收稿：2020-12-11，
纸质出版：2021-10-07
移动端阅览
合成孔径雷达（SAR）因其对地观测全天候、全天时优势，成为多云多雨天气限制下洪水动态监测中不可或缺的数据来源之一。由于GEE（Google Earth Engine）云计算平台的兴起和短重访Sentinel-1数据的可获取性，洪水监测与灾害评估目前正面向动态化、广域化快速发展。顾及洪水淹没区土地覆盖变化的复杂性和发生时间的不确定性，基于时序Sentinel-1A卫星数据提出了针对大尺度范围、连续长期的汛情自动检测及动态监测方法。该方法首先，利用图像二值化分割时序SAR数据实现水体时空分布粗制图，逐像素计算时间序列中被识别为水体候选点的频率。然后，利用Sentinel-2光学影像对精度较粗的初期SAR水体提取结果进行校正，得到精细的水体分布图。最后，针对不同频率区间的淹没特点，采用差异化的时序异常检测策略识别淹没范围：对低频覆水区利用欧氏距离检测时序断点，以提取扰动强度大、淹没时间短的洪涝灾害区；对高频覆水区利用标准分数（Z-Score）检测时序断点，以提取季节性水体覆盖区。在GEE平台上利用该方法，实现了2020-05—10长江中下游地区全域洪水淹没范围时空信息的自动、快速、有效监测，揭示了不同区域汛情发展模式的差异性。本文提出的洪水快速监测方法对大尺度下的汛情动态监测、灾害定量评估和快速预警响应具有重要的现实意义。
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.
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
陈军 , 杜培军 , 谭琨 . 2015 . 一种改进的全极化SAR图像MCSM-Wishart非监督分类方法 . 国土资源遥感 , 27 ( 2 ): 15- 21 [ DOI: 10.6046/gtzyyg.2015.02.03 http://dx.doi.org/10.6046/gtzyyg.2015.02.03 ]
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 [ DOI: 10.3390/rs11020107 http://dx.doi.org/10.3390/rs11020107 ]
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 [ DOI: 10.1016/j.rse.2020.111664 http://dx.doi.org/10.1016/j.rse.2020.111664 ]
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
董程 , 冯民权 . 2020 . 长江流域洪水汛情分布与水位-流量特征关系分析 . 西安理工大学学报 , 36 ( 3 ): 316- 322 [ DOI: 10.19322/j.cnki.issn.1006-4710.2020.03.007 http://dx.doi.org/10.19322/j.cnki.issn.1006-4710.2020.03.007 ]
Du P J . 2002 . Research on filtering of RADARSAT images . Journal of China University of Mining and Technology , 31 ( 2 ): 132 - 137
杜培军 . 2002 . RADARSAT图象滤波的研究 . 中国矿业大学学报 , 31 ( 2 ): 132- 137 [ DOI: 10.3321/j.issn:1000-1964.2002.02.006 http://dx.doi.org/10.3321/j.issn:1000-1964.2002.02.006 ]
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
杜培军 , 王欣 , 蒙亚平 , 林聪 , 张鹏 , 卢刚 . 2020 . 面向地理国情监测的变化检测与地表覆盖信息更新方法 . 地球信息科学学报 , 22 ( 4 ): 857 - 866
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
范一大 , 吴玮 , 王薇 , 刘明 , 温奇 . 2016 . 中国灾害遥感研究进展 . 遥感学报 , 20 ( 5 ): 1170- 1184 [ DOI: 10.11834/jrs.20166171 http://dx.doi.org/10.11834/jrs.20166171 ]
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
冯宝飞 , 许银山 , 陈桂亚 . 2020 . 2020年7月长江洪水及水库群防洪效益分析 . 人民长江 , 51 ( 12 ): 88- 93 [ DOI: 10.16232/j.cnki.1001-4179.2020.12.018 http://dx.doi.org/10.16232/j.cnki.1001-4179.2020.12.018 ]
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 [ DOI: 10.1117/1.JRS.8.083646 http://dx.doi.org/10.1117/1.JRS.8.083646 ]
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
谷鑫志 , 曾庆伟 , 谌华 , 陈尔学 , 赵磊 , 于飞 , 涂宽 . 2019 . 高分三号影像水体信息提取 . 遥感学报 , 23 ( 3 ): 555 - 565
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 [ DOI: 10.3390/rs12111761 http://dx.doi.org/10.3390/rs12111761 ]
Kaplan G and Avdan U . 2018 . Monthly analysis of wetlands dynamics using remote sensing data . ISPRS International Journal of Geo-Information , 7 ( 10 ): 411 [ DOI: 10.3390/ijgi7100411 http://dx.doi.org/10.3390/ijgi7100411 ]
Lee J S . 1981 . Refined filtering of image noise using local statistics . Computer Graphics and Image Processing , 15 ( 4 ): 380 - 389 [ DOI: 10.1016/S0146-664X(81)80018-4 http://dx.doi.org/10.1016/S0146-664X(81)80018-4 ]
