ISCRAM 2009 Conference <ul><li>Early flood detection and mapping for humanitarian response </li></ul><ul><li>JRC Global Fl...
Floods –  underestimated natural disasters <ul><li>Floods cause major human suffering </li></ul><ul><ul><li>78% of all pop...
Floods –  frequent, recurring natural disasters <ul><ul><li>Top 10 floods in last 10 years </li></ul></ul><ul><ul><ul><li>...
Global Disaster Alert and Coordination System <ul><li>GDACS </li></ul><ul><ul><li>Multi-hazard disaster alert system for h...
Monitoring floods <ul><li>Global flood early warning? </li></ul><ul><ul><li>Modelling and forecasting:  no global coverage...
Flood observation from space? <ul><li>Water from space </li></ul><ul><ul><li>Typical and unique spectral signature at most...
Passive microwave remote sensing <ul><li>Upwelling radiation </li></ul><ul><li>Atmospheric attenuation: low </li></ul><ul>...
Passive microwave information <ul><li>Brightness temperature </li></ul><ul><ul><li>Grey Body and Black Body </li></ul></ul...
Novel normalization methodology: Water signal T b  dry wet T b dry wet Dry pixel Wet pixel Influence of clouds is eliminat...
Calibration site <ul><li>Manual selection </li></ul><ul><ul><li>Must be dry </li></ul></ul><ul><ul><li>Must be close to ob...
Anomaly detection <ul><li>Magnitude: relative importance  of peaks in time series </li></ul>ISCRAM Conference 11 May 2009 ...
GFDS data and products <ul><li>Brightness temperature </li></ul><ul><ul><li>Gridded image </li></ul></ul><ul><li>Signal im...
Rapid flood mapping ISCRAM Conference 11 May 2009 Flooded area mapped by the Dartmouth Flood Observatory based on MODIS op...
Map and observation site ISCRAM Conference 11 May 2009 First media reports
Africa, early March 2009 ISCRAM Conference 11 May 2009 Okavango delta Barrier lakes Etosha Pan Caprivi floods Etosha flood...
Floods Caprivi, Namibia, 2009 Flood map based on AMSR-E passive microwave data at 36.5GHz, processed using the JRC Global ...
Daily flood detection ISCRAM Conference 11 May 2009 http://www.gdacs.org/floods
Conclusions <ul><li>Near real time flood monitoring and mapping </li></ul><ul><ul><li>Turning Remote Sensing data into Flo...
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2009 De Groeve Iscram Conference

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Global flood detection and mapping system, developed by Joint Research Centre of the European Commission and integrated in the Global Disaster Alert and Coordination System (GDACS).

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2009 De Groeve Iscram Conference

  1. 1. ISCRAM 2009 Conference <ul><li>Early flood detection and mapping for humanitarian response </li></ul><ul><li>JRC Global Flood Detection System </li></ul><ul><li>Joint Research Center </li></ul><ul><li>Institute for the Protection and the Security of the Citizen </li></ul><ul><li>GlobeSec – Global Security and Crisis Management </li></ul><ul><li>Critech </li></ul><ul><li>Tom De Groeve, Ph. D. </li></ul>
  2. 2. Floods – underestimated natural disasters <ul><li>Floods cause major human suffering </li></ul><ul><ul><li>78% of all population affected by disasters </li></ul></ul><ul><ul><li>Floods affect 0.5 billion people / year </li></ul></ul><ul><ul><ul><li>2 billion / year by 2050 </li></ul></ul></ul><ul><ul><li>46% of disasters are floods </li></ul></ul><ul><li>International aid for floods </li></ul><ul><ul><li>1/3 of all humanitarian aid </li></ul></ul><ul><ul><li>DG ECHO: €36 million 2002-2007 </li></ul></ul>Figures from EM-DAT, OCHA, ECHO ISCRAM Conference 11 May 2009
