Multi-season RapidEye imagery improves the classification of wetland and dryland communities in a subtropical coastal region

Remote sensing is considered a valuable tool for monitoring the impacts of global change on vegetation species composition, condition and distribution. Multi-season imagery has been shown to improve the classification of vegetation communities though the contribution of the winter season in multi-seasonal classifications remains to be assessed. The capability of multi-season images of RapidEye, a new generation high spatial resolution (5 m) space-borne sensor containing an additional red-edge band, was evaluated for the classification of several wetland and dryland communities. RapidEye images were obtained for four seasons (winter, spring, summer and autumn) between 2011 and 2012 for a subtropical coastal region of South Africa. The separability of nine wetland and dryland communities was assessed for each season using the Partial Least Square Random Forest (PLS-RF) algorithm. The four-seasons approach yielded a higher overall classification accuracy (OA = 86 +/- 2.8%) when compared to using any single-season classification. The highest single-season accuracies were obtained in spring (80 +/- 2.9%), summer (80 +/- 3.1%), and autumn (79 +/- 3.4%) compared to the winter (66 +/- 3.1%). A three-season combination of autumn, winter and spring yielded the highest average OA (86 +/- 3.1%), maximised the user's accuracies and minimised the number of comparable pairs confused. The inclusion of indices in the classification scenarios showed a minor (+/- 1 percentage points) difference in the average overall and user's accuracies compared to the classification results where only bands were used. The red-edge band of RapidEye increased the overall and average user's accuracy for most of the scenarios by 2-6 percentage points and thus contributed to the separability of communities which are dominated by evergreen tree species.