Improving MODIS land cover classification by combining MODIS spectral and angular signatures in a Canadian boreal forest

This study explores the use of reflectance anisotropy as described by the Bidirectional Reflectance Distribution Function (BRDF) as an additional source of information to improve land surface classification accuracies in a Canadian boreal forest region through the use of a decision tree classifier (C4.5). This effort primarily uses a daily rolling version of the operational algorithm developed for Direct Broadcast to generate 500 m 16-day daily rolling data sets in the study region. Descriptive statistic and statistically rigorous techniques are used to assess classification accuracies based on confusion matrices and a 10-fold cross-validation method. The results show that the inclusion of additional 7-band model anisotropic parameter group (volumetric (VOL) plus geometric (GEO)) with spectral feature group (nadir BRDF-adjusted reflectance (NABR) plus Enhanced Vegetation Index (EVI)) is most useful in classification, increasing overall accuracies by 5.68%. The most improvements of per-class accuracies are...