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LANCE UWG Meeting, November 16, 2010

UWG Member: Robert Brakenridge

Affiliation: University of Colorado

Director, Dartmouth Flood Observatory

http://floodobservatory.colorado.edu/

Email: Robert.Brakenridge@Colorado.edu

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LANCE Data Products

• Currently publishing 9 LANCE-derived “NASA Experimental Flood Maps” for southern Africa

• These use MODIS bands 1,2,7 reflectances, in Geotiff format

• Data processed first at GSFC, then maps are transmitted to Flood Observatory web site via ftp push

• Latency at GSFC to be described by F. Policelli• For map production, classified 250 m water pixels are

integrated into GIS layers for each 10 deg x 10 deg map quadrangle

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Automated mapping of surface water changes using the LANCE MODIS rapid response processor.

MODIS data (four scenes, Terra and Aqua, over two days) are used to classify water pixels using an algorithm developed at the Dartmouth Flood Observatory. Data are processed at NASA's Goddard Space Flight Center, and transmitted to the Flood Observatory for display here.

Authorship: Sun, J., Slayback, D., Policelli, F., Brakenridge, G., and Kettner, A., 2010, "NASA Experimental Flood Maps,Current Floodwater", http://floodobservatory.colorado.edu/LanceModis.html.

Project supported by NASA grant NNX09AV26G to Dartmouth College (G.R. Brakenridge). Overall principle investigator is F. Policelli, NASA Goddard Space Flight Center

DFO Map (updated during major flooding)

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Sample ofGSFC automated flood water mapping.

No current flooding: no water pixels above the reference water mask.

Flooding occurs annually in this region (large variability in surface water extent). Past flooding is illustrated in the DFO map.

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An earlier format of DFO’s MODIS-based flood map.

Prior events can be illustrated by year, as here, or merged into the “maximum imaged inundation” (next slide).

Here, SRTM water mask is used as reference water.

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Current format of DFO’s online MODIS-based flood mapping.

Maximum imaged inundation is larger, each year,as new floods are mapped.

Current flooding is now shown in dark blue; will then be included in 2010 map layer.

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Applications

• Who uses these data?• DFO web site access statistics help address this question. In the 7-

day period ending 24-Jul-2009, for example, these statistics applied:• Average successful requests per day: 9,066

Average successful requests for pages per day: 6,367Distinct files requested: 14,567Distinct hosts served: 5,884Data transferred: 76.53 gigabytes Average data transferred per day: 5.30 gigabytes .

• Users include research, educational, disaster alert and relief, and national and regional water ministry personnel.

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User ExamplesDisaster alert and relief users (only):• ReliefWeb (global hub for humanitarian information, UN Office for the Coordination of

Humanitarian Affairs (OCHA)• OCHA (UN Office for the Coordination of Humanitarian Affairs), Regional Office for Asia

and the Pacific• Sentinel Asia (Disaster Management Support System in the Asia-Pacific Region,

Japanese Space Agency (JAXA)• The Pacific Disaster Center• The GDACS (Global Disaster Alert and Coordination System) project of the European

Commission• Thomson Reuters Foundation AlertNet (alerting humanitarians to emergencies)• HEWS (Humanitarian Early Warning Service of the World Food Program, UN) • GRIP (Global Risk Identification Programme, hosted by the UN Development

Programme, UNDP) • The U.S. NOAA Satellite and Information Service, National Climatic Data Center • UN-Habitat Disaster Assessment Portal • U.S. Geological Survey Maps on Demand (products related to hurricanes Katrina and

Rita) • Map of U.S. Government Humanitarian Assistance to Burma, USAID

(http://myanmar.humanitarianinfo.org/map center/Maps)/

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Latency

• 3-hour latency is less critical than is rapid (daily) automatic generation of the end product (geotiffs of the subsets showing water pixels)

• However, users may come to expect to obtain new data within a few hours of expected refresh data

• During flood events, automatic refresh of web site-based maps and GIS data could provide significant humanitarian and economic benefits.

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Comments on the Present LANCE System

• We are now initiating use of LANCE-derived data after while still using RapidFire subsets for non-automated mapping

• We hope for many more map sheets to be included within LANCE

• There are many steps from LANCE reflectances to useful flood maps. Higher level processing must also be supported.

• Any interruptions within LANCE will negatively affect our output and end users.

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Future Changes• Need to expand capacity to near-global coverage.

• Either within LANCE or outside, daily refresh of surface water pixels needs further processing in order to generate many other useful and needed products, e.g.,

I. Daily global flood detection display. Flood events in many regions are not known prior to news reports. MODIS commonly sees these events first and tracks them through waxing and waning stages over many weeks (for example, 2010, Pakistan)

II. MODIS 250 m “mean water extent” (wet season and dry season) water masks are critical to determine flood magnitude..

III. Annual and Period-of-Record “maximum (flood) and minimum (hydrologic drought) water extent also need to be mapped and will these data products will be facilitated by automated, daily, updating.

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Example of DFO mapping during Pakistan’s Great Flood of 2010.

Waning stages; dark blue is flooded land; red was recently flooded, lighter reds show flooding in previous years

Lance data will allow routine, global, recording of these major events in near real time

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