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.contributor.funder	Environmental Protection Agency
dc.contributor.funder	Irish Government
dc.date.accessioned	2016-02-17T10:07:56Z
dc.date.available	2016-02-17T10:07:56Z
dc.date.issued	2015
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.description.status	Peer reviewed	en
dc.description.version	Published Version	en
dc.format.mimetype	application/pdf	en
dc.identifier.articleid	e0133583
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.doi	10.1371/journal.pone.0133583
dc.identifier.issn	1932-6203
dc.identifier.issued	8	en
dc.identifier.journaltitle	PLOS ONE	en
dc.identifier.uri	https://hdl.handle.net/10468/2298
dc.identifier.volume	10	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

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

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