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All Journal HAYATI Journal of Biosciences MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management MEDIA KONSERVASI Indonesian Journal of Geography REKA GEOMATIKA Geoplanning : Journal of Geomatics and Planning IJOG : Indonesian Journal on Geoscience Journal of Degraded and Mining Lands Management Indonesian Journal of Geospatial Journal of Mathematical and Fundamental Sciences Journal of Engineering and Technological Sciences Indonesian Journal of Biotechnology Forum Geografi Indonesian Journal of Science and Technology JURNAL ILMIAH GEOMATIKA Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital International Journal of Remote Sensing and Earth Sciences (IJReSES) JURNAL GEOGRAFI Geografi dan Pengajarannya Indonesian Journal of Electrical Engineering and Computer Science Indonesian Journal on Geoscience Indonesian Journal on Geoscience Bulletin of Geology Geosfera Indonesia Jurnal Penginderaan Jauh dan Pengolahan Data Citra Digital
Ketut Wikantika
Centre for Remote Sensing, Bandung Institute of Technology (ITB) and Faculty of Earth Sciences and Technology, Bandung Institute of Technology (ITB)
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Immunology & microbiology Materials Science & Nanotechnology Mathematics Physics Social Sciences

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Journal : Geosfera Indonesia

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Advanced Machine Learning Techniques for Tidal Marsh Classification: A Random Forest Approach using Sentinel-2A Simarmata, Nirmawana; Wikantika, Ketut; Darmawan, Soni; Harto, Agung Budi
Geosfera Indonesia Vol. 9 No. 3 (2024): GEOSFERA INDONESIA
Publisher : Department of Geography Education, University of Jember, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/geosi.v9i3.52186

Abstract

Tidal marshes play a vital role in coastal ecosystems, functioning in climate change mitigation, water filtration, and protection from coastal erosion. However, mapping and monitoring of these ecosystems is often hampered by difficult accessibility and dynamic environmental conditions. This research aims to improve tidal marsh classification accuracy by applying a Random Forest (RF) algorithm supported by Sentinel-2A satellite imagery. This image provides various spectral parameters and vegetation indices, including B1, GNDVI, BSI, and NDWI. Three RF models with varying parameters were tested to determine their effectiveness in tidal marsh classification. The model with 26 parameters (Model 3) performed best, with the lowest RMSE value of 0.22, the highest AUC of 0.87, and the highest overall accuracy of 95%. These results show that combining critical spectral parameters in the RF model can significantly improve the classification accuracy and biomass estimation in tidal marshes. This study also confirmed the effectiveness of Random Forest in addressing the challenges of high-accuracy mapping and monitoring. These findings provide a solid foundation for tidal marsh ecosystem conservation and management applications and support the application of machine learning in coastal ecosystem mapping for better and more accurate results.
Co-Authors Abd. Rasyid Syamsuri Adhi Wibowo Adriana Hiariej, Adriana Agung B. Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agung Budi Harto Agus Handoyo Harsolumakso Agus Handoyo Harsolumakso, Agus Handoyo Agus Sutanto Agus Sutanto Ahmad Luthfi Hadiyanto Akihiko Kondoh Aldyansyah, Muhammad Aminah Kastuari Anesta, Aqilla Fitdhea Anggun Tridawati Aqilla Fitdhea Anesta Armi Susandi Armi Susandi Ary Setijadi Prihatmanto Asep Saepuloh Asep Saepuloh Asep Yusup Saptari Asep Yusup Saptari, Asep Yusup Asmi M. Napitu Asmi M. Napitu Aswin Rahadian Bambang Widarsono Bobby S. Dipokusumo Dandy A. Novresiandi Darmawan S Darmawan S, Darmawan Dedi Irawadi Deni Suwardi Desti Ayunda Dudung M Hakim Dudung Muhally Hakim Dudung Muhally Hakim Fahmi, Muhammad Nurul Farah Nafisa Ariadji Fauziah, Afi Fenny M. Dwivany FENNY MARTHA DWIVANY Ghazali, Mochamad Firman Ghozali, M. Firman Giasintha Stefani Golok Jaya, La Ode Muhammad Hary Nugroho Herru Lastiadi Setiawan Himasari Hanan Husna Nugrahapraja I Nyoman Dibia I NYOMAN RAI I Wayan Nuarsa Imam A. Sadisun Intan Fatmawati Irland Fardani Ishak H. Ismullah Jevon A. Telaumbanua Karlia Meitha Katmoko Ari Sambodo Katmoko Ari Sambodo, Katmoko Ari LILIK BUDIPRASETYO Lissa F. Yayusman Luky Adrianto Lumbantobing, Marlonroi Mamad Sugandi Marlonroi Lumbantobing Mila Olivia Trianaputri Mirelva, Prima Rizky Mochamad Firman Ghazali Mochamad Firman Ghazali Nengah Widiadnyana Nengah Widiadnyana Nisrina Sukriandi Nurjanna Joko Trilaksono Prihanggo, Maundri Prila Ayu Dwi Prastiwi Purnama, Yustika Retno Dammayatri Rian Nurtyawan Riantini Virtriana S. Suliantara Satria Bijaksana Shafarina Wahyu Trisyanti Sigit Nur Pratama Simarmata, nirmawana Soni Darmawan Sony Darmawan, Sony Sugandi, Mamad Sukristiyanti Sukristiyanti Supriadi A Supriadi A, Supriadi Susantoro, Tri Muji Suwardhi, Deni Tahjudil Witra Tan, Alex Tohir, Rizki Kurnia Tombayu A. Hidayat Topik Hidayat Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro Tri Muji Susantoro, Tri Muji Trianaputri, Mila Olivia Tridawati, Anggun Trika Agnestasia Tarigan Yayusman, Lissa Fajri Yudi Setiawan