Integrated Shoreline Dynamics Assessment Using QSCAT and Random Forest Along the Phetchaburi Coastline, Thailand

Uma Seeboonruang; Kanana Limpakdeeswat; Pinit Tanachaichoksirikun; Kornvisith Silarom

doi:10.48084/etasr.17033

Authors

Uma Seeboonruang Department of Civil Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Kanana Limpakdeeswat Department of Civil Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Pinit Tanachaichoksirikun Department of Civil Engineering, School of Engineering, King Mongkut's Institute of Technology Ladkrabang, Thailand
Kornvisith Silarom School of Management Science, Sukhothai Thammathirat Open University, Thailand

Volume: 16 | Issue: 2 | Pages: 34737-34744 | April 2026 | https://doi.org/10.48084/etasr.17033

Received: 18 December 2025 | Revised: 30 January 2026 and 16 February 2026 | Accepted: 20 February 2026 | Online: 4 April 2026

Corresponding author: Pinit Tanachaichoksirikun

Abstract

The Phetchaburi coastline faces significant erosion risks, exacerbated by human activities and climate change. This research evaluates shoreline shifts and mangrove cover modifications along the 88.5 km coastline, utilizing the QGIS Shoreline Change Assessment Tool (QSCAT) plugin and Random Forest (RF) classification on Landsat 8-9 OLI/TIRS images from 2017 to 2023. The integration of these methods provides a robust framework for monitoring coastal changes in a data-scarce, microtidal environment. Results indicate a highly dynamic coastline with a maximum erosion distance of -108.71 m and a maximum accretion of +122.95 m. Statistical analysis of 1,766 transects reveals a critical erosion trend, with a maximum erosion rate reaching -38.66 m/year. Overall, 50.74% of the transects experienced erosion, while 48.98% showed accretion. The RF model demonstrated high effectiveness in land use classification, achieving an overall accuracy of 96.50% and a Kappa coefficient of 0.92. Spatiotemporal analysis reveals a strong link between mangrove degradation and increased erosion zones, emphasizing the protective function of vegetation. These findings provide essential quantitative benchmarks for developing targeted coastal management and restoration strategies in the Gulf of Thailand.

Keywords:

coastal erosion, geoinformatics, mangrove restoration, QSCAT, random forest

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References

L. Mentaschi, M. I. Vousdoukas, J.-F. Pekel, E. Voukouvalas, and L. Feyen, "Global long-term observations of coastal erosion and accretion," Scientific Reports, vol. 8, no. 1, Aug. 2018, Art. no. 12876. DOI: https://doi.org/10.1038/s41598-018-30904-w

A. Luijendijk, G. Hagenaars, R. Ranasinghe, F. Baart, G. Donchyts, and S. Aarninkhof, "The State of the World’s Beaches," Scientific Reports, vol. 8, no. 1, p. 6641, Apr. 2018. DOI: https://doi.org/10.1038/s41598-018-24630-6

S. Ritphring, C. Somphong, K. Udo, and S. Kazama, "Projections of Future Beach Loss due to Sea Level Rise for Sandy Beaches along Thailand’s Coastlines," Journal of Coastal Research, vol. 85, no. sp1, pp. 541–545, May 2018. DOI: https://doi.org/10.2112/SI85-109.1

C. Somphong, K. Udo, S. Ritphring, H. Shirakawa, and S. Kazama, "Adaptation assessment to future beach loss due to sea level rise in Thailand," Coastal Engineering Proceedings, no. 36, Dec. 2018, Art. no. 13. DOI: https://doi.org/10.9753/icce.v36.risk.13

S. Saramul and T. Ezer, "Spatial variations of sea level along the coast of Thailand: Impacts of extreme land subsidence, earthquakes and the seasonal monsoon," Global and Planetary Change, vol. 122, pp. 70–81, Nov. 2014. DOI: https://doi.org/10.1016/j.gloplacha.2014.08.012

F. Duriyapong and K. Nakhapakorn, "Coastal vulnerability assessment at Phetchaburi province, Thailand," Songklanakarin Journal of Science and Technology, vol. 33, no. 3, pp. 357-365, 2011.

Marine and coastal resources information of Phetchaburi Province, Ministry of Natural Resources and Environment, Thailand, 2018.

S. Vongvisessomjai, "Effect of global warming in Thailand," Songklanakarin Journal of Science and Technology, vol. 32, no. 4, pp. 431-444, Aug. 2010.

O. Sarajit, K. Nakhapakorn, S. Jirakajohnkool, K. Tienwong, and A. Pansuwan, "Assessing coastal composite vulnerability indices on seasonal change in Phetchaburi, Thailand," EnvironmentAsia, vol. 8, no. 1, pp. 115–123, 2015.

A. Faiboon and W. Sangmanee, "Geospatial Monitoring and Forecasts of Coastal Engineering Structures’ Shoreline Transformational Impact at Songkhla Lake Mouth," Princess of Naradhiwas University Journal, vol. 11, no. 3, pp. 165–179, Jan. 2019.

