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Original Articles

A method to improve the spatial features of NDVI data series

Pages 407-420 | Received 05 Mar 2012, Accepted 10 Oct 2012, Published online: 17 Feb 2017

References

  • Baret F., Guyot G. (1991)—Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment, 46: 213–222.
  • Boardman J.W. (1994)—Geometric mixture analysis of imaging spectrometery data. Proceedings International Geoscience and Remote Sensing Symposium, 4: 2369–2371.
  • Bolle H.J., Eckardt M., Koslowsky D., Maselli F., Melia-Miralles J., Menenti M., Olesen F.S., Petkov L., Rasool I., Van de Griend A. (2006)—Mediterranean Land-surface Processes Assessed from Space. Springer, Series: Regional Climate Studies 2006, XXVIII, 760 pp.
  • Conese C., Maselli F. (1992)—Use of Error Matrices to Improve Area Estimates with Maximum Likelihood Classification Procedures. Remote Sensing of Environment, 40: 113–124. doi: http://dx.doi.org/10.1016/0034-4257(92)90009-9.
  • Conese C., Maselli F. (1993)—Selection of optimum bands from remotely sensed scenes through Mutual Information Analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 48(3):2–11. doi: http://dx.doi.org/10.1016/0924-2716(93)90059-V.
  • ENVI (2012)—ENVI Tutorial: Using SMACC to Extract Endmembers.
  • Gilabert M.A., Conese C., Maselli F. (1994)—An Atmospheric Correction Method for the Automatic Retrieval of Surface Reflectances from TM Images. International Journal of Remote Sensing, 15(10):2065–2086. doi: http://dx.doi.org/10.1080/01431169408954228.
  • Gruningen J., Ratkowski A.J., Hoke M.L. (2004)—The Sequential Maximum Angle Convex Cone (SMACC) Endmember Model. Proceedings SPIE, Algorithms for Multispectral and Hyper-spectral and Ultraspectral Imagery, 5425–1, Orlando FL.
  • Houghton R.A. (2005)—Aboveground forest biomass and the global carbon balance. Global Change Biology, 11(6):945–958. doi: http://dx.doi.org/10.1111/j.1365-2486.2005.00955.x.
  • Kerdiles H., Grondona M.O. (1995)—NOAA-AVHRR NDVI decomposition and subpixel classification using linear mixing in the Argentinean Pampa. International Journal of Remote Sensing, 16(7):1303–1325. doi: http://dx.doi.org/10.1080/01431169508954478.
  • Maselli F. (2001)—Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analyses. Remote Sensing of Environment, 75:2938. doi: http://dx.doi.org/10.1016/S0034-4257(00)00153-X.
  • Maselli F. (2002)—Improved estimation of environmental parameters through locally calibrated multivariate regression analyses. Photogrammetric Engineering and Remote Sensing, 68(11): 1163–1171.
  • Maselli F., Gilabert M.A., Conese C. (1998)—Integration of high and low resolution NDVI data for monitoring vegetation in Mediterranean environments. Remote Sensing of Environment, 63:208–218. doi: http://dx.doi.org/10.1016/S0034-4257(97)00131-4.
  • Maselli F., Rembold F. (2002)—Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments. International Journal of Remote Sensing, 23(12): 2475–2488. doi: http://dx.doi.org/10.1080/01431160110104755.
  • Maselli F., Chiesi M. (2005)—Integration of high and low resolution satellite data to estimate pine forest productivity in a Mediterranean coastal area. IEEE Transactions on Geoscience and Remote Sensing, 43:135–143. doi: http://dx.doi.org/10.1109/TGRS.2004.839434.
  • Maselli F., Papale D., Puletti N., Chirici G., Corona P. (2009)—Combining remote sensing and ancillary data to monitor the gross productivity of water-limitedforest ecosystems. Remote Sensing of Environment, 113:657–667. doi: http://dx.doi.org/10.1016/j.rse.2008.11.008.
  • Nascimento J.M.P., Dias J.M.B. (2005)—Does independent component analysis play a role in unmixing hyperspectral data? IEEE Transactions on Geoscience and Remote Sensing, 43: 175–187. doi: http://dx.doi.org/10.1109/TGRS.2004.839806.
  • Richard J.A., Jia X., (1999)—Remote Sensing Digital Image Analysis, 3rd ed., New York, Spring Verlag,.
  • Roy D.P., Ju J., Lewis P., Schaaf C., Gao F., Hansen M., Lindquist E. (2008)—Multitemporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data. Remote Sensing of Environment, 112: 3112–3130. doi: http://dx.doi.org/10.1016/j.rse.2008.03.009.
  • Running S.W., Nemani R.R., Heinsch F.T., Zhao M., Reeves M., Hashimoto H. (2004)—A Continuous Satellite-Derived Measure of Global Terrestrial Primary Production. Bioscience, 54: 547–560. doi: http://dx.doi.org/10.1641/0006-3568(2004)054[0547:ACSM0G]2.0.C0;2.
  • Waring H.R, Running S.W. (2007)—Forest Ecosystems. Analysis at Multiples Scales. 3rd edition. Academic Press, San Diego.
  • Van Leeuwen W.J.D., Orr B.J., Marsh S.E., Herrmann S.M. (2006)—Multi-sensor NDVI data continuity: Uncertainties and implications for vegetation monitoring applications. Remote Sensing of Environment,100:67–81. doi: http://dx.doi.org/10.1016/j.rse.2005.10.002.
  • Venkateswarlu N.B., Raju P.S.V.S.K. (1992)—Fast isodata clustering algorithms. Pattern Recognition, 25(3):335–442. doi: http://dx.doi.org/10.1016/0031-3203(92)90114-X.
  • Zhang H., Zhang Yand Duan F. (2010)—A robust andfast geometry based unmixing algorithm for hyperspectral imagery. International Journal of Remote Sensing, 31(20):5481–5493. doi: http://dx.doi.org/10.1080/01431160903376357.