Drone ecology: Using UAVs to monitor and map tropical forest phenology

Abstract

Tropical forest phenology links with biodiversity and ecosystem process but knowledge gaps remain surrounding the dynamics and drivers of all phenological types, its influence on invertebrate distribution, and how fragmentation may change phenological resources available to higher taxa. Over 1 year, we monitored a 16 ha area of logged tropical forest in Malaysian Borneo. Using UAV imagery within a machine learning pipeline we showed the potential of UAVs to identify individual tree crowns and track their phenology through time but highlighted limitations in classifying spectral outliers into phenological events. In parallel, we ground-truthed the UAV phenology data for 135 of these trees, representing 45 taxa and 17 plant families, demonstrating phenology was taxon specific and highly asynchronous across the landscape, with no one environmental factor determining phenological change. Furthermore, using a grid of canopy-set traps we monitored invertebrate biomass weekly, showing they maintain a low-level, patchy distribution across the landscape but are not influenced by phenology. Finally, in a 10-year spatially explicit simulation of forest fragmentation, we showed that reducing forest area, significantly reduced and de-stabilised temporal phenological resource availability in a 7,200 ha area of forest. The asynchrony displayed by tropical forest phenology demonstrates it is a complex process to monitor and despite leveraging the high spatiotemporal resolution of UAV data and the power of machine learning, there are still challenges that need to be addressed. However, our detailed overview of forest phenology demonstrates it can be susceptible to fragmentation and future climatic change, and even though invertebrates were not affected here, this could have reverberating consequences for higher taxa and ecosystem function. Therefore, we would do well to successfully tackle the challenges of UAV monitoring in the tropics, to rapidly and automatedly assess phenological change and help understand the impacts of these environmental changes on phenology.