Map Units for LANDFIRE: Integrating Vegetation Classification and Map Legends

LANDFIRE DELIVERABLES FIRE BEHAVIOR/FIRE EFFECTS Layers Fire behavior fuel models Canopy bulk density Canopy base height Canopy cover Canopy height FCC Fuelbeds Fuel Loading Models VEGETATION Existing vegetation composition Existing vegetation structure Biophysical Settings FIRE ECOLOGY Layers Historical fire return interval Historical fire severity Historical fire regime Current Succession Class Vegetation departure Fire Regime Condition Classes

LANDFIRE DELIVERABLES FIRE BEHAVIOR/FIRE EFFECTS Fire behavior fuel models Canopy bulk density Canopy base height Canopy cover Canopy height FCC Fuelbeds Fuel Loading Models VEGETATION Existing vegetation composition Existing vegetation structure Biophysical Settings FIRE ECOLOGY Historical fire return interval Historical fire severity Historical fire regime Current Succession Class Vegetation departure Fire Regime Condition Classes

LANDFIRE Map Unit Development All Lands & Vegetative Communities –Same level of detail Forestlands, Shrublands, and Grasslands Repeatable –Quick and affordable Target Map Accuracies: –60 to 80 percent map accuracy Consistent for the Map Extent (National) –Map units mean the same thing in Florida as they do in Colorado

Map Unit Requirements Identifiable from field or plot data Map-able 30 meter resolution 60-80% accurate Scalable link with existing classifications Model-able provide required model inputs

LANDFIRE Vegetation Layers PotentialVegetationType (PVT) or Biophysical Setting (BpS)ExistingVegetation Type (Species Composition) ExistingStructuralStage

Existing Vegetation Type: Other Efforts Alliances and Associations of the USNVC (Grossman and others 1998) Sagebrush cover type map (SAGEMAP 2002) and classification (Reid and others 2002) developed by USGS Forest Cover Types of the United States and Canada, Society of American Foresters (Eyre 1980) Rangeland Cover Types of the United States, Society for Range Management (Shiflet 1994) GAP Cover Types for the eleven western states complied by the BLM

Map Unit Requirements Identifiable from field or plot data through dominance of species or groups of species on the plots through individual or groups of indicator species on plots ield Keydichotomous Field Key with “field and floristic criteria” Sequence TableSequence Table for plot data with “ floristic criteria”

Sequence Tables Criteria Absolute cover for lifeforms Relative cover for floristic criteria

Sequence Tables Criteria Absolute cover for lifeforms Relative cover for floristic criteria Automation BpS_EVT_Key_Classifier Summaries by BpS, EVT, BpS/EVT Constancy/Cover by BpS, EVT, BpS/EVT

Map Unit Requirements Map-able Final Cleanup Sequence Tables: Mappers incorporate QA/QC for plots during mapping process and update MAT (contains training plots) NatureServe runs through unclassified plot data and applies a qualitative classification LFRDB determines what plot data is releasable and not releasable Final MAT posted and available

Map Unit Requirements Scalable Meet different scaling needs, fine to broad, by linking to existing classification; crosswalks Maintain continuity between maps of different scales.

Map Unit Requirements Model-able Provide “correct” combos for fuel lookup tables and inputs Anomalous combinations of vegetation composition, structure and site potential will not have plot data and thus no associated fuels inputs

Mapping Fuels for LANDFIRE: Integrating Remote Sensing, GIS, and Biophysical Modeling

Why are Fuels Important? The one factor over which we have the most control

Fuels Maps The Most Important Fire Management Layer Potential Uses Predict future growth of firePredict future growth of fire Develop fire danger, hazard, risk layersDevelop fire danger, hazard, risk layers Plan future fires and prioritize treatmentsPlan future fires and prioritize treatments Simulate fire effects-smoke, tree mortalitySimulate fire effects-smoke, tree mortality Evaluate management alternativesEvaluate management alternatives Predict future growth of firePredict future growth of fire Develop fire danger, hazard, risk layersDevelop fire danger, hazard, risk layers Plan future fires and prioritize treatmentsPlan future fires and prioritize treatments Simulate fire effects-smoke, tree mortalitySimulate fire effects-smoke, tree mortality Evaluate management alternativesEvaluate management alternatives

What Are Fuels? Live and dead biomass WBiomass when burned: WContributes to fire propagation WProduces smoke WGenerates heat to kill flora & fauna

Challenges in Mapping Fuels UCanopy obstruction UFuel bed diversity UFire behavior fuel models UFuel complexity UFuel variability UFine resolutions UCanopy obstruction UFuel bed diversity UFire behavior fuel models UFuel complexity UFuel variability UFine resolutions

Fuel Bed Diversity Many categories of fuels Litter and Duff Dead and Live Crown foliage and branchwood Dead and Live Crown foliage and branchwood Downed Dead Woody Twig and Branchwood Downed Dead Woody Twig and Branchwood Live and Dead Shrub and Herbaceous Live and Dead Shrub and Herbaceous Logs Live and Dead Tree Regeneration Live and Dead Tree Regeneration Cones, Buds, Mosses, Lichens Cones, Buds, Mosses, Lichens Arboreal Mosses and Lichens

Fuel Complexity {Each fuel type important to one, but not all, fire applications {Fire behavior needs description of fine fuels {Smoke prediction requires description of all fuel types {Fuel models and fuel classifications must be robust {Each fuel type important to one, but not all, fire applications {Fire behavior needs description of fine fuels {Smoke prediction requires description of all fuel types {Fuel models and fuel classifications must be robust

