Costs and benefits of uncrewed aerial systems at long term vegetation research sites in drylands

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Anna Roser, Marie-Anne de Graaff and T. Trevor Caughlin, Department of Biological Sciences, Boise State University, Boise, ID, Josh Enterkine and Nancy F. Glenn, Geosciences, Boise State University, Boise, ID, Juan Miguel Requena-Mullor, School of Environment and Sustainability, University of Michigan, Ann Arbor, MI, Alex Boehm, Pat Clark and Fred Pierson, USDA-ARS, Boise, ID

Background/Question/Methods
The structure and composition of vegetation in drylands is highly variable at fine spatial extents. It is difficult to predict how drylands will respond to climate change, in part, because of the fine scale variability of plants. This is study tests Uncrewed Aerial Systems (UAS) to map and model photosynthetic cover and community composition of plants in three sagebrush ecosystems, which are metrics to gauge productivity and community structure. For both objectives, a series of surveys were conducted using fine-scale spatial resolution (1-4 cm pixel-1) multispectral UAS data collected in Reynolds Creek Experimental Watershed in Southwestern Idaho, USA. Data were collected at three sites along an elevation and precipitation gradient. We used a Bayesian logistic regression to model photosynthetic cover from vegetation indices. The modeled photosynthetic cover was compared to field data consisting of point frame plots (n = 30) at each site. We also tested Object-Based Image Analysis (OBIA) for classification of UAS surveys into plant functional types.
Results/Conclusions
We found the Modified Soil Adjusted Vegetation index (MSAVI) had the highest accuracy when modeling photosynthetic cover at each site (62-93%). Correlations between field and UAS-derived cover estimates showed significant positive relationships at the Low Sage (r = 0.75, p<0.0001) and Mountain Big Sage site (r = 0.55, p = 0.002), but not at Wyoming Big Sage (r = 0.10, p = 0.61). Classification accuracies for plant functional types were: Wyoming Big Sage (70%), Low Sage (73%), and Mountain Big Sage (78%).
We found significant differences in the accuracy of products between the three sagebrush sites. This research highlights the impact that heterogeneity of vegetation can have on analysis. As a result, in some ecosystems it may be necessary to alter data collection protocols based on the structural and compositional differences in vegetation. It’s also noteworthy that UAS imagery can be used to model photosynthetic cover at fine spatial scale resolution, with coverage and sampling density that greatly exceeds field data. UAS surveys captured the heterogeneity between sagebrush sites, and presented options to measure leaf-level composition and structure at landscape levels. UAS data provides unique insights to vegetation surveys because of the larger extent and higher sampling density then field data. These findings are relevant to any researcher who works at small spatial scales and encounters heterogeneity within an ecosystem.