The project will combine three disparate data sets and perform the following: photogrammetric analysis of ALAN emissions from buildings in Manhattan using image data; identification and calculation of bird density in the air above the city from radar-derived observations; a statistical analysis of NYC Audubon-owned bird death count and location data in lower Manhattan; and a publicly-accessible visualization of each of the previous points. The ultimate goal of the project is to produce light maps of the area of interest, successful radar estimates of airborne bird density, an interactive visualization combining these data in a way that clarifies bird risk, and a series of experiments measuring the correlation of ALAN and bird density, considering nights of both high and low overall brightness.
Previous work to quantify bird attraction to urban ALAN has been limited in spatial and temporal scales. One study investigated the effects of the 9/11 Memorial Tribute Lights in New York City on the behaviors of nocturnally migrating birds, finding that the annual event over seven years influenced the migration of 1.1 million birds (Van Doren et al., 2017). On the other hand, La Sorte et al. studied migration patterns across the northeast using satellite imagery to infer light levels and citizen science data to infer bird counts - while large in spatial scope, their study was limited in resolution. For example, light level "pixels" from the satellite data covered 3.3km2 each (La Sorte et al., 2017).
More quantitative approaches to evaluate artificial light emissions at night have been performed in an urban context by Dobler et al., who utilized ALAN emissions from buildings in the New York City skyline to measure human behavior based on the identification of on/off light transitions. Patterns of light intensity and sudden shifts in average brightness of a building could be discerned with these methods (Dobler et al., 2015). The goal of this work is to determine if a similar pulse is present in bird counts over time and if that is geospatially and temporally correlated to the lighting variability.