Fuels & Fuel Treatments
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This study analyzed existing permanent monitoring plot data collected between 1995 and 2010 to assess achievement of management objectives related to prescribed fire in ponderosa pine forests. Following first entry fire, ponderosa pine (Pinus ponderosa var. scopulorum) and Gambel oak (Quercus gambelii) overstory and midstory densities declined between 10% and 45% and effectively shifted the Gambel oak diameter distribution toward larger trees. Second entry fires had a greater effect, reducing ponderosa pine and Gambel oak overstory and midstory densities between 24% and 92%. Diameter distributions of both species shifted toward fewer, larger trees following second entry fires. Total fuel load was reduced by <20% in first entry fires and by half in second entry fires. Several objectives identified by the National Park Service (e.g., overstory ponderosa pine reduction) were not achieved with either fire entry; however, power analysis indicated that sample sizes were not adequate to fully detect long term changes following first entry fires.
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The MoD-FIS tool seasonally modulates fuel model data in the Great Basin and Southwest regions. MoD-FIS incorporates seasonal variability of herbaceous cover. These fine fuel measurements are then used to capture changes to fire behavior fuel models based on the current fire season herbaceous production.
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This synthesis updated the Conservation Effects Assessment Project (CEAP) review and broadened the discussion of prescribed fire as a global management practice. It reviews and summarizes prescribed fire literature available through Web of Science using search terms in the title. The majority of literature (40%) evaluated plant responses to fire with fire behavior and management (29%), wildlife and arthropods (12%), soils (11%), and air quality (4%) evaluated less frequently. Generally, fire effects on plants are neutral to positive and the majority of negative responses lasted less than 2 years. Similarly, soil responses were recovered within 2 yr after burning. However, most studies did not report how long treatments were in place (62%) or the size of experimental units (52%).
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The USGS developed a dataset that estimates 2017 herbaceous annual percent cover predicted on May 1st with an emphasis on annual grasses. These data were developed to provide land managers and researchers with early-season, near-real-time predictions of spatially explicit percent cover predictions of herbaceous annual vegetation in the study area.
This data comes with several caveats. First, as an early-season dataset, it will not reflect the end-of-season estimated percent cover of annual grass in many areas. In fact, some areas with annual grass cover will reflect no cover at this early date. Second, these estimates should be viewed as relative abundances. Third, each pixel in the dataset represent 250-meters and can include a geolocation error of up to 125 meters. Comparing this dataset to similar datasets with different spatial resolutions can lead to substantial differences between datasets. Fourth, this dataset represents annual herbaceous for 2017 forecast on May 1. This dataset is a forecast, and mapping could improve with later map development dates (e.g., July 1). This forecast is considered accurate and reasonable given this early season of mapping.
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This dataset provides an estimate of 2015 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The information is designed to provide a near-real-time estimate of cheatgrass in the northern Great Basin for 2015 to optimize land management efforts to control cheatgrass, preserve critical greater sage-grouse habitat, and inform fire control and prevention. Timely maps of dynamic cheatgrass percent cover are needed in early summer for these purposes. Research shows that cheatgrass percent cover is spatially and temporally highly variable in arid and semiarid environments because cheatgrass germination and growth is highly sensitive to annual weather, especially precipitation totals and timing. Precipitation totals and timing are also spatially and temporally highly variable in these environments; therefore, this dataset is only representative of cheatgrass percent cover during 2015 and does not represent any other time period.
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This dataset provides an estimate of 2016 cheatgrass percent cover in the northern Great Basin at 250 meter spatial resolution. The information is designed to provide a near-real-time estimate of cheatgrass in the northern Great Basin for 2016 to optimize land management efforts to control cheatgrass, preserve critical greater sage-grouse habitat, and inform fire control and prevention. Timely maps of dynamic cheatgrass percent cover are needed in early summer for these purposes. Research shows that cheatgrass percent cover is spatially and temporally highly variable in arid and semiarid environments because cheatgrass germination and growth is highly sensitive to annual weather, especially precipitation totals and timing. Precipitation totals and timing are also spatially and temporally highly variable in these environments; therefore, this dataset is only representative of cheatgrass percent cover during 2016 and does not represent any other time period.
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This dataset contains a time series (2000-2013) of cheatgrass percent cover maps covering the western and central areas of the northern Great Basin. The time series of cheatgrass percent cover maps was developed for two primary reasons: To better understand cheatgrass percent cover dynamics in the northern Great Basin and to develop a dataset that can be used as proxy for annual actual cheatgrass production thereby serving as the dependent variable in the cheatgrass dieoff model.
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This study was designed to quantify how the properties (size, shape, and fuel chemistry) of masticated fuels change with age and how these changes affect their burn characteristics (flame height, rate of spread, heat flux, and below fuel bed temperatures).
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This guide is designed to provide direction on the critique and modification of LANDFIRE geospatial data products for local applications. It is not so much a “cookbook” or “how-to” guide, as the specifics vary greatly by data product, intended use, scale, and location. Rather, it presents primary considerations for using and modifying the data for use in local applications and provide examples and demonstrations of available tools and methods for completing common critique and modification tasks.
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This study adapted and applied four rangeland health indicators to data compiled by the U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station’s Forest Inventory and Analysis (FIA) program for research locations on the Fishlake National Forest in central Utah. These data can be used by local forest managers to determine the health status of the local forest and to identify the proportion of sites that may be functioning at risk.