Project Team: Southeast Asia Disasters Team
Team Location: NASA Goddard Space Flight Center, Greenbelt, Maryland
Dr. John Bolten (NASA Goddard Space Flight Center)
Fritz Policelli (NASA Goddard Space Flight Center)
Joseph Spruce (NASA Stennis Space Center)
Matt Handy (NASA Goddard Space Flight Center)
Samuel Weber (DEVELOP)
Alexa Nieves (DEVELOP)
Jason Abkowitz (DEVELOP)
Michael Gao (DEVELOP)
This study developed a near real-time flood monitoring capability for the Lower Mekong Water Basin (LMB), the largest river basin in Southeast Asia and home to more than 60 million people. The region has seen rapid population growth and socio-economic development, agricultural expansion, and stream-flow regulation. The basin supports substantial rice farming and other agrarian activities, which typically heavily depend upon seasonal flooding. Floods due to typhoons and other severe weather events, however, can result in disasters that cost millions of dollars and cause hardships to millions of people.
This study used historical and NASA Land Atmosphere Near real-time Capability for EOS (LANCE) near real-time Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250-meter resolution Normalized Difference Vegetation Index (NDVI) products to map flood impact zones within the LMB in near real-time. In doing so, NDVI change products were derived by comparing NDVI during the wet season to a baseline NDVI from the dry season. The method identified flooded areas, which were discernable by decreases in NDVI compared to non-flooded conditions. NDVI change product computation was automated for updating a near real-time system, as part of the Committee on Earth Observing Satellites (CEOS) Disaster Risk Management Observation Strategy. The system is a Web-based Û÷Flood Dashboard’ that will showcase MODIS flood monitoring products, along with other flood mapping and weather data products. This flood dashboard enables end-users to view and assess a variety of geospatial data to monitor flood impact areas in near real-time, and provides a platform for further data aggregation for flood prediction modeling and post-event assessment.