estimated value. Pixel values of water body are very low
and almost same. The standard deviation of water body
should be low value. But obtain result shows large standard
deviation value for water body. Such situation may be
caused the boundary of water area which included other
land cover. Ground resolution of thermal band image is 60m
x 60m. On the other hand ground resolution of other band
images are 30m x 30m. Boundary of water body includes
some other land cover pixels. The standard deviation was
affected from these pixel values. In order to avoid such
influence, we introduced quantiles. We used median and
interquartile range instead of mean value and standard
deviation.
Interquartile ;
Median
range
Urban 39 170-
Water body 50 20
Bare groundl 46 173
Bare ground2 39 183
Soil 39 172
Forest 57 130
Vegetationl 50 64
Vegetation2 47 120
Vegetation3 37 181
Table 2 Median and interquartile range of thermal band
data.
By comparing two tables, we could get the result which
showed superiority of median and interquartile range for
accurate thermal environment. Interquartile range is around
twice of S.D.. Both median and interquartile range are
less than mean value and twice of S.D. in water body.
4. CONCLUSIONS
Urmugi was selected for the target city in this study. Land
cover information was obtained from Landsat ETM image
by using classification algorithm. Quantile was introduced
for analyzing the thermal environment in the target area.
Obtained result showed the potential of more accurate
analysis by using quantile statistics.
References
[1] Inamu K., Hosomura, T, 2007. A Study On the
Interaction Between Urumqi Urban Land Use and Urban
Heat Island Using Remote Sensing Data. ACRS 2007 The
28" Asian Conference on Remote Sensing (12-16
November 2007)
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