汶川大地震灾情综合地理信息遥感监测与信息服务系统
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摘要
结合此次汶川大地震灾情综合地理信息监测与评估工作,着重探讨航空航天遥感技术在地震灾情监测与评估中的方法和技术路线。通过集成多平台和多传感器数据,根据不同区域受灾严重程度不同的情况,研究制定了地震灾区灾情综合地理信息监测指标;通过综合震前震后多源数据,制定了快速几何处理、快速数据质量综合分析、快速变化提取、快速目标判读和次生滑坡灾害空间危险性评估的技术流程,实现了汶川大地震震区灾情综合地理信息的解译、制图和统计评估。在此基础上,开发了汶川地震灾情综合地理信息服务系统,实现了灾情监测信息的综合管理、可视化查询和统计分析。并对当前工作中存在的问题进行了探讨。
Earthquake is one of most serious natural disasters in the world.It has wide geographic coverage,huge damage,and long time influence for recovery.The earthquake happened in Wenchuan on May 12,2008 caused great damage in 42 counties in Sichuan province,23 counties in Gansu province,and 19 counties in Shaanxi province.In addition,it also has much influence on Chongqing,Yunnan,Shanxi,Guizhou,and Hubei provinces.Because of the severely destroyed infrastructures,complicated topography and bad weather condition after the earthquake,the disaster situation assessment from ground investigation is very difficult to undertake.In this situation,feasible approaches should be investigated.In this paper,we take the remote sensing monitoring and assessment of the Wenchuan Earthquake disaster situation as a case study and discuss the technical approaches and methods on application of space and airborne remote sensing technology for earthquake disaster situation monitoring and assessment.Firstly,by integrating multi-platform multi-sensor data,we defined the earthquake disaster situation monitoring indexes according to two different disaster severity levels.For areas with high-level disaster loss,aerial photos and high-resolution satellite images with less than 1 meter resolution are used as the major data sources,where four major categories with detailed subclasses are classified,i.e.,1) urban and rural residential areas;2) urban and rural infrastructures such as industrial and mining lands,highways,railways,bridges,tunnels,electric facilities,telecommunication facilities,channels,dikes,reservoirs,and dams;3) geological and environmental change information such as earthquake lakes,rock slumps,landslides,and turbidities;and 4) destroyed farmlands such as croplands and forest lands.For areas with low-level disaster loss,high and medium resolution optical and SAR satellite images before and after the earthquake are used as the major data sources,where four major categories with less detailed subclasses are classified,i.e.,1) urban and rural residential areas;2) urban and rural infrastructures such as industrial and mining lands,highways,railways,and large reservoirs;3) geological and environmental change information such as earthquake lakes,rock slumps,landslides,and turbidities;and 4) destroyed farmlands such as croplands and forest lands.By combining the multi-temporal,multi-source data before and after the earthquake,we establish the technical flows for quick geometric processing,quick data quality analysis and assessment,quick change detection,quick target interpretation,quick spatial risk assessment for landslides induced by earthquake,and the synthesis of these methods for disaster monitoring.For geometric processing,the ImageInfo software module PixelGrid is used.For image fusion,the data fusion module of ImageInfo is used.For interpretation of different area of images,the ArcGIS software is used.Additionally,the SINMAP module under ArcGIS software is also used for the validation of the extracted geological risk areas.The approach has been successfully applied in monitoring,mapping,and statistical assessment of the earthquake disaster situation.In addition,we also develope an application system which could provide functionalities for disaster information management,visualization,and statistical analysis.It utilize 2D WebGIS user interface and 3D virtual simulation environment to manage the disaster database and visualize the monitored results as 2D maps,statistic tables,graphs,and 3D models,and provide functionalities for measure and analysis.Finally,we also addressed and discussed some practical problems when applying this approach that should be well justified in the future,including the development of new geometric correction algorithm without control points,and the standardization of the workflow described in this paper for better accuracy.
