Spracklen, BD and Spracklen, DV orcid.org/0000-0002-7551-4597 (2019) Identifying European Old-Growth Forests using Remote Sensing: A Study in the Ukrainian Carpathians. Forests, 10 (2). ARTN 127. ISSN 1999-4907
Abstract
Old-growth forests are an important, rare and endangered habitat in Europe. The ability to identify old-growth forests through remote sensing would be helpful for both conservation and forest management. We used data on beech, Norway spruce and mountain pine old-growth forests in the Ukrainian Carpathians to test whether Sentinel-2 satellite images could be used to correctly identify these forests. We used summer and autumn 2017 Sentinel-2 satellite images comprising 10 and 20 m resolution bands to create 6 vegetation indices and 9 textural features. We used a Random Forest classification model to discriminate between dominant tree species within old-growth forests and between old-growth and other forest types. Beech and Norway spruce were identified with an overall accuracy of around 90%, with a lower performance for mountain pine (70%) and mixed forest (40%). Old-growth forests were identified with an overall classification accuracy of 85%. Adding textural features, band standard deviations and elevation data improved accuracies by 3.3%, 2.1% and 1.8% respectively, while using combined summer and autumn images increased accuracy by 1.2%. We conclude that Random Forest classification combined with Sentinel-2 images can provide an effective option for identifying old-growth forests in Europe.
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Copyright, Publisher and Additional Information: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). | ||||
Keywords: | old-growth forest; multispectral satellite imagery; random forest; forest classification | ||||
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Institution: | The University of Leeds | ||||
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) > Inst for Climate & Atmos Science (ICAS) (Leeds) | ||||
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Depositing User: | Symplectic Publications | ||||
Date Deposited: | 18 Apr 2019 09:07 | ||||
Last Modified: | 18 Apr 2019 09:07 | ||||
Status: | Published | ||||
Publisher: | MDPI | ||||
Identification Number: | https://doi.org/10.3390/f10020127 | ||||
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