Evaluating satellite sensor-derived indices for Lyme disease risk prediction

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.
• 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.