wildland fire emissions study – phase 2
DESCRIPTION
Wildland Fire Emissions Study – Phase 2. Research in progress by the CAMFER fire group: Peng Gong, Ruiliang Pu, Presented by Nick Clinton. For WRAP FEJF Meeting. Purpose. - PowerPoint PPT PresentationTRANSCRIPT
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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
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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.”
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U.C. Berkeley –3
User Interface
VegetationCrosswalk
FuelModels
EmissionEstimation
Fuel Loading
FuelConsumption
VegetationCoverage
UserParameters
Sum
Modular System
Fire HistoryMap
EmissionsReporting
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Vegetation Data
• The GAP vegetation layer– Statewide coverage– Less complex than
other vegetation layers such as CALVEG
– 1990 source data
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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).
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Fire History – Agency Data
• CDF fire polygons• Historical database• Completeness??• Remote sensing
based fire map
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Algorithms
A. 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
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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
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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)
,.NDVIpostofmean
NDVIpreofmeanCRatio
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Fire History – RS Data
• Overlay of CDF and CAMFER data
• 1996 and 1999 (big fire years)
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Overlay of CDF and CAMFER
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Quantitative Comparison
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• 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.
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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…
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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
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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?
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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