Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation

Rangeland ecosystems encompass vast areas, covering 239 million ha in the United States and 3.6 billion ha globally¹. Rangelands provide a wide array of ecosystem services and management of rangelands involves multiple land uses. In the western US, rangelands provide wildlife habitat, water storage, carbon sequestration, and forage for domestic livestock². Rangelands are subject to various disturbances, including invasive species, wildfires, infrastructure development, and natural resource extraction (e.g., oil, gas, and coal)³. Vegetation monitoring is critical to sustaining resource management within rangelands and other ecosystems throughout the world^4,5,6. Vegetation monitoring in rangelands is often used to assess rangeland health, habitat suitability for wildlife species, and to catalogue changes in landscapes due to invasive species, wildfires, and natural resource extraction^7,8,9,10. While the goals of specific monitoring programs may vary, monitoring programs that fit the needs of multiple stakeholders while being statistically reliable, repeatable, and economical are desired^5,7,11. Although land managers recognize the importance of monitoring, it is often seen as unscientific, uneconomical, and burdensome⁵.

Traditionally, rangeland monitoring has been conducted with a variety of methods including ocular or visual estimation¹⁰, Daubenmire frames¹², plot charting¹³, and line point intercept along vegetation transects¹⁴. While ocular or visual estimation is time-efficient, it is subject to high observer bias¹⁵. Other traditional methods, while also subject to high observer bias, are often inefficient due to their time and cost requirements^6,15,16,17. The time required to implement many of these traditional methods is often too burdensome, making it difficult to obtain statistically valid sample sizes, resulting in unreliable population estimates. These methods are often applied based on convenience rather than stochastically, with observers choosing where they collect data. Additionally, reported and actual sample locations frequently differ, causing confusion for land managers and other stakeholders reliant upon vegetation monitoring data¹⁸. Recent research has demonstrated that image-based vegetation monitoring is time- and cost-effective^6,19,20. Increasing the amount of data that can be sampled within a given area in a short amount of time should improve statistical reliability of the data compared to more time-consuming traditional techniques. Images are permanent records that can be analyzed by multiple observers after field data are collected⁶. Additionally, many cameras are equipped with global positioning systems (GPS), so images can be geotagged with a collection location^18,20. Use of computer-generated sampling points, accurately located in the field, should reduce observer bias whether the image is acquired with a handheld camera or by an unmanned aerial system because it reduces an individual observer’s inclination to use their opinion of where sample locations should be placed.

Aside from being time-consuming, costly, and subject to high observer bias, traditional natural resource monitoring frequently fails to adequately characterize heterogeneous rangeland due to low sample size and concentrated sampling locations²¹. Spatially balanced sampling designs distribute sample locations more evenly across an area of interest to better characterize natural resources^21,22,23,24. These designs can reduce sampling costs, because smaller sample sizes are required to achieve statistical accuracy relative to simple random sampling²⁵.

In this method, a spatially balanced sampling design known as balanced acceptance sampling (BAS)^22,24 is combined with image-based monitoring to assess rangeland vegetation. BAS points are optimally spread over the area of interest²⁶. However, this does not guarantee that points will be ordered in an optimal route for visitation²⁰. Therefore, BAS points are arranged using a route optimization algorithm that solves the travelling salesperson problem (TSP)²⁷. Visiting the points in this order determines an optimal path (i.e., least distance) connecting the points. BAS points are transferred into a geographic information system (GIS) software program and then into a handheld data collection unit equipped with GPS. After BAS points are located, images are taken with a GPS-equipped camera as well as an unmanned aerial system operated using flight software. Upon entering the field, a technician walks to each point to acquire 1 m² monopod-mounted camera images with 0.3 mm ground sample distance (GSD) at each BAS point while a UAS flies to the same points and acquires 2.4 mm-GSD images. Subsequently, vegetation cover data are generated using ‘SamplePoint’²⁸ to manually classify 36 points/image. Vegetation cover data generated from the analysis of ground-level and UAS imagery is compared as well as reported acquisition times for each method. In the representative study, two adjacent, 10-acre rangeland plots were used. Finally, other applications of this method and how it may be modified for future projects or projects in other ecosystems is discussed.