Burn Severity

Burn Severity

Burn severity (dNBR or Differenced Normalized Burn Ratio) is a remote sensing change detection technique utilizing the two Landsat TM/ETM+ bands most responsive to fire-induced environmental change (Lutes et al. 2006). dNBR is best described as the magnitude of environmental change occurring during a fire. Attempting to link dNBR to biomass consumption is one of the primary challenges this study addresses.

Calculating dNBR

Forest Fires in Three Western Ecoregions: Relating Burn Patterns to Biomass Consumption

Benjamin Koziol, School of Natural Resources & Environment, University of Michigan

This poster explores linking remotely sensed data on forest fire severity to a spatial fuel consumption model. Data on fuel conditions are obtained from a fuel loading map developed by the Forest Service. The initial fuel model employed in this project is Consume, a biomass consumption model that uses inputs from the Forest Service fuel conditions map and estimated moisture levels at the time of the burn. Coupling the fuel model with the spatial fuel map, biomass consumption under different moisture regimes can be evaluated on a landscape scale. Recent research has focused on the utility of the burn severity maps in understanding a fire’s impacts, particularly the quantity of fuel consumed during a burn. Presented here is an initial exploration of applying a portion of the spatial model to three forest fires.

Fire Descriptions

McNally Rattle Rodeo

Dates July 21 - August 29, 2002 July 2002 June 18 - July 7, 2002

Location Southern California Near Moab, Utah Northeast Arizona

Acres Burned 150,670 74,730 462,614

0 50 100 150 200 250 3000

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70Fuel Consumption by Moisture & Fuel Type (tons/acre)

Fuel Moisture (%)

Ton

s/A

cre

0 50 100 150 200 250 3001.5

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0 50 100 150 200 250 3002

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Moisture (%)

Con

sum

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Rattle

0 50 100 150 200 250 3002

2.5

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Moisture (%)

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McNally

FCCS Code Description Acres

0 Urban - agriculture - barren 1925.93934

17 Red fir forest 3024.34747

20 Western juniper / Huckleberry oak forest 11428.4262

22 Lodgepole pine forest 21263.6157

37 Ponderosa pine - Jeffrey pine forest 90928.3555

44 Scrub oak - Chaparral shrubland 14296.4301

55 Western juniper / Sagebrush savanna 2302.6762

210 Pinyon - Juniper forest 189.702801

Rattle


0 Urban - agriculture - barren 1127.09706

30 Turbinella oak - Ceanothus - Mountain mahogany shrubland 1357.72052

34 Interior Douglas-fir - Ponderosa pine / Gambel oak forest 9065.92577

42 Trembling aspen / Engelmann spruce forest 5801.16948

59 Subalpine fir - Engelmann spruce - Douglas-fir - Lodgepole pine 844.433218


218 Gambel oak / Sagebrush shrubland 12468.1221

273 Engelmann spruce - Douglas-fir - White fir - Interior ponderosa 7281.87434

Rodeo


27 Ponderosa pine - Two-needle pine - Juniper forest 52319.054

55 Western juniper / Sagebrush savanna 13.3436906


211 Interior ponderosa pine forest 255485.861

236 Tobosa - Grama grassland 242.187984

273 Engelmann spruce - Douglas-fir - White fir - Interior ponderosa 976.090965

Consume is a series of fire consumption equations derived from generalized modeling (Prichard et al. 2003). These equations take the form of exponential functions with proportion consumed per acre varying according to fuel moisture levels in different compartments of the forest (eg. overstory, duff, 1000-hr downed woody fuels).

Summary Tables from GIS

Model Applied in

MatLab

How can this model be linked with remote sensing to estimate biomass consumption? That is the golden question. Other fire modeling platforms, such as FlamMap, explicitly incorporate fuel moisture estimation as a function of weather patterns and terrain (Stratton 2004).

Fusing these two modeling frameworks may provide insights into the spatial distribution of dNBR within a fire. Are dNBR values correlated with fuel moisture, fuel type, or some combination of the two? It is possible that dNBR is independent of fuel moisture and consumption, affected by other properties such as charring or vegetation state prior to burning.

The Data!!

The graphic below shows the three western ecoregions comprising the study area. Ecoregions were taken from Omernik (1987). FCCS (Fuel Characteristics Classification System) are overlayed by fire points and polygons (Sandberg 2001). Raster cells in the FCCS layer represent different fuel types. Each FCCS code has associated attributes describing that fuel’s loading parameters in tons per acre (depth for duff). Resolution of the fuels layer is very coarse (1 sq. km). Coarse resolution was chosen to account for the variety of data fused which includes remotely sensed land cover and, for example, Kuchler’s (1974) potential vegetation map.

Fu

el L

oad

ing

ReferencesKuchler, A. W. "A New Vegetation Map of Kansas." Ecology 55.3 (1974): 586-604.Lutes, Duncan C., et al. Firemon: Fire Effect Monitoring and Inventory System. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Gen. Tech. Rep. RMRS-GTR-164-CD, (2006).Omernick, James M. "Map Supplement: Ecoregions of the Conterminous United States." Annals of the Association of American Geographers 77.1 (1987): 118-25.Prichard, Susan J., Roger D. Ottmar, and Gary K. Anderson. Consume 3.0 User's Guide. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, (2003).Sandberg, David V., Roger D. Ottmar, and Geoffrey H. Cushon. "Characterizing Fuels in the 21st Century." International Journal of Wildland Fire 10 (2001): 381-87.Stratton, Richard D. "Assessing the Effectiveness of Landscape Fuel Treatments on Fire Growth and Behavior." Journal of Forestry (2004): 32-40.

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dNBR Category

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esComparison of dNBR Level by Fuel Type

dnBR Response by Fuel Type

Future Directions

Documents

Burn Severity