Advertisement

Post-flood land use damage estimation using improved Normalized Difference Flood Index (NDFI3) on Landsat 8 datasets: December 2014 floods, Kelantan, Malaysia

  • Wan Kah Mun 
  • Lawal Billa
Original Paper
  • 37 Downloads

Abstract

Floods in Malaysia have been increasing in frequency and magnitude as reflected in the Kelantan Flood event in 2014 that resulted in a huge loss of lives and properties. Whereas remote sensing (RS) and geographical information system (GIS) tools have been extensively applied in flood disaster management, there are few reports and studies on the impact of floods on the land use/land cover environment in a post-disaster assessment. In this study, an integrated modelling approach was developed that used Landsat 8 OLI TIRS (Operational Land Imager (OLI) and Thermal Infrared Sensor) data, flood indexing and classification processes to estimate the impact of flood on the environment. The Normalized Difference Flood Index-3 (NDFI3) is an improvement on NDFI2 that takes into account the effects of cloud shadow in the images when extracting flood index areas. The flood model developed showed good agreement when compared with flooded areas shown in SAR (synthetic-aperture radar) image. The results of the flood extent as a proxy for damage estimation showed that the total flooded area was 502.34 km2 for the Kelantan Flood event in 2014, with plantation and built-up area accounting for 43 and 34.6% respectively. The least affected land uses/land covers were deforested area and forest, which accounted for 12.2 and 10.2% respectively. The RS and GIS technique developed in this post-disaster damage assessment is effective, relatively inexpensive and simple to implement by local authorities in support of post-flood disaster planning and decision-making.

Keywords

Landsat 8 OLI TIRS Normalized Difference Flood Index (NDFI3Land cover classification Flood damage estimate Kelantan Flood 2014 Malaysia 

Notes

Acknowledgments

The authors are thankful to the School of Biosciences, Faculty of Science, University of Nottingham Malaysia Campus collective fund for supporting this research project. Thanks are also given to the Department of Environment Malaysia for providing basic data and supporting information that were used in the research project.

