Research On Flood Disaster Monitoring Based On Multi-source Satellite Dat

As global warming is intensified,extreme precipitation events become more frequent and result in flood disasters throughout the world.Since China is one of the areas with the most frequent flood disasters in the world,which seriously threatens the life and property safety of Chinese people.Thus,monitoring and extraction of surface water areas in real-time is of paramount importance.Investigation method using the traditional artificial monitoring flooding exist cycle is long,dangerous,spend a lot of defects such as resources,and satellite remote sensing technology has a wide range,high efficiency and large amount of information,can make up for the inadequacy of traditional technology,the monitoring and evaluation has been flooding,water resources research in areas such as widely used.Today,the algorithms for flood disaster monitoring from polar-orbiting satellites has been quite mature.With the help of high spatial resolution images,accurate extraction of flood submerges can be realized.However,the revisit cycle of polar-orbiting satellites often varies from several days,and they are unable to deal with the flood disasters that occur in the data vacancy period.Feng Yun-4A(FY-4A)is China’s new generation of geostationary meteorological satellite,it carries onboard Advanced Geosynchronous Radiation Imager(AGRI)and allows for observing the earth atmosphere at visible,near infrared,mid-infrared and longwave infrared wavelengths.Its high temporal and spatial resolution can effectively compensate for the long revisit period of polar-orbiting satellites in flood disaster monitoring.This thesis develops the theory and method of water monitoring using multi-source satellite data and establishes a flood disaster monitoring through both polar-orbiting and geostationary meteorological satellites.The main research contents and results of this thesis include:(1)Evaluation of multiple water index models based on polar-orbiting satellite data.Based on Sentinel-2 image data,a new Water Spectral Similarity Index(WSSI)model is constructed by comparing and analyzing the extraction effects of 10 common band index models.The water recognition results of high-resolution images combined with visual interpretation are used as the truth value,and f1-Score,false alarm rate and missed detection rate are used to evaluate all models from three aspects: water recognition accuracy,water-land mixed pixel detection ability and threshold stability.The flood disaster situation in the middle and lower reaches of Yangtze River is studied by WSSI algorithm,and the change trend of flood disaster area was analyzed.The results show that the optimal segmentation threshold of WSSI ensures the accuracy of WSSI in large range water body information recognition.In the past three years,dongting Lake,Poyang Lake and Taihu Lake experienced the most severe flood disaster in 2020,and the water area expanded significantly.(2)Cloud removal processing of meteorological satellite data and flood disaster monitoring application.Multi-temporal rapid synthesis algorithm is used to complete cloud removal of meteorological satellite data,and then clear sky images of flood disasters in the study area are obtained,so as to facilitate subsequent water extraction and disaster assessment work smoothly.According to the band characteristics of FY4 A AGRI,the cloud detection model used by predecessors in multi-temporal rapid synthesis algorithm is improved to realize the improvement of cloud removal image quality.Based on the clear sky images of the study area obtained by cloud removal,the algorithm applicable to meteorological satellite data in the algorithm evaluation results is used to complete the extraction of flood submergence area.The results show that the flood information extracted from meteorological satellite data is significantly consistent with the water information extracted from high-resolution data.The work of this paper on the one hand,solve in the research of water body recognition in the face of complex scene will happen when the best segmentation threshold fluctuation problem;on the other hand,to a certain extent,overcome the cloud based on optical image sensor interference problems in the extraction,the surface water can be achieved against floods of wide-area real-time monitoring,provide the foundation for flood prevention and control work,It provides basic data support for relevant departments to understand the specific disaster situation and make macro decisions.