Interactions between urban vegetation and surface urban heat islands: a case study in the Boston metropolitan region

Abstract

Many studies have used thermal data from remote sensing to characterize how land use and surface properties modify the climate of cities. However, relatively few studies have examined the impact of elevated temperature on ecophysiological processes in urban areas. In this paper, we use time series of Landsat data to characterize and quantify how geographic variation in Boston's surface urban heat island (SUHI) affects the growing season of vegetation in and around the city, and explore how the quality and character of vegetation patches in Boston affect local heat island intensity. Results from this analysis show strong coupling between Boston's SUHI and vegetation phenology at the scale of both individual landscape units and for the region as a whole, with significant detectable signatures in both surface temperature and growing season length extending 15 km from Boston's urban core. On average, land surface temperatures were about 7°C warmer and the growing season was 18-22 days longer in Boston relative to adjacent rural areas. Within Boston's urban core, patterns of temperature and timing of phenology in areas with higher vegetation amounts (e.g., parks) were similar to those in adjacent rural areas, suggesting that vegetation patches provide an important ecosystem service that offsets the urban heat island at local scales. Local relationships between phenology and temperature were affected by the intensity of urban land use surrounding vegetation patches and possibly by the presence of exotic tree species that are common in urban areas. Results from this analysis show how species composition, land cover configuration, and vegetation patch sizes jointly influence the nature and magnitude of coupling between vegetation phenology and SUHIs, and demonstrate that urban vegetation provides a significant ecosystem service in cities by decreasing the local intensity of SUHIs.

Impacts of Urbanization on Vegetation Phenology over the Past Three Decades in Shanghai, China

“…Recent research has suggested that the growing season of vegetation in cities is longer compared with the surrounding rural regions because of UHI effects [9,17,26,27,33,34]. Our results support this conclusion, providing a refined characterization of interactions between composition and configuration of local LCLU types and spatial patterns of vegetation phenology.…”

“…Melaas et al [31,32] extended the algorithm in a way that allowed the detection of interannual variability in phenology and validated the method in North American temperate and boreal deciduous forest. These approaches have only recently been applied to urban areas [33,34], and there remain substantially unrealized potential for leveraging them to better understand how urbanization affects phenological changes. More importantly, landscape patterns not only reflect the urban development and their socioeconomic drivers [35][36][37], but also significantly influence UHI [38].…”

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

“…This also indicates that base pair and software selection may lead to different results than when looking at features or specific locations within the municipality. This supports previous findings that EOS in urban vegetation patches is influenced by the percent impervious surface of surrounding patches [56].…”

“…This coincides with the case study by Melaas et al [56], which showed that the amount of impervious surface area in surrounding vegetation patches influences the timing of SOS. Although we do not show a clear trend with the difference in SOS growing as mean percent imperviousness does, for all three selection methods the difference in mean days becomes larger between urban and exurban areas for the two most developed classes when compared to the two least developed classes.…”

Spatial and Temporal Analysis of the Mitigating Effects of Industrial Relocation on the Surface Urban Heat Island over China

“…However, due to the sparse distribution of observation stations, spatially continuous analysis is difficult. To solve this problem, the use of satellite data for the detection and assessment of SUHIs has been attempted [22][23][24][25][26]. Remote sensing data have wall-to-wall continuous coverage of urban areas [27].…”