Thermal impacts of greenery, water, and impervious structures in Beijing's Olympic area: A spatial regression approach. (February 2019)

• Record Type: Journal Article
• Authors: Dai, Zhaoxin; Guldmann, Jean-Michel; Hu, Yunfeng
• Is Part Of: Ecological indicators. Volume 97(2019)
• Page Start: 77
• Page End: 88
• Publication Date: 2019-02
• Subjects: Urban heat island -- Land uses -- Trees -- Grass -- Spatial autocorrelation; Environmental monitoring -- Periodicals; Environmental management -- Periodicals; Environmental impact analysis -- Periodicals; Environmental risk assessment -- Periodicals; Sustainable development -- Periodicals; 333.71405
• DOI: 10.1016/j.ecolind.2018.09.041
• Languages: English
• ISSNs: 1470-160X

Abstract

Highlights: Detailed land-use measures with 1 m accuracy explain the urban heat island (UHI). Building shades have negative effects on surface temperatures. Spatial regressions account for spatial autocorrelations and spillovers. Nonlinear and interaction effects of grass, trees and water are modeled. The results can be used in urban planning to mitigate the UHI at fine scales. Abstract: This paper explores the urban land-use determinants of the urban heat island (UHI) in Beijing's Olympic Area, using different statistical models, land surface temperatures (LST) derived from Landsat 8 remote sensing, and land-use data derived from 1-m high-resolution imagery. Data are captured over grids of different sizes. Spatial regressions are necessary to capture neighboring effects, particularly when the grid unit is small. Grass, trees, water bodies, and shades have all significant and negative effects on LST, whereas buildings, roads and other impervious surfaces have all significant and positive effects. The results also point to significant nonlinear and interaction effects of grass, trees and water, particularly when the grid cell size is small (60 m-90 m). Trees are found to be the most important predictor of LST. When the grids are smaller than 180 m, the indirect impacts are larger than the direct ones, whereas, the opposite takes place for larger grids. Because of their strong performance (R 2 ranging from 0.839 to 0.970), the models can be used for predicting the impacts of land-use changes on the UHI and as tools for urban planning. Finally, extensive uncertainty and sensitivity analyses show that the models are very reliable in terms of both input data accuracy and estimated coefficients precision.