Perbandingan Polaritas VV dan VH dalam Penerapan Algoritma NDFI pada Pemetaan Banjir Kota Palembang
DOI:
https://doi.org/10.32493/jtsi.v7i1.36209Keywords:
NDFI, Flood, Palembang, Remote Sensing, SentinelAbstract
Frequent floods in Indonesia, particularly in Palembang, South Sumatra, pose significant economic and social challenges due to land-use alterations, river overflow, and intense precipitation. The region's geographical, geological, and demographic features, compounded by global climate change, worsen the situation. Flood mapping and monitoring, utilizing satellites like Sentinel-1 with Synthetic Aperture Radar (SAR), are pivotal for mitigating these disasters. Sentinel-1's SAR technology aids wetland monitoring, acting as a natural water absorber and minimizing flood risks. Data from Sentinel-1, especially in VV and VH polarizations, offer profound insights into hydrological systems influencing floods. SAR efficiently comprehends Earth's environment, facilitating high-precision and rapid flood mapping using the NDFI method in Palembang. This study compares VV and VH polarizations in the NDFI algorithm to identify the most suitable polarization for accurate flood mapping. Results show VV data achieves 97.8% mapping accuracy compared to VH's 50%. The high accuracy of VV data signifies superior flood area detection. Moreover, VV's sigma0 value (backscatter) at -1.47 dB exceeds VH's approximately -20.47 dB, indicating stronger signal intensity. Hence, VV-polarized data, considering its performance, proves more effective for flood mapping.
Keywords: NDFI; Flood; Polarization; Sentinel
References
Ahdityas, R., Sukmono, A., & Sasmito, B. (2023). Analisis Kualitas Perairan Waduk Cacaban Dengan Menggunakan Data Ccitra Landsat 8 & 9 Multitemporal. Jurnal Geodesi Undip, 161–170.
Anggoro, A., Zamdial, Z., Hartono, D., Bakhtiar, D., Herliany, N. E., & Utami, M. A. F. (2020). Pemetaan Habitat Perairan Dangkal Menggunakan Citra Resolusi Menengah Dengan Metode Klasifikasi Berbasis Piksel (Studi Kasus Pulau Tikus). Jurnal Enggano, 5(1), 78–90. Https://Doi.Org/10.31186/Jenggano.5.1.78-90
Ariyantoni, J., & Aries Rokhmana, C. (2020). Evaluasi Polarisasi Citra Sar (Syhthetic Aperture Radar) Untuk Klasifikasi Obyek Tutupan Lahan. Jurnal Geodesi Dan Geomatika, 22–29.
Aryawati, R. (2021). Fitoplankton Sebagai Bioindikator Pencemaran Organik Di Perairan Sungai Musi Bagian Hilir Sumatera Selatan. Jurnal Ilmu Dan Teknologi Kelautan Tropis, 13(1), 163–171. Https://Doi.Org/10.29244/Jitkt.V13i1.25498
Bashiir, M. F., & Kurniadin, N. (2021). Deteksi Kerusakan Perkotaan Akibat Gempa Bumi Di Kota Palu Menggunakan Data Satelit Sentinel-1. Buletin Poltanesa, 22(1). Https://Doi.Org/10.51967/Tanesa.V22i1.330
Bioresita, F., Ngurawan, M. G. R., & Hayati, N. (2022). Identifikasi Sebaran Spasial Genangan Banjir Memanfaatkan Citra Sentinel-1 Dan Google Earth Engine (Studi Kasus: Banjir Kalimantan Selatan). Geoid, 17(1), 108–118.
Bmkg. (2023). Curah Hujan Kota Palembang.
Cao, W., Qiao, Z., Gao, Z., Lu, S., & Tian, F. (2021). Use Of Unmanned Aerial Vehicle Imagery And A Hybrid Algorithm Combining A Watershed Algorithm And Adaptive Threshold Segmentation To Extract Wheat Lodging. Physics And Chemistry Of The Earth, Parts A/B/C, 123, 103016. Https://Doi.Org/Https://Doi.Org/10.1016/J.Pce.2021.103016
Cian, F., Marconcini, M., & Ceccato, P. (2018). Normalized Difference Flood Index For Rapid Flood Mapping: Taking Advantage Of Eo Big Data. Remote Sensing Of Environment, 209(February), 712–730. Https://Doi.Org/10.1016/J.Rse.2018.03.006
Lutfi, M., Arsanto, A. T., Faishol Amrulloh, M., & Kulsum, U. (2023). Penanganan Data Tidak Seimbang Menggunakan Hybrid Method Resampling Pada Algoritma Naive Bayes Untuk Software Defect Prediction. In Informatics Journal (Vol. 8, Issue 2).
