Author:
Chen Shihan,Yang Yuanjian,Deng Fei,Zhang Yanhao,Liu Duanyang,Liu Chao,Gao Zhiqiu
Abstract
Abstract. Due to rapid urbanization and intense human activities, the urban
heat island (UHI) effect has become a more concerning climatic and
environmental issue. A high-spatial-resolution canopy UHI monitoring method
would help better understand the urban thermal environment. Taking the city
of Nanjing in China as an example, we propose 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. Firstly, the observed
environmental parameters, e.g., surface albedo, land use/land cover,
impervious surface, and anthropogenic heat flux (AHF), around densely
distributed meteorological stations were extracted from satellite images.
These parameters were used as independent variables to construct an RF model
for predicting air temperature. The correlation coefficient between the
predicted and observed air temperature in the test set was 0.73, and the
average root-mean-square error was 0.72 ∘C. Then, the spatial
distribution of CUHII was evaluated at 30 m resolution based on the output
of the RF model. We found that wind speed was negatively correlated with
CUHII, and wind direction was strongly correlated with the CUHII offset
direction. The CUHII reduced with the distance to the city center, due to
the decreasing proportion of built-up areas and reduced AHF in the same
direction. The RF model framework developed for real-time monitoring and
assessment of high spatial and temporal resolution (30 m and 1 h) CUHII
provides scientific support for studying the changes and causes of CUHII,
as well as the spatial pattern of urban thermal environments.
Funder
National Natural Science Foundation of China
Nanjing University
Cited by
34 articles.
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