Urban heat island impacts on plant phenology: intra-urban variability and response to land cover

Abstract

Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start/end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012-2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ∼5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains >50% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.

Intra-Urban Microclimate Effects on Phenology

“…The goals of the final study [19] compare to the goals of our study. Zipper et al [16] used two methods to establish start of season, end of season, and growing season length for Dane County, Wisconsin-including the City of Madison and its metropolitan area.…”

Seasonal and Spatial Characteristics of Urban Heat Islands (UHIs) in Northern West Siberian Cities

“…Further selection of an additional predictor variable (Trs; Equation (4)) increases the R 2 value to 0.71: ∆Ts = 1.23 log P + 0.20Trs − 3.73 (4) For winter, the initial model (excluding the outlier Surgut) has one predictor variable (log(P); Equation (5)): ∆Tw = 1.42 log P − 0.73…”

“…Urban areas alter the weather and climate, and feedbacks influence human health and energy consumption; these two aspects alone are enough to motivate interest in UHI studies. UHIs affect the climate of cities [3], shift plant phenology [4] and develop habitats for introduced or invasive species of plants and animals [5]. With rising global warming, the intensity of the UHI is also likely to increase, and its effects will become more significant in the future [6].…”

Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

“…Similar to Zipper et al [51], prior to any comparisons that aggregated the years together all data were normalized to account for different temperature-based seasons. We normalized for each phenological parameter by year according to the expected exurban value for each land class by subtracting the mean value of the exurban area from each data point for each year.…”

“…For example, it was assumed that any influences a homeowner may have on phenology due to landscaping practices (i.e., watering the lawn in the spring) and vegetation transplanting (i.e., planting grass for a lawn) were just as likely to occur regardless if the individual lives in a rural and urban settings. This is an important assumption, as it has been observed that phenological traits can differ between vegetation types [51]. However, making comparisons between the urban and rural environments of developed areas with the same land cover classes controlled for this, as opposed to comparing dissimilar land cover classes that may have dissimilar landscape management techniques.…”