Correlating Leaf Area Index of Ponderosa Pine with Hyperspectral CASI Data

References

• Chen, Z., Curran, P.J., and Hansom, J.D. 1992. “Derivative Reflectance Spectroscopy to Estimate Suspended Sediment Concentration,” Remote Sensing of the Environment, 40(1), 67–77.
   Web of Science ®Google Scholar
• Curran, P.J., Dungan, J.L., and Gholz, H.L. 1992. “Seasonal LAI in Slash Pine Estimated with Landsat TM,” Remote Sensing of the Environment, 39(1), 3–14.
   Web of Science ®Google Scholar
• Curran, P.J. 1989. “Remote Sensing Foliar Chemistry,” Remote Sensing of the Environment, 30: 271–278.
   Web of Science ®Google Scholar
• Curran, P.J., and Williamson, H.D. 1987. “GLAI Estimation Using Estimates of Red, Near Infrared, and Middle Infrared Radiance,” P. E. & R.S., 53(2), 181–186.
   Web of Science ®Google Scholar
• Curran, P.J. 1983. “Multispectral Remote Sensing for the Estimation of Green Leaf Area Index,” Phil. Trans. R. Soc. Lond. A, 309, 257–270.
   Web of Science ®Google Scholar
• Demetriades-Shah, T. H., Steven, M. D., and Clark, J.A. 1990. “High Resolution Derivative Spectra in Remote Sensing,” Remote Sensing of the Environment, 33, 55–64.
   Web of Science ®Google Scholar
• Dick, K., and Miller, J.R. 1991. “Derivative Analysis Applied to High Resolution Optical Spectra of Freshwater Lakes,” Proceedings of the Fourteenth Canadian Symposium on Remote Sensing, Calgary, Alberta, pp. 400–403.
   Google Scholar
• Feng, Y., and Miller, J.R. 1991. “Vegetation Green Reflectance at High Spectral Resolution as a Measure of Leaf Chlorophyll Content,” Proceedings of the Fourteenth Canadian Symposium on Remote Sensing, Calgary, Alberta, pp. 351–355.
   Google Scholar
• Freemantle, J.R., Pu, R., and Miller, J.R. 1992. “Calibration of Imaging Spectrometer Data to Reflectance Using Pseudo-Invariant Features,” Proceedings of the Fifteenth Canadian Symposium on Remote Sensing, Toronto, Ontario, 1–4 June, pp.452–455.
   Google Scholar
• Gower, S.T., and Norman, J.M. 1992. “Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations Using Li-Cor-2000,” Ecology, in press.
   Google Scholar
• Gutman, G.G. 1991. —“Vegetation Indices from AVHRR: An Update and Future Prospects,” Remote Sensing of the Environment, 35, 121–136.
   Web of Science ®Google Scholar
• Hall, F.G., Huemmrich, K.F., and Goward, S.N. 1990. “Use of Narrow-Band Spectral to Estimate the Fraction of Absorbed Photosynthetically Active Radiation,” Remote Sensing of the Environment, 32, 47–54.
   Web of Science ®Google Scholar
• Herwitz, S.R., Peterson, D.L., and Eastman, J.R. 1990. “Thematic Mapper Detection of Changes in the Leaf Area of Closed Canopy Pine Plantations in Central Massachusetts,” Remote Sensing of the Environment, 29, 129–140.
   Google Scholar
• Horler, D.N.H., Dockray, M., and Barber, J. 1983. “The Red Edge of Plant Leaf Reflectance,” International Journal of Remote Sensing, 4, 273–288.
   Web of Science ®Google Scholar
• Huete, A.R., Jackson, R.D., and Post, D.F. 1985. “Spectral Response of a Plant Canopy with Different Soil Backgrounds,” Remote Sensing of the Environment, 17, 37–53.
   Web of Science ®Google Scholar
• Kosmas, C.S., Curi, N., Bryant, R.B., and Franzmeier, D.P. 1984. “Characterization of Iron Oxide Minerals by Second-Derivative Visible Spectroscopy,” Soil Sci. Soc. Am. J., 48, 401–405.
   Web of Science ®Google Scholar
• LI-COR Inc. 1990. “LAI-2000 Plant Canopy Analyzer,” Instruction and Manual, Lincoln, Nebraska, 14 pp.
   Google Scholar
• Lathrop, R.G. Jr., and Pierce, L.L. 1991. “Ground-based Canopy Transmittance and Satellite Remotely Sensed Estimates for Estimation of Coniferous Forest Canopy Structure,” Remote Sensing of the Environment, 36, 179–188.
   Web of Science ®Google Scholar
• Major, D.J., Baret, F., and Guyot, G. 1990. “A Ratio Vegetation Index Adjusted for Soil Brightness,” International Journal of Remote Sensing, Vol. 11, No. 5, 727–740.
