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Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes

View ORCID ProfileJakob J Assmann, View ORCID ProfileJeffrey T Kerby, View ORCID ProfileAndrew M Cunliffe, View ORCID ProfileIsla H Myers-Smith
doi: https://doi.org/10.1101/334730
Jakob J Assmann
1School of GeoSciences, The University of Edinburgh, Edinburgh, UK
2School of Biology, The University of Edinburgh, Edinburgh, UK
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Jeffrey T Kerby
3Neukom Institute for Computational Science, Institute of Arctic Studies, Dartmouth College, USA
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Andrew M Cunliffe
1School of GeoSciences, The University of Edinburgh, Edinburgh, UK
4School of Geography, University of Exeter, Exeter, UK
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Isla H Myers-Smith
1School of GeoSciences, The University of Edinburgh, Edinburgh, UK
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Abstract

Emerging drone technologies have the potential to revolutionise ecological monitoring. The rapid technological advances in recent years have dramatically increased affordability and ease of use of Unmanned Aerial Vehicles (UAVs) and associated sensors. Compact multispectral sensors, such as the Parrot Sequoia (Paris, France) and MicaSense RedEdge (Seattle WA, USA) capture spectrally accurate high-resolution (fine grain) imagery in visible and near-infrared parts of the electromagnetic spectrum, providing supplement to satellite and aircraft-based imagery. Observations of surface reflectance can be used to calculate vegetation indices such as the Normalised Difference Vegetation Index (NDVI) for productivity estimates and vegetation classification. Despite the advances in technology, challenges remain in capturing consistently high-quality data, particularly when operating in extreme environments such as the high latitudes. Here, we summarize three years of ecological monitoring with drone-based multispectral sensors in the remote Canadian Arctic. We discuss challenges, technical aspects and practical considerations, and highlight best practices that emerged from our experience, including: flight planning, factoring in weather conditions, and geolocation and radiometric calibration. We propose a standardised methodology based on established principles from remote sensing and our collective field experiences, using the Parrot Sequoia sensor as an example. With these good practises, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

Acknowledgements

Much of this manuscript would have not been possible without the valuable input from Chris MacLellan and Andrew Gray at the NERC Field Spectroscopy Facility at the Grant Institute in Edinburgh. We would also like to thank Andrew Gray for providing feedback on an earlier version of this manuscript and Tom Wade from the University of Edinburgh Airborne GeoSciences Facility University of Edinburgh Airborne GeoScience facility for his ongoing support of our drone-based endeavours in the Arctic.

Funding for this research was provided by NERC through the ShrubTundra standard grant (NE/M016323/1), a NERC E3 Doctoral Training Partnership PhD studentship for Jakob Assmann (NE/L002558/1), a research grant from the National Geographic Society (CP-061R-17) and a Parrot Climate Innovation Grant for Jeffrey Kerby, a NERC support case for use of the NERC Field Spectroscopy Facility (738.1115), equipment loans from the University of Edinburgh Airborne GeoSciences Facility and the NERC Geophysical Equipment Facility (GEF 1063 and 1069).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted May 30, 2018.
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Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes
Jakob J Assmann, Jeffrey T Kerby, Andrew M Cunliffe, Isla H Myers-Smith
bioRxiv 334730; doi: https://doi.org/10.1101/334730
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Vegetation monitoring using multispectral sensors – best practices and lessons learned from high latitudes
Jakob J Assmann, Jeffrey T Kerby, Andrew M Cunliffe, Isla H Myers-Smith
bioRxiv 334730; doi: https://doi.org/10.1101/334730

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