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The protocol presented in this paper utilizes route optimization, balanced acceptance sampling, and ground-level and unmanned aircraft system (UAS) imagery to efficiently monitor vegetation in rangeland ecosystems. Results from images obtained from ground-level and UAS methods are compared.
Rangeland ecosystems cover 3.6 billion hectares globally with 239 million hectares located in the United States. These ecosystems are critical for maintaining global ecosystem services. Monitoring vegetation in these ecosystems is required to assess rangeland health, to gauge habitat suitability for wildlife and domestic livestock, to combat invasive weeds, and to elucidate temporal environmental changes. Although rangeland ecosystems cover vast areas, traditional monitoring techniques are often time-consuming and cost-inefficient, subject to high observer bias, and often lack adequate spatial information. Image-based vegetation monitoring is faster, produces permanent records (i.e., images), may result in reduced observer bias, and inherently includes adequate spatial information. Spatially balanced sampling designs are beneficial in monitoring natural resources. A protocol is presented for implementing a spatially balanced sampling design known as balanced acceptance sampling (BAS), with imagery acquired from ground-level cameras and unmanned aerial systems (UAS). A route optimization algorithm is used in addition to solve the ‘travelling salesperson problem’ (TSP) to increase time and cost efficiency. While UAS images can be acquired 2–3x faster than handheld images, both types of images are similar to each other in terms of accuracy and precision. Lastly, the pros and cons of each method are discussed and examples of potential applications for these methods in other ecosystems are provided.
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 world4,5,6. 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 extraction7,8,9,10. 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 desired5,7,11. 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 requirements6,15,16,17. 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-effective6,19,20. 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 location18,20. 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 resources21,22,23,24. 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.
1. Defining area of study, generating sample points and travel path, and field preparation
Figure 1: A depiction of the study areas of interest. This location is on a grazing allotment south of Cheyenne in Laramie County, WY, USA (Imagery Source: Wyoming NAIP Imagery 2017). Please click here to view a larger version of this figure.
2. Field data collection and postprocessing
Figure 2: The user interface of Mission Hub. The map depicts the drone flight path along a series of 30 BAS points across one of the study sites while the popup window shows image acquisition parameters at each waypoint. Figure 2 is specific to Site 1, though it is similar in appearance to Site 2. Please click here to view a larger version of this figure.
Figure 3: The waypoint flight mission in Litchi’s mission execution application running on an Android smartphone. Unique waypoint IDs are shown in purple and represent the relative order in which images were taken at various points in the study area. The numbers at each waypoint, such as 7(6), indicate the integer values of heights above the ground at which images were taken (first number) and heights above the home point or drone launch site (second number). Notice the distances between successive waypoints that are labeled on the map. Figure 3 is specific to Site 1, though it is similar in appearance to Site 2. Please click here to view a larger version of this figure.
3. Image analysis
NOTE: All Steps can be found in the ‘tutorial’ section on www.SamplePoint.org; a supplemental ‘tutorial.pdf’ file is attached.
4. Statistical analysis
UAS image acquisition took less than half the time of ground-based image collection, while the analysis time was slightly less with ground-based images (Table 1). Ground-based images were higher resolution, which is likely the reason they were analyzed in less time. Differences in walking path times between sites were likely due to start and end points (launch site) being located closer to Site 1 than Site 2 (Figure 1). Differences in acquisition time between platforms was p...
The importance of natural resource monitoring has long been recognized14. With increased attention on global environmental issues, developing reliable monitoring techniques that are time- and cost-efficient is increasingly important. Several previous studies showed that image analysis compares favorably to traditional vegetation monitoring techniques in terms of time, cost, and providing valid and defensible statistical data6,31. Ground-le...
The authors declare no conflict of interest. The software used in this study was available to authors either as open-source or through institutional permits. No authors are sponsored by any software used in this study and acknowledge that other software programs are available that are capable of doing similar research.
This research was funded in majority by Wyoming Reclamation and Restoration Center and Jonah Energy, LLC. We thank Warren Resources and Escelara Resources for funding the Trimble Juno 5 unit. We thank Jonah Energy, LLC for continuous support to fund vegetation monitoring in Wyoming. We thank the Wyoming Geographic Information Science Center for providing the UAS equipment utilized in this study.
Name | Company | Catalog Number | Comments |
ArcGIS | ESRI | GPS Software | |
DJI Phantom 4 Pro | DJI | UAS | |
G700SE | Ricoh | GPS-equipped camera | |
GeoJot+Core | Geospatial Experts | GPS Software | Used to extract image metadata |
Juno 5 | Trimble | Handheld GPS device | |
Litchi Mission Hub | Litchi | Mission Hub Software | We chose Litchi for its terrain awareness and its ability to plan robust waypoint missions |
Program R | R Project | Statistical analysis/programming software | |
SamplePoint | N/A | Image analysis software |
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