Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China

Yu, Siyi; Chen, Zuoqi; Yu, Bo; Wang, Lei; Wu, Bin; Wu, Jianping; Zhao, Feng

doi:10.1016/j.scitotenv.2020.138229

Cited by 108 publications

(40 citation statements)

References 60 publications

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“…Moreover, in summer, the sun-facing side of a tall building, featuring a high albedo, shades the low-albedo natural surfaces behind it [59,60]. MBV was positively correlated with summer LST at the pixel scale (Figure 3), implying that a high building energy consumption can increase the anthropogenic heat release, contributing to the accumulation of U-LSTs [14,61,62]. SVF was found to be positively correlated with U-LST, contributing to high seasonal U-LSTs at various spatial scales, and the warming effects at the city block scale were higher than those at the pixel scale.…”

Section: Differences In the Effects Of Built-up Infrastructure Parameters On Seasonal U-lsts Between City Block And Pixel Scalesmentioning

confidence: 99%

“…They found that the links between normalized difference impervious surface index and U-LST increased with the rising spatial scales. Recently, given that the use of Light Detection and Ranging (LiDAR) point clouds data brings the possibility to obtain high-resolution 2D and 3D USPs [13], Yu et al [14] explored the relationships between 3D landscape patterns and LSTs over an area of approximately 665.637 km 2 in central Shanghai, China, using the extreme Gradient Boosting tree regression method. They found that 3D USPs yield higher explanatory capabilities for U-LSTs than 2D USPs.…”

Section: Introductionmentioning

confidence: 99%

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How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

Cao

et al. 2021

Remote Sensing

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Identifying the driving factors of urban land surface temperatures (U-LSTs) is critical in improving urban thermal environments and in supporting the sustainable development of cities. Previous studies have demonstrated that two- and three-dimensional (2D and 3D) urban structure parameters (USPs) largely influence seasonal U-LSTs. However, the effects of 2D and 3D USPs on seasonal U-LSTs at different spatial scales still await a general explanation. In this study, we used very-high-resolution remotely sensed data to investigate how 2D and 3D USPs impact seasonal U-LSTs at different spatial scales (including pixel and city block scales). In addition, the influences of various functional zones on U-LSTs were analyzed. The results show that, (1) generally, the links between USPs and U-LSTs at the city block scale were more obvious than those at the pixel scale, e.g., the Pearson correlation coefficient (r) between U-LST and the mean building height at the city block scale (summer: r = −0.156) was higher than that at the pixel scale (summer: r = −0.081). Tree percentage yielded a considerable cooling effect on summer U-LSTs on both the pixel (r = −0.199) and city block (r = −0.369) scales, and the effect was more obvious in regions with tall trees. (2) The independently total explained variances (R2) of 3D USPs on seasonal U-LSTs were considerably higher than those of 2D USPs in most urban functional zones (UFZs), suggesting the distinctive roles of 3D USPs in U-LST regulation at the local scale. Three-dimensional USPs (R2 value = 0.66) yielded more decisive influences on summer U-LSTs than 2D USPs did (R2 value = 0.48). (3) Manufacturing zones yielded the highest U-LST, followed by residential and commercial zones. Notably, it is found that the explained variances of the total study area for seasonal U-LSTs were significantly lower than those of each UFZ, suggesting the different roles of 2D and 3D USPs played in various UFZs and that it is critical to explain U-LST variations by using UFZs.

