Revealing the impact of Urban spatial morphology on land surface temperature in plain and plateau cities using explainable machine learning DOI

Zi Wang,

Rui Zhou, Jin Rui

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: unknown, P. 106046 - 106046

Published: Dec. 1, 2024

Language: Английский

A first Chinese building height estimate at 10 m resolution (CNBH-10 m) using multi-source earth observations and machine learning DOI Creative Commons
Wanben Wu, Jun Ma, Ellen Banzhaf

et al.

Remote Sensing of Environment, Journal Year: 2023, Volume and Issue: 291, P. 113578 - 113578

Published: April 10, 2023

Language: Английский

Citations

162

Landscape metrics in assessing how the configuration of urban green spaces affects their cooling effect: A systematic review of empirical studies DOI
Yilun Li, Chao Ren, Janice Ho

et al.

Landscape and Urban Planning, Journal Year: 2023, Volume and Issue: 239, P. 104842 - 104842

Published: July 26, 2023

Language: Английский

Citations

61

Quantifying the effects of 2D/3D urban landscape patterns on land surface temperature: A perspective from cities of different sizes DOI

Hongchao Xu,

Chunlin Li,

Yuanman Hu

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 233, P. 110085 - 110085

Published: Feb. 7, 2023

Language: Английский

Citations

56

Quantifying tree canopy coverage threshold of typical residential quarters considering human thermal comfort and heat dynamics under extreme heat DOI
Yingnan Li,

Dongli Lin,

Yuhan Zhang

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 233, P. 110100 - 110100

Published: Feb. 14, 2023

Language: Английский

Citations

54

Quantifying the nonlinear relationship between block morphology and the surrounding thermal environment using random forest method DOI
Yuejing Gao, Jingyuan Zhao, Li Han

et al.

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 91, P. 104443 - 104443

Published: Feb. 1, 2023

Language: Английский

Citations

48

Enhanced observations from an optimized soil-canopy-photosynthesis and energy flux model revealed evapotranspiration-shading cooling dynamics of urban vegetation during extreme heat DOI
Zhaowu Yu, Jiaqi Chen, Jike Chen

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 305, P. 114098 - 114098

Published: March 11, 2024

Language: Английский

Citations

46

Influences of urban spatial factors on surface urban heat island effect and its spatial heterogeneity: A case study of Xi'an DOI
Duo Xu, Yiquan Wang, Dian Zhou

et al.

Building and Environment, Journal Year: 2023, Volume and Issue: 248, P. 111072 - 111072

Published: Nov. 29, 2023

Language: Английский

Citations

42

Exploring the seasonal effects of urban morphology on land surface temperature in urban functional zones DOI
Yefei Liu, Weijie Zhang, Wenkai Liu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 103, P. 105268 - 105268

Published: Feb. 9, 2024

Language: Английский

Citations

39

Exploring the Nonlinear Interplay between Urban Morphology and Nighttime Thermal Environment DOI
Xinyue Gu,

Zhiqiang Wu,

Xintao Liu

et al.

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 101, P. 105176 - 105176

Published: Jan. 5, 2024

Language: Английский

Citations

33

Spatiotemporal changes in urban forest carbon sequestration capacity and its potential drivers in an urban agglomeration: Implications for urban CO2 emission mitigation under China’s rapid urbanization DOI Creative Commons

Wenhai Hong,

Zhibin Ren, Yüjie Guo

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 159, P. 111601 - 111601

Published: Jan. 26, 2024

Urban forests can absorb carbon dioxide for urban CO2 emission mitigation. However, the potential capacity of forest sequestration (CS) and its drivers remain unclear in agglomerations under rapid urbanization. In our study, net primary productivity (NPP) built-up areas was reconstructed Harbin-Changchun agglomeration (HCUA) from 2000 to 2020 reflect CS, spatial CS patterns were further explored using Geodetector model. Our results showed that HCUA has experienced urbanization over past 20 years. Across gradient, higher new developing than old developed all The increased gradually 2020, especially large areas. skewed toward low (<100 g·m−2) medium value (100–300 class distributions years; however, proportion high (>300 show an overall increasing trend small, low-altitude total 0.35 Mt·C·yr−1 2.06 could offset approximately 2.23 % emissions 2000, 5.08 2020. Natural factors, such as temperature, mainly determined changes distribution. addition, we found morphology build-up area, construction height, population density, gross national product, significantly influence CS. We there may exist threshold area product affecting variation. interaction between natural anthropogenic factors had stronger explanatory power variation study help city managers formulate low-carbon development strategies address negative impacts climate change realize cities.

Language: Английский

Citations

21