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IMED 2018: Landcover/habitat

Yoshinori Nakazawa, Ph.D., Poxvirus Branch, US Centers for Disease Control and Prevention: Landcover/habitat

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IMED 2018: Landcover/habitat

  1. 1. National Center for Emerging and Zoonotic Infectious Diseases Landcover/habitat International Meeting on Emerging Diseases and Surveillance November 2018 Yoshinori Nakazawa
  2. 2.  Satellite imagery products – Vegetation Indexes (NDVI, EVI), Reconstructed Solar‐Induced Fluorescence (RSIF), Net Primary Productivity (NPP)  Phenology – Growing season, senescence, etc.  Cases/examples Landcover/habitat
  3. 3.  Satellite imagery products – Various sources: AHHRR, MODIS, LandSat, ESA Sentinel‐2, Worldview, Ikonos, Quickbird, etc. – Multispectral imagery (red, green, blue, and near‐infrared) – Various spatial and temporal resolutions Satellite imagery
  4. 4.  Satellite imagery products – Various sources: AHHRR, MODIS, LandSat, ESA Sentinel‐2, Worldview, Ikonos, Quickbird, etc. – Multispectral imagery (red, green, blue, and near‐infrared) – Various spatial and temporal resolutions Satellite imagery
  5. 5. Satellite imagery
  6. 6.  Seasonal changes in vegetative growth and decline. Phenology: Veg Index Time Grow Maturity Senescence 0 1 Jan Dec “the study of periodic plant and animal life cycle events and how these are influenced by seasonal and interannual variations in climate”
  7. 7.  Seasonal changes in vegetative growth and decline. Phenology Veg Index Time 0 1 Jan Dec
  8. 8. Phenology JAN 1 –JAN 16 2011 MAR 22 – APR 6 2011
  9. 9. Phenology JUL 12 – JUL 27 2011 OCT 16 – OCT 31 2010
  10. 10. Phenology 0.25 0.3 0.35 0.4 0.45 0.5 0.55 0.6 Mean EVI Baleko Boende Bongoy Bosenge Inganda Lifomi Lomela River Tokumbu
  11. 11. Phenology Peterson AT et al. 2005. Transactions of the Royal Society of Tropical Medicine and Hygiene 99: 647-655. Dengue (Aedes aegypti) in Mexico Point-occurrence of Aedes aegypti drawn from larval surveys (Laboratorio de Entomologia, InDRE) Monthly samples from eastern and southern Mexico. Monthly composites of NDVI for 1995. From AVHRR.
  12. 12. Phenology Dengue in Mexico
  13. 13. Phenology Dengue in Mexico - Produce more accurate prediction of transmission risk areas - Test the ability of the model to predict future events Occurrence data divided into monthly subsets Use environmental data specific to each month to create a model Project each model into conditions in other months Mt  Et+1; Et+2; Et+3; … Mt+1  Et+2; Et+3; Et+4; …
  14. 14. Blue: All data and all months Red:Month specific (n,n-n-1 and n-n-2) Phenology Dengue in Mexico
  15. 15. Percent correctly predicted Human dengue cases Month (1995) Number of test points Any >50% of models >80% of models Number of test points % correct % area predicted present (150km buffer) P June 22 100.0 * 72.7 * 54.5 * 92 70.7 55.0 * July 28 100.0 X 82.1 * 67.9 * 195 74.4 55.7 * August 40 100.0 + 75.0 * 35.0 * 714 62.5 38.9 * September 25 100.0 X 80.0 * 16.0 599 78.0 36.7 * October 19 94.7 X 78.9 21.1 356 60.1 49.0 * November 25 100.0 X 76.0 52.0 + 30 26.7 49.1 * December 22 100.0 X 95.5 + 81.8 * - - - * Phenology Dengue in Mexico
  16. 16. Landcover/habitat
  17. 17. Landcover/habitat
  18. 18. Landcover/habitat
  19. 19. Landcover/habitat
  20. 20. Landcover/habitat
  21. 21. Landcover/habitat
  22. 22. Landcover/habitat 100 m 300 m Land Use Case Community P value Case Community P value Water 0 0.003 0.70 0 0.02 0.31 Forest 0.03 0.2 0.71 0.18 0.14 0.17 Disturbed 0.40 0.26 0.09 0.54 0.36 0.01 Developed 0.61 0.71 0.25 0.27 0.45 0.02 Flooded 0 0.01 0.48 0.01 0.03 0.21 Geographic regions surrounding case homes vs.general community
  23. 23. For more information, contact CDC 1‐800‐CDC‐INFO (232‐4636) TTY: 1‐888‐232‐6348 www.cdc.gov The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. Thank you!

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