Abstract
The abundance and diversity of plants and insects are important indicators of biodiversity, overall ecosystem health and agricultural production. Bees in particular are interesting indicators as they provide a key ecosystem service in many agricultural crops. Worldwide, habitat loss and fragmentation, agricultural intensification and climate change are important drivers of plant and bee decline. Monitoring of plants and bees is a crucial first step to safeguard their diversity and the services they provide but traditional in situ methods are time consuming and expensive. Remote sensing and Earth observation have the advantages that they can cover large areas and provides repeated, spatially continuous and standardized information. However, to date it has proven challenging to use these methods to assess small-scaled species-level biodiversity components with this approach. Here we surveyed bees and flowering plants using conventional field methods in 30 grasslands along a land-use intensity gradient in the Southeast of the Netherlands. We took RGB (true colored Red-Green–Blue) images using an Unmanned Aerial Vehicle (UAV) from the same fields and tested whether remote sensing can provide accurate assessments of flower cover and diversity and, by association, bee abundance and diversity. We explored the performance of different machine learning methods: Random Forest (RF), Neural Networks (NNET) and Support-Vector Machine (SVM). To evaluate the effect of the spatial resolution on the accuracy of the estimates, we tested all approaches using images at the original spatial resolution (∼ 0.5 cm) and re-sampled at 1 cm, 2 cm and 5 cm. We generally found significant relationships between UAV RGB derived estimates of flower cover and in situ estimates of flower cover and bee abundance and diversity. The highest resolution images generally resulted in the strongest relationships, with RF and NNET methods producing considerably better results than SVM methods (flower cover RF R2 = 0.8, NNET R2 = 0.79; bee abundance RF R2 = 0.65, NNET R2 = 0.54, bee species richness RF R2 = 0.62, NNET R2 = 0.52; bee species diversity RF R2 = 0.54, NNET R2 = 0.46). Our results suggest that methods based on the coupling of UAV imagery and machine learning methods can be developed into valuable tools for large-scale, standardized and cost-effective monitoring of flower cover and therefore of an important aspect of habitat quality for bees.