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

1. National Center for Emerging and Zoonotic Infectious Diseases
Landcover/habitat
International Meeting on Emerging Diseases and Surveillance
November 2018
Yoshinori Nakazawa

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.  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.  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. Satellite imagery

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.  Seasonal changes in vegetative growth and decline.
Phenology
Veg Index
Time
0
1
Jan Dec

8. Phenology JAN 1 –JAN 16 2011 MAR 22 – APR 6 2011

9. Phenology JUL 12 – JUL 27 2011 OCT 16 – OCT 31 2010

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. 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. Phenology Dengue in Mexico

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. Blue: All data and all months Red:Month specific (n,n-n-1 and n-n-2)
Phenology Dengue in Mexico

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. Landcover/habitat

17. Landcover/habitat

18. Landcover/habitat

19. Landcover/habitat

20. Landcover/habitat

21. Landcover/habitat

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. 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!