Abstract:Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues.

It is currently unclear what role microhabitat land cover plays in determining the seasonal spatial distribution of Aedes aegypti and Culex quinquefasciatus, disease vectors of dengue and West Nile Virus, respectively, in Tucson, AZ. We compared mosquito abundance to sixteen land cover variables derived from 2010 NAIP multispectral data and 2008 LiDAR height data. Mosquitoes were trapped with 30-49 traps from May to October of 2010 and 2011. Variables were extracted for five buffer zones (10-50 m radii at 10 m intervals) around trapping sites. Stepwise regression was performed to determine the best scale for observation and the influential land cover variables. The 30 m radius buffer was determined to be the best for observing the land cover-mosquito abundance relationship. Ae. aegypti presence was positively associated with structure and medium height trees and negatively associated with bare earth; Cx. quinquefasciatus presence was positively associated with pavement and medium height trees and negatively associated with shrubs. These findings emphasize vegetation, impervious surfaces, and soil influences on mosquito presence in an urban setting. Lastly, the land cover-mosquito abundance relationships were used to produce risk maps of seasonal presence that highlight high risk areas in Tucson, which may be useful for focusing mosquito control program actions. Journal of Vector Ecology 37 (2): 407-418. 2012.