• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.2184-2189
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• 2009

China is one of the countries in which flood occurs most frequently in the world and with the current economic growth; flood disaster causes more and more economic losses. Chinese government pays more attention to flood monitoring and assessment by space technology. Since1983, NOAA(AVHRR), Landsat-TM, LANDSAT-ETM+, JERS-1, SPOT, ERS-2, Radarsat-1, CBERS-1, Envisat have been used for flood monitoring and assessment. Due to the bad weather conditions during flood, microwave remote sensing is the major tools for flood monitoring. Envisat is one of the best satellite with powerful SAR. Its application for flood monitoring has been studied and its near real time(NRT) application can be realized on the basis of real-time delivery of image. During the 2005, 2006 and 2007 flood seasons, over the 31 NRT flood monitoring based on Envisat, had been carried out in Yangtze, Songua, Huaihe, pearl river basin. The result shows that Envisat SAR is very useful data source for flood disaster monitoring and assessment.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.507-515
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• 2002

Remote Sensing Monitoring and Loss Estimated System of Flood Disaster based on GIS is an integrated system comprised flood disaster information receiving and collection, flood disaster simulation, and flood disaster estimation. When the system receives and collects remote sensing monitoring and conventional investigation information, the distributional features of flood disaster on space and time is obtained by means of image processing and information fusion. The economic loss of flood disaster can be classified into two pus: direct economic loss and indirect economic loss. The estimation of direct economic loss applies macroscopic economic analysis methods, i.e. applying Product (Industry and Agriculture Gross Product or Gross Domestic Product - GDP) or Unit Synthetic Economic Loss Index, direct economic loss can be estimated. Estimating indirect economic loss applies reduction coefficient methods with direct economic loss. The system can real-timely ascertains flood disaster and estimates flood Loss, so that the science basis fur decision-making of flood control and relieving disaster may be provided.

• Proceedings of the KSRS Conference
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• 2002.10a
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• pp.175-182
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• 2002

The paper introduces the application of flood monitoring and assessment by remote sensing and GIS in china and describes the frame of operational system for practical performance of flood disaster mitigation. In addition, The operational system for flood monitoring and assessment in RSTAC/ MWR and its application in the floods of 1998,1999 in china are introduced.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.236-236
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• 2015

Flood is one of the major natural disasters affecting millions of people. Thailand also, frequently faces with this type of disaster. Especially, 2011 mega flood in Central Thailand, inundated highway severely attributed to the failure of national economic and risk to life. Lesson learned from such an extreme event caused flood monitoring and warning becomes one of the sound mitigations. The highway flood hazard mapping accomplished in this research is one of the strategies. This is due to highway flood is the potential risk to life and limb, and potential damage to property. Monitoring and warning therefore help reducing live and property losses. In this study, degree of highway flood hazard was assessed by weighting factors for each cause of the highway flood using Multi Criteria Analysis (MCA) based Analytic Hierarchy Process (AHP). These weighting factors are the essential information to classify the degree of highway flood hazard to enable pinpoint on flood monitoring and flood warning in hazard areas. The highway flood causes were then investigated. It was found that three major factors influence to the highway flood are namely the highway characteristics, the hydrological characteristics and the land topography characteristics. The weight of importance for each cause of the highway flood in the whole country was assessed by weighting 3 major factors influence to the highway flood. According to the result of MCA analysis, the highway, the hydrological and the land topography characteristics were respectively weighted as 35, 35 and 30 percent influence to the cause of highway flood. These weighting factors were further utilized to classify the degree of highway flood hazard. The Weight Linear Combination (WLC) method was used to compute the total score of all highways according to each factor. This score was later used to categorize highway flood as high, moderate and low degree of hazard levels. Highway flood hazard map accomplished in this research study is applicable to serve as the handy tool for highway flood warning. However, to complete the whole warning process, flood water level monitoring system for example the camera gauge should be installed in the hazard highway. This is expected to serve as a simple flood monitor as part of the warning system during such extreme or critical event.

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.280.2-286
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• 1998

Flood is the main natural disaster mostly in the world. It is a care problem to prevent flood disaster generally. The frequency of flood disaster is high and the distributing field is wide, the 50 percent population and 70 percent properties distribute at the threaten field of flood disaster in China. Flood disaster has caused a huge amount of economical losses and these losses have an increasing trend. Along with the development of reducing natural disaster action, it has become one of the most attentive problems for monitoring flood, preventing flood and forecasting flood efficiently. Remote sensing has the characteristics of large spatial observing areas, wide spectrum ranges, and imaging far away from the targets, imaging capabilities all weather. Spatial remote sensing information, which records the full, processes of the disaster's occurrence and development in real-time. It is a scientific basis for management, planning and decision-making. Through systemic analyzing the RS monitoring theory, based on compounding RS information, the technology and method of monitoring flood disaster are studied.

