Data Land
Resume:
Theor. Appl. Climatol. **, *** *** (****)
DOI **.1007/s00704-006-0273-1
Printed in The Netherlands
1
Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China
2
Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
3
Institute of Atmospheric Physics, Chinese Academy of Sciences, Chao yang district, Beijing, China
4
School of Forestry and Wildlife Sciences, Auburn, Alabama, USA
Assessing the effect of land use= land cover change
on the change of urban heat island intensity
J. F. He1, J. Y. Liu2, D. F. Zhuang2, W. Zhang3, and M. L. Liu2;4
With 6 Figures
Received January 24, 2006; revised May 11, 2006; accepted August 31, 2006
Published online February 28, 2007 # Springer-Verlag 2007
Summary (Esserl, 1989; Grunblatt et al., 1992; Wei and Fu,
1998; Lambin et al., 1999; Hillel and Rosenzweig,
Due to rapid economic development, China has experienced
2002; Gao et al., 2003). The urban heat island
one of the greatest rates of change in land use=land cover
(UHI) effect, the phenomena where air and surface
during the last two decades. This change is mainly urban
expansion and cultivated land reduction in urban growth temperatures of urban areas are higher than those
regions, both of which play an important role in regional of its surrounding rural areas, has been rigorously
climate change. In this paper, the variation of the urban heat
researched (e.g. Oke, 1982; Owen, 1998). UHI
island (UHI) caused by urbanization has been evaluated with
was described by Roth et al. (1989) as one of
an analysis of land use change in China. First, meteorological
the most clearly established examples of inadver-
observation stations were grouped by different land cover
tent modi cation of climate. Numerous studies
types (dry land, paddy eld, forest, grassland, water eld,
urban, rural inhabitable area, industrial and mineral land, and have shown that urbanization can produce radi-
waste land) throughout China. These stations were sub- cal changes in the radiative, thermodynamic, and
divided into urban and non-urban classes. Then, a new
water processes of the land surface and modify
method was proposed to determine the UHI intensity from
local climate in terms of for example, tempera-
the difference between the observed and the interpolated air
ture, precipitation, and cloudiness (Carlson and
temperature of urban type weather stations. The results
indicate that the trends of UHI intensity in different land use Arthur, 2000; Huff and Changnon, 1972; Oke,
change regions are spatially correlated with regional land use 1974). There is increasing evidence that global
and its change pattern. During 1991 2000, the estimated
warming is a result of anthropogenic activity dur-
UHI intensity has increased by 0.11 C per decade in the
ing the past fty years (IPCC, 2001). Global warm-
spring and has uctuated in other seasons throughout China
ing can be partitioned into (1) the urban heat
resulting from land use change.
island effect, (2) the effect of deforestation, (3)
the effect of secular micro-climate shift, (4) the
1. Introduction
in uence of general global warming with par-
ticular reference to the tropics (Harger, 1995). It
In recent years, scientists have recognized that
land cover=land use changes induced by human is therefore important to eliminate the urban heat
island effect from observed temperature records
activities have large impacts on regional climate
218 J. F. He et al.
in order to assess monthly, seasonal, and annual which is derived from high resolution remote
averaged long-term temperature trends (Karl sensed data and covers the period from the end
et al., 1988; Jones et al., 1990; Wang et al., 1990; of the 1980s to 2000 (Liu et al., 2003, 2005a), this
Matsushita et al., 2004). For example, a reanalysis study develops a new method of estimating how
of global temperature data over the past 50 years the intensity of the UHI has changed during this
that is insensitive to surface dynamics has been ten year period and how much was the result of
produced (Kalnay et al., 1996; Kistler et al., 2001) land use change.
and the data have been used to assess the long-term
climate change, its causes and its impact on carbon
2. Data sources
and water cycles.
