Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

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dc.contributor.author Devaney, John
dc.contributor.author Barrett, Brian
dc.contributor.author Barrett, Frank
dc.contributor.author Redmond, John
dc.contributor.author O'Halloran, John
dc.date.accessioned 2016-02-17T10:07:56Z
dc.date.available 2016-02-17T10:07:56Z
dc.date.issued 2015
dc.identifier.citation Devaney J, Barrett B, Barrett F, Redmond J, O`Halloran J (2015) Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies. PLoS ONE 10(8): e0133583. doi:10.1371/journal.pone.0133583
dc.identifier.volume 10 en
dc.identifier.issued 8 en
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10468/2298
dc.identifier.doi 10.1371/journal.pone.0133583
dc.description.abstract Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. en
dc.description.sponsorship Environmental Protection Agency (Science, Technology, Research and Innovation for the Environment (STRIVE) Programme 2011-CCRP-FS-1.1); Irish Government under National Development Plan (NDP) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Public Library of Science en
dc.rights © 2015 Devaney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ en
dc.subject Land cover en
dc.subject Alos-Palsar en
dc.subject Tropical deforestation en
dc.subject Mapping deforestation en
dc.subject Brazilian Amazonia en
dc.subject Boreal forests en
dc.subject Spatial data en
dc.subject Classification en
dc.subject Backscatter en
dc.subject Biomass en
dc.title Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother John O'Halloran, School of Biological, Earth and Environmental Sciences, University College Cork, Cork, Ireland. +353-21-490-3000 Email: j.ohalloran@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.internal.wokid WOS:000359353300008
dc.contributor.funder Environmental Protection Agency
dc.contributor.funder Irish Government
dc.description.status Peer reviewed en
dc.identifier.journaltitle PLOS ONE en
dc.internal.IRISemailaddress j.ohalloran@ucc.ie en
dc.identifier.articleid e0133583


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© 2015 Devaney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Except where otherwise noted, this item's license is described as © 2015 Devaney et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
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