Utilizing NASA Earth Observations to Enhance Flood Impact Products and Mitigation in the Lower Mekong Water Basin

Wednesday, 17 December 2014: 1:55 PM
Colin Doyle1, Michael Gao1, Joseph Spruce2, John D Bolten3 and Samuel Weber1, (1)NASA DEVELOP National Program, Goddard Space Flight Center, Greenbelt, MD, United States, (2)Computer Sciences Corporation, Stennis Space Center, MS, United States, (3)NASA GSFC, Greenbelt, MD, United States
This presentation discusses results of a project to develop 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 sixty million people. The region has seen rapid population growth and socio-economic development, fueling unsustainable deforestation, agricultural expansion, and stream-flow regulation. The basin supports substantial rice farming and other agrarian activities, which heavily depend upon seasonal flooding. But, floods due to typhoons and other severe weather events can result in disasters that cost millions of dollars and cause hardships to millions of people. This study uses near real time and historical Aqua and Terra MODIS 250-m resolution Normalized Difference Vegetation Index (NDVI) products to map flood and drought impact within the LMB. In doing so, NDVI change products are derived by comparing from NDVI during the wet season to a baseline NDVI from the dry season. The method records flood events, which cause drastic 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 Disaster Risk Management Observation Strategy. The system is a web-based ‘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 floods and flood impacts in near real-time, as well provides a platform for further data aggregation for flood prediction modeling and post-event assessment.