Leng Y and Li N . 2017 . Improved change detection method for flood monitoring . Journal of Radars , 6 ( 2 ): 204 - 212
冷英 , 李宁 . 2017 . 一种改进的变化检测方法及其在洪水监测中的应用 . 雷达学报 , 6 ( 2 ): 204- 212 [ DOI: 10.12000/JR16139 http://dx.doi.org/10.12000/JR16139 ]
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 [ DOI: 10.3390/rs12152413 http://dx.doi.org/10.3390/rs12152413 ]
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
李玉 , 杨蕴 , 赵泉华 . 2019 . 结合改进的降斑各向异性扩散和最大类间方差的SAR图像水体提取 . 地球信息科学学报 , 21 ( 6 ): 907 - 917
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 [ DOI: 10.1016/j.isprsjprs.2019.10.017 http://dx.doi.org/10.1016/j.isprsjprs.2019.10.017 ]
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.]
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 [ DOI: 10.1080/2150704X.2016.1260178 http://dx.doi.org/10.1080/2150704X.2016.1260178 ]
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 [ DOI: 10.5194/nhess-9-303-2009 http://dx.doi.org/10.5194/nhess-9-303-2009 ]
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 [ DOI: 10.1080/01431169608948714 http://dx.doi.org/10.1080/01431169608948714 ]
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 [ DOI: 10.1016/J.JHYDROL.2011.03.051 http://dx.doi.org/10.1016/J.JHYDROL.2011.03.051 ]
Otsu N . 1979 . A threshold selection method from gray-level histograms . IEEE Transactions on Systems, Man, and Cybernetics , 9 ( 1 ): 62 - 66 [ DOI: 10.1109/TSMC.1979.4310076 http://dx.doi.org/10.1109/TSMC.1979.4310076 ]
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 [ DOI: 10.1038/nature20584 http://dx.doi.org/10.1038/nature20584 ]
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
闪丽洁 , 张利平 , 张艳军 , 佘敦先 , 夏军 . 2018 . 长江中下游流域旱涝急转事件特征分析及其与ENSO的关系 . 地理学报 , 73 ( 1 ): 25- 40 [ DOI: 10.11821/dlxb201801003 http://dx.doi.org/10.11821/dlxb201801003 ]
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
孙亚勇 , 李小涛 , 杨锋杰 , 黄诗峰 . 2014 . 基于星载SAR数据的山区水体提取方法研究 . 中国水利水电科学研究院学报 , 12 ( 3 ): 258- 263 [ DOI: 10.13244/j.cnki.jiwhr.2014.03.006 http://dx.doi.org/10.13244/j.cnki.jiwhr.2014.03.006 ]
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 [ DOI: 10.1016/j.isprsjprs.2020.04.001 http://dx.doi.org/10.1016/j.isprsjprs.2020.04.001 ]
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 ): e 0237324 [ DOI: 10.1371/journal.pone.0237324 http://dx.doi.org/10.1371/journal.pone.0237324 ]
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 [ DOI: 10.3390/rs10081286 http://dx.doi.org/10.3390/rs10081286 ]
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 [ DOI: 10.1016/j.rse.2017.07.015 http://dx.doi.org/10.1016/j.rse.2017.07.015 ]
Wahlstrom M and Guha-Sapir D . 2015 . The human cost of weather-related disasters 1995-2015. Geneva, Switzerland: UNISDR
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
喻静敏 , 马力 , 黄珊 , 贺媛卉 . 2020 . 基于天空地协同的超标准洪水监测体系研究 . 人民长江 , 51 ( 6 ): 29 - 32 , 112 [ DOI: 10.16232/j.cnki.1001-4179.2020.06.006 http://dx.doi.org/10.16232/j.cnki.1001-4179.2020.06.006 ]
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