Nationwide Risk Mapping Framework: The risk was characterized and measured by a combination of exposure and vulnerability to a hazard based on areas where a hazard was posing an immediate threat to human lives, property, or the environment. Hazard and exposure were identified by tangible factors using topographic and demographic distribution data. Vulnerability, though intangible, was measured as a characteristic of the elements of interest (i.e., assets, agricultural products). In this paper, risk was defined as potential damage areas focusing on the rice fields of the annual largest flood risk associated with hazard and exposure, in order to estimate the rice field proxy risk. This study applied the concept of flood risk to the 2007 floods of Bangladesh in the case of the cyclic 10-year flood event. Three procedures for the assessment of areas of direct monetary damage: (a) hazard, (b) exposure, and (c) risk, respectively.
Floodwater Detection: Following the significant 2007 flood in Bangladesh, it became necessary to improve a surface water detection method to identify flood areas by using water indices. For flood mapping in a nationwide, comprehensive approach, it was important to determine floodwater pixels more accurately through the development of a floodwater index. The MODIS time series data were processed in four different patterns to compare the three water indices of NDWI, LSWI, MLSWI combining bands 2 and 6 (MLSWI 2 and 6), and MLSWI combining bands 2 and 7 (MLSWI 2 and 7).
Verification of Flood water: Several zones were selected at sites two and three for verification of the existence of floodwater (i.e., site two: Majibari Union = 18.8 km2, and site three: Koijuri Union = 28.8 km2). Over 100 ground truth sample points were obtained from the field survey in each Union to distinguish floodwater and non-flooded areas. The stratified random sample points were all located in the area of 30%–100% damage level in the Sirajganj district along the Brahmaputra River. Floodwater and non-flooded areas in the Sirajganj district were verified by the high-spatial-resolution (10 m) ALOS AVNIR-2 images. The water-related pixels of MLSWI were confirmed by a comparison with the water pixels from NDVI threshold classification and differential NDVI, focusing on NIR (Band 4: 760–890 nm) of AVNIR-2 in the Sirajganj district.
Flood water Depth and Duration: According to the In situ hydrograph created from the river water level as an indicator, determined the flood duration, not only by the eight- and 16-day MODIS data, but also by observing the in situ flood hydrograph five times a day. Daily MODIS and eight-day composite data provide a reasonable estimate of the dynamic extent of floodplain inundation at the regional scale. The MODIS eight-day composite data can be used as a surrogate for daily images to determine the flood duration. Though not suited to capture flash floods, MODIS eight-day composite data captured the 2007 floods in Bangladesh that lasted for 14 days. We confirmed the eight- and 16-day flood durations derived from MODIS eight-day composite data by checking daily surface reflectance (MOD09GA and MYD09GA) during the peak flood period.
Exposed Rice Field: To estimate the exposure area of rice fields, a hybrid approach was applied to identify irrigated paddy fields before the flooding of 2007, by combining data between existing paddy fields from MODIS-derived VI products with a quality check (MODIS-EVI and NDVI) and extracted paddy fields from two land cover data; GLCNMO 2008 and GMIA ver.5. For extracting and combining two rice products, the rice fields from GLCNMO 2008 were recalculated for all input pixels containing over 10% of rice croplands from GMIA ver.5 data products by using the block statistic function in the same boundary rectangle (approx. 10 km spatial resolution). Since there was no reliable evidence-based map of national land-use and cropland in Bangladesh, adopted this hybrid approach to minimize the ambiguity of identifying rice fields despite the difference in their production years and spatial resolutions. MODIS-EVI can provide estimates of the spatial distribution of rice phenology, the amount of rice crop per year, and the cropping pattern. With MODIS-EVI alone, however, it was difficult to determine areas of existing rice fields because EVI detects all vegetation. GMIA/GLCNMO was then used with EVI to identify existing irrigated paddy fields with improved accuracy.
Risk Area Estimation: Nationwide flood risk was estimated for damage assessment in terms of flood hazard (i.e., inundation area, depth, duration and frequency), exposure (i.e., agricultural estates) and vulnerability (i.e., sensitivity to economic damage). A proxy map was an alternative risk map for identifying and quantifying risk at a macro level. In this study, the risk proxy map of the rice fields was characterized as a result of the combination of hazard and exposure in rice fields based on the 2007 flood conditions. Mainly, a large flood causes significant damage to rice at the transplanting and pre-growing stages during June to August in Bangladesh. On a large scale in this study, economic damage evaluation in agriculture was performed only for Aman rice, focusing on rapid flood risk assessment by using a simple remote sensing-based approach and moderate estimates (i.e., approx. 500 m spatial resolution). To estimate the risk area affected by a hazard, flood stage-damage curves of rice crops were applied to the floodwater depth and duration, using temporal and spatial dynamics for nationwide flood risk instead of inadequate information of actual risk phenomena.