Flood Disaster Management

Flood mapping: The Susquehanna river (Binghamton, NY) flooded its banks in 2006 - the WASP system was able to map 44 linear miles in two hoursFlood mapping: The Susquehanna river (Binghamton, NY) flooded its banks in 2006 - the WASP system was able to map 44 linear miles in two hours

UB's Landscape-based Environmental System Analysis & Modeling (LESAM) Group and RIT's Laboratory for Imaging Algorithms and Systems (LIAS) have established track records in remote sensing of hydrology and flood events. Examples include UB's GeoWEPP program and flood mapping using RIT's WASP sensors.

In June 2006 over 10 inches of rain fell within a 36-hour period in the Binghamton, NY, region. The Susquehanna river rose rapidly and caused historic flooding. New York State Gas and Electric (NYSEG), a local energy utility, was quickly faced with a catastrophe. The flooding caused the pilot lights on furnaces to extinguish, thus allowing houses to fill with natural gas and resulting in explosions. NYSEG desperately needed to map the location and extent of flooding - they used a manual, helicopter-based approach, which took three days to complete. RIT's WASP system was used to remap the entire area as part of a 2007 demonstration and was able to provide geo-registered flood extent data within two hours after take-off (see top-right).

Lidar structural data: Lidar data collected over the Seneca Nation of Indians in upstate New York. These data can be used to assess topography, riverbed obstructions, and map water depth, when coupled with a flood extent mapLidar structural data: Lidar data collected over the Seneca Nation of Indians in upstate New York. These data can be used to assess topography, riverbed obstructions, and map water depth, when coupled with a flood extent map

The IPLER project intends to formalize some of these abilities that would enable real-time, geo-registered down-linked flood products directly to emergency responders. This will be accomplished by studying the phenomenology of near-infrared flood characterization and by using different remote sensing modalities, e.g., light detection and ranging (lidar). Lidar is useful for mapping topography, riverbed obstructions, etc. The image on the left shows lidar data in point-cloud (range) format. Algorithm development is required to go from high data volume point clouds to useful structural products, such as topography and vegetation structure. This research will form one of the core activities of the IPLER initiative, given the usefulness of structural data in rural (left) and urban (below) environments.

Lidar over RIT's campus: Point-cloud lidar data collected over an urban area. Algorithms are required to extract building structures, map evacuation routes, etc.Lidar over RIT's campus: Point-cloud lidar data collected over an urban area. Algorithms are required to extract building structures, map evacuation routes, etc.

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We are collaborating with an IPLER partner, Kucera International, to collect lidar data and develop operational algorithms. MS students will be sponsored as part of the research and education component of IPLER, which will allow these students to develop into informed researchers, technologists, or disaster responders upon graduation.