IMPROVEMENT OF THERMAL ESTIMATION AT LAND COVER BOUNDARY BY 
    
USING QUANTILE 
Tsukasa Hosomura 
Division of Information System Design, School of Science and Engineering, 
Tokyo Denki University, Hatoyama, Hiki, Saitama, 350-0394, Japan 
hosomura@mail.dendai.ac.ip 
  
Commission VIil/8 
KEY WORDS: Statistics, High Resolution, Classification, Infrared, Accuracy Analysis, Urban, Data Analysis 
ABSTRACT: 
Land cover classification was conducted for Landsat ETM image of Urmqi. Maximum likelihood classification algorism was used for 
this purpose. Classification classes were urban, water body, forest, soil, bare groundl, bare ground2, vegetationl, vegetation2 and 
vegetation3. Mask image of each land cover was created from the obtained classification image. Thermal band image of each land 
cover was extracted by using the mask image. In general, mean value and standard deviation are calculated for the thermal band 
image. However, these values were affected by the difference of ground resolution. In this study, we introduced quantiles to avoid this 
problem. Quantiles are points taken at regular intervals from the cumulative distribution function. Quantiles showed the effectiveness 
of decreasing the error caused from the difference of ground resolution. 
1. INTRODUCTION 
The air temperature at the city center is higher than that of 
the surrounding non-urban areas so that it looks like an 
island. This phenomenon is so called “Urban Heat Island”. 
In early days, it has attracted attentions as an environmental 
problem unique to urban area. The investigation area of 
Urmqi, which is the regional capital of Xinjiang Uyghr 
Autonomous Region in the dry northwestern part of China, 
has gained rapid development in recent decades. Together 
With economic development, the landscape has changed 
significantly. Land use/cover change has significant impacts 
on regional environment. Land surface temperature is an 
important indicator for assessment of regional environment 
especially in big cities such as Urumqi where urban heat 
island can usually be relatively obvious. 
In this study we aim at specifying the urban expansion 
characteristics of Urmqi City using Landsat ETM images to 
detect and evaluate the land use and land cover change and 
analyze the relationship between land use and heat 
environment of Urmgqi city. 
Land cover classification was conducted for Landsat ETM 
image of Urmgi. Thermal band image of each land cover 
was extracted by using the mask image. In general, mean 
value and standard deviation are used for statistic analysis. 
However, difference of ground resolution between thermal 
infrared image and other band image influence these 
statistic variables. In this study, we introduced quantiles to 
avoid this problem. 
2. CONCEPT OF QUANTILE 
Quantiles are points taken at regular intervals from the 
cumulative distribution function. Quantiles showed the 
effectiveness of decreasing the error caused from the 
difference of ground resolution. The median is the central 
value of the distribution, such that half the points are less 
than or equal to it and half are larger than or equal to it. The 
quantiles divide the distribution into four equal parts, called 
fourths. The second quantile is the median. The interquartile 
range corresponds to the distance between the first quantile 
and the third quantile. 
3. THERMAL BAND DATA ANALYSIS 
In order to analyze relationship between land cover and heat