Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree c...Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.展开更多
Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for c...Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.展开更多
Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of Ch...Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.展开更多
Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable...Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.展开更多
A LM-2C launch vehicle was launched from the Taiyuan Satellite Launch Center on November 19, 2012, carrying HJ-1C, a technology demonstration satellite and the Fengniao satellite. The three satellites were placed to t...A LM-2C launch vehicle was launched from the Taiyuan Satellite Launch Center on November 19, 2012, carrying HJ-1C, a technology demonstration satellite and the Fengniao satellite. The three satellites were placed to the preset orbits respectively. Developed by DFH Satellite Co., Ltd., HJ-1C is a SAR Earth observation satellite for civilian use, which展开更多
Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme...Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4 A images. The maximum between-class variance algorithm(OTSU;developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4 A is highly correlated with that from the high-resolution satellite datasets Gaofen-1(GF-1) and Sentinel-1 A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4 A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4 A satellite data, advantages of the wide coverage, fast acquisition,and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.展开更多
Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of...Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.展开更多
The geographical condition is a very important component of a country’s national condition,and geographical conditions monitoring(GCM)has been of great concern to the Chinese government.GCM has a close relation with...The geographical condition is a very important component of a country’s national condition,and geographical conditions monitoring(GCM)has been of great concern to the Chinese government.GCM has a close relation with‘Digital China’and is a concrete embodiment of Digital China.This paper discusses the content and classification of GCM.In accordance with application areas,GCM can be divided into fundamental monitoring,thematic monitoring,and disaster monitoring.The application areas perspective includes the content of the three other perspectives,like the monitoring elements,the monitoring scope,and the monitoring cycle and fully reflects the essence of the GCM.Fundamental monitoring mainly focuses on monitoring all of the geographical elements,which provides a basis for follow-up thematic monitoring;thematic monitoring is a special type of designated subject monitoring that concerns the public or the government;disaster monitoring focuses on the dynamic monitoring of the pre-disaster and disaster periods for natural disasters.The monitoring results will provide timely information for governments so that they can meet management or decision-making requirements.A GCM case study in the area of the Qinghai−Tibet plateau was made,and some concrete conclusions were drawn.Finally,this paper presents some thoughts on conducting GCM.展开更多
Environment and Disasters Monitoring Microsatellite Constellation with high spatial resolution,high temporal resolution and high spectral resolution characteristics was put forward by China.HJ-1B satellite,one of the ...Environment and Disasters Monitoring Microsatellite Constellation with high spatial resolution,high temporal resolution and high spectral resolution characteristics was put forward by China.HJ-1B satellite,one of the first two small optical satellites,had a CCD camera and an infrared camera,which would provide an important new data source for snow monitoring.In the present paper,through analyzing the sensor and data characteristics of HJ-1B,we proposed a new infrared normalized difference snow index(INDSI) referring to the traditional normalized difference snow index(NDSI).The accuracy of these two automatic snow recognition methods was estimated based on a supervised classification method.The accuracy of the traditional NDSI method was 97.761 9% while that of the new INDSI method was 98.617 1%.展开更多
Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such ...Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such as topography and surface properties),but also by climate events.It is necessary to study rainstorm disaster-causing factors,hazard-formative environments,and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide.According to a global flood disaster dataset for the last 20 years,the top four flood disaster causes(accounting for 96.8%in total)related to rainstorms,from most to least influential,are heavy rain(accounting for 61.6%),brief torrential rain(16.7%),monsoonal rain(9.4%),and tropical cyclone/storm rain(9.1%).A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years.Taking the 7-day accumulated rainfall,3-and 12-h maximum rainfall,24-h rainfall,rainstorm threshold,and others as the main parameters,a rainstorm intensity index is constructed.Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data,population data,and economic data.Finally,a satellite remote sensing RDRM model is developed,incorporating the above three factors(rainstorm intensity index,hazard-formative environment factor,and hazard-affected body factor).The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.展开更多
