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Am. J. Trop. Med. Hyg., 57(6), 1997, pp. 687-692
Copyright © 1997 by The American Society of Tropical Medicine and Hygiene

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Landscape Characterization of Peridomestic Risk for Lyme Disease Using Satellite Imagery

Sheri W. Dister, Durland Fish, Shannon M. Bros, Denise H. Frank AND Byron L. Wood
Johnson Controls World Services, Inc., and Ecosystem Science and Technology Branch, National Aeronautics and Space Administration Ames Research Center, Moffett Field, California; Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, Connecticut; Department of Biological Sciences, San Jose State University, San Jose, California; Department of Community and Preventative Medicine, New York Medical College, Valhalla, New York

Remotely sensed characterizations of landscape composition were evaluated for Lyme disease exposure risk on 337 residential properties in two communities of suburban Westchester County, New York. Properties were categorized as no, low, or high risk based on seasonally adjusted densities of Ixodes scapularis nymphs, determined by drag sampling during June and July 1990. Spectral indices based on Landsat Thematic Mapper data provided relative measures of vegetation structure and moisture (wetness), as well as vegetation abundance (greenness). A geographic information system (GIS) was used to spatially quantify and relate the remotely sensed landscape variables to risk category. A comparison of the two communities showed that Chappaqua, which had more high-risk properties (P < 0.001), was significantly greener and wetter than Armonk (P < 0.001). Furthermore, within Chappaqua, high-risk properties were significantly greener and wetter than lower-risk properties in this community (P < 0.01). The high-risk properties appeared to contain a greater proportion of broadleaf trees, while lower-risk properties were interpreted as having a greater proportion of nonvegetative cover and/or open lawn. The ability to distinguish these fine scale differences among communities and individual properties illustrates the efficiency of a remote sensing/GIS-based approach for identifying peridomestic risk of Lyme disease over large geographic areas.




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Copyright © 1997 by the American Society of Tropical Medicine and Hygiene.