The intensity of the urban heat island (UHI) is The intensively validated NLCD are used as land
directly related to the rate of urbanization, land use use change background for this study. The NLCD
patterns, and building density. It can be assessed in contains land use data in vector format with a
several ways: measuring the UHI by comparing scale of 1:100,000 and gridded land use percen-
present to earlier pre-urban conditions (Lowry, tage data at a one square kilometre resolution.
1977), measuring the UHI through the urban-to- The NLCD, constructed by the Chinese Academy
rural temperature difference (Lowry, 1977; Karl of Sciences and the Ministry of Science and
et al., 1988); and measuring the UHI in terms of Technology, is based on remote sensed data
surface (or skin) temperature through the use of Landsat TM=ETM (Thematic Mapper=Enhanced
airborne or satellite remote sensing (Roth et al., Thematic Mapper) covering three periods: the
1989; Gallo and Tarpley, 1996; Streutker, 2003). end of the 1980s (1985 1990), 1995=1996, and
The selection of the most appropriate method 2000 (Liu et al., 2003). According to quality as-
depends mainly on the observed data available. sessment, the accuracy of classi cation for urban
China, the biggest developing county in land areas is 96.32% and the bias of polygon bound-
area, has experienced rapid urbanization in recent aries for each land use type is less than 45 m
years (Liu et al., 2005a, b). Some recent studies (Liu et al., 2003). The gridded percentage of land
have shown the effect of large scale urbanization use represents the areal composition of each land
on regional temperature change in China (Zhang use type within each one square kilometre unit
et al., 2003; Zhou et al., 2004), but they failed to (Liu et al., 2002). To characterize the spatial pat-
directly estimate the temporal and spatial variabil- tern of variability in intensity of UHI from land
ity of the UHI intensity impacted by the real land use change, the map of national land use change
use change (especially urban sprawl). Using the zoning (Liu et al., 2003) was used as regional
National Land Cover Dataset (NLCD) of China, divisions.
Fig. 1. Spatial distribution of
meteorological observation sta-
tions and their corresponding
ground surface land use types
Assessing the effect of land use=land cover change on the change of urban heat island intensity 219
Daily temperature data used in this study are improvement and extension in order to eliminate
collected from the 673 meteorological stations uncertainty and to discover the effect of regional
of the China Historical Climatological Network land use change over a long time period by using
and include records from 1991 to 2000 (China high resolution data. In general, signi cant spa-
Meteorological Administration, http:==211.147. tial differences (elevation and latitude) would
16.25=ywwz=constitute=wls01.php). At rst, the exist between urban and non-urban pair stations
tabular air temperature data are converted into if their distance is over 100 kilometres (km). Un-
spatial data with point vector format in Geo- fortunately, many urban stations in China are far
graphical Information Systems (GIS). Then, by from the surrounding non-urban stations. It is dif-
overlaying the station point vector layer with cult to nd enough urban and non-urban pairs to
the NLCD land use layer, the satellite-based land estimate the intensity of UHI at a national scale.
use classes (dry land, paddy eld, forest, grass- Here, a new method is proposed to assess the
land, water eld, urban, rural inhabitable area, intensity of UHI. It identi es the difference be-
industrial and mineral land, and waste land) are tween the observed air temperature by an urban
designated to meteorological observation stations. weather station and the interpolated temperature
Stations with the urban land use feature are de- by non-urban stations. The interpolated tempera-
ned as urban type weather stations, and those tures from non-urban stations are set as back-
with other land use features are classed as non- ground temperature of the urban station. The
urban types. According to this de nition, 273 estimated intensity of UHI (HI) for one urban
stations (40% of total numbers) were urban in station can be expressed as
2000. There were 46 weather stations experienc- HI Tu Tb 1
ing urbanization (from non-urban to urban) from
where Tu is the observed temperature of the
1991 to 2000, accounting for 6% of the total
urban station; Tb is the background temperature
stations (Fig. 1). In this study, observations are
of the urban station. Figure 2 describes the de -
used to identify which stations (urban type) are
nition of urban background temperature of the
affected by the UHI effect and which are not,
urban station. The interpolation method is from
with the exception of the 46 weather stations ex-
Thornton et al. (1997) which is based on the spa-
periencing urbanization.
tial convolution of a truncated Gaussian weight-
ing, i.e.
