Prompt Proxy Mapping of Flood Damaged Rice Fields Using MODIS-Derived Indices

Remote Sensing

Youngjoo Kwak²  Bhuyan Arifuzzanman³  Yoichi Iwami²  Guy J-P. Schumann¹  Magaly Koch¹ 

[1] International Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, 1-6 Minamihara, Tsukuba 305-8516, JapanInternational Centre for Water Hazard and Risk Management (ICHARM) under the auspices of UNESCO, 1-6 Minamihara, Tsukuba 305-8516, Japan;Flood Forecasting & Warning Center, Bangladesh Water Development Board, Dhaka 1000, Bangladesh;

关键词: flood mapping;    MLSWI;    EVI;    rice crop;    flood risk map;    MODIS;   

DOI  :  10.3390/rs71215805

来源: mdpi

【 摘 要 】

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).

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.