SCAVI: A Sunlit Canopy Adjusted Vegetation Index

REFERENCES

• Adams, J.B., Smith, M.O., and Gillespie, A.R. 1993. “Imaging spectroscopy: interpretation based on spectral mixture analysis.” In Topics in Remote Sensing 4: Remote Geochemical Analysis edited by Pieters and Englert, 145–166. New York, NY: Cambridge University Press.
   Google Scholar
• Bannari, A., Morin, D., Bonn, F., and Huete, A.R. 1995. “A review of vegetation indices.” Remote Sensing Reviews, Vol 13(No. 1–2): pp. 95–120.
   Google Scholar
• Baret, F., Jacquemoud, S., and Hanocq, JF. 1993. “The soil line concept in remote sensing.” Remote Sensing Reviews, Vol. 7(No. 1): pp. 65–82.
   Google Scholar
• Chen, J.M. 1996. “Evaluation of vegetation indices and a modified simple ratio for boreal applications.” Canadian Journal of Remote Sensing, Vol. 22(No. 3): pp. 229–242.
   Google Scholar
• Chen, J.M., Rich, P.M., Gower, S.T., Norman, J.M., and Plummer, S. 1997. “Leaf area index of boreal forests: theory, techniques, and measurements.” Journal of Geophysical Research, Vol. 102(No. D24): pp. 29,42929,443.
   Web of Science ®Google Scholar
• Chen, X., Vierling, L.A., Rowell, E., DeFelice, T. 2004. “Using LiDAR and effective LAI data to evaluate IKONOS and Landsat 7 ETM+ vegetation cover estimates in a ponderosa pine forest.” Remote Sensing of Environment, Vol. 91(No.1): pp. 14–26.
   Google Scholar
• Clark, R.N., Swayze, G., Wise, R., Livo, E., Hoefen, T., Kokaly, R., Sutley, S. 2007. U.S. Geological Survey, Digital Spectral Library splib06a: Data Series 231. Accessed October 9, 2014. http://speclab.cr.usgs.gov/spectral.lib06.
   Google Scholar
• Clevers, J.G. 1989. “The applications of a weighted infrared-red vegetation index for estimating LAI by correcting for soil moisture.” Remote Sensing of Environment, Vol. 29 pp. 25–37.
   Web of Science ®Google Scholar
• Dennison, P.E. and Roberts, D.A. 2003. “Endmember selection for multiple endmember SMA using endmember average RSME.” Remote Sensing of Environment, Vol. 87: pp. 123–135.
   Web of Science ®Google Scholar
• Fontana, F.M., Coops, N.C., Khlopenkov, K.V., Trishchenko, A.P., Riffler, M., and Wulder, M.A. 2012. “Generation of a novel 1 km NDVI dataset over Canada, the northern United States, and Greenland based on historical AVHRR data.” Remote Sensing of Environment, Vol. 121: pp. 171–185.
   Web of Science ®Google Scholar
• Goel, N.S. and Qin, W. 1994. “Influences of canopy architecture on relationships between various vegetation indices, LAI and FPAR.” Remote Sensing Reviews, Vol. 10: pp.309–347.
   Google Scholar
• Hall, F.G., Huemmrich, K.F., Strebel, D., Goetz, S., Nickeson, J. and Woods, K. 1992. Biophysical, morphological, canopy optical property and productivity data: Superior National Forest. NASA GSFC Rpt 104568. Greenbelt, MD. USA: NASA.
   Google Scholar
• Hall, F.G., Shimabukuro, Y., and Huemmrich, K.F. 1995. “Remote sensing of forest biophysical structure in boreal stands of picea mariana using mixture decomposition and geometric reflectance models.” Ecological Applications, Vol. 5: pp. 993–1013.
   Web of Science ®Google Scholar
• Huete, A.R., 1988. “A soil-adjusted vegetation index (SAVI).” Remote Sensing of Environment, Vol. 25: pp. 295–309.
   Web of Science ®Google Scholar
• Huete, A.R., Didan, K., Miura, T., Rodriguez, E., Gao, X., and Ferreira, L. 2002. “Overview of the radiometric and biophysical performance of the MODIS vegetation indices.” Remote Sensing of Environment, Vol. 83: pp. 195–213.
   Web of Science ®Google Scholar
• Jiang, Z., Huete, A., Didan, K., and Miura T. 2008. “Development of a two-band enhanced vegetation index without a blue band.” Remote Sensing of Environment, Vol. 112: pp. 3833–3845.
   Web of Science ®Google Scholar
• Jordan, C.F. 1969. “Derivation of leaf area index from quality of light on the forest floor.” Ecology, Vol. 50: pp. 663–666.
   Web of Science ®Google Scholar
• King, D.J., Olthof, I., Pellikka, P.K.E., Seed, E.D., and Butson, C. 2005. “Modelling and mapping forest ice storm damage using remote sensing and environmental data.” Natural Hazards (Special Issue on Remote Sensing) Vol. 35: pp. 321–342.
