Determining the Influence of Building Density
on Heat Island Effect Using Baidu Map
and Remote Sensing
Shili Chen, Wei Lang, Xun Li, Chong Shen and Qi Fan
Abstract
This study investigated central urban area of Guangzhou as
an example. Data on the types of land use in 2000 - 2009
and building data were used to analyze the changes in the
intensity of urban heat island (
UHI
) by using the meteorologi-
cal and numerical weather and research forecasting (
WRF
)
model. The correlation analysis method was used to analyze
the temperature and building density to explore the decisive
influence of building density on the
UHI
effect. Results showed
that the
WRF
model can be used to simulate the temperature
and humidity performance in the
UHI
effects. Moreover, urban
building density have a great coefficient with
UHI
. The
UHI
ef-
fect is relatively weak when a few buildings sparsely distrib-
uted, various building densities have a substantial influence
on the
UHI
effect. In general, urban development in Guang-
zhou has enhanced human activities and changed the type
of land use, thereby considerably influencing the
UHI
effect.
Introduction
Given the rapid development of urbanization and industrial-
ization, the considerable heat island effect has substantially
affected the daily lives of urban residents and the urban
ecological environment worldwide. Urban heat island (
UHI
) is
a typical representative of urban change caused by urbaniza-
tion.
UHI
is the phenomenon, in which the urban surface and
atmospheric temperature are higher than those of the sur-
rounding non-urban environment; this phenomenon is caused
by land use, thermodynamic power, and release of human
heat, which are considerably common in metropolitan areas
(Oke, 1982; Buyantuyev and Wu, 2010).
Lake Howard, a British climate scientist, first recorded the
phenomenon in 1833 in his book
London Climate
, in which
the main idea is that the temperature in the city center is
higher than that in the suburbs. This climate characteristic
is known as the “heat island effect” (Luke, 1818). Manley
proposed the concept of
UHI
for the first time in 1958 (Mnaley,
1958). Four methods are commonly used to detect the heat is-
land effect, namely, point observation, numerical simulation,
meteorological data, and remote sensing research methods. In
particular, the extensiveness of the observational data and the
development of remote sensing technology have expanded the
method from point to surface and have obtained the research
results on the phenomena and laws of the
UHI
effect (Unwin,
1980). Simultaneously, laboratory development (Cenedese,
2003) and numerical simulation (Kusaka, 2000) further pro-
moted the study of the
UHI
effect.
In recent years, many scholars mainly focused on the
shape and structure (Miao
et al
., 2009), energy change
(Champollion
et al
., 2009; Ryu and Baik, 2012), interaction
mechanisms, and simulations (Freitas
et al
., 2007; King and
Davis, 2007) in the heat island research of cities. The scholars
determined that urbanization has changed the atmospheric
dynamics and heat exchange properties of underlying sur-
faces. Moreover, surface cover and land use have changed
rapidly, thereby promoting the formation of
UHI
. In particular,
many scholars have emphasized that changes in land use
and vegetation cover are the major factors in the formation
and evolution of the
UHI
effects (Kolokotroni and Giridharan,
2008). Each type of land use has different thermal or radiolog-
ical characteristics and is often characterized by high temper-
ature for urban land use, whereas natural elements (e.g., bare
soil, vegetation, and water) have cooling effects (Sun, 2012;
Sun
et al
., 2012). Thus, the expansion of cities causes changes
in the type of land use, thereby resulting in a corresponding
change in the
UHI
effects. Data on changes in the type of land
use were obtained using remote sensing images. The simula-
tion of heat island strength using the weather and research
forecasting (
WRF
) model is still relatively limited.
Many scholars believed that the pattern, structure, and
material composition of an urban space are closely related
with heat island (Connors
et al
., 2013; Wu, 2014). Zhang
et
al
.(2008) considered that no rational allocation of various
types of surface composition on landscape structure caused
the formation of heat island; thus, urban planning and design,
which consider the elements of landscape pattern, is based
on the overall macro of a city
(Guo et al., 2015; Kuang et al
.,
2015; Zhang
et al
., 2008; Zhang
et al
., 2013; Zhou
et al
., 2011).
In recent years, the renovation of the urban construction lay-
out has changed the original natural landscape because of the
rapid progress of urbanization and the real estate industry in
China. The rapid increase of the volume and building density
in the urban construction area become the major factor that
affects the heat island effect. Building density, which indi-
cates the ratio of the coverage of the buildings’ footprints to
the size of the area of interest (Zhang
et al
., 2017), relatively
reflects the building-intensive and empty rate, as well as the
living environment, traffic, green conditions, and ecological
environment, of the city. Moreover, numerous scholars have
used remote sensing data to extract urban building informa-
tion (Huang and Zhang, 2012; Huang
et al
., 2014; Hussain
et al
., 2013). In the process of rapid urbanization, urban
Shili Chen, Wei Lang, and Xun Li are with the Department
of Urban and Regional Planning, Sun Yat-sen University,
Guangzhou 510275, China; and the Urbanization Institute of
Sun Yat-sen University, Guangzhou 510275, China (lixun@
mail.sysu.edu.cn;
).
Chong Shen and Qi Fan are with the School of Atmospheric
Sciences, Sun Yat-sen University, Guangzhou 510275, China.
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 9, September 2018, pp. 549–558.
0099-1112/18/549–558
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.9.549
PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
September 2018
549
