Cláudio C. F. Barbosa

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Chlorophyll-a (chl-a) is a central water quality parameter that has been estimated through remote sensing bio-optical models. This work evaluated the performance of three well established reflectance based bio-optical algorithms to retrieve chl-a from in situ hyperspectral remote sensing reflectance datasets collected during three field campaigns in the(More)
To assess seasonal changes in phytoplanktonic chlorophyll distributions in Amazon floodplain lakes, a linear mixing model was applied to Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data acquired at four river stages: rising (April), high (June), decreasing (September), and low (November). The study area is located in a floodplain reach(More)
Wetland extent, vegetation cover, and inundation state were mapped for the first time at moderately high (100 m) resolution for the entire lowland Amazon basin, using mosaics of Japanese Earth Resources Satellite (JERS-1) imagery acquired during low- and high-water seasons in 1995–1996. A wetlands mask was created by segmentation of the mosaics and(More)
Uncertainties in the estimates of water constituents are among the main issues concerning the orbital remote sensing of inland waters. Those uncertainties result from sensor design, atmosphere correction, model equations, and in situ conditions (cloud cover, lake size/shape, and adjacency effects). In the Amazon floodplain lakes, such uncertainties are(More)
A methodology is described for mapping the Amazon Basin Wetlands using region growing segmentation and region classification of multi-date JERS-1 data. The proposed methodology includes the following steps: imagery segmentation, feature extraction, unsupervised region classification, merging and mapping of unsupervised classes to three basic ones, editing,(More)
The objective of this paper was to estimate turbidity in the Curuai floodplain during high water level. Spatial regression models were developed using fraction images derived from a Linear Spectral Mixture Model (LSMM) applied to a MODIS/Terra image and turbidity in-situ data. As the turbidity in-situ data showed spatial autocorrelation, they had been(More)
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