Too big, too small, or just right? The influence of multispectral image size on mosquito population predictions in the greater Toronto area

Outbreaks of West Nile Virus (WNV) and St. Louis Encephalitis (SLE) are projected to increase in frequency and intensity with climate change, underlining the need to develop better mosquito borne disease (MBD) fore- casting systems. Spread by Culex, WNV and SLE have seemingly random spatial and temporal outbreaks, making such outbreaks difficult to predict. However, recent studies have found that mosquito abundance and WNV/SLE transmission are strongly correlated, providing researchers with a foundation for the development MBD forecasting systems. Mosquito populations are impacted by several environmental variables, such as humidity, temperature, vegetation, and available breeding habitat. Mosquito-population forecasting models are beginning incorporate spectral data, such as the normalized difference vegetation index (NDVI). Vegetation is a crucial habitat for some mosquito species, and spectral data offers the best estimate of this habitat virtually anywhere on Earth. Additionally, vegetation offers a proxy for understanding how water flows across a landscape, a crucial consideration in urban areas with high landscape heterogeneity. This research explores how the spatial scale (extent) of multispectral imagery used in mosquito population prediction models influences mosquito population forecasts, specifically in the Greater Toronto Area. We derive three monthly time series of standard spectral indices from multispectral imagery over the Greater Toronto Area from 2004 to 2015; each time series is derived from images taken over the same locations, but using images taken over different spatial footprints. We then explore how spectral indices across the three spatial scales perform as predictors for combined Cx. restuans and Cx. pipiens populations.