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Processing remote sensing data for solving environmental problems - Dan G. Blumberg Ben-Gurion University of the Negev Intelligent Analysis of Environmental Data (S4 ENVISA Workshop 2009)
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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;
Ben-Gurion University of the Negev
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
Ben-Gurion University of the Negev
Data transformation: admin to grid
Ben-Gurion University of the Negev
תמונת לילה של המזרח התיכון
Defense meteorological satellites
Ben-Gurion University of the Negev
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
Ben-Gurion University of the Negev
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
Ben-Gurion University of the Negev
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
Ben-Gurion University of the Negev
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
Ben-Gurion University of the Negev
Mortality risk due to multiple hazards
Ben-Gurion University of the Negev
Economic risk due to multiple hazards
Ben-Gurion University of the Negev
proposals
• EU call for environment• EU call for security
– How can available data be harnessed for environmental issues?