Publication Date


Date of Final Oral Examination (Defense)


Type of Culminating Activity


Degree Title

Master of Science in Biology



Major Advisor

Nancy F. Glenn, Ph.D.


Pat Clark, Ph.D.


Trevor Caughlin, Ph.D.


Marie-Anne de Graaff, Ph.D.


Drylands cover 41% of the global land surface and provide ecosystem services to 38% of the world’s population. Dryland ecosystems have already been degraded or threatened by the increased rates of wildfire and invasive annual grasses, as well as changes in precipitation patterns. We cannot protect, mitigate, or restore drylands without comprehensive vegetation surveys. To understand ecosystem processes, we need to know the composition of vegetation at the patch and plant scales. Field observations are limited in coverage, and are expensive and time intensive. Data from Unmanned Aircraft Systems (UAS) will fill the niche between field data and medium scale remotely sensed data, and support the potential for upscaling. UAS-based remote sensing will also help extend the spatiotemporal scope of field surveys, improving efficiency and effectiveness. This study aims to test UAS methods to estimate two important vegetation metrics (1) fractional photosynthetic cover and (2) fractional cover of plant functional types.

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. Each site is characterized by a different type of sagebrush: Wyoming Big Sage, Low sage, and Mountain big sage. The first study in this thesis tests multiple vegetation indices at each site to assess their accuracy in modeling photosynthetic cover. We found the Modified Soil Adjusted Vegetation index (MSAVI) had the highest accuracy when modeling photosynthetic cover at each site (62-93%). The modeled photosynthetic cover was compared to field data consisting of point frame plots (n = 30) at each site. Correlations between field and UAS-derived cover estimates showed significant positive relationships at the Low Sage (r = 0.75, pr = 0.55, p = 0.002), but not at Wyoming Big Sage (r = 0.10, p = 0.61). These results demonstrate methods to estimate photosynthetic cover at fine scales in three types of sagebrush using UAS imagery. Additionally, these results suggest that UAS surveys has high correlation with field measurements at mid and high elevation sagebrush sites, but more studies are needed in low elevation sites to understand the potential of integrating UAS and field observations of photosynthetic cover.

Our second study quantified fractional cover of plant functional types in the same three sagebrush sites listed above. First, we tested Object-Based Image Analysis (OBIA) for classification of UAS surveys into plant functional types. We assessed the accuracy of the maps using confusion matrices; overall classification accuracies were strong: Wyoming Big Sage (70%), Low Sage (73%), and Mountain Big Sage (78%). The classified maps of plant functional types were compared to data from field plots (n = 30) at each site. We found significant positive correlations for shrubs (r = 0.58; 0.83), forbs (r = 0.39; 0.94), and bare ground (r = 0.61; 0.70) at our Low Sage and Mountain Big Sage. However we did not find significant relationships for the gramminoid class at any site (r = 0.18; 0.3; 0.32). Second, we tested the application of OBIA to sum shrub abundance from UAS imagery. Abundance data from field plots (n= 24 per site) were tested for agreement with UAS imagery. We found no correlation at any site with field observations at the 10m2 scale (r = -0.22; 0.12; 0.26). Overall, we were able to calculate percent cover for large-unit plant functional types, such as shrubs, trees, and some forbs. Accuracy for gramminoid classification was low due to small plant size, confounding soil reflectance, and grasses that grew beneath shrub canopies.

This research demonstrates that UAS methods can be used to estimate photosynthetic cover and map plant functional types. Using UAS surveys also increased coverage and sampling density of data when compared to traditional field observations. These findings help land managers, restoration experts, and other researchers who monitor, manage, and protect dryland ecosystems by demonstrating an accurate and less expensive approach to collecting ecosystem data.