| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 4465155 | International Journal of Applied Earth Observation and Geoinformation | 2009 | 7 Pages |
CHRIS/PROBA data collected in the Brazilian Amazônia in 4 view angles (−36°, nadir, +36°, +55°) and 62 bands (410–1000 nm range) were evaluated for the discrimination between primary forest and 3 stages of secondary succession after deforestation: initial (SS1; <5 years), intermediate (SS2; 5–15 years) and advanced (SS3; >15 years). Single view angle and multiangular approaches (nadir plus anisotropic information derived from reflectance ratios between view angles) were tested for discrimination. Both approaches used principal components analysis (PCA) applied to pixel spectra representative of each class in order to reduce data dimensionality at each dataset, to enhance separability between the classes, and to provide input variables for multiple discriminant analysis (MDA). The results showed that the off-nadir viewing improved discrimination between the successional stages. Discrimination between SS2 and SS3 was enhanced with PCA at +36° view angle. Primary forest and SS3 presented a more anisotropic behavior than SS2 and SS1, especially in the backward scattering direction (positive view angles) in which great amounts of sunlit canopy components were viewed by the sensor. MDA classification results showed that the multiangular approach produced an overall improvement in the discrimination. From the single (nadir) to the multiangular approach, classification accuracy using a separate set of pixels increased from 83.3% to 98.3% for SS1, 53.3% to 70.0% for SS2, and 58.3% to 76.7% for SS3. The nadir and multiangular classifications were statistically different at a 0.05% level of significance. Kappa statistics increased from 0.63 to 0.82. The results showed that multiangular data can improve the differentiation between primary forest and old stages of natural vegetation regrowth, which have been reported in the literature as the most difficult classes to be mapped in the Amazonian environment.