   Web of Science ®Google Scholar
• Miller, J.R., Wu, J., Boyer, M. G., Belanger, M., and Hare, E.W. 1991. “Season Patterns in Leaf Reflectance Red Edge Characteristics,” International Journal of Remote Sensing, Vol. 12, No. 7, 1509–1523.
   Web of Science ®Google Scholar
• Miller, J.R., Hare, E.W., and Wu, J. 1990. “Quantitative Characterization of the Vegetation Red Edge Reflectance: 1) An Inverted-Gaussian Reflectance Model,” International Journal of Remote Sensing, Vol. 11, No. 10, 1775–1795.
   Google Scholar
• Miller, J.R., Boyer, M.G., Wu, J., Gauthier, R.P., Hare, E.W. and Belanger, M. 1988. “Detection of Spectral Effects in Individual Tree Crowns of Metal-injected Trees Using High Resolution Pushbroom Imagery,” Proceedings of the Sixteenth Congress of the International Society for Photogrammetry and Remote Sensing, 27, Part B, pp. 847–856.
   Google Scholar
• Nemani, R.R., and Running, S.W. 1989. “Testing a Theoretical Climate-Soil-Leaf Area Hydrologic Equilibrium of Forests Using Satellite Data and Ecosystem Simulation,” Agricultural and Forest Meteorology, 44, 245–260.
   Web of Science ®Google Scholar
• Peterson, D.L., Aber, J.D., Matson, D.A., Card, D.H., Swanberg, N., Wessman, C., and Spanner, M. 1988. “Remote Sensing of Forest Canopy and Leaf Biochemical Contents,” Remote Sensing of the Environment, 24, 85–108.
   Web of Science ®Google Scholar
• Peterson, D.L., Spanner, M.A., Running, S.W., and Teuber, K.B. 1987. “Relationship of Thematic Mapper Simulator Data to Leaf Area Index of Temperate Coniferous Forests,” Remote Sensing of the Environment, 22, 323–341.
   Web of Science ®Google Scholar
• Rencher, A.C., and Pun, F.C. 1980. “Inflation of R2 in Best Subset Regression,” Technometrics, Vol. 22, No. 1, pp. 49–53.
   Web of Science ®Google Scholar
• Rock, B.N., Hoshizaki, T., and Miller, J.R. 1988. “Comparison of the in situ and Airborne Spectral Estimates of the Blue Shift Associated with Forest Decline,” Remote Sensing of the Environment, 24, 109–127.
   Web of Science ®Google Scholar
• Running, S.W., Nemani, R.R., Peterson, D.L., Band, L.E., Potts, D.F., Pierce, L.L., and Spanner, M.A. 1989. “Mapping Regional Forest Evapotranspiration and Photosynthesis by Coupling Satellite Data with Ecosystem Simulation,” Ecology, 70(4), pp. 1090–1101.
   Web of Science ®Google Scholar
• Running, S.W., Peterson, D.L., Spanner, M.A., and Teuber, K.B. 1986. “Remote Sensing of Coniferous Forest Leaf Area,” Ecology, 67(1), pp. 273–276.
   Web of Science ®Google Scholar
• Savitzky, A. and Golay, M.J.E. 1964. “Smoothing and Differentiation of Data by Simplified Least Squares Procedures,” Anal. Chem., Vol. 36, No. 8, pp. 1627–1639.
   Web of Science ®Google Scholar
• Spanner, M.A., Pierce, L.L., Running, S.W., and Peterson, D.L. 1990a. “The Seasonality of AVHRR Data of Temperate Coniferous Forests: Relationship with Leaf Area Index,” Remote Sensing of the Environment, 33, 97–112.
   Web of Science ®Google Scholar
• Spanner, M.A., Pierce, L.L., Peterson, D.L., and Running, S.W. 1990b. “Remote Sensing of Temperate Coniferous Forest Leaf Area Index, The Influence of Canopy Closure, Understory Vegetation and Background Reflectance,” International Journal of Remote Sensing, Vol. 11, No. 1, 95–111.
   Web of Science ®Google Scholar
• Tang, S. 1984. Multivariate Statistical Analysis, Beijing: Forestry Press of China.
   Google Scholar
• Wessman, C.A., Aber, J.D., and Peterson, D.L. 1989. “An Evaluation of Imaging Spectrometry for Estimating Forest Canopy Chemistry,” International Journal of Remote Sensing, Vol. 10, No. 8, 1293–1316.
   Web of Science ®Google Scholar
• Wessman, C.A., Aber, J.D., Peterson, D.L., and Melillo, J.M. 1988. “Remote Sensing of Canopy Chemistry and Nitrogen Cycling in Temperate Forest Ecosystems,” Nature, Vol. 335, No. 6186, pp. 154–156.
   Web of Science ®Google Scholar