Comparison of three satellite sensors at three spatial scales to predict larval mosquito presence in Connecticut wetlands

Abstract

Satellite imagery can be used to identify suitable habitat for mosquitoes in areas inaccessible or lacking sufficient ground-based information about the environment but current applications are limited by the spatial and spectral resolution of the sensors. Here, models used to compare prediction of the presence of Anopheles punctipennis larvae in Connecticut wetlands were built using stepwise logistic regression and compared by Akaike's Information Criterion (AIC). Vegetation indices were extracted from three satellite sensor scenes (Hyperion, ASTER and Landsat-TM) at three scales (pixel, wetland perimeter, and wetland area). The best models were developed using ASTER (ROC=0.80, p=0.01, AIC 65.37) and Hyperion (ROC=0.81, p<0.01, AIC 66.40) at the wetland area level. The Disease Water Stress Index (DWSI), a measure of leaf water content, and Normalized Difference Vegetation Index (NDVI) were significant in many of the models. This comparison of satellite based models demonstrates higher spatial and spectral resolution of ASTER and Hyperion resulted in more parsimonious models than Landsat-TM models. The need for continued research and development into sensors with increased spatial and spectral resolution and the development of mosquito specific indices is discussed.