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Remote Sens. 2017, 9(6), 536; doi:10.3390/rs9060536

Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

1
,
2,3,* , 2,* and 3
1
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Key Laboratory of Virtual Geographic Environment of Ministry of Education, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
2
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
3
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Dale A. Quattrochi and Prasad S. Thenkabail
Received: 25 March 2017 / Revised: 13 May 2017 / Accepted: 24 May 2017 / Published: 27 May 2017
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Abstract

Canopy layer heat islands (CLHIs) in urban areas are a growing problem. In recent decades, the key issues have been how to monitor CLHIs at a large scale, and how to optimize the urban landscape to mitigate CLHIs. Taking the city of Wuhan as a case study, we examine the spatiotemporal trends of the CLHI along urban-rural gradients, including the intensity and footprint, based on satellite observations and ground weather station data. The results show that CLHI intensity (CLHII) decays exponentially and significantly along the urban-rural gradients, and the CLHI footprint varies substantially and especially in winter. We then quantify the driving factors of the CLHI by establishing multiple linear regression (MLR) models with the assistance of ZY-3 satellite data (with a spatial resolution of 2.5 m), and obtain five main findings: (1) built-up area had a significant positive effect on daily mean CLHII in summer and a negative effect in winter; (2) vegetation had significant inhibiting effects on daily mean CLHII in both summer and winter; (3) absolute humidity has a significant inhibiting effect on daily mean CLHII in summer and a positive effect in winter; (4) anthropogenic heat emissions exacerbated the daily mean CLHII by about 0.19 °C (90% confidence interval −0.06–0.44 °C) on 17 September 2013 and by about 0.06 °C (−0.06–0.19 °C) on 23 January 2014; and (5) if most of the urban area is transformed into roads (i.e., an extreme case), we estimate that the daily mean CLHII would reach 1.41 °C (0.38–2.44 °C) on 17 September 2013 and 0.14 °C (0.08–0.2 °C) on 23 January 2014 in Wuhan metropolitan area. Overall, the results provide new insights into quantifying the CLHI and its driving factors, to enhance our understanding of urban heat islands. View Full-Text
Keywords: urban heat island (UHI); surface air temperature; spatiotemporal trend; NDVI; NDBI; absolute humidity and nighttime lights urban heat island (UHI); surface air temperature; spatiotemporal trend; NDVI; NDBI; absolute humidity and nighttime lights
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Li, L.; Huang, X.; Li, J.; Wen, D. Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing. Remote Sens. 2017, 9, 536.

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