Efficacy of Mapping Grassland Vegetation for Land Managers and Wildlife Researchers Using sUAS

The proliferation of small unmanned aerial systems (sUAS) is making very high-resolution imagery attainable for vegetation classifications, potentially allowing land managers to monitor vegetation in response to management or wildlife activities and offering researchers opportunities to further examine relationships among wildlife species and their habitats. The broad adoption of sUAS for remote sensing among these groups may be hampered by complex coding, expensive equipment, and time-consuming protocols. We used a consumer sUAS, semiautomated flight planning software, and graphical user interface GIS software to classify grassland vegetation with the aim of providing a user-friendly framework for managers and ecological researchers. We compared the overall accuracy from classifications using this sUAS imagery (89.22%) to classifications using freely available National Agriculture Imagery Program imagery (76.25%) to inform decisions about cost and accuracy. We also compared overall accuracy between manual classification (89.22%) and random forest classification (69.26%) to aid with similar decisions. Finally, we examined the impact of resolution and the addition of a canopy height model on classification accuracy, obtaining mixed results. Our findings can help new users make informed choices about imagery sources and methodologies, and our protocols can serve as a template for those groups wanting to perform similar vegetation classifications on grassland sites without the need for survey-grade equipment or coding. These should help more land managers and researchers obtain appropriate grassland vegetation classifications for their projects within their budgetary and logistical constraints.

Automated summary

The study explores the use of small unmanned aerial systems for vegetation classification, comparing different classification methods to help users choose appropriate imagery sources and methodologies, providing feasible vegetation classification protocols.