Uncertainty in urban forest canopy assessment: Lessons from Seattle,WA, USA

Increasing urbanization around the globe is leading to concern over the loss of tree canopy within cities, but quantifying urban forest canopy cover can be difficult. We discuss methods of assessing canopy cover within cities, and then use a case study of Seattle, WA, USA to examine issues of uncertainty in canopy cover assessment. We find that uncertainty is often not reported, and when reported, may be biased. Based on these findings, we provide a list of recommendations for those undertaking canopy cover assessment in complex urban environments.     

Evaluating the potential for urban heat-island mitigation by greening parking lots

Artificial urban land uses such as commercial and residential buildings, roads, and parking lots covered by impervious surfaces can contribute to the formation of urban heat islands (UHIs), whereas vegetation such as trees, grass, and shrubs can mitigate UHIs. Considering the increasing area of parking lots with little vegetation cover in Nagoya, Japan, this study evaluated the potential for UHI mitigation of greening parking lots in Nagoya. The relationships between land surface temperature (LST) and land use/land cover (LULC) in different seasons were analyzed using multivariate linear regression models. Potential UHI mitigation was then simulated for two scenarios: (1) grass is planted on the surface of each parking lot with coverage from 10 to 100% at an interval of 10% and (2) parking lots are covered by 30% trees and 70% grass. The results show that different LULC types play different roles in different seasons and times. On average, both scenarios slightly reduced the LST for the whole study area in spring or summer. However, for an individual parking lot, the maximum LST decrease was 7.26 °C in summer. This research can help us understand the roles of vegetation cover and provide practical guidelines for planning parking lots to mitigate UHIs.     

Quantifying the cool island effects of urban green spaces using remote sensing Data

Urban Heat Island (UHI) leads to increased energy consumption, aggravated pollution and threatened health of citizens. Urban green spaces mitigate UHI effects, however, it is still unclear how the green space characteristics and its surrounding environment affects the green space cool island (GCI). In this study, land surface temperature (LST) and land cover types within the outmost ring road of Shanghai, China were obtained from Landsat 8 data and high-resolution Google Earth data. The GCI effects were defined in three aspects: GCI range (GR), amplitude of temperature drop (TA) and temperature gradient (TG). Pearson correlation analysis was processed to get the relationship between the aspects and impact factors. The results indicated that the GCI principle could be explained by the thermal conduct theory. The efficient methods to decrease LST of green spaces include increasing green space area while staying below the threshold, adding complexity of green space shape, decreasing impervious surfaces and enlarging the area of water bodies. For the surrounding environment of the green spaces, increasing vegetation and water body fractions or decreasing impervious surfaces will help to strengthen GCI effects. The findings can help urban planners to understand GCI formation and design cool green spaces to mitigate UHI effects.     

Effect of urban green patterns on surface urban cool islands and its seasonal variations

Urban green spaces often form urban cool islands (UCIs), which are important for human health and urban sustainability. Previous studies have emphasized the cooling effects of urban green spaces on their surrounding areas at landscape level. Less attention, however, has been directed to effects of urban green space patterns on their own UCIs at patch level. In this study, we focused on the effects of spatial patterns of urban green patches on their own surface UCIs. The urban green spaces of Beijing, China, were extracted from one QuickBird image and were classified as Trees, Shrubs, Grass, Crops, River and Lake. Land surface temperatures (LSTs) were derived from four Landsat images, each in one season. The UCI was represented by the minimum LST of each urban green patch. Results showed spatial patterns of urban green patches had significant effects on their UCIs in four seasons. In detail, the size, edge and connectivity of urban green spaces all affected the UCIs negatively, and the influence was stronger in warm seasons. Shape of urban green space also had effects on UCIs, but the effects were stronger in cool seasons. Great differences were found between predictive values of metrics for different green types. Shape metrics were more important for indicating UCIs of River, Trees and Crops than were patch size and connectivity. However, patch size and connectivity metrics were more effective in determining UCIs of Shrubs, Grass and Lake than were shape metrics. Further, among shape metrics, only shape index was a good indicator of UCIs. The results of this study suggest that a combination of specific urban green types and pattern metrics are a prerequisite for analyzing the influence of urban green patterns on UCIs and for urban green design.