Pn
3. Methods Wi;r Ti 0 1 zb zi
Tb i 1 Pn 2
i 1 Wi;r
3.1 Estimation of UHI intensity 8
at the regional scale Rb
2
Wi;r 3
: exp Rb e ; r Rb
As previously described, the traditional methods r
used to estimate the intensity of UHI required
Fig. 2. (a) Urban and its neigh-
bor non-urban stations (b) cal-
culation of urban background
temperature of urban station
220 J. F. He et al.
where Ti is the observed temperature around the Finally, the probability distributions of the
number of UHI affected sites Pk for each sea-
non-urban station i; zb is the elevation of the pre- m
dicting urban station; zi is the elevation of the son at the regional scale is calculated.
X 1; if F k m
non-urban station i; Wi,r is the lter weight of k j
Pm 6
the non-urban station i that related with radial 0; else
j
distance r from the predicting site; Rb is the trun-
where m represents the number of years that site j
cation distance from the predicting site; is a
represented positive UHI intensity in season k, the
unit-less shape parameter of the Gaussian func-
value scope of m is from 0 to 10. The probability
tion; n is the total number of non-urban weather
of Pk is equal to Pk =sum of urban stations.
stations that have a distance less than Rb from the m m
predicting urban station; 0 and 1 are the coef-
cients of linear regression, which is calculated 3.3 Validation of the method
from station pairs by the 2-stage least squares ap-
To validate this method, we camed out experi-
proximation method to re ect the altitude effect
ments to randomly select urban and non-urban
on temperature. Rb is assigned as 150 km and
stations upon which to calculate UHI intensities
as 3.0 after many tests to achieve the least extra-
for the study period. A station is de ned as an
polation error. In the processing of interpolation,
urban type based on the possibility of an urban
weighting averages of the temperature of non-
effect within a certain distance from the station.
urban stations within 150 km of the urban station
A one kilometre distance is selected as the buf-
will remove the effects of latitudinal differences,
fering radius around the station to run the overlay
and the calculated vertical temperature reduction
procedure with NLCD urban layer by GIS. If the
rate of elevation will reduce the elevation effects
urban area covers more than 75% of the buffering
on the interpolated temperature.
zone of the station, the station is re-classi ed as
In this paper, we calculated the variability
an urban type, otherwise, as a non-urban station.
of UHI intensity for each urbanization affect-
ed weather station by seasonal average air tem-
perature for the periods: Dec. Feb. (winter),
Mar. May (spring), June Aug. (summer) and
Sep. Nov. (autumn).
3.2 Analysis of statistical features
of estimated UHI intensity
In order to estimate regional UHI intensity effec-
tively, a discrete mathematics method is used. At
rst, for each site in each season, the estimated
UHI intensity is transformed from oat format
into integer format so that only 0 or 1 is assigned
(Eq. 4).
(
0; HIik; j 0
Dk; j 4
1; HIik; j > 0
i
where k represents each season; i represents each
year; j represents each urban weather station.
Then, the frequency of the UHI effect Fjk (i.e.
UHI intensity is 1) at each station for each season
during 1991 2000 is calculated.
X
Fjk Dk; j 5 Fig. 3. Statistical distributions of Pk for both urban type
i m
(a) and non-urban type (b) stations
i
Assessing the effect of land use=land cover change on the change of urban heat island intensity 221
The statistical distributions of Pk for both urban ability of UHI intensity at the station level with
m
and non-urban type stations are then calculated. land use change zoning maps (Liu et al., 2003)
Signi cant differences between them are found. (Fig. 5). Table 1 describes the change area of
For urban stations, when m 10, the probability main land use types in different land use change
of Pk is the largest in all m and when m 0, the zones. From the percentage of urban stations in-
m
probability of Pk is small; while for the non- tensively affected by UHI (we refer to the value
m
of Pk from formula 6) in each zone, the spatial
urban stations, the probability distribution of 10
Pk is close to the ideal situation that Pk is equal differences in urbanization effect on temperature
m m
to m 0 and m 10 (Fig. 3). This indicates that would be discovered. Our results show that Zone 4
our method effectively detects the UHI effect. (Huang-Huai-Hai and Yangtze River delta culti-
vated land to urban land transform area) has the
strongest UHI effect from 1991 to 2000 (Figs. 5
4. Results
and 6).
