Advertisement

Natural Hazards

, Volume 94, Issue 3, pp 1099–1116 | Cite as

Direct tangible damage assessment for regional snowmelt flood disasters with HJ-1 and HR satellite images: a case study of the Altay region, northern Xinjiang, China

  • Siquan Yang
  • Haixia He
  • Weitao Chen
  • Lizhe Wang
Original Paper
  • 34 Downloads

Abstract

Regional snowmelt flood disasters (RSFDs) can cause significant direct tangible damage which generally refers to the physical destruction due to direct contact with the flood water, such as damage to buildings, croplands, livestock, and infrastructure. Information about people, habitations, and infrastructure affected by the flood is essential for disaster responders and the humanitarian community to plan and coordinate emergency response activities. However, this direct tangible damage information obtained in the ground is limited, incomplete, contradictory, and sometimes impossible to obtain in a short time. Earth observation satellites help overcome operational uncertainties after the RSFDs. Here, we present an improved rapid direct tangible damage assessment model using HJ-1 and GF-1/2 satellite images. We selected the Altay region in northern Xinjiang, China, as the study area, and investigated a RSFD occurring in spring 2017. A series of HJ-1 and GF-1 images were used to track the flood extent over the duration of the disaster, and the maximum affected flood area was assigned as the area in which direct tangible damage occurred. Pre-disaster GF-2 images were then used to estimate direct tangible damage to habitations (2375 households and 6388 rooms), infrastructure (102 km of roads), and affected population (7125) in the flood area, which covered an area of 185,240 m2. Our method is an effective approach for the design of rescue plans and disaster subsidy programs.

Keywords

Snowmelt flood disaster Tangible damage HJ-1 GF-2 Remote sensing 

Notes

Acknowledgements

This work was supported jointly by the China Geological Survey (No. 12120115063201), the National Natural Science Foundation of China (No. 41401605), and National Key Research and Development Program of China (Project No. 2017YFC1502900). The authors would like to thank Zhao Mingjing for her assistance with laboratory work, as well as the helpful comments made by anonymous reviewers.

Author contributions

All authors have made significant contributions to the manuscript. Siquan Yang and Haixia He conceived of, designed, and performed the experiments and wrote the manuscript. Weitao Chen and Lizhe Wang helped to analyze the results and revise the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

