ed as 
  
  
  
Novo, Evlyn 
  
Table 2 — Proportion of Pixels classified in the different water type classes. 
  
  
  
  
  
  
  
  
  
Selected River Sections Background Black Water Clear Water White Water 
(^) (7) (70) (6) 
100m | 258m | 100m | 258m | 100m | 258m | 100m | 258m 
Santo António do Icá 86.88 85.47 - - 0.42 0.35 12.70 14.18 
Mamiá 66.64 54.21 - 4.79 11.67 12.00 21.69 29.00 
Manaus 44.10 43.43 13.99 15.59 2.38 7.64 39.53 33.34 
Madeira 51.58 50.39 6.79 6.20 2.77 4.66 39.45 38.16 
Trombetas 67.42 47.93 - 10.53 9.17 14.63 23.41 26.91 
Tapajós 28.68 32.13 2.25 4.80 35.23 30.45 33.84 32.60 
Xingu 43.51 47.7 15.91 17.50 15.33 17.50 25.24 21.85 
  
  
  
  
  
  
  
  
  
  
  
Reaches dominated by larger rivers of white water were not deeply affected by changes in resolution. Those are the 
cases of Santo Antônio do Iça, where classification results differ at about 15 %. At the Manaus reach, the change in 
resolution affected the discrimination between clear water and white water pixels. White water pixels were classified as 
clear water probably as an artifact from the adopted resampling scheme. 
Results in Table 2 show that in most of the reaches the White Water pixels are coherently classified at both spatial 
resolutions. They differ in less than 10% except for Mamiä reach where there is a 25% difference between the 
resolutions. 
Based on the preliminary results one can support the use of WFI/CBERS images for mapping water types at the 
Amazon River basin. The accuracy of the results must be better assessed because they will vary according to the 
complexity of the reaches under study. 
CONCLUSIONS 
The experiment allows to conclude that the 258 m spatial resolution of the WFI/CBERS data is not a strong limitation 
to map the water pathways from the river to the floodplain. Although the resolution affects the discrimination between 
the three water types at certain reaches, they do not affect the mapping of White Water, allowing the detection of water 
path from the white water rivers to the floodplain. 
REFERENCES 
Clark, W. J. Cloud Cover as a Facto in the Utilization of Landsat Data for Limnological Research. Remote Sensing of 
Environment 13:453-560, 1983. 
Fisher, Jr. T.R.; Parsley, P. E. Amazon Lakes: Water Storage and nutrient stripping by algae. Limnology and 
Oceanography 24(3)547-553, 1979. 
Junk, W.J.; Furch, K. The physical and chemical properties of Amazonian waters and their relationship with the biota. 
IN: G.T. Prance; T.E. Lovejoy, Ed. Key Environments: Amazonia. Pergamon Press, Oxford. Pp. 3-17, 1985. 
Kirk, J.T.O. Light and photosynthesis in aquatic ecosystems. New York, 2.ed., Cambridge University Press, 509 p., 
1994, 
Lathrop, R. G.; Lillesand, T. M. Monitoring Water Quality and River Plume Transport in Green Bay, Lake Michigan 
with SPOT-1 Imagery. Photogrammetric Engeneering and Remote Sensing 55(3):349-354, 1989. 
Mertes, L.A. Floodplain Development and Sediment Transport In the Solimóes-Amazon River, Brazil. Thesis 
Submitted in partial fulfillment of the requirements for the degree o Master of Science. University of Washington, 1985. 
Mertes, L.A.K.; Smith, M.O.; Adams, J.B. Estimating suspended sediment concentrations in surface waters of the 
Amazon River wetlands from Landsat Images. Remote Sensing of Environment,43:281-301, 1993. 
Mertes, L.A.K.; Daniel, D.L.; Melack, J.M.; Nelson, B.; Martinelli, L.A.; Forsberg, B.R. Spatial Patterns of hydrology, 
feomorphology an dvegetation on the floodplain of the Amazon River in Brazil form a remote sensing perspective. 
Geomorphology, 13, 215-232, 1995. 
  
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1031 
| 
| 
] 
| 
i 
a  u 
ue SSH