Evaluation and Mapping of Rice Flood Damage Using Domestic Remotely Sensed Data in China

  • Huifang Wang
  • Xiaoyi FangEmail author
  • Wei Guo
  • Yonghong Liu
  • Qingzu Luan
  • Shuo Zhang
  • Yanhu Gao
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)


It has great significance to study quick monitoring of rice flood disaster and applying timely remedial measures in the disaster area. The purpose of this research paper was to evaluate the rice flood damage, which happened on July to August 2009 in Shouxian, Huoqiu, Bengbu and Huainan are mainly rice producing areas in Anhui Province in China, using two images from domestic remotely sensing data from China that named Huan Jing satellite (HJ-CCD images). One was pre-flood disaster and the other was post-flood disaster. According to the change characteristics of NDVI (Normalized Difference Vegetation Index) of forty field-sampling points of post-flood in study area, the flood damage degrees (light, moderate and serious) were been classified also. The verification of the classification accuracy calculated by confusion matrix that based on 40 field-sampling verification points. Accuracy results showed that Kappa coefficient (κ) was 0.6907 and the overall accuracy was 80.0%. At the same time, extract nine vegetation indices calculated from HJ-CCDs data were to build the model to inverted LAI of rice, and analyze the growth of rice after flood disaster stress. Hence, this study descriptive that multi-temporal and multispectral imagery domestic remotely sensing data from China (HJ-1 CCD images) are sufficient to assess rice flood disaster areas, specify relative damage degrees and growth analysis after flood stress.


Remote sensing NDVI change characters detection Rice flood disaster degree HJ-CCD 



This research was supported and funded by the National Natural Science Foundation of China (41401415 and 41501481), and Beijing Meteorological Bureau science and technology projects (BMBKJ201704003).


  1. 1.
    Mirza, M.M.Q.: Climate change and extreme weather events: can developing countries adapt? Clim. Policy 3, 233–248 (2003)CrossRefGoogle Scholar
  2. 2.
    Tai, F.J., Yuan, Z.L., Wu, X.L., Zhao, P.F., Hu, X.L., Wang, W.: Identification of membrane proteins in maize leaves, altered in expression under drought stress through polyethylene glycol treatment. Plant Omics 4(5), 250–256 (2011)Google Scholar
  3. 3.
    Cuijin, L.: A statistical analysis of the storm flood disasters in China. J. Catastrophology (1), 59–63 (1996)Google Scholar
  4. 4.
    Hap, M., Akhtar, M., Muhammad, S., et al.: Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan. Egypt. J. Remote Sens. Space Sci. 15, 135–141 (2012)Google Scholar
  5. 5.
    Liang, Y., Liu, K., Zhou, S., et al.: The technical method of monitoring of flood disaster due to torrential rain using EOS-MODIS. Torrential Rain Disaster 27(1), 64–66 (2008)Google Scholar
  6. 6.
    Mouri, G., Minoshima, D., Golosov, V., et al.: Probability assessment of flood and sediment disasters in Japan using the total runoff-integrating pathways model. Int. J. Disaster Risk Reduct. 3(1), 31–43 (2013)CrossRefGoogle Scholar
  7. 7.
    Subash, N., Mohan, H.S.R., Sikka, A.K.: Quantitative assessment of influence of monsoon rainfall variability on rice production over India. J. Agro Meteorol. 11(2), 109–116 (2009)Google Scholar
  8. 8.
    Wang, F.: Derivating rice parameters from hyperspectral reflectance and its system development, and rice cultivated areas extraction using remote sensing, Zhejiang University (2007)Google Scholar
  9. 9.
    Liu, Z., Huang, J., Wang, F., Wang, Y.: Adjusted-normalized difference vegetation index for estimating leaf area index of rice. Sci. Agric. Sinica 41(10), 3350–3356 (2008)Google Scholar
  10. 10.
    Rouse, J.W., Hass, R.H., Schell, J.A., et al.: Monitoring the vernal advancement of retrogradation of natural vegetation NASA/GSFC, Type III, Final report. Greenbelt, MD, 371 (1974)Google Scholar
  11. 11.
    Gitelson, A.A., Merzlyak, M.N., Chivkunova, O.B.: Optical properties and nondestructive estimation of anthocyanin content in plant leaves. J. Photochem. Photobiol. 74(1), 38–45 (2001)CrossRefGoogle Scholar
  12. 12.
    Huete, A., Justice, C.: MODIS vegetation index (MOD 13) algorithm theoretical basis documents, Vision 3 (1999)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Huifang Wang
    • 1
  • Xiaoyi Fang
    • 1
    Email author
  • Wei Guo
    • 2
  • Yonghong Liu
    • 1
  • Qingzu Luan
    • 1
  • Shuo Zhang
    • 1
  • Yanhu Gao
    • 1
  1. 1.Beijing Municipal Climate CenterBeijingChina
  2. 2.Henan Agricultural UniversityZhengzhouChina

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