Our study also shows that the variability of
4.1 The trend and seasonal variability of UHI
UHI intensity is correlated with land use change
intensity in China
during the period of 1991 to 2000 (Fig. 6). The
The intensity of UHI varies seasonally. The urban
greatest increase in UHI intensity took place in
effect on temperature is more signi cant in winter
the area with rapid urbanization. Urbanization is
and autumn than that in spring and summer. For
a widespread phenomenon in China due to its
example, more stations have large UHI intensity
fast economic growth and the increasing desire
in the rst two seasons (Fig. 4). For each season,
by people for an urban life style. During this study
we found no signi cant trend in HI except in spring
period, remote sensing data discovered that more
with an increasing rate of 0.11 C per decade
than 70% of the increased urban area is located
(y 0.011x 0.605, R2 0.517) (Fig. 4). HI uc-
in Region 4 (Huang-Huai-Hai and Yangtze River
tuated from 1.0 to 1.2 C (mean 1.1 C) in winter,
delta region), Region 5 (Sichuan basin region),
from 0.6 to 0.8 C (mean 0.66 C) in spring, from
and Region 11 (East-southern seaside of China)
0.6 to 0.7 C (mean 0.65 C) in summer, and from
(Liu et al., 2003, 2005a). Table 2 gives the urban
0.7 to 0.8 C (mean 0.76 C) in autumn.
area change ratios of the four main urban growth
regions every ve years during 1990 2000. Most
4.2 The spatial pattern of UHI intensity of the newly increased urban area in Region 4
and its relationship with land use (about 1100 thousand hectares) is from farmland
