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We analysed spectral and textural attributes from the Advanced Land Imager (ALI)/EO-1 for land-cover mapping and inspected their correlation with biophysical parameters of primary and secondary forests from Eastern Amazon. An artificial neural network (ANN) technique selected the most relevant spectral/textural attributes, which were combined for classification of the ALI scene. From the ANN land-cover map, areas classified as primary forest (PF), initial (SS1), intermediate (SS2) and advanced (SS3) stages of secondary succession were studied. Biophysical parameters were determined from field inventory of 40 sample plots. Results showed an overall classification accuracy of 79% using reflectance and 89% using the combined data set. The combined data set included the reflectance of ALI bands 3–9 and the texture metrics mean (bands 3–4; 6–8) and dissimilarity (band 8). The reflectance of the near-infrared/shortwave infrared bands and their texture mean decreased from SS1 to SS3/PF. The gradient between primary and secondary forests controlled the correlations of reflectance with biophysical parameters. While the aboveground biomass, basal area, leaf area index, tree height and canopy cover increased from SS1 to SS3/PF, the reflectance decreased with the development of canopy structure and the resultant canopy shadows. The mean was the only texture metric correlated with biophysical parameters.
