OOS 18-8
Quantifying foreclosure effects on land cover using remotely sensed data
Following the 2006 housing bubble, home foreclosure rates in the United States underwent steep increases with an estimated 9 million foreclosures having occurred since 2007. One of the impacts of foreclosure is a loss of owners managing their yards, reducing or removing entirely lawn maintenance practices including mowing, watering, and fertilization. Foreclosure-related changes in yard management may have important consequences for urban land cover, which depend on regional climate. In temperate climates, reduced management may lead to an increase in the height and biomass of grass, shrubs, and trees on abandoned properties. In arid climates, however, reduced management may lead to decreased vegetation biomass because of increasing water stress. The U.S. foreclosure crisis, through the lens of changes in yard maintenance, allows us an unprecedented experiment to examine interactions between human drivers at the household, neighborhood, and municipal/regional scale on urban ecosystem processes. In this analysis, we use a dense time series of satellite-based remote sensing imagery of Phoenix, Arizona and Cleveland, Ohio to examine the spatiotemporal patterns of changes in vegetation dynamics as a function of socioeconomic and biophysical factors.
Results/Conclusions
Our initial results from a small number of sites in Phoenix, Arizona indicate an increasing amplitude of variation in the spectral responses in neighborhoods with increased foreclosure, with an overall negative year-to-year trend in both high and low foreclosure regions that may be tied to regional climatic effects. Time series from sites with no foreclosures resemble highly managed lawns in golf courses, whereas time series from sites following foreclosures resemble those of unmanaged, desert parklands. We conclude that foreclosure can alter urban land cover, but that the direction and magnitude of effects will depend on regional climate. This approach offers a novel way of detecting foreclosures and their impacts in a near-real-time fashion.