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Can digital image classification be used as a standardised method for surveying peatland vegetation cover?

This paper, published in a Special Issue of the journal Ecological Indicators to mark 20 years of data collection at ECN terrestrial sites, describes the use of digital image classification techniques as an approach to surveying peatland vegetation to functional type level.
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© 2016 Elsevier Ltd.
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Baxendale, CL., Ostle, NJ., Wood, CM., Oakley, S., Ward, SE. (2016). Can digital image classification be used as a standardised method for surveying peatland vegetation cover? Ecological Indicators68, 150-156. DOI: 10.1016/j.ecolind.2015.11.035.

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The ability to carry out systematic, accurate and repeatable vegetation surveys is an essential part of long-term scientific studies into ecosystem biodiversity and functioning. However, current widely used traditional survey techniques such as destructive harvests, pin frame quadrats and visual cover estimates can be very time consuming and are prone to subjective variations. We investigated the use of digital image techniques as an alternative way of recording vegetation cover to plant functional type level on a peatland ecosystem. Using an established plant manipulation experimental site at Moor House NNR (an Environmental Change Network site), we compared visual cover estimates of peatland vegetation with cover estimates using digital image classification methods, from 0.5 m × 0.5 m field plots. Our results show that digital image classification of photographs taken with a standard digital camera can be used successfully to estimate dwarf-shrub and graminoid vegetation cover at a comparable level to field visual cover estimates, although the methods were less effective for lower plants such as mosses and lichens. Our study illustrates the novel application of digital image techniques to provide a new way of measuring and monitoring peatland vegetation to the plant functional group level, which is less vulnerable to surveyor bias than are visual field surveys. Furthermore, as such digital techniques are highly repeatable, we suggest that they have potential for use in long-term monitoring studies, at both plot and landscape scales.