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Evaluating satellite sensor-derived indices for Lyme disease risk prediction.

Sarah E Rodgers1, Thomas N Mather

  • 1Center for Vector-Borne Disease, University of Rhode Island, Kingston 02881-0804, USA.

Journal of Medical Entomology
|April 20, 2006
PubMed
Summary
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Landsat Thematic Mapper (TM) wetness and greenness indices could predict blacklegged tick (Ixodes scapularis) abundance in lower-than-average abundance years. However, these indices were not effective in predicting tick abundance in higher-than-average abundance years.

Area of Science:

  • Ecology
  • Remote Sensing
  • Medical Entomology

Background:

  • Blacklegged ticks (Ixodes scapularis) are vectors for Lyme disease.
  • Predicting tick abundance is crucial for understanding and mitigating Lyme disease risk.
  • Satellite-derived vegetation indices offer potential for ecological monitoring.

Purpose of the Study:

  • To evaluate the utility of Landsat Thematic Mapper (TM)-derived wetness and greenness indices for predicting nymphal blacklegged tick abundance in Rhode Island.
  • To determine if these indices can forecast tick distribution across different abundance levels.

Main Methods:

  • Utilized Landsat TM data from 1995, 1997, and 2002.
  • Calculated wetness and greenness indices from satellite imagery.
  • Compared index values between sites with low, moderate, and high nymphal blacklegged tick abundance.

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  • Performed statistical analyses (P-values) to assess significance.

    • Main Results:

    • In 1995 and 1997, significant differences in wetness and greenness indices were observed between low/moderate and high tick abundance sites.
    • These significant differences suggest potential predictive capability in years with lower average tick abundance.
    • In 2002, a year with higher average tick abundance, the indices were not significant predictors.

    Conclusions:

    • Landsat TM-derived wetness and greenness indices may predict blacklegged tick abundance in years with below-average tick populations.
    • The predictive utility of these indices appears limited in years with higher-than-average tick abundance.
    • These remotely sensed indices are unlikely to be broadly useful for modeling non-endemic Lyme disease risk across large regions.