The objective of this study was to estimate rice crop damage over the entire Cambodia during a large flood event from July to November 2011. An integrated approach was applied to detect and monitor flood areas with flood depth and duration for near real-time rice crop damage estimation in 2011 by using MODIS time-series imagery. The combined data consists of developed MLSWI, EVI from MODIS, new FID from DEM, land use, and simplified empirical damage curves. These data are expected to play an important role in emergency response efforts and rapid risk assessment for high-risk flood areas in the Cambodian floodplain. A rice crop damage map will be generated, showing areas with different damage levels based on flood duration and floodwater depth, including 25% (8 days, below 1.5 m), 50% (8 days, over 1.5 m; 16 days, below 1.5 m), and 100% (16 days, over 1.5 m). The resulting map was validated and shows about 80% consistency with the government census based on field-scale investigation and survey. Index Terms-Cambodian floodplain, damage curves, large flood, MODIS, rice crop damage.

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Measurements of flood flow have been conducted intensively at major control sections in Japan for storing reliable hydrological data for use in a long-term river planning. However, such a measurement pays attention only to flow passing through one cross-section; thus, spatial flow features are not available, while river channel changes its feature at every flood event. In this research, we performed concurrent measurements of a snowmelt flood of the Uono River using an image analysis and radio-controlled acoustic Doppler current profiler (ADCP). In the image analysis with STIV (Space-time Image Velocimetry), three video cameras were used to cover a river reach of about 300m by changing their view angles at every location. On the other hand, the boat-mounted ADCP was remotely controlled to form a zigzag trajectory to cover the same reach. The accuracy and limitation of STIV was made clear through a comparison with ADCP data and a spatial distribution of correction factor from surface to depth-averaged velocity was found to have a weak correlation with a large-scale bed slope.

River inundation satellite images are restricted to make realtime flood inundation maps in many cases. However, such images have significant potential to predict the time, place and scale of a flooding event, and can be very useful in emergency response efforts. The estimation of water extent boundary and flood volume is important to determine a fundamental hazard in flood risk assessment. In this study, an attempt was made to detect surface water in a severe flood event (the 2011 Thai flood) by applying modified remote sensing indices to near-real-time MODIS images. Flood volumes were also calculated for detected flood areas by using a proposed flood inundation level (FIL) model with the Digital Elevation Model (DEM). FILs were verified through field investigation. The results show that the MODIS-FIL combined approach is feasible for automatic, instant flooding detection.Index Terms-MODIS, flood inundation level (FIL), flood volume, DEM