Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
권호 표지
DOI QR코드

DOI QR Code

The Comparison of Thermal Infrared Satellite Observation for Plume Assessment of Thermal Discharge

온배수 확산 평가를 위한 열적외선 위성관측 비교

  • Received : 2015.05.12
  • Accepted : 2015.06.10
  • Published : 2015.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

To examine the effect of thermal discharge from nuclear power plants, Sea Surface Temperature (SST) is one of the most important variables measured by satellite remote sensing. However, the study was not much comparison of field data and satellite SST from operational Landsat 8 Thermal Infrared Sensor(TIRS) and Landsat 7 ETM+. The Landsat 8 TIRS have 2 spilt Thermal Infrared channels but ETM+ uses one channel for extracting of SST. In spite of that this research carried out that Landsat 7 ETM+ have more profitable for correction of SST than Landsat 8 TIRS. The used 15 Landsat 7 and 8 Thermal Infrared data of path/row 114-36 were processed by SST algorithm of ENVI and IDL. The in-situ SST data from KHOA(Korea Hydrographic and Oceanographic Administration) compared with satellite SST and the accuracy of extracted SST were assessed by each field sites in-situ point data with time series satellite SST.

해수 표층 수온은 원자력발전소의 온배수 영향을 조사하기 위해서 위성원격탐사에 의해 관측되는 가장 중요한 정보들 중 하나이다. 하지만 Landsat 7 위성과 Landsat 8 위성의 열적외선 센서로부터 추출한 표층수온과 실측치를 비교한 연구는 부족하다. Landsat 8 위성은 표층수온을 추출하기 위해 열적외선 센서에 두 개의 분리된 밴드를 가지고 있지만, Landsat 7은 한 개의 밴드를 사용하고 있다. 그럼에도 불구하고 본 연구에서는 Landsat 7 ETM+센서가 Landsat 8 TIRS 보다 표층수온의 보정에 유용하다는 것을 제시하였다. 본 연구에서는 Landsat 114-36 지역의 15개 위성자료를 가지고 ENVI와 IDL을 이용한 표층수온 알고리즘을 처리하였다. 국립해양조사원으로부터 수집한 표층수온 실측자료와 위성에서 추출한 표층수온을 비교하였고, 위성관측 시계열 자료와 측정지점의 실측자료를 통해 정확도를 비교하였다.

Keywords

References

  1. Ahn YH, Shanmugam P, Lee JH, Kang YQ. 2006. Application of satellite infrared data for mapping of thermal plume contamination in coastal ecosystem of Korea, Marine environmental research, 61, 186-201. https://doi.org/10.1016/j.marenvres.2005.09.001
  2. Barsi JA, Schott JR, Palluconi FD, Heider D L, Hook SJ, Markham BL. 2003. LANDSAT TM and ETM+ thermal band calibration, Canadian Journal of Remote Sensing, 29(2), 141-153. https://doi.org/10.5589/m02-087
  3. Casey KS, Cornillon P. 1999. A comparison of satellite and in situ based sea surface temperature climatologies. Journal of Climate, 12, 1848-1863. https://doi.org/10.1175/1520-0442(1999)012<1848:ACOSAI>2.0.CO;2
  4. Deschamps P, Phulpin T. 1980. Atmospheric Correction of Infrared Measurements of Sea Surface Temperature Using Channels at 3.7, 11 and 12 Mm. Bound. Layer Meteorol. 18, 131-143. https://doi.org/10.1007/BF00121320
  5. Gibbons D, Wukelic G, Leighton J, Doyle M. 1989. Application of Landsat Thematic Mapper data for coastal thermal plume analysis at Diablo Canyon. Photogrammetric Engineering and Remote Sensing, 55, 903-909.
  6. Irons JR, Dwyer JL, Barsi JA. 2012. The Next Landsat Satellite: The Landsat Data Continuity Mission. Remote Sens. Environ. 122, 11-21. https://doi.org/10.1016/j.rse.2011.08.026
  7. ITT Visual Information Solutions. 2009. ENVI Environment for Visualizing Images, Version 4.7, Boulder, USA.
  8. Mossa M. 2006. Field measurements and monitoring of wastewater discharge in sea water. Estuarine, Coastal and Shelf Science, 68, 509-514. https://doi.org/10.1016/j.ecss.2006.03.002
  9. McMillin LM. 1975. Estimation of Sea Surface Temperatures from Two Infrared Window Measurements with Different Absorption. J. Geophys. Res., 80, 5113-5117. https://doi.org/10.1029/JC080i036p05113
  10. NASA, Landsat Project Science Office. 2003. Landsat 7 Science Data Users Handbook. NASA(http://www.gsfc.nasa.gov/IAS/handbook/handbook_toc.html).
  11. Skokovic D, Sobrino JA, Jimenez-Munoz JC, Soria G, Julien Y, Mattar C and Jordi Cristobal, 2014, Calibration and Validation of Land Surface Temperature for Landsat 8 TIRS Sensor, Land product Validation and Evolution, ESA/ESRIN Frascati (Italy), 28-30.
  12. Thomas A, Byrne D, Weatherbee R. 2002. Coastal sea surface temperature variability from Landsat infrared data. Remote Sensing of Environment, 81, 262-272. https://doi.org/10.1016/S0034-4257(02)00004-4
  13. Yu Y, Privette JL, Pinheiro AC. 2008. Evaluation of Split-Window Land Surface Temperature Algorithms for Generating Climate Data Records. Geosci. IEEE Trans Remote Sens., 46, 179-192. https://doi.org/10.1109/TGRS.2007.909097
  14. Yokoyama R, Konda M. 1996. Sea surface effects on the sea surface temperature estimation by remote sensing. International Journal of Remote Sensing, 17, 1293-1302. https://doi.org/10.1080/01431169608948704

Cited by

  1. 발전소 폐열을 이용한 농업시설용 히트펌프시스템의 난방 성능 분석 vol.26, pp.4, 2017, https://doi.org/10.12791/ksbec.2017.26.4.317