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

Published: June 14, 2020

doi:

Michael F. Curran^2,3, Paddington Hodza, Samuel E. Cox, Shawn G. Lanning, Blair L. Robertson, Timothy J. Robinson, Peter D. Stahl^2,3

¹Wyoming Reclamation and Restoration Center,University of Wyoming, ²Department of Ecosystem Science and Management,University of Wyoming, ³Program in Ecology,University of Wyoming, ⁴Wyoming Geographic Information Science Center,University of Wyoming, ⁵Wyoming State Office,Bureau of Land Management, ⁶School of Mathematics and Statistics,University of Canterbury, ⁷Department of Statistics,University of Wyoming

Summary

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.

Abstract

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.

Introduction

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.

Protocol

1. Defining area of study, generating sample points and travel path, and field preparation Definition of the area of study Use a GIS software program to draw a polygon graphic(s) around the area(s) of interest. This study was conducted on two 10-acre plots within a grazing allotment in Laramie County, WY, USA (Figure 1). Ensure that those areas that are not intended to be within the sample frame are excluded from the polygon (e.g., water bodies, building st…

Representative Results

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…

Discussion

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…

Divulgazioni

The authors have nothing to disclose.

Acknowledgements

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.

Materials

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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