Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants
Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-spe...
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doaj-5a56e23e4ede4e0c865f72a6ddbea4712020-11-25T01:32:46ZengMDPI AGRemote Sensing2072-42922020-01-0112339110.3390/rs12030391rs12030391Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island DeterminantsXiali Luan0Zhaowu Yu1Yuting Zhang2Sheng Wei3Xinyu Miao4Zheng Y. X. Huang5Shuqing N. Teng6Chi Xu7School of Life Sciences, Nanjing University, Nanjing 210023, ChinaDepartment of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen, Copenhagen 1958, DenmarkSchool of Life Sciences, Nanjing University, Nanjing 210023, ChinaSchool of Architecture and Urban Planning, Nanjing University, Nanjing 210093, ChinaSchool of Life Sciences, Nanjing University, Nanjing 210023, ChinaCollege of Life Sciences, Nanjing Normal University, Nanjing 210023, ChinaSchool of Life Sciences, Nanjing University, Nanjing 210023, ChinaSchool of Life Sciences, Nanjing University, Nanjing 210023, ChinaUrban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25−5 km) and spatial extents (20−60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect.https://www.mdpi.com/2072-4292/12/3/391land surface temperatureurban heat islandnatural surfacehuman activitypoint of interestmulti-scale analysisscaling |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Xiali Luan Zhaowu Yu Yuting Zhang Sheng Wei Xinyu Miao Zheng Y. X. Huang Shuqing N. Teng Chi Xu |
spellingShingle |
Xiali Luan Zhaowu Yu Yuting Zhang Sheng Wei Xinyu Miao Zheng Y. X. Huang Shuqing N. Teng Chi Xu Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants Remote Sensing land surface temperature urban heat island natural surface human activity point of interest multi-scale analysis scaling |
author_facet |
Xiali Luan Zhaowu Yu Yuting Zhang Sheng Wei Xinyu Miao Zheng Y. X. Huang Shuqing N. Teng Chi Xu |
author_sort |
Xiali Luan |
title |
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants |
title_short |
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants |
title_full |
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants |
title_fullStr |
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants |
title_full_unstemmed |
Remote Sensing and Social Sensing Data Reveal Scale-Dependent and System-Specific Strengths of Urban Heat Island Determinants |
title_sort |
remote sensing and social sensing data reveal scale-dependent and system-specific strengths of urban heat island determinants |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-01-01 |
description |
Urban natural surfaces and non-surface human activities are key factors determining the urban heat island (UHI), but their relative importance remains highly controversial and may vary at different spatial scales and focal urban systems. However, systematic studies on the scale-dependency system-specificity remain largely lacking. Here, we selected 32 major Chinese cities as cases and used Landsat 8 images to retrieve land surface temperature (LST) and quantify natural surface variables using point of interest (POI) data as a measure of the human activity variable and using multiple regression and relative weight analysis to study the contribution and relative importance of these factors to LST at a range of grain sizes (0.25−5 km) and spatial extents (20−60 km). We revealed that the contributions and relative importance of natural surfaces and human activities are largely scale-dependent and system-specific. Natural surfaces, especially vegetation cover, are often the most important UHI determinants for a majority of scales, but the importance of non-surface human activities is increasingly pronounced at a coarser spatial scale with respect to both grain and spatial extent. The scaling relations of the UHI determinants and their relative importance were mostly linear-like at the city-collective level, but highly diverse across individual cities, so reducing non-surface heat emissions could be the most effective measure in particular cases, especially at relatively large spatial scales. This study advances the understanding of UHI formation mechanisms and highlights the complexity of the scale issue underpinning the UHI effect. |
topic |
land surface temperature urban heat island natural surface human activity point of interest multi-scale analysis scaling |
url |
https://www.mdpi.com/2072-4292/12/3/391 |
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