Optimal temporal windows for mapping fynbos seep wetlands using unmanned aerial vehicle data

Abstract

Despite growing international interest in seasonal effects on wetland vegetation mapping, there is a notable lack of research focused on South Africa’s unique fynbos wetlands, leaving a critical gap in understanding the spatiotemporal dynamics of fynbos ecosystems. This study aimed to assess the ability of Parrot Sequoia and MicaSense RedEdge-M UAV data collected during six seasonal periods between 2018 and 2020 to discriminate between fynbos wetland vegetation species. It also identifies the most suitable time of year for accurate species-level classification. The highest classification accuracy (OA = 98.0%) was achieved in late winter and early summer (OA = 90.1%), while the lowest (OA = 57.2%) occurred in mid-autumn. Most species attained high user and producer accuracies, though Erica serrata and Tetraria thermalis were more inconsistently classified. A Kruskal–Wallis test revealed a significant effect of seasonality on user and producer accuracy as well as kappa (p < 0.05). A Wilcoxon rank-sum test indicated that the accuracy metrics were not significantly different (p > 0.05) when different sensors were used within the same season. The results suggest that conservation agencies and researchers should collect remote sensing data at the end of winter to take advantage of phenological differences between plant species.