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Transferability and the effect of colour calibration during multi-image classification of Arctic vegetation change

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Abstract

Mapping changes in vegetation cover is essential for understanding the consequences of climate change on Arctic ecosystems. Classification of ultra-high spatial-resolution (UHR, < 1 cm) imagery can provide estimates of vegetation cover across space and time. The challenge of this approach is to assure comparability of classification across many images taken at different illumination conditions and locations. With warming, vegetation at higher elevation is expected to resemble current vegetation at lower elevation. To investigate the value of classification of UHR imagery for monitoring vegetation change, we collected visible and near-infrared images from 108 plots with hand-held cameras along an altitudinal gradient in Greenland and examined the classification accuracy of shrub cover on independent images (i.e. classification transferability). We implemented several models to examine if colour calibration improves transferability based on an in-image calibration target. The classifier was trained on different number of images to find the minimum training subset size. With a training set of ~ 20% of the images the overall accuracy levelled off at about 81% and 68% on the non-calibrated training and validation images, respectively. Colour calibration improved the accuracy on training images (1–4%) while it only improved the classifier transferability significantly for training sets < 20%. Linear calibration only based on the target’s grey series improved transferability most. Reasonable transferability of Arctic shrub cover classification can be obtained based only on spectral data and about 20% of all images. This is promising for vegetation monitoring through multi-image classification of UHR imagery acquired with hand-held cameras or Unmanned Aerial Systems.

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Acknowledgements

The research was initiated with a WSL internal innovative research grant and supported by a Villum Young Investigator Grant (VKR023456), an Aarhus University (AU), Research Foundation grant (AUFF-E-2015-FLS-8-73) and an AU Science and Technology Synergy grant. Fieldwork was possible due to funding from the AU Arctic Research Centre. We are grateful to Rok Kreslin and Peter Peer for providing their code in MATLAB for the DT method; to Constantinos Tsirogiannis for his valuable help in code optimization and to Ditte Grube Barild for geo-referencing the images.

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Authors and Affiliations

  1. Department of Bioscience, Section for Ecoinformatics & Biodiversity, Center for Biodiversity Dynamics in a Changing World, Aarhus University, Ny Munkegade 116, 8000, Aarhus C, Denmark

    Samira Kolyaie, Urs Albert Treier, Gary Richard Watmough, Bjarke Madsen, Peder Klith Bøcher & Signe Normand

  2. Swiss Federal Research Institute WSL, Remote Sensing Group, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland

    Urs Albert Treier, Achilleas Psomas & Ruedi Bösch

  3. Department of Bioscience, Arctic Research Centre, Aarhus University, Ny Munkegade 116, 8000, Aarhus C, Denmark

    Urs Albert Treier & Signe Normand

  4. School of Geosciences, University of Edinburgh, Edinburgh, EH8 9XP, UK

    Gary Richard Watmough

  5. Swiss Federal Research Institute WSL, Landscape Dynamics Group, Zürcherstrasse 111, 8903, Birmensdorf, Switzerland

    Signe Normand

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  1. Samira Kolyaie
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Kolyaie, S., Treier, U.A., Watmough, G.R. et al. Transferability and the effect of colour calibration during multi-image classification of Arctic vegetation change. Polar Biol 42, 1227–1239 (2019). https://doi.org/10.1007/s00300-019-02491-7

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