Physically based classification and satellite mapping of biophysical characteristics in the southern boreal forest

Journal of Geophysical Research Atmospheres

Volumn 102, Issue 24, 1997, Pages 29567-29580

  Hall, Forrest G ^a   Knapp, David E ^b   Huemmrich, Karl F ^a,b  

^a NASA GODDARD SPACE FLIGHT CENTER   (United States)

^b HUGHES STX CORPORATION   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BIOPHYSICAL CHARACTERISTIC; BIOPHYSICAL CLASSIFICATION; BOREAL ECOSYSTEM-ATMOSPHERE STUDY; BOREAL FOREST; BOREAS; CANOPY REFLECTANCE; IMAGE CLASSIFICATION; LAND COVER; MAPPING; REMOTE SENSING; SATELLITE IMAGERY; VEGETATION COVER;

CANADA;

CONIFEROPHYTA; PICEA MARIANA;

EID: 0031433968     PISSN: 01480227     EISSN: None     Source Type: Journal    
DOI: 10.1029/97jd02578     Document Type: Article

References (36)

1. Badhwar, G. D., J. G. Carnes, and W. W. Austin, Use of Landsat-derived temporal profiles for corn-soybeans feature extraction and classification, Remote Sens. Environ., 12, 57-59, 1982.
2. Ball, G. H., and D. J. Hall, A Clustering Technique for Summarizing Multivariate Data, Behavioral Sciences, 12(2), 153-155, 1967.
3. Black, T. A., et al., Annual cycles of water vapor and carbon dioxide fluxes in and above a boreal aspen stand, Global Change Biol., 2, 219-299, 1996.
4. Frolking, S., et al., Modeling temporal variability in the carbon balance of a spruce/moss boreal forest, Global Change Biol., 2, 343-366, 1996.
5. Goel, N. S., Models of vegetation canopy reflectance and their use in estimation of biophysical parameters from reflectance data, Remote Sens. Rev., 4, 1-121, 1988.
6. Goel, N. S., and T. Grier, Estimation of canopy parameters for inhomogeneous vegetation canopies from reflectance data, III, TRIM: A model for radiative transfer in heterogeneous three-dimensional canopies, Remote Sens. Environ., 25, 255-293, 1988.
7. Goel, N. S., and R. L. Thompson, Inversion of vegetation canopy reflectance models for estimating agronomic variables, V, Estimaon of LAI and average leaf angle using measured canopy reflectances, Remote Sens. Environ., 16, 69-85, 1984.
8. Hall, F. G., and G. D. Badhwar, Signature-extendable technology: Global space-based crop recognition, IEEE Trans. Geosci. Remote Sens., GE-25, 93-103, 1987.
9. Hall, F. G., D. B. Botkin, D. E. Strebel, K. D. Woods, and S. J. Goetz, Forest succession and dynamics from remote sensing, Ecology, 72(2), 628-640, 1991.
10. Hall, F. G., K. F. Huemmrich, D. E. Strebel, S. J. Goetz, J. E. Nickeson, and K. D. Woods, Biophysical, morphological, canopy optical property, and productivity data from the Superior National Forest, NASA Tech. Memo. 104568, 1992.
11. Hall, F. G., Y. E. Shimabukuro, and K. F. Huemmrich, Remote sensing of forest biophysical structure using mixture decomposition and geometric reflectance models, Ecol. Appl., 5(4), 993-1013, 1995.
12. Hall, F. G., D. R. Peddle, and E. F. LeDrew, Remote sensing of biophysical variables in boreal forest stands of Picea Mariana, Int. J. Remote Sens., 17(15), 3077-3081, 1996.
13. Horowitz, H. M., J. T. Lewis, and A. P. Pentland, Estimating proportions of objects from multispectral scanner data, final report, NASA contract NAS9-14123, NASA CR 141826, 1975.
14. Huemmrich, K. F., A simple addition to the SAIL model to describe discontinuous canopy reflectance: The GeoSail model, Remote Sens. Environ., in press, 1997.
15. Jasinski, M. F., and P. S. Eagleson, Estimation of subpixel vegetation cover using red-infrared scatter grams, IEEE Trans. Geosci. Remote Sens., 28(2), 253-267, 1990.
16. Kettig, R. L., and D. A. Landgrebe, Classification of spectral image data by extraction and classification of homogeneous objects, IEEE Transactions of Geoscience Electronics, 14, 19-25, 1976.
