Processing remote sensing data for solving environmental problems<br />Dan G. Blumberg<br />Ben-Gurion University of the N...
Bridging the gap<br />The good<br />Huge remote sensing DBs<br />Mullti-temporal<br />Multi frequency<br />Signals are the...
Case studies<br />Climate change in central Asia<br />Very little wind data <br />5 reliable stations<br />Model (ECWM) da...
(SVM)  classification<br />The field validation<br />P. 4<br />
Geographic distribution of natural hazards<br />
Who Was Affected?<br />The Demography of Tsunami-Affected Population<br />Dan Blumberg, Deborah Balk and Yuri Gorokhovich<...
Overview<br />Population <br />exposure<br />composition<br />measurement<br />Crude death rates<br />Socioeconomic profil...
Population exposure<br />What do we know about the spatial distribution of human population?<br />People do not live unifo...
The 2004 Tsunami<br />אירע באוקיינוס ההודי ב- 26 בדצמבר 2004<br />בעקבות רעש אדמה בעוצמה של MS 9.15<br />Created by NOAA<b...
רעש האדמה של סומטרה אנדמן<br />Sumatra Andaman<br />26 דצמבר 2004<br />תזוזה של 15 מ לאורך 1200 ק&quot;מ באזור ההפחתה שבו ...
Affected countries<br />Indonesia<br />India<br />Bangladesh<br />Thailand<br />Sri Lanka<br />Sumalia<br />
Population density<br />Asia—particularly south and southeast Asia—are the most densely populated place on earth<br />Coas...
Demographic Composition<br />Age distribution: Asia is young.  <br />Proportion of population &lt; 15 yrs ranges between 2...
Population estimation<br />Who was exposed?<br />Who was at risk? <br />Who was affected? <br />Lost lives<br />Lost livel...
Who was exposed to the tsunami?<br />Wave heights were reported to be 10 m at their maximum<br />Persons below roughly 10 ...
Why is population estimation tricky? <br />Data formats are not easily comparable<br />Population data come from censuses:...
Coastlinesmust match, but often don’t<br />Shorelines of data sources do not match:<br /><ul><li> Black shoreline: ESRI
 Red shoreline: Administrative Units, BPS
The finer the scale the more the differences matter</li></li></ul><li>Data transformation: admin to grid<br />
Defense meteorological satellites<br />תמונת לילה של המזרח התיכון<br />
population data<br />shoreline<br />2 km buffer<br />Vector and raster data combination<br />Population data are now Gridd...
Shuttle Radar Topography Mission<br />טסה ב 2000 על ה- Endeavour<br />2 מערכות מכ&quot;ם) - band ארה&quot;ב) ו X-band<br /...
מיפוי טופוגרפי מן החלל<br />95.30<br />95.40<br />95.50<br />5.20<br />5.10<br />
1998/04/09<br />1998/12/31<br />לפני ואחרי הצונאמיRADARSATהדמאת <br />
2005/01/02<br />לפני ואחרי הצונאמי: סרי לנקהRADARSATהדמאת <br />2002/12/27<br />
מס&apos; ימים לאחר האירוע (2004)<br />(1998) לפני האירוע<br />Radarsat inundation<br />
Radarsat inundation<br />Inundated areas<br />Object oriented detection<br />
Urban areas<br />Need for high res data<br />Ikonos<br />EROS-1A<br />Quickbird<br />
הדמאות Ikonos של אזורים אורבניים<br />לפני האירוע          לאחר האירוע  <br />
EROS-1A<br />1.9 מטר<br />
Detected changed areas from the Landsat images<br />Landsat scene (30 meters resolution) of northern tip of Sumatra<br />
Estimation population in changed areas<br />Areas of detectable change (light green) <br />Area of analysis  = Northern Ac...
Socio-economic conditions of the affected region<br />The relative well-off areas hit hardest<br />
Where are people now? <br />Much harder to assess<br />Displaced persons estimate <br />UNFPA estimates that 500,000 girls...
Where are the displaced persons?