that has been cultivated for a long time. The ur-
To study the relationship between UHI inten-
banization process has greatly affected the local
sity and land use change, we linked the vari-
environment and climate systems in the last de-
cade as has been shown in this study in the case
of temperature in Regions 4, 6 and 7. The urban
weather stations with greatest UHI effect are
broadly distributed across these areas. In North
China and the Loess Plateau region (Region 6),
a large area covered by natural vegetation (re-
duction 1,695,820 ha.) and water (reduction
116,348 ha.) has been transformed into cropland
and urban areas. This would induce signi cant
environmental risks because of the fragile phys-
ical background (Zhang et al., 2003). The main
feature of land use change in Central China
(Region 8) is the transformation of paddy elds,
pothole pools, bottomland and lakes and the ex-
pansion of urban land. In the southeastern hill
forest, the land=cultivated land transformation
Fig. 4. Urban heat island intensity distributions in different
seasons during 1991 2000 (unit: C) area (Region 9), farmland and forestland re-
222 J. F. He et al.
Fig. 5. The distribution map of urban stations which are affected by UHI overlaying with land use change zoning map in four
seasons. The black dot represents urban station; the size of them represents the seasonal average UHI intensity of urban
station; the number of polygon is the code of land use change zone, which are, 1 north-east China Da=xiao xing an mountains
forestry=grass land ! cultivated land transform area, 2 east part of north east China forestry=grass land ! cultivated land
transform area, 3 north-east China plain dry land ! paddy eld transform area, 4 Huang-Huai-Hai and Yangtze river delta
cultivated land ! urban land transform area, 5 Sichuan basin cultivated land ! urban transform area, 6 north China=Loess
Plateau farming and herding grass land ! cultivated land transform area, 7 north-west China cultivated land expansion and
waste land transform area, 8 central China interchange between paddy elds, pothole pools, bottomland and lakes and
urbansprawl area, 9 south eastern hill forest land ! cultivated land transform area, 10 south eastern seaside grass land ! man
made forestry transform area, 11 south-east seaside urban sprawl area, 12 south-western forest-grass land and forest grass
land ! cultivated land transform area, 13 Qinghai-Tibet Plateau little change area
veal a mixed distribution. Water area changes fre- Plateau region, belonging to stable and less change
quently. Though urbanization expands in these areas), urban stations are either not or only very
regions, due to the temperature regulation func- slightly affected by the UHI effect. However, be-
tion of water, the climate environment is hard- cause of development, some urban stations in
ly affected by land use change. Therefore, in big cities in these regions have experienced the
Regions 1, 2, 3, 5, 8, 9 and 13 (Qinghai-Tibet UHI effect.
Assessing the effect of land use=land cover change on the change of urban heat island intensity 223
Table 1. Land use change area in different land use change zone
Zoning code Paddy eld Dry eld Forestry Grassland Unused eld Water eld Urban
758***-****** 42924
1-242**-****** 10260 6310
2-135***-****** 17192
391**-****** 649**-****** 50233
349***-****** 219**-****** 117955
5 969 23 599 80747
169****-****** 116348
872812
7-132**-****** 527*-****** 126***-*****
8-134*-****-***** 48079
246**-*****-**** 20030 66
56231 634*-****** 7268
10-249***-***** 59455
11-278*-***** 192767
13 0-906*-****-***** 2987
Unit: Ha. Positive value means increase, negative value means decrease.
and the scale of study. At the regional scale, if
urban expansion cannot be successfully detected
and considered in analyzing the station data, the
bias in estimating the UHI intensity and the tem-
perature trend would be large. The method we
used in this study is appropriate in studying the
UHI effect when the availability of urban rural
pairs and meteorological stations is limited. At
the regional scale, urban land use changes cannot
be detected and may cause some bias in UHI
studies (Brian et al., 1998). However, we selected
the non-urban stations free of the in uence of
Fig. 6. The relationship between UHI spatial distribution
UHI, in some cases; microclimate differences
and the land use dynamic change spatial distribution
cannot be removed completely within a range of
150 km. Also, the interpolation method itself
5. Discussion
may introduce some uncertainties because of the
sparse distribution of the stations.
UHI is a special meteorological phenomenon in
In order to evaluate our results we compared our
urban areas but is controlled by the physical
study with that of Weng and Yang (2004) which
characteristics and structure of the land surface
examines the impact of urban development on
within and around the urban area such as soil
UHI through a historical analysis of urban rural
moisture, vegetation communities and imper-
air temperature differences. Weng and Yang (2004)
vious surface coverage. Detecting the UHI effect
indicates that during 1985 1995 and 1996 2000,
relies on the availability of meteorological data
Table 2. Urban area change ratio in main 4 urban growth regions every ve years during 1991 2000
Code Urban area Increased area Increased area Ratio during Ratio during
of 1990s during 1990 1995 during 199*-****-**** 199*-****-****
4 1143462.12 418811.35 69101.79 36.63 4.42
5 73057.95 19332.61 21261.72 26.46 23.01
8 135546.88 22887.80 3187.50 16.86 2.01
11 254623.56 101871.23 6541.32 40.01 1.83
Area unit: Ha.
