Obtaining accurate population estimates of plants has been an integral part of the listing, recovery, and delisting of species under the U.S. Endangered Species Act (ESA) of 1973 and for monitoring vegetation in response to livestock grazing management. However, obtaining such estimates for many plant species remains a daunting and labor-intensive task. The use of small unmanned aircraft systems (sUAS or drones) may provide an effective alternative to ground surveys for rare and endangered plants.

The objective of our study was to evaluate the effectiveness of using sUAS (DJI Phantom 4 Pro with a 20 MP camera) to survey for Wright fishhook cactus (Sclerocactus wrightiae L.D.Benson), a small (1-8 cm diameter) endangered plant species endemic to Utah, located in southwest USA desert grazing lands. This species functions in enhancing soil stability, providing nectar for pollinating insect species, and increasing biodiversity in hot arid environments.

We used georectified images overlaid with grid plots in ArcGIS Pro to 1) assess the effectiveness of very high resolution remotely sensed imagery for detecting and counting individual cacti and then compared these with ground surveys and 2) determine the optimal altitude (10 m, 15 m, or 20 m) and associated resolution for identifying individual cactus plants.

Our results demonstrated that the lowest altitude flights (10 m) provided the best detection rates (from 26.6% at 20m to 67% at 10m; p<0.001) and counts (p<0.001). We generated population estimates based on the inclusion of error terms in the analysis. We suggest that sUAS can be effectively used to locate cactus within grazing land areas, but should be coupled with ground surveys for higher accuracy and reliability. We suggest that sUAS surveys can be effectively conducted for locating cactus populations within the flowering period and for documenting known populations outside of the flowering period. While sUAS remote sensing did not provide a complete census of Wright fishhook cactus plants, likely due to its small, obscure, thorny, low-growing structure, nonetheless this tool can be effective in early plant population detection, monitoring populations in response to grazing activities, and preventing potential soil/plant disturbance resulting from ground-based surveys.

We conducted survey flights and collected images using drone technology. Specially, we utilized a DJI Phantom 4 Pro (SZ DJI Technology Co. Ltd. Shenzhen, China) with a standard DJI RGB camera (Table 1). NIR/RGB images were obtained using a Sony Qx1 camera transported by a 3DR Solo RTF quadcopter. Each flight was programmed using the Pix4D capture application (Pix4D S.A. Lausanne, Switzerland) on an iPad:6th Gen (Apple, Cupertino, California). Flight images were stitched into an orthomosaic using Pix4D (Pix4D S.A. Lausanne, Switzerland). Images were georectified using GPS coordinate locations of distinct cactus plants in each image as ground control. Images were loaded into ArcGIS Pro where orthomasics were georectified using GPS coordinate locations of distinct cactus plants in each image as ground control.

On the ground sample plots were censured by foot for cacti immediately following the three flights. Each cactus location was marked using the same GPS, and the following attributes were recorded: location (UTM), diameter (cm), number of stems, and any damage or disturbance to the plant.

Cactus counts between the different survey altitudes and ground censuses were compared using two techniques: 1) a validation data matrix adapted from Rominger and Meyer (2019) and 2) mixed effects modeling (glmer and lmer). In all analyses, 14 flights were used for analysis at each flight altitude. One of the original 15 flights was not included due to distortion caused by high winds at the time of the flight. Generalized and linear mixed-effects regression (glmer and lmer) were conducted in R (R Core Team 2018) using packages lme4 (Bates et al.), lmerTest (Kuznetsova et al., 2017), MuMIn (Barton) to analyze cactus detection rates (%) and cactus counts (#) relative to flight altitude.