The first study identified landscape features associated with exposure risk for Lyme disease at the municipality level. The purpose of the second study was to determine if satellite remote sensing data could characterize landscape differences at a finer scale, which are related to variations in peridomestic exposure risk within communities. In this study, spectral indices derived from Landsat Thematic Mapper (TM) data were used to describe the landscape composition of residential properties in two Lyme disease-endemic communities of Westchester County, New York, i.e., Armonk and Chappaqua. The study included 337 properties, all of which contained some wooded area. Woods are a more favorable environment for survival of tick larvae and nymphs than lawns or herbaceous vegetation. The wooded habitat on the properties had been previously sampled for Ixodes scapularis nymphs. Tick density determined from the sample data was used to assign risk levels to the properties. The locations of the sampled residences were digitized into the GIS from high-resolution black and white aerial photography.
A transformation was performed on the TM data to generate "greenness" and "wetness" spectral indices for each pixel in the study area. The means and standard deviations of these spectral indices, as well as topographic information, were determined for a 3x3-pixel area (approximately 90x90 meters) centered on each residence. Within a single community, a multivariate ANOVA showed that high-risk properties had significantly higher mean greenness and wetness than low-risk or no-risk risk properties (p <0.01). These properties appeared to contain a greater proportion of broadleaf trees, while lower risk properties were interpreted as having a greater proportion of non-vegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties using satellite data illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic Lyme disease risk over large geographic areas.
For more information about this research, contact Louisa Beck.