Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data

Wildfire activity in the western United States is expanding and many western forests are struggling to regenerate postfire. Accurate estimates of forest regeneration following wildfire are critical for postfire forest management planning and monitoring forest dynamics. National or regional forest in...

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Autores principales:	George C. Gaines, David L. R. Affleck
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	forest inventory wildland fire forest regeneration bias estimation forest inventory and analysis monitoring trends in burn severity Forestry SD1-669.5 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/6c4fc483bc214e9f8c1a1bc6ab65e4f9
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id	oai:doaj.org-article:6c4fc483bc214e9f8c1a1bc6ab65e4f9
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spelling	oai:doaj.org-article:6c4fc483bc214e9f8c1a1bc6ab65e4f92021-11-15T07:00:41ZSmall Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data2624-893X10.3389/ffgc.2021.761509https://doaj.org/article/6c4fc483bc214e9f8c1a1bc6ab65e4f92021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/ffgc.2021.761509/fullhttps://doaj.org/toc/2624-893XWildfire activity in the western United States is expanding and many western forests are struggling to regenerate postfire. Accurate estimates of forest regeneration following wildfire are critical for postfire forest management planning and monitoring forest dynamics. National or regional forest inventory programs can provide vegetation data for direct spatiotemporal domain estimation of postfire tree density, but samples within domains of administrative utility may be small (or empty). Indirect domain expansion estimators, which borrow extra-domain sample data to increase precision of domain estimates, offer a possible alternative. This research evaluates domain sample sizes and direct estimates in domains spanning large geographic extents and ranging from 1 to 10 years in temporal scope. In aggregate, domain sample sizes prove too small and standard errors of direct estimates too high. We subsequently compare two indirect estimators—one generated by averaging over observations that are proximate in space, the other by averaging over observations that are proximate in time—on the basis of estimated standard error. We also present a new estimator of the mean squared error (MSE) of indirect domain estimators which accounts for covariance between direct and indirect domain estimates. Borrowing sample data from within the geographic extents of our domains, but from an expanded set of measurement years, proves to be the superior strategy for augmenting domain sample sizes to reduce domain standard errors in this application. However, MSE estimates prove too frequently negative and highly variable for operational utility in this context, even when averaged over multiple proximate domains.George C. GainesDavid L. R. AffleckFrontiers Media S.A.articleforest inventorywildland fireforest regenerationbias estimationforest inventory and analysismonitoring trends in burn severityForestrySD1-669.5Environmental sciencesGE1-350ENFrontiers in Forests and Global Change, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	forest inventory wildland fire forest regeneration bias estimation forest inventory and analysis monitoring trends in burn severity Forestry SD1-669.5 Environmental sciences GE1-350
spellingShingle	forest inventory wildland fire forest regeneration bias estimation forest inventory and analysis monitoring trends in burn severity Forestry SD1-669.5 Environmental sciences GE1-350 George C. Gaines David L. R. Affleck Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
description	Wildfire activity in the western United States is expanding and many western forests are struggling to regenerate postfire. Accurate estimates of forest regeneration following wildfire are critical for postfire forest management planning and monitoring forest dynamics. National or regional forest inventory programs can provide vegetation data for direct spatiotemporal domain estimation of postfire tree density, but samples within domains of administrative utility may be small (or empty). Indirect domain expansion estimators, which borrow extra-domain sample data to increase precision of domain estimates, offer a possible alternative. This research evaluates domain sample sizes and direct estimates in domains spanning large geographic extents and ranging from 1 to 10 years in temporal scope. In aggregate, domain sample sizes prove too small and standard errors of direct estimates too high. We subsequently compare two indirect estimators—one generated by averaging over observations that are proximate in space, the other by averaging over observations that are proximate in time—on the basis of estimated standard error. We also present a new estimator of the mean squared error (MSE) of indirect domain estimators which accounts for covariance between direct and indirect domain estimates. Borrowing sample data from within the geographic extents of our domains, but from an expanded set of measurement years, proves to be the superior strategy for augmenting domain sample sizes to reduce domain standard errors in this application. However, MSE estimates prove too frequently negative and highly variable for operational utility in this context, even when averaged over multiple proximate domains.
format	article
author	George C. Gaines David L. R. Affleck
author_facet	George C. Gaines David L. R. Affleck
author_sort	George C. Gaines
title	Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
title_short	Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
title_full	Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
title_fullStr	Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
title_full_unstemmed	Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data
title_sort	small area estimation of postfire tree density using continuous forest inventory data
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/6c4fc483bc214e9f8c1a1bc6ab65e4f9
work_keys_str_mv	AT georgecgaines smallareaestimationofpostfiretreedensityusingcontinuousforestinventorydata AT davidlraffleck smallareaestimationofpostfiretreedensityusingcontinuousforestinventorydata
_version_	1718428501956100096

Small Area Estimation of Postfire Tree Density Using Continuous Forest Inventory Data

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