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Using a hybrid demand-allocation algorithm to enable distributional analysis of land use change patterns

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dc.contributor.authorBrooks, Evan B.en
dc.contributor.authorCoulston, John W.en
dc.contributor.authorRiitters, Kurt H.en
dc.contributor.authorWear, David N.en
dc.contributor.departmentForest Resources and Environmental Conservationen
dc.date.accessioned2021-01-29T15:24:05Zen
dc.date.available2021-01-29T15:24:05Zen
dc.date.issued2020-10-15en
dc.description.abstractFuture land use projections are needed to inform long-term planning and policy. However, most projections require downscaling into spatially explicit projection rasters for ecosystem service analyses. Empirical demand-allocation algorithms input coarse-level transition quotas and convert cells across the raster, based on a modeled probability surface. Such algorithms typically employ contagious and/or random allocation approaches. We present a hybrid seeding approach designed to generate a stochastic collection of spatial realizations for distributional analysis, by 1) randomly selecting a seed cell from a sample ofncells, then 2) converting patches of neighboring cells based on transition probability and distance to the seed. We generated a collection of realizations from 2001-2011 for the conterminous USA at 90m resolution based on varying the value ofn, then computed forest area by fragmentation class and compared the results with observed 2011 forest area by fragmentation class. We found that realizations based on values ofn <= 256 generally covered observed forest fragmentation at regional scales, for approximately 70% of assessed cases. We also demonstrate the potential of the seeding algorithm for distributional analysis by generating 20 trajectories of realizations from 2020-2070 from a single example scenario. Generating a library of such trajectories from across multiple scenarios will enable analysis of projected patterns and downstream ecosystem services, as well as their variation.en
dc.description.notesE.B.B was funded through the Joint Venture Agreement 14-JV-11330145-108 between the USDA Forest Service and Virginia Tech. J.W.C, K.H.R, and D.N.W were supported by the USDA Forest Service.en
dc.description.sponsorshipUSDA Forest ServiceUnited States Department of Agriculture (USDA)United States Forest Service [14-JV-11330145-108]; Virginia Tech. [14-JV-11330145-108]en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0240097en
dc.identifier.issn1932-6203en
dc.identifier.issue10en
dc.identifier.othere0240097en
dc.identifier.pmid33057344en
dc.identifier.urihttp://hdl.handle.net/10919/102129en
dc.identifier.volume15en
dc.language.isoenen
dc.rightsCC0 1.0 Universalen
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/en
dc.titleUsing a hybrid demand-allocation algorithm to enable distributional analysis of land use change patternsen
dc.title.serialPlos Oneen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.dcmitypeStillImageen

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