Assessment of Mangrove Cover Change Based on Combining Remote Sensing Technique and Hydrodynamic Model Simulation

Authors

  • Nguyen Van Thinh

    Joint Vietnam - Russia Tropical Science and Technology Research Center, Ho Chi Minh City 700000, Vietnam
  • Ngo Trung Dung

    Joint Vietnam - Russia Tropical Science and Technology Research Center, Ho Chi Minh City 700000, Vietnam
  • Nguyen Trong Hiep

    Joint Vietnam - Russia Tropical Science and Technology Research Center, Ho Chi Minh City 700000, Vietnam
  • Do Phong Luu

    Joint Vietnam - Russia Tropical Science and Technology Research Center, Ho Chi Minh City 700000, Vietnam
  • Dang Truong An

    1. Department of Oceanology, Meteorology and Hydrology, University of Science, Ho Chi Minh City 700000, Vietnam; 2. Viet Nam National University-Ho Chi Minh, Ho Chi Minh City 700000, Vietnam

DOI:

https://doi.org/10.30564/jees.v7i2.7730
Received: 8 November 2024 | Revised: 19 November 2024 | Accepted: 25 November 2024 | Published Online: 24 January 2025

Abstract

Mangrove ecosystems along Vietnam's coastline face significant degradation due to human activities, despite their crucial role in coastal protection against natural hazards. This study aims to assess the spatial and temporal changes in mangrove coverage along Vietnam's southern coast by integrating remote sensing techniques with hydrodynamic model simulations. The research methodology combines the Collect Earth tool analysis of Spot-4 and Planet satellite imagery (2000-2020) with Mike 21-HD two-dimensional (2D) hydrodynamic modeling to evaluate mangrove coverage changes by simulating shoreline erosion. Results analysis reveals that a significant increase of 109.83 ha in mangrove area within Vinh Chau Town of Soc Trang Province during the period 2010-2020, predominantly in the eastern region. Hydrodynamic simulations demonstrate that the coastal zone is primarily influenced by the interaction of nearshore currents, East Sea tides, and seasonal monsoon wave patterns. The model results effectively capture the complex interactions between these hydrodynamic factors and mangrove distribution. These findings not only validate the effectiveness of combining remote sensing and hydrodynamic modeling for mangrove assessment but also provide crucial insights for sustainable coastal ecosystem management. The study's integrated approach offers a robust framework for monitoring mangrove dynamics and developing evidence-based conservation strategies, highlighting the importance of maintaining these vital ecosystems for coastal protection.

Keywords:

Mangrove; Wave; Current; Hydrodynamic Modeling; Satellite

References

[1] Bherwani, H., Niwalkar, A., Kapley, A., Biniwale, R., 2024. Mapping and valuation of ecosystem services of mangroves: a detailed study from the Andaman Island. Landscape and Ecological Engineering. 21, 1–12.

[2] FAO, 2007. The World's Mangroves 1980-2005. FAO Forestry Paper 153. 30 December 2007.

[3] Cunningham, E., 2019. The role of mangroves in coastal protection. Wood Environment & Infrastructure Solutions UK Limited. Doc Ref: 41607rep03i1

[4] Mukherjee, N., Sutherland, W., Dicks, L., Hugé, J., Koedam, N., Dahdouh-Guebas, F., 2014. Ecosystem Service Valuations of Mangrove Ecosystems to Inform Decision Making and Future Valuation Exercises. PloS one. 9(9). e107706.

[5] Zeng, H., Jia, M., Zhang, R., Wang, Z., Mao, D., Ren, C., Zhao, C., 2023. Monitoring the light pollution changes of China's mangrove forests from 1992-2020 using nighttime light data. Frontiers in Marine Science. 10, P.1187702. DOI: https://doi.org/10.3389/fmars.2023.1187702

[6] Riddin, T., Adams, J., Rajkaran, A., Machite, A., Peer, N., Suarez, E., 2024. IUCN Red List of Ecosystems, Mangroves of The Agulhas. Available from: https://doi.org/10.32942/X2H322 (Cited 01 June 2024)

[7] Berlinck, C., Lima, L.H.A., Pereira, A.M.M., Carvalho Jr, E.A.R. , Paula, R.C., Thomas, W.M., Morato, R.G. , 2021. The Pantanal is on fire and only a sustainable agenda can save the largest wetland in the world. Brazilian Journal of Biology. 82(42), e244200.

[8] Phan, L.K., van Thiel de Vries, J.S., Stive, M.J., 2015. Coastal Mangrove Squeeze in the Mekong Delta. Journal of Coastal Research. 31(2), 233–243.

[9] Linh, P. K., Son, T. H., 2021. Hydrodynamic characteristics and the role of mangrove forests in coastal erosion protection and climate change adaptation in the Mekong Delta. Journal of Water Resources Science and Technology. 66, 1–11.

[10] Hong, P. N., 1999. Vietnam's Mangrove Forests. Agriculture Publishing House: Hanoi, p. 173

[11] Mazda, Y., Magi, M., Kogo, M., Hong, P.N., 1997. Mangroves as a coastal protection from waves in the Tong King delta, Vietnam. Mangroves and Salt Marshes. 1(2). 127–135.

