Determining the Granularity and Randomness of Burned Areas from Prescribed and Natural Fires

Robert Kremens1, Anthony Bova2, Matthew Dickenson2, Jason Faulring1, Colin Hardy3, Anthony Vodacek1

1Rochester Institute of Technology2USDA Forest Service Northeast Research Station3USDA Forest Service Rocky Mountain Research Station

ABSTRACTForested areas that have burned naturally without interference from man (fire suppression) show a marked variation in the size, shape and location of burned versus unburned areas. In order to achieve the goal of restoring landscapes to pre-suppression conditions, it is necessary to determine if prescribed fire produces similar patterns of variability and randomness as natural fires occurring in unsuppressed areas on a landscape scale. Understanding and quantifying the variations in landscapes can be performed using several classification machine vision techniques that have been developed for other purposes. We use a time-resolved multi-band remote sensing technique to observe fires using the WASP airborne imaging platform, which gives us the ability immediately after a fire to determine which areas have not burned (i.e., insignificant thermal flux emitted). We can then measure the variability produced by prescribed fires and compare this to the variability produced by natural fires in unsuppressed areas. We show results for prescribed fires in Ohio. The patterns obtained from natural fires and our subset of prescribed fires are markedly different, and suggest that new ignition methods or lighting patterns may be required to obtain natural burning patterns during prescribed fire treatments. We will discuss future plans for experiments over varied forest types in the continental United States. This work was supported by the National Aeronautics and Space Administration under Grant NAG5-10051 and by the USDA Forest Service under Joint Venture Agreement 03-JV-1222048-049 with the Rocky Mountain Research Station.

ObjectivesDescribe the motivation for this workDescribe the experimental technique, including the airborne and ground based data collection systemsDescribe the image analysis methodsClaim success for our method!Explain what we missed because of a slow fire season

Motivation for this research

We need to know whether the heat and fire effect patterns for prescribed fires are similar to wildfiresIs prescribed fire capable of restoring a natural (pre-suppression) ecology/pattern?Determining the scale and pattern of unburned and heat-affected areas is laborious using field techniques Field methods may be spatially inaccurate Single overhead images are snapshots of an instant in the time history of the fire and do not represent total heat outputPost fire VIS-NIR images do not capture understory fires or lightly affected regions and do not tell fire intensity directlyTime-series infrared images captured over the complete course of the fire can determine the heat release patterns

Secondary goalsValidate airborne flux measurement against known fire observables like fuel burn-up

Verify usefulness of ground sensors as in-situ calibration devices for airborne data collection

Learn the SNAFUs

We combine overhead and ground based data collection to measure total fire energy outputAircraft cameras sample ground at the same point as ground sensorsClocks are synchronized on aircraft and ground (GPS clock)Deploy several (2-5) of these ground sensors per fire

Collection method and processing chainCapture a time series of airborne IR images (using WASP imager)Simultaneously capture ground calibration data and burn plot samples (pre- and post- fire)Geo- and ortho-rectify images, create mosaicsMask areas not matching 0-heat boundary conditionsTime integrate images to produce total flux map

where L = sensor radiance, Dn = digital data from detector, Dt = time interval between frames, C = calibration derived from ground sensors, f = frame number, n = total number of frames

Collection method and processing chain (2)6. Calibrate image data using ground sensors (surface-leaving flux may be obtained from sensor-reaching radiance)7. Analyze fire patterns using established means (e.g. FRAGSTATS)8. Compare with extensive field sampling of fuel consumption and visual post-burn condition

A little bit about the WASP camera6 km swath from 3 km (10kft)COTS High Performance Position MeasurementCOTS Camera for VNIR Proven aerial mapping camera 4k x 4k pixel format 12 bit quantization High quality Kodak CCDCOTS Cameras for SWIR, MWIR, LWIR Rugged industrial/aerospace equipment 640 x 512 pixel format 14 bit quantization < 0.05K NEDTMeasurement AccuracyPosition3 mRoll/Pitch 0.03 degHeading0.10 deg1.5 km at nadir

