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
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 System
Fire HistoryMap
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
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
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)
,.NDVIpostofmean
NDVIpreofmeanCRatio
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