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Solar radiation grids were produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This solar radiation grid was produced using the Area Solar Radiation tool in ArcGIS 10.1, using inputs of the associated 30m DEM.
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Conditional Flame Length (CFL) is an estimate of the mean flame lengths for each pixel, and was predicted for the 2050-2070 period in the Rio Grande area using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway. CFL...
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Fire type predicted for the 2020-2040 period in the Rio Grande area with five classes: 1) shrub vegetation with torching flames; 2) shrub vegetation without torching flames; 3) forest with torching flames; 4) forest without torching flames; 5) grass or non-vegetation. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model...
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Burn probability (BP) raster dataset predicted for the 2020-2040 period in the Rio Grande area was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5 Representative Concentration Pathway.
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Burn probability (BP) for Fireline Intensity Class 1 (FIL1) with flame lengths in the range of 0-0.6 m predicted for the 2080-2100 period in the Rio Grande area. This raster dataset was generated using: 1) data developed from the 2014 Fire Program Analysis (FPA) system; 2) geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. 2011); and 3) climate predictions developed using the Multivariate Adaptive Constructed Analogs (MACA) method (Abatzoglou and Brown 2011) which downscaled model output from the GFDL-ESM-2m global climate model of the Coupled Model Inter-Comparison Project 5 for the 8.5...
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Globally, changing fire regimes due to climate is one of the greatest threats to ecosystems and society. This dataset presents projections of historic and future fire probability for the southcentral U.S. using downscaled climate projections and the Physical Chemistry Fire Frequency Model (PC2FM, Guyette et al., 2012). Climate data from 1900-1929 and projected climate data for 2040-2069 and 2070-2099 were used as model inputs to the Physical Chemistry Fire Frequency Model (Guyette et al. 2012) to estimate fire probability. Baseline and future time period data are from three global climate models (GCMs): CGCM, GFDL, and HadCM3. The nine associated data sets (tiffs) represent estimated change in mean fire probability...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
Solar radiation grids were produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This solar radiation grid was produced using the Area Solar Radiation tool in ArcGIS 10.1, using inputs of the associated 30m DEM.
Solar radiation grids were produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This solar radiation grid was produced using the Area Solar Radiation tool in ArcGIS 10.1, using inputs of the associated 30m DEM.
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the zip folder there are 5 raster tiffs. i. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit ii. XXX_pre_refl.tif The at-sensor-reflectance of the prefire landsat scene, named with the PolyID unique identifier for the...
This imagery was collected and produced for a set of large fires sampled from within the Great Northern Landscape Conservation Cooperative study area. This imagery and associated metrics was produced using Landsat 5 and 7. This set of imagery and remote sensing metrics have the following file structure: 1. Each sub-folder in the Fires LC Map folder represents an individual fire. 2. Within the folder there are 8 raster tiffs. 1. XXX_post_refl.tif The at-sensor-reflectance of the postfire landsat scene, named with the PolyID unique identifier for the fire, stored in 8-bit i. Band 1 of the Tiff is Band 3 (Red) of Landsat ii. Band 2 of the Tiff is Band 4 (NIR) of Landsat iii. Band 3 of...
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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 Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area Burn Probability predicted for 2020 to 2040 for Rio Grande study area Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Fire probability for 1900-1929 using GFDL baseline climate values BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2008) BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Northern Rubber Boa BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Mule Deer Class B Summer BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Coopers Hawk BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Sonora Mojave Semi-Desert Chaparral BLM REA MAR 2012 CONUS Thematic Burn Severity Mosaic (2008) Burn Probability for Fireline Intensity Class 1, predicted for 2080 to 2100 for Rio Grande study area Conditional Flame Length predicted for 2050 to 2070 for Rio Grande study area Fire type predicted for 2020 to 2040 for Rio Grande study area Burn Probability predicted for 2020 to 2040 for Rio Grande study area BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Northern Rubber Boa BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Mule Deer Class B Summer BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Coopers Hawk BLM REA CBR 2010 Modeled Future Bioclimate 2050 - Sonora Mojave Semi-Desert Chaparral Fire probability for 1900-1929 using GFDL baseline climate values