Description: The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy cover describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. Canopy cover is generated separately for tree cover life forms using training data and a series of geospatial data layers. Percentage tree canopy cover training data are generated using digital orthophotographs and/or high spatial resolution satellite data for multiple sites. Relationships between the training data and the combination of multitemporal Landsat data, digital elevation data and biophysical gradient data layers are determined using regression tree analysis (Cubist). Correlation (R) values are generated through cross-validation using the regression tree software, and provide a means for assessing accuracy. The derived regression tree equations are then applied to the geospatial data to create 30m resolution datasets of canopy cover. Canopy cover values are then binned into discrete classes (up to 10 bins at 10 percent intervals). The final canopy cover layers were evaluated and rectified through a series of QA/QC measures. This was to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. All non - forest values, including herbaceous and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0.The time period for this data set is not applicable. In other words, it is not possible to characterize this data set with a single date, nor is it logical to use a range.
Description: The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy cover describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. Canopy cover is generated separately for tree cover life forms using training data and a series of geospatial data layers. Percentage tree canopy cover training data are generated using digital orthophotographs and/or high spatial resolution satellite data for multiple sites. Relationships between the training data and the combination of multitemporal Landsat data, digital elevation data and biophysical gradient data layers are determined using regression tree analysis (Cubist). Correlation (R) values are generated through cross-validation using the regression tree software, and provide a means for assessing accuracy. The derived regression tree equations are then applied to the geospatial data to create 30m resolution datasets of canopy cover. Canopy cover values are then binned into discrete classes (up to 10 bins at 10 percent intervals). The final canopy cover layers were evaluated and rectified through a series of QA/QC measures. This was to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. All non - forest values, including herbaceous and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0.The time period for this data set is not applicable. In other words, it is not possible to characterize this data set with a single date, nor is it logical to use a range.
Description: The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy cover describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. Canopy cover is generated separately for tree cover life forms using training data and a series of geospatial data layers. Percentage tree canopy cover training data are generated using digital orthophotographs and/or high spatial resolution satellite data for multiple sites. Relationships between the training data and the combination of multitemporal Landsat data, digital elevation data and biophysical gradient data layers are determined using regression tree analysis (Cubist). Correlation (R) values are generated through cross-validation using the regression tree software, and provide a means for assessing accuracy. The derived regression tree equations are then applied to the geospatial data to create 30m resolution datasets of canopy cover. Canopy cover values are then binned into discrete classes (up to 10 bins at 10 percent intervals). The final canopy cover layers were evaluated and rectified through a series of QA/QC measures. This was to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. All non - forest values, including herbaceous and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0.The time period for this data set is not applicable. In other words, it is not possible to characterize this data set with a single date, nor is it logical to use a range.
Description: The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. DATA SUMMARY: Canopy cover describes percent cover of tree canopy in a stand. A spatially-explicit map of canopy cover supplies information for fire behavior models such as FARSITE (Finney 1998) to determine surface fuel shading for calculating dead fuel moisture and for calculating wind reductions. In FARSITE, canopy characteristics are used to compute shading, wind reduction factors, spotting distances, crown fuel volume, spread characteristics of crown fires and incorporate the effects of ladder fuels for transitions from a surface to crown fire. Canopy characteristics refer to the tree canopy. Where there are tree canopies, i.e. existing vegetation types that are forest and woodland, LANDFIRE has attributed the grid with canopy characteristics with some exceptions. There will be no canopy characteristics in fuel types where the tree canopy is considered a part of the surface fuel and the surface fire behavior fuel model is chosen as such. This is because LANDFIRE assumes the potential burnable biomass in the tree canopy has been accounted for in the surface fuel model parameters. For example, young or short conifer stands where the trees are represented by a shrub type fuel model will not have canopy characteristics. Canopy cover is generated separately for tree cover life forms using training data and a series of geospatial data layers. Percentage tree canopy cover training data are generated using digital orthophotographs and/or high spatial resolution satellite data for multiple sites. Relationships between the training data and the combination of multitemporal Landsat data, digital elevation data and biophysical gradient data layers are determined using regression tree analysis (Cubist). Correlation (R) values are generated through cross-validation using the regression tree software, and provide a means for assessing accuracy. The derived regression tree equations are then applied to the geospatial data to create 30m resolution datasets of canopy cover. Canopy cover values are then binned into discrete classes (up to 10 bins at 10 percent intervals). The final canopy cover layers were evaluated and rectified through a series of QA/QC measures. This was to ensure that the life-form of the cover code matched the life-form of the existing vegetation type. All non - forest values, including herbaceous and shrub systems and non-burnable types such as urban, barren, snow and ice and agriculture, were coded as 0.The time period for this data set is not applicable. In other words, it is not possible to characterize this data set with a single date, nor is it logical to use a range.