Abstract
Abstract. Flooding is one of the major disasters occurring in various parts of the world. Estimation of economic loss due to flood often becomes necessary for flood damage mitigation. This present practice to carry out post flood survey to estimate damage, which is a laborious and time-consuming task. This paper presents a framework of rapid estimation of flood damage using SAR earth observation satellite data.In Nakhon Si Thammarat, a southern province in Thailand, flooding is a recurrent event affecting the entire province, especially the urban area. Every year, it causes lives and damages to infrastructure, agricultural production and severely affects local economic development. In order to monitor and estimate flood damages in near-real time, numerous techniques can be used, from a simply digitizing on maps, to using detailed surveys or remote sensing techniques. However, when using the last-mentioned technique, the results are conditioned by the time of data acquisition (day or night) as well as by weather conditions. Although, these impediments can be surpassed by using RADAR satellite imagery. The aim of this study is to delineate the land surface of Chian Yai, Pak Phanang and Hua Sai districts of that was affected by floods in December 2018 and January 2019. For this case study, Sentinel-1 C-Band SAR data provided by ESA (European Space Agency) were used. The data sets were taken before and after the flood took place, all within 1 days and were processed using Sentinel Toolbox. Cropland mapping has been carried out to assess the agricultural loss in study area using Sentinel-1 SAR data. The thematic accuracy has been assessed for cropland classification for test site shows encouraging overall accuracy as 82.63 % and kappa coefficients (κ) as 0.78.
Highlights
1.1 BackgroundIn recent years, the number of weather and hydrological calamities has been steadily increased, at the global level being affected hundreds of millions of people every year, especially in south Asian countries
The threshold value of backscattering coefficient or sigma naught obtained for flooded area and water bodies detection was 0.05
Water features for pre-flood image is mapped to identify permanent water bodies existing in study area
Summary
1.1 BackgroundIn recent years, the number of weather and hydrological calamities has been steadily increased, at the global level being affected hundreds of millions of people every year, especially in south Asian countries. Floods represent the most generous natural disaster that may occur at different levels, having an impact on environment, ecology, agriculture and infrastructure. Damage and loss assessment are significant for flood management, but it is always challenging task in context with its complexity in dealing with big data, damage types, spatial and temporal scales i.e. depth of analysis (Menoni et al, 2016; Dingtao et al, 2015). Damage evaluation depends on an assumption like spatial and temporal boundary selection and financial contrast like depreciated values or alternative cost, classification of the thing at risk, quantification of the uncovered asset values and tactics for describing susceptibility (Merz et al, 2010). Cost of distinctive sorts of natural disasters consists of direct cost, indirect cost, intangible effect and value of mitigation (Meyer et al, 2012)
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