한국수자원학회논문집 (Journal of Korea Water Resources Association)

  • 제48권2호
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  • Pages.79-90
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  • 2015
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  • 2799-8746(pISSN)
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  • 2799-8754(eISSN)
한국수자원학회 (Korea Water Resources Association)
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Landsat 8 이미지영상을 이용한 영양염류농도 추정; 금강을 대상으로

Estimation of Water Quality using Landsat 8 Images for Geum-river, Korea

  • 임지상 (성균관대학교 수자원전문대학원 수자원학과) ;
  • 백종진 (성균관대학교 건설환경시스템공학과) ;
  • 김형록 (성균관대학교 수자원전문대학원 수자원학과) ;
  • 최민하 (성균관대학교 수자원전문대학원 수자원학과)
  • Lim, Jisang (Dept. of Water Resources, Graduate School of Water Resources, Sungkyunkwan Univ.) ;
  • Baik, Jongjin (Dept. of Civil, Architectural and Environmental System Engineering, Sungkyunkwan Univ.) ;
  • Kim, Hyunglok (Dept. of Water Resources, Graduate School of Water Resources, Sungkyunkwan Univ.) ;
  • Choi, Minha (Dept. of Water Resources, Graduate School of Water Resources, Sungkyunkwan Univ.)
  • 투고 : 2014.12.02
  • 심사 : 2015.01.02
  • 발행 : 2015.02.28
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초록

2013년 3월에 발사된 Landsat 8 인공위성의 이미지데이터를 이용하여 금강유역을 대상으로 수질인자에 대한 평가를 수행하였다. 본 연구의 목적은 다양한 수질인자 중 녹조에 직접적인 영향을 미치는 총질소와 총인의 농도를 추정함으로써 궁극적으로 수생태계에 악영향을 미치는 녹조의 발생을 모니터링 하는 것이다. 현장실측데이터와 인공위성 데이터간의 상관관계를 규명하기 위하여 Pearson' 상관계수를 이용하여 그 관계를 파악하였다. Landsat 8이 촬영되는 시기를 포함하는 총 20개의 현장실측 데이터가 수집되었으며 Landsat 8의 11개의 밴드중, 밴드2, 3, 4의 반사도 값이 총인과 총질소를 탐지하는데 있어서 가장 상관성 높은 것으로 나타났다. 총질소는 유의수준 0.05에서 밴드2(0.48), 3(0.62), 4(0.57)과 높은 양의 상관관계를 보였으며, 총인의 경우, 유의수준 0.01에서 밴드2(0.59), 3(0.59), 4(0.58)로 높은 양의 상관관계를 나타냈다. 5번 밴드는 유의수준을 벗어남으로써 두 수질인자를 탐지하는데 상관성이 떨어지는 것으로 나타났다. 상관성이 높았던 밴드간의 조합을 통해서 총질소와 총인에 대한 각각의 최적 회귀식이 다중 회귀식을 근거로 구축되었다. 유도된 회귀식으로 계산된 총질소와 총인의 농도값은 통계기법인 Bias와RMSE를 이용하여 현장실측데이터들과 비교 검증되었다. 최종적으로, 2014년 4월 21과 2013년 11월 12일에 대한 맵핑을 수행함으로써 총질소와 총인의 공간적인 분포를 시각적으로 확인할 수 있었다.

In this study, the water quality parameters of Geum-river were estimated using Landsat 8 satellite image data which had launched in March 2013. The goal of this research is to predict HAB and to monitor spatial pattern of total nitrogen (TN) and total phosphorus (TP) because both TN and TP are the dominant factors of the growth of harmful algal blooms (HABs). To investigate the relationship between satellite band reflectance and in situ measurement value, Pearson' correlation coefficient analysis was used. The band2, 3, 4 and 5 reflectance values among 11 bands of Landsat 8 were used which was highly associated with detecting TN and TP. The 20 in situ data set with satellite's overpass time were identified. TN showed positive relation with band 2 (0.48), band3 (0.62), band4 (0.57) at a significance level of p<0.05. TP also showed high correlation for band2 (0.59), band3 (0.59), band4 (0.58) at a significance level of p<0.01. The optimal regression equation models were constructed for TN and TP based on multiple regression equations. The estimated concentration based on derived regression equations of TN and TP were compared with in situ measurement data. Finally, the spatial pattern of the two parameters was able be monitored through mapping on November 12, 2013 and April 21, 2014.

키워드

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