Understanding the contributions of land parcel features to intra‐surface urban heat island intensity and magnitude: A study of downtown Shanghai, China

Abstract

This study investigated the fine‐scale land parcel features and their influences on the intra‐surface urban heat island (SUHI) of four dense urban functional zones in downtown Shanghai. A thermal sharpening method was employed to map the high‐resolution intra‐SUHI intensity (SUHII) and magnitude (SUHIM) by integrating the cokriging interpolation of Landsat 8 thermal band 10 and Quickbird datasets. Together with three area proportions (area proportion of the cooling surface/CoolAP, area proportion of the buildings/BuildAP, and area proportion of the other impervious surface/OtherAP), the mean distance between the nearest neighboring buildings (BuildDist) and the building average story number (BuildSN) were used to characterize the land parcel features. The results showed that both the composition and the design attributes of land parcels were significantly related to the intra‐SUHI indicators (SUHII and SUHIM). Additionally, remarkable seasonal variations existed in the intra‐SUHI indicators. The results of the partial least square regression models explained 64.6–65.2% of the variance of the SUHIIs and 63.0–63.9% of the variance of the SUHIMs on hot days and 11.70–18.90% of the variance of the SUHIIs and 11.10–15.90% of the variance of the SUHIMs on cool and chilly days, respectively. This study provided compelling evidence that for a given land parcel, when fixing the effects of BuildAP or OtherAP, high levels of CoolAP, BuildSN, and BuildDist will help create a cooling microclimate on hot days. In contrast, high‐density land parcels with sparse cooling surface exhibit much higher SUHII and SUHIM levels than do low‐density parcels with dominant cooling surface.

Examining Land Use/Land Cover Change and the Summertime Surface Urban Heat Island Effect in Fast-Growing Greater Hefei, China: Implications for Sustainable Land Development

“…Urban areas, which are subject to a local warming effect, or combined local and global warming effects, are the fundamental units for climate change adaptation and mitigation [13]. Thus, how cities cope with the UHI effect, particularly optimizing land use patterns towards sustaining regional ecosystems, will significantly affect UHI mitigation and adaption to climate change [14][15][16][17][18][19][20].…”

Analyzing the Spatial Heterogeneity of the Built Environment and Its Impact on the Urban Thermal Environment—Case Study of Downtown Shanghai

“…Rather than using the generalized concept of the UHI effect as measured by the LST difference between urban and rural areas, the intra-SUHI intensity in this study is defined as the LST difference between the impervious surfaces and the BGS (vegetated land and water bodies) in a given land parcel. The parcel-based intra-SUHII is calculated as follows [38]:…”

Extraction and Spatio-Temporal Analysis of Impervious Surfaces over Dongying Based on Landsat Data

“…This can lead to information loss and accuracy reduction when using traditional methods [25]. Therefore, many approaches have been developed for the mapping of impervious surfaces at the subpixel level, such as decision tree, regression tree, artificial neural network, and Linear Spectral Mixture Analysis (LSMA) [11,26,27]. Among these, LSMA is the most widely used for solving the spectral mixing problem, as it shows better performance than other methods in many fields, such as land cover classification [28], wetland classification [29], and urban classification [30].…”