224 J. F. He et al.
Table 3. The UHI intensity of Guangzhou city measured in two ways
Year 199*-****-**** 199*-****-**** 199*-****-**** 2000
0.308 0.125 0.100 0.050
Weng s result 0.542 0.458 0.492 0.400 0.658 0.058
0.203 0.2 0.025
Estimated in our paper 0.548 0.608 0.593 0.718 0.703 0.025 0.12
0.006 0.150 0.101 0.318 0.045 0.106 0.075 0.170
Diff. 0.075 0.033
R2: 0.822
the average intensity of the UHI is 0.7 C=year If the weather station is located at the bound-
and 0.13 C=year, respectively, in the station lo- ary of an urban area, it may not be detected even
cated in Guangzhou, a big city in southeast China though it is surely affected by the UHI. The noise
(Table 3). The intensity measured in Weng s paper produced by the microclimate of the urban area
is lower than ours and the R2 0.822. The main where weather stations are located may disguise
reason for this difference is that the rural station the UHI effect (Karl et al., 1988). Also, environ-
has been affected by UHI after 1990 due to urban mental variables, such as wind speed, cloud cov-
area enlargement and the rapid increase of its tem- er, atmospheric aerosol, air pollutions, and water
perature. The high R2-value, close to 1, shows that vapor content, would have an impact on the UHI
the precision of our estimated UHI intensity at this effect. This indicates that many factors can con-
station is relatively high and reasonable. Those trol the UHI effects occurring in an urban area.
urban stations with negative HI during 1990 Our study has indicated that the pattern of land
2000 are not considered as UHI affected stations use change is very important for the dynamics of
in our study. For example, we did not select the local climate. For example, both the growth of
weather station located in Guangzhou City as a UHI the urban area and the drastic reduction in lake
affected station because its HI was not always areas produced signi cant UHI effect in the ur-
positive during 1990 2000. The negative HI may ban area (Jazcilevich et al., 2000). In contrast, the
be mainly due to the impact of other land-use expansion of the water surface can buffer the
changes on non-urban stations (Kalnay and Cay, variation of surface temperature and so decrease
2003), or may potentially be related to: (i) the the UHI effect.
difference of geographic environment (longitude
and latitude, elevation, land use type) of the
6. Conclusions
weather stations, or (ii) the location of the weather
stations. In Fig. 3, we nd that about half of the It is feasible to estimate the UHI effect on urban
urban stations show no urban heat island effect weather stations at the regional scale by com-
(i.e. there are several negative HI in those sta- paring observed temperature with interpolated
tions). We did not discuss how to identify and background temperatures from near non-urban
detect whether an urban station was affected by stations. Our results show that many weather sta-
UHI when the station revealed negative HI. Ap- tions were under the UHI effect during all sea-
parently, the estimation of UHI intensity is sensi- sons, especially in winter and autumn, and they
tive to methodology. For different countries and were broadly distributed during the period of
regions, the land use change, especially urbaniza- 1991 2000. The intensity of UHI revealed sea-
tion, effect on the intensity of the UHI effect is sonal variation. In winter and autumn the urban
estimated to be 0.11 C=year 0.91 C=year (Karl effect on temperature was more signi cant than
et al., 1988; Wang et al., 1990; Streutker, 2003). in spring and summer. The average UHI intensity
was 1.1 C in winter, 0.66 C in spring, 0.65 C in
We do not assess the effect of UHI on temperature
summer, and 0.76 C in autumn during this ten-
trend series warming as do many studies such as
Kalnay and Cai (2003), Zhou et al. (2004), Li et al. year period.