  3. 3. Floods – frequent, recurring natural disasters <ul><ul><li>Top 10 floods in last 10 years </li></ul></ul><ul><ul><ul><li>Floods kill few people but affect a lot </li></ul></ul></ul><ul><li>(EM-DAT CRED) </li></ul><ul><li>Latest flood disasters (ReliefWeb) </li></ul><ul><ul><li>Southern Africa, Mar-Apr 2009 </li></ul></ul><ul><ul><li>Bolivia, Feb 2009 </li></ul></ul><ul><ul><li>Guyana, Dec 2008 </li></ul></ul><ul><ul><li>Malaysia, Dec 2008 </li></ul></ul><ul><ul><li>Brazil, Nov 2008... </li></ul></ul><ul><li>Affecting the poor more </li></ul>ISCRAM Conference 11 May 2009 Country Date Killed Haiti 23 May 2004 2665 India 24 Jul 2005 1200 Bangladesh 21 Jul 2007 1110 India 3 Jul 2007 1103 India (Flash Flood) 11 Jun 2008 1063 Algeria 10 Nov 2001 921 India 20 Jun 2004 900 India 30 Aug 2008 900 India 18 Sep 2000 884 India 2 Aug 2000 867 Country Date Total Affected China 23 Jun 2003 150 million China 15 Jun 2007 105 million China (Flash flood) 8 Jun 2002 80 million India 21 Jun 2002 42 million Bangladesh 20 Jun 2004 36 million China 15 Jul 2004 33 million India 20 Jun 2004 33 million India 18 Sep 2000 24 million India 2 Aug 2000 22 million India 24 Jul 2005 20 million
  4. 4. Global Disaster Alert and Coordination System <ul><li>GDACS </li></ul><ul><ul><li>Multi-hazard disaster alert system for humanitarian response </li></ul></ul><ul><ul><li>Earthquakes </li></ul></ul><ul><ul><li>Tropical Cyclones </li></ul></ul><ul><ul><li>Volcanoes </li></ul></ul><ul><ul><li>Floods </li></ul></ul><ul><li>Floods </li></ul><ul><ul><li>Replace manually compiled media-based list of floods by objective satellite based monitoring </li></ul></ul>ISCRAM Conference 11 May 2009
  5. 5. Monitoring floods <ul><li>Global flood early warning? </li></ul><ul><ul><li>Modelling and forecasting: no global coverage </li></ul></ul><ul><ul><li>National / regional systems: not interoperable </li></ul></ul><ul><li>Global discharge monitoring? </li></ul><ul><ul><li>Global Runoff Data Centre: no global nor timely coverage </li></ul></ul><ul><ul><li>Costly : 1 million km of river globally (89M$/year for US) </li></ul></ul><ul><ul><ul><li>Local systems: not interoperable </li></ul></ul></ul><ul><li>Media monitoring? </li></ul><ul><ul><li>Dartmouth Flood Observatory, EM-DAT database </li></ul></ul><ul><ul><li>Automatic media monitoring </li></ul></ul><ul><ul><li>Reporting is not systematic : language dependent; qualitative, biased </li></ul></ul>ISCRAM Conference 11 May 2009
  6. 6. Flood observation from space? <ul><li>Water from space </li></ul><ul><ul><li>Typical and unique spectral signature at most wavelengths </li></ul></ul><ul><li>Challenges </li></ul><ul><ul><li>Coverage: global? </li></ul></ul><ul><ul><li>Revisit time: daily? </li></ul></ul><ul><ul><li>Cloud coverage: influence? </li></ul></ul><ul><ul><li>Data distribution </li></ul></ul>ISCRAM Conference 11 May 2009
  7. 7. Passive microwave remote sensing <ul><li>Upwelling radiation </li></ul><ul><li>Atmospheric attenuation: low </li></ul><ul><li>Resolution: 10km </li></ul><ul><li>Swath width: ~3000km </li></ul><ul><li>Revisit time: ~daily </li></ul>ISCRAM Conference 11 May 2009 36.5GHz
  8. 8. Passive microwave information <ul><li>Brightness temperature </li></ul><ul><ul><li>Grey Body and Black Body </li></ul></ul><ul><li>Influences </li></ul><ul><ul><li>Physical temperature T </li></ul></ul><ul><ul><li>Roughness of surface </li></ul></ul><ul><ul><li>Material: dielectric constant </li></ul></ul><ul><ul><ul><li>In particular: H 2 O content </li></ul></ul></ul><ul><ul><li>Atmospheric attenuation and emission </li></ul></ul><ul><li>Emissivity at 36.5GHz * </li></ul>ISCRAM Conference 11 May 2009 * Rees, 1990. Physical Principles of Remote Sensing. ** Sharkov, 2003. Passive Microwave Remote Sensing of the Earth T T T b ε ** Material ε Water 0.3 - 0.5 Minerals 0.75 – 0.95 Sea Ice 0.75 – 0.95 T