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Section: Differences In the Effects Of Built-up Infrastructure Parameters On Seasonal U-lsts Between City Block And Pixel Scalesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

Cao

et al. 2021

Remote Sensing

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“…Recently, urban environmental studies have been produced in 3D considering spatiotemporal morphological databases for urban green infrastructure [70], wind loading on scaled down fractal tree models [71], landscape patterns and land surface temperatures [72], and urban street canyons [73]. Laser-scanned 3D models can help take advantage of subtle topographic differences to support water management, capture significant site features, and provide an accurate site inventory that could reduce the cost of displaced terrain and replanted trees.…”

Section: Introductionmentioning

confidence: 99%

Integration of Aerobiological Information for Construction Engineering Based on LiDAR and BIM

et al. 2022

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In green urban areas, the allergenic factor is important when selecting trees to improve the quality of life of the population. An application of laser imaging detection and ranging (LiDAR) in building information modelling (BIM) is the capture of geo-referenced geometric information of the environment. This study presents the process of digitalisation of a green infrastructure inventory based on the geolocation and bioparameters of the cypress species. The aerobiological index (IUGZA) was estimated by developing green infrastructure BIM models at different detail levels and with a new BIM dimension (6D) for the urban environment. The novelty of the study is the modelling of urban information for evaluating the potential environmental impact related to the allergenicity of the urban green infrastructure using LiDAR through BIM. The measurements of cypress trees based on bioparameters and distances were applied to the IUGZA. This innovation for describing the current 3D environments and designing new scenarios in 6D may prevent future problems in urban areas during construction projects.

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“…Landscape patterns refer to the spatial structure characteristics of the landscape in addition to the type, number, and spatial configuration of landscape component units, which is a comprehensive spatial expression of landscape heterogeneity. The development of methods for quantifying landscape patterns not only helps to understand the interaction of patterns and processes but also has significant implications for applying the concepts of landscape ecology to sustainable landscape planning [ 8 , 9 ]. Landscape pattern metrics are commonly used quantitative methods for landscape pattern analysis [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

Ren

Zhao

et al. 2021

PLoS ONE

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Three-dimensional landscape patterns are an effective means to study the relationship between landscape pattern evolution and eco-environmental effects. This paper selects six districts in Xi’an as the study area to examine the spatial distribution characteristics of the three-dimensional architectural landscape in the city’s main urban area using three-dimensional information on the buildings in 2020 with the support of GIS. In this study, two new architectural landscape indices—landscape height variable coefficient and building rugosity index—were employed in landscape pattern analysis, whilst a system of rigorous and comprehensive three-dimensional architectural landscape metrics was established using principal component analysis. A mathematical model of weighted change of landscape metrics based on the objective weighting method was applied to carry out scale analysis of the landscape patterns. Spatial statistical analysis and spatial autocorrelation analysis were conducted to comprehensively study the differentiation of three-dimensional architectural landscape spatial patterns. The results show that the characteristic scale of the three-dimensional landscape pattern in Xi’an’s main urban area is around 8 km. Moreover, the three-dimensional landscape of the buildings in this area is spatially positively correlated, exhibiting a high degree of spatial autocorrelation whilst only showing small spatial differences. The layout of the architectural landscape pattern is disorderly and chaotic within the second ring, whilst the clustering of patch types occurs near the third ring. Moreover, the building density in the Beilin, Lianhu, and Xincheng districts is large, the building height types are rich, and the roughness of the underlying surface is high, such that these are key areas to be improved through urban renewal. The height, volume, density, morphological heterogeneity, and vertical roughness of the architectural landscape vary amongst functional areas within the study area. This paper is the first to apply the study of spatial heterogeneity of three-dimensional landscape patterns to Xi’an. It does so in order to provide a quantitative basis for urban landscape ecological design for urban renewal and the rational planning of built-up areas, which will promote the sustainable development of the city’s urban environment.

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Exploring the relationship between 2D/3D landscape pattern and land surface temperature based on explainable eXtreme Gradient Boosting tree: A case study of Shanghai, China

Cited by 108 publications

References 60 publications

How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

How Do Two- and Three-Dimensional Urban Structures Impact Seasonal Land Surface Temperatures at Various Spatial Scales? A Case Study for the Northern Part of Brooklyn, New York, USA

Integration of Aerobiological Information for Construction Engineering Based on LiDAR and BIM

Analysis of the spatial differentiation and scale effects of the three-dimensional architectural landscape in Xi’an, China

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