processing remote sensing data for solving environmental problems - dan g. blumberg ben-gurion...

Ben-Gurion University of the Negev

Processing remote sensing data for solving environmental problems

Dan G. BlumbergBen-Gurion University of the

Negev

Ben-Gurion University of the Negev

Bridging the gap

The good• Huge remote sensing

DBs– Mullti-temporal– Multi frequency– Signals are there– Archival source

The bad• Huge remote sensing

DBs– Not same time– Not same sensor– Not same frequencies

• Lack of ground truth

The Ugly: without ground truth it is hard to use RS data

Ben-Gurion University of the Negev

Case studiesClimate change in central Asia• Very little wind data

– 5 reliable stations– Model (ECWM) data– The two don’t match

SVM and object oriented classification• Works well but difficult to

validate

Ben-Gurion University of the Negev

(SVM) classification

P. 4

The field validation

Ben-Gurion University of the Negev

Geographic distribution of natural hazards

Ben-Gurion University of the Negev

Who Was Affected? The Demography of Tsunami-Affected

Population

Dan Blumberg, Deborah Balk and Yuri Gorokhovich

Center for International Earth Science Information NetworkColumbia University

Ben-Gurion University of the Negev

Ben-Gurion University of the Negev

The 2004 Tsunami

אירע באוקיינוס ההודי ב- 26

בדצמבר 2004

בעקבות רעש אדמה בעוצמה M של

S

9.15Created by NOAAAnimation provided by Vasily V. Titov, Associate Director, Tsunami Inundation Mapping Efforts (TIME), NOAA/PMEL - UW/JISAO, USA

Ben-Gurion University of the Negev

• 2004דצמבר 26• ק"מ באזור ההפחתה שבו הלוח ההודי1200 מ לאורך 15תזוזה של

צונח מתחת ללוח בורמה

רעש האדמה של סומטרה אנדמן

Sumatra Andaman

Ben-Gurion University of the Negev

Indonesia

India

Bangladesh

Thailand

Sri Lanka

Sumalia

Affected countries

Ben-Gurion University of the Negev

Population density• Asia—particularly

south and southeast Asia—are the most densely populated place on earth

• Coastal zones have disproportionately high population densities– 450 persons/km2,

Asia– vs. 175, globally

• Coastal areas are more urban

Source: CIESIN, GRUMP v1 (alpha)

Ben-Gurion University of the Negev

Demographic Composition

• Age distribution: Asia is young. – Proportion of population < 15 yrs ranges

between 25-35% as compared with 20% or lower in North America and Europe

• Household size and composition.– Larger, extended, with traditions of fosterage

• Gender– Displacement affects women and men

differently

Ben-Gurion University of the Negev

Population estimation

• Who was exposed?• Who was at risk? • Who was affected?

– Lost lives– Lost livelihoods– Displacement

Ben-Gurion University of the Negev

Who was exposed to the tsunami?

• Wave heights were reported to be 10 m at their maximum– Persons below roughly 10 meters, in elevation

• At close distance to the coastline– In most places, the waves were reported to go no more than 1-2

km inland from the coast• Except in parts of Sumatra were there were reported as far inland

as 4-5 kilometers

• Additional damage from the earth quake– And perhaps interactions with flooding

• How to quantify the number of persons exposed?

Ben-Gurion University of the Negev

Why is population estimation tricky?

