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.
Flood mapping, particularly hazard and risk mapping, is an imperative process and a fundamental part of emergency response and risk management. This paper aims to produce a flood risk proxy map of damaged rice fields over the whole of Bangladesh, where monsoon river floods are dominant and frequent, affecting over 80% of the total population. This proxy risk map was developed to meet the request of the government on a national level. This study represents a rapid, straightforward methodology for estimating rice-crop damage in flood areas of Bangladesh during the large flood from July to September 2007, despite the lack of primary data. We improved a water detection algorithm to achieve a better discrimination capacity to discern flood areas by using a modified land surface water index (MLSWI). Then, rice fields were estimated utilizing a hybrid rice field map from land-cover classification and MODIS-derived indices, such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results showed that the developed method is capable of providing instant, comprehensive, nationwide mapping of flood risks, such as rice field damage. The detected flood areas and damaged rice fields during the 2007 flood were verified by comparing them with the Advanced Land Observing Satellite (ALOS) AVNIR-2 images (a 10 m spatial resolution) and in situ field survey data with moderate agreement (K = 0.57).
As part of the contribution to flood disaster risk reduction, it is important to identify and characterize flood areas, locations, and durations. Multiple satellite-based flood mapping and monitoring are an imperative process and the fundamental part of risk assessment in disaster risk management. In this paper, the MODIS-derived synchronized floodwater index (SfWi) was used to detect the maximum extent of a nationwide flood based on annual time-series data of 2015 in order to maximize the application of optical satellite data. The selected three major rivers-i.e., Ganges, Brahmaputra, and Meghna (GBM), transboundary rivers running through the great floodplain delta lying between Bangladesh and eastern India-show that a propensity of flood risk was revealed by the temporal and spatial dynamics of the maximum flood extent during the 2015 monsoon season. Resultant flood maps showed that SfWi-indicated flood areas were small but more accurate than those derived from the single use of the MODIS-derived water index. The return period of SfWi-indicated maximum flood extent was confirmed to be about 20 years based on historical flood records.
Abstract:Climate change is anticipated to escalate flood impacts, and thus it is important to assess flood risk closely in terms of extent and location. This study aimed to assess present and future flood risks, particularly flood risk change, over the Asia-Pacific region with consideration of climate change impacts by using a topography-based analysis method. By analyzing the output of the super-high-resolution global atmospheric general circulation model, it was found that future flood risk will increase in response to extreme rainfall under climate change. Results of this study also indicated that flood risk will further increase in the far future than in the near future . Analyses of inundation area and flood inundation depth (FID) also showed upward trends; most of flood plains in the AsiaPacific region may experience a 0-50cm increase in FID.
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