The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
260 
Buddenbaum, H., Schlerf, M., and Hill, J., 2005. 
Classification of coniferous tree species and age classes using 
hyperspectral data and geostatistical methods. International 
Journal of Remote Sensing, 26(24), 5453-5465. 
Campbell, K. and Landry, S., 1999. City of Tampa Urban 
Ecological Analysis, The Florida center for Community design 
and Research, University of South Florida. 
Carleer, A. and Wolff, E., 2004. Exploition of very high 
resolution satellite data for tree species identification, 
Photogrammetric Engineering and Remote Sensing, 70(1), 
135-140. 
Carreiras, J. M. B., Pereira, J. M. C., and Pereira, J. S., 2006. 
Estimation of tree canopy cover in evergreen oak woodlands 
using remote sensing. Forest Ecology and Management, 223 
(1-3), 45-53. 
Clark, M. L., Roberts, D. A., and Clark, D. B., 2005. 
Hyperspectral discrimination of tropical rain forest tree species 
at leaf to crown scales. Remote Sensing of Environment, 96, 
375-398. 
Cochrane, M. A., 2000. Using vegetation reflectance 
variability for species level classification of hyperspectral data. 
International Journal of Remote Sensing, 21(10), 2075-2087. 
Congalton, R., Miguel-Ayanz, J., and Gallup, B., 1991. Remote 
sensing techniques for hardwood mapping. Contract Rep. to 
California Dept, of Forestry and Fire Protection, Sacremento, 
California. 
Datt, B., 1999. A new reflectance index for remote sensing of 
chlorophyll content in higher plants: tests using Eucalyptus 
leaves. J. Plant Physiol., 154, 30-36. 
Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L.B., and 
Pearlman, J.S., 2003. Preprocessing EO-1 Hyperion 
hyperpsectral data to support the application of agricultural 
indexes. IEEE Trans. Geosci. Remote Sens., Vol. 41, pp. 1246- 
1259. 
Franklin, S. E., 1994. Discrimination of subalpine forest 
species and canopy density using digital CASI, SPOT, and 
Landsat TM data. Photogrammetric Engineering and Remote 
Sensing, 60, 1233-1241. 
Galvao, L. S., Formaggio, A. R., and Tisot, D. A., 2005. 
Discrimination of sugarcane varieties in Southeastern Brazil 
with EO-1 Hyperion data. Remote Sensing of Environment, 94, 
523-534. 
Gao, B.C., 1996. NDWI - A normalized difference water 
index for remote sensing of vegetation liquid water from space. 
Remote Sensing of Environment, 58, 257-266. 
Gong, P. and Chen, J., 1992. Boundary uncertainties in 
digitized maps I: some possible determination methods. In 
Proceedings of the 1992 Geographic Information System & 
Land Information System, November, 1992, San Jose, 
California, 274-281. 
Gong, P., Biging, G. S., Lee, S. M., Mei, X., Sheng, Y., Pu, R., 
Xu, B., Schwarz, K. P., and Mostafa, M., 1999. Photo 
ecometrics for forest inventory. Geographic Information 
Sciences, 5(1), 9-14. 
Gong, P., Pu, R. and Yu, B., 1997. Conifer species recognition: 
An exploratory analysis of in situ hyperspectral data. Remote 
Sensing of Environment, 62, 189-200r 
Gong, P., Pu, R., and Heald, R. C., 2002. Analysis of in situ 
hyperspectral data for nutrient estimation of giant sequoia. 
International Journal of Remote Sensing, 23(9), 1827-1850. 
Johansen, K. and Phinn, S., 2006. Mapping structural 
parameters and species composition of riparian vegetation 
using IKONOS and Landsat ETM plus data in Australian 
tropical savannahs. Photogrammetric Engineering and Remote 
Sensing, 72 (1), 71-80. 
Jordan, C. F., 1969. Derivation of leaf area index from quality 
of light on the forest floor. Ecology, 50, 663-666. 
Nagendra, H., 2001. Using remote sensing to assess 
biodiversity. International Journal of Remote Sensing, 22(12), 
2377-2400. 
Peñuelas, J., and Filella, L, 1998. Visible and near-infrared 
reflectance techniques for diagnosing plant physiological status. 
Trends in Plant Science, 3, 151-156. 
Peñuelas, J., Filella, L, Lloret, R, Muñoz, R, and Vilajeliu, M., 
1995. Reflectance assessment of mite effects on apple trees. Int. 
J. Remote Sens, 16, 2727-2733. 
Peñuelas, J., Gamón, J.A., Fredeen, A.L., Merino, J., and Field, 
C.B., 1994. Refelctance indices associated with physiological 
changes in nitrogen- and water-limited sunflower leaves. 
Remote Sens. Environ., 48, 135-146. 
Peñuelas, J., Piñol, J., Ogaya, R., and Filella, L, 1997. 
Estimation of plant water concentration by the reflectance 
water index WI (R900/R970). Int. J. Remote Sens., 18, 2869- 
2875. 
Pu, R., Foschi, L., and Gong, R, 2004. Spectral feature analysis 
for assessment of water status and health level of coast live oak 
(Quercus Agrifolia) leaves. International Journal of Remote 
Sensing, 25(20), 4267-4286. 
Pu, R., Ge, S., Kelly, N.M., and Gong, R, 2003. Spectral 
absorption features as indicators of water status in Quercus 
Agrifolia leaves. International Journal of Remote Sensing, 
24(9), 1799-1810. 
Rouse, J. W., Haas, R. H., Schell, J. A., and Deering, D. W., 
1973. Monitoring vegetation systems in the Great Plains with 
ERTS,” in Proceedings, Third ERTS Symposium, vol. 1, pp. 
48-62, 1973. 
SAS Institute Inc., 1991. SAS/STA User’s Guide, Release 6.03 
Edition, Gary, NC: SAS Institute Inc., USA, 1028pp. 
Thenot, F., Méthy, M., and Winkel, T. (2002). The 
Photochemical reflectance index (PRI) as a water-stress index. 
Int. J. Remote Sens., 23, 5135-5139. 
Thomas, J.R., and Gausman. H.W., 1977. Leaf reflectance vs. 
leaf chlorophyll and carotenoid concentration for eight crops. 
Agron. J. 69, 799-802. 
Tian, Q., Tong, Q., Pu, R., Guo, X., and Zhao, C., 2001. 
Spectroscopic determination of wheat water status using 1650- 
1850 nm spectral absorption features. International Journal of 
Remote Sensing, 22(12), 2329-2338. 
Wang, L., Sousa, W. R, Gong, R, and Biging, G. S., 2004. 
Comparison of IKONOS and QuickBird images for mapping 
mangrove species on the Caribbean coast of Panama. 
Engineering and Remote Sensing, 91(3-4), 432-440. 
Xiao, Q., Ustin, S. L., and McPherson, E. G, 2004. Using 
AVIRIS data and multiple-masking techniques to map urban 
forest tree species. International Journal of Remote Sensing, 
25(24), 5637-5654.