WASP has detected a 15 cm charcoal test fire at 3300m AGLLWIRMWIRSWIR

Typical WASP ortho- and geo- rectified data product (LWIR Tmax = 800C)

WASP II quick factsResolution at 1500m AGL 2m IR, 0.3m VISBands: visible (RGB or color IR) 0.9-1.8mm, 3-5mm, 8-11mmFrame rate: 0.5 Hz Vis, 30 Hz IR14 bit dynamic range ~ 12 bit effectiveView Angle, 50o fixed, 110o scanningCoverage @ 120kts @3100m AGL: 100,000 ac/hrNEDT: measured less than 0.05 K in MWIRAbsolute geolocation accuracy~10mGeolocation repeatability: ~1.5m

The WASP airborne sensor mounted in the aircraft The ground based IR flux-weather stations used to calibrate the airborne sensor and obtain in-fire weather data for fire behavior calculations

A little bit about the ground sensorsIn this experiment, the ground sensors allow us to calibrate absolutely and remove the effects of atmospheric absorptionUsed for experiments on 6 firesMeasure weather and infrared flux, optionally several other variablesWe have measured key factors for IR remote sensing: burn scar cool down rate, surface flux (outward), emissivityIn-fire weather has been measured, showing interesting weather phenomena during flaming front passage

The ground sensors were used to absolutely calibrate the airborne images..

Chart3

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1328.1045978585

1615.0619651269

4031.2302262528

3933.9743562281

3413.0294095296

3533.27156322124062.9

2725.2771122691

2308.1923242294

1697.7606285257

1357.6692401467

1203.5270489457

1185.1553643914

1047.2945483663

975.27419578991849.62

855.4612039838

832.5841784577

803.6800307701

800.3537170921

741.8723195584

776.6969339219

830.1968568537

889.3478799768

730.4454000904

731.2554108266

665.711308449617.7

598.8484661544

591.1086606911

598.0703332585

598.8484661544

544.5956353431

588.0384865938

593.4209288863

600.4075089712

547.7100192544

550.8392335343

543.8193511089

544.5956353431

488.0259494528

481.8782534416

532.2854658686

483.4096760718

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478.8263759158

469.7580660963

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475.0320334391

Surface Flux

Airborne Flux

30 second sample period - Sample #

Surface Flux, W/cm2

Surface Flux vs Measured Airborne FluxVinton Furnace Ohio PF April 2004 - Logger Unit 6N 39.187567 W 82.379167 262m Elevation

Notes

Notes:

