Affiliation:
1. School of Landscape Architecture, Central South University of Forestry and Technology, Changsha 410004, China
2. School of Architecture and Art, Central South University, Changsha 410075, China
3. School of Architecture, Changsha University of Science and Technology, Changsha 410076, China
Abstract
Optimizing urban spatial morphology is one of the most effective methods for improving the urban thermal environment. Some studies have used the local climate zones (LCZ) classification system to examine the relationship between urban spatial morphology and Surface Urban Heat Islands (SUHIs). However, these studies often rely on single-time-point data, failing to consider the changes in urban space and the time-series LCZ mapping relationships. This study utilized remote sensing data from Landsat 5, 7, and 8–9 to retrieve land surface temperatures in Changsha from 2005 to 2020 using the Mono-Window Algorithm. The spatial-temporal evolution of the LCZ and the Surface Urban Heat Island Intensity (SUHII) was then examined and analyzed. This study aims to (1) propose a localized, long-time LCZ mapping method, (2) investigate the spatial-temporal relationship between the LCZ and the SUHII, and (3) develop a more convenient SUHI assessment method for urban planning and design. The results showed that the spatial-temporal evolution of the LCZ reflects the sequence of urban expansion. In terms of quantity, the number of built-type LCZs maintaining their original types is low, with each undergoing at least one type change. The open LCZs increased the most, followed by the sparse and the composite LCZs. Spatially, the LCZs experience reverse transitions due to urban expansion and quality improvements in central urban areas. Seasonal changes in the LCZ types and the SUHI vary, with differences not only among the LCZ types but also in building heights within the same type. The relative importance of the LCZ parameters also differs between seasons. The SUHI model constructed using Boosted Regression Trees (BRT) demonstrated high predictive accuracy, with R2 values of 0.911 for summer and 0.777 for winter. In practical case validation, the model explained 97.86% of the data for summer and 96.77% for winter. This study provides evidence-based planning recommendations to mitigate urban heat and create a comfortable built environment.
Funder
Hunan Provincial Natural Science Foundation General Project
Hunan Provincial Social Science Achievement Evaluation Committee Key Project
Hunan Provincial Philosophy and Social Science Planning Fund Office
Reference71 articles.
1. Masson-Delmotte, V., Pörtner, H.O., Skea, J., Zhai, P., and Roberts, D. (2019). Special Report: Global Warming of 1.5 °C, Intergovernmental Panel on Climate Change (IPCC).
2. China Meteorological Administration (2023). Blue Book on Climate Change in China 2023.
3. Aerospace Information Research Institute, Chinese Academy of Sciences (2023, August 08). Remote Sensing Monitoring Database for the Expansion of Typical Cities in China in the Past 50 Years. Available online: https://aircas.cas.cn/dtxw/kydt/202103/t20210304_5969166.html.
4. Beating the urban heat: Situation, background, impacts and the way forward in China;He;Renew. Sustain. Energy Rev.,2022
5. Relationship of land surface and air temperatures and its implications for quantifying urban heat island indicators—An application for the city of Leipzig (Germany);Schwarz;Ecol. Indic.,2012