A Remote Sensing Approach for Surface Urban Heat Island Modeling in a Tropical Colombian City Using Regression Analysis and Machine Learning Algorithms

Remote Sensing

Iñigo Molina¹  Jesús Velasco¹  Julián Garzón¹  Andrés Calabia² 

[1] Department of Surveying and Cartography Engineering, Universidad Politécnica de Madrid, 28031 Madrid, Spain;School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;

关键词: Surface Urban Heat Island (SUHI);    Land Surface Temperature (LST);    Principal Component Analysis (PCA);    Multiple Linear Regression (MLR);    Machine Learning;    Naïve Bayes;   

DOI  :  10.3390/rs13214256

来源: DOAJ

【 摘 要 】

The Surface Urban Heat Islands (SUHI) phenomenon has adverse environmental consequences on human activities, biophysical and ecological systems. In this study, Land Surface Temperature (LST) from Landsat and Sentinel-2 satellites is used to investigate the contribution of potential factors that generate the SUHI phenomenon. We employ Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) techniques to model the main temporal and spatial SUHI patterns of Cartago, Colombia, for the period 2001–2020. We test and evaluate the performance of three different emissivity models to retrieve LST. The fractional vegetation cover model using Sentinel-2 data provides the best results with R² = 0.78, while the ASTER Global Emissivity Dataset v3 and the land surface emissivity model provide R² = 0.27 and R² = 0.26, respectively. Our SUHI model reveals that the factors with the highest impact are the Normalized Difference Water Index (NDWI) and the Normalized Difference Build-up Index (NDBI). Furthermore, we incorporate a weighted Naïve Bayes Machine Learning (NBML) algorithm to identify areas prone to extreme temperatures that can be used to define and apply normative actions to mitigate the negative consequences of SUHI. Our NBML approach demonstrates the suitability of the new SUHI model with uncertainty within 95%, against the 88% given by the Support Vector Machine (SVM) approach.

【 授权许可】

Unknown