Fire Extent, Severity and Patchiness Mapping using Daily

Satellite Data

Stefan W MaiermaitecDarwin

to reduce Greenhouse Gas emissions by reducing:

1. fire severity

2. fire extent

3. fuel load

Fundamental Principle of Savanna Fire Projects

GHG emission equation (simplified):

E = P · A · FL · BE · EF

E: GHG emittedP: patchiness factor → fire severityA: fire extent / area burnt → fire extentFL: fuel load → fuel loadBE: combustion completeness (“burning efficiency”) → fire severityEF: emission factor

to reduce Greenhouse Gas emissions by reducing:

1. fire severity

2. fire extent

3. fuel load

Fundamental Principle of Savanna Fire Projects

→ these variables have to be measured (estimated) repeatedly

Daily Near Real-Time Fire Extent Mapping

Fire Severity Mapping

Daily Near Real-Time Fire Extent Mapping

Fire Severity Mapping

2-3 images per day per satellite

5 images per day

~1,800 images (1.8TB) per year

Data Source: MODIS

calibration atmospherecorrectionviewing geometry

correctionburnt areamapping

Processing Steps

near real-time mapping < 2h

Example: Near Real-Time Burnt Area Map

Example: Day of Burn

fire stopped by creek

fire jumped creek

fire jumped creek

daily progression of fire

Accuracy (Top-End)

overall accuracy

commission error

omission error

MODIS Daily Burnt Area (250m) 95.2% 3.7% 6.2%

NASA (MCD45) (500m) 90.7% 3.2% 15.8%

NAFI (manual) (250m) 94.6% 4.6% 6.7%

Conclusion – Daily Fire Extent Mapping

● automatic (twice) daily mapping of fire extent possible● fire extent maps available within 2h of satellite overpass● Top End: automatic mapping as accurate as manual mapping● accuracy in other areas has to be systematically assessed

(→ algorithm might have to be adjusted)● automatic system is treating every image exactly the same

→ accuracy does not change→ no on-going validation necessary(manual mapping treats every image slightly differently)

Daily Near Real-Time Fire Extent Mapping

Fire Severity Mapping

Fire is not Fire

fire severity

fine scale patchiness

Patchiness: Spatial Scale

northern Australia, on-ground transects within fire perimeters:

83% (EDS) / 93% (LDS) burnt

87% (EDS) / 89% (LDS) of unburnt patches are ≤ 5m

Oliveira, et al. (2015). Ecological Implications of Fine-Scale Fire Patchiness and Severity in Tropical Savannas of Northern Australia. Fire Ecology, 11, 10-31.

Patchiness: Temporal Constraints

1 day after fire

10 days after fire

Spectral Unmixing of MODIS Pixels (Fraction Pixel Burnt)

on-ground satellite sensor

30%

70%

un-mixing

pixel

unburnt burnt

Example 1: Tropical Savanna Northern Australia

Example 2: Tropical Savanna Northern Australia – Day of Burn

Example 2: Tropical Savanna Northern Australia – Fraction Pixel Burnt

day-time fire

night-time fire

Example 3: Tropical Savanna Northern Australia – Fraction Pixel Burnt

Photo: Rohan Fischer

Comparison Season vs Fraction Pixel Burnt (Patchiness)

http://www.maitec.com.au/gallery/fire_animations/index.html

Fraction Pixel Burnt WALFA

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10

0.01

0.01

0.02

0.02

0.03

0.03

0.04

456789101112

fraction pixel burnt

frequ

ency

Trend EDS/LDS Fraction Pixel Burnt WALFA

late dry season fires

have become patchier

(smaller fraction pixel

burnt values)

not accounted for!!!

start of fire management

Conclusion – Fraction Pixel Burnt

● physical measure of fire severity (not an index)● no field calibration necessary● scale (sensor) independent● field comparison looks good, proper validation necessary● analysis of archive provides interesting insights (e.g. LDS fires patchier)

● we can measure patchiness factor (P) and combustion completeness (BE) !!!● no need to use a poor surrogate

(e.g. season, project budget, number of helicopter hours)● fire management does more than “only” shift fires from LDS to EDS● time to update the decade old method for emissions accounting !!!

stefan.maier@maitec.com.au

Slide 1Slide 2Slide 3Slide 4Slide 5Slide 6Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Slide 13Slide 14Slide 15Slide 16Slide 17Slide 18Slide 19Slide 20Slide 21Slide 22Slide 23Slide 24Slide 25