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Updated: May 23, 2025

Exploring the Effects of Atmospheric Forcings on Evaporation: Experimental Integration of the Atmospheric Boundary Layer and Shallow Subsurface
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Relationship between urban form and surface temperature based on XGBoost SHAP interpretable machine learning model.

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Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|May 17, 2025
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Summary
This summary is machine-generated.

Urban morphology significantly impacts land surface temperature (LST). Building density and nighttime lights increase LST, while vegetation and open spaces decrease it, offering insights for urban heat management.

Keywords:
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Area of Science:

  • Environmental Science
  • Urban Planning
  • Remote Sensing

Background:

  • Increasing urbanization and high-rise buildings worldwide necessitate understanding urban morphology's impact on land surface temperature (LST).
  • Mitigating urban heat islands (UHIs) and optimizing urban planning require detailed analysis of 2D and 3D urban forms.
  • Previous studies often focused on linear relationships, overlooking complex nonlinear interactions between urban factors and LST.

Purpose of the Study:

  • To investigate the influence of 13 urban 2D/3D morphological factors on LST in Changsha.
  • To reveal the nonlinear impacts and contributions of these factors using advanced modeling techniques.
  • To provide scientific insights for sustainable urban heat management and planning.

Main Methods:

  • Extraction of 13 urban 2D/3D morphological factors from multi-source remote sensing data for Changsha.
  • Pearson correlation analysis to assess linear relationships between LST and morphological factors.
  • Application of XGBoost and SHAP models to uncover nonlinear impacts and contributions of factors to LST.

Main Results:

  • High LST concentrated in building-dense areas; low LST in green spaces and along rivers.
  • Positive correlations found for Normalized Difference Building Index (NDBI), Nighttime Lighting (NTL), and Proportion of Construction Land (PCL); negative correlations for Normalized Difference Vegetation Index (NDVI) and Sky View Coefficient (SVF).
  • NDBI, NTL, NDVI, and Elevation (DEM) were most influential, explaining 60.9% of LST variance, with specific nonlinear effects observed for each factor.

Conclusions:

  • Urban morphology, particularly NDBI, NTL, NDVI, and DEM, significantly influences LST through complex nonlinear relationships.
  • Green spaces (NDVI) and open spaces (SVF) effectively mitigate urban heat, while built-up areas (NDBI, PCL) and NTL exacerbate it.
  • The XGBoost-SHAP model is effective for understanding these nonlinear urban heat dynamics, informing strategies for green, low-carbon urbanization.