Development of Method for LST (Land Surface Temperature) Detection Using Big Data of Landsat TM Images and AWS

Development of Method for LST (Land Surface Temperature) Detection Using Big Data of Landsat TM Images and AWS

Myung-Hee Jo¹, Sung Jae Kim², Jin-Ho Lee

¹ Department of Aeronautical Satellite System Engineering, Kyungpook National University, Gajangdong Sangju, Gyeongsangbuk-do, Korea,[email protected]

² Institute of Spatial Information Technology Research, GEO C&I Co., Ltd, Daegu, Korea

3

School of Mechanical Engineering at Yonsei University, Seoul, Korea

ABSTRACT

Worldwide climate change phenomena and rapid industrialization caused serious environmental problem. One of the major implications of urbanization is the increase of surface temperature and development of Urban Heat Island. Surface temperature is increased by anthropogenic heat discharges due to energy consumption, increased land surface coverage by artificial materials having high heat capacities and conductivities, and the associated decrease in vegetation and water pervious surfaces which reduce the surface temperature through evapotranspiration. Landsat ETM images are widely used to observe and model the biophysical characteristics of the land surface. In addition to the development of Land use/cover maps band 6 of the landsat imagery is useful for deriving the surface temperature. In this paper we analyze the results of the LST estimation from landsat data and discuss the associates constraints and challenges.

In this study, land surface temperature derived from landsat TM satellite imagery (145 scenes) and meteorological data observed at the Automatic Weather Observation (AWS) from 1984-2009 were used as input variables for the evaluation of LST in Seoul City, Korea. For the landsat images data and AWS date where link and pre-processing such as geometric correction was performed. AWS observed surface heat converted data correlated with temperature and atmospheric temperature and wind direction, humidity, sea level pressure, and multiple regression analysis obtained by setting the interval of highly variable surface temperature with landsat images correlation analysis was performed.

For accurate indicator analysis NASA model was utilized to extract the surface heat. This research is to analyze and identify the correlation between the surface temperature and the linear equations obtain to calculate the correction factor to develop a model for LST in Korea. The results of this study will contribute to the strategies necessary for the sustainable management in urban revitalization planning in the future.

Keyword: Land surface temperature, Landsat, Urban Heat Island, NASA Model

INTRODUCTION

Large scale changes in surface temperature partially due to urban development and population centralization are now increasing the air pollution substances by essentially causing landscape changes, increase of temperature, and wind field in the city. In addition, urban heat island effect of forming high temperature in the city causes social issues in the city development and public hygiene.

Eventually a study dealing with urban heat analysis by utilizing GIS as well as satellite remote sensing has been actively conducted. Therefore, it is now feasible to perform characteristics of heat distribution via cutting-edge image and AWS data mining techniques. Data mining techniques were applied based on the heat ultraviolet rays temperature data on the surface and air temperature resources of large scaled AWS in the research area intending to clarify relation between air temperature and earth surface temperature, acquire accuracy of modification factor, and ultimately suggest the optimal environmental factors.

MATERIALS AND METHODS Subject of Study and Data

Seoul, a targeted research place, has a total area of 605.28 km² acquiring 30 AWSs. In addition, it

is also available to extract information and apply data mining by using large scaled climate observing

data that have been accumulated for the multiple years in the past. In addition, Seoul is also an urbanized area that acquired extracted data of earth surface temperature for previously accumulated ultraviolet rays landsat images and urban heat island effect. This study utilized landsat TM images (145 scenes) that had been recorded for 26 years (1984~2009) in the area of Seoul with less than 30%

of cloudy cover and precipitation, temperature, wind direction, wind speed, moisture, and air pressure observed in 30 AWSs in the area of Seoul for the 26 years (1984~2009) as input variables for the evaluation.

RESULTS AND DISCUSSIONS

Extracting Earth Surface Temperature by Using Satellite Ultraviolet Sensor

First of all, entire-process procedures on the image are required in order to use satellite image data.

Entire-process procedures are a phase prior to work analysis that removes radiation-related geometric and radioactive error and processes or converts into the form for making it feasible to process resource in the steps of acquiring resources. Landsat TM resources provide items corrected with basic radioactive errors correction and geometric error were used in this study.

As for the first phase, a targeted research area was selected within 500m of radius of AWS observatory without sheath changes in the area of Seoul followed by implementation of GIS analysis in order to select a particular structure for site sheath in each AWS.

Figure 1: Procedures of Selecting Structure for Representing Earth Surface in Each AWS

As for the second phase, spectrum intensity of radiation was converted from the landsat TM band

6 preparing for earth surface temperature distribution by using NASA model. In addition, Julian

calendar was used for applying NASA model intending to utilize corrective factor in an astronomical

unit considering the distance between the earth and the sun.