   Web of Science ®Google Scholar
• Leboeuf, A., and Fournier, R.A. 2013. “Estimating stand attributes of boreal forests using digital aerial photography and a shadow fraction method.” Canadian Journal of Remote Sensing, Vol. 39(No. 3): pp. 217–231, doi: doi: 10.5589/m13-030.
   Web of Science ®Google Scholar
• Liang, S. (Ed.): 2008, Advances in Land Remote Sensing: System, Modeling, Inversion and Application. New York, NY: Springer.
   Google Scholar
• Luther, J.E., Fournier, R.A., Houle, M., Leboeuf, A., and Piercey, D.E. 2012. “Application of shadow fraction models for mapping attributes of conifer forests in eastern Canada.” Canadian Journal of Forest Research, Vol. 4: pp. 1750–1757. doi: 10.1139/x2012-118.
   Web of Science ®Google Scholar
• Myneni, R., Hall, F.G., Sellers, P.J., Marshak, A. 1995. “The interpretation of spectral vegetation indexes.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 33(No. 2): pp. 481–486.
   Web of Science ®Google Scholar
• ORNL-SNF, 2010. Oak Ridge National Laboratory DAAC: Superior National Forest Datasets. Accessed October 9, 2014. http://daac.ornl.gov/SNF/snf.shtml.
   Google Scholar
• Peddle, D.R., Boon, S., Glover, A., Hall, F.G. 2010. “Forest structure without ground data: adaptive full-blind multiple forward-mode canopy reflectance model inversion in a mountain pine beetle damaged forest.” International Journal of Remote Sensing, Vol. 31(No. 8): pp. 2123–2128.
   Web of Science ®Google Scholar
• Peddle, D.R., Brunke, S.P., Hall, F.G., 2001. A comparison of spectral mixture analysis and 10 vegetation indices for estimating boreal forest biophysical information from airborne data. Canadian Journal of Remote Sensing. 27(6):627–635.
   Web of Science ®Google Scholar
• Peddle, D.R., Hall, F.G., and LeDrew, E.F. 1999. “Spectral mixture analysis and geometric optical reflectance modeling of boreal forest biophysical structure.” Remote Sensing of Environment, Vol. 67(No. 3): pp. 288–297.
   Web of Science ®Google Scholar
• Peddle, D.R., Johnson, R., Cihlar, J., Latifovic, R. 2004. “Large area forest classification and biophysical parameter estimation using the 5-Scale canopy reflectance model in multiple forward mode.” Remote Sensing of Environment, Vol. 89: pp. 252–263.
   Web of Science ®Google Scholar
• Peddle, D.R., and Smith, A.M. 2005. “Spectral mixture analysis of agricultural crops: endmember validation and biophysical estimation in potato plots.” International Journal of Remote Sensing, Vol. 26(No. 22): pp. 4959–4979.
   Web of Science ®Google Scholar
• Perry Jr., C.R., and Lautenschlager, L.F. 1984. “Functional equivalence of spectral vegetation indices.” Remote Sensing of Environment, Vol. 14: pp. 169–182.
   Web of Science ®Google Scholar
• Pinty, B., and Verstraete, M.M. 1992. “GEMI: a non-linear index to monitor global vegetation from satellites.” Vegetation, Vol. 101(No. 1): pp. 15–20.
   Google Scholar
• Qi, J., Chehbouni, A., Huete, A.R., Kerr, Y., and Sorooshian, S. 1994. “A modified soil adjusted vegetation index.” Remote Sensing of Environment, Vol. 48: pp. 119–126.
   Web of Science ®Google Scholar
• Rocha, A., and Shaver, G. 2009. “Advantages of a two band EVI calculated from solar and PAR fluxes.” Agricultural and Forest Meteorology, Vol. 149: pp. 1560–1563.
   Web of Science ®Google Scholar
• Roujean, J.-L., and Breon, F.M. 1995. “Estimating PAR absorbed by vegetation from bidirectional reflectance measurements.” Remote Sensing of Environment, Vol. 51: pp. 375–384.
   Web of Science ®Google Scholar
• Rouse, J. ( Jr.), Haas, R., Deering, D., Schell, J. 1973. Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetation. NASA GSFC Rpt, Greenbelt, MD. Accessed October 9, 2014. . http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/19740004927_1974004927.pdf
   Google Scholar
• Seed, E.D., and King, D.J. 2003. “Shadow brightness and shadow fraction relations with effective leaf area index: importance of canopy closure and view angle in mixedwood boreal forest.” Canadian Journal of Remote Sensing, Vol. 29(No. 3): pp. 324–335. doi:
   Web of Science ®Google Scholar
• Shimabukuro, Y.S., and Smith, J.A. 1991. “The least-squares mixing models to generate fraction images derived from remote sensing multispectral data.” IEEE Transactions on Geoscience and Remote Sensing, Vol. 29(No. 1): pp. 16–20.
   Web of Science ®Google Scholar
• Tucker, C.J. 1979. “Red and photographic infrared linear combinations for monitoring vegetation.” Remote Sensing of Environment, Vol. 8(No. 2): pp. 127–150.
   Web of Science ®Google Scholar