School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Fei Deng
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Yanhao Zhang
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Duanyang Liu
Key Laboratory of Transportation Meteorology, China Meteorological
Administration, Nanjing 210008, China
China Meteorological Administration, Nanjing Joint Institute For Atmospheric Sciences, Nanjing 210008, China
Chao Liu
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
Zhiqiu Gao
Collaborative Innovation Centre on Forecast and Evaluation of
Meteorological Disasters, School of Atmospheric Physics, Nanjing University
of Information Science & Technology, Nanjing 210044, China
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4,484
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6,045
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128
174
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BibTeX: 128
EndNote: 174
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Cumulative views and downloads
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Total article views: 5,076 (including HTML, PDF, and XML)
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3,883
1,097
96
5,076
168
118
160
HTML: 3,883
PDF: 1,097
XML: 96
Total: 5,076
Supplement: 168
BibTeX: 118
EndNote: 160
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Total article views: 969 (including HTML, PDF, and XML)
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601
351
17
969
140
10
14
HTML: 601
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XML: 17
Total: 969
Supplement: 140
BibTeX: 10
EndNote: 14
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Total article views: 6,045 (including HTML, PDF, and XML)
Thereof 5,951 with geography defined
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Total article views: 5,076 (including HTML, PDF, and XML)
Thereof 5,015 with geography defined
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Total article views: 969 (including HTML, PDF, and XML)
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This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by...
