Data for Multilabel Classification of Peatland Plant Species from High-Resolution Drone Images

Biodiversity monitoring programs are essential for detecting changes in species distributions and correlating these changes with biotic and abiotic factors. This information is crucial for identifying early problems before they become too difficult to address and for implementing effective management strategies. Traditionally, biodiversity monitoring for small plant species has relied on the quadrat method, which requires botanists to identify species in the field. While this method has its advantages, it is limited by the availability of botanists, restricting the scale of monitoring programs. In this study, we explored the potential of using high-resolution photos and artificial intelligence to estimate small plant species cover in peatlands, thereby reducing the need for field-based species identification by botanists. Our approach involves dividing quadrat images into smaller tiles, applying a multi-label classification model to each tile, and calculating species cover based on the identified tiles. Data were collected from 32 sites across Quebec, and images were annotated for five common species: Chamaedaphne calyculata, Kalmia angustifolia, Andromeda polifolia, Rhododendron groenlandicum, and Larix laricina. Our model achieved a global F1 score of 71.68%, with the highest-performing species (Larix laricina) reaching 87.17%. Although some species showed lower performance, the estimated species cover by our model in a whole quadrat was comparable to traditional methods. Our results demonstrate that this method offers significant advantages for monitoring broad changes in vegetation. This dataset contains the quadrat pictures, the annotations, and the code that we used to train a multi-label classification model to identify shrub-species in Quebec peatlands.