Advertisement

Information Technology of Satellite Image Processing for Monitoring of Floods and Drought

  • Dmitry Mozgovoy
  • Volodymyr HnatushenkoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1080)

Abstract

The information technology of automated processing of optical and radar data from Sentinel 1A/B and Sentinel 2A/B satellites in order to ensure all-weather monitoring of floods and droughts was developed and tested. Comparison of the processing results confirmed a rather high efficiency of water object recognition both in the two-polarization radar data of the C-band and in the multispectral data of the visible and near-IR ranges. Both methods showed similar results in quality of recognition of water objects with a small number of unrecognized or falsely recognized objects (on average less than 10% of the area of recognized objects). The information technology for automated processing of optical and radar data from Sentinel satellites for monitoring floods and droughts is independent from weather conditions over the monitored territory (imaging is possible even with 100% cloudiness). Owing to the high degree of automation of data processing in the developed information technology, it can be used to promptly inform about the course and consequences of floods and droughts not only representatives of government services and commercial structures, but also the general public.

Keywords

Satellite imagery Information technology Monitoring Floods Drought Radar Polarization Image processing 

References

  1. 1.
    Ban, H.-J., Kwon, Y.-J., Shin, H., Ryu, H.-S., Hong, S.: Flood monitoring using satellite-based RGB composite imagery and refractive index retrieval in visible and near-infrared bands. Remote Sens. 9, 313 (2017)CrossRefGoogle Scholar
  2. 2.
    Chen, X., Jiang, J., Li, H.: Drought and flood monitoring of the Liao River Basin in Northeast China using extended GRACE data. Remote Sens. 10, 1168 (2018)CrossRefGoogle Scholar
  3. 3.
    Hnatushenko, V.V., Hnatushenko, Vik.V., Mozgovoy, D.K., Vasiliev, V.V:. Satellite technology of the forest fires effects monitoring. Scientific Bulletin of National Mining University, Issue 1 (151), pp. 70–76 (2016)Google Scholar
  4. 4.
    Psomiadis, E., Soulis, K.X., Zoka, M., Dercas, N.: Synergistic approach of remote sensing and GIS techniques for flash-flood monitoring and damage assessment in Thessaly plain area, Greece. Water 11, 448 (2019)CrossRefGoogle Scholar
  5. 5.
    Washaya, P., Balz, T., Mohamadi, B.: Coherence change-detection with Sentinel-1 for natural and anthropogenic disaster monitoring in urban areas. Remote Sens. 10, 1026 (2018)CrossRefGoogle Scholar
  6. 6.
    Hnatushenko, V.V., Mozgovoy, D.K., Vasyliev, V.V.: Satellite monitoring of deforestation as a result of mining. Scientific bulletin of National Mining University - State Higher Educational Institution “National Mining University”. Dnipropetrovsk № 5 (161), pp. 94–99 (2017)Google Scholar
  7. 7.
    Jeyaseelan, A.T.: Drought & flood assessment and monitoring using remote sensing and GIS. In: Dun, D. (ed.) Satellite Remote Sensing and GIS Applications in Agricultural Meteorology, p. 291. World Meteorological Organization: Hyderabad, India; Geneva, Switzerland (2003)Google Scholar
  8. 8.
    Hnatushenko, V.V., Mozgovoy, D.K., Vasyliev, V.V., Kavats, O.O.: Satellite Monitoring of Consequences of Illegal Extraction of Amber in Ukraine. Scientific bulletin of National Mining University. - State Higher Educational Institution “National Mining University”. Dnipropetrovsk № 2 (158), pp. 99–105 (2017)Google Scholar
  9. 9.
    Mason, D.C., Davenport, I.J., Neal, J.C., Schumann, G.J.-P., Bates, P.D.: Near real-time flood detection in urban and rural areas using high-resolution Synthetic Aperture Radar images. IEEE Trans. Geosci. Remote Sens. 50, 3041–3052 (2012)CrossRefGoogle Scholar
  10. 10.
    Garkusha, I.N., Hnatushenko, V.V., Vasyliev, V.V.: Using Sentinel-1 data for monitoring of soil moisture. In: 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA (2017).  https://doi.org/10.1109/IGARSS.2017.8127291
  11. 11.
    Garkusha, I.N., Hnatushenko, V.V., Vasyliev, V.V.: Research of influence of atmosphere and humidity on the data of radar imaging by Sentinel-1. In: IEEE 37th International Conference on Electronics and Nanotechnology (ELNANO) (2017).  https://doi.org/10.1109/ELNANO.2017.7939787
  12. 12.
    Mozgovoy, D., Hnatushenko, V., Vasyliev, V.: Accuracy evaluation of automated object recognition using multispectral aerial images and neural network. In: Proceedings of the SPIE Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060H (2018).  https://doi.org/10.1117/12.2502905
  13. 13.
    Hnatushenko, V.V., Mozgovoy, D.K., Serikov, I.Ju., Vasyliev, V.V.: Automatic vegetation classification using multispectral aerial images and neural network. System technologies. Dnipro № 6 (107), pp. 66–72 (2016)Google Scholar
  14. 14.
    Mozgovoy, D.K., Hnatushenko, V.V., Vasyliev, V.V.: Automated recognition of vegetation and water bodies on the territory of megacities in satellite images of visible and IR bands. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. IV(3), 167–172 (2018).  https://doi.org/10.5194/isprs-annals-IV-3-167-2018CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Physics, Electronics and Computer SystemsOles Gonchar Dnipro National UniversityDniproUkraine
  2. 2.Department of Information Systems and TechnologiesDnipro University of TechnologyDniproUkraine

Personalised recommendations