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dc.contributor.authorButers, Todd
dc.contributor.authorBateman, Bill
dc.contributor.authorRobinson, Todd
dc.contributor.authorBelton, David
dc.contributor.authorDixon, Kingsley
dc.contributor.authorCross, Adam
dc.date.accessioned2021-07-19T03:02:16Z
dc.date.available2021-07-19T03:02:16Z
dc.date.issued2019
dc.identifier.citationButers, T.M. and Bateman, P.W. and Robinson, T. and Belton, D. and Dixon, K.W. and Cross, A.T. 2019. Methodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration. Remote Sensing. 11 (10): Article No. 1180.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/84620
dc.identifier.doi10.3390/rs11101180
dc.description.abstract

The last decade has seen an exponential increase in the application of unmanned aerial vehicles (UAVs) to ecological monitoring research, though with little standardisation or comparability in methodological approaches and research aims. We reviewed the international peer-reviewed literature in order to explore the potential limitations on the feasibility of UAV-use in the monitoring of ecological restoration, and examined how they might be mitigated to maximise the quality, reliability and comparability of UAV-generated data. We found little evidence of translational research applying UAV-based approaches to ecological restoration, with less than 7% of 2133 published UAV monitoring studies centred around ecological restoration. Of the 48 studies, > 65% had been published in the three years preceding this study. Where studies utilised UAVs for rehabilitation or restoration applications, there was a strong propensity for single-sensor monitoring using commercially available RPAs fitted with the modest-resolution RGB sensors available. There was a strong positive correlation between the use of complex and expensive sensors (e.g., LiDAR, thermal cameras, hyperspectral sensors) and the complexity of chosen image classification techniques (e.g., machine learning), suggesting that cost remains a primary constraint to the wide application of multiple or complex sensors in UAV-based research. We propose that if UAV-acquired data are to represent the future of ecological monitoring, research requires a) consistency in the proven application of different platforms and sensors to the monitoring of target landforms, organisms and ecosystems, underpinned by clearly articulated monitoring goals and outcomes; b) optimization of data analysis techniques and the manner in which data are reported, undertaken in cross-disciplinary partnership with fields such as bioinformatics and machine learning; and c) the development of sound, reasonable and multi-laterally homogenous regulatory and policy framework supporting the application of UAVs to the large-scale and potentially trans-disciplinary ecological applications of the future.

dc.languageEnglish
dc.publisherMDPI
dc.relation.sponsoredbyhttp://purl.org/au-research/grants/arc/IC150100041
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectScience & Technology
dc.subjectLife Sciences & Biomedicine
dc.subjectPhysical Sciences
dc.subjectTechnology
dc.subjectEnvironmental Sciences
dc.subjectGeosciences, Multidisciplinary
dc.subjectRemote Sensing
dc.subjectImaging Science & Photographic Technology
dc.subjectEnvironmental Sciences & Ecology
dc.subjectGeology
dc.subjectecological restoration
dc.subjectdrone
dc.subjectUAS
dc.subjectrehabilitation
dc.subjectrevegetation
dc.subjectHIGH-RESOLUTION
dc.subjectHYPERSPECTRAL IMAGERY
dc.subjectINFRARED IMAGERY
dc.subjectLOW-ALTITUDE
dc.subjectUAV
dc.subjectSYSTEM
dc.subjectDRONES
dc.subjectCLASSIFICATION
dc.subjectVEGETATION
dc.subjectAIRCRAFT
dc.titleMethodological ambiguity and inconsistency constrain unmanned aerial vehicles as a silver bullet for monitoring ecological restoration
dc.typeJournal Article
dcterms.source.volume11
dcterms.source.number10
dcterms.source.titleRemote Sensing
dc.date.updated2021-07-19T03:02:15Z
curtin.note

© 2019 The Authors. Published by MDPI Publishing.

curtin.departmentSchool of Molecular and Life Sciences (MLS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidCross, Adam [0000-0002-5214-2612]
curtin.contributor.orcidBateman, Bill [0000-0002-3036-5479]
curtin.contributor.orcidRobinson, Todd [0000-0003-3314-3748]
curtin.contributor.orcidBelton, David [0000-0002-2879-7918]
curtin.contributor.orcidDixon, Kingsley [0000-0001-5989-2929]
curtin.contributor.orcidButers, Todd [0000-0001-7018-9388]
curtin.contributor.researcheridCross, Adam [F-5450-2012]
curtin.contributor.researcheridRobinson, Todd [G-9400-2013]
curtin.contributor.researcheridBelton, David [Q-5423-2016]
curtin.contributor.researcheridDixon, Kingsley [A-8133-2016] [B-1042-2011]
curtin.identifier.article-numberARTN 1180
dcterms.source.eissn2072-4292
curtin.contributor.scopusauthoridCross, Adam [55829876800]
curtin.contributor.scopusauthoridBateman, Bill [7006469998]
curtin.contributor.scopusauthoridRobinson, Todd [8610563900]
curtin.contributor.scopusauthoridBelton, David [36920327800]
curtin.contributor.scopusauthoridDixon, Kingsley [35556048900] [55498810700] [57203078005]


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