References

  1. Amini JA (2010) method for generating floodplain maps using IKONOS images and DEMs. Int J Remote Sens 31:2441–2456.  https://doi.org/10.1080/01431160902929230 CrossRefGoogle Scholar
  2. Andrews ED, Erman DC (1986) Persistence in the size distribution of surficial bed material during an extreme snowmelt flood. Water Resour Res 22:191–197.  https://doi.org/10.1029/WR022i002p00191 CrossRefGoogle Scholar
  3. Atif I, Mahboob MA, Waheed A (2015) Spatio-temporal mapping and multi-sector damage assessment of 2014 flood in Pakistan using remote sensing and GIS. Indian J Sci Technol 8(35):1–11.  https://doi.org/10.17485/ijst/2015/v8i35/76780 CrossRefGoogle Scholar
  4. Ban H-J, Kwon Y-J, Shin H, Ryu H-S, Hong S (2017) Flood monitoring using satellite-based RGB composite imagery and refractive index retrieval in visible and near-infrared bands. Remote Sens 9:313.  https://doi.org/10.3390/rs9040313 CrossRefGoogle Scholar
  5. Berz G, Kron W, Loster T, Rauch E, Schimetschek J, Schmieder J, Siebert A, Smolka A, Wirtz A (2001) World map of natural hazards—a global view of the distribution and intensity of significant exposures. Nat Hazards 23:443–465.  https://doi.org/10.1023/A:1011193724026 CrossRefGoogle Scholar
  6. Blasco F, Bellan MF, Chaudhury MU (1992) Estimating the extent of floods in Bangladesh using SPOT data. Remote Sens Environ 39(3):167–178.  https://doi.org/10.1016/0034-4257(92)90083-V CrossRefGoogle Scholar
  7. Boyle SJ, Tsanis IK, Kanaroglou PS (1998) Developing geographic information systems for land use impact assessment in flooding conditions. J Water Resour Plan Manag 124:89–98.  https://doi.org/10.1061/(ASCE)0733-9496(1998)124:2(89) CrossRefGoogle Scholar
  8. Chen J, Cao X, Peng S, Ren H (2017) Analysis and applications of GlobeLand30: a review. ISPRS Int J Geo-Inf 6(8):230.  https://doi.org/10.3390/ijgi6080230 CrossRefGoogle Scholar
  9. Criss RE (2016) Statistics of evolving populations and their relevance to flood risk. J Earth Sci 27:2–8.  https://doi.org/10.1007/s12583-015-0641-9 CrossRefGoogle Scholar
  10. Díaz-Delgado R, Aragonés D, Afán I, Bustamante J (2016) Long-term monitoring of the flooding regime and hydroperiod of Doñana Marshes with landsat time series (1974–2014). Remote Sens 8:775.  https://doi.org/10.3390/rs8090775 CrossRefGoogle Scholar
  11. Domenikiotis C, Loukas A, Dalezios NR (2003) The use of NOAA/AVHRR satellite data for monitoring and assessment of forest fires and floods. Nat Hazards Earth Syst Sci 3:115–128CrossRefGoogle Scholar
  12. Dutta D, Herath S, Musiake K (2003) A mathematical model for flood loss estimation. J Hydrol 277(1–2):24–49.  https://doi.org/10.1016/S0022-1694(03)00084-2 CrossRefGoogle Scholar
  13. Feng Q, Gong J, Liu J, Li Y (2015) Flood Mapping based on multiple endmember spectral mixture analysis and random forest classifier—the case of Yuyao, China. Remote Sens 7:12539–12562.  https://doi.org/10.3390/rs70912539 CrossRefGoogle Scholar
  14. Fengqing J, Cheng Z, Guijin M et al (1980) Magnification of flood disasters and its relation to regional precipitation and local human activities since the 1980s in Xinjiang, Northwestern China. Nat Hazards 36(3):307–330.  https://doi.org/10.1007/s11069-005-0977-z CrossRefGoogle Scholar
  15. Haas EM, Bartholomé E, Combal B (2009) Time series analysis of optical remote sensing data for the mapping of temporary surface water bodies in sub-Saharan western Africa. J Hydrol 370:52–63.  https://doi.org/10.1016/j.jhydrol.2009.02.052 CrossRefGoogle Scholar
  16. Hammond MJ, Chen AS, Djordjević S, Butler D, Mark O (2015) Urban flood impact assessment: a state-of-the-art review. Urban Water J. 12:14–29.  https://doi.org/10.1080/1573062X.2013.857421 CrossRefGoogle Scholar
  17. Jeyaseelan A (2003) Droughts and floods assessment and monitoring using remote sensing and GIS. In: Satellite remote sensing and GIS applications in agricultural meteorology. pp 291–313Google Scholar
  18. Klemas V (2014) Remote sensing of floods and flood-prone areas: an overview. J Coastal Res 31(4):1005–1013.  https://doi.org/10.2112/JCOASTRES-D-14-00160.1 CrossRefGoogle Scholar
  19. Knox JC (2000) Sensitivity of modern and Holocene floods to climate change. Quat Sci Rev 19:439–457.  https://doi.org/10.1016/S0277-3791(99)00074-8 CrossRefGoogle Scholar
  20. Kruse FA (1988) Use of airborne imaging spectrometer data to map minerals associated with hydrothermally altered rocks in the northern grapevine mountains, Nevada, and California. Remote Sens Environ 24(1):31–51.  https://doi.org/10.1016/0034-4257(88)90004-1 CrossRefGoogle Scholar
  21. Kumar R, Acharya P (2016) Flood hazard and risk assessment of 2014 floods in Kashmir Valley: a space-based multisensor approach. Nat Hazards 84:437–464.  https://doi.org/10.1007/s11069-016-2428-4 CrossRefGoogle Scholar
  22. Lim J, Lee K (2017) Investigating flood susceptible areas in inaccessible regions using remote sensing and geographic information systems. Environ Monit Assess 189(3):96.  https://doi.org/10.1007/s10661-017-5811-z CrossRefGoogle Scholar
  23. Makkeasorn A, Chang N-B, Li J (2009) Seasonal change detection of riparian zones with remote sensing images and genetic programming in a semi-arid watershed. J Environ Manage 90:1069–1080.  https://doi.org/10.1016/j.jenvman.2008.04.004 CrossRefGoogle Scholar
