Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas

The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of...

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Autores principales:	Dakota McCarty, Jaekyung Lee, Hyun Woo Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	urban heat island urbanization shapley additive explanation park characteristic extreme gradient boost Dallas Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/3693ad1783c6463dbfb55592b755faf9
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id	oai:doaj.org-article:3693ad1783c6463dbfb55592b755faf9
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spelling	oai:doaj.org-article:3693ad1783c6463dbfb55592b755faf92021-11-25T19:03:14ZMachine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas10.3390/su1322126782071-1050https://doaj.org/article/3693ad1783c6463dbfb55592b755faf92021-11-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/22/12678https://doaj.org/toc/2071-1050The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.Dakota McCartyJaekyung LeeHyun Woo KimMDPI AGarticleurban heat islandurbanizationshapley additive explanationpark characteristicextreme gradient boostDallasEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 12678, p 12678 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	urban heat island urbanization shapley additive explanation park characteristic extreme gradient boost Dallas Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	urban heat island urbanization shapley additive explanation park characteristic extreme gradient boost Dallas Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Dakota McCarty Jaekyung Lee Hyun Woo Kim Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
description	The urban heat island effect has been studied extensively by many researchers around the world with the process of urbanization coming about as one of the major culprits of the increasing urban land surface temperatures. Over the past 20 years, the city of Dallas, Texas, has consistently been one of the fastest growing cities in the United States and has faced rapid urbanization and great amounts of urban sprawl, leading to an increase in built-up surface area. In this study, we utilize Landsat 8 satellite images, Geographic Information System (GIS) technologies, land use/land cover (LULC) data, and a state-of-the-art methodology combining machine learning algorithms (eXtreme Gradient Boosted models, or XGBoost) and a modern game theoretic-based approach (Shapley Additive exPlanation, or SHAP values) to investigate how different land use/land cover classifications impact the land surface temperature and park cooling effects in the city of Dallas. We conclude that green spaces, residential, and commercial/office spaces have the largest impacts on Land Surface Temperatures (LST) as well as the Park’s Cooling Intensity (PCI). Additionally, we have found that the extent and direction of influence of these categories depends heavily on the surrounding area. By using SHAP values we can describe these interactions in greater detail than previous studies. These results will provide an important reference for future urban and park placement planning to minimize the urban heat island effect, especially in sprawling cities.
format	article
author	Dakota McCarty Jaekyung Lee Hyun Woo Kim
author_facet	Dakota McCarty Jaekyung Lee Hyun Woo Kim
author_sort	Dakota McCarty
title	Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
title_short	Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
title_full	Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
title_fullStr	Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
title_full_unstemmed	Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas
title_sort	machine learning simulation of land cover impact on surface urban heat island surrounding park areas
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3693ad1783c6463dbfb55592b755faf9
work_keys_str_mv	AT dakotamccarty machinelearningsimulationoflandcoverimpactonsurfaceurbanheatislandsurroundingparkareas AT jaekyunglee machinelearningsimulationoflandcoverimpactonsurfaceurbanheatislandsurroundingparkareas AT hyunwookim machinelearningsimulationoflandcoverimpactonsurfaceurbanheatislandsurroundingparkareas
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Machine Learning Simulation of Land Cover Impact on Surface Urban Heat Island Surrounding Park Areas

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