Seagrass ecosystem biodiversity mapping in part of Rote Island using multi-generation PlanetScope imagery

Abstract

Remote sensing offers an effective and efficient solution to provide information on the biodiversity of seagrass ecosystems, which is currently lacking in most parts of the world. Therefore, this study aimed to map the biodiversity of seagrass ecosystems in parts of Rote Island, which is one of the seagrass biodiversity hotspots, using multi-generation PlanetScope magery to see how they compare. The most frequently used biodiversity indicators were identified, including the major benthic habitat (coral, seagrass, macroalgae, bare substrate) and the composition of seagrass species based on life forms. We also aim to understand the actual biodiversity indicators of seagrass ecosystems captured by PlanetScope imagery. To achieve this, field data was integrated with the resulting ISODATA classification results to assess what ISODATA class clusters represent in the field, and new classification schemes are developed accordingly. The random forest algorithm was used to carry out the classification, with seagrass field data serving as training data. Independent field data was subsequently used to assess the accuracy. The results showed that the accuracy of benthic habitat and seagrass mapping ranged from 60%-70%. However, through the use of a classification scheme built on ISODATA clustering, the spatial distribution of classes and accuracy of all PlanetScope images was significantly improved to > 90%. This highlighted the importance of understanding which indicators of seagrass biodiversity were effectively captured by PlanetScope images to achieve higher mapping accuracy. Overall, this approach optimized the ability of PlanetScope images to map seagrass biodiversity while obtaining a higher number of biodiversity indicator classes and mapping accuracy than the commonly used biodiversity indicator classification scheme.