  9. 9. Novel normalization methodology: Water signal T b dry wet T b dry wet Dry pixel Wet pixel Influence of clouds is eliminated by comparing dry and wet signal Water has a lower brightness temperature than land 1 2 3 1 2 3 1 2 3 ISCRAM Conference 11 May 2009 flood signal
  10. 10. Calibration site <ul><li>Manual selection </li></ul><ul><ul><li>Must be dry </li></ul></ul><ul><ul><li>Must be close to observation site for similar atmospheric effects </li></ul></ul><ul><li>Automatic selection </li></ul><ul><ul><li>Choose ‘hottest’ pixel nearby </li></ul></ul>ISCRAM Conference 11 May 2009 Optimization of calibration window size and percentile. M w > 0 C w = 0
  11. 11. Anomaly detection <ul><li>Magnitude: relative importance of peaks in time series </li></ul>ISCRAM Conference 11 May 2009 avg ( s ) sd ( s ) = σ 3 σ 2 σ σ m = 3 m = 2 m = 1
  12. 12. GFDS data and products <ul><li>Brightness temperature </li></ul><ul><ul><li>Gridded image </li></ul></ul><ul><li>Signal image </li></ul><ul><li>Magnitude image </li></ul><ul><ul><li>Using </li></ul></ul><ul><ul><ul><li>average of signal image </li></ul></ul></ul><ul><ul><ul><li>standard deviation of signal image </li></ul></ul></ul><ul><li>Flood maps </li></ul><ul><ul><li>Threshold magnitude (2 or 4) </li></ul></ul><ul><ul><ul><li>Google animation </li></ul></ul></ul><ul><ul><ul><li>Daily maps, animations </li></ul></ul></ul>ISCRAM Conference 11 May 2009 <ul><li>Observation sites </li></ul><ul><ul><li>Points (sites) </li></ul></ul><ul><ul><li>Lines (along river) </li></ul></ul><ul><ul><li>Areas (regions, buffers) </li></ul></ul>Time after satellite passes Processing step ~3h Download of data +2 minutes Swath data inserted in grid +2 minutes Update of all sites and maps
  13. 13. Rapid flood mapping ISCRAM Conference 11 May 2009 Flooded area mapped by the Dartmouth Flood Observatory based on MODIS optical imagery Flooded area mapped by JRC based on AMSR-E microwave data
  14. 14. Map and observation site ISCRAM Conference 11 May 2009 First media reports
  15. 15. Africa, early March 2009 ISCRAM Conference 11 May 2009 Okavango delta Barrier lakes Etosha Pan Caprivi floods Etosha floods Upper Zambezi
  16. 16. Floods Caprivi, Namibia, 2009 Flood map based on AMSR-E passive microwave data at 36.5GHz, processed using the JRC Global Flood Detection technique. GLIDE: FL-2009-000062-NAM Datum/Projection: WGS1984/Geographic Map production: JRC Background map: Global Discovery Contact: tom.de-groeve@jrc.it 100% water 0% water
  17. 17. Daily flood detection ISCRAM Conference 11 May 2009 http://www.gdacs.org/floods
  18. 18. Conclusions <ul><li>Near real time flood monitoring and mapping </li></ul><ul><ul><li>Turning Remote Sensing data into Flood Events, useful for early alert </li></ul></ul><ul><ul><li>Integration with multi-hazard system + international response community through GDACS </li></ul></ul><ul><li>Further work </li></ul><ul><ul><li>Coupling with other systems </li></ul></ul><ul><ul><ul><li>rainfall, weather based (e.g. TRMM flood potential) </li></ul></ul></ul><ul><ul><li>Examining potential for other applications </li></ul></ul><ul><ul><ul><li>Measuring impact on agriculture, population </li></ul></ul></ul><ul><ul><ul><li>Tasking satellite acquisitions for high resolution mapping </li></ul></ul></ul><ul><ul><li>Improving technique </li></ul></ul><ul><ul><ul><li>Additional satellite sensors </li></ul></ul></ul><ul><ul><ul><li>Reducing noise </li></ul></ul></ul>ISCRAM Conference 11 May 2009

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