摘要:热岛效应已成为城市化进程中备受关注的城市生态环境问题。有关城市热岛效应的研究对区域在地表升温过程中的空间分异关注尚不足。通过设计区域增温敏感性指数，借助增强回归树（boosted regression tree，BRT）机器学习算法，量化城市不同区域在地表升温过程中的敏感性差异。结果表明：（1）上海市各辖区增温敏感性从高至低依次是：黄浦区、杨浦区、虹口区、静安区、普陀区、徐汇区、长宁区、宝山区、闵行区、浦东新区、嘉定区、奉贤区、松江区、青浦区、金山区、崇明区；（2）区域增温敏感性在不同的温度梯度下具有明显差异，在高温梯度下区域间指数值的差异最大；（3）地表覆被的增温敏感性存在明显的区域分异特性；（4）基于BRT的增温敏感性指数具有较好的指示意义和较高的灵敏度。在城市热环境持续恶化背景下，探讨城市异质空间中地表温度变化的敏感性可为城市热环境调控提供科学依据。



Abstract:The urban heat island effect has become a prominent urban environmental issue in the process of urbanization. However, attempts to differentiate regional sensitivities in the process of surface warming are rare. In this study, we designed the regional warming sensitivity index by adopting the boosted regression tree (BRT) approach. With this index, we quantified regional sensitivity differences in the process of surface warming. The results show that: 1) the warming sensitivities of Shanghai's jurisdictions in descending order are Huangpu District, Yangpu District, Hongkou District, Jing'an District, Putuo District, Xuhui District, Changning District, Baoshan District, Minhang District, Pudong New District, Jiading District, Fengxian District, Songjiang District, Qingpu District, Jinshan District, and Chongming District. 2) The difference of regional warming sensitivity is obvious under different temperature gradients, and the regional variability of temperature changes is large under the highest temperature gradient. 3) The warming sensitivity of land covers has obvious regional difference. 4) The regional warming sensitivity index based on the BRT method has a good indicating significance and high sensitivity. The exploration of regional warming sensitivity could provide scientific bases for regulating the urban thermal environment in deteriorating urban thermal environments.





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