Application of Satellite Data Spatiotemporal Fusion in Predicting Seasonal NDVI

위성영상 시공간 융합기법의 계절별 NDVI 예측에서의 응용

  • 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)
  • 김예화 (서울대학교 협동과정 조경학) ;
  • 주경영 (서울대학교 대학원) ;
  • 성선용 (서울대학교 협동과정 조경학) ;
  • 이동근 (서울대학교 조경지역시스템공학부)
  • Received : 2017.03.06
  • Accepted : 2017.04.04
  • Published : 2017.04.30


Fine temporal and spatial resolution of image data are necessary to monitor the phenology of vegetation. However, there is no single sensor provides fine temporal and spatial resolution. For solve this limitation, researches on spatiotemporal data fusion methods are being conducted. Among them, FSDAF (Flexible spatiotemporal data fusion) can fuse each band in high accuracy.In thisstudy, we applied MODIS NDVI and Landsat NDVI to enhance time resolution of NDVI based on FSDAF algorithm. Then we proposed the possibility of utilization in vegetation phenology monitoring. As a result of FSDAF method, the predicted NDVI from January to December well reflect the seasonal characteristics of broadleaf forest, evergreen forest and farmland. The RMSE values between predicted NDVI and actual NDVI (Landsat NDVI) of August and October were 0.049 and 0.085, and the correlation coefficients were 0.765 and 0.642 respectively. Spatiotemporal data fusion method is a pixel-based fusion technique that can be applied to variousspatial resolution images, and expected to be applied to various vegetation-related studies.


Supported by : 산림청, 환경부, 서울대학교


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