dang, K. B. Dang, C. C. Ngo,
T. P. N. Pham, B. Benjamin, T. K. C. Giap, Assessment of Shoreline Changes for Setback Zone Establishment from Son Tra (Da Nang city) to Cua Dai (Hoi An city), Vietnam, Science of the Earth, Vol. 42, 2020, pp. 363-383, https://doi.org/10.15625/0866-7187 /42/4/15410.
[15] M. E. B. V. Puijenbroek, C. Nolet, A. V. D. Groot, J. M. Suomalainen, M. J. P. M. Riksen,
F. Berendse, J. Limpens, Exploring the Contributions of Vegetation and Dune Size to Early Dune Development Using Unmanned Aerial Vehicle (UAV) Imaging, Biogeosciences, Vol. 14, 2017, pp. 5533-5549, https://doi.org/10.5194/bg-14-5533-2017.
[16] M. Dai, X. Leng, B. Xiong, K. Ji, Sea-land Segmentation Method for SAR Images Based on Improved BiSeNet, Journal of Radars, Vol. 9, 2020, pp. 886-897, https://doi.org/10.12000/JR20089.
[17] R. Gens, Remote Sensing of Coastlines: Detection, Extraction and Monitoring, International Journal of Remote Sensing, Vol. 31, 2010, pp. 1819-1836.
[18] L. Kumar, M. S. Afzal, M. M. Afzal, Mapping Shoreline Change Using Machine Learning: A Case Study from the Eastern Indian Coast, Acta Geophysica, Vol. 68, 2020, pp. 1127-1143, https://doi.org/10.1007/s11600-020-00454-9.
[19] S. Toure, O. Diop, K. Kpalma, A. S. Maiga, Shoreline Detection Using Optical Remote Sensing: A review, ISPRS International Journal of Geo-Information, Vol. 8, 2019, https://doi.org/10.3390/ijgi8020075.
[20] J. G. Titus, C. Richman, Maps of Lands Vulnerble to Sea Level Rise: Modeled Elevations Along the US Atlantic and Gulf Coasts, Climate Research, Vol. 18, 2001, pp. 205-228, https://doi.org/10.3354/cr018205.
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[22] P. Marchesiello, N. M. Nguyen, N. Gratiot,
H. Loisel, E. J. Anthony, C. S. Dinh, T. Nguyen,
R. Almar, E. Kestenare, Erosion of the Coastal Mekong Delta: Assessing Natural Against Man Induced Processes, Continental Shelf Research, Vol. 181, 2019, pp. 72-89, https://doi.org/10.1016/j.csr.2019.05.004.
[23] H. M. Phan, Q. Ye, A. J. H. M. Reniers, M. J. F. Stive, Tidal Wave Propagation Along the Mekong Deltaic Coast, Estuarine, Coastal and Shelf Science, Vol. 220, 2019, pp. 73-98, https://doi.org/10.1016/j.ecss.2019.01.026.
[24] L. Harris, R. Nel, S. Holness, K. Sink,
D. Schoeman, Setting Conservation Targets for Sandy Beach Ecosystems, Estuarine, Coastal and Shelf Science, Vol. 150, 2014, pp. 45-57, https://doi.org/10.1016/j.ecss.2013.05.016.
[25] L. Rayne, M. C. Gatto, L. Abdulaati, M. A. Haddad, M. Sterry, N. Sheldrick, D. Mattingly, Detecting Change At Archaeological Sites in North Africa Using Open-source Satellite Imagery, Remote Sensing, Vol. 12, 2020, pp. 1-29, https://doi.org/10.3390/rs12223694.
[26] X. Y. Tong, G. S. Xia, Q. Lu, H. Shen, S. Li,
S. You, L. Zhang, Land-cover Classification with High-Resolution Remote Sensing Images Using Transferable Deep Models, Remote Sensing of Environment, Vol. 237, 2020, pp. 111322, https://doi.org/10.1016/j.rse.2019.111322.
[27] Q. Hu, W. Wu, T. Xia, Q. Yu, P. Yang, Z. Li,
Q. Song, Exploring the use of Google Earth Imagery and Object-Based Methods in Land Use/Cover Mapping, Remote Sensing, Vol. 5, 2013, pp. 6026-6042, https://doi.org/10.3390/rs5116026.
[28] F. Müller, S. Bicking, K. Ahrendt, K. B. Dang,
I. Blindow, C. Fürst, P. Haase, M. Kruse, T. Kruse, L. Ma, M. Perennes, I. Ruljevic, G. Schernewski, C. Schimming, A. Schneiders, H. Schubert,
U. Tappeiner, P. Wangai, W. Windhorst, J. Zeleny, Assessing Ecosystem Service Potentials to Evaluate Terrestrial, Coastal and Marine Ecosystem Types in Northern Germany – An expert-based Matrix Approach, Ecological Indicators, Vol. 112, 2020.
[29] T. T. Nguyen, T. D. Pham, C. T. Nguyen, J. Delfos, R. Archibald, K. B. Dang, N. B. Hoang, W. Guo, H. H. Ngo, A Novel Intelligence Approach Based Active and Ensemble Learning for Agricultural Soil Organic Carbon Prediction Using Multispectral and SAR Data Fusion, Science of the Total Environment, Vol. 804, 2022, pp. 150187, https://doi.org/10.1016/j.scitotenv.2021.150187.
[30] K. B. Dang, V. B. Dang, V. L. Ngo, K. C. Vu,
H. Nguyen, D. A. Nguyen, T. D. L. Nguyen,
T. P. N. Pham, T. L. Giang, H. D. Nguyen,
T. H. Do, Application of Deep Learning Models to Detect Coastlines and Shorelines, Journal of Environmental Management, Vol. 320, 2022,
pp. 115732, https://doi.org/10.1016/j.jenvman.2022.115732.
[31] E. A. Himmelstoss, R. E. Hederson, K. M. G. A. S. Farris, Digital Shoreline Analysis System (DSAS) Version 5.0 User Guide: U.S. Geological Survey Open-File Report, 2018.