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Inventory, Monitoring, & Analysis


In recent years airborne Light Detection and Ranging (LiDAR) technology has received a great deal of attention. Using airborne LiDAR, analysts have successfully related height measurements to forest characteristics such as tree size, basal area, and number of trees.
Remotely sensed radiation, attractive for its spatial and temporal coverage, offers a means of inferring energy deposition in fires (e.g. on soils, fuels and tree stems) but coordinated remote and in situ (in-flame) measurements are lacking.
Remotely sensed data are commonly used as predictor variables in spatially explicit models depicting landscape characteristics of interest (response) across broad extents, at relatively fine resolution. To create these models, variables are spatially registered to a known coordinate system and used to link responses with predictor variable values.
The concept of ecological integrity has been applied widely to management of aquatic systems, but still is considered by many to be too vague and difficult to quantify to be useful for managing terrestrial systems, particularly across broad areas.
Whitebark pine (Pinus albicaulis Engelm.) has recently experienced high mortality due to multiple stressors, and future population viability may rely on natural regeneration. We assessed whitebark pine seedling densities throughout the US Rocky Mountains and identified stand, site, and climatic variables related to seedling presence based on data from 1,217 USDA Forest Service Forest Inventory and Analysis plots.
Monitoring and classifying forest disturbance using Landsat time series has improved greatly over the past decade, with many new algorithms taking advantage of the high-quality, cost free data in the archive.
Conifers in the Pinaceae and Cupressaceae from dry environments have been shown to broadly differ in their stomatal sensitivity to soil drying that result in isohydric versus anisohydric water use behavior, respectively.
The United States Forest Inventory and Analysis (FIA) program has been monitoring national forest resources in the United States for over 80 years; presented here is a synthesis of research applications for FIA data. A review of over 180 publications that directly utilize FIA data is broken down into broad categories of application and further organized by methodologies and niche research areas.
This report presents a summary of the most recent inventory of Arizona’s forests based on field data collected between 2001 and 2014. The report includes descriptive highlights and tables of forest and timberland area, numbers of trees, biomass, volume, growth, mortality, and removals. Most sections and tables are organized by forest type or forest-type group, species group, diameter class, or owner group.
Accurate characterization of Carbon (C) consequences of forest disturbances and management is critical for informed climate mitigation and adaptation strategies.