Flood Damage Detection Algorithm Using Sentinel-2 Images (Case Study: Golestan Flood of March 2019)

Document Type : Research Article

Authors

1 Professor, Dept. of Watershed Management, Sari University of Agric. & Natural Res., PoBox 578

2 M.Sc. in Remote Sensing & GIS, Higher Education Institute of Haraz, Amol

3 M.Sc. Student in Watershed Management, Sari University of Agric. & Natural Res., PoBox 578

Abstract

Remote sensing data can be used with reasonable accuracy today for areas without statistics to investigate natural events. One of these sudden and damaging phenomena is flooding. April flood in Golestan province has caused much damage in various areas of housing, agriculture, forestry and so on. In the present study, using satellite data and presenting flood damage detection algorithm, this phenomenon is investigated. The highest difference in the NDVI index derived from Sentinel-2 data was calculated in the study period of March 2019 and late April 2019 in the study area. These results show that some areas have been severely damaged by floods, in which severity and duration of rainfall have had a significant role. These areas, which are estimated at an area of 70908 hectares, include agricultural, forest, residential, etc. Among the advantages of the proposed algorithm are the ease and speed of calculation, the capability to apply in urban areas, the high accuracy with reduced impact of other phenomena. The overall accuracy of the present algorithm is estimated to be 93.5% using ground control points. The accuracy of the results shows that the flood damage algorithm has a good accuracy to distinguish the areas under flood damage from areas safe from danger.

Keywords

References

[1]. Jonkman SN. Global perspectives on loss of human life caused by floods. Nat. Hazards Dordr. (Dordr). 2005; 34: 151–175.
[2]. Leopold LB. Hydrology for urban planning. US Geological Survey, Washington, DC. (Available from: http://eps. berkeley. edu/people/lunaleopold/); 1968.
[3]. Solaimani K. Flood hazard zonation in Mazandaran. Project final report, Mazandaran Management and planning Organisation; 2006. 430 p. [Persian]
[4]. Hammer T.R. Stream channel enlargement due to urbanization. Water Resources Research. 1972; 8: 1530-1540.
[5].  Schueler T. The importance of imperviousness watershed protection. 2nd int. conf. on urban drainage. 1994; 322-330.
[6]. Paul MJ, Meyer JL. Streams in the urban landscape. Annual review of ecology and systematic. 2001; 333-365.
[7]. Solaimani K. Hydrology and Quantitative Modeling of Urban Floods in GIS and SWM Environment. Iranian Remote Sensing & GIS Association; 2015. 322 p. [Persian].
[8]. Boccardo P, Tonolo FG. Remote sensing role in emergency mapping for disaster response. In Engineering Geology for Society and Territory-Volume 5. Springer, Cham; 2015.p. 17-24.
[9]. Giordan D, Notti D, Villa A. Zucca F, Calò F, Pepe A. Lowcost, multiscale and multi-sensor application for flooded area mapping. Nat. Hazards Earth Syst. Sci. 2018; 1493–1516.
[10].            Kumar R, Singh R, Gautam H, Pandey MK. Flood hazard assessment of August 20, 2016f loods in Satna District, Madhya Pradesh, India. Remote Sens. Appl. Soc. Environ. 2018; 11: 104–118.
[11].            Schumann GJP. Preface: remote sensing in flood monitoring and management. Remote Sens. (Basel). 2015; 7: 17013–17015.
[12].            Rokni K, Ahmad A, Selamat A, Hazini S. Water feature extraction and change detection using multitemporal landsat imagery. Remote Sens. (Basel). 2014; 6: 4173–4189. [Persian]
[13].            Townsend PA, Walsh SJ. Modeling floodplain inundation using an integrated GIS with radar and optical remote sensing. Geomorphology. 1998; 21: 295-312.
[14].            Ronald T, Eguchi RT, Huyck CK, Ghos S, Adams BJ. The Application of Remote Sensing Technologies for Disaster Management. The 14th World Conference on Earthquake Engineering October. 2008; 12-17
[15].            Shrestha R, Di L, Yu G, Shao Y, Kang L, Zhang B. Detection of flood and its impact on crops using NDVI-Corn case. In Agro- Geoinformatics (Agro-Geoinformatics). Second International Conference; 2013.p. 200-204.
[16].            EIpki A, Rezai Y, Heydari Mozaffar M, Tarabzadeh Khorasani H. Evaluation and Survey of Surface Flood Changes Using Satellite Images (Case Study: Ilam Province). Hamedan Institute of Higher Education and Development. MA Thesis. 2016. [Persian].
[17].            Liping Di, Eugene Yu, Shrestha R, Lin L. DVDI: A New Remotely Sensed Index for Measuring Vegetation Damage Caused by Natural Disasters. In IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium; 2018.p. 9067-9069.
[18].            Sarah Di, Liying G, Lin L. Rapid Estimation of Flood Crop Loss by Using DVDI. 7th International Conference on Agro-geoinformatics (Agro-geoinformatics). Hangzhou, China. DOI: 10.1109/Agro-Geoinformatics; 2018.
[19].            Wu H, Adler RF, Tian Y, Huffman GJ, Li H, Wang J. Real‐time global flood estimation using satellite‐based precipitation and a coupled land surface and routing model. Water Resources Research. 2014; 50(3): 2693-2717.
[20].            Rajabizadeh Y, Ayyoubzadeh S.A, Zahiri A. Flood survey of Golestan province in 2017-2018 and providing solutions for its control and management in the future. Iranian Journal of Ecohydrology. 2019 (a); 6(4): 921-942. [Persian].
[21].            Rajabizadeh Y, Ayyoubzadeh S.A, Ghomshi M. Investigation of floods in Khuzestan province during the water year 2018-2019 and providing solutions for its control and management in the future. Iranian Journal of Ecohydrology. 2019 (b); 6(4): 1069-1084. [Persian].
[22].            Goffi A, Stroppiana D, Brivio PA, Bordogna G, Boschetti M. Towards an automated approach to map flooded areas from Sentinel-2 MSI data and soft integration of water spectral features. International Journal of Applied Earth Observation and Geoinformation. 2020; 84: 101951.
[23].            www.usgs.gov/centers/eros/science/usgs-eros-archive-sentinel-2?qt-science center objects
Iranian journal of Ecohydrology
Volume 7, Issue 2
July 2020
Pages 303-312

Files

History

  • Receive Date: 07 November 2019
  • Revise Date: 08 April 2020
  • Accept Date: 08 April 2020
  • First Publish Date: 21 June 2020
  • Publish Date: 21 June 2020

Share

How to cite

Statistics