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Processing remote sensing data for solving environmental problems - Dan G. Blumberg Ben-Gurion University of the Negev

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

  1. 1. Processing remote sensing data for solving environmental problems<br />Dan G. Blumberg<br />Ben-Gurion University of the Negev<br />
  2. 2. Bridging the gap<br />The good<br />Huge remote sensing DBs<br />Mullti-temporal<br />Multi frequency<br />Signals are there<br />Archival source<br />The bad<br />Huge remote sensing DBs<br />Not same time<br />Not same sensor<br />Not same frequencies<br />Lack of ground truth<br />The Ugly: without ground truth it is hard to use RS data<br />
  3. 3. Case studies<br />Climate change in central Asia<br />Very little wind data <br />5 reliable stations<br />Model (ECWM) data<br />The two don’t match<br />SVM and object oriented classification<br />Works well but difficult to validate<br />
  4. 4. (SVM) classification<br />The field validation<br />P. 4<br />
  5. 5. Geographic distribution of natural hazards<br />
  6. 6. Who Was Affected?<br />The Demography of Tsunami-Affected Population<br />Dan Blumberg, Deborah Balk and Yuri Gorokhovich<br />Center for International Earth Science Information Network<br />Columbia University <br />Ben-Gurion University of the Negev<br />
  7. 7. Overview<br />Population <br />exposure<br />composition<br />measurement<br />Crude death rates<br />Socioeconomic profiles of the exposed areas<br />Where are people now? <br />A hazardous world: a multi-hazard approach<br />
  8. 8. Population exposure<br />What do we know about the spatial distribution of human population?<br />People do not live uniformly with respect to <br />National borders<br />Coastlines<br />Other geographic features, including hazard-prone regions<br />Some hazard prone regions may “attract” population, e.g., volcanic soils<br />Coastal zones support fishing, and access to markets (historically)<br />People move<br />Daily movement—commuting to work, markets, schools<br />Seasonal movements—tourist, labor-migration<br />Longer term movements—life-cycle (childbearing, retirement), permanent migration, forced migration <br />
  9. 9. The 2004 Tsunami<br />אירע באוקיינוס ההודי ב- 26 בדצמבר 2004<br />בעקבות רעש אדמה בעוצמה של MS 9.15<br />Created by NOAA<br />Animation provided by Vasily V. Titov, Associate Director, Tsunami Inundation Mapping Efforts (TIME), NOAA/PMEL - UW/JISAO, USA<br />
  10. 10. רעש האדמה של סומטרה אנדמן<br />Sumatra Andaman<br />26 דצמבר 2004<br />תזוזה של 15 מ לאורך 1200 ק&quot;מ באזור ההפחתה שבו הלוח ההודי <br /> צונח מתחת ללוח בורמה<br />
  11. 11. Affected countries<br />Indonesia<br />India<br />Bangladesh<br />Thailand<br />Sri Lanka<br />Sumalia<br />
  12. 12. Population density<br />Asia—particularly south and southeast Asia—are the most densely populated place on earth<br />Coastal zones have disproportionately high population densities<br />450 persons/km2, Asia<br />vs. 175, globally<br />Coastal areas are more urban<br />Source: CIESIN, GRUMP v1 (alpha)<br />
  13. 13. Demographic Composition<br />Age distribution: Asia is young. <br />Proportion of population &lt; 15 yrs ranges between 25-35% as compared with 20% or lower in North America and Europe<br />Household size and composition.<br />Larger, extended, with traditions of fosterage<br />Gender<br />Displacement affects women and men differently<br />
  14. 14. Population estimation<br />Who was exposed?<br />Who was at risk? <br />Who was affected? <br />Lost lives<br />Lost livelihoods<br />Displacement<br />
  15. 15. Who was exposed to the tsunami?<br />Wave heights were reported to be 10 m at their maximum<br />Persons below roughly 10 meters, in elevation<br />At close distance to the coastline<br />In most places, the waves were reported to go no more than 1-2 km inland from the coast<br />Except in parts of Sumatra were there were reported as far inland as 4-5 kilometers<br />Additional damage from the earth quake<br />And perhaps interactions with flooding<br />How to quantify the number of persons exposed?<br />
  16. 16. Why is population estimation tricky? <br />Data formats are not easily comparable<br />Population data come from censuses:<br />Irregular-shaped units <br />“Who slept here” or usual residence; <br />
  17. 17. Coastlinesmust match, but often don’t<br />Shorelines of data sources do not match:<br /><ul><li> Black shoreline: ESRI
  18. 18. Red shoreline: Administrative Units, BPS
  19. 19. The finer the scale the more the differences matter</li></li></ul><li>Data transformation: admin to grid<br />
  20. 20. Defense meteorological satellites<br />תמונת לילה של המזרח התיכון<br />
  21. 21. population data<br />shoreline<br />2 km buffer<br />Vector and raster data combination<br />Population data are now Gridded (i.e., rasterized)<br />Shoreline is vector (convert to raster)<br />
  22. 22. Shuttle Radar Topography Mission<br />טסה ב 2000 על ה- Endeavour<br />2 מערכות מכ&quot;ם) - band ארה&quot;ב) ו X-band<br />מודד הפרשי פאזה בין 2 אנטנות על מנת ליצור אינטרפרוגרמה<br />
  23. 23. מיפוי טופוגרפי מן החלל<br />95.30<br />95.40<br />95.50<br />5.20<br />5.10<br />
  24. 24.
