Drone-Based Characterization of Seagrass Habitats in the Tropical Waters of Zanzibar

Abstract

Unmanned automatic systems (UAS) are increasingly being applied as an alternative to more costly time-consuming traditional methods for mapping and monitoring marine shallow-water ecosystems. Here, we demonstrate the utility of combining aerial drones with in situ imagery to characterize the habitat conditions of nine shallow-water seagrass-dominated areas on Unguja Island, Zanzibar. We applied object-based image analysis and a maximum likelihood algorithm on the drone images to derive habitat cover maps and important seagrass habitat parameters: the habitat composition; the seagrass species; the horizontal- and depth-percent covers, and the seascape fragmentation. We mapped nine sites covering 724 ha, categorized into seagrasses (55%), bare sediment (31%), corals (9%), and macroalgae (5%). An average of six seagrass species were found, and 20% of the nine sites were categorized as “dense cover” (40–70%). We achieved high map accuracy for the habitat types (87%), seagrass (80%), and seagrass species (76%). In all nine sites, we observed clear decreases in the seagrass covers with depths ranging from 30% at 1–2 m, to 1.6% at a 4–5 m depth. The depth dependency varied significantly among the seagrass species. Areas associated with low seagrass cover also had a more fragmented distribution pattern, with scattered seagrass populations. The seagrass cover was correlated negatively (r2 = 0.9, p < 0.01) with sea urchins. A multivariate analysis of the similarity (ANOSIM) of the biotic features, derived from the drone and in situ data, suggested that the nine sites could be organized into three significantly different coastal habitat types. This study demonstrates the high robustness of drones for characterizing complex seagrass habitat conditions in tropical waters. We recommend adopting drones, combined with in situ photos, for establishing a suite of important data relevant for marine ecosystem monitoring in the Western Indian Ocean (WIO).

Keywords

• UAS
• remote sensing
• shallow-water ecosystems
• seascape fragmentation
• image classification
• maximum likelihood algorithm