Früher|Nächster

Dynamic monitoring on flooding situation in the Middle and Lower Reaches of the Yangtze River Region using Sentinel-1A time series

  • role： First author第一作者
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

  • Email：gsc@smail.nju.edu.cn
  • Introduction：郭山川，1992年生，男，博士研究生，研究方向为时序SAR影像处理与分析。E-mail： gsc@smail.nju.edu.cn
GUO Shanchuan123,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

  • Email：dupjrs@126.com
  • Introduction：杜培军，1975年生，男，教授，研究方向为城市环境遥感。E-mail： dupjrs@126.com
DU Peijun123*,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

MENG Yaping123,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

WANG Xin123,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

TANG Pengfei123,  
  • Affiliation：

    School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China

    Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China

    Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, Nanjing 210023, China

LIN Cong123,  
  • Affiliation：

    Geoinformatics Unit, RIKEN Center for Advanced Intelligence Project (AIP), Tokyo 103-0027, Japan

XIA Junshi4

Resümee

Synthetic Aperture Radar (SAR) is an indispensable data source for the dynamic monitoring of flood events due to the capacity for all-weather and all-time sensing. At present, flooding mapping and damaging assessment are developed rapidly towards dynamic monitoring and large scale, owing to the emergence of cloud computing platforms, such as Google Earth Engine (GEE) and accessibility of short-revisit Sentinel-1A radar data. A novel method is proposed for detecting flood events and continuous monitoring of the inundated areas at a large scale based on the time-series processing and analysis from Sentinel-1A images, considering the complexity of land cover changes within the flooded areas and the temporal uncertainty of flood events. First, the binary segmentation threshold for the water extraction was determined by the maximum interclass variance algorithm, and accordingly, the coarse maps of spatiotemporal distribution of water body were generated from Sentinel-1A time series. Second, the frequency of candidate water pixels was computed through these coarse maps. The initial fine water map in the temporal sequence was refined by the water map retrieved by the NDWI from the Sentinel-2 optical image. In view of the distinctive inundation characteristics of different frequency areas, a differentiated sequential anomaly detection strategy was proposed to identify the flooded areas and the seasonal water areas. The change feature maps of backscattering coefficient were established by the Sentinel-1A time series. Euclidean distance was used to detect the sequential breakpoint in low-frequency water-covered area, which is regarded as the flooding disaster area with high disturbance intensity and short flooding time. Temporal Z-score was introduced to detect the breakpoint in high-frequency water-covered area, which is considered the continuous inundating area.Result We automatically, rapidly, and effectively detected the flood events in the middle and lower reaches of the Yangtze River Region between May and October in 2020 and monitored the inundated area change during flooding, using the proposed method deployed on the GEE. The results exhibited that the flooding areas, such as the middle reaches of the Huaihe River, the lower reaches of the Xinyi River, and the Chaohu Lake Basin, and displayed the max extents of seasonal water, such as Poyang Lake, Dongting Lake, and Shijiu Lake during extreme weather with serious precipitation. The storage, diversion, and release of the flood water in the Yangtze River Plain demonstrated a lower loss manner due to the function of the free Yangtze-linking lakes, such as Poyang Lake and Dongting Lake. In the Huaihe River Basin, the extreme rainy weather is more likely to result in flood disasters and causes public damage. The flood processing in the rainy season was described in a spatial fashion, and the regional differentiation of inundation patterns was revealed through the spatiotemporal information of the middle and lower reaches of the Yangtze River Region. The proposed rapid and robust flood monitoring method is greatly practical and important to the dynamic monitoring of flood situation, quantitative assessment of flood disaster, and rapid early warning response.

Schlüsselwort

flooding monitoring;water mapping;SAR time series;Google Earth Engine;Middle and Lower Reaches of the Yangtze River Region;Sentinel-1A

