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Monitoring Trends in Burn Severity Puerto Rico (Map Service)
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Direct Download
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Monitoring Trends in Burn Severity Conterminous United States (Map Service)
공공데이터포털
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.,
Monitoring Trends in Burn Severity Hawaii (Map Service)
공공데이터포털
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Direct Download
Monitoring Trends in Burn Severity Alaska (Map Service)
공공데이터포털
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.
MTBS Wildfire Burn Severity Mosaics
공공데이터포털
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.�Map Services
Monitoring Trends in Burn Severity (MTBS) Puerto Rico
공공데이터포털
Burn severity layers are thematic images depicting severity as unburned to low, low, moderate, high, and increased greenness (increased post-fire vegetation response). The layer may also have a sixth class representing a mask for clouds, shadows, large water bodies, or other features on the landscape that erroneously affect the severity classification. This data has been prepared as part of the Monitoring Trends in Burn Severity (MTBS) project. Due to the lack of comprehensive fire reporting information and quality Landsat imagery, burn severity for all targeted MTBS fires are not available. Additionally, the availability of burn severity data for fires occurring in the current and previous calendar year is variable since these data are currently in production and released on an intermittent basis by the MTBS project.
LANDFIRE Remap Forest Canopy Cover (CC) Puerto Rico US Virgin Islands
공공데이터포털
LANDFIRE's (LF) 2016 Remap (Remap) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand, CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In disturbed locations CC is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Cover (FVC) forested classes. In some instances, LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. LF Remap Annual Disturbance products are incorporated into CC to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. Reporting of the pre-disturbance scenario helps to calculate CC, by providing information about vegetation impacted by a disturbance. Then, vegetation adjustments are modeled in disturbance areas based on disturbance type and severity. CC is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CC supplies information to fire behavior models in order to; determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. CC also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.
LANDFIRE Remap Forest Canopy Cover (CC) Puerto Rico US Virgin Islands
공공데이터포털
LANDFIRE's (LF) 2016 Remap (Remap) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand, CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In disturbed locations CC is calculated from linear regression equations derived from Forest Vegetation Simulator (FVS) plot data output, but at non-disturbed locations it is assigned the midpoint of Fuel Vegetation Cover (FVC) forested classes. In some instances, LF Remap assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. LF Remap Annual Disturbance products are incorporated into CC to provide informed changes by disturbance type, severity, and time since disturbance (TSD). Annual Disturbance products provide a pre-disturbance scenario represented by LF Remap existing vegetation products. Reporting of the pre-disturbance scenario helps to calculate CC, by providing information about vegetation impacted by a disturbance. Then, vegetation adjustments are modeled in disturbance areas based on disturbance type and severity. CC is then used in the calculation of Canopy Bulk Density (CBD) and Canopy Base Height (CBH). CC supplies information to fire behavior models in order to; determine the probability of crown fire initiation, provide input in the spotting model, calculate wind reductions, and to calculate fuel moisture conditioning. CC also has capable fuels functionality. Capable fuels calculate TSD assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.
LANDFIRE Remap Fuel Vegetation Cover (FVC) Puerto Rico US Virgin Islands
공공데이터포털
The LANDFIRE (LF) 2016 Remap (Remap) Fuel Vegetation Cover (FVC) represents a modified pre-disturbance version of the Existing Vegetation Cover (EVC) product from previous LF versions. LF Remap EVC is mapped as continuous estimates of canopy cover for tree, shrub, and herbaceous lifeforms with a potential range from 10% to 100%. To translate continuous EVC values into fuel model assignments, EVC values are binned to correspond with the bins from previous LF versions. FVC leverages fuel transition assignments related to disturbed areas by re-establishing pre-disturbance vegetation and is developed using the full suite of LF vegetation releases, plus the most recent 10 years of disturbance data. FVC is a capable fuels product that calculates Time Since Disturbance (TSD) assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.
LANDFIRE Remap Fuel Vegetation Cover (FVC) Puerto Rico US Virgin Islands
공공데이터포털
The LANDFIRE (LF) 2016 Remap (Remap) Fuel Vegetation Cover (FVC) represents a modified pre-disturbance version of the Existing Vegetation Cover (EVC) product from previous LF versions. LF Remap EVC is mapped as continuous estimates of canopy cover for tree, shrub, and herbaceous lifeforms with a potential range from 10% to 100%. To translate continuous EVC values into fuel model assignments, EVC values are binned to correspond with the bins from previous LF versions. FVC leverages fuel transition assignments related to disturbed areas by re-establishing pre-disturbance vegetation and is developed using the full suite of LF vegetation releases, plus the most recent 10 years of disturbance data. FVC is a capable fuels product that calculates Time Since Disturbance (TSD) assignments for disturbed areas using an "effective year." For example, year 2020 fuels may be calculated for the year 2020. the new process considers all the existing disturbances included in LF Remap and adjusts the TSD for these to the effective year (2020 in the example), making the products "2020 capable fuels." More information about capable fuels can be found at https://www.landfire.gov/lf_remap.php.
LANDFIRE 2023 Forest Canopy Cover (CC) Puerto Rico US Virgin Islands
공공데이터포털
LANDFIRE's 2023 Update (LF 2023) Forest Canopy Cover (CC) describes the percent cover of the tree canopy in a stand. CC is a vertical projection of the tree canopy cover onto an imaginary horizontal plane. In fire behavior models, CC supplies information to determine the probability of crown fire initiation, provides input in the spotting model, and aids in calculating wind reductions and fuel moisture conditioning. To create CC, LANDFIRE’s Existing Vegetation Cover (EVC) product must be produced first. EVC is a continuous scaled product which assigns cover to all life forms in the LF data. CC is then derived from EVC by assigning bins of 10% for fuel production and use in fire behavior software. CC is used in the calculation of Forest Canopy Bulk Density (CBD) and Base Height (CBH). To designate disturbed areas where CC is modified, the aggregated Annual Disturbance products from 2014 to 2023 in the LF Fuel Disturbance (FDist) product are used. All existing disturbances between 2014-2023 are represented in LF 2023, and the products are intended to be used in 2024 (the year of release). When using any product from the LF 2023 fuel product suite, users should consider adjusting fuel layers for disturbances that occurred after the end of the 2023 fiscal year (after October 1st, 2023). Disturbances that occurred after the end of the 2023 fiscal year are not accounted for within LF 2023 fuel products. Learn more about LF 2023 at https://landfire.gov/data/lf2023.