You are here

Keyword: estimation

Relative species richness and community completeness: Birds and urbanization in the Mid-Atlantic States

Publications Posted on: December 16, 2019
The idea that local factors govern local richness has been dominant for years, but recent theoretical and empirical studies have stressed the influence of regional factors on local richness. Fewer species at a site could reflect not only the influence of local factors, but also a smaller regional pool.

Doing more with the core: Proceedings of the 2017 Forest Inventory and Analysis (FIA) Science Stakeholder Meeting; 2017 October 24- 26; Park City, UT

Publications Posted on: October 17, 2017
The Forest Service’s Forest Inventory and Analysis Program (FIA) is the primary source of information about our forests’ status and trends. A network of nationally consistent field observations forms FIA’s core, and active collaboration with clients and peer organizations ensures that the resulting inventory remains agile, comprehensive, and relevant.

Forest Inventory ESTimation & Analysis (FIESTA)

Projects Posted on: January 22, 2015
Forest Inventory ESTimation & Analysis (FIESTA) is a research tool for analysts who use data from the Forest Inventory and Analysis program and work in the open-source, R statistical programming environment.

Estimating forest characteristics using NAIP imagery and ArcObjects

Publications Posted on: August 27, 2014
Detailed, accurate, efficient, and inexpensive methods of estimating basal area, trees, and aboveground biomass per acre across broad extents are needed to effectively manage forests.

Trends in standing biomass in Interior West forests: Reassessing baseline data from periodic inventories

Publications Posted on: September 17, 2013
Trends in U.S. forest biomass and carbon are assessed using Forest Inventory and Analysis (FIA) data relative to baseline assessments from the 1990s. The integrity of baseline data varies by state and depends largely on the comparability of periodic versus annual forest inventory data.

Is lodgepole pine mortality due to mountain pine beetle linked to the North American Monsoon?

Publications Posted on: September 17, 2013
Regional precipitation patterns may have influenced the spatial variability of tree mortality during the recent mountain pine beetle (Dendroctonus ponderosa) (MPB) outbreak in the western United States. Data from the Forest Inventory and Analysis (FIA) Program show that the outbreak was especially severe in the state of Colorado where over 10 million lodgepole pines (Pinus contorta Dougl. Ex Loud.) succumbed to MPB between 2002 and 2009.

Mapping aspen in the Interior West

Publications Posted on: September 17, 2013
Quaking aspen (Populus tremuloides Michx.) is a critical species that supports wildlife and livestock, watershed function, the forest products industry, landscape diversity, and recreation opportunities in the Interior West (Bartos and Campbell 1998).

A comparison of FIA plot data derived from image pixels and image objects

Publications Posted on: February 06, 2013
The use of Forest Inventory and Analysis (FIA) plot data for producing continuous and thematic maps of forest attributes (e.g., forest type, canopy cover, volume, and biomass) at the regional level from satellite imagery can be challenging due to differences in scale.

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

Publications Posted on: February 06, 2013
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.

An efficient estimator to monitor rapidly changing forest conditions

Publications Posted on: February 06, 2013
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-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.