With polar orbiting meteorological satellites FY-1 and NOAA,flooding was monitored in the areas of the Huaihe River basin and the Taihu Lake region during June and July 1991. All satellite images from FY-1 and NOAA fo...With polar orbiting meteorological satellites FY-1 and NOAA,flooding was monitored in the areas of the Huaihe River basin and the Taihu Lake region during June and July 1991. All satellite images from FY-1 and NOAA for concerned areas before and during flooding were examined.Those of cloud-free,with small amount of cumulus or thin cirrus were selected to exam the situation.Navigation and projec- tion were carefully performed,to ensure the projected images at different time overlap accurately with each other in 1—2 pixels. Channel 1 (CH1) and Channel 2 (CH2) data of FY-1 and NOAA satellites with wavelength of 0.58—0.68μm and 0.725—1.1μm were used to monitor the flooding.Albedo of Channel 2 and normalized vegetation index (NDVI) were adopted as indicators to identify water body from land.With histogram and man-machine interactive methods,analysis was done.In cloud-free condition,the two indicators identified the same area and scope of the water body. Totally cloud-free image in a large area is quite rare.To understand flood process,it is necessary to use more fre- quent images.It was investigated to distinguish water from land in partly cloudy condition.The result showed that when there is small amount of cumulus or thin cirrus,satellite images are still valuable in monitoring water body.In case of monitoring area covered with cirrus,vegetation index is useful,and while there is small amount of cumulus on land, albedo of Channel 2 can be used. Ten images from May 16 to August 18 of 1991 were examined.The results show that in the Lixiahe area,Jiangsu Province,the area submerged in total was the largest;along main stream of the Huaihe River,the Chuhe River,and around the Chaohu Lake,a large percentage of area submerged;while in the Taihu Lake area,less field submerged. Flood monitoring was performed for 87 counties in the region concerned.These counties were put in order accord- ing to the percentage of submerged area in total.This order showed the extent of disaster at one view point.展开更多
文摘Flood disasters can have a serious impact on people's production and lives, and can cause hugelosses in lives and property security. Based on multi-source remote sensing data, this study establisheddecision tree classification rules through multi-source and multi-temporal feature fusion, classified groundobjects before the disaster and extracted flood information in the disaster area based on optical imagesduring the disaster, so as to achieve rapid acquisition of the disaster situation of each disaster bearing object.In the case of Qianliang Lake, which suffered from flooding in 2020, the results show that decision treeclassification algorithms based on multi-temporal features can effectively integrate multi-temporal and multispectralinformation to overcome the shortcomings of single-temporal image classification and achieveground-truth object classification.
基金Supported by Huzhou Science and Technology Program(2013GY06)Research Project of Huzhou Municipal Meteorological Bureau(hzqx201602)
文摘Based on the needs of characteristic agricultural production for meteorological services in Huzhou City,we use C# programming language to develop the meteorological disaster monitoring and early warning platform for characteristic agriculture in Huzhou City. This platform integrates the functions of meteorological and agricultural information monitoring,disaster identification and early warning,fine weather forecast product display,and data query and management,which effectively enhances the capacity of meteorological disaster monitoring and early warning for characteristic agriculture in Huzhou City,and provides strong technical support for the meteorological and agricultural departments in the agricultural meteorological services.
基金This work was supported by the National Key Research and Development Program of China(Grants No.2018YFC1508302 and 2018YFC1508301)the Natural Science Foundation of Hubei Province of China(Grant No.2019CFB507).
文摘Summer floods occur frequently in many regions of China,affecting economic development and social stability.Remote sensing is a new technique in disaster monitoring.In this study,the Sihu Basin in Hubei Province of China and the Huaibei Plain in Anhui Province of China were selected as the study areas.Thresholds of backscattering coefficients in the decision tree method were calculated with the histogram analysis method,and flood disaster monitoring in the two study areas was conducted with the threshold method using Sentinel-1 satellite images.Through satellite-based flood disaster monitoring,the flooded maps and the areas of expanded water bodies and flooded crops were derived.The satellite-based monitoring maps were derived by comparing the expanded area of images during a flood disaster with that before the disaster.The difference in spatiotemporal distribution of flood disasters in these two regions was analyzed.The results showed that flood disasters in the Sihu Basin occurred frequently in June and July,and flood disasters in the Huaibei Plain mostly occurred in August,with a high interannual vari-ability.Flood disasters in the Sihu Basin were usually widespread,and the affected area was between Changhu and Honghu lakes.The Huaibei Plain was affected by scattered disasters.The annual mean percentages of flooded crop area were 14.91%and 3.74% in the Sihu Basin and Huaibei Plain,respectively.The accuracies of the extracted flooded area in the Sihu Basin in 2016 and 2017 were 96.20% and 95.19%,respectively.
