Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques


  • Ni Made Trigunasih Soil Sciences and Environment, Faculty of Agriculture, Udayana University, Jl. Raya Kampus UNUD, Bukit Jimbaran, Kuta Selatan, Badung-Bali 80361
  • I Wayan Narka Soil Sciences and Environment, Faculty of Agriculture, Udayana University, Jl. Raya Kampus UNUD, Bukit Jimbaran, Kuta Selatan, Badung-Bali 80361
  • Moh Saifulloh Spatial Data Infrastructure Development Center (PPIDS), Udayana University, Jl. Raya Kampus UNUD, Bukit Jimbaran, Kuta Selatan, Badung-Bali 80361



eruption, land cover, machine learning, Mount Agung-Bali, remote sensing, Sentinel-2A


Volcanic eruptions are natural disasters with significant environmental and societal impacts. Timely detection and monitoring of volcanic eruptions are crucial for effective hazard assessment, mitigation strategies, and emergency response planning. Remote sensing technology has emerged as a valuable tool for detecting and assessing the effects of volcanic eruptions. One of the challenges in remote sensing image processing is handling large data dimensions that are difficult to address using traditional methods. Machine learning approaches offer a suitable solution to tackle these challenges. Machine learning demonstrates increasing computational capabilities, the ability to handle big data and automation. This study aimed to compare different machine learning classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Gaussian Mixture Model (GMM), and K-Nearest Neighbors (KNN). The data utilized in this study was derived from Sentinel-2A Multi-Spectral Instrument (MSI) imagery, which was tested in areas affected by the eruption of Mount Agung, Bali Province, in 2017. The results indicated that the GMM algorithm performed the best among the machine learning classifiers, achieving an Overall Accuracy (OA) value of 82.04%. It was followed by RF (78.86%) and KNN (77.55%). The areas affected by volcanic eruptions were determined by overlaying disaster-prone regions with areas mapped using the machine learning approach. The total affected area was measured as 29.89 km2, with an additional 3.31 km2 outside the designated zone. The findings of this study serve as a guideline for governmental entities, stakeholders, and communities to implement effective mitigation efforts for disaster risk reduction.


Andaru, R., Rau, J.Y., Syahbana, D.K., Prayoga, A.S. and Purnamasari, H.D. 2021. The use of UAV remote sensing for observing lava dome emplacement and areas of potential lahar hazards: An example from the 2017–2019 eruption crisis at Mount Agung in Bali. Journal of Volcanology and Geothermal Research 415, doi:10.1016/j.jvolgeores.2021.107255

Batta, M. 2020. Machine learning algorithms - a review. International Journal of Science and Research 9(1):381-386, doi:10.21275/ART20203995.

Bensoussan, A., Li, Y., Nguyen, D.P.C., Tran, M.B., Yam, S.C.P. and Zhou, X. 2022. Machine learning and control theory. Handbook of Numerical Analysis 23, doi:10.1016/bs.hna.2021.12.016.

Bergsma, E.W.J. and Almar, R. 2020. Coastal coverage of ESA’ Sentinel 2 mission. Advances in Space Research 6511, doi:10.1016/j.asr.2020.03.001.

Blum, A. 2007. Machine Learning Theory. Carnegie Melon University, School of Computer Science.

Borgogno-Mondino, E., De Palma, L. and Novello, V. 2020. Investigating Sentinel 2 multi-spectral imagery efficiency in describing the spectral response of vineyards covered with plastic sheets. Agronomy 1012, doi:10.3390/agronomy10121909.

Brownlee, J. 2016. K-Nearest Neighbors for Machine Learning. In: Machine Learning Algorithms, Machine Learning Mastery, /.

Carn, S.A., Watts, R.B., Thompson, G. and Norton, G.E. 2004. Anatomy of a lava dome collapse: The 20 March 2000 event at Soufrière Hills Volcano, Montserrat. Journal of Volcanology and Geothermal Research 131(3-4):241-264, doi: 10.1016/S0377-0273(03)0364-0.

Favorskaya, M.N., Favorskaya, A.V., Petrov, I.B. and Jain, L.C. 2021. Recent advances in numerical methods, machine learning, and computer science. In Smart Innovation, Systems and Technologies 215, doi:10.1007/978-981-33-4619-2_1.

Harsanto, P. 2015. River morphology modeling at the downstream of Progo River post-eruption 2010 of Mount Merapi. Procedia Environmental Sciences 28, doi:10.1016/j.proenv.2015.07.021.

Hashi, A.O., Abdirahman, A.A., Elmi, M.A., Hashi, S.Z.M. and Rodriguez, O.E.R. 2021. A real-time flood detection system based on machine learning algorithms with an emphasis on deep learning. International Journal of Engineering Trends and Technology 69(5):249-256, doi:10.14445/22315381/IJETT-V69I5P232.

