Wildland Fire Emissions Study – Phase 2

For WRAP FEJF Meeting

Research in progress by the CAMFER fire group:Peng Gong, Ruiliang Pu, Presented by Nick Clinton

U.C. Berkeley –2

Purpose“…to develop a method for producing

coherent, consistent, spatially and temporally resolved GIS based emission estimates for wildfire and prescribed burning.”

U.C. Berkeley –3

User Interface

VegetationCrosswalk

FuelModels

EmissionEstimation

Fuel Loading

FuelConsumption

VegetationCoverage

UserParameters

Sum

Modular SystemFire History

Map

EmissionsReporting

Vegetation Data

• The GAP vegetation layer– Statewide coverage– Less complex than

other vegetation layers such as CALVEG

– 1990 source data

National Inputs• The spatial inputs are the

NFDRS fuel model grid (seen left) and a grid of remotely sensed fire detections (both 1km resolution).

• Utilizes the same emissions equations as with polygon processing.

• Requires crosswalk of FOFEM fuel models to NFDRS fuel models (proof of concept).

Fire History – Agency Data

• CDF fire polygons• Historical database• Completeness??• Remote sensing

based fire map

AlgorithmsA. Hotspot Detection (modified to CCRS’)

Y ES

YES

YES

NO

NO

NO

AVHRR data preparation

Algorithm applied to each pixel

Test # 1T3 > 315 K?

Test # 2T3 –T4>=14 K?

Test # 3T4>=260 K?

Fire clear pixels

Eliminate cloudy pixel

Eliminate warm background, e.g., bare soil

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

YES

NO

Test # 4Contextual info

R2<=30%?R2<=8 neighb P ave-1?T3>8 neighb P ave+5?

Test # 5Wild land cover types?

Test # 8|R1-R2|>1%?

Test # 7R1+R2<=75%?

Test # 6T4-T5<4.0 K and

T3-T4>=19 K?

Test # 9One of neighbor P passes

the 8 tests above?

True fire pixels False fire pixels

Eliminate highly reflecting clouds & surface and warm background

Eliminate urban, agriculture,dune, desert, water body

Eliminate single fire pixel

Eliminate sunglint pixels

Eliminate highly reflecting clouds & surface

Eliminate thin clouds with warm background

Single date fire mask

AlgorithmsB. Burnt Scar mapping (modified to CCRS’ HANDS) with - Two NDVI composites of an interesting interval - One corresponding hotspot composite (fire mask) Step 1. Normalize NDVIpost to NDVIpre

normalized NDVIpost = Ratio.C * NDVIpost

Step 2. Calculate NDVI differencenormalized NDVIpost – NDVIpre

Step 3. Confirm hotspot pixels using NDVI difference (CBP)

,.NDVIpostofmeanNDVIpreofmeanCRatio

Fire History – RS Data

• Overlay of CDF and CAMFER data

• 1996 and 1999 (big fire years)

Overlay of CDF and CAMFER

Quantitative Comparison

• Variation in mapping success between different ecosystem types.

• The amount of variation differs between methods (monthly or annual differencing), and between years.

• In general, the CAMFER method is more successful in the forest type.

Overlay of CDF and CAMFER• RED is now RS

detections. Green is Jepson ecoregion

• Lambert Conformal Conic Projection

• No Post-processing (filtering, nearest neighbor relationship to hotspots)

• Slightly reduced accuracy

• Potential for more data refinement by incorporating hotspots…

Overlay of CDF and CAMFER

• Green is annual NDVI differencing.

• Blue is monthly NDVI differencing

• Neither method is effective in detecting the entire burn area

Overlay of CDF and CAMFER

• Hotspots (Red) overlaid on the monthly and annual NDVI differencing

• Increase or at least negligible decrease in NDVI, especially over an annual time scale

• Problems with temporal resolution in hotspot detection

• Potential for more dynamic thresholding in burn scar mapping?

Temporal Decomposition of RS Data

• Remotely sensed burn scar polygons can be decomposed to daily polygons based on a nearest neighbor relationship using hot spot detections

• Facilitates temporal allocation of emissions

• Useful to dispersion modeling, emissions tracking