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)

1. Processing remote sensing data for solving environmental problems Dan G. Blumberg Ben-Gurion University of the Negev

2. 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

3. Case studies Climate 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

4. (SVM) classification The field validation P. 4

5. Geographic distribution of natural hazards

6. Who Was Affected? The Demography of Tsunami-Affected Population Dan Blumberg, Deborah Balk and Yuri Gorokhovich Center for International Earth Science Information Network Columbia University Ben-Gurion University of the Negev

7. Overview Population exposure composition measurement Crude death rates Socioeconomic profiles of the exposed areas Where are people now? A hazardous world: a multi-hazard approach

8. Population exposure What do we know about the spatial distribution of human population? People do not live uniformly with respect to National borders Coastlines Other geographic features, including hazard-prone regions Some hazard prone regions may “attract” population, e.g., volcanic soils Coastal zones support fishing, and access to markets (historically) People move Daily movement—commuting to work, markets, schools Seasonal movements—tourist, labor-migration Longer term movements—life-cycle (childbearing, retirement), permanent migration, forced migration

9. The 2004 Tsunami אירע באוקיינוס ההודי ב- 26 בדצמבר 2004 בעקבות רעש אדמה בעוצמה של MS 9.15 Created by NOAA Animation provided by Vasily V. Titov, Associate Director, Tsunami Inundation Mapping Efforts (TIME), NOAA/PMEL - UW/JISAO, USA

10. רעש האדמה של סומטרה אנדמן Sumatra Andaman 26 דצמבר 2004 תזוזה של 15 מ לאורך 1200 ק"מ באזור ההפחתה שבו הלוח ההודי צונח מתחת ללוח בורמה

11. Affected countries Indonesia India Bangladesh Thailand Sri Lanka Sumalia

12. 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)

13. 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

14. Population estimation Who was exposed? Who was at risk? Who was affected? Lost lives Lost livelihoods Displacement

15. 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?

16. 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;

18. Red shoreline: Administrative Units, BPS

20. Defense meteorological satellites תמונת לילה של המזרח התיכון

21. population data shoreline 2 km buffer Vector and raster data combination Population data are now Gridded (i.e., rasterized) Shoreline is vector (convert to raster)

22. Shuttle Radar Topography Mission טסה ב 2000 על ה- Endeavour 2 מערכות מכ"ם) - band ארה"ב) ו X-band מודד הפרשי פאזה בין 2 אנטנות על מנת ליצור אינטרפרוגרמה

23. מיפוי טופוגרפי מן החלל 95.30 95.40 95.50 5.20 5.10

25. 1998/04/09 1998/12/31 לפני ואחרי הצונאמיRADARSATהדמאת

26. 2005/01/02 לפני ואחרי הצונאמי: סרי לנקהRADARSATהדמאת 2002/12/27

27. מס' ימים לאחר האירוע (2004) (1998) לפני האירוע Radarsat inundation

28. Radarsat inundation Inundated areas Object oriented detection

29. Urban areas Need for high res data Ikonos EROS-1A Quickbird

30. הדמאות Ikonos של אזורים אורבניים לפני האירוע לאחר האירוע

31. EROS-1A 1.9 מטר

32. Detected changed areas from the Landsat images Landsat scene (30 meters resolution) of northern tip of Sumatra

33. 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)

35. Socio-economic conditions of the affected region The relative well-off areas hit hardest

37. Where are the displaced persons?

38. How to reach them?

39. What are their needs?

40. Reconstruction

41. Rebuild with sustainability in mind

43. Answers to longer term questions Were geophysical and environmental properties protective in some places? Have recent population dynamics and related behavioral change altered some of the underlying geophysical benefits E.g., Protective ecosystems Scenario building. What if this—or other hazards—happened elsewhere? These questions presuppose a basic understanding of the population distributionat the time of the event, and even in the recent past

44. 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

45. Mortality risk due to multiple hazards

46. Economic risk due to multiple hazards

47. proposals EU call for environment EU call for security How can available data be harnessed for environmental issues?