Quantifying the effects of urban land forms on land surface temperature and modelling the spatial variation using machine learning

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.

Keywords:

• NDVI
• NDWI
• DBSI
• SVM
• K-NN
• LST

Disclosure statement

No potential conflict of interest was reported by the authors.