(2004). These studies indicate that the UHI ef- The urban stations affected by UHI are
fect causes the temperature warming trends of broadly distributed in the Huang-Huai-Hai River
0.05 C=decade 0.011=decade depending on dif- and Yangtze River Delta areas, Southeast China,
ferent regions and analysis methods. the Loess Plateau of North China, and Wulumuqi
Assessing the effect of land use=land cover change on the change of urban heat island intensity 225
Harger JRE (1995) Air-temperature and ENSO effects in
city-Shihezi city in Northwest China. These areas
Indonesia, the Philippines and El Salvador. ENSO pat-
have experienced rapid urbanization and other
terns and changes from 1866 1993. Atmos Environ 29:
intensive land use change during this study pe- 1919 1942
riod. We found that the variability of UHI inten- Hillel D, Rosenzweig C (2002) Deserti cation in relation to
sity has a strong spatial connection with the climate variability and change. Adv Agron 77: 1 38
pattern of land use, i.e. the in uence of the UHI Huff FA, Changnon SA Jr (1972) Climatological assessment
of urban effects on precipitation at St. Louis. J Appl
effect on urban stations is not only related to land
Meteor 11: 823 842
use change type, but also to land use change
IPCC (2001) Climate change 2001: Working Group I: The
structures. In regions where land use change type Scienti c Basis, IPCC third assessment report
represented the sharp expansion of an urban area Jones PD, Groisman PY, Coughlan M, Plummer N, Wang
and the reduction in water area, the urban sta- WC, Karl TR (1990) Assessment of urbanization effects
in time series of surface temperature over land. Nature
tions were often easily affected by UHI. On the
347: 1669 1772
other hand, in regions where land use change type
Kalnay E, Cai M (2003) Impact of urbanization and land-use
was the expansion of urban area accompanied by change on climate. Nature 423: 528 531
an increase in water area or vegetation area, the Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D,
urban stations were seldom affected by UHI. So, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y,
reductions in natural vegetation (forest and grass) Celliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC,
Ropelewski C, Wang J, Leetmaa A, Reynolds R, Jenne R,
and water area (such as wetland and lake) would
Joseph D (1996) The NCEP=NCAR 40-year reanalysis
change microclimate patterns of temperature and
project. Bull Amer Meteor Soc 77: 437 471
make the environment more fragile and climate Karl TR, Diaz HF, Kukla G (1988) Urbanization: its de-
elements more sensitive to global temperature tection and effect in the United States climate record.
change. J Climate 1: 1099 1123
Kistler R, Kalnay E, Collins W, Saha S, White G, Woollen J,
Chelliah M, Ebisuzaki W, Kanamitsu M, Kousky V, et al
(2001) The NCEP-NCAR 50-year reanalysis: monthly
Acknowledgments
means CD-ROM and documentation. Bull Amer Meteor
This research was supported by China s Ministry of Science Soc 82: 247 268
and Technology (MOST) 973 Program (2002CB412500), Lambin EF, Baulies X, Bockstael N, Fischer G, Krug T,
and the Resources and Environment Scienti c Data Center, Leemans R, Moran EF, Rindfuss RR, Sato Y, Skole D,
Chinese Academy of Sciences. We thank three anonymous Turber BL II, Vogel C (1999) IGBP Report No.48=IHDP
reviewers and Dr. P. Gong for their very helpful comments Report No. 10: Land-use and land-cover change imple-
and suggestions. mentation strategy. IGBP Secretariat, The Royal Swedish
Academy of Sciences, pp 75 85
Li Q, Zhang H, Liu X, Huang J (2004) Urban heat island
References effect on annual mean temperature during the last 50 years
in China. Theor Appl Climatol 79: 165 174
Brian P, Dan Moses S-K, Craig KS, Hashem A (1998)
Liu J, Tian H, Liu M, Zhuang D, Melillo JM, Zhang Z
Existing Climate Data Sources and Their Use in Heat
(2005a) China s changing landscape during the 1990s:
Island Research, http:==eetd.lbl.gov=EA=Reports=41973=
Large-scale land transformations estimated with satel-
Existing Climate Data Sources and Their Use in Heat
lite data. Geophys Res Lett 32: L02405, doi:10.1029=
Island Research
2004GL021649
Carlson TN, Arthur ST (2000) The impact of land use=land
Liu J, Liu M, Tian HQ, Zhuang D, Zhang Z, Zhang W, Tang X,
cover changes due to urbanization on surface microcli-
Deng X (2005b) Spatial and temporal patterns of China s
mate and hydrology: A satellite perspective. Global Planet
cropland during 1990 2000: An analysis based on Landsat
Change 25: 49 65
TM data. Remote Sensing Environ 98: 442 456
Esser G (1989) Global land-use changes from 1860 to
Liu JY, Zhang ZX, Zhuang DF, Wang YM, Zhou WC, Zhang
1980 and future projections to 2500. Ecol Model 44:
SW, Li RD, Jiang N, Wu SX (2003) A study on the spatial-
307 316
temporal dynamic changes of land-use and driving forces
Gallo KP, Tarpley JD (1996) The comparison of vegetation
analyses of China in the 1990s. Geographical research
index and surface temperature composites for urban heat-
(Chinese version) 22: 1 12
island analysis. Int J Remote Sensing 17: 3071 3076
Liu JY, Liu ML, Zhuang DF, Zhang ZX, Deng XZ (2002)
Gao XJ, Luo Y, Lin WT, Zhao ZC, Giorgi F (2003) Simula-
Analyses of the recent spatial land use change pattern in
tion of effects of land use change on climate in China by a
China. Sci China Ser D 32: 1031 1040
regional climate model. Adv Atomos Sci 20: 583 592
Lowry WP (1977) Empirical estimation of urban effects
Grunblatt J, Ottichilo WK, Sinange RK (1992) A GIS
on climate: A problem analysis. J Appl Meteor 16:
approach to deserti cation assessment and mapping.
