Impact of Urban Climate Landscape Patterns on Land Surface Temperature in Wuhan, China

Abstract

Facing urban warming, mitigation and adaptation strategies are not efficient enough to tackle excessive urban heat, especially at the local scale. The local climate zone (LCZ) classification scheme is employed to examine the diversity and complexity of the climate response within a city. This study suggests that zonal practice could be an efficient way to bridge the knowledge gap between climate research and urban planning. Urban surfaces classified by LCZ are designated as urban climate landscapes, which extends the LCZ concept to urban planning applications. Selecting Wuhan as a case study, we attempt to explore the climatic effect of landscape patterns. Thermal effects are compared across the urban climate landscapes, and the relationships between patch metrics and land surface temperature (LST) are quantified. Results indicate that climate landscape layout is a considerable factor impacting local urban climate. For Wuhan, 500 m is an optimal scale for exploring landscape pattern-temperature relationships. Temperature contrast between surrounding landscape patches has a major influence on LST. Generally, fragmental landscape patches contribute to heat release. For most climate landscape types, patch metrics also have a significant effect on thermal response. When three metrics are included as predictive variables, 53.3% of the heating intensity variation can be explained for the Large Lowrise landscape, while 57.4% of the cooling intensity variation can be explained for the Water landscape. Therefore, this article claims that land-based layout optimization strategy at local scale, which conforms to planning manner, should be taken into account in terms of heat management.

Gis-Based Mapping of Local Climate Zones Using Fuzzy Logic and Cellular Automata

“…LCZ G (Water) displayed high-night time temperature because of its high specific capacity, which result to a slower release of heat at night The lowest night-time mean temperature (visualized as blue in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W19, 2019 PhilGEOS x GeoAdvances 2019, 14-15 November 2019, Manila, Philippines night-time LST in Figure 10) was identified in LCZ A (Dense trees) and LCZ F (Bare soil or sand). The variations in daytime and night-time LST (Figure 11) showed consistency with profiles from other literature (Yang et al, 2017;Wang et al, 2017;Cai et al, 2018). Earth's surface absorbs heat during the day and radiates it back at night.…”

A Geographically Weighted Regression Analysis of the Underlying Factors Related to the Surface Urban Heat Island Phenomenon

“…In addition, the appropriate scale also depends on landscape matrix selection. For instance, the SHDI showed the strongest correlation with LST at the 700 m scale which was not the optimal solution if other landscape matrices were taken into account, as indicated by a case study of Wuhan, China [53]. In this sense, further research is warranted to examine the scale sensitivity of their relationships in the future to lead to more comprehensive understanding.…”

Seasonal Variation of the Spatially Non-Stationary Association Between Land Surface Temperature and Urban Landscape

“…Referring to previous studies [40,[64][65][66], a matching with tolerable multicollinearity (VIF < 10) was identified as the final indicator system for later modeling in this study. All variables are resampled to the city block scale of 500 m in urban climatology research [70], as identified by multiple related studies to be the optimal spatial scale for exploring LST and landscape association in Wuhan [27,71]. The city block scale is also recommendable for city-scale study as it is the smallest urban unit where homogeneity is possible while also large enough to generate localized breeze systems [70].…”