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Properties of Endogenous Post-Stratified Estimation using remote sensing data

Post-stratification is commonly used to improve the precision of survey estimates. In traditional poststratification methods, the stratification variable must be known at the population level. When suitable covariates are available at the population level, an alternative approach consists of fitting a model on the covariates, making predictions for the population and...

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Author(s):

John Tipton; Jean Opsomer; Gretchen Moisen

Year:

2013

Keywords:

endogenous post-stratification, forest inventory analysis, Landsat ETM+, Random Forest, improved precision

Source:

Remote Sensing of Environment. 139: 130-137.

Small-area estimation of forest attributes within fire boundaries

Wildfires are gaining more attention every year as they burn more frequently, more intensely, and across larger landscapes. Generating timely estimates of forest resources within fire perimeters is important for land managers to quickly determine the impact of fi res on U.S. forests. The U.S. Forest Service?s Forest Inventory and Analysis (FIA) program needs tools to...

Author(s):

T. Frescino; G. Moisen; K. Adachi; J. Breidt

Year:

2014

Keywords:

wildfires, specified fire perimeter, small-area estimation, Forest Inventory and Analysis (FIA), FIESTA

Source:

The International Forestry Review. 16(5): 408.

Model-assisted estimation of forest resources with generalized additive models

Multiphase surveys are often conducted in forest inventories, with the goal of estimating forested area and tree characteristics over large regions. This article describes how design-based estimation of such quantities, based on information gathered during ground visits of sampled plots, can be made more precise by incorporating auxiliary information available from...

Author(s):

Jean D. Opsomer; F. Jay Breidt; Gretchen G. Moisen; Goran Kauermann

Year:

2007

Keywords:

calibration, multiphase survey estimation, nonparametric regression, systematic sampling, variance estimation

Source:

Journal of the American Statistical Association. 102: 400-416.

Model-assisted survey regression estimation with the lasso

In the U.S. Forest Service?s Forest Inventory and Analysis (FIA) program, as in other natural resource surveys, many auxiliary variables are available for use in model-assisted inference about finite population parameters. Some of this auxiliary information may be extraneous, and therefore model selection is appropriate to improve the efficiency of the survey...

Author(s):

Kelly S. McConville; F. Jay Breidt; Thomas C. M. Lee; Gretchen G. Moisen

Year:

2017

Keywords:

adaptive lasso, calibration estimation, complex surveys, generalized regression estimation, model-assisted inference, model selection

Source:

Journal of Survey Statistics and Methodology. 5: 131-158.

Improving FIA trend analysis through model-based estimation using landsat disturbance maps and the forest vegetation simulator

The Forest Inventory and Analysis (FIA) Program's panel system, in which 10-20 percent of the sample is measured in any given year, is designed to increase the currency of FIA reporting and its sensitivity to factors operating at relatively fine temporal scales. Now that much of the country has completed at least one measurement cycle over all panels, there is an...

Author(s):

Sean P. Healey; Gretchen G. Moisen; Paul L. Patterson

Year:

2012

Keywords:

statistics, estimation, sampling, modeling, remote sensing, forest health, data integrity, environmental monitoring, cover estimation, international forest monitoring

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]: 427-431.

An efficient estimator to monitor rapidly changing forest conditions

Extensive 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-strat