Abstract

Roadsides are important urban public spaces where residents are in direct contact with the thermal environment. Understanding the effects of different vegetation types on the roadside thermal environment has been an important aspect of recent urban research. Although previous studies have shown that the thermal environment is related to the type and configuration of vegetation, remote sensing-based technology is not applicable for extracting different vegetation types at the roadside scale. The rapid development and usage of street view data provide a way to solve this problem, as street view data have a unique pedestrian perspective. In this study, we explored the effects of different roadside vegetation types on land surface temperatures (LSTs) using street view images. First, the grasses–shrubs–trees (GST) ratios were extracted from 19,596 street view images using semantic segmentation technology, while LST and normalized difference vegetation index (NDVI) values were extracted from Landsat-8 images using the radiation transfer equation algorithm. Second, the effects of different vegetation types on roadside LSTs were explored based on geographically weighted regression (GWR), and the different performances of the analyses using remotely sensed images and street view images were discussed. The results indicate that GST vegetation has different cooling effects in different spaces, with a fitting value of 0.835 determined using GWR. Among these spaces, the areas with a significant cooling effect provided by grass are mainly located in the core commercial area of Futian District, which is densely populated by people and vehicles; the areas with a significant cooling effect provided by shrubs are mainly located in the industrial park in the south, which has the highest industrial heat emissions; the areas with a significant cooling effect provided by trees are mainly located in the core area of Futian, which is densely populated by roads and buildings. These are also the areas with the most severe heat island effect in Futian. This study expands our understanding of the relationship between roadside vegetation and the urban thermal environment, and has scientific significance for the planning and guiding of urban thermal environment regulation.

Highlights

  • GST, normalized difference vegetation index (NDVI) and land surface temperature (LST) data were extracted from street view images (GST) and Landsat-8 images (NDVI and land surface temperatures (LSTs))

  • We we introduce introduce the the pyramid pyramid scene scene parsing parsing network network (PSPNet), an improved semantic segmentation network based on a full convolutional (PSPNet), an improved semantic segmentation network based on a full convolutional netnetwork (FCN)

  • The research results can expand the scientific understanding of the influence of urban vegetation structures on LSTs and provide references with which urban planners can mitigate the urban heat island effect by optimizing the spatial distribution of urban vegetation

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Summary

Introduction

There is evidence that the replacement of the natural environment by the urban fabric can lead to significant temperature increases as well as significant heat island effects [1]. In warm climate regions, the heat island effect significantly increases energy expenditures and carbon emissions [2], and these changes critically impact urban ecology and have major adverse effects on public health [3,4]. To address this issue, improved urban greenery is emerging as an effective mitigation measure and is becoming an important component of urban landscape design [5]. Rapid urbanization has greatly altered original green space patterns, resulting in cities facing a dilemma involving limited land

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