Conifer species recognition: An exploratory analysis of in situ hyperspectral data

Remote Sensing of Environment

Volumn 62, Issue 2, 1997, Pages 189-200

  Gong, Peng ^a   Ruilianp, Pu ^a   Bin, Yu ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HYPERSPECTRAL DATA ANALYSIS; TREE SPECIES RECOGNITION;

ALGORITHMS; DATA REDUCTION; NEURAL NETWORKS; PLANTS (BOTANY); RADIOMETRY; SPECTRUM ANALYSIS;

REMOTE SENSING;

ALGORITHM; CONIFER; CONIFER SPECIES; HYPERSPECTRAL DATA; NEAR INFRARED WAVELENGTH; NEURAL NETWORK; REMOTE SENSING; SPECIES RECOGNITION; TREE SPECIES;

EID: 0344074641     PISSN: 00344257     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0034-4257(97)00094-1     Document Type: Article

References (29)

1. ANCAL, Inc. (1995), C-SPEC Data Acquisition and Manipulation Program, Users Guide & Operating Instructions, Version 1.5. Ancal, Inc., Las Vegas.
2. Boardman, J. W. (1989), Inversion of imaging spectrometry data using singular value decomposition. In Proceeding of the IGARSS'89, Vancouver, BC, Canada, pp. 2069-2072.
3. Card, D. H., Peterson, D. L., and Matson P. A. (1988), Prediction of leaf chemistry by use of visible and near infrared reflectance spectroscopy. Remote Sens. Environ. 26:123-147.
4. Curran, P. (1989), Remote sensing of foliar biochemistry. Remote Sens. Environ. 30:271-278.
5. Daughtry, C. S. T., Biehl, L. L., and Ranson, K. J. (1989)., A new technique to measure the spectral properties of conifer needles. Remote Sens. Environ. 33:55-64.
6. Goetz, A. F. H., Vane, G., Solomonson, J. E., and Rock, B. N. (1985), Imaging spectrometry for earth remote sensing. Science 228:1147-1153.
7. Gong, P. (1996), Integrated analysis of spatial data from multiple sources: using evidential reasoning and an artifical neural network for geological mapping. Photogramm. Eng. Remote Sens. 62(5):513-523.
8. Gong, P., Pu, R., and Miller, J. R. (1992), Correlating leaf area index of ponderosa pine with hyperspectral CASI data. Can. J. Remote Sens. 18(4):275-292.
9. Gong, P., Miller, J. R., and Spanner, M. (1994), Forest canopy closure from classification and spectral unmixing of scene components - multisensor evaluation of an open canopy. IEEE Trans. Geosci. Remote Sens. 32(5):1067-1080.
10. Gong, P., Pu, R., and Miller, J. R. (1995), Coniferous forest leaf area index estimation along with Oregon transect using Compact Airborne Spectrographic Imager data. Photogramm. Eng. Remote Sens. 61(9):1107-1117.
11. Gong, P., Pu, R., and Chen, J. (1996), Mapping ecological land systems and classification uncertainties from digital elevation and forest cover data using neural networks. Photogramm. Eng. Remote Sens. 62(11):1249-1260.
12. Coward, S. N., Huemmrich, K. F., and Waring, R. H. (1994), Visible-near infrared spectral reflectance of Landscape components in western Oregon. Remote Sens. Environ. 47:190-203.
13. Hepner, G. F., Logan, T., Ritter, N., and Bryant, N. (1990), Artificial neural network classification using minimal training set. Programm. Eng. Remote Sens. 56:469-473.
14. Huete, A. R., Jackson, R. D., and Post, D. F. (1985), Spectral responses of a plant canopy with different soil backgrounds. Remote Sens. Environ. 17:37-53.
15. Johnson, L. F., Hlavka, G. A., and Peterson, D. L. (1994), Multivariate analysis of AVIRIS data for canopy biochemical estimation along the Oregon Transect. Remote Sens. Environ. 47:216-230.
16. Leblon, B., Gallant, L., and Granberg, H. (1996), Effects of shadowing types on ground-measured visible and near-infrared shadow reflectances. Remote Sens. Environ. 58:322-328.
17. Li, X., and Strahler, A. (1986), Geometric-optical bi-directional reflectance modeling of a coniferous forest canopy. IEEE Trans. Geosci. Remote Sens. GE-24:906-919.
18. Liang, S., and Strahler, A. H. (1993), An analytic BRDF model of canopy radiative transfer and its inversion. IEEE Trans. Geosci. Remote Sens. 31 (5): 1081-1092.
19. Matson, P. A., Johnson, L. F., Miller, J. R., Billow, C. R., and Pu, R. (1994), Seasonal patterns and remote spectral estimation of canopy chemstry across the Oregon Transect. Ecol. Appl. 4(2):280-298.
20. McCormack, M. D., Zaucha, D. E., and Dushek, D. W. (1993), First-break refraction event picking and seismic data trace editing using neural networks. Geophysics 58:67-78.
21. Miller, J. R., Wu, J., Boyer, M. G., Belanger, M., and Hare, E. W. (1991), Season patterns in leaf reflectance red edge characteristics. Int. J. Remote Sens. 12:1509-1523.
22. Pao, Y. (1989), Adaptive Pattern Recognition and Neural Networks, Addison and Wesley, New York, pp. 113-299.
23. Pu, R., and Gong, P. (1996), Band selection from hyperspectral data using a fuzzy clustering technique for tree species identification. In Proceedings of Geoinformatics '96. West Palm Beach, FL, pp. 464-471.
24. Qi, J., Moran, M. S., and Dedieu, G. (1995), Normalization of sun/view angle effects using spectral albedo-based vegetation indices. Remote Sens. Environ. 52:207-217.
25. Rumelhart, D. E., Hinton, G. E., and Williams R. J. (1986). Learning internal representations by error propagation. In Parallel Distributed Processing-Explorations in the Microstructure of Cognition (D. E. Rumelhart and J. L. McClelland, Eds.), MIT Press, Cambridge, MA, Vol. 1, pp. 318-362.
26. SAS Institute, Inc. (1985), SAS User's Guide: Statistics, 5th ed., Cart, NC.
27. van Leeuwen, W. J. D., and Huete, A. R. (1996), Effects of standing litter on the biophysical interpretation of plant canopies with spectral indices. Remote Sens. Environ. 55:123-138.
28. Williams, D. L. (1991), A comparison of spectral reflectance properties at the needle, branch, and canopy level for selected conifer species. Remote Sens. Environ. 35:79-91.
29. Yin, Y., and Xu, X. (1991), Applying neural net technology for multi-objective land use planning. J. Environ. Manage. 32:349-356.