Abstract

This study explores the impact on land surface temperature due to the spatial clustering of urban landforms with normalized difference vegetation index, normalized difference water index and dry bare-soil index. In order to determine the contribution of different land use/land cover classes in affecting the land surface temperature, the contribution index was used for summer and winter seasons. For analyzing the intensity of land surface temperature at the local scale, landscape index was used. Results depicted that the contribution of the source and sink landscapes weakens the intensity of land surface temperature in the winter season. However, the contribution of the source and sink landscape promoted the intensity of land surface temperature in the summer season. Furthermore, this study evaluated the predictive performance of four machine learning models, including K-Nearest Neighbor (K-NN) regression, Neural Networks (NN), Random Trees (RT) regression and Support Vector Machine (SVM) regression for land surface temperature.

摘要

本研究利用归一化差异植被指数、归一化差异水分指数和干旱裸土指数探讨城市地貌空间聚类对地表温度的影响。为了确定不同土地利用/土地覆盖等级对地表温度的影响，贡献指数用于夏季和冬季。为了分析局部尺度的地表温度强度，使用了景观指数。结果表明，源汇景观的贡献减弱了冬季地表温度的强度。然而，源汇景观的贡献促进了夏季地表温度的强度。此外，