Upland vegetation mapping using Random Forests with optical and radar satellite data

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dc.contributor.author Barrett, Brian
dc.contributor.author Raab, Christoph
dc.contributor.author Cawkwell, Fiona
dc.contributor.author Green, Stuart
dc.date.accessioned 2017-12-08T13:33:40Z
dc.date.available 2017-12-08T13:33:40Z
dc.date.issued 2016-11-28
dc.identifier.citation Barrett, B., Raab, C., Cawkwell, F. and Green, S. (2016) 'Upland vegetation mapping using Random Forests with optical and radar satellite data', Remote Sensing in Ecology and Conservation, 2(4), pp. 212-231. doi: 10.1002/rse2.32 en
dc.identifier.volume 2
dc.identifier.issued 4
dc.identifier.startpage 212
dc.identifier.endpage 231
dc.identifier.issn 2056-3485
dc.identifier.uri http://hdl.handle.net/10468/5132
dc.identifier.doi 10.1002/rse2.32
dc.description.abstract Uplands represent unique landscapes that provide a range of vital benefits to society, but are under increasing pressure from the management needs of a diverse number of stakeholders (e.g. farmers, conservationists, foresters, government agencies and recreational users). Mapping the spatial distribution of upland vegetation could benefit management and conservation programmes and allow for the impacts of environmental change (natural and anthropogenic) in these areas to be reliably estimated. The aim of this study was to evaluate the use of medium spatial resolution optical and radar satellite data, together with ancillary soil and topographic data, for identifying and mapping upland vegetation using the Random Forests (RF) algorithm. Intensive field survey data collected at three study sites in Ireland as part of the National Parks and Wildlife Service (NPWS) funded survey of upland habitats was used in the calibration and validation of different RF models. Eight different datasets were analysed for each site to compare the change in classification accuracy depending on the input variables. The overall accuracy values varied from 59.8% to 94.3% across the three study locations and the inclusion of ancillary datasets containing information on the soil and elevation further improved the classification accuracies (between 5 and 27%, depending on the input classification dataset). The classification results were consistent across the three different study areas, confirming the applicability of the approach under different environmental contexts. en
dc.description.sponsorship Environmental Protection Agency (Science, Technology, Research and Innovation for the Environment (STRIVE) en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher John Wiley & Sons Ltd. en
dc.relation.uri http://onlinelibrary.wiley.com/doi/10.1002/rse2.32/abstract
dc.rights © 2016, the Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. en
dc.rights.uri https://creativecommons.org/licenses/by-nc/4.0/
dc.subject Radar en
dc.subject Random forests en
dc.subject Remote sensing en
dc.subject Satellite data en
dc.subject Uplands en
dc.subject Vegetation mapping en
dc.title Upland vegetation mapping using Random Forests with optical and radar satellite data en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Fiona Cawkwell, Geography, University College Cork, Cork, Ireland. +353-21-490-3000 Email: f.cawkwell@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.contributor.funder Environmental Protection Agency
dc.description.status Peer reviewed en
dc.identifier.journaltitle Remote Sensing in Ecology and Conservation en
dc.internal.IRISemailaddress f.cawkwell@ucc.ie en


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© 2016, the Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Except where otherwise noted, this item's license is described as © 2016, the Authors. Remote Sensing in Ecology and Conservation published by John Wiley & Sons Ltd on behalf of Zoological Society of London. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
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