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Assessing estimation techniques for missing plot observations in the U.S. forest inventoryAuthor(s): Grant M. Domke; Christopher W. Woodall; Ronald E. McRoberts; James E. Smith; Mark A. Hatfield
Source: In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 177-181.
Publication Series: Paper (invited, offered, keynote)
Station: Northern Research Station
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DescriptionThe U.S. Forest Service, Forest Inventory and Analysis Program made a transition from state-by-state periodic forest inventories--with reporting standards largely tailored to regional requirements--to a nationally consistent, annual inventory tailored to large-scale strategic requirements. Lack of measurements on all forest land during the periodic inventory, along with access issues and misidentification of forest plots as nonforest, have resulted in plot-level data gaps spread in the FIA database. In this study, we examined several approaches that compensate for missing observations with respect to the deviation and precision of stratified estimates of carbon stocks per unit area using data from the FIA database. Preliminary estimates of live tree carbon stocks per unit area calculated using all missing data approaches were well within one standard error of the baseline estimates for the Lake States study region.
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CitationDomke, Grant M.; Woodall, Christopher W.; McRoberts, Ronald E.; Smith, James E.; Hatfield, Mark A. 2012. Assessing estimation techniques for missing plot observations in the US forest inventory. In: Morin, Randall S.; Liknes, Greg C., comps. Moving from status to trends: Forest Inventory and Analysis (FIA) symposium 2012; 2012 December 4-6; Baltimore, MD. Gen. Tech. Rep. NRS-P-105. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. [CD-ROM]: 177-181.
Keywordsstatistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
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