DOI QR코드

DOI QR Code

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

Abstract

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.

Keywords

Phenology;Vegetation index;Satellite image fusion;MODIS;Landsat

Acknowledgement

Supported by : 산림청, 환경부, 서울대학교

References

  1. Brown, M.E., D.J. Lary, A. Vrieling, D. Stathakis, and H. Mussa, 2008. Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS. International Journal of Remote Sensing, 29(24): 7141-7158. https://doi.org/10.1080/01431160802238435
  2. Cammalleri, C., M.C. Anderson, F. Gao, C.R. Hain, and W.P. Kustas, 2014. Mapping daily evapotranspiration at field scales over rainfed and irrigated agricultural areas using remote sensing data fusion. Agricultural and Forest Meteorology, 186: 1-11. https://doi.org/10.1016/j.agrformet.2013.11.001
  3. Chai, T., and R.R. Draxler, 2014. Root mean square error (RMSE) or mean absolute error (MAE)? ? Arguments against avoiding RMSE in the literature. Geosci. Model Dev., 7(3): 1247-1250. https://doi.org/10.5194/gmd-7-1247-2014
  4. Fan, H., X. Fu, Z. Zhang, and Q. Wu, 2015. Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data. Remote Sensing, 7(5): 6041-6058. https://doi.org/10.3390/rs70506041
  5. Feng, G., J. Masek, M. Schwaller, and F. Hall, 2006. On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance. IEEE Transactions on Geoscience and Remote Sensing, 44(8): 2207-2218. https://doi.org/10.1109/TGRS.2006.872081
  6. FLAASH, M., 2009. Atmospheric Correction Module: QUAC and FLAASH User's Guide, Version 4.7, Boulder, CO, USA.
  7. Gamon, J.A., K.F. Huemmrich, C.Y. Wong, I. Ensminger, S. Garrity, D.Y. Hollinger, A. Noormets, and J. Penuelas, 2016. A remotely sensed pigment index reveals photosynthetic phenology in evergreen conifers. Proc Natl Acad Sci U S A, 113(46): 13087-13092. https://doi.org/10.1073/pnas.1606162113
  8. Gao, F., M.C. Anderson, X. Zhang, Z. Yang, J.G. Alfieri, W.P. Kustas, R. Mueller, D.M. Johnson, and J.H. Prueger, 2017. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment, 188: 9-25. https://doi.org/10.1016/j.rse.2016.11.004
  9. Goward, S.N., G.D. Cruickshanks, and A.S. Hope, 1985. Observed Relation between Thermal Emission and Reflected Spectral Radiance of a Complex Vegetated Landscape. Remote Sensing of Environment, 18: 137-146. https://doi.org/10.1016/0034-4257(85)90044-6
  10. Huete, A., K. Didan, T. Miura, E.P. Rodriguez, X. Gao, and L.G. Ferreira, 2002. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(12): 195-213. https://doi.org/10.1016/S0034-4257(02)00096-2
  11. Jarihani, A., T. McVicar, T. Van Niel, I. Emelyanova, J. Callow, and K. Johansen, 2014. Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of "Indexthen-Blend" and "Blend-then-Index" Approaches. Remote Sensing, 6(10): 9213-9238. https://doi.org/10.3390/rs6109213
  12. Jin, Y., S. Sung, D. Lee, G. Biging, and S. Jeong, 2016. Mapping Deforestation in North Korea Using Phenology-Based Multi-Index and Random Forest. Remote Sensing, 8(12): 997. https://doi.org/10.3390/rs8120997
  13. Lee, H., and S. Noh, 2013. Advanced Statistical Analysis: Theory and Practice. Moonwoosa.
  14. Malingreau, J.P., 1989. The vegetation index and the study of vegetation dynamics. Ispra Courses. Springer Netherlands.
  15. Marsett, R.C., J. Qi, P. Heilman, S.H. Biedenbender, M. Carolyn Watson, S. Amer, M. Weltz, D. Goodrich, and R. Marsett, 2006. Remote Sensing for Grassland Management in the Arid Southwest. Rangeland Ecology & Management, 59(5): 530-540. https://doi.org/10.2111/05-201R.1
  16. McRoberts, R., and E. Tomppo, 2007. Remote sensing support for national forest inventories. Remote Sensing of Environment, 110(4): 412-419. https://doi.org/10.1016/j.rse.2006.09.034
  17. Morisette, J.T., F.A. Heinsch, and S.W. Running, 2006. Monitoring Global Vegetation Using Moderate Resolution Satellites. Eos Trans. AGU, 87(50): 568-568. https://doi.org/10.1029/2006EO500009
  18. Pat S., and J. Chavez, 1996. Image-Based Atmospheric Corrections-Revisited and Improved. Photogrammetric Engineering and Remote Sensing, 62: 1025-1036.
  19. Roy, D.P., J. Ju, P. Lewis, C. Schaaf, F. Gao, M. Hansen, and E. Lindquist, 2008. Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112(6): 3112-3130. https://doi.org/10.1016/j.rse.2008.03.009
  20. Shen, M., Y. Tang, J. Chen, X. Zhu, and Y. Zheng, 2011. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agricultural and Forest Meteorology, 151(12): 1711-1722. https://doi.org/10.1016/j.agrformet.2011.07.003
  21. Wald, L., 2002. Data fusion: definitions and architectures: fusion of images of different spatial resolution. Presses des MINES.
  22. Yang, X., and C.P. Lo, 2002. Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area. International Journal of Remote Sensing, 23(9): 1775-1798. https://doi.org/10.1080/01431160110075802
  23. Zhou, Y., J. Chen, X.-h. Chen, X. Cao, and X.-l. Zhu, 2013. Two important indicators with potential to identify Caragana microphylla in xilin gol grassland from temporal MODIS data. Ecological Indicators, 34: 520-527. https://doi.org/10.1016/j.ecolind.2013.06.014
  24. Zhu, X., J. Chen, F. Gao, X. Chen, and J.G. Masek, 2010. An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions. Remote Sensing of Environment, 114(11): 2610-2623. https://doi.org/10.1016/j.rse.2010.05.032
  25. Zhu, X., E.H. Helmer, F. Gao, D. Liu, J. Chen, and M.A. Lefsky, 2016. A flexible spatiotemporal method for fusing satellite images with different resolutions. Remote Sensing of Environment, 172: 165-177. https://doi.org/10.1016/j.rse.2015.11.016