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위성영상 시공간 융합기법의 계절별 NDVI 예측에서의 응용

Application of Satellite Data Spatiotemporal Fusion in Predicting Seasonal NDVI

  • 김예화 (서울대학교 협동과정 조경학) ;
  • 주경영 (서울대학교 대학원) ;
  • 성선용 (서울대학교 협동과정 조경학) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Jin, Yihua (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Zhu, Jingrong (Graduate School, Seoul National University) ;
  • Sung, Sunyong (Interdisciplinary Program in Landscape Architecture, Seoul National University) ;
  • Lee, Dong Kun (Department of Landscape Architecture and Rural System Engineering, Seoul National University)
  • 투고 : 2017.03.06
  • 심사 : 2017.04.04
  • 발행 : 2017.04.30

초록

시간해상도와 공간해상도가 높은 영상 자료는 효과적인 식생의 모니터링을 위해서 필수적이다. 하지만 단일 센서를 통한 영상은 공간해상도와 시간해상도가 높은 자료를 동시에 제공할 수 없는 한계점이 있다. 최근에는 위성영상의 공간적 해상도를 높이고 시간해상도를 보완하기 위해서 시공간 융합연구가 진행되고 있다. 그 중에서도 FSDAF(Flexible spatiotemporal data fusion) 방법론은 위성영상의 각 밴드를 융합하는 방법으로 적절한 것으로 나타났다. 본 연구에서는 FSDAF 융합기법을 활용하여 MODIS NDVI와 Landsat 영상으로 계산한 NDVI를 융합 후 검증을 실시하였으며 식생 계절 모니터링에서의 활용가능성을 제시하였다. 그 결과, 1월부터 12월까지 융합을 통해 NDVI 예측한 영상은 활엽수, 침엽수, 농지의 계절적인 특징을 잘 반영하고 있었다. 융합된 결과의 검증을 위하여 8월과 10월의 예측한 NDVI와 실제 값(Landsat NDVI) 간의 RMSE 값을 계산한 결과 각각 0.049와 0.085, 상관계수는 0.765, 0.642로 비교적 일치한 것으로 나타났다. 본 연구에서 활용된 FSDAF 시공간 융합 기법은 픽셀기반의 융합기법으로 다양한 공간스케일의 영상과도 융합 가능할 것이며 다양한 식생 관련 연구에 활용될 것으로 기대된다.

과제정보

연구 과제 주관 기관 : 산림청, 환경부, 서울대학교

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