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An efficient estimator to monitor rapidly changing forest conditionsAuthor(s): Raymond L. Czaplewski; Michael T. Thompson; Gretchen G. Moisen
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]: 416-420.
Publication Series: Paper (invited, offered, keynote)
Station: Northern Research Station
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DescriptionExtensive expanses of forest often change at a slow pace. In this common situation, FIA produces informative estimates of current status with the Moving Average (MA) method and post-stratification with a remotely sensed map of forest-nonforest cover. However, MA "smoothes out" estimates over time, which confounds analyses of temporal trends; and post-stratification limits gains from remote sensing. Time-series estimators, like the Kalman Filter (KF), better detect and analyze unexpected or rapid changes in dynamic forests. KF is a recursive multivariate model-based estimator that separates complex time-series of panel estimates and multi-sensor remotely sensed data into a sequence of smaller and more manageable components. Population-level results are disaggregated into expansion factors that assure additivity and simplify small area and small domain estimation. Other statistics gauge fit of alternative models to annual FIA panel data, which permits quantitative rankings among alternative cause-effect hypotheses.
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CitationCzaplewski, Raymond L.; Thompson, Michael T.; Moisen, Gretchen G. 2012. An efficient estimator to monitor rapidly changing forest conditions. 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]: 416-420.
Keywordsstatistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring
- Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.
- Opportunities to improve monitoring of temporal trends with FIA panel data
- Optimized endogenous post-stratification in forest inventories
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