Show simple item record

dc.contributor.authorJohansen, Kasper
dc.contributor.authorErskine, Peter D.
dc.contributor.authorMcCabe, Matthew
dc.date.accessioned2018-11-18T11:01:51Z
dc.date.available2018-11-18T11:01:51Z
dc.date.issued2018-10-29
dc.identifier.citationJohansen K, Erskine PD, McCabe MF (2019) Using Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines. Journal of Cleaner Production 209: 819–833. Available: http://dx.doi.org/10.1016/j.jclepro.2018.10.287.
dc.identifier.issn0959-6526
dc.identifier.doi10.1016/j.jclepro.2018.10.287
dc.identifier.urihttp://hdl.handle.net/10754/629885
dc.description.abstractMine sites are routinely required to rehabilitate their post-mining landforms with a safe, stable and sustainable land-cover. To assess these post-mining landforms, traditional on-ground field monitoring is generally undertaken. However, these labour intensive and time-consuming measurements are generally insufficient to catalogue land rehabilitation efforts across the large scales typical of mining sites (>100 ha). As an alternative, information derived from Unmanned Aerial Vehicles (UAV) can be used to map rehabilitation success and provide evidence of achieving rehabilitation site requirements across a range of scales. UAV based sensors have the capacity to collect information on rehabilitation sites with extensive spatial coverage in a repeatable, flexible and cost-effective manner. Here, we present an approach to automatically map indicators of safety, stability and sustainability of rehabilitation efforts, and demonstrate this framework across three coalmine sites. Using multi-spectral UAV imagery together with geographic object-based image analysis, an empirical classification system is proposed to convert these indicators into a status category based on a number of criteria related to land-cover, landform, erosion, and vegetation structure. For this study, these criteria include: mapping tall trees (Eucalyptus species); vegetation extent; senescent vegetation; extent of bare ground; and steep slopes. Converting these land-cover indicators into appropriate mapping categories on a polygon basis indicated the level of rehabilitation success and how these varied across sites and age of the rehabilitation activity. This work presents a framework and workflow for undertaking a UAV based assessment of safety, stability and sustainability of mine rehabilitation and also provides a set of recommendations for future rehabilitation assessment efforts.
dc.description.sponsorshipWe would like to thank Andrew Fletcher for help to develop this project and for collecting and processing UAV imagery. The Australian Coal Industry's Research Program (ACARP) monitors helped us develop site specific criteria and access sites and data. Funding to undertake this work was provided by ACARP through their support of Project C24031.
dc.publisherElsevier BV
dc.relation.urlhttp://www.sciencedirect.com/science/article/pii/S0959652618333201
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Journal of Cleaner Production. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Cleaner Production, [209, , (2018-10-29)] DOI: 10.1016/j.jclepro.2018.10.287 . © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectIndicators
dc.subjectMonitoring
dc.subjectObject-based image analysis
dc.subjectOpen cut mine
dc.subjectRehabilitation
dc.subjectUAV
dc.titleUsing Unmanned Aerial Vehicles to assess the rehabilitation performance of open cut coal mines
dc.typeArticle
dc.contributor.departmentWater Desalination and Reuse Research Center (WDRC)
dc.contributor.departmentBiological and Environmental Sciences and Engineering (BESE) Division
dc.contributor.departmentEnvironmental Science and Engineering Program
dc.identifier.journalJournal of Cleaner Production
dc.eprint.versionPost-print
dc.contributor.institutionCentre for Mined Land Rehabilitation, Sustainable Minerals Institute, The University of Queensland, Brisbane, QLD, 4072, , Australia
kaust.personJohansen, Kasper
kaust.personMcCabe, Matthew
refterms.dateFOA2018-11-18T11:07:04Z
dc.date.published-online2018-10-29
dc.date.published-print2019-02


Files in this item

Thumbnail
Name:
Journal_Cleaner_Production_v23_.pdf
Size:
1.788Mb
Format:
PDF
Description:
Accepted Manuscript

This item appears in the following Collection(s)

Show simple item record