129 135
J Arid Environ 23: 81 102
J. F. He et al.: Assessing the effect of land use=land cover change on the change of urban heat island intensity
226
Matsushita B, Xu M, Chen J, Kameyama S, Tamura M (2004) Wei HL, Fu CB (1998) Study of the sensitivity of a regional
Estimation of regional net primary productivity (NPP) model in response to land cover change over northern
using a process-based ecosystem model: How important China. Hydrol Process 12: 2249 2265
is the accuracy of climate data? Ecol Model 178: 371 338 Weng QH, Yang SH (2004) Managing the adverse thermal
Oke TR (1974) Review of urban climatology 1968 1973. effects of urban development in a densely populated
Tech Note No. 134, WMO, 132 pp Chinese city. J Environ Manage 70: 145 156
Oke TR (1982) The energetic basis of the urban heat island. Zhang GY, Dong WJ, Ye DZ, Fu ZB (2003) New proof to
Quart J Roy Meteor Soc 108: 1 24 validate the effect of vegetation on summer climate in
Owen TW, Carlson TN, Gillies RR (1998) An assessment of China. Chinese Sci Bull 48: 91 95
satellite remotely-sensed land cover parameters in quan- Zhou LM, Dickinson RE, Tian YH, Fang JY, Li QX,
titatively describing the climatic effect of urbanization. Int Kaufmann RK, Tucker CJ, Myneni RB (2004) Evidence
J Remote Sensing 19: 1663 1681 for a signi cant urbanization effect on climate in China.
Roth M, Oke TR, Emery WJ (1989) Satellite-derived urban PNAS 101: 9540 9544
heat islands from three coastal cities and the utilization of
such data in urban climatology. Int J Remote Sensing 10: Authors addresses: J. F. He (e-mail: ***@****.**.**),
1699 1720 Institute of Remote Sensing Applications, Chinese Academy
Streutker DM (2003) Satellite-measured growth of the urban of Sciences, P.O. Box 9718, Beijing 100101, China; J. Y. Liu
heat island of Houston, Texas. Remote Sensing Environ (correspondence; e-mail: *****@******.**.**), D. F. Zhuang,
85: 282 289 Institute of Geographical Sciences and Natural Resources
Thornton PE, Running SW, White MA (1997) Generating Research, Chinese Academy of Sciences, P.O. Box 9719,
surfaces of daily meteorological variables over large Beijing 100101, China; W. Zhang, Institute of Atmospheric
regions of complex terrain. J Hydrol 190: 214 251 Physics, Chinese Academy of Sciences, Chao yang district,
Wang WC, Zeng Z, Karl TR (1990) Urban heat islands in Beijing 100029, China; M. L. Liu, School of Forestry and
China. Geophys Res Lett 17: 2377 2380 Wildlife Sciences, Auburn, Alabama, USA.
Contact this candidate