• Korean Journal of Remote Sensing
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• v.12 no.1
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• pp.60-80
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• 1996

AVHRR(Advanced Very High Resolution Radiometer) on NOAA satellite provides data in five spectral, one in visible range, one in near infrared and three in thermal range. In this paper, application of NOAA/AVHRR data is studied for environment monitoring such as cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index, forest fire, flood, snow cover and so on. The analyses for cloud top temperature, surface temperature, albedo, sea surface temperature, vegetation index and forest fire showed reasonable agreement. But monitoring for flood and snow cover was uneasy due to the limitations such as cloud contamination, low spatial resolution. So this research had only simple purpose to identify well-defined waterbody for dynamic monitoring of flood. Based on development of these basic algorithms, we have a plan to further reseach for environment monitoring using AVHRR data.

• Proceedings of the KSR Conference
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• 2004.06a
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• pp.224-228
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• 2004

In these days, as it frequently happens that water level in the river exceeds the design floods due to irregular heavy rainfall, so it is required, first of an, to manage with railroad bridge safely. Because train control criterion under heavy rainfall is still not prepared and automatic flood monitoring system for railroad bridges is not used, worry over invisible economic loss due to train passage delay is in existence. Therefore, it is important to secure the safety and detect the risk like turnover or failure of railroad bridges through systematic disaster prevention system. And the transition from conventional monitoring method to real time monitoring method supported by sensors and communication system with high technology is rapidly needed. This research is on developing the real time flood monitoring system which prevents railroad disasters in advance by detecting continuously the water level of railroad bridge through CCD camera and water level gauge.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1145-1149
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• 2006

The typhoon Rusa passed through the Korean peninsula from the west-southern part to the east-northern part in the summer season of 2002. The flash flood due to the Rusa was occurred over the Korean peninsula and especially the damage was concentrated in Kangnung, Yangyang, Kosung, and Jeongsun areas of Kangwon-Do. Since the latter half of the 1990s the flash flood has became one of the frequently occurred natural disasters in Korea. Flash floods are a significant threat to lives and properties. The government has prepared against the flood disaster with the structural and nonstructural measures such as dams, levees, and flood forecasting systems. However, since the flood forecasting system requires the rainfall observations as the input data of a rainfall-runoff model, it is not a realistic system for the flash flood which is occurred in the small basins with the short travel time of flood flow. Therefore, the flash flood forecasting system should be constructed for providing the realistic alternative plan for the flash flood. To do so, firstly, Flash Flood Monitoring and Prediction (FFMP) Model must be developed suitable to Korea terrain. In this paper, We develop the FFMP model which is based on GIS, Radar techniques and hydro-geomorphologic approaches. We call it the F2MAP model. F2MAP model has three main components (1) radar rainfall estimation module for the Quantitative Precipitation Forecasts (QPF), (2) GIS Module for the Digital terrain analysis, called TOPAZ(Topographic PArametiZation), (3) hydrological module for the estimation of threshold runoff and Flash Flood Guidance(FFG). For the performance test of the model developed in this paper, F2MAP model applied to the Kangwon-Do, Korea, where had a severe damage by the Typhoon Rusa in August, 2002. The result shown that F2MAP model is suitable for the monitoring and the prediction of flash flood.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.962-964
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• 2003

In 2002 Thailand was faced with severe flooding in the North, Northeast and Central parts of the country caused by heavy rainfall of the monsoonal depression which brought about significant damages. According to the report by the Ministry of Interior and the Ministry of Agricultural and Co-operatives, the total damages were estimated to be about 6 billion bath. More than 850,000 farmers and 10 million livestock were effected. An area of 1,450,000 ha of farmland in 59 Provinces were put under water for a prolonged period. Satellite imageries were employed for mapping and monitoring the flood-inundated areas, flood damage assessment, flood hazard zoning and post-flood survey of river configuration and protection works. By integrating satellite data with other updated spatial and non-spatial data, likely flood zones can be predicted beforehand. Some examples of satellite data application to flood dis aster mitigation in Thailand during 2002 using mostly Radarsat-1 data and Landsat-7 data were illustrated and discussed in the paper. The results showed that satellite data can clearly identify and give information on the status, flooding period, boundary and damage of flooding. For comprehensive flood mitigation planning, other geo-informatic data, such as the elevation of topography, hydrological data need to be integrated. Ground truth data of the watershed area, including the water level, velocity, drainage pattern and direction were also useful for flood forecasting in the future.

• Korean Journal of Remote Sensing
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• v.31 no.4
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• pp.293-302
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• 2015

Water body extraction is significant for flood disaster monitoring using satellite imagery. Conventional methods have focused on finding an index, which highlights water body and suppresses non-water body such as vegetation or soil area. The Normalized Difference Water Index (NDWI) is typically used to extract water body from satellite images. The drawback of NDWI, however, is that some man-made objects in built-up areas have NDWI values similar to water body. The objective of this paper is to propose a new method that could extract correctly water body with built-up areas in before and after images of flood. We first create a two-element feature vector consisting of NDWI and a Near InfRared band (NIR) and then select a training site on water body area. After computing the mean vector and the covariance matrix of the training site, we classify each pixel into water body based on Mahalanobis distance. We also register before and after images of flood using outlier removal and triangulation-based local transformation. We finally create a change map by combining the before-flooding water body and after-flooding water body. The experimental results show that the overall accuracy and Kappa coefficient of the proposed method were 97.25% and 94.14%, respectively, while those of the NDWI method were 89.5% and 69.6%, respectively.