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Figure 7. The result of the pH (left) and Aluminum saturation (right) in the pasture chronosequence. 
As a pre-selection procedure for pasture land implementation chronossequence sites, this method seems promissory as 
for minimizing the fieldwork. With this logic, four study sites were selected for soil sampling representing pasture 
settling chronossequence. The dynamics of soil fertility (increase and decay over time) in pasture chronossequence (also 
including the primary forest as a control) is illustrated in the Figure 7. 
The dynamics of the soil fertility in the pasture chronossequence such as pH, aluminum saturation and phosphorus 
observed in this study were similar to the results of the other studies like Serráo et al. (1982), Teixeira & Bastos, (1989). 
For more detail about soil fertility analysis, see Numata (1999). 
5 CONCLUSIONS 
The remote sensing method presented in this study was useful for the identification of pasture chronossequence, by 
allowing the visualization of the spatial and temporal changes in land cover, in a simple and readily available manner, 
leading to the optimization of fieldwork. The temporal information obtained from remote sensing corresponded to the 
dynamics of soil fertility in the pasture chronossequence. To improve the classification, it is important to include other 
spectral fraction images, such as NPV and soil, characterizing better the land covers type. In term of the soil fertility In 
term of the impact of the conversion of forest into pasture in soil fertility in Amazon, it is recommended to analyze the 
relationship between soil fertility dynamics and pasture productivity in the pasture chronossequence, characterizing 
pasture biomass dynamics measured through some remote sensing parameter. 
ACKNOWLEDGMENTS 
The authors acknowledge the EMBRAPA/RO and Mr. Eraldo Matricardi from the Secretary of the State of Rondonia 
for the support for the fieldwork. We also acknowledge Dr. Yoshio E. Shimabukuro and Dr. Diogenes S. Alves from 
INPE (Brazilian Institute for Space Research) for many suggestion in term of the image processing. 
REFERENCES 
Adams, J.B. et al. 1995. Calssification of multitemporal images based in fractions of endmembers: application to land 
cover change in the Brazilian Amazon. Remote Sensing of Environment, 52, pp. 137-154. 
Alves, D. S.; Skole, D. L. 1996Characterizing land cover dynamics using multitemporal imagery. International Journal 
of Remote Sensing, 17( 4), pp. 835-839. 
Alves, D.S et al. 1998 Analise comparativa de tecnicas de classificacao de imagens do sensor Landsat/TM para 
caracterizacao de areas desflorestadas. [CD-ROM]. In: Simposio Brasileiro de Sensoriamento Remoto, 9., Santos, 
Brazil. Secao oral. 
Falesi, Í. C.; Veiga, J. B. 1987. O solo da Amazónia e as pastagens cultivadas. In: Dias Filho, M. B. ed. Pastagem na 
regiáo amazónica. : FEALQ, Piracicaba, pp.1-27. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1037