Relationships between forest stand parameters and Landsat TM spectral responses in the Brazilian Amazon Basin

Estimation of forest stand parameters such as aboveground biomass in a large area using remotely sensed data has considerable significance for sustainable management and utility of natural resources. In practice, selecting suitable image data for such purposes remains difficult due to a poor understanding of forest stand parameters and remotesensing spectral response relationships, particularly in moist tropical regions. This paper explores relationships between forest stand parameters and Landsat Thematic Mapper (TM) spectral responses through analyses of three study areas in the eastern Amazon basin (Altamira, Bragantina, and Ponta de Pedras). Six TM bands and many vegetation indices are examined through integration of spectral responses and field vegetation inventory data. Pearson’s correlation coefficients are used to interpret relationships between forest stand parameters and TM data. This study concludes that single band TM5 and linear transformed indices such as PC1 (the first component in a principal component analysis), KT1 (brightness of the tasseled cap transform), and albedo are most strongly correlated with forest stand parameters, somewhat independent of biophysical environments. Many vegetation indices that use TM4 and TM3 data, such as the atmospherically resistant vegetation index, the atmospheric and soil vegetation index, and the normalized difference vegetation index, are weakly correlated with selected forest stand parameters. In contrast, vegetation indices using band TM5 data improve correlations with selected forest stand parameters in Altamira forests that are characterized by a complex stand structure. Forest stand structure and associated canopy shadow affect the forest stand parameters and TM spectral response relationships. This paper provides a better understanding of relationships that have a potential of being important for developing stand parameter estimation models and for improvement of vegetation classification accuracy.