References

  1. Alias NE, Mohamad H, Wan YC, Yusop Z (2016) Rainfall analysis of the Kelantan big yellow flood 2014. Journal Teknologi (Sciences & Engineering) 78: 9–4 (2016) 83–90 https://www.researchgate.net/publication/308792068_Rainfall_analysis_of_the_Kelantan_big_yellow_flood_2014. Accessed 7 December 2017
  2. Baharuddin KA, Wahab SFA, Ab Rahman NHN, Mohamad NAN, Kamauzaman THT, Noh AYM, Majob MYA (2015) The record-setting flood of 2014 in Kelantan: challenges and recommendations from an emergency medicine perspective and why the medical campus stood dry. The Malaysian Journal of Medical Sciences 22(2):1–7Google Scholar
  3. Billa L, Mansor S, Mahmud AR (2011) Pre-flood inundation mapping for flood early warning. Journal of Flood Risk Management 4(4):318–327CrossRefGoogle Scholar
  4. Billa L, Mansor S, Mahmud AR (2012) Mesoscale grid rainfall estimation from AVHRR and GMS data. Int J Remote Sens 33(9):2892–2908CrossRefGoogle Scholar
  5. BMGN (2012) Landuse report 2012 Kuala Lumpur: Department of City and Village Development Peninsular Malaysia Landuse Information DepartmentGoogle Scholar
  6. Boschetti M, Nutini F, Manfron G, Brivio PA, Nelson A (2014) Comparative analysis of normalised difference spectral indices derived from MODIS for detecting surface water in flooded rice cropping systems. PLoS One 9(2):e88741CrossRefGoogle Scholar
  7. Chen Y, Huang C, Ticehurst C, Merrin L, Thew P (2013) An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping. Wetlands 33(5):823–835CrossRefGoogle Scholar
  8. Department of Statistics (2010) Preliminary Count Report. Department of Statistics, Malaysia. http://www.statistics.gov.my/portal/download/download_POPULATION.php?cat=1&id_file=3. Accessed 17 March 2016
  9. Elmahdy SI, Mostafa MM (2013) Remote sensing and GIS applications of surface and near-surface hydromorphological features in Darfur region, Sudan. Int J Remote Sens 34(13):4715–4735CrossRefGoogle Scholar
  10. Han-qiu XU (2005) A study on information extraction of water body with the modified normalized difference water index (MNDWI). Journal of Remote Sensing 5:589–595Google Scholar
  11. Herath S (2003) Flood damage estimation of urban catchment using remote sensing and GIS. International training Program on Total Disaster Risk Management, 10–13 June 2003Google Scholar
  12. Ho LTK, Umitsu M,Yamaguchi Y, (2010) Flood hazard mapping by satellite images and SRTM DEM in the Vu Gia–Thu Bon alluvial plain, Central Vietnam. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, Commission VIII, working group VIII/1, XXXVIII, (Part 8):275–80 Kyoto Japan 2010Google Scholar
  13. Kia MB, Pirasteh S, Pradhan B, Mahmud AR, Sulaiman WNA, Moradi A (2012) An artificial neural network model for flood simulation using GIS: Johor River basin, Malaysia. Environmental Earth Sciences 67(1):251–264CrossRefGoogle Scholar
  14. Konadu DD, Fosu C (2009) Digital elevation models and GIS for watershed modelling and flood prediction–a case study of Accra Ghana. In: Yanful EK (ed) Appropriate technologies for environmental protection in the developing world. Springer Science Business Media, Dordrecht, pp 325–332Google Scholar
  15. KERREGGA (2014) Gambar Banjir; Stadium Sultan Muhammad Sudah Jadi Cacam kolan renang. http://gigitankerengga.blogspot.my/2014/12/gambar-banjir-stadium-sultan-muhammad.html. Accessed 7 December 2017
  16. Loo YY, Billa L, Singh A (2015) Effect of climate change on seasonal monsoon in Asia and its impact on the variability of monsoon rainfall in Southeast Asia. Geosci Front 6(6):817–823CrossRefGoogle Scholar
  17. Lowe WH, Likens GE (2005) Moving headwater streams to the head of the class. BioScience 55(3):196–197CrossRefGoogle Scholar
  18. The Malay Mail Online (2015) Nearly half of flood victims return home as situation improves. https://www.dosm.gov.my/v1/index.php?r=column/cone&menu_id=Yk9tK2xVSmRXbzRvTU9rSlR4OE1nZz09 Accessed 15 April 2016
  19. Mason DC, Davenport IJ, Neal JC, Schumann GJP, Bates PD (2012) Near real-time flood detection in urban and rural areas using high-resolution synthetic aperture radar images. IEEE Trans Geosci Remote Sens 50(8):3041–3052CrossRefGoogle Scholar
  20. Merz B, Kreibich H, Schwarze R, Thieken A (2010) Review article “assessment of economic flood damage”. Nat Hazards Earth Syst Sci 10(8):1697–1724CrossRefGoogle Scholar
  21. Paiva RC, Collischonn W, Tucci CE (2011) Large scale hydrologic and hydrodynamic modeling using limited data and a GIS based approach. J Hydrol 406(3):170–181CrossRefGoogle Scholar
  22. Portal Kelantan (2016) Kelantan state government’s official portal.? http://www.kelantan.gov.my/index.php?option=com_content&view=article&id=128&Itemid=167&lang=bm. Accessed 17 March 2016
  23. Pradhan B (2009) Flood susceptible mapping and risk area delineation using logistic regression, GIS and remote sensing. J Spat Hydrol 9(2):1–17Google Scholar
  24. Pradhan B, Hagemann U, Tehrany M, Prechtel N (2014) An easy to use ArcMap based texture analysis program for extraction of flooded areas from TerraSAR-X satellite image. Comput Geosci 63:34–43.  https://doi.org/10.1016/j.cageo.2013.10.011 CrossRefGoogle Scholar
  25. Pradhan B, Tehrany MS, Jebur MN (2016) A new semi-automated detection mapping of flood extent from TerraSAR-X satellite image using rule-based classification and Taguchi optimization techniques. IEEE Transactions on Geoscience & Remote Sensing 54(7):4331–4342.  https://doi.org/10.1109/TGRS.2016.2539957 CrossRefGoogle Scholar
  26. Pradhan B, Youseef AM (2011) A 100-year maximum flood susceptibility mapping using integrated hydrological and hydrodynamic models: Kelantan River Corridor, Malaysia. Journal of Flood Risk Management 4(3):189–202CrossRefGoogle Scholar
  27. Sanyal J, Lu XX (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33(2):283–301CrossRefGoogle Scholar
  28. Smith DJ (1994) Flood damage estimation. A Review of Urban Stage Damage Curves and Loss Functions, Water SA 20:231–238Google Scholar
  29. Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydrol Process 11(10):1427–1439CrossRefGoogle Scholar
  30. Sridharan R, Das TP, Ahmed SM, Bhardwaj A (2013) Lessons from Kedarnath tragedy of Uttarakhand Himalaya, India. Curr Sci 105(11):1472–1474Google Scholar
  31. The Sun Daily (2015) State of devastation. http://www.thesundaily.my/news/1299690. Accessed 05 April 2016
  32. Toriman ME, Hassan AJ, Gazim MB, Mokhtar M, Mastura SAS, Jaafar O, Karim O, AbdulAziz NA (2009) Integration of 1-d hydrodynamic model and GIS approach in flood management study in Malaysia. Research Journal of Earth Sciences 1(1):22–27Google Scholar
  33. UNSW (1981) Economic evaluation methodology of flood damage in Australia. University of New South Wales (UNSW), SydneyGoogle Scholar
  34. USGS (2016a) Landsat 8 (L8) Data Users Handbook - Section 4. http://landsat.usgs.gov/l8handbook_ section4.php Accessed 10 March 2016
  35. USGS (2016b) Landsat 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor). https://lta.cr.usgs.gov/L8. Accessed 05 March 2016
  36. Velasco V, Gogu R, Vázquez-Suñè E, Garriga A, Ramos E, Riera J, Alcaraz M (2013) The use of GIS-based 3D geological tools to improve hydrogeological models of sedimentary media in an urban environment. Environmental Earth Sciences 68(6):2145–2162CrossRefGoogle Scholar
  37. Weather Spark (2016) Average Weather For Kota Bharu, Malaysia. https://weatherspark.com/y/114202/Average-Weather-in-Kota-Bharu-Malaysia-Year-Round Accessed 20 March 2015
  38. Webster PJ, Toma VE, Kim HM (2011) Were the 2010 Pakistan floods predictable? Geophys Res Lett 38(4).  https://doi.org/10.1029/2010GL046346
  39. Xu H (2006) Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033CrossRefGoogle Scholar
  40. Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens Environ 118:83–94CrossRefGoogle Scholar
  41. Ziegler AD, Lim HS, Tantasarin C, Jachowski NR, Wasson R (2012) Floods, false hope, and the future. Hydrol Process 26(11):1748–1750CrossRefGoogle Scholar

Copyright information

© Saudi Society for Geosciences 2018

Authors and Affiliations

  1. 1.School of BiosciencesUniversity of Nottingham Malaysia CampusSemenyihMalaysia
  2. 2.School of Environmental and Geographical SciencesSemenyihMalaysia

Personalised recommendations