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A MODIS-Based Automated Flood Monitoring System for Southeast AsiaFlood disasters in Southeast Asia result in significant loss of life and economic damage. Remote sensing information systems designed to spatially and temporally monitor floods can help governments and international agencies formulate effective disaster response strategies during a flood and ultimately alleviate impacts to population, infrastructure, and agriculture. Recent destructive flood events in the Lower Mekong River Basin occurred in 2000, 2011, 16 2013, and 2016 (http://ffw.mrcmekong.org/historical_rec.htm, April 24, 2017). The large spatial distribution of flooded areas and lack of proper gauge data in the region makes accurate monitoring and assessment of impacts of floods difficult. Here, we discuss the utility of applying satellite-based Earth observations for improving flood inundation monitoring over the flood-prone Lower Mekong River Basin. We present a methodology for determining near real-time surface water extent associated with current and historic flood events by training surface water classifiers from 8-day, 250-meter Moderate-resolution Imaging Spectroradiometer (MODIS) data spanning the length of the MODIS satellite record. The Normalized Difference Vegetation Index (NDVI) signature of permanent water bodies (MOD44W; Carroll et al., 2009) is used to train surface water classifiers which are applied to a time period of interest. From this, an operational nowcast flood detection component is produced using twice daily imagery acquired at 3-hour latency which performs image compositing routines to minimize cloud cover. Case studies and accuracy assessments against radar-based observations for historic flood events are presented. The customizable system has been transferred to regional organizations and near real-time derived surface water products are made available through a web interface platform. Results highlight the potential of near real-time observation and impact assessment systems to serve as effective decision support tools for governments, international agencies, and disaster responders.
Document ID
20180002842
Acquisition Source
Goddard Space Flight Center
Document Type
Accepted Manuscript (Version with final changes)
External Source(s)
doi:10.1016/j.jag.2017.05.006
Authors
A Ahamed (Universities Space Research Association Columbia, Maryland, United States)
J D Bolten (Goddard Space Flight Center Greenbelt, Maryland, United States)
Date Acquired
May 16, 2018
Publication Date
May 22, 2017
Publication Information
Publication: International Journal of Applied Earth Observation and Geoinformation
Publisher: Elsevier
Volume: 61
Issue Publication Date: September 1, 2017
ISSN: 0303-2434
DOI: doi: 10.1016/j.jag.2017.05.006
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
GSFC-E-DAA-TN42818
Funding Number(s)
CONTRACT_GRANT: NNG11HP16A
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
Mekong River
Image Classification

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