基金partially funded by Sao Paulo Research Foundation(FAPESP),Brazil,grant numbers#2015/18808-0,#2018/23064-8,#2019/23382-2.
文摘Weather events put human lives at risk mostly when people might occupy areas susceptible to natural disasters.Deploying Professional Weather Stations(PWS)in vulnerable areas is key for monitoring weather with reliable measurements.However,such professional instrumentation is notably expensive while remote sensing from a number of stations is paramount.This imposes challenges on the large-scale weather station deployment for broad monitoring from large observation networks such as in Cemaden—The Brazilian National Center for Monitoring and Early Warning of Natural Disasters.In this context,in this paper,we propose a Low-Cost Automatic Weather Station(LCAWS)system developed from Commercial Off-The-Shelf(COTS)and open-source Internet of Things(IoT)technologies,which provides measurements as reliable as a reference PWS for natural disaster monitoring.When being automatic,LCAWS is a stand-alone photovoltaic system connected wirelessly to the Internet in order to provide real-time reliable end-to-end weather measurements.To achieve data reliability,we propose an intelligent sensor calibration method to correct measures.From a 30-day uninterrupted observation with sampling in minute resolution,we show that the calibrated LCAWS sensors have no statistically significant differences from the PWS measurements.As such,LCAWS has opened opportunities for reducing maintenance costs in Cemaden's observational network.
文摘A LM-2C launch vehicle was launched from the Taiyuan Satellite Launch Center on November 19, 2012, carrying HJ-1C, a technology demonstration satellite and the Fengniao satellite. The three satellites were placed to the preset orbits respectively. Developed by DFH Satellite Co., Ltd., HJ-1C is a SAR Earth observation satellite for civilian use, which
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)。
文摘Fengyun-4 A(FY-4 A) belongs to the second generation of geostationary meteorological satellite series in China. Its observations with high frequency and resolution provide a better data basis for monitoring of extreme weather such as sudden flood disasters. In this study, the flood disasters occurred in Bangladesh, India, and some other areas of South Asia in August 2018 were investigated by using a rapid multi-temporal synthesis approach for the first time for removal of thick clouds in FY-4 A images. The maximum between-class variance algorithm(OTSU;developed by Otsu in 2007) and linear spectral unmixing methods are used to extract the water area of flood disasters. The accuracy verification shows that the water area of flood disasters extracted from FY-4 A is highly correlated with that from the high-resolution satellite datasets Gaofen-1(GF-1) and Sentinel-1 A, with the square correlation coefficient R2 reaching 0.9966. The average extraction accuracy of FY-4 A is over 90%. With the rapid multi-temporal synthesis approach used in flood disaster monitoring with FY-4 A satellite data, advantages of the wide coverage, fast acquisition,and strong timeliness with geostationary meteorological satellites are effectively combined. Through the synthesis of multi-temporal images of the flood water body, the influence of clouds is effectively eliminated, which is of great significance for the real-time flood monitoring. This also provides an important service guarantee for the disaster prevention and reduction as well as economic and social development in China and the Asia-Pacific region.
文摘Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.
基金This work was funded by National Basic Surveying and Mapping Research Program-Automatic Classification with Multisource Remote Sensing Data in Complex Vegetation Coverage Area,National Key Technology Research and Development Program of the Ministry of Science and Technology of China[grant number 2012BAH28B01]National Natural Science Foundation of China[grant number 41371406].
文摘The geographical condition is a very important component of a country’s national condition,and geographical conditions monitoring(GCM)has been of great concern to the Chinese government.GCM has a close relation with‘Digital China’and is a concrete embodiment of Digital China.This paper discusses the content and classification of GCM.In accordance with application areas,GCM can be divided into fundamental monitoring,thematic monitoring,and disaster monitoring.The application areas perspective includes the content of the three other perspectives,like the monitoring elements,the monitoring scope,and the monitoring cycle and fully reflects the essence of the GCM.Fundamental monitoring mainly focuses on monitoring all of the geographical elements,which provides a basis for follow-up thematic monitoring;thematic monitoring is a special type of designated subject monitoring that concerns the public or the government;disaster monitoring focuses on the dynamic monitoring of the pre-disaster and disaster periods for natural disasters.The monitoring results will provide timely information for governments so that they can meet management or decision-making requirements.A GCM case study in the area of the Qinghai−Tibet plateau was made,and some concrete conclusions were drawn.Finally,this paper presents some thoughts on conducting GCM.
