• Login
    View Item 
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    •   Home
    • UA Faculty Research
    • UA Faculty Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Integrating drone imagery with existing rangeland monitoring programs

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    GIllan_EMAS_2020.pdf
    Size:
    2.286Mb
    Format:
    PDF
    Description:
    Final Accepted Manuscript
    Download
    Author
    Gillan, Jeffrey K
    Karl, Jason W
    van Leeuwen, Willem J D
    Affiliation
    Univ Arizona, Sch Nat Resources & Environm
    Univ Arizona, Sch Geog & Dev
    Issue Date
    2020-04-06
    Keywords
    adaptive management
    drone
    Ecological inventory and monitoring
    Rangelands
    Remote sensing
    Unmanned aerial system
    
    Metadata
    Show full item record
    Publisher
    SPRINGER
    Citation
    Gillan, J.K., Karl, J.W. & van Leeuwen, W.J. Integrating drone imagery with existing rangeland monitoring programs. Environ Monit Assess 192, 269 (2020). https://doi.org/10.1007/s10661-020-8216-3
    Journal
    ENVIRONMENTAL MONITORING AND ASSESSMENT
    Rights
    © Springer Nature Switzerland AG 2020.
    Collection Information
    This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
    Abstract
    The recent availability of small and low-cost sensor carrying unmanned aerial systems (UAS, commonly known as drones) coupled with advances in image processing software (i.e., structure from motion photogrammetry) has made drone-collected imagery a potentially valuable tool for rangeland inventory and monitoring. Drone-imagery methods can observe larger extents to estimate indicators at landscape scales with higher confidence than traditional field sampling. They also have the potential to replace field methods in some instances and enable the development of indicators not measurable from the ground. Much research has already demonstrated that several quantitative rangeland indicators can be estimated from high-resolution imagery. Developing a suite of monitoring methods that are useful for supporting management decisions (e.g., repeatable, cost-effective, and validated against field methods) will require additional exploration to develop best practices for image acquisition and analytical workflows that can efficiently estimate multiple indicators. We embedded with a Bureau of Land Management (BLM) field monitoring crew in Northern California, USA to compare field-measured and imagery-derived indicator values and to evaluate the logistics of using small UAS within the framework of an existing monitoring program. The unified workflow we developed to measure fractional cover, canopy gaps, and vegetation height was specific for the sagebrush steppe, an ecosystem that is common in other BLM managed lands. The correspondence between imagery and field methods yielded encouraging agreement while revealing systematic differences between the methods. Workflow best practices for producing repeatable rangeland indicators is likely to vary by vegetation composition and phenology. An online space dedicated to sharing imagery-based workflows could spur collaboration among researchers and quicken the pace of integrating drone-imagery data within adaptive management of rangelands. Though drone-imagery methods are not likely to replace most field methods in large monitoring programs, they could be a valuable enhancement for pressing local management needs.
    Note
    12 month embargo; published online: 6 April 2020
    ISSN
    0167-6369
    EISSN
    1573-2959
    PubMed ID
    32253518
    DOI
    10.1007/s10661-020-8216-3
    Version
    Final accepted manuscript
    ae974a485f413a2113503eed53cd6c53
    10.1007/s10661-020-8216-3
    Scopus Count
    Collections
    UA Faculty Publications

    entitlement

    Related articles

    • Modeling vegetation heights from high resolution stereo aerial photography: an application for broad-scale rangeland monitoring.
    • Authors: Gillan JK, Karl JW, Duniway M, Elaksher A
    • Issue date: 2014 Nov 1
    • Ground-level Unmanned Aerial System Imagery Coupled with Spatially Balanced Sampling and Route Optimization to Monitor Rangeland Vegetation.
    • Authors: Curran MF, Hodza P, Cox SE, Lanning SG, Robertson BL, Robinson TJ, Stahl PD
    • Issue date: 2020 Jun 14
    • A comparison of drone imagery and ground-based methods for estimating the extent of habitat destruction by lesser snow geese (Anser caerulescens caerulescens) in La Pérouse Bay.
    • Authors: Barnas AF, Darby BJ, Vandeberg GS, Rockwell RF, Ellis-Felege SN
    • Issue date: 2019
    • Rangeland Condition Monitoring: A New Approach Using Cross-Fence Comparisons of Remotely Sensed Vegetation.
    • Authors: Kilpatrick AD, Lewis MM, Ostendorf B
    • Issue date: 2015
    • Drone-Based Environmental Monitoring and Image Processing Approaches for Resource Estimates of Private Native Forest.
    • Authors: Srivastava SK, Seng KP, Ang LM, Pachas A'A, Lewis T
    • Issue date: 2022 Oct 17
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.