Data from: Using drones to detect vegetation differences in a highly managed habitat with applications to bird research

This is a supporting dataset. Background. Habitat restoration is a necessary step in the conservation and management of plant and animal species. Pitch pine-scrub oak barrens are rare ecosystems that provide critical habitat for specialized and rare species and require active restoration efforts. Unmanned aerial vehicles (i.e., drones) may be well-suited for use in monitoring continual and rapid changes in plant and animal communities in response to restoration efforts. The goal of this study was to use drone imagery to describe habitats in an actively managed pitch pine-scrub oak ecosystem and to examine whether this imagery can detect differences in vegetation density associated with habitat, recent management activities, and tagged bird locations. Methods. Using a commercially available drone, I captured and processed aerial imagery of a pine barrens with an open-source photogrammetric processing program. Point cloud data were processed in ArcGIS Pro, and vegetation density was quantified across the four habitats (treated pitch pine forest, pitch pine forest, shrub oak thicket, and hardwood forest). Furthermore, I described and compared vegetation density between recently disturbed (i.e., within the last two years) and past disturbed (>2 years) areas of the most often managed habitats (i.e., treated pitch pine forest and scrub oak thickets). In addition, I compared vegetation density in drone imagery between used and available locations for Eastern Whip-poor-wills during the day and at night. Results. In August 2024, a drone flight covering a 3.05 km2 area of a pine barrens captured 3,372 aerial images. Vegetation density differed by habitat type, with scrub oak having the highest and deciduous forest the lowest. Within actively managed habitats, scrub oak and treated pitch pine habitats, the mean vegetation density was greater in recently disturbed sites (i.e., < 2 years post-disturbance) than in sites with longer disturbance histories (i.e., >2 years). Using tagged Eastern Whip-poor-wills, I generated ‘used’ locations during the day (n =90) and night (n =192), paired ‘available’ locations, and associated vegetation density with all points. Vegetation density was lower at daytime locations than at night locations and at used locations than at available locations. Discussion. The UN Decade of Ecosystem Restoration has highlighted the importance of effective and timely monitoring of restoration efforts. With one drone flight, I captured fine-scale imagery that, when processed, could detect differences in vegetation density across habitats, management, and wildlife locations. This study demonstrated that drones and relatively simple image processing can be practical tools for restoration when quantifying and monitoring vegetation differences in dynamic ecosystems, such as actively managed pine barrens. Note: This work was partially funded by the William P. Wharton Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.