  25. 25. 1998/04/09<br />1998/12/31<br />לפני ואחרי הצונאמיRADARSATהדמאת <br />
  26. 26. 2005/01/02<br />לפני ואחרי הצונאמי: סרי לנקהRADARSATהדמאת <br />2002/12/27<br />
  27. 27. מס&apos; ימים לאחר האירוע (2004)<br />(1998) לפני האירוע<br />Radarsat inundation<br />
  28. 28. Radarsat inundation<br />Inundated areas<br />Object oriented detection<br />
  29. 29. Urban areas<br />Need for high res data<br />Ikonos<br />EROS-1A<br />Quickbird<br />
  30. 30. הדמאות Ikonos של אזורים אורבניים<br />לפני האירוע לאחר האירוע <br />
  31. 31. EROS-1A<br />1.9 מטר<br />
  32. 32. Detected changed areas from the Landsat images<br />Landsat scene (30 meters resolution) of northern tip of Sumatra<br />
  33. 33. Estimation population in changed areas<br />Areas of detectable change (light green) <br />Area of analysis = Northern Aceh Province <br />10 km coastal buffer (yellow) <br />4 km coastal buffer (not shown)<br />4 km coast buffer on western and northern coasts only (red outline)<br />
  34. 34.
  35. 35. Socio-economic conditions of the affected region<br />The relative well-off areas hit hardest<br />
  36. 36. Where are people now? <br />Much harder to assess<br />Displaced persons estimate <br />UNFPA estimates that 500,000 girls and women have been displaced in Sri Lanka alone<br /> Short-term needs are different from medium and longer-term ones<br /><ul><li>Recovery efforts
  37. 37. Where are the displaced persons?
  38. 38. How to reach them?
  39. 39. What are their needs?
  40. 40. Reconstruction
  41. 41. Rebuild with sustainability in mind
  42. 42. Learn from assessments of our vulnerabilities</li></li></ul><li>How many lives might a warning system have saved?<br />Distance to epicenter<br />Effects of earthquake<br />Effects of tsunami<br />Infrastructure<br />Civil alert system?<br />Use of local knowledge<br />Which type of warning system?<br />Not all are alike<br />Would there have been anywhere to go?<br />Up? High ground or buildings?<br />Away—Indonesians had further to go than Sri Lankans<br />
  43. 43. Answers to longer term questions <br />Were geophysical and environmental properties protective in some places? <br />Have recent population dynamics and related behavioral change altered some of the underlying geophysical benefits<br />E.g., Protective ecosystems<br />Scenario building. What if this—or other hazards—happened elsewhere?<br />These questions presuppose a basic understanding of the population distributionat the time of the event, and even in the recent past<br />
  44. 44. Lessons learned<br />For analysis:<br />Baseline information is NOT ready for use<br />Data sharing issues arise and pose legal issues<br />Data integration is skill and time intensive<br />For policy:<br />Short-term recovery, and medium and long-run development pose much different but closely related questions<br />We have a better idea of the right parameters to construct early warning<br />Consider the risk of multiple and different hazards <br />
  45. 45. Mortality risk due to multiple hazards <br />
  46. 46. Economic risk due to multiple hazards<br />
  47. 47. proposals<br />EU call for environment<br />EU call for security<br />How can available data be harnessed for environmental issues?<br />

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