基金HJ-1 Satellite data Application Research Project(2008A01A1300)National High Technology Research and Development Program(2009AA12Z101)Key Project of Knowledge Innovation Program of Chinese Academy of Sciences(KZCX2-YW-Q03-07)
文摘Environment and Disasters Monitoring Microsatellite Constellation with high spatial resolution,high temporal resolution and high spectral resolution characteristics was put forward by China.HJ-1B satellite,one of the first two small optical satellites,had a CCD camera and an infrared camera,which would provide an important new data source for snow monitoring.In the present paper,through analyzing the sensor and data characteristics of HJ-1B,we proposed a new infrared normalized difference snow index(INDSI) referring to the traditional normalized difference snow index(NDSI).The accuracy of these two automatic snow recognition methods was estimated based on a supervised classification method.The accuracy of the traditional NDSI method was 97.761 9% while that of the new INDSI method was 98.617 1%.
基金Supported by the National Key Research and Development Program of China(2018YFC1506500)Open Research Fund of Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SZKT2016001)。
文摘Methods of rainstorm disaster risk monitoring(RDRM)based on retrieved satellite rainfall data are studied.Due to significant regional differences,the global rainstorm disasters are not only affected by geography(such as topography and surface properties),but also by climate events.It is necessary to study rainstorm disaster-causing factors,hazard-formative environments,and hazard-affected incidents based on the climate distribution of precipitation and rainstorms worldwide.According to a global flood disaster dataset for the last 20 years,the top four flood disaster causes(accounting for 96.8%in total)related to rainstorms,from most to least influential,are heavy rain(accounting for 61.6%),brief torrential rain(16.7%),monsoonal rain(9.4%),and tropical cyclone/storm rain(9.1%).A dynamic global rainstorm disaster threshold is identified by using global climate data based on 3319 rainstorm-induced floods and rainfall data retrieved by satellites in the last 20 years.Taking the 7-day accumulated rainfall,3-and 12-h maximum rainfall,24-h rainfall,rainstorm threshold,and others as the main parameters,a rainstorm intensity index is constructed.Calculation and global mapping of hazard-formative environmental factor and hazard-affected body factor of rainstorm disasters are performed based on terrain and river data,population data,and economic data.Finally,a satellite remote sensing RDRM model is developed,incorporating the above three factors(rainstorm intensity index,hazard-formative environment factor,and hazard-affected body factor).The results show that the model can well capture the rainstorm disasters that happened in the middle and lower reaches of the Yangtze River in China and in South Asia in 2020.
文摘With polar orbiting meteorological satellites FY-1 and NOAA,flooding was monitored in the areas of the Huaihe River basin and the Taihu Lake region during June and July 1991. All satellite images from FY-1 and NOAA for concerned areas before and during flooding were examined.Those of cloud-free,with small amount of cumulus or thin cirrus were selected to exam the situation.Navigation and projec- tion were carefully performed,to ensure the projected images at different time overlap accurately with each other in 1—2 pixels. Channel 1 (CH1) and Channel 2 (CH2) data of FY-1 and NOAA satellites with wavelength of 0.58—0.68μm and 0.725—1.1μm were used to monitor the flooding.Albedo of Channel 2 and normalized vegetation index (NDVI) were adopted as indicators to identify water body from land.With histogram and man-machine interactive methods,analysis was done.In cloud-free condition,the two indicators identified the same area and scope of the water body. Totally cloud-free image in a large area is quite rare.To understand flood process,it is necessary to use more fre- quent images.It was investigated to distinguish water from land in partly cloudy condition.The result showed that when there is small amount of cumulus or thin cirrus,satellite images are still valuable in monitoring water body.In case of monitoring area covered with cirrus,vegetation index is useful,and while there is small amount of cumulus on land, albedo of Channel 2 can be used. Ten images from May 16 to August 18 of 1991 were examined.The results show that in the Lixiahe area,Jiangsu Province,the area submerged in total was the largest;along main stream of the Huaihe River,the Chuhe River,and around the Chaohu Lake,a large percentage of area submerged;while in the Taihu Lake area,less field submerged. Flood monitoring was performed for 87 counties in the region concerned.These counties were put in order accord- ing to the percentage of submerged area in total.This order showed the extent of disaster at one view point.
