Discrimination And Biophysical Characterization Of Brazilian Cerrado Physiognomies With Eo-1 Hyperspectral Hyperion

作者: Edson E. Sano , Laerte G. Ferreira , Tomoaki Miura , Alfredo R. Huete

DOI:

关键词:

摘要: The Brazilian savanna, locally known as "cerrado", is the most intensely stressed biome with both natural- and human-induced pressures. In this study, we aimed to improve discrimination characterization of cerrado physiognomies using hyperspectral Hyperion imagery, first space-borne imaging spectrometer onboard NASA's Earth Observing-1 (EO-1) platform. A image was acquired over Brasilia National Park (BNP) on July 20, 2001. Various optical measures which took a full advantage remote sensing were employed applied atmospherically-corrected data. These included 1st-order derivative-based green vegetation index (1st-DGVI), ligno-cellulose absorption index, spectral albedo, shortwave-infrared (SWIR) unmixing. All these correlated well field-estimates landscape component cover fractions. Especially, SWIR unmixing results simultaneously biophysically-characterized discriminated physiognomies. preliminary analyses showed great potential in characterizing/discriminating land covers cerrado.

参考文章(10)
D. L. Skole, W. H. Chomentowski, W. A. Salas, A. D. Nobre, Physical and human dimensions of deforestation in Amazonia BioScience. ,vol. 44, pp. 314- 322 ,(1994) , 10.2307/1312381
J RATTER, The Brazilian Cerrado Vegetation and Threats to its Biodiversity Annals of Botany. ,vol. 80, pp. 223- 230 ,(1997) , 10.1006/ANBO.1997.0469
Zhikang Chen, Christopher D. Elvidge, David P. Groeneveld, Monitoring Seasonal Dynamics of Arid Land Vegetation Using AVIRIS Data Remote Sensing of Environment. ,vol. 65, pp. 255- 266 ,(1998) , 10.1016/S0034-4257(98)00036-4
F. Seyler, V. Chaplot, F. Muller, C. E. P. Cerri, M. Bernoux, V. Ballester, C. Feller, C. C. C. Cerri, Pasture mapping by classification of Landsat TM images. Analysis of the spectral behaviour of the pasture class in a real medium-scale environment: The case of the Piracicaba Catchment (12 400 km 2, Brazil) International Journal of Remote Sensing. ,vol. 23, pp. 4985- 5004 ,(2002) , 10.1080/01431160210146217
J. Roughgarden, S. W. Running, P. A. Matson, What Does Remote Sensing Do For Ecology? Ecology. ,vol. 72, pp. 1918- 1922 ,(1991) , 10.2307/1941546
Christopher D. Elvidge, Zhikang Chen, Comparison of broad-band and narrow-band red and near-infrared vegetation indices Remote Sensing of Environment. ,vol. 54, pp. 38- 48 ,(1995) , 10.1016/0034-4257(95)00132-K
Gregory P Asner, David B Lobell, A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation Remote Sensing of Environment. ,vol. 74, pp. 99- 112 ,(2000) , 10.1016/S0034-4257(00)00126-7
M. C. Hansen, R. S. Defries, J. R. G. Townshend, R. Sohlberg, Global land cover classification at 1 km spatial resolution using a classification tree approach International Journal of Remote Sensing. ,vol. 21, pp. 1331- 1364 ,(2000) , 10.1080/014311600210209
H. Franca, A. W. Setzer, AVHRR temporal analysis of a savanna site in Brazil International Journal of Remote Sensing. ,vol. 19, pp. 3127- 3140 ,(1998) , 10.1080/014311698214226