17. Larsen, J. A., The Boreal Ecosystem, pp. 328-329, Academic, San Diego, Calif., 1980.
18. Li, X., and A. H. Strahler, Geometrical-optical modeling for a conifer forest canopy, IEEE Trans. Geosci. Remote Sens., GE-23, 705-721, 1985.
19. Lloyd, D., A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery, Int. J. Remote Sens., 11 (12), 2269-2279, 1990.
20. Loechel, S. E., C. L. Walthall, E. Brown de Colstoun, J. Chen, B. L. Markham, and J. Miller, Variability of boreal forest reflectances as measured from a helicopter platform, J. Geophys. Res., this issue.
21. Mather, P. M., Preprocessing of training data for multispectral image classification, in paper presented at the 13th Annual Conference on Advances in Digital Image Processing, Remote Sens. Soc., Nottingham, England, 1987.
22. Novo, E. M. M., and Y. E. Shimabukuro, Spectral mixture analysis of inland tropical waters, Int. J. Remote Sens., 15(6), 1351-1356, 1994.
23. Sellers, P. J., F. G. Hall, G. Asrar, D. E. Strebel, and R. E. Murphy, The first ISLSCP field experiment (FIFE), Bull. Am. Meteorol. Soc., 1(69), 22-27, 1988.
24. Sellers, P. J., F. G. Hall, G. Asrar, D. E. Strebel, and R. E. Murphy, An overview of the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE), J. Geophys. Res., 97, 18,345-18,371, 1992.
25. Sellers, P. J., S. O. Los, C. J. Tucker, C. O. Justice, D. A. Dazlich, G. J. Collatz, and D. A. Randall, A global 1-"degree" by 1-"degree" NDVI data set for climate studies, 2, The generation of global fields of terrestrial biophysical parameters from the NDVI, Int. J. Remote Sens., 15, 3519-3545, 1994.
26. Sellers, P., et al., The Boreal Ecosystem-Atmosphere Study (BOREAS): An overview and early results from the 1994 field year, Bull. Am. Meteorol. Soc., 76(9), 1549-1577, 1995.
27. Settle, J. J., Contextual classification of remotely sensed data, in Proceedings on Mathematics and Its Applications in Remote Sensing, edited by S. R. Brooks, Oxford Univ. Press, New York, 1989.
28. Shimabukuro, Y. E., and J. A. Smith, Fraction images derived from Landsat TM and MSS data for monitoring reforested areas, Can. J. Remote Sens., 21(1), 67-74, 1995.
29. Shimabukuro, Y. E., B. N. Holben, and C. J. Tucker, Fraction images derived from NOAA AVHRR data for studying the deforestation in the Brazilian Amazon, Cover, Int. J. Remote Sens., 15(3), 517-520, 1994.
30. Smith, O. S., S. L. Ustin, J. B. Adams, and A. R. Gillespie, Vegetation in deserts, I, A regional measure of abundance from multispectral images, Remote Sens. Environ., 31, 1-26, 1990a.
31. Smith, O. S., S. L. Ustin, J. B. Adams, and A. R. Gillespie, Vegetation deserts, II, A regional measure of abundance from multispectral images, Remote Sens. Environ., 31, 1-26, 1990b.
32. Steyaert, L. T., F. G. Hall, and T. R. Loveland, Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1-km AVHRR and Landsat TM data, J. Geophys. Res., this issue.
33. Strebel, D. E., D. R. Landis, K. F. Huemmrich, and B. W. Meeson, Collected data of the First ISLSCP Field Experiment, in Surface Observations and Non-Image Data Sets, vol. 1, CDROM, Field Exp. Data Arch., Oak Ridge Natl. Lab., Oak Ridge, Tenn., 1994.
34. Swain, P. H., and S. M. Davis, Remote Sensing: The Quantitative Approach, McGraw-Hill, New York, 1978.
35. Townshend, J. R. G., C. O. Justice, W. Li, C. Gurney, and J. McManus, Global land cover classification by remote sensing: Present capabilies and future possibilities, Remote Sens. Environ., 35, 243-256, 1991.
36. Verhoef, W., Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model, Remote Sens. Environ., 16, 125-141, 1984.