Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS

Myung Hee Jo, Sung Jae Kim, Jin Ho Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication34th Asian Conference on Remote Sensing 2013, ACRS 2013
PublisherAsian Association on Remote Sensing
Pages1599-1604
Number of pages6
Volume2
ISBN (Print)9781629939100
Publication statusPublished - 2013 Jan 1
Event34th Asian Conference on Remote Sensing 2013, ACRS 2013 - Bali, Indonesia
Duration: 2013 Oct 202013 Oct 24

Other

Other34th Asian Conference on Remote Sensing 2013, ACRS 2013
CountryIndonesia
CityBali
Period13/10/2013/10/24

Fingerprint

Temperature
Big data
Atmospheric temperature
Evapotranspiration
Urban planning
Satellite imagery
Sea level
Linear equations
Land use
Regression analysis
Climate change
Specific heat
NASA
Thermal conductivity
Atmospheric humidity
Energy utilization
Hot Temperature
Processing
Water

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications

Cite this

Jo, M. H., Kim, S. J., & Lee, J. H. (2013). Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013 (Vol. 2, pp. 1599-1604). Asian Association on Remote Sensing.
Jo, Myung Hee ; Kim, Sung Jae ; Lee, Jin Ho. / Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS. 34th Asian Conference on Remote Sensing 2013, ACRS 2013. Vol. 2 Asian Association on Remote Sensing, 2013. pp. 1599-1604
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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.",
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Jo, MH, Kim, SJ & Lee, JH 2013, Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS. in 34th Asian Conference on Remote Sensing 2013, ACRS 2013. vol. 2, Asian Association on Remote Sensing, pp. 1599-1604, 34th Asian Conference on Remote Sensing 2013, ACRS 2013, Bali, Indonesia, 13/10/20.

Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS. / Jo, Myung Hee; Kim, Sung Jae; Lee, Jin Ho.

34th Asian Conference on Remote Sensing 2013, ACRS 2013. Vol. 2 Asian Association on Remote Sensing, 2013. p. 1599-1604.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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N2 - 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.

AB - 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.

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Jo MH, Kim SJ, Lee JH. Development of method for LST (Land surface temperature) detection using big data of Landsat TM images and AWS. In 34th Asian Conference on Remote Sensing 2013, ACRS 2013. Vol. 2. Asian Association on Remote Sensing. 2013. p. 1599-1604