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The fourth dimension in FIAAuthor(s): Francis A. Roesch
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]: 421-426.
Publication Series: Paper (invited, offered, keynote)
Station: Northern Research Station
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DescriptionIn the past, the goal of forest inventory was to determine the extent of the timber resource. Predictions of how the resource was changing were made by comparing differences between successive inventories. The general view of the associated sample design included selection probabilities based on land area observed at a discrete point in time. That is, time was not considered part of the sample design because it was not considered an element of the sampled population. Over the last few decades, the general goal of Forest Inventory and Analysis (FIA) has been changing to monitoring the dynamic forest ecosystem. However, much of the literature discussing FIA's new annual monitoring system, its sample design, and estimators is still based on an areal probability paradigm. In Roesch (2008; Forest Science 54(4): 455-464), I pointed out why it is usually necessary to include the dimension of time when describing the sampled population and the sample design for FIA and similar forest inventory systems. Here, I further explore the inferential advantages of replacing the areal probability paradigm with a three-dimensional probability paradigm with an application.
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CitationRoesch, Francis A. 2012. The fourth dimension in FIA. 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]: 421-426.
Keywordsstatistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
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