Multi‐angle Imaging Spectroradiometer (MISR) data, collected in four bands and at nine view angles in the Brazilian Amazon region, were used to describe view‐angle effects on the spectral response and discrimination of three forest types; close and open lowland forests, open submontane forest and green/emerging pastures. A principal‐component analysis (PCA) was applied over 450 bidirectional reflectance factor (BRF) MISR spectra (10 pixels, five land covers and nine view angles) to characterize the spectral‐angular variability in the dataset and to identify the best view direction to enhance land cover discrimination. The analysis was extended into the images of the different cameras, which were classified for the presence of the forest covers using the minimum distance of the pixels to the average PC1 and PC2 scores of each forest class calculated from spectra analysis. Results showed an increase in the mean reflectance over the spectral bands (brightness) of the land covers from nadir to extreme viewing, as indicated by the first principal component, especially in the backward direction due to the predominance of sunlit view vegetation components. The transition from the backward (sunlit view surface components) to the forward (shaded view surface components) scattering directions was also characterized by changes in the shape of the BRF spectra, as indicated by decreasing PC2 score or near‐infrared/blue ratio values. The variations in the MISR BRF followed the regularities expected from theory. PCA results also indicated that the best viewing to discriminate the forest types was the backward scattering direction (−26.1° view angle), whereas the less favourable viewing was the forward scattering direction under the view shading condition (e.g. +45.6° view angle). The overall classification accuracy for the three forest types increased from 52.4% at +45.6° view angle to 78.7% at nadir, and to 95.0% at a −26.1° view angle. From nadir to extreme view angles, directional effects produced a NDVI decrease for the forest types and an NDVI increase for the green and especially emerging pastures. Results demonstrated that data acquisition in off‐nadir viewing may improve the discrimination and mapping of the Amazonian land cover types.
Instituto de Estudos Avançados (IEAv), Rodovia dos Tamoios, km 5,5, Torrão de Ouro, 12228‐840, São José dos Campos, SP, Brazil 2:
Instituto Nacional de Pesquisas Espaciais (INPE), Divisão de Sensoriamento Remoto, Caixa Postal 515, 12245‐970, São José dos Campos, SP, Brazil