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

Abstract

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.

Editorial: Special Issue on Geographical Analysis and Modeling of Urban Heat Island Formation

“…Integrating machine learning algorithms with remote sensing data is an important topic that has received considerable attention. Applying regression analysis and machine learning algorithms, Garzón et al evaluated modeling techniques to assess the impact of various elements on surface UHIs [21]. In this paper, an attempt was made to illustrate the applicability of machine learning algorithms in the surface mapping of UHI intensities by quantifying surface UHIs using different contributing parameters.…”

Using GAMs to Explore the Influence Factors and Their Interactions on Land Surface Temperature: A Case Study in Nanjing

“…Given the profound impact of LST on agricultural productivity, industrial operations, and societal well-being, it is of great importance to comprehend its spatiotemporal dynamics and analyze the underlying driving forces. This will not only facilitate a more effective response to the diverse challenges posed by climate change but also can serve as a crucial foundation for the government and relevant departments in formulating scientific and precise strategies [5][6][7].…”

Urban Heat Island Intensity Prediction in the Context of Heat Waves: An Evaluation of Model Performance

“…Various techniques are employed to map urban heat islands in cities [8][9][10]. These include satellite remote sensing for large-scale temperature assessment, ground-based sensors and weather stations for real-time and precise data collection, aerial thermography using infrared cameras mounted on aircraft or drones to obtain detailed thermal images, on-site temperature measurements using portable thermometers or thermographic devices, and simulation models that incorporate urban geometry, land use, vegetation, and solar radiation to predict and map heat islands [11][12][13][14][15][16][17][18][19]. A combination of these techniques and data sources is crucial to gain a comprehensive understanding of heat islands, enabling informed decision-making in urban planning, mitigation strategies, and the informed safeguarding of residents' health [13,20].…”