Seeing both the forest and the trees: An automated workflow for ultra‐high‐resolution drone imaging of tropical canopies

Tropical forest canopies host diverse tree and liana assemblages, yet key foliar and reproductive traits needed for taxonomic identification of canopy plants are often difficult to see from the forest floor, creating a bottleneck in inventories.

We introduce a drone‐based workflow that automatically acquires hundreds of close‐up, ultra‐high‐resolution photographs (~0.4 mm pixel ⁻¹ ) of selected tree crowns per day, enabling efficient botanical and ecological studies of canopy plants (demo: https://youtu.be/80goMEifpc4 ). The approach leverages a compact DJI Mavic 3 Enterprise (M3E) with wide‐angle and telephoto cameras to (i) generate a high‐resolution digital surface model and orthomosaic via structure‐from‐motion photogrammetry and (ii) capture close‐up photographs from ~6 m above the canopy at preplanned locations.

Photographs are accurately (<13 cm error) geolocated with real‐time kinematic (RTK) global navigation satellite system to support spatial co‐registration with photogrammetric products. In field deployments, we acquired close‐up photographs for up to ~300 selected canopy trees per day and maintained RTK corrections over operational distances up to ~3 km, illustrating the potential of the method for plot‐ to landscape‐scale studies. The level of visual detail in the close‐up photographs of canopy crowns was sufficient to support expert identification in a majority of cases in our tropical forest study sites despite their high taxonomic diversity.

Although developed for tropical trees and lianas, the workflow is applicable to other forest and vegetation types for botanical, phenological and ecological applications. We outline strengths and limitations of the workflow to guide use and also provide harpia , an open‐source Python library to program automatic close‐up missions with the M3E (code: https://doi.org/10.5281/zenodo.22116831 ).