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dc.contributor.authorAssmann, JJ
dc.contributor.authorMyers-Smith, IH
dc.contributor.authorKerby, JT
dc.contributor.authorCunliffe, AM
dc.contributor.authorDaskalova, GN
dc.date.accessioned2021-04-14T06:43:10Z
dc.date.issued2020-11-24
dc.description.abstractData across scales are required to monitor ecosystem responses to rapid warming in the Arctic and to interpret tundra greening trends. Here, we tested the correspondence among satellite- and drone-derived seasonal change in tundra greenness to identify optimal spatial scales for vegetation monitoring on Qikiqtaruk - Herschel Island in the Yukon Territory, Canada. We combined time-series of the Normalised Difference Vegetation Index (NDVI) from multispectral drone imagery and satellite data (Sentinel-2, Landsat 8 and MODIS) with ground-based observations for two growing seasons (2016 and 2017). We found high cross-season correspondence in plot mean greenness (drone-satellite Spearman's ρ 0.67-0.87) and pixel-by-pixel greenness (drone-satellite R 2 0.58-0.69) for eight one-hectare plots, with drones capturing lower NDVI values relative to the satellites. We identified a plateau in the spatial variation of tundra greenness at distances of around half a metre in the plots, suggesting that these grain sizes are optimal for monitoring such variation in the two most common vegetation types on the island. We further observed a notable loss of seasonal variation in the spatial heterogeneity of landscape greenness (46.2%-63.9%) when aggregating from ultra-fine-grain drone pixels (approx. 0.05 m) to the size of medium-grain satellite pixels (10-30 m). Finally, seasonal changes in drone-derived greenness were highly correlated with measurements of leaf-growth in the ground-validation plots (mean Spearman's ρ 0.70). These findings indicate that multispectral drone measurements can capture temporal plant growth dynamics across tundra landscapes. Overall, our results demonstrate that novel technologies such as drone platforms and compact multispectral sensors allow us to study ecological systems at previously inaccessible scales and fill gaps in our understanding of tundra ecosystem processes. Capturing fine-scale variation across tundra landscapes will improve predictions of the ecological impacts and climate feedbacks of environmental change in the Arctic.en_GB
dc.description.sponsorshipNatural Environment Research Council (NERC)en_GB
dc.description.sponsorshipNational Geographic Societyen_GB
dc.description.sponsorshipParrot Climate Innovation Granten_GB
dc.description.sponsorshipAarhus University Research Foundationen_GB
dc.description.sponsorshipEuropean Union Horizon 2020en_GB
dc.identifier.citationVol. 15 (120, article 125002en_GB
dc.identifier.doi10.1088/1748-9326/abbf7d
dc.identifier.grantnumberNE/M016323/1en_GB
dc.identifier.grantnumberNE/L002558/1en_GB
dc.identifier.grantnumberCP-061R-17en_GB
dc.identifier.grantnumber754513en_GB
dc.identifier.urihttp://hdl.handle.net/10871/125351
dc.language.isoenen_GB
dc.publisherIOP Publishingen_GB
dc.relation.urlhttps://github.com/jakobjassmann/qhi_phen_tsen_GB
dc.rights© 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.en_GB
dc.subjectArctic tundraen_GB
dc.subjectvegetation monitoringen_GB
dc.subjectlandscape phenologyen_GB
dc.subjectsatelliteen_GB
dc.subjectdronesen_GB
dc.subjectUAV and RPASen_GB
dc.subjectNDVIen_GB
dc.subjectscaleen_GB
dc.titleDrone data reveal heterogeneity in tundra greenness and phenology not captured by satellitesen_GB
dc.typeArticleen_GB
dc.date.available2021-04-14T06:43:10Z
dc.identifier.issn1748-9318
dc.descriptionThis is the final version. Available on open access from IOP Publishing via the DOI in this recorden_GB
dc.descriptionData availability statement The data and code that support the findings of this study are openly available at the following URL: (https://github.com/jakobjassmann/qhi_phen_ts).en_GB
dc.identifier.journalEnvironmental Research Lettersen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2020-10-08
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-11-24
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2021-04-14T06:39:11Z
refterms.versionFCDVoR
refterms.dateFOA2021-04-14T06:43:21Z
refterms.panelCen_GB


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© 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
Except where otherwise noted, this item's licence is described as © 2020 The Author(s). Published by IOP Publishing Ltd. Open access. Original content from this work may be used under the terms of the Creative Commons Attribution 4.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.