Martha Anggraeni, N. (2023). Analisis Dampak Perubahan Iklim Dan Pola Angin Pada Lingkungan Global. Jurnal Pendidikan, Sains, Dan Teknologi, 02(4), 1041–1047. Http://Jurnal.Minartis.Com/Index.Php/Jpst/
Muin, A., Rakuasa, H., & Kunci, K. (2023). Pemanfaat Geographic Artificial Intelligence (Geo-Ai) Untukidentifikasi Daerah Rawan Banjir Di Kota Ambon. Gudang Jurnal Multidisiplin Ilmu, 58–63. Https://Doi.Org/10.59435/Gjmi.V1i2.24
Paulus Siregar, V., Sofian, I., Jaya, I., & Bambang Wijanarto, A. (2020). Dynamic Of Coastal Inundation In Jakarta Based On Data Multi-Temporal Satellites Using Water Index And Radar Polarization. J. Ilmu Dan Teknologi Kelautan Tropis, 12(3), 885–901. Https://Doi.Org/10.29244/Jitkt.V12i3.20711
Rachmayanti, H., Musa, R., & Mallombasi, A. (2022). Studi Pengaruh Perubahan Tata Guna Lahan Terhadap Debit Banjir Dengan Menggunakan Software Hec Hms (Studi Kasus Das Saddang). Jurnal Konstruksi, 01(01), 1–9.
Ramadhani, D. I., Damayanti, O., Thaushiyah, O., & Kadafi, A. R. (2022). Penerapan Metode K-Means Untuk Clustering Desa Rawan Bencana Berdasarkan Data Kejadian Terjadinya Bencana Alam. Jurikom (Jurnal Riset Komputer), 9(3), 749. Https://Doi.Org/10.30865/Jurikom.V9i3.4326
Septian, M. R. D., Febriani, & Nilawati, A. R. (2019). Perbandingan Deteksi Tepi Sobel Dan Robert Untuk Pendeteksian Kesamaan Citra Berdasarkan Warna. Jurnal Ilmiah Teknologi Dan Rekayasa, 24(2), 131–140. Https://Doi.Org/10.35760/Tr.2019.V24i2.2391
Setiawan, I. N. K. D. (2022). Klasterisasi Wilayah Rentan Bencana Alam Berupa Gerakan Tanah Dan Gempa Bumi Di Indonesia. Prosiding Seminar Nasional Official Statistics, 669–676.
Surampudi, S., Kumar, V., & Yarrakula, K. (2021). Flood Index Estimation Using L-Band Sar Data For Assam Flood Prone Regions . Ieee International Geoscience And Remote Sensing Symposium, 8301–8304.
Suspidayanti, L., & Aries Rokhmana, C. (2021). Identifikasi Fase Pertumbuhan Padi Menggunakan Citra Sar (Synthetic Aperture Radar) Sentinel-1. Elipsoida : Jurnal Geodesi Dan Geomatika, 4(1), 9–15.
Syarkiyah, D., Pramudita, A. A., & Arseno, A. (2022). Deteksi Lintasan Misil Dengan Metode Identifikasi Polarisasi Gelombang Vertikal Dan Horizontal (Detection Of Missile Trajectories By Using The Identification Method Of Vertical Dan Horizontal Wave Polarization). E-Proceeding Of Engineering, 8(6), 2913–2922.
Vanama, V. S. K., Rao, Y. S., & Bhatt, C. M. (2021). Change Detection Based Flood Mapping Using Multi-Temporal Earth Observation Satellite Images: 2018 Flood Event Of Kerala, India. European Journal Of Remote Sensing, 54(1), 42–58.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Maudhy Az-zahra, Ade Silvia Handayani, Lindawati Lindawati
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
Jurnal Teknologi Sistem Informasi dan Aplikasi have CC BY-NC or an equivalent license as the optimal license for the publication, distribution, use, and reuse of scholarly work.
In developing strategy and setting priorities, Jurnal Teknologi Sistem Informasi dan Aplikasi recognize that free access is better than priced access, libre access is better than free access, and libre under CC BY-NC or the equivalent is better than libre under more restrictive open licenses. We should achieve what we can when we can. We should not delay achieving free in order to achieve libre, and we should not stop with free when we can achieve libre.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License
YOU ARE FREE TO:
- Share - copy and redistribute the material in any medium or format
- Adapt - remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms