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

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 w....

Protocol

1. Defining area of study, generating sample points and travel path, and field preparation

1. Definition of the area of study
   1. 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).
   2. 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.......

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 data^6,31. Ground-le.......

Materials

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