T. Baodindam, T. Thammakornkul, A. Tiangtrong, N. Noipow, and T. Mangmoon, "Coastline changes using Digital Shoreline Analysis System (DSAS): Case study Songkhla Province," in Proceedings of the 8th National Conference on Science and Technology (NCOST) and the 2nd International Conference on Science and Technology (INCOST), Rajamangala University of Technology Suvarnabhumi, 2024, pp. 119-129.

C. Saengsupavanich, "Detached breakwaters: Communities’ preferences for sustainable coastal protection," Journal of Environmental Management, vol. 115, pp. 106–113, Jan. 2013. DOI: https://doi.org/10.1016/j.jenvman.2012.11.029

C. Chawalit et al., "Geoinformatics and Machine Learning for Shoreline Change Monitoring: A 35-Year Analysis of Coastal Erosion in the Upper Gulf of Thailand," ISPRS International Journal of Geo-Information, vol. 14, no. 2, Feb. 2025, Art. no. 94. DOI: https://doi.org/10.3390/ijgi14020094

C. Kuenzer, A. Bluemel, S. Gebhardt, T. V. Quoc, and S. Dech, "Remote Sensing of Mangrove Ecosystems: A Review," Remote Sensing, vol. 3, no. 5, pp. 878–928, Apr. 2011. DOI: https://doi.org/10.3390/rs3050878

P. Nithinarangru and S. Ritphring, "The comparative study of shoreline detection methods," in Proceedings of the 24th National Convention on Civil Engineering, Udonthani, Thailand, 2019, pp. 2106-2113.

L. P. Facun et al., "QGIS Shoreline Change Analysis Tool (QSCAT): A fast, open-source shoreline change analysis plugin for QGIS," Environmental Modelling & Software, vol. 184, Jan. 2025, Art. no. 106263. DOI: https://doi.org/10.1016/j.envsoft.2024.106263

L. T. de Lima, S. Fernández-Fernández, J. M. de A. Espinoza, M. da G. Albuquerque, and C. Bernardes, "End Point Rate Tool for QGIS (EPR4Q): Validation Using DSAS and AMBUR," ISPRS International Journal of Geo-Information, vol. 10, no. 3, Mar. 2021, Art. no. 162. DOI: https://doi.org/10.3390/ijgi10030162

E. A. Himmelstoss, R. E. Henderson, M. G. Kratzmann, and A. S. Farris, "Digital Shoreline Analysis System (DSAS) version 5.0 user guide," US Geological Survey, Open-File Report 2018-1179, 2018. DOI: https://doi.org/10.3133/ofr20181179

E. R. Thieler, E. A. Himmelstoss, J. L. Zichichi, and A. Ergul, "The Digital Shoreline Analysis System (DSAS) version 4.0—An ArcGIS extension for calculating shoreline change," US Geological Survey, Open-File Report 2009-1004, 2009. DOI: https://doi.org/10.3133/ofr20081278

L. Breiman, "Random Forests," Machine Learning, vol. 45, no. 1, pp. 5–32, Oct. 2001. DOI: https://doi.org/10.1023/A:1010933404324

M. Belgiu and L. Drăguţ, "Random forest in remote sensing: A review of applications and future directions," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, pp. 24–31, Apr. 2016. DOI: https://doi.org/10.1016/j.isprsjprs.2016.01.011

M. Pal, "Random forest classifier for remote sensing classification," International Journal of Remote Sensing, vol. 26, no. 1, pp. 217–222, Jan. 2005. DOI: https://doi.org/10.1080/01431160412331269698

L. K. Phan, J. S. M. van T. de Vries, and M. J. F. Stive, "Coastal Mangrove Squeeze in the Mekong Delta," Journal of Coastal Research, vol. 31, no. 2, pp. 233–243, Mar. 2015. DOI: https://doi.org/10.2112/JCOASTRES-D-14-00049.1

Phetchaburi Provincial Coastal and Marine Resources Committee and Office of Marine and Coastal Resources 3, Annual report on marine and coastal resources and coastal erosion situation in Phetchaburi Province, Ministry of Natural Resources and Environment, Thailand, 2023.

W. Truttung, "Study of shoreline change using geographic information system," Royal Thai Naval Academy Journal of Science and Technology, vol. 3, no. 1, pp. 55–65, 2020.