Earthquake is one of most serious natural disasters in the world.It has wide geographic coverage,huge damage,and long time influence for recovery.The earthquake happened in Wenchuan on May 12,2008 caused great damage in 42 counties in Sichuan province,23 counties in Gansu province,and 19 counties in Shaanxi province.In addition,it also has much influence on Chongqing,Yunnan,Shanxi,Guizhou,and Hubei provinces.Because of the severely destroyed infrastructures,complicated topography and bad weather condition after the earthquake,the disaster situation assessment from ground investigation is very difficult to undertake.In this situation,feasible approaches should be investigated.In this paper,we take the remote sensing monitoring and assessment of the Wenchuan Earthquake disaster situation as a case study and discuss the technical approaches and methods on application of space and airborne remote sensing technology for earthquake disaster situation monitoring and assessment.Firstly,by integrating multi-platform multi-sensor data,we defined the earthquake disaster situation monitoring indexes according to two different disaster severity levels.For areas with high-level disaster loss,aerial photos and high-resolution satellite images with less than 1 meter resolution are used as the major data sources,where four major categories with detailed subclasses are classified,i.e.,1) urban and rural residential areas;2) urban and rural infrastructures such as industrial and mining lands,highways,railways,bridges,tunnels,electric facilities,telecommunication facilities,channels,dikes,reservoirs,and dams;3) geological and environmental change information such as earthquake lakes,rock slumps,landslides,and turbidities;and 4) destroyed farmlands such as croplands and forest lands.For areas with low-level disaster loss,high and medium resolution optical and SAR satellite images before and after the earthquake are used as the major data sources,where four major categories with less detailed subclasses are classified,i.e.,1) urban and rural residential areas;2) urban and rural infrastructures such as industrial and mining lands,highways,railways,and large reservoirs;3) geological and environmental change information such as earthquake lakes,rock slumps,landslides,and turbidities;and 4) destroyed farmlands such as croplands and forest lands.By combining the multi-temporal,multi-source data before and after the earthquake,we establish the technical flows for quick geometric processing,quick data quality analysis and assessment,quick change detection,quick target interpretation,quick spatial risk assessment for landslides induced by earthquake,and the synthesis of these methods for disaster monitoring.For geometric processing,the ImageInfo software module PixelGrid is used.For image fusion,the data fusion module of ImageInfo is used.For interpretation of different area of images,the ArcGIS software is used.Additionally,the SINMAP module under ArcGIS software is also used for the validation of the extracted geological risk areas.The approach has been successfully applied in monitoring,mapping,and statistical assessment of the earthquake disaster situation.In addition,we also develope an application system which could provide functionalities for disaster information management,visualization,and statistical analysis.It utilize 2D WebGIS user interface and 3D virtual simulation environment to manage the disaster database and visualize the monitored results as 2D maps,statistic tables,graphs,and 3D models,and provide functionalities for measure and analysis.Finally,we also addressed and discussed some practical problems when applying this approach that should be well justified in the future,including the development of new geometric correction algorithm without control points,and the standardization of the workflow described in this paper for better accuracy.
引文
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[2]Zang J X,Yang G J,Automatic Land Use and Land CoverChange Detection with One Temporary Remote Sensing Image[J].Journal ofRemote Sensing,2005,9(3):294—299.[张继贤,杨贵军.单一时相遥感数据土地利用与覆盖变化自动检测方法[J].遥感学报,2005,9(3):294—299].
[3]Pack R T,Tarboton D G,Goodwin C N,et al.SINMAP 2.AStability Index Approach to Terrain Stability Hazard Mapping,Technical Description and Users guide for version2.0[R],UtahState University,USA,2005.
[4]Liu Z J,Wang J,Zhang J X,et al.Study on Landslide HazardZonation Based on Factor Weighting-rating in Wan County,ThreeGorges Reservoir Area[A].Symposium of 2007 EnvironmentalRemote Sensing Annual Conference[C].Dalian,Liaoning,2007.[刘正军,王坚,张继贤等.基于因子层分级加权的三峡库区万州滑坡风险分区评估研究.[A]2007环境遥感学术年会——自然灾害遥感专题研讨会论文集,[C]辽宁大连,2007].
[5]Hu D Y,Li J,Chen Y Het al.2007,GIS-Based Landslide Spa-tial Prediction Methods,a Case Study in Cameron Highland,Ma-laysia[J].Journal ofRemote Sensing,2007,11(6):852—859.[胡德勇,李京,陈云浩等.GIS支持下滑坡灾害空间预测方法研究[J].遥感学报,2007,11(6):852—859].
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