Hu, Y., Li, Z., Wang, L., Chen, B., Zhu, W., Zhang, S., Du, J., Zhang, X., Yang, J., Zhou, M., Liu, Z., Wang, S., Miao, C., Zhang, L. and Peng, J. 2022. Rapid interpretation and analysis of the 2022 eruption of Hunga TongaâƒHunga HaÌ“apai volcano with integrated remote sensing techniques. Geomatics and Information Science of Wuhan University 472, doi:10.13203/j.whugis20220050.

Huang, G.B., Zhu, Q.Y. and Siew, C.K. 2006. Extreme learning machine: Theory and applications. Neurocomputing 70(1-3):489-501, doi:10.1016/j.neucom.2005.12.126.

Jamali, A. 2021. Land use land cover mapping using advanced machine learning classifiers. Ekologia (Bratislava) 40(3):286-300, doi:10.2478/eko-2021-0031.

Kobayashi, N., Tani, H., Wang, X. and Sonobe, R. 2020. Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication 4(1):67-90, doi:10.1080/24751839.2019.1694765.

Komorowski, J.C., Jenkins, S., Baxter, P.J., Picquout, A., Lavigne, F., Charbonnier, S., Gertisser, R., Preece, K., Cholik, N., Budi-Santoso, A. and Surono. 2013. Paroxysmal dome explosion during the Merapi 2010 eruption: Processes and facies relationships of associated high-energy pyroclastic density currents. Journal of Volcanology and Geothermal Research 261:260-294, doi:10.1016/j.jvolgeores.2013.01.007.

Lavigne, F., Degeai, J.P., Komorowski, J.C., Guillet, S., Robert, V., Lahitte, P., Oppenheimer, C., Stoffel, M., Vidal, C.M., Suronoh, Pratomo, I., Wassmer, P., Hajdas, I., Hadmoko, D.S. and De Belizal, E. 2013. Source of the great A.D. 1257 mystery eruption unveiled Samalas volcano, Rinjani Volcanic Complex, Indonesia. Proceedings of the National Academy of Sciences of the United States of America 110(42):16742-16747, doi:10.1073/pnas.1307520110.

Loukika, K.N., Keesara, V.R., and Sridhar, V. 2021. Analysis of land use and land cover using machine learning algorithms on Google Earth engine for Munneru river basin, India. Sustainability (Switzerland) 13(24):13758, doi:10.3390/su132413758.

Ma, Z., Mei, G. and Piccialli, F. 2021. Machine learning for landslides prevention: a survey. Neural Computing and Applications 33(17):10881-10907, doi:10.1007/s00521-020-05529-8.

Malawani, M.N., Lavigne, F., Gomez, C., Mutaqin, B.W. and Hadmoko, D.S. 2021. Review of local and global impacts of volcanic eruptions and disaster management practices: The Indonesian example. Geosciences (Switzerland) 11(3):109, doi:10.3390/geosciences11030109.

Negnevitsky, M. and Pavlovsky, V. 2005. Neural networks approach to online identification of multiple failures of protection systems. IEEE Transactions on Power Delivery 20(2):588-594, doi:10.1109/TPWRD.2004.843451.

Negnevitsky, M., Lim, M.J.H., Hartnett, J. and Reznik, L. 2005. Email communications analysis: How to use computational intelligence methods and tools? Proceedings of the 2005 IEEE International Conference on Computational Intelligence for Homeland Security and Personal Safety, CIHSPS 2005, doi:10.1109/CIHSPS.2005.1500603.

Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y. and Ranagalage, M. 2020. Sentinel-2 data for land cover/use mapping: A review. Remote Sensing 12(14):2291, doi:10.3390/rs12142291.

Portugal, I., Alencar, P. and Cowan, D. 2018. The use of machine learning algorithms in recommender systems: A systematic review. Expert Systems with Applications 97(1):205-227, doi:10.1016/j.eswa.2017.12.020

Prins, A.J. and Van Niekerk, A. 2020. Crop type mapping using LiDAR, Sentinel-2 and aerial imagery with machine learning algorithms. Geo-spatial Information Science 24(2):211-227, doi:10.1080/10095020.2020.1782776.

Rahmawati, P.I., Trianasari, N. and Martin, A.A.N.Y. 2019. The Economic Impact of Mount Agung Eruption on Bali Tourism. Proceedings of the International Conference on Tourism, Economics, Accounting, Management, and Social Science, doi:10.2991/teams-18.2019.18.

Ravichandran, T., Gavahi, K., Ponnambalam, K., Burtea, V. and Mousavi, J.S. 2021. Ensemble-based machine learning approach for improved leak detection in water mains. Journal of Hydroinformatics 23(2):307-323, doi:10.2166/HYDRO.2021.093.

Ruiz-Real, J.L., Uribe-Toril, J., Torres, J.A. and Pablo, J.D.E. 2021. Artificial intelligence in business and economics research: Trends and future. Journal of Business Economics and Management 22(1):98-117, doi:10.3846/jbem.2020.13641.

Russell, S. and Bohannon, J. 2015. Artificial intelligence. Fears of an AI pioneer. Science New York.

