대한원격탐사학회지 (Korean Journal of Remote Sensing)

  • 제30권2호
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  • Pages.275-292
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  • 2014
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  • 1225-6161(pISSN)
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  • 2287-9307(eISSN)
대한원격탐사학회 (Korean Society of Remote Sensing)
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정지궤도 천리안위성 해양관측센서 GOCI의 Tasseled Cap 변환계수 산출연구

A Study of Tasseled Cap Transformation Coefficient for the Geostationary Ocean Color Imager (GOCI)

  • 신지선 (연세대학교 지구시스템과학과) ;
  • 박욱 (연세대학교 지구시스템과학과) ;
  • 원중선 (연세대학교 지구시스템과학과)
  • Shin, Ji-Sun (Department of Earth system sciences, Yonsei University) ;
  • Park, Wook (Department of Earth system sciences, Yonsei University) ;
  • Won, Joong-Sun (Department of Earth system sciences, Yonsei University)
  • 투고 : 2014.03.14
  • 심사 : 2014.04.12
  • 발행 : 2014.04.30
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초록

이 연구에서는 Geostationary Ocean Color Imager(GOCI) 센서에 적용할 수 있는 고유의 Tasseled Cap Transformation(TCT) 계수를 제시하고 있다. TCT는 다중밴드 센서 자료로부터 지표의 특성을 분석하는 전통적인 영상변환 방법 중 하나로 새로운 다중밴드 광학센서가 관측을 시작하는 경우 센서의 특성 차이로 인하여 각각의 육상관측 위성센서에 적합한 TCT 계수들이 장기 분석을 통하여 수립되어야 한다. GOCI 센서는 해양관측이 주 목적으로 개발되었으나 영상의 상당 부분은 육지를 관측하고 있으며 밴드 구성은 육지관측에도 일반적으로 이용되는 Visible-Near InfraRed(VNIR) 영역의 정보를 포함하고 있다. 또한 GOCI 센서의 높은 시간 해상도는 지표의 일별 변화의 관측에도 유용하게 사용될 수 있다. 이러한 장점을 이용하여 GOCI 센서에 대한 고유한 TCT가 제공된다면 GOCI 센서의 관측범위 내에서 준 실시간으로 지표변화에 대한 분석과 해석이 가능할 것이다. TCT는 일반적으로 "Brightness", "Greenness", "Wetness"의 세 가지 정보를 포함하지만, ShortWave InfraRed(SWIR) 파장대역이 없는 GOCI 센서의 경우에는 "Wetness"의 정보를 얻을 수 없다. GOCI 센서의 높은 시간 해상도의 활용을 극대화하기 위해서는 "Wetness"의 정보가 제공되어야 한다. "Wetness"의 정보를 얻기 위해 GOCI 주성분 분석(Principal Component Analysis: PCA) 공간을 MODIS TCT 공간에 선형 회귀하는 방법이 사용되었다. 이 연구에서 산출된 GOCI TCT 계수는 정지궤도의 특성에 의해 관측 시간대별로 다른 변환계수를 가질 수 있다. 이 차이를 알아보기 위하여 GOCI TCT 자료와 MODIS TCT 자료 사이의 상관관계가 비교되었다. 그 결과, "Brightness"와 "Greenness"는 4시 자료, "Wetness"는 2시 자료의 변환계수가 선택되었다. 최종적으로 산출된 변환계수의 적절성을 평가하기 위하여 GOCI TCT 자료는 MODIS TCT 영상 및 여러 육상 파라미터들과 비교되었다. GOCI TCT 영상은 MODIS TCT 영상보다 지표 피복의 분류가 더 세밀하게 표현되었으며, GOCI TCT 공간의 지표 피복 분포도 유의미한 결과를 보여줬다. 또한 GOCI TCT의 "Brightness", "Greenness", "Wetness" 자료는 Albedo($R^2$ = 0.75), Normalized Difference Vegetation Index(NDVI) ($R^2$ = 0.97), Normalized Difference Moisture Index(NDMI) ($R^2$ = 0.77)와 각각 비교적 높은 상관관계가 나타났다. 이러한 결과들은 적절한 TCT 계수의 산출이 이루어졌다는 것을 보여준다.

The objective of this study is to determine Tasseled Cap Transformation (TCT) coefficients for the Geostationary Ocean Color Imager (GOCI). TCT is traditional method of analyzing the characteristics of the land area from multi spectral sensor data. TCT coefficients for a new sensor must be estimated individually because of different sensor characteristics of each sensor. Although the primary objective of the GOCI is for ocean color study, one half of the scene covers land area with typical land observing channels in Visible-Near InfraRed (VNIR). The GOCI has a unique capability to acquire eight scenes per day. This advantage of high temporal resolution can be utilized for detecting daily variation of land surface. The GOCI TCT offers a great potential for application in near-real time analysis and interpretation of land cover characteristics. TCT generally represents information of "Brightness", "Greenness" and "Wetness". However, in the case of the GOCI is not able to provide "Wetness" due to lack of ShortWave InfraRed (SWIR) band. To maximize the utilization of high temporal resolution, "Wetness" should be provided. In order to obtain "Wetness", the linear regression method was used to align the GOCI Principal Component Analysis (PCA) space with the MODIS TCT space. The GOCI TCT coefficients obtained by this method have different values according to observation time due to the characteristics of geostationary earth orbit. To examine these differences, the correlation between the GOCI TCT and the MODIS TCT were compared. As a result, while the GOCI TCT coefficients of "Brightness" and "Greenness" were selected at 4h, the GOCI TCT coefficient of "Wetness" was selected at 2h. To assess the adequacy of the resulting GOCI TCT coefficients, the GOCI TCT data were compared to the MODIS TCT image and several land parameters. The land cover classification of the GOCI TCT image was expressed more precisely than the MODIS TCT image. The distribution of land cover classification of the GOCI TCT space showed meaningful results. Also, "Brightness", "Greenness", and "Wetness" of the GOCI TCT data showed a relatively high correlation with Albedo ($R^2$ = 0.75), Normalized Difference Vegetation Index (NDVI) ($R^2$ = 0.97), and Normalized Difference Moisture Index (NDMI) ($R^2$ = 0.77), respectively. These results indicate the suitability of the GOCI TCT coefficients.

키워드

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