References

  1. 1.
    Chen J, Du P J and Tan K. 2015. An improved unsupervised classification scheme for polarimetric SAR image with MCSM-Wishart. Remote Sensing for Land and Resources, 27(2): 15-21
  2. 2.
    Chini M, Pelich R, Pulvirenti L, Pierdicca N, Hostache R and Matgen P. 2019. Sentinel-1 InSAR coherence to detect floodwater in urban areas: Houston and Hurricane Harvey as a test case. Remote Sensing, 11(2): 107
  3. 3.
    DeVries B, Huang C Q, Armston J, Huang W L, Jones J W and Lang M W. 2020. Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine. Remote Sensing of Environment, 240: 111664
  4. 4.
    Dong C and Feng M Q. 2020. Analysis of the relationship between flood distribution and water level-discharge characteristics in The Yangtze River. Journal of Xi’an University of Technology, 36(3): 316-322
  5. 5.
    Du P J. 2002. Research on filtering of RADARSAT images. Journal of China University of Mining and Technology, 31(2): 132-137
  6. 6.
    Du P J, Wang X, Meng Y P, Lin C, Zhang P and Lu G. 2020. Effective change detection approaches for geographic national condition monitoring and land cover map updating. Journal of Geo-information Science, 22(4): 857-866
  7. 7.
    Fan Y D, Wu W, Wang W, Liu M and Wen Q. 2016. Research progress of disaster remote sensing in China. Journal of Remote Sensing, 20(5): 1170-1184
  8. 8.
    Feng B F, Xu Y S and Chen G Y. 2020. Flood control benefit of reservoir group in Changjiang River flood, 2020. Yangtze River, 51(12): 88-93
  9. 9.
    Ghofrani Z, Mokhtarzade M, Sahebi M R and Beykikhoshk A. 2014. Evaluating coverage changes in national parks using a hybrid change detection algorithm and remote sensing. Journal of Applied Remote Sensing, 8(1): 083646
  10. 10.
    Gu X Z, Zeng Q W, Shen H, Chen E X, Zhao L, Yu F and Tu K. 2019. Study on water information extraction using domestic GF-3 image. Journal of Remote Sensing, 23(3): 555-565
  11. 11.
    Huth J, Gessner U, Klein I, Yesou H, Lai X J, Oppelt N and Kuenzer C. 2020. Analyzing water dynamics based on Sentinel-1 time series—a study for Dongting Lake Wetlands in China. Remote Sensing, 12(11): 1761
  12. 12.
    Kaplan G and Avdan U. 2018. Monthly analysis of wetlands dynamics using remote sensing data. ISPRS International Journal of Geo-Information, 7(10): 411
  13. 13.
    Lee J S. 1981. Refined filtering of image noise using local statistics. Computer Graphics and Image Processing, 15(4): 380-389
  14. 14.
    Leng Y and Li N. 2017. Improved change detection method for flood monitoring. Journal of Radars, 6(2): 204-212
  15. 15.
    Li Y, Niu Z G, Xu Z Y and Yan X. 2020. Construction of high spatial-temporal water body dataset in China based on Sentinel-1 archives and GEE. Remote Sensing, 12(15): 2413
  16. 16.
    Li Y, Yang Y and Zhao Q H. 2019. Waterbody extraction from SAR imagery based on improved speckle reducing anisotropic diffusion and maximum between-cluster variance. Journal of Geo-Information Science, 21(6): 907-917
  17. 17.
    Liang J Y and Liu D S. 2020. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 159: 53-62
  18. 18.
    Louis J, Debaecker V, Pflug B, Main-Knorn M, Bieniarz J, Mueller-Wilm U, Cadau E and Gascon F. 2016. Sentinel-2 Sen2Cor: L2A processor for users//Proceedings Living Planet Symposium 2016. Prague, Czech Republic: [s.n.]
  19. 19.
    Lu S L, Jia L, Zhang L, Wei Y P, Baig M H A, Zhai Z K, Meng J H, Li X S and Zhang G F. 2017. Lake water surface mapping in the Tibetan Plateau using the MODIS MOD09Q1 product. Remote Sensing Letters, 8(3): 224-233
  20. 20.
    Martinis S, Twele A and Voigt S. 2009. Towards operational near real-time flood detection using a split-based automatic thresholding procedure on high resolution TerraSAR-X data. Natural Hazards and Earth System Sciences, 9(2): 303-314
  21. 21.
    McFeeters S K. 1996. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features. International Journal of Remote Sensing, 17(7): 1425-1432
  22. 22.
    Nobre A D, Cuartas L A, Hodnett M, Rennó C D, Rodrigues G, Silveira A, Waterloo M and Saleska S. 2011. Height above the nearest drainage—a hydrologically relevant new terrain model. Journal of Hydrology, 404(1/2): 13-29
  23. 23.
    Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66
  24. 24.
    Pekel J F, Cottam A, Gorelick N and Belward A S. 2016. High-resolution mapping of global surface water and its long-term changes. Nature, 540(7633): 418-422
  25. 25.
    Shan L J, Zhang L P, Zhang Y J, She D X and Xia J. 2018. Characteristics of dry-wet abrupt alternation events in the middle and lower reaches of the Yangtze River Basin and their relationship with ENSO. Acta Geographica Sinica, 73(1): 25-40
  26. 26.
    Sun Y Y, Li X T, Yang F J and Huang S F. 2014. Study on the mountain water extraction method of the space-borne SAR Image. Journal of China Institute of Water Resources and Hydropower Research, 12(3): 258-263
  27. 27.
    Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S and Brisco B. 2020. Google Earth Engine for geo-big data applications: a meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164: 152-170
  28. 28.
    Tiwari V, Kumar V, Matin M A, Thapa A, Ellenburg W L, Gupta N and Thapa S. 2020. Flood inundation mapping-Kerala 2018; Harnessing the power of SAR, automatic threshold detection method and Google Earth Engine. PLoS ONE, 15(8): e0237324
  29. 29.
    Tsyganskaya V, Martinis S, Marzahn P and Ludwig R. 2018. Detection of temporary flooded vegetation using Sentinel-1 time series data. Remote Sensing, 10(8): 1286
  30. 30.
    Veloso A, Mermoz S, Bouvet A, Le Toan T, Planells M, Dejoux J F and Ceschia E. 2017. Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications. Remote Sensing of Environment, 199: 415-426
  31. 31.
    Wahlstrom M and Guha-Sapir D. 2015. The human cost of weather-related disasters 1995-2015. Geneva, Switzerland: UNISDR
  32. 32.
    Yu J M, Ma L, Huang S and He Y H. 2020. Research on monitoring system for exceeding-standard flood based on space-air-ground integrated monitoring. Yangtze River, 51(6): 29-32, 112

Lesen Sie die ganze Passage

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website