  24. Malinowski R, Groom G, Schwanghart W, Heckrath G (2015) Detection and delineation of localized flooding from WorldView-2 multispectral data. Remote Sens 7:14853–14875.  https://doi.org/10.3390/rs71114853 CrossRefGoogle Scholar
  25. Marks D, Kimball J, Tingey D, Link T (1998) The sensitivity of snowmelt processes to climate conditions and forest cover during rain-on-snow: a case study of the 1996 Pacific Northwest flood. Hydrol Process 12(10):1569–1587CrossRefGoogle Scholar
  26. Mason DC, Schumann GJP, Neal JC, Garcia-Pintado J (2012) Automatic near real-time selection of flood water levels from high resolution synthetic aperture radar images for assimilation into hydraulic models: a case study. Remote Sens Environ 124:705–716CrossRefGoogle Scholar
  27. Merz B, Kreibich H, Thieken A, Schmidtke R (2004) Estimation uncertainty of direct monetary flood damage to buildings. Nat Hazards Earth Syst Sci 4:153–163CrossRefGoogle Scholar
  28. Merz B, Thieken A, Gocht M (2007) Flood risk mapping at the local scale: concepts and challenges. Flood Risk Manag Eur 25:231–251.  https://doi.org/10.1007/978-1-4020-4200-3_13 CrossRefGoogle Scholar
  29. Merz B, Kreibich H, Schwarze R, Thieken A (2010) Review article “Assessment of economic flood damage”. Nat Hazards Earth Syst Sci 10:1697–1724CrossRefGoogle Scholar
  30. Mohammadi A, Costelloe JF, Ryu D (2017) Application of time series of remotely sensed normalized difference water, vegetation and moisture indices in characterizing flood dynamics of large-scale arid zone floodplains. Remote Sens Environ 190:70–82.  https://doi.org/10.1016/j.rse.2016.12.003 CrossRefGoogle Scholar
  31. Mueller N, Lewis A, Roberts D, Ring S, Melrose R, Sixsmith J, Lymburner L, McIntyre A, Tan P, Curnow S, Ip A (2016) Water observations from space: mapping surface water from 25 years of Landsat imagery across Australia. Remote Sens Environ 174:341–352.  https://doi.org/10.1016/j.rse.2015.11.003 CrossRefGoogle Scholar
  32. Ogilvie A, Belaud G, Delenne C, Bailly J-S, Bader J-C, Oleksiak A, Ferry L, Martin D (2015) Decadal monitoring of the Niger Inner Delta flood dynamics using MODIS optical data. J Hydrol 523:368–383.  https://doi.org/10.1016/j.jhydrol.2015.01.036 CrossRefGoogle Scholar
  33. Okamoto K, Yamakawa S, Kawashima H (1998) Estimation of flood damage to rice production in North Korea in 1995. Int J Remote Sens 19(2):365–371.  https://doi.org/10.1080/014311698216332 CrossRefGoogle Scholar
  34. Parker DJ, Green CH, Thompson PM (1987) Urban flood protection benefits: a project appraisal guide. Gower Technical Press, AldershotGoogle Scholar
  35. Rahman MS, Di L (2017) The state of the art of spaceborne remote sensing in flood management. Nat Hazards 85:1223–1248.  https://doi.org/10.1007/s11069-016-2601-9 CrossRefGoogle Scholar
  36. Sakamoto T, Van Nguyen N, Kotera A, Ohno H, Ishitsuka N, Yokozawa M (2007) Detecting temporal changes in the extent of annual flooding within the Cambodia and the Vietnamese Mekong Delta from MODIS time-series imagery. Remote Sens Environ 109:295–313.  https://doi.org/10.1016/j.rse.2007.01.011 CrossRefGoogle Scholar
  37. Sanyal J, Lu XX (2004) Application of remote sensing in flood management with special reference to monsoon Asia: a review. Nat Hazards 33:283–301.  https://doi.org/10.1023/B:NHAZ.0000037035.65105.95 CrossRefGoogle Scholar
  38. Schumann GJ-P, Stampoulis D, Smith AM, Sampson CC, Andreadis KM, Neal JC, Bates PD (2016) Rethinking flood hazard at the global scale. Geophys Res Lett 43:10249–10256.  https://doi.org/10.1002/2016GL070260 CrossRefGoogle Scholar
  39. Smith LC (1997) Satellite remote sensing of river inundation area, stage, and discharge: a review. Hydro Process 11(10):1427–1439.  https://doi.org/10.1002/(SICI)1099-1085(199708)11:103.0.CO;2-S CrossRefGoogle Scholar
  40. Smith K, Ward R (1998) Floods: physical processes and human impacts. Wiley, ChichesterGoogle Scholar
  41. Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150.  https://doi.org/10.1016/0034-4257(79)90013-0 CrossRefGoogle Scholar
  42. Van Der Sande C (2001) River flood damage assessment using IKONOS imagery. Nat Hazards Project-Floods 1:1–78Google Scholar
  43. Van Der Sande CJ, De Jong SM, De Roo APJ (2003) A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. Int J Appl Earth Obs 4(3):217–229.  https://doi.org/10.1016/S0303-2434(03)00003-5 CrossRefGoogle Scholar
  44. White GF (1945) Human adjustments to floods: a geographical approach to the flood problem in the United States. Doctoral Dissertation and Research paper. Department of Geography, University of ChicagoGoogle Scholar
  45. Yamazaki D, Trigg MA, Ikeshima D (2015) Development of a global ~ 90m water body map using multi-temporal Landsat images. Remote Sens Environ 171:337–351.  https://doi.org/10.1016/j.rse.2015.10.014 CrossRefGoogle Scholar
  46. Zeinivand H, Smedt FD (2010) Prediction of snowmelt floods with a distributed hydrological model using a physical snow mass and energy balance approach. Nat Hazards 54:451–468.  https://doi.org/10.1007/s11069-009-9478-9 CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2018

Authors and Affiliations

  1. 1.National Disaster Reduction Center of ChinaBeijingChina
  2. 2.Faculty of Computer Science and Geological Survey of CUGChina University of GeosciencesWuhanChina
  3. 3.Faculty of Geo-Information Science and Earth Observation (ITC)University of TwenteEnschedeThe Netherlands

Personalised recommendations