Using Drones to Monitor Broad-Leaved Orchids (Dactylorhiza majalis) in High-Nature-Value Grassland

Abstract

Dactylorhiza majalis is a threatened indicator species for the habitat quality of nutrient-poor grassland sites. Environmentalists utilize the species to validate the success of conservation efforts. Conventionally, plant surveys are field campaigns where the plant numbers are estimated and their spatial distribution is either approximated by GPS or labor-intensively measured by differential GPS. In this study, we propose a monitoring approach using multispectral drone-based data with a very high spatial resolution (~3 cm). We developed the magenta vegetation index to enhance the spectral response of Dactylorhiza majalis in the drone data. We integrated the magenta vegetation index in a random forest classification routine among other vegetation indices and analyzed feature impact on model decision making using SHAP. We applied an image object-level median filter to the classification result to account for image artefacts. Finally, we aggregated the filtered result to individuals per square meter using an overlaying vector grid. The SHAP analysis showed that magenta vegetation index had the highest impact on model decision making. The random forest model could reliably classify Dactylorhiza majalis in the drone data (F1 score: 0.99). We validated the drone-derived plant count using field mappings and achieved good results with an RMSE of 12 individuals per square meter, which is within the error margin stated by experts for a conventional plant survey. In addition to abundance, we revealed the comprehensive spatial distribution of the plants. The results indicate that drone surveys are a suitable alternative to conventional monitoring because they can aid in evaluating conservation efforts and optimizing site-specific management.