Keyword: environmental monitoring

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

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.

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

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?

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

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

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

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

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.

Applying inventory methods to estimate aboveground biomass from satellite light detection and ranging (LiDAR) forest height data

Light Detection and Ranging (LiDAR) returns from the spaceborne Geoscience Laser Altimeter (GLAS) sensor may offer an alternative to solely field-based forest biomass sampling. Such an approach would rely upon model-based inference, which can account for the uncertainty associated with using modeled, instead of field-collected, measurements.

Strategies for assessing inter- and intra-specific variation in tree biomass in the interior west

Wildfire hazard mitigation and bioenergy harvesting have emerged as forest management priorities throughout the Interior West (IW) of the USA. Regional forest inventory and forecasting applications are therefore increasingly focused on tree biomass, including biomass in traditionally non-merchantable components. Yet accurate biomass equations for the latter components are typically lacking, even for major commercial species.

Properties of the endogenous post-stratified estimator using a random forests model

Post-stratification is used in survey statistics as a method to improve variance estimates. In traditional post-stratification methods, the variable on which the data is being stratified must be known at the population level. In many cases this is not possible, but it is possible to use a model to predict values using covariates, and then stratify on these predicted values.