DYNAMIC OF COASTAL INUNDATION IN JAKARTA BASED ON DATA MULTI-TEMPORAL SATELLITES USING WATER INDEX AND RADAR POLARIZATION

  • Asmadin Asmadin Fakultas Perikanan dan Ilmu Kelautan, Universitas Halu Oleo, Kendari
  • Vincentius Paulus Siregar Departemen Ilmu dan Teknologi Kelautan, FPIK-IPB University, Bogor
  • Ibnu Sofian Badan Informasi Geospasial (BIG), Cibinong, Bogor
  • Indra Jaya Departemen Ilmu dan Teknologi Kelautan, FPIK-IPB University, Bogor
  • Antonius Bambang Wijanarto Badan Informasi Geospasial (BIG), Cibinong, Bogor
Keywords: inundation, Jakarta, landsat 8, sentinel 1a, polarization, water index

Abstract

Combining baseline data of remote sensing systems active and passive has many advantages in monitoring coastal inundation dynamically. It has advanced the surface water information gaps in coastal areas, especially areas covered by clouds and shadows. The main objective of this study was to assess the dynamics of coastal inundation in Jakarta based on multi-temporal data optics of Landsat 8 and Synthetic Aperture Radar (SAR) Sentinel 1A. The method of this research used two water index algorithms. They are Modified Normalized Difference Water Index (MNDWI) and Dynamic Surface Water Extent (DSWE) based on spectral reflectance values and empirical formulas. The other method is using the coefficient backscattering of water from a single polarization of Vertical Verticals (VV) and Vertical Horizontal (VH). The study results show that the use of both satellite data baseline of 8, 9, 15, and 16 days is quite effective, applying inundation dynamics for 8-49 days. Based on the threshold value of MNDWI > 0.123 and the backscattering coefficient of -19dB are quite efficient to extract satellite data information. The empirical algorithms result in the feature of inundation, especially along the coastal dikes, reservoirs, mangrove ecosystems, and built-up lands. Satellite monitoring results show that the peak of inundation occurred on 30 May 2016 and was still visible on 15 June 2016. The combination of remote sensing methods is quite effective and efficient for monitoring inundation dynamically.

Downloads

Download data is not yet available.

References

Abidin, H.Z., H. Andreas, I. Gumilar, & I.R.R. Wibowo. 2015. On correlation between urban development, land subsidence and flooding phenomena in Jakarta. Proc. IAHS, 370: 15–20. https://doi.org/10.5194/piahs-370-15-2015

Ali, M. 2016. Akibat banjir listrik di pemukiman pantai Mutiara padam. 4 Juni 2016; 01:24 WIB. https://www.liputan6.com/news/read/2523156/akibat-banjir-listrik-di-permukiman-pantai-mutiara-padam.

Amitrano, D., G. Di Martino, A. Iodice, F. Mitidieri, M.N. Papa, D. Riccio, & G. Ruello. 2014. Sentinel-1 for Monitoring Reservoirs: A Performance Analysis. Remote Sens., 6: 10676-10693. https://doi.org/10.3390/rs61110676

Anggraini, N., B. Trisakti, & T.E.B. Soesilo. 2012. Pemanfaatan data satelit untuk analisis potensi genangan dan dampak kerusakan akibat kenaikan muka air laut. J. Pengindraan Jauh & Pengolahan Data Citra Digital, 9(2): 140-150. http://jurnal.lapan.go.id/index.php/jurnal_inderaja/article/view/1788

Betbeder, J., S. Rapinel, Corpetti, E. Pottier, S. Corgne, & L. Hubert-Moy. 2014. Multitemporal classification of TerraSAR-X data for wetland vegetation mapping. J. Appl. Remote Sens., 8: 1-16. https://doi.org/10.1117/1.JRS.8.083648

Boon. 2007. World Tides User Manual Version 1.03. USA.24 p.

