Wildland Fire Emissions Study

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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

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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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Modular System
User Interface
Vegetation Coverage
Fire History Map
Emissions Reporting
User Parameters
Vegetation Crosswalk
Fuel Models
Emission Estimation
Fuel Loading
Fuel Consumption
Sum
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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)

AVHRR data preparation
Algorithm applied to each pixel
Test 1 T3 gt 315 K?
Fire clear pixels
Test 2 T3 T4gt14 K?
Eliminate warm background, e.g., bare soil
Test 3 T4gt260 K?
Eliminate cloudy pixel
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Test 4 Contextual info R2lt30?R2lt8 neighb P
ave-1? T3gt8 neighb P ave5?
NO
Eliminate highly reflecting clouds surface and
warm background
YES
Test 5 Wild land cover types?
Eliminate urban, agriculture, dune, desert, water
body
Test 6 T4-T5lt4.0 K and T3-T4gt19 K?
Eliminate thin clouds with warm background
Test 7 R1R2lt75?
Eliminate highly reflecting clouds surface
NO
Test 8 R1-R2gt1?
Eliminate sunglint pixels
YES
Test 9 One of neighbor P passes the 8 tests
above?
Eliminate single fire pixel
True fire pixels
False fire pixels
Single date fire mask
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Algorithms

• B. 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 difference
• normalized NDVIpost NDVIpre
• Step 3. Confirm hotspot pixels using NDVI
  difference (CBP)

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• A CBP is assumed to have a negative NDVI
  difference
• Step 4. Calculate NDVI difference statistics
  (mean, SD) of CBP for each landscape type
• Step 5. Select potential burnt scar pixels (BSPs)
  
• A BSP NDVI difference ltmean cSD (CBP), c can
  be 01
• Step 6. Apply a sieve filter to BSPs
• Filter out a burnt patch of lt 2 pixels
• Step 7. Confirm a BSP with a neighbor CBP and
  later on a neighbor BSP to create connected burn
  patches
• One to four neighbor CBP, BSP to be used for the
  confirmation
• Step 8. Filter out a BSP patch of lt 2 pixels and
  false burnt patch
• Step 9. Output burnt area mask (in TIFF format)

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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