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A warming climate, fire exclusion, and land cover changes are altering the conditions that produced historical fire regimes and facilitating increased recent wildfire activity in the northwestern United States. Understanding the impacts of changing fire regimes on forest recruitment and succession, species distributions, carbon cycling, and ecosystem services is critical, but challenging across broad spatial scales. One important and understudied aspect of fire regimes is the unburned area within fire perimeters; these areas can function as fire refugia across the landscape during and after wildfire by providing habitat and seed sources. With increasing fire activity, there is speculation that fire intensity and...
Wildfire refugia are forest patches that are minimally-impacted by fire and provide critical habitats for fire-sensitive species and seed sources for post-fire forest regeneration. Wildfire refugia are relatively understudied, particularly concerning the impacts of subsequent fires on existing refugia. We opportunistically re-visited 122 sites classified in 1994 for a prior fire refugia study, which were burned by two wildfires in 2012 in the Cascade mountains of central Washington, USA. We evaluated the fire effects for historically persistent fire refugia and compared them to the surrounding non-refugial forest matrix. Of 122 total refugial (43 plots) and non-refugial (79 plots) sites sampled following the 2012...
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Geospatial data were developed to characterize pre-fire biomass, burn severity, and biomass consumed for the Black Dragon Fire that burned in northern China in 1987. Pre-fire aboveground tree biomass (Mh/ha) raster data were derived by relating plot-level forest inventory data with pre-fire Landsat imagery from 1986 and 1987. Biomass data were generated for individual species: Dahurian larch (Larix gmelinii Rupr. Kuzen), white birch (Betula platyphylla Suk), aspen (Populus davidiana Dode and Populus suaveolens Fischer), and Mongolian Scots pine (Pinus sylvestris var. mongolica Litvinov). A raster layer of total aboveground tree biomass was also generated. Burned area was manually delineated using the normalized...
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In Alaska, recent research has identified particular areas of the state where both a lack of soil moisture and warming temperatures increase the likelihood of wildfire. While this is an important finding, this previous research did not take into account the important role that melting snow, ice, and frozen ground (permafrost) play in replenshing soil moisture in the spring and summer months. This project will address this gap in the characterization of fire risk using the newly developed monthly water balance model (MWBM). The MWBM takes into account rain, snow, snowmelt, glacier ice melt, and the permafrost layer to better calculate soil moisture replenishment and the amount of moisture that is lost to the atmosphere...
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This dataset represents 25 parallel longitudinal profiles that were extracted from terrestrial lidar point clouds taken during six survey periods. The six lidar surveys were conducted between 7 October 2010 and 8 October 2013. Over that time a colluvial hollow eroded into a fluvial channel. The longitudinal profiles show the topography of the colluvial hollow for each survey period. The width of the original colluvial hollow was approximately 1.25 m, and a longitudinal profile was extracted every 5 cm for the entire length of the hollow, resulting in 25 parallel longitudinal profiles. These data can be used to observe the transition of the colluvial hollow to a fluvial channel and furthermore they show the development...
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The FSim burn probability was used to determine the burn probability of the white sturgeon range in the ecoregion. This layer was used to examine wildfire risk to areas within the white sturgeon range.
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large wildland fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2010. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This...
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
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The Monitoring Trends in Burn Severity (MTBS) project assesses the frequency, extent, and magnitude (size and severity) of all large fires (includes wildfire, wildland fire use, and prescribed fire) in the conterminous United States (CONUS), Alaska, Hawaii, and Puerto Rico for the period of 1984 through 2011. All fires reported as greater than 1,000 acres in the western U.S. and greater than 500 acres in the eastern U.S. are mapped across all ownerships. MTBS produces a series of geospatial and tabular data for analysis at a range of spatial, temporal, and thematic scales and are intended to meet a variety of information needs that require consistent data about fire effects through space and time. This map layer...
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.
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This dataset is a raster of predicted suitable bioclimate using statistical correlations between known habitat and current climate (1950-1999 average) , and then projecting that niche into the future. The future timeslices used are 2020's, which is an average of 2020-2029, and 2050's which is 2050-2059. The Values 1-6 show the degree of model agreement (For example: areas with a value of 1 is where only 1 GCM predicted suitability; pixels with a value of 6 are where 6 GCMs predicted suitability, ect). *see Maxent output pdfs for more details about model inputs and settings.


map background search result map search result map Improving Characterizations of Future Wildfire Risk in Alaska Fourmile Canyon Wildfire Longitudinal Profile Data Pre-fire biomass, burn severity, biomass consumption, and fire perimeter data for the 1987 Black Dragon Fire in China BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2010) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (1999) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2005) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2004) BLM REA NGB 2011 Fsim Burn Probability in White Sturgeon Areas (4km) BLM REA NWP 2011 FI C 2000 MTBS BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Bighorn Sheep BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Sonoran Mojave Mixed Salt Desert Scrub BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Inter-Mountain Basins Mixed Salt Desert Scrub BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Mojave Mid Elevation Mixed Desert Scrub Fourmile Canyon Wildfire Longitudinal Profile Data Pre-fire biomass, burn severity, biomass consumption, and fire perimeter data for the 1987 Black Dragon Fire in China BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2010) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (1999) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2005) BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2004) BLM REA NGB 2011 Fsim Burn Probability in White Sturgeon Areas (4km) BLM REA NWP 2011 FI C 2000 MTBS BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Bighorn Sheep BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Sonoran Mojave Mixed Salt Desert Scrub BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Inter-Mountain Basins Mixed Salt Desert Scrub BLM REA MBR 2010 Modeled Future Bioclimate 2050 - Mojave Mid Elevation Mixed Desert Scrub Improving Characterizations of Future Wildfire Risk in Alaska