Brisco, B., M. Kapfer, T. Hirose, B. Tedford, & J. Liu. 2011. Evaluation of C-band polarization diversity and polarimetry for wetland mapping. Can. J. Remote Sens., 37: 82–92. https://doi.org/10.5589/m11-017

Carroll, M.L. & T.V. Loboda. 2017. Multi-decadal surface water dynamics in North American Tundra. Remote Sens., 9(5): 497. https://doi.org/10.3390/rs9050497

Cazals, C., S. Rapinel, P.L. Frison, A. Bonis, G. Mercier, C. Mallet, S. Corgne, & J.P. Rudant. 2016. Mapping and Characterization of Hydrological Dynamics in a Coastal Marsh Using High Temporal Resolution Sentinel-1A Images. Remote Sens., 8(7): 570. https://doi.org/10.3390/rs8070570

Congedo, L. 2016. Semi-automatic classification plugin documentation. Release 6.0.1.1. 198p. https://doi.org/10.13140/RG.2.2.29474.02242/1

DeVries, B., C. Huang, M.W. Lang, J.W. Jones, W. Huang, I.F. Creed, & M.L. Carroll. 2017. Automated quantification of surface water inundation in Wetlands Using Optical Satellite Imagery. Remote Sens., 9(8): 807. https://doi.org/10.3390/rs9080807

Du, Z., W. Li, D. Zhou, L. Tian, F. Ling, H. Wang, Y. Gui, & B. Sun. 2014. Analysis of Landsat-8 OLI imagery for land surface water mapping. Remote Sens. Lett, 5(7): 672–681. https://doi.org/10.1080/2150704X.2014.960606

Ferretti, A., A. Monti-Guarnieri, C. Prati, F. Rocca, & D. Massonnet. 2007. InSAR Principles: Guidelines for SAR Interferometry Processing and Interpretation. ESA Publications, TM-19. 40p. https://www.esa.int/esapub/tm/tm19/TM-19_ptA.pdf

Hall-Atkinson, C. & L.C. Smith. 2001. Delineation of delta ecozones using interferometric SAR phase coherence: Mackenzie River Delta, NWT. Can. Remote Sens. Environ., 78: 229–238. https://doi.org/10.1016/S0034-4257(01)00221-8

Huang, C., Y. Chen, & J. Wu. 2014. Mapping spatio-temporal flood inundation dynamics at large river basin scale using time-series flow data and MODIS imagery. Int. J. Appl. Earth Obs. Geoinf., 26(1): 350-362. https://doi.org/10.1016/j.jag.2013.09.002

Irons, J.R., J.L. Dwyer, & J.A. Barsi. 2012. The next Landsat satellite: The Landsat Data Continuity Mission. Remote Sens. Environ., 122: 1-21. https://doi.org/10.1016/j.rse.2011.08.026

Jamalludin, K.I. Fatoni, & T.M. Alam. 2016. Identifikasi banjir rob periode 2013–2015 di Kawasan Pantai Utara Jakarta. J. Chart Datum, 2(2): 105-116. https://doi.org/10.37875/chartdatum.v2i2.97

Ji, L., L. Zhang, & B. Wylie. 2009. Analysis of dynamic thresholds for the normalized difference water index. Photogramm. Eng. Remote Sens., 75: 1307-1317. https://doi.org/10.14358/PERS.75.11.1307

Jones, J.W. 2015. Efficient wetland surface water detection and monitoring via landsat: comparison with in situ Data from Everglades Depth Estimation Network. Int. J. Remote Sens., 7: 12503-12538. https://doi.org/0.3390/rs70912503

Lang, M.W. & E.S. Kasischke. 2008. Using c-band synthetic aperture radar data to monitor forested wetland hydrology in Maryland's Coastal Plain, USA. IEEE Trans. Geosci. Remote Sens., 46(2): 535-546. https://doi.org/10.1109/TGRS.2007.909950

McFeeters, S.K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features Int. J. Remote Sens., 17: 1425–1432. https://doi.org/10.1080/01431169608948714