Fuel Variability þFuels are continuous not discrete þHighly variable in space and time þRelated to many factors UStand history UBiophysical setting UCommunity composition UStand Structure þFuels are continuous not discrete þHighly variable in space and time þRelated to many factors UStand history UBiophysical setting UCommunity composition UStand Structure

Fuel Mapping Approaches UField Reconnaissance URemote Sensing UField Reconnaissance URemote Sensing

ºCorrelated with many ecosystem attributes ºGoverns fuel dynamics ºClassifications available ºCorrelated with many ecosystem attributes ºGoverns fuel dynamics ºClassifications available Fuel Mapping Strategy Species Composition Stand Structure Biophysical Setting Species Composition Stand Structure Biophysical Setting

Fuel Variability Example FBFM 2 - Conifer Grass FBFM 2 - Conifer Grass FBFM 5 - Live Shrub FBFM 5 - Live Shrub FBFM 9 - Pine Litter FBFM 9 - Pine Litter

These systems characterize the physical and chemical properties of atmospherically transmitted radiation. The reflected radiation is coupled with atmospheric models and fitted to geographic location, time and date to determine apparent surface reflectance. Remote sensing This remotely sensed data can be either directly or indirectly related to identifiable materials such as shade, various soils, non-photosynthetic vegetation, green biomass, live fuel moisture, diverse vegetation species and unique land-cover types.

LANDFIRE Fuels Two major divisions of fuels are recognized by the LANDFIRE project – surface fuels and canopy fuels.  Surface fuels are those biomass components that occur on the ground (less than six feet tall) and are the fuels that carry a surface fire. Live or dead, herbaceous or shrub, downed dead woody, litter, and duff Fire Behavior Fuel models have been developed to predict fire behavior.  Canopy fuels are those aerial biomass components higher than six feet that can carry a crown fire and are usually consumed in the crown fire. 1) Bulk density (kg m-3), 2) Canopy cover (%), 3) Canopy height (m), 4) Canopy base height (m).

Ancillary Layers Base Vegetation Layers FARSITE Input Layers

METHODS Creating the surface fuel maps –Create lookup tables for FBFM13 –Creating the surface fuel model maps from the lookup tables Creating the surface fuel maps –Create lookup tables for FBFM13 –Creating the surface fuel model maps from the lookup tables

Fire Behavior Fuel Models þDescribes expected fire behavior þNot a description of actual fuel conditions þComplicated procedure to construct models þFuel model construction subjective þAssessment in field is subjective þDescribes expected fire behavior þNot a description of actual fuel conditions þComplicated procedure to construct models þFuel model construction subjective þAssessment in field is subjective

METHODS continued Creating the canopy fuel maps –Calculate all four canopy characteristics for all plots in the LANDFIRE reference database with comprehensive tree data using the FUELCALC program –Classification and regression trees were used to link the calculated reference data to Landsat satellite imagery and a series of 30-meter, spatially-explicit gradient layers representing climate, fire ecology, soil, and topography. Creating the canopy fuel maps –Calculate all four canopy characteristics for all plots in the LANDFIRE reference database with comprehensive tree data using the FUELCALC program –Classification and regression trees were used to link the calculated reference data to Landsat satellite imagery and a series of 30-meter, spatially-explicit gradient layers representing climate, fire ecology, soil, and topography.

METHODS continued Performing QA/QC procedures –Create fuels QA/QC ruleset –Check all layers for data gaps –Check all layers for logic inconsistencies within LANDFIRE layers Performing accuracy assessment –Calculate accuracy of statistical models –Calculate classification accuracy of fuel model keys –Calculate pixel accuracy of fuel maps –Calculate mapping accuracy of fuel maps Performing QA/QC procedures –Create fuels QA/QC ruleset –Check all layers for data gaps –Check all layers for logic inconsistencies within LANDFIRE layers Performing accuracy assessment –Calculate accuracy of statistical models –Calculate classification accuracy of fuel model keys –Calculate pixel accuracy of fuel maps –Calculate mapping accuracy of fuel maps

LANDFIRE Fuel Layers  Standard 13 Fire Behavior Fuel Models (FBFM13).  Canopy bulk density (CBD)  Canopy cover (CC)  Canopy height (CH)  Canopy base height (CBH)

Fire Behavior Fuel Model

Canopy Base Height

Canopy Height

Canopy Cover

Crown Bulk Density

LANDFIRE Fuel Layers  Standard 13 Fire Behavior Fuel Models (FBFM13).  Canopy bulk density (CBD)  Canopy cover (CC)  Canopy height (CH)  Canopy base height (CBH)

LANDFIRE Fuel Layers  Standard 13 Fire Behavior Fuel Models (FBFM13).  Canopy bulk density (CBD)  Canopy cover (CC)  Canopy height (CH)  Canopy base height (CBH) plus:  New 40 Fire Behavior Fuel Models (FBFM40)  Fuel Characteristic Classification System (FCCS)  Fuel Loading Models (FLM)

Other Analysis Tools New Fuel Models and Fuel Classifications New Fire behavior, Fire effects Fuel Models

New set of 40 fire behavior fuel models (FBFM40) The new set of 40 fire behavior fuel models (FBFM40) are hierarchically organized by fuel strata and fuel loading. The 40 fuel models have already been implemented into the BEHAVE fire modeling system and the FARSITE fire growth model. Subtle modifications in fuelbeds as a result of fuel treatment activities should be represented by these 40 fuel models.

Fuel Characteristic Classification System (FCCS) National Fuelbed Map The FCCS Fuelbed concept was developed by the Fire and Environmental Research Applications (FERA) at PNW, Seattle. It includes complete descriptions of typical fuel situations around the nation. The FCCS summarizes fuel by component using canopy, ground, and surface fuel stratifications. LANDFIRE Prototype Zone 16

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