摘要
洪水灾害是世界上发生最频繁、危害最严重的自然灾害之一,快速准确获取洪水受影响区域是洪水灾损评估的重要信息,可为政府制定应急救援决策提供重要的数据支持。针对传统洪水评估方法存在的时效性差、工作量大和费时费力等不足,本研究以广西“西江2020年第1号洪水”灾情为案例,首先对NPP-VIIRS DNB夜光遥感数据进行统计量法辐射归一化处理;其次构建市级、县级尺度的人口评估模型进行模型适宜性和尺度适宜性分析,基于多项式最优模型反演受灾人口数量并进行对比验证;再其次基于洪水发生前后夜光亮度变化提取识别灾后受灾影响变化区域,并监测灾后恢复进程;最后构建灾情可视化管理系统,可实现灾情信息实时共享。研究结果可得反演受灾人口的准确率为79.4%,与新闻报道具有一致性,表明基于NPP-VIIRS夜光遥感数据评估洪水受影响区域及受灾人口的准确性较高,具有可行性;同时基于WebGIS将灯光密度、灾情统计、多媒体数据进行集成开发,实现灾情动态信息在线可视化展示,可为政府及公众及时掌握灾情进而有效制定应急救援措施提供重要的数据支持。
Flood disaster is one of the most frequent and serious natural disasters in the world.Rapid and accurate acquisition of flood affected areas is an important information for flood damage assessment,which can provide im-portant data support for the government to make emergency rescue decisions.In view of the shortcomings of tradi-tional flood assessment methods,such as poor effectiveness,heavy workload,time-consuming and labor-consum-ing,this study takes the disaster situation of"Xijiang River Flood No.1 in 2020"as a case,firstly,the NPP-VIIRS DNB nighttime light remote sensing data are normalized by statistical method.Secondly,the population evaluation model at city and county scale is constructed to analyze the model suitability and scale suitability,and the number of people affected by the disaster was inversed based on the polynomial optimal model and the change of nighttime light before and after the flood is extracted to identify the affected areas and monitor the recovery process.Finally,a disaster visualization management system is constructed to realize the real-time sharing of disaster information.The results show that the accuracy of the inversion of the affected population in this study is 79.4%,which is consistent with the news reports.It indicates that the accuracy of the estimation of the affected area and the affected population in different regions based on NPP-VIIRS nighttime light remote sensing data is high and feasible.At the same time,the integrated development based on Web GIS,nighttime light density,disaster situation statistics and multimedia data can realize the online visualization of disaster dynamic information,which can provide important data support for the government and the public to master the disaster situation in time and formulate emergency rescue measures effectively.
作者
何原荣
王晓荣
柴春芳
余德清
郑渊茂
李栋坤
HE Yuanrong;WANG Xiaorong;CHAI Chunfang;YU Deqing;ZHENG Yuanmao;LI Dongkun(Big Data Institute of Digital Natural Disaster Monitoring in Fujian,Xiamen University of Technology,Xiamen 361024,China.2.Remote Sensing Monitoring of Ecological Environment in Dongting Lake,Key Laboratory of Hunan Province,Changsha 410007,China)
出处
《自然灾害学报》
CSCD
北大核心
2022年第3期93-105,共13页
Journal of Natural Disasters
基金
福建省自然科学基金面上项目(2020J01263)
宁夏回族自治区重点研发计划项目(2021BEG03001)
洞庭湖区生态环境遥感监测湖南省重点实验室开放课题(DTH Key lab.2021024)
福建省建设科技研究开发项目(2020-K-60)。