box below,
and spatial
nployed in
veen urban
gether with
alWeighted | :
on (GWR) i
ysis
on surface
statistical
| analysis is
ban surface
üch can be
:R. Table 1
dels. Under
important
an surface
e
endent variable
pende nt variables
'SIS
a of all of
ch in Hong
ler stations
ogical data
ese stations
LO) for this
ats. Within
ing to 957
where the
with the
isive urban
sparsely in
ii, Sha Tin,
ly focusing
which are
study area.
long Kong
fish ponds
ea includes
a few industrial development areas located in Tuen Mun,
Tsuen Wan, Tai Po and Yuen Long. Together with the
mountains topography, the diverse land use in the study area
provides practical meaning for the study of local effect on
surface thermal anomalies under the subtropical climate.
The study period ranges from 2003 to 2006 when the ASTER
data used for this site study is available. The ASTER images
of these years are obtained from the achieved database in the
Land Process Distributed Active Archive Center through the
Earth Observing System Data Gateway (EDG), which would
be demonstrated and discussed in the subsequent part of this
section. Since Hong Kong is a highly developed city with
mature urban infrastructure. During this study period from
2003 to 2006, Hong Kong experienced no pervasive urban
development but with a few percentages of urban sprawl
recognized.
C ma dm Là
*
Sheu cut
*
lox Far Sinh dura Park
S.
Figure 2. Study area with weather stations
In this research ASTER LIB data and Land Surface
Temperature (LST) product AST_08 data were both acquired
for this research. ASTER L1B data is used to derive some of
the ecological parameters. The surface temperature of
AST 08 can be regarded as valid for cloud-free pixels, the
reported accuracy of surface temperature estimation achieved
proved to be within 1.5K. To facilitate further analysis, the
surface temperature of AST 08 data in units of degrees
Kelvin has been converted to degrees Celsius (°C). The
primary descriptive statistics of surface temperature during
these observation periods are given in the Table 2 to provide
some background information about the surface temperature
distribution within the observation periods.
DATE | TIMEOFDAY ] MIN MAX 1 MEAN | STD
2005-04-17 | 16075 18.15 44.75 | 26.74 238
2005-10-23 11:02:39.38 1715 41.85 | 26.60 3.18
2005-10-01 22:34:53.98 2215 53.85 | 28.27 150
2004-11-21 | 11:02:10.94 14.95 42.85 | 25.57 334
2004-1005 | 241137 1195 225 | 19.95 236
2008-11-03 | 11:03:35.52 22325 | 49.45 | 3235 3.46
| 2003-10-28 | 22:35:49.75 1485 | 2225 | 2103 182
Table 2. Descriptive statistics of surface temperature for the
images used in this research
For the analysis of the relationship between local surface
temperature and urban environmental measures by linear
regression analysis, there were 1070 sample points with
systematic sampling covering the whole study area chosen as
the observations for correlation analysis which was
strategically located within the whole study area. All the
values of each correlation variables corresponding to each
sample point have been calculated.
4. RESULTS AND DISCUSSIONS
Geographical Weighted Regression (GWR) analysis is
chosen to measure the possible spatial varying relationship
between the referred local environmental factors and surface
temperature with the use of a software package GWR 3.0
(Fotheringham et al, 2002). In this calculation, the
significance of potential spatial variation in relationship is
measured and verified with a Monte Carlo statistical test. P-
value for each variable is given and compared to offer a
formal evaluation about the significance of spatial variation
within each influential factor coefficient.
Because the adaptive kernel is more capable to reflect the
scale of local variation resulting from the equal amount of
sample data at each regression point as compared to the fixed
kernel (Su et al, 2005), the adaptive kernel with AIC
(Akaike Information Criterion) minimization bandwidth
selection is adopted for GWR analysis in this study. On the
other hand it would provide one valued reference indicating
the grain size of local varying patterns existed and the further
inference of local fragmentation in the relationship pattern
within whole study area can be made.
The Monte Carlo significance test of spatial variation
corresponding to each variable is summarized in Table 3,
with the grey color rows showing the nighttime models,
others corresponding with the daytime models. The
goodness-of-fit of GWR models within each image time
period can be evaluated with the adjusted R-square in this
table. In general, the R-square has greatly improved to over
70%. The highest value of adjusted R-square (0.772) appears
in daytime model of 11-21-2004. On the other hand, the R-
square value of daytime models is consistently higher than
the values of nighttime models, which implies that the
daytime surface temperature can be better modeled with
current environmental variables than nighttime surface
temperature.
In summary the F values of GWR models at each time show
that the relationships between urban environmental factors
and local surface temperature are significant. When
comparing the calculated bandwidth of each GWR model
between daytime and nighttime, it can be found that the
bandwidths of nighttime models is larger than the ones of
daytime models, this also confirmed the relative stable
surface heating pattern during nighttime than during daytime.
It is noted that the bandwidth of GWR model during
10/01/2005 is the biggest (184) among the daytime and
nighttime models. This may be due to the strong
“smoothing” effect of even ‘noise’ introduced by the stripped
LST image and the highest relative humidity (89.1%) at this
image time.The standard residual distribution of the models
confirmed that spatial association has been successfully
removed with GWR model,
During daytime the Intercept and Building square footage
demonstrate spatial association correlation with local surface
temperature. While during nighttime these spatial variations
of parameter coefficients tend to be less obvious. Except
these factors referred above, no significant spatial association