[12] O'Leary, F., 2022. Wetland loss in the Ñeembucú Wetlands Complex, Paraguay, using remote sensing. Available from: https://www.biorxiv.org/content/10.1101/2022.01.03.474818v1 (Cited 10 July 2024). Doi: https://doi.org/10.1101/2022.01.03.474818

[13] Vu, D. T., 2011. The Role of Mangroves for Reducing High Waves During Typhoon in Dai Hop (Kien Thuy, Hai Phong). Vietnam Journal of Marine Science and Technology. 11(1), 43–55. (in Vietnamese)

[14] Mazda, Y., Magi, M., Ikeda, Y., Kurokawa, T., Asano, T., 2006. Wave reduction in a mangrove forest dominated by Sonneratia sp. Wetlands Ecology and Management. 14, 365–378.

[15] Suzuki, T., Zijlema, M., Burger, B., Meijer, M.C., Narayan, S., 2012. Wave dissipation by vegetation with layer schematization in SWAN. Coastal Engineering. 59(1), 64–71.

[16] Horstman, E. M., Dohmen-Janssen, C. M., Narra, P. M. F., Van den Berg, N. J. F., Siemerink, M., Hulscher, S. J. M. H., 2014. Wave attenuation in mangroves: a quantitative approach to field observations. Coastal engineering, 94, 47–62.

[17] Butera, M.K., 1983. Remote Sensing of Wetlands. IEEE Transactions on Geoscience and Remote Sensing. GE-21(3), 383–392.

[18] Ghosh, M., Kumar, L., Roy, C., 2016. Mapping Long-Term Changes in Mangrove Species Composition and Distribution in the Sundarbans. Forests. 7(12), 305. DOI: https://doi.org/10.3390/f7120305

[19] Karakıs, S., Marangoz, A., Buyuksalih, G., 2006. Analysis of segmentation parameters in ecognition software using high resolution quickbird ms imagery. Proceedings of The ISPRS Workshop on Topographic Mapping from Space; February 14-16, 2006; Ankara, Turkey. pp. 14–16.

[20] O'Reilly, J.E., Maritorena, S., Siegel, D., O'Brien, M.C., Toole, D., Mitchell, B.G., 2000. Ocean color chlorophyll a algorithms for SeaWiFS, OC2, and OC4: Version 4. SeaWiFS Postlaunch Calibration and Validation Analyses. 11, 9–23.

[21] Subramaniam, A., Brown, C.W., Hood, R.R., Carpenter, E.J., Capone, D.G., 2001. Detecting Trichodesmium blooms in SeaWiFS imagery. Deep Sea Research Part II: Topical Studies in Oceanography. 49(1–3), 107–121.

[22] Melin, F., 2009. Global Distribution of the Random Uncertainty Associated With Satellite-Derived Chl a. IEEE Geoscience and Remote Sensing Letters. 7(1), 220–224.

[23] Gitelson, A.A., Dall'Olmo, G., Moses, W., Rundquist, D.C., Barrow, T., Fisher, T., Gurlin, D., Holz, J., 2008. A Simple semi-analytical model for remote estimation of chlorophyll-a in turbid waters: Validation. Remote Sensing of Environment. 112(9), 3582–3593.

[24] Al-Naimi, N., Raitsos, D.E., Ben-Hamadou, R., Soliman, Y., 2017. Evaluation of Satellite Retrievals of Chlorophyll-a in the Arabian Gulf. Remote Sensing. 9(3), 301.

[25] Sivakumar, R., Prasanth, B.S.V., Ramaraj, M., 2022. An empirical approach for deriving specific inland water quality parameters from high spatio-spectral resolution image. Wetlands Ecology and Management. 30(2), 405–422.

[26] Jalbuena, R.L., Peralta, R.V., Tamondong, A.M., 2015. Object-based image analysis for mangroves extraction using LIDAR datasets and orthophoto. Proceedings of The Asian Association on Remote Sensing (AARS) Proceedings, 24-28 October 2015, Quezon City, Metro Manila Philippines, pp.1-8

[27] Cárdenas, N.Y., Joyce, K.E., Maier, S.W., 2017. Monitoring mangrove forests: Are we taking full advantage of technology?. International Journal of Applied Earth Observation and Geoinformation. 63(310), 1–14.

[28] Thanh C, N., An T, D., Khuong NT, T., 2022. Monsoonal sediment transport along the subaqueous Mekong Delta: An analysis of surface sediment grain-size changes. Ocean Systems Engineering. 12(4), 403–411

[29] Kavzoglu, T., Yildiz, M., 2014. Parameter-Based Performance Analysis of Object-Based Image Analysis Using Aerial and Quikbird-2 Images. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences. 2(7), 31–37.

[30] Lee, S.K., Dang, T.A., Tran, T.H., 2018. Combining rainfall–runoff and hydrodynamic models for simulating flow under the impact of climate change to the lower Sai Gon-Dong Nai River basin. Paddy and Water Environment. 16, 457–465.

[31] Baatz, M., Schape , A., 2000. Multiresolution segmentation: an optimization approach for high quality multiscale image segmentation. In: Strobl, J., Blaschke, T., Griesbner, G. (Eds.). Angewandte geographische informationsverarbeitung, XII. Wichmann Verlag: Karlsruhe, Germany. pp. 2–1

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How to Cite

Nguyen Van Thinh, Ngo Trung Dung, Nguyen Trong Hiep, Do Phong Luu, & Dang Truong An. (2025). Assessment of Mangrove Cover Change Based on Combining Remote Sensing Technique and Hydrodynamic Model Simulation. Journal of Environmental & Earth Sciences, 7(2), 151–160. https://doi.org/10.30564/jees.v7i2.7730

Issue

Vol. 7 , Iss. 2 (February 2025)

Article Type

Article

License

Copyright © 2025 Nguyen Van Thinh, Ngo Trung Dung, Nguyen Trong Hiep, Do Phong Luu, Dang Truong An

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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