1. Room temperature assumed to be the same as sensor temperature for calibration

2. Radiant (brightness) temperature measured for calib ration with OMEGA handheld radiometer

3. Unit 3 failed - no data on ADC channels

4. Unit 1 had cable melt-down due to fire shelter shield blowing off cable - fasten with FG tape!

5. What the hell have I been doing? Wrong calibration proceedurefix it today (NOV 8 2004)

6

Unit 1

29.12

29.35

29.41

29.39

29.41

29.09

29.08

29.06

29.27

28.89

29.11

29.3

28.98

29.42

29.3

29.24

29.29

30.22

30.36

29.660000002

30.210000002

39.4500000055

41.2300000113

55.1600000111

43.8800000238

37.7500000131

54.2500000887

Sample

Temp, C

Surface Equiv. Radiant TemperatureOhio PF April 2004 - Unit 1

Unit 3

5

Data Capture Failure

Sample

Temp, C

Surface Equiv. Radiant TemperatureOhio PF April 2004 - Unit 3

Sheet2

Sheet2

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467.5113514416

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463.784830701

464.5283378547

467.5113514416

469.7580660963

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469.0082593392

470.5087756911555.118

472.0129062916

473.5206569397

473.5206569397

474.275891595

475.0320334391

473.5206569397

475.0320334391

478.82637591582240.54

478.0656872299

475.0320334391

474.275891595

475.0320334391

477.3059093665

478.0656872299

1328.1045978585

1615.0619651269

4031.2302262528

3933.9743562281

3413.0294095296

3533.27156322124062.9

2725.2771122691

2308.1923242294

1697.7606285257

1357.6692401467

1203.5270489457

1185.1553643914

1047.2945483663

975.27419578991849.62

855.4612039838

832.5841784577

803.6800307701

800.3537170921

741.8723195584

776.6969339219

830.1968568537

889.3478799768

730.4454000904

731.2554108266

665.711308449617.7

598.8484661544

591.1086606911

598.0703332585

598.8484661544

544.5956353431

588.0384865938

593.4209288863

600.4075089712

547.7100192544

550.8392335343

543.8193511089

544.5956353431

488.0259494528

481.8782534416

532.2854658686

483.4096760718

479.5879761519

478.8263759158

469.7580660963

470.5087756911

471.2603888479

472.0129062916

475.0320334391

Surface Flux

Airborne Flux

30 second sample period - Sample #

Surface Flux, W/cm2

Surface Flux vs Measured Airborne FluxVinton Furnace Ohio PF April 2004 - Logger Unit 6N 39.187567 W 82.379167 262m Elevation

Unit 6

36.7506093579469.7580660963

36.3795817362467.5113514416

36.0085603098465.272742783

35.7612161523463.784830701

35.8848878841464.5283378547

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36.8742866021470.5087756911

36.7506093579469.7580660963

36.6269327979469.0082593392

36.8742866021470.5087756911

37.1216431377472.0129062916

37.3690023941473.5206569397

37.3690023941473.5206569397

37.4926830392474.275891595

37.6163643604475.0320334391

37.3690023941473.5206569397

37.6163643604475.0320334391

38.2347810614478.8263759158

38.1110963796478.0656872299

37.6163643604475.0320334391

37.4926830392474.275891595

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38.1110963796478.0656872299

128.65361303611328.1045978585

148.78478100421615.0619651269

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48.4120442437544.5956353431

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48.8705775601547.7100192544

49.329329103550.8392335343

48.297445187543.8193511089

48.4120442437544.5956353431

39.7190489792488.0259494528

38.7295264563481.8782534416

46.5801232327532.2854658686

38.9769031361483.4096760718

38.3584664113479.5879761519

38.2347810614478.8263759158

36.7506093579469.7580660963

36.8742866021470.5087756911

36.9979645291471.2603888479

37.1216431377472.0129062916

37.6163643604475.0320334391

Eq. BB Temperature

Surface Flux

30 second sample period - Sample #

Equivalent BB Temperature, C

Surface Flux,W/m2

Surface Equivalent Radiant Temperature and Surface FluxVinton Furnace Ohio PF April 2004 - Logger Unit 6N 39.187567 W 82.379167 262m Elevation

Calibration + Data

Cheap and Easy calibration of Dexter Research 2M Thermopile

Full aperture on black plate All calculations for voltage done in mV

Radiometric temperaure (e=1) measured by OMEGA handheld radiometer

Cal Plate covers full aperture of 2M sensorRadianceSB constant5.67E-08airborne calibration

Assume emissivity of 0.95Cal K4.32E+01Data for Ohio Prescribed Fire April 20040.58

TempTemp (K)Incident FluxSensor radianceDetector Reaching RDet VCal KMeasured Fmv/cnt ->1.217< - DC OffsetA/D Sensitivity1.21971273658.2

The Vinton Furnace, OH experiment3 hour fire duration, ~25 over flights by WASP sensor

40 ground truth stations (manually sampled before and after)

3 WX/fire intensity calibration stations

Two line ignition (upper and lower line), hilly terrain

Maple/oak/hickory forest, open with leaf duff and

Time Resolved Imaging Example

Five image sequence from prescribed fire in Vinton Furnace OH Images centered on a flux-weather stationLine ignition from top and bottomFire coalesces on sensor package near center of imageTime resolved flux measured from image and flux-WX stationTime integral shows fire effects

Integral Fire Intensity

Fire intensity/severity map from the time integral of a 10 exposure sequence

DiscussionDefinite patterns generated by ignition torches/methodHighest heat areas tended to be nearest ignition lineGood agreement was obtained with fuel burn-up and other measures of fire intensity