Spatio-temporal patterns in green infrastructure as driver of land surface temperature variability: The case of Sydney

被引:29
|
作者
Bartesaghi-Koc, Carlos [1 ]
Osmond, Paul [2 ]
Peters, Alan [3 ]
机构
[1] Deakin Univ, Fac Sci Engn & Built Environm, Sch Architecture & Built Environm, Geelong, Vic, Australia
[2] Univ New South Wales UNSW Sydney, Fac Built Environm, Sydney, NSW, Australia
[3] Univ Adelaide, Sch Architecture & Built Environm, Adelaide, SA, Australia
关键词
Surface urban heat island (SUM); Urban climate; Heat mitigation; Urban greening; Spatial autocorrelation; Urban planning and design; Hotspots; LOCAL CLIMATE ZONES; URBAN HEAT-ISLAND; EMISSIVITY RETRIEVAL; SEASONAL VARIABILITY; CLASSIFICATION; VEGETATION; COVER; MITIGATION; RESOLUTION; CITIES;
D O I
10.1016/j.jag.2019.101903
中图分类号
TP7 [遥感技术];
学科分类号
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要
The 'green infrastructure typology' (GIT) scheme is a standardised framework to map and classify urban landscapes into 34 standard classes, each defined by a specific land cover composition and spatial configuration of vegetation. Previous studies have confirmed that GIT classifications can be successfully derived from airborne remote sensing data; nonetheless, the promotion of the GIT scheme as a framework for the assessment of ecosystem services such as 'climate moderation' requires further validations using a range of study areas with different vegetation conditions, and datasets from different seasons and times of the day. This study expands on previous research and evaluates the quality of thermal delineations by examining the spatio-temporal patterns and intra-/inter-typology differences of land surface temperatures (LSTs) using Sydney as case study. Further, this paper discusses the advantages and disadvantages of the classification framework and methods for mapping and assessing the thermal conditions of green infrastructure (GI). Evidence indicates a strong spatial dependericy of LSTs that may have significant implications in the interpretation and precision of numerical or predictive models. Results for spatial clustering demonstrate that the GIT scheme can be implemented for a rapid identification of hotspots to prioritise urban areas for heat mitigation. Statistical results confirm that LST differences among GIT classes are statistically significant for different times of the day and seasons. Significant thermal contrasts were found for most GITs at daytime (86.9% in summer and 85.5% in winter) and night-time (80.9% in summer and 73.8% in winter). Temperature differences are more distinguishable in summer and daytime, due to longer solar exposure of surfaces. It was found that the cooling effects of pervious surfaces, water and trees are significantly disturbed by transmission of heat from surrounding impervious materials. Despite good thermal differentiations among GITs, a considerable intra-variability of LSTs was detected in classes with a large proportion of impervious materials with contrasting radiative properties. This causes numerous complexities and challenges that should be explored by future studies.
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页数:17
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