Local Climate Zone in Xi’an City: A Novel Classification Approach Employing Spatial Indicators and Supervised Classification

Abstract

The Local Climate Zone (LCZ), as a foundational element of urban climate zone classification proposed by Oke and Stewart, categorizes urban surface types based on 10 influential parameters affecting the urban heat island effect, such as building density, surface reflectivity, sky view factor, and surface roughness length. This method divides cities into 17 different Local Climate Zones (LCZs) to standardize climate observations and promote global climate research exchange, offering valuable insights for heat island studies. In this study, we enhance the existing local climate zones spatial classification approach by focusing on Xi’an city’s urban layout and architectural features. By using urban spatial indicators and employing a supervised classification approach and a spatial clustering method with land parcels as statistical units, we investigate typical urban areas and classify Xi’an’s land parcels into 17 or 15 distinct local climate zones. Subsequently, through the evaluation of two distinct classification methods, the most suitable urban microclimate zoning method for Xi’an city was selected. This optimization of the local climate zoning representation introduces a spatial classification method tailored to urban climate planning and control, utilizing urban spatial indicators and remote sensing data. The resulting urban climate zoning map not only supports sample selection for urban heat environment parameter observation but also aids urban planners in identifying spatial distribution patterns for climate zoning.

Keywords

• local climate zone
• urban heat island
• multi-source data
• supervised spatial classification