• Data formats are not easily comparable– Population data come from

censuses:• Irregular-shaped units • “Who slept here” or usual

residence;

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Shorelines of data sources do not match: Black shoreline: ESRI Red shoreline: Administrative Units, BPS The finer the scale the more the differences matter

Coastlines must match, but often don’t

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Data transformation: admin to grid

Ben-Gurion University of the Negev

תמונת לילה של המזרח התיכון

Defense meteorological satellites

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shoreline

population data

Vector and raster data combination

Population data are now Gridded (i.e., rasterized)

Shoreline is vector (convert to raster)

2 km buffer

Ben-Gurion University of the Negev

• Endeavour - על ה2000טסה ב • X-band ארה"ב) ו band - (מערכות מכ"ם 2• אנטנות על מנת ליצור 2מודד הפרשי פאזה בין

אינטרפרוגרמה

Shuttle Radar Topography Mission

Ben-Gurion University of the Negev

95.30 95.40

5.20

5.10

95.50

מיפוי טופוגרפי מן החלל

Ben-Gurion University of the Negev

Ben-Gurion University of the Negev

הדמאת RADARSAT לפני ואחרי הצונאמי

1998/04/091998/12/31

Ben-Gurion University of the Negev

הדמאת RADARSAT לפני ואחרי הצונאמי: סרי לנקה

2002/12/272005/01/02

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לפני האירוע (1998)

Radarsat inundation

מס' ימים לאחר )2004האירוע (

Ben-Gurion University of the NegevDepartment of Geography and Environmental Development

Ben-Gurion University of the Negev

• Inundated areas• Object oriented detection

Radarsat inundation

Ben-Gurion University of the Negev

Need for high res data

Ikonos

EROS-1A

Quickbird

Urban areas

Ben-Gurion University of the Negev

לפני האירוע לאחר האירוע

של אזורים אורבניים Ikonos הדמאות

Ben-Gurion University of the Negev

Ben-Gurion University of the Negev

Department of Geography and Environmental Development

מטר 1.9

EROS-1A

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Detected changed areas from the Landsat images

Landsat scene (30 meters resolution) of northern tip of Sumatra

Ben-Gurion University of the Negev

Estimation population in changed areas• Areas of detectable

change (light green) • Area of analysis =

Northern Aceh Province – 10 km coastal buffer

(yellow) – 4 km coastal buffer

(not shown)– 4 km coast buffer on

western and northern coasts only (red outline)

Ben-Gurion University of the Negev

within 1km of coast, under 10 m in

elevation

within 4km of coast, under 10 m in

elevation(bolded area) (bolded area)

1 Aceh 4,228,487 118,613 519,040

2 Sumatera Utara 12,444,168 134,404 584,315

3 Sumatera Barat 4,384,543 107,006 389,338

4 Riau 6,161,865 n/a n/a

5 Jambi 2,646,455 n/a n/a

6 Bengkulu 1,818,350 20,946 127,743

7 Sumatera Selatan 7,775,072 n/a n/a

8 Lampung 7,147,519 4,333 6,094

Sumatra Total 46,606,459 385,302 1,626,529

Sumatra Population by Province, 2005 est.

# ProvinceTotal

Population

Ben-Gurion University of the Negev

Socio-economic

conditions of the affected

region

Poverty estimateIn all exposed

regions

In highly exposed regions

Low poverty (IMR under 30) 9% 29%Moderate poverty (IMR between 30 and 60) 22% 71%High poverty (IMR above 60) 69% 0%

Source: CIESIN, DHS, MICS.

The relative well-off areas hit hardest

Ben-Gurion University of the Negev

Where are people now? • Much harder to assess

– Displaced persons estimate • UNFPA estimates that 500,000 girls and women have

been displaced in Sri Lanka alone Short-term needs are different from medium and longer-term ones

Recovery efforts Where are the

displaced persons? How to reach them? What are their

needs? Reconstruction

Rebuild with sustainability in mind

Learn from assessments of our vulnerabilities

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Lessons learned• For analysis:

– Baseline information is NOT ready for use• Data sharing issues arise and pose legal issues

– Data integration is skill and time intensive

• For policy:– Short-term recovery, and medium and long-run development

pose much different but closely related questions– We have a better idea of the right parameters to construct early

warning– Consider the risk of multiple and different hazards

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Mortality risk due to multiple hazards

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Economic risk due to multiple hazards

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proposals

• EU call for environment• EU call for security

– How can available data be harnessed for environmental issues?