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Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest

Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resol...

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Autores principales: Marsik, Matthew, Staub, Caroline G., Kleindl, William J., Hall, Jaclyn M., Fu, Chiung-Shiuan, Yang, Di, Stevens, Forrest R., Binford, Michael W.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111890/
https://www.ncbi.nlm.nih.gov/pubmed/30152814
http://dx.doi.org/10.1038/sdata.2018.165
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author Marsik, Matthew
Staub, Caroline G.
Kleindl, William J.
Hall, Jaclyn M.
Fu, Chiung-Shiuan
Yang, Di
Stevens, Forrest R.
Binford, Michael W.
author_facet Marsik, Matthew
Staub, Caroline G.
Kleindl, William J.
Hall, Jaclyn M.
Fu, Chiung-Shiuan
Yang, Di
Stevens, Forrest R.
Binford, Michael W.
author_sort Marsik, Matthew
collection PubMed
description Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software.
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spelling pubmed-61118902018-08-31 Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest Marsik, Matthew Staub, Caroline G. Kleindl, William J. Hall, Jaclyn M. Fu, Chiung-Shiuan Yang, Di Stevens, Forrest R. Binford, Michael W. Sci Data Data Descriptor Forests in the United States are managed by multiple public and private entities making harmonization of available data and subsequent mapping of management challenging. We mapped four important types of forest management, production, ecological, passive, and preservation, at 250-meter spatial resolution in the Southeastern (SEUS) and Pacific Northwest (PNW) USA. Both ecologically and socio-economically dynamic regions, the SEUS and PNW forests represent, respectively, 22.0% and 10.4% of forests in the coterminous US. We built a random forest classifier using seasonal time-series analysis of 16 years of MODIS 16-day composite Enhanced Vegetation Index, and ancillary data containing forest ownership, roads, US Forest Service wilderness and forestry areas, proportion conifer and proportion riparian. The map accuracies for SEUS are 89% (10-fold cross-validation) and 67% (external validation) and PNW are 91% and 70% respectively with the same validation. The now publicly available forest management maps, probability surfaces for each management class and uncertainty layer for each region can be viewed and analysed in commercial and open-source GIS and remote sensing software. Nature Publishing Group 2018-08-28 /pmc/articles/PMC6111890/ /pubmed/30152814 http://dx.doi.org/10.1038/sdata.2018.165 Text en Copyright © 2018, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Marsik, Matthew
Staub, Caroline G.
Kleindl, William J.
Hall, Jaclyn M.
Fu, Chiung-Shiuan
Yang, Di
Stevens, Forrest R.
Binford, Michael W.
Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title_full Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title_fullStr Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title_full_unstemmed Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title_short Regional-scale management maps for forested areas of the Southeastern United States and the US Pacific Northwest
title_sort regional-scale management maps for forested areas of the southeastern united states and the us pacific northwest
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111890/
https://www.ncbi.nlm.nih.gov/pubmed/30152814
http://dx.doi.org/10.1038/sdata.2018.165
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