Figure 2: Example of Earth Surface Heat Based on NASA Model

As for the third phase, raster data earth surface temperature data were converted to numerical form in order to analyze an accurate interrelationship with AWS data. Up to one decimal was represented in a range of AWS data for efficient analysis by rounding up the second or later decimals of the value.

Figure 3: Example of Data Conversion of Earth Surface Heat

Development of AWS Data Mining Technique Analysis

Data mining is a procedure of extracting new and meaningful information from large scaled

resources to be utilized for decision-making process. In this study, the following AWS data mining

procedures were implemented to acquire improved correction factor in the AWS observation

resources and satellite observed data.

Figure 4: Data Mining of AWS Resources

For the fourth phase, interrelated variables were analyzed via multiple regression analysis.

Multiple regression analysis was used when there are two or more quantitative data with one qualitative data as an outcome variable leading to identify cause-and-effect relations of a variable with other variables to interrelations of previously observed earth surface temperature and other factors.

Figure 5: Example of Interrelation of Temperature, Moisture, Precipitation, Wind Speed, and Pressure on the Earth Surface

For the fifth phase, interval analyses were selected depending on the interrelated variables. Air

temperature in the AWS observation location and earth surface might influence on the temperature

value that specific intervals were selected for each air environment condition. In addition, intervals

were divided into 3 sub-intervals on a consistent manner according to the frequency on the normal

distribution curve.

Figure 6: Selection of Intervals for Analysis of Interrelated Variables

Finally, corrective variables were calculated via correlation analysis. Corrective factors are required for preparing heat distribution via interrelation analysis between AWS observed resources and satellite ultraviolet rays sensor observed resources were calculated. Improved corrective factors are expected to provide and enhanced accuracy in the statistical perspective over corrective factors obtained in the short term period in the previous studies and also to be applied for establishing heat distribution chart in Seoul by area using KOMPSAT-3A in the future.

Figure 7: Calculation of Corrective Factors via Correlation Analysis

CONCLUSIONS

This study is available to be incorporated to most studies dealing with preparation for precise earth

surface temperature map, urban heat island effect, and heat energy distribution via improvement of

earth surface heat environment analysis by utilizing corrective factors. In addition, it is anticipated to

establish infrastructure of the follow-up study of earth heat environment in a macro-perspective by incorporating the data mining analyzing technique to the AWS. Furthermore, it is expected to apply Korean-type heat corrective factors on the KOMPSAT-3A and NARO scientific satellite as a data of satellite heat sensor in Korea making Korea as the main power in the field of satellite.

REFERENCES:

(1) Analysis of Urban Heat-Island Effect Using ASTER and ETM+ Data: Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. S Kato, Y Yamaguchi, Remote Sensing of Environment, 99(1), p44-54, 2005

(2) An Analysis of Urban Thermal Characteristics and Associated Land Cover in Tampa Bay and Las Vegas using Landsat satellite data., G Xian, M Crane, Remote sensing of environment, 104(2), p147–156, 2006

(3) Assessment with Satellite Data of the Urban Heat Island Effects in Asian Mega Cities., H Tran, D Uchihama, S Ochi, Y Yasuoka, International Journal of Applied Earth Observation and Geoinformation, 8(1), p34~48, 2006

(4) Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery., F Yuan, ME Bauer, Remote Sensing of Environment, 106(3), p375–386, 2007

(5) Estimation of Land Surface Temperature–Vegetation Abundance Relationship for Urban Heat Island Studies, Q Weng, D. Lu, J Schubring, Remote Sensing of Environment, 89(4), Pages 467–

483, 2004

(6) Remote Sensing of the Urban Heat Island and its Changes in Xiamen City of SE China., H Xu, B CHEN, Journal of Environmental Sciences, 16(2), p276~281, 2004

(7) Remote Sensing Image-Based Analysis of the Relationship Between Urban Heat Island and Land Use/Cover Changes., XL Chen, HM Zhao, PX Li, ZY Yin, Remote sensing of environment, 104(2), p133–146, 2006

(8) Satellite-Measured Growth of the Urban Heat Island of Houston, Texas., DR Streutker, Remote Sensing of Environment., 85(3), p282~289, 2003

(9) Spectral Mixture Analysis of ASTER Images for Examining the Relationship Between Urban Thermal Features and Biophysical Descriptors in Indianapolis, Indiana, USA. D Lu, Q. Weng, 104(2), p157~167, 2006

(10) Thermal Infrared Remote Sensing for Urban Climate and Environmental Studies: Methods,

applications, and trends.,Q. Weng, ISPRS Journal of Photogrammetry and Remote Sensing,

64(4), p335~344, 2009