Salih, M.M. 2021. Developing spectral reflectance measurement system for environmental remote sensing applications. International Journal of Design and Nature and Ecodynamics 16(1):33-39, doi:10.18280/ijdne.160105.

Saputra, D.D., Sari, R.R., Hairiah, K., Widianto, Suprayogo, D. and van Noordwijk, M. 2022. Recovery after volcanic ash deposition: vegetation effects on soil organic carbon, soil structure and infiltration rates. Plant and Soil 474(1-2):163-179, doi:10.1007/s11104-022-05322-7.

Schmidt, A., Leadbetter, S., Theys, N., Carboni, E., Witham, C.S., Stevenson, J.A., Birch, C.E., Thordarson, T., Turnock, S., Barsotti, S., Delaney, L., Feng, W., Grainger, R.G., Hort, M.C., Höskuldsson, Ã., Ialongo, I., Ilyinskaya, E., Jóhannsson, T., Kenny, P., … Shepherd, J. 2015. Satellite detection, long-range transport, and air quality impacts of volcanic sulfur dioxide from the 2014-2015 flood lava eruption at Bárðarbunga (Iceland). Journal of Geophysical Research 120(18), doi:10.1002/2015JD023638.

Shetty, S., Gupta, P.K., Belgiu, M. and Srivastav, S.K. 2021. Assessing the effect of training sampling design on the performance of machine learning classifiers for land cover mapping using multi-temporal remote sensing data and Google Earth engine. Remote Sensing 13(8):1433, doi:10.3390/rs13081433.

Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P. and Homayouni, S. 2020. Support vector machine versus random forest for remote sensing image classification: a meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13:6308-6322, doi:10.1109/JSTARS.2020.3026724.

Simurda, C., Magruder, L.A., Markel, J., Garvin, J.B. and Slayback, D.A. 2022. ICESat-2 Applications for Investigating Emerging Volcanoes. Geosciences (Switzerland) 12(1):40, doi:10.3390/geosciences12010040.

Sutomo, and Wahab, L. 2019. Changes in vegetation on Mount Agung Volcano Bali, Indonesia. Journal of Tropical Biodiversity and Biotechnology 4(2):54-61, doi:10.22146/jtbb.41008.

Suwa, H. and Yamakoshi, T. 1999. Sediment discharge by storm runoff at volcanic torrents affected by eruption. Zeitschrift Fur Geomorphologie, Supplementband 114.

Syifa, M., Kadavi, P.R., Lee, C.W. and Pradhan, B. 2020. Landsat images and artificial intelligence techniques used to map volcanic ashfall and pyroclastic material following the eruption of Mount Agung, Indonesia. Arabian Journal of Geosciences 133:1-12, doi:10.1007/s12517-020-5060-2.

Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.A. and Rahman, A. 2020. Land-use land-cover classification by machine learning classifiers for satellite observations-A review. Remote Sensing 12(7):1135, doi:10.3390/rs12071135.

Tavares, P.A., Beltrão, N.E.S., Guimarães, U.S. and Teodoro, A.C. 2019. Integration of sentinel-1 and sentinel-2 for classification and LULC mapping in the urban area of Belém, eastern Brazilian Amazon. Sensors (Switzerland) 19(5):1140, doi:10.3390/s19051140.

Thouret, J.C., Lavigne, F., Suwa, H., Sukatja, B. and Surono. 2007. Volcanic hazards at Mount Semeru, East Java (Indonesia), with emphasis on lahars. Bulletin of Volcanology 70:221-244, doi:10.1007/s00445-007-0133-6.

Thupae, R., Isong, B., Gasela, N. and Abu-Mahfouz, A.M. 2018. Machine learning techniques for traffic identification and classification in SDWSN: A survey. Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, doi:10.1109/IECON.2018.8591178.

Uddin, S., Khan, A., Hossain, M.E. and Moni, M.A. 2019. Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making 19(1):281, doi:10.1186/s12911-019-1004-8.

Wang, B., Jia, K., Liang, S., Xie, X., Wei, X., Zhao, X., Yao, Y. and Zhang, X. 2018). Assessment of Sentinel-2 MSI spectral band reflectances for estimating fractional vegetation cover. Remote Sensing 10(12):1927, doi:10.3390/rs10121927.

Wang, Z., Ritou, M., Da Cunha, C., and Furet, B. 2020. Contextual classification for smart machining based on unsupervised machine learning by Gaussian mixture model. International Journal of Computer Integrated Manufacturing 33(10-11):1042-1054, doi:10.1080/0951192X.2020.1775302.

Wu, A., Dong, H., Wu, Q. and Ling, L. 2011. A survey of application-level protocol identification based on machine learning. Proceedings - 2011 4th International Conference on Information Management, Innovation Management and Industrial Engineering, ICIII 2011, 3, doi:10.1109/ICIII.2011.331.








How to Cite

Trigunasih, N. M., Narka, I. W., & Saifulloh, M. (2023). Mapping eruption affected area using Sentinel-2A imagery and machine learning techniques. Journal of Degraded and Mining Lands Management, 11(1), 5073–5083.



Research Article