2009 de groeve iscram conference

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ISCRAM 2009 Conference

Early flood detection and mapping for humanitarian response

JRC Global Flood Detection System

Joint Research CenterInstitute for the Protection and the Security of the CitizenGlobeSec – Global Security and Crisis ManagementCritech

Tom De Groeve, Ph. D.

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Floods – underestimated natural disasters

• Floods cause major human suffering– 78% of all population affected by

disasters– Floods affect 0.5 billion people / year

2 billion / year by 2050

– 46% of disasters are floods

• International aid for floods– 1/3 of all humanitarian aid

– DG ECHO: €36 million 2002-2007

Figures from EM-DAT, OCHA, ECHO

ISCRAM Conference 11 May 2009

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Floods – frequent, recurring natural disasters

Country Date KilledHaiti 23 May 2004 2665India 24 Jul 2005 1200Bangladesh 21 Jul 2007 1110India 3 Jul 2007 1103India (Flash Flood) 11 Jun 2008 1063Algeria 10 Nov 2001 921India 20 Jun 2004 900India 30 Aug 2008 900India 18 Sep 2000 884India 2 Aug 2000 867

Country Date Total AffectedChina 23 Jun 2003 150 millionChina 15 Jun 2007 105 millionChina (Flash flood) 8 Jun 2002 80 millionIndia 21 Jun 2002 42 millionBangladesh 20 Jun 2004 36 millionChina 15 Jul 2004 33 millionIndia 20 Jun 2004 33 millionIndia 18 Sep 2000 24 millionIndia 2 Aug 2000 22 millionIndia 24 Jul 2005 20 million

• Top 10 floods in last 10 yearsFloods kill few people but affect a lot

(EM-DAT CRED)

• Latest flood disasters (ReliefWeb)– Southern Africa, Mar-Apr 2009– Bolivia, Feb 2009– Guyana, Dec 2008– Malaysia, Dec 2008– Brazil, Nov 2008...

• Affecting the poor more


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Global Disaster Alert and Coordination System

• GDACS– Multi-hazard disaster alert

system for humanitarian response

– Earthquakes– Tropical Cyclones– Volcanoes– Floods

• Floods– Replace manually

compiled media-based list of floods by objective satellite based monitoring


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Monitoring floods

• Global flood early warning?– Modelling and forecasting: no global coverage– National / regional systems: not interoperable

• Global discharge monitoring?– Global Runoff Data Centre: no global nor timely coverage– Costly: 1 million km of river globally (89M$/year for US)

Local systems: not interoperable

• Media monitoring?– Dartmouth Flood Observatory, EM-DAT database– Automatic media monitoring– Reporting is not systematic: language dependent; qualitative, biased


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Flood observation from space?

• Water from space– Typical and unique spectral

signature at most wavelengths

• Challenges– Coverage: global?– Revisit time: daily?– Cloud coverage: influence?– Data distribution


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Passive microwave remote sensing

• Upwelling radiation• Atmospheric attenuation: low

• Resolution: 10km• Swath width: ~3000km• Revisit time: ~daily


36.5GHz

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Passive microwave information

• Brightness temperature– Grey Body and Black Body

• Influences– Physical temperature T– Roughness of surface– Material: dielectric constant

In particular: H2O content– Atmospheric attenuation and

emission

• Emissivity at 36.5GHz *


TTb .

* Rees, 1990. Physical Principles of Remote Sensing.** Sharkov, 2003. Passive Microwave Remote Sensing of the Earth

Material ε

Water 0.3 - 0.5

Minerals 0.75 – 0.95

Sea Ice 0.75 – 0.95

TT T

TTb

ε

**

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Novel normalization methodology: Water signal

Tb

dry

wet

Tb

dry

wet

Dry pixelWet 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 31 2 3


flood signal

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Calibration site

• Manual selection– Must be dry– Must be close to observation site

for similar atmospheric effects

• Automatic selection– Choose ‘hottest’ pixel nearby


Mw > 0

Cw = 0

Optimization of calibration window size and percentile.

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Anomaly detection

• Magnitude: relative importance of peaks in time series


)(

)(

ssd

savgsm

avg(s)

sd(s) = σ3σ

2σ σ

m = 3

m = 2m = 1

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GFDS data and products

• Brightness temperature– Gridded image

• Signal image

• Magnitude image– Using

average of signal image standard deviation of signal

image

• Flood maps– Threshold magnitude (2 or 4)

Google animation Daily maps, animations

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


• Observation sites– Points (sites)– Lines (along river)– Areas (regions, buffers)

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Rapid flood mapping

Flooded area mapped by the Dartmouth Flood Observatory based on MODIS optical imagery

Flooded area mapped by JRC based on AMSR-E microwave data


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Map and observation siteISCRAM Conference 11 May 2009

First media reports

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Africa, early March 2009ISCRAM Conference 11 May 2009

Okavango deltaOkavango delta

Barrier lakesBarrier lakes

Etosha PanEtosha Pan

Caprivi floodsCaprivi floods

Etosha floodsEtosha floods

Upper ZambeziUpper Zambezi

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Floods Caprivi, Namibia, 2009

100% water 0% water

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/GeographicMap production: JRCBackground map: Global Discovery

Contact: [email protected]

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Daily flood detectionISCRAM Conference 11 May 2009

http://www.gdacs.org/floodshttp://www.gdacs.org/floods

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Conclusions

• Near real time flood monitoring and mapping– Turning Remote Sensing data into Flood Events, useful for early alert– Integration with multi-hazard system + international response community

through GDACS

• Further work– Coupling with other systems

rainfall, weather based (e.g. TRMM flood potential)

– Examining potential for other applications Measuring impact on agriculture, population Tasking satellite acquisitions for high resolution mapping

– Improving technique Additional satellite sensors Reducing noise


2009 de groeve iscram conference

Technology

brightness temperature

flash flood

atmospheric attenuation

signal image

humanitarian response

iscram conference 11

revisit time

global