Map
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This interactive map identifies frequently threatened towns and cities, including the different sizes and distances of wildfires from nearby communities.
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A mobile-friendly, current, interactive fire risk map.
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ModelMap software, created by specialists working for the Rocky Mountain Research Station, automates and simplifies the map modeling process, allowing researchers and land managers to visualize complicated geospatial data, develop predictions, and communicate it all to stakeholders and other researchers.
Access MoD-FIS tool.
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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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.
Access community planning tools.
The Community Planning Assistance for Wildfire (CPAW) program provides communities with expertise in land use planning, forestry, risk assessment, and research to identify and reduce local wildfire risks and costs. Learn even more background and access other tools at Headwaters Economics.
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Explore the Great Basin LCC, its projects, events, story maps, and news.
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