D. A. Friess et al., "The State of the World’s Mangrove Forests: Past, Present, and Future," Annual Review of Environment and Resources, vol. 44, no. 1, pp. 89–115, Oct. 2019. DOI: https://doi.org/10.1146/annurev-environ-101718-033302

U. Thampanya, J. E. Vermaat, S. Sinsakul, and N. Panapitukkul, "Coastal erosion and mangrove progradation of Southern Thailand," Estuarine, Coastal and Shelf Science, vol. 68, no. 1, pp. 75–85, June 2006. DOI: https://doi.org/10.1016/j.ecss.2006.01.011

Y. Mazda, M. Magi, M. Kogo, and P. N. Hong, "Mangroves as a coastal protection from waves in the Tong King delta, Vietnam," Mangroves and Salt Marshes, vol. 1, no. 2, pp. 127–135, June 1997. DOI: https://doi.org/10.1023/A:1009928003700

R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sensing of Environment, vol. 37, no. 1, pp. 35–46, July 1991. DOI: https://doi.org/10.1016/0034-4257(91)90048-B

J. W. Rouse, R. H. Haas, J. A. Schell, and D. W. Deering, "Monitoring vegetation systems in the Great Plains with ERTS," in Third Earth Resources Technology Satellite-l Symposium. Volume I: Technical Presentations Section A. Goddard Space Flight Center: Washington, D.C, December 10-14, 1973. National Aeronautics and Space Administration: Washington, D.C., pp.309-317.

B. Bidorn, N. Phanomphongphaisarn, C. Rukvichai, and P. Kongsawadworakul, "Evolution of Mangrove Muddy Coast in the Western Coast of the Upper Gulf of Thailand Over the Past Six Decades," in Estuaries and Coastal Zones in Times of Global Change, 2020, pp. 429–442. DOI: https://doi.org/10.1007/978-981-15-2081-5_25

A. Kumar and A. K. Gorai, "Application of transfer learning of deep CNN model for classification of time-series satellite images to assess the long-term impacts of coal mining activities on land-use patterns," Geocarto International, vol. 37, no. 26, pp. 11420–11440, Dec. 2022. DOI: https://doi.org/10.1080/10106049.2022.2057595

A. Kumar and A. K. Gorai, "Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region," Computers & Geosciences, vol. 170, Jan. 2023, Art. no. 105251. DOI: https://doi.org/10.1016/j.cageo.2022.105251

A. L. McIvor, I. Möller, T. Spencer, and M. Spalding. Reduction of wind and swell waves by mangroves. Natural Coastal Protection Series: Report 1, Cambridge Coastal Research Unit Working Paper 40. Cambridge, UK: The Nature Conservancy and Wetlands International, 2012.

A. L. Fathah, B. Semedi, F. C. Wardana, and A. Isdianto, "Remote Sensing-Based Estimation of Mangrove Above-Ground Carbon Using Sentinel-2 Vegetation Indices and Random Forest," Engineering, Technology & Applied Science Research, vol. 15, no. 6, pp. 29598–29604, Dec. 2025. DOI: https://doi.org/10.48084/etasr.14335

A. Kumar and A. Kumar Gorai, "A comparative evaluation of deep convolutional neural network and deep neural network-based land use/land cover classifications of mining regions using fused multi-sensor satellite data," Advances in Space Research, vol. 72, no. 11, pp. 4663–4676, Dec. 2023. DOI: https://doi.org/10.1016/j.asr.2023.08.057

P. Imlamai, S. Worachananant, and J. Phaksopa, "Monitoring study of coastal morphological change from coastal engineering structures at Mrigadayavan Palace, Phetchaburi Province using geo-informatics," Burapha Science Journal, vol. 28, no. 2, pp. 730–748, 2023.

G. M. Foody, "Status of land cover classification accuracy assessment," Remote Sensing of Environment, vol. 80, no. 1, pp. 185–201, Apr. 2002. DOI: https://doi.org/10.1016/S0034-4257(01)00295-4

M. Spalding, A. McIvor, F. H. Tonneijck, S. Tol, and P. van Eijk. Mangroves for coastal defence: Guidelines for coastal managers and policy makers. Cambridge, UK: Wetlands International and The Nature Conservancy, 2014.

E. B. Barbier et al., "Coastal Ecosystem-Based Management with Nonlinear Ecological Functions and Values," Science, vol. 319, no. 5861, pp. 321–323, Jan. 2008. DOI: https://doi.org/10.1126/science.1150349

S. Temmerman, P. Meire, T. J. Bouma, P. M. J. Herman, T. Ysebaert, and H. J. De Vriend,"Ecosystem-based coastal defence in the face of global change," Nature, vol. 504, no. 7478, pp. 79–83, Dec. 2013. DOI: https://doi.org/10.1038/nature12859

D. M. Alongi, "Mangrove forests: Resilience, protection from tsunamis, and responses to global climate change," Estuarine, Coastal and Shelf Science, vol. 76, no. 1, pp. 1–13, Jan. 2008. DOI: https://doi.org/10.1016/j.ecss.2007.08.024

C. Giri et al., "Status and distribution of mangrove forests of the world using earth observation satellite data," Global Ecology and Biogeography, vol. 20, no. 1, pp. 154-159, 2011. DOI: https://doi.org/10.1111/j.1466-8238.2010.00584.x

J. Kongwongjan, "Land use change effect on coastal erosion in Phuket Province," M.S. thesis, Dept. Management of Environmental Resources, Prince of Songkla University, Thailand, 2012.