Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data

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dc.contributor.authorFagan, M. E.
dc.contributor.authorMorton, D.C.
dc.contributor.authorCook, B.D.
dc.contributor.authorMasek, J.
dc.contributor.authorZhao, F.
dc.contributor.authorNelson, R.F.
dc.contributor.authorHuang, C.
dc.date.accessioned2018-11-06T14:52:28Z
dc.date.available2018-11-06T14:52:28Z
dc.date.issued2018-10
dc.description.abstractThe southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.en_US
dc.description.sponsorshipFunding for this study was provided by a NASA Postdoctoral Program fellowship (M. Fagan) and NASA's Carbon Monitoring System and Carbon Cycle Science Programs.en_US
dc.description.urihttps://www.sciencedirect.com/science/article/pii/S0034425718303341en_US
dc.format.extent12 pagesen_US
dc.genrejournal articles post-printen_US
dc.identifierdoi:10.13016/M2251FP8K
dc.identifier.citationM.E. Fagan, D.C. Morton, B.D. Cook, J. Masek, F. Zhao, R.F. Nelson, C. Huang, Mapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing data, Remote Sensing of Environment Volume 216, October 2018, Pages 415-426, https://doi.org/10.1016/j.rse.2018.07.007en_US
dc.identifier.urihttps://doi.org/10.1016/j.rse.2018.07.007
dc.identifier.urihttp://hdl.handle.net/11603/11878
dc.language.isoen_USen_US
dc.publisherElsevier Inc.en_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Geography and Environmental Systems Department Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 United States*
dc.rightsAccess to this article will be available from Nov 1. 2020
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectMappingen_US
dc.subjectpine plantationsen_US
dc.subjectstructuralen_US
dc.subjectspectralen_US
dc.subjecttemporalen_US
dc.subjectremote sensing dataen_US
dc.titleMapping pine plantations in the southeastern U.S. using structural, spectral, and temporal remote sensing dataen_US
dc.typeTexten_US

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