Novak, L.M. & C.M. Netishen. 1992. Polarimetric Synthetic Aperture Radar Imaging. Int J Imaging Syst Technol., 4: 306-318. https://doi.org/10.1002/ima.1850040410

Ouma, Y.O. & R. Tateishi. 2006. A water index for rapid mapping of shoreline changes of five East African Rift Valley lakes: An empirical analysis using Landsat TM and ETM+ data. Int. J. Remote Sens., 27: 3153-3181. https://doi.org/10.1080/01431160500309934

O’Grady, D., M. Leblanc, & A. Bass. 2013. Relationship of local incidence angle with satellite radar backscatter for different surface conditions. Int J App Ear Obs Geoinf., 24: 42–53. https://doi.org/10.1016/j.jag.2013.02.005

O’Grady, D., M. Leblanc, & A. Bass. 2014. The use of radar satellite data from multiple incidence angles improves surface water mapping. Remote Sens Env., 140: 652–664. https://doi.org/10.1016/j.rse.2013.10.006

Potin, P., B. Rosich, J. Roeder, & P. Bargellini. 2014. Sentinel-1 mission operations concept In Proc. IEEE IGARSS. 1465-1468 pp.

Ramdhani, J. 2017. Warga sebut tanggul jebol di Muara Angke sejak Mei 2016. 10 Januari 2017; 13:22 WIB. https://news.detik.com/berita/d-3392121/warga-sebut-tanggul-jebol-di-muara-angke-sejak-mei-2016.

Rignot, E. & J. Mouginot. 2012. Ice flow in Greenland for the international polar year 2008–2009. Geophys. Res. Lett., 39: L11501. https://doi.org/10.1029/2012GL051634

Schubert, A., D. Small, N. Miranda, D. Geudtner, & E. Meier. 2015. Sentinel-1A Product Geolocation Accuracy: Commissioning Phase Results. Remote Sens., 7: 9431-9449. https://doi.org/10.3390/rs70709431

Schumann, G.J.P. & D.K. Moller. 2015. Microwave remote sensing of flood inundation, Phys Chem Earth, 83-84: 84-95. https://doi.org/10.1016/j.pce.2015.05.002

Simarmata, H.A. 2018. Phenomenology in adaptation planning an empirical study of flood-affected people in Kampung Muara Baru Jakarta. Springer. Singapore. 203p.

Sutrisno, D., R. Windiastuti, I. Sofyan, & D. Ramdhani. 2011. Spatial Modeling of Sea Level Rise Projections for Health Vulnerability Estimation. Globë, 13(2): 102-111. http://jurnal.big.go.id/index.php/GL/article/view/92/89

Sundermann, L., O. Schelske, & P. Hausmann. 2014. Mind the risk-A global ranking of cities under threat from natural disasters. Swiss Re. Zurich, Swiztzerland. 30 p.

Takagi H., D. Fujii, M. Esteban, & X. Yi. 2017. Effectiveness and limitation of coastal dykes in Jakarta: The Need for Prioritizing Actions against Land Subsidence. Sust., 9(4): 619. http://doi.org/10.3390/su9040619.

Xu, H. 2006. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens., 27: 3025–3033. https://doi.org/10.1080/01431160600589179

White, L., B. Brisco, M. Dabboor, A. Schmitt, & A. Pratt. 2015. A Collection of SAR Methodologies for Monitoring Wetlands Remote Sens., 7: 7615-7645. https://doi.org/10.3390/rs70607615

Wibowo, A. 2012. Kerentanan lingkungan laut tiap provinsi di Indonesia. J. Ilmu dan Teknologi Kelautan Tropis, 4(1): 145-162. https://doi.org/10.29244/jitkt.v4i1

Yusuf, A.A. & H.A. Francisco. 2009. Climate Change Vulnerability Mapping for Southeast Asia. 26p. http://www.eepsea.org/pub/tr/12324196651Mapping_Report.pdf

Published
2020-12-31