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ARS Home » Pacific West Area » Logan, Utah » Forage and Range Research » Research » Publications at this Location » Publication #398550
Research Project: Improved Plant Genetic Resources and Methodologies for Rangelands, Pastures, and Turf Landscapes in the Semiarid Western U.S.

Location: Forage and Range Research

Title: Mapping floral resources in montane landscapes using unmanned aerial systems and two-step random forest classifications

Author
item TABOR, JESSE - Utah State University
item Hernandez, Alexander
item Cox-Foster, Diana
item Love, Byron
item McCabe, Lindsie
item Koch, Jonathan
item Robbins, Matthew

Submitted to: Drones
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/30/2024
Publication Date: 8/14/2024
Citation: Tabor, J., Hernandez, A.J., Cox-Foster, D.L., Love, B.G., Mccabe, L.M., Koch, J., Robbins, M.D. 2024. Mapping floral resources in montane landscapes using unmanned aerial systems and two-step random forest classifications. Drones. https://doi.org/10.1016/j.rama.2024.06.016.
DOI: https://doi.org/10.1016/j.rama.2024.06.016

Interpretive Summary: Flowers provide many ecosystem services such as food for insect pollinators, they enhance recreational landscapes, and the resulting fruits and seeds from flowers are essential for the survival of many animal species. There is a growing need for several stakeholders such as bee keepers, scientists, and land managers to obtain accurate spatial estimates of floral resources in natural, non-agricultural settings. Maps of flower on the landscape provide not only biodiversity indicators but also give a good idea of the quantity and quality of resources that are available for pollinators.

Technical Abstract: Monitoring floral biodiversity is a critical step in understanding our ecosystems. However, manual methods to quantify flowering vegetation are costly in time and personnel. In large landscapes, these limited methods may not capture the spatial and temporal variation of floral resources. Recent advances in sensors and unmanned aerial vehicle (UAV) platforms offer op-portunities to characterize the dynamic distribution of floral resources at the landscape level. In this study, UAV imagery and a multi-step machine learning classification analysis were used to quantify floral resources in non-agricultural environments, where topography, vegetation, and inflorescence size were variable. Seven flowering species covering an area of 2,138 m2 were clas-sified throughout our study, equaling 0.5% of the overall landscape. We determined the period of flowering for important species based on the temporal changes of the floral area classified from UAV images. Models performed well considering the extreme rarity of flowers in the UAV images. The use of UAVs appears to be a feasible method for characterizing floral resources in non-agricultural settings. Classifications would benefit from a more robust and comprehensive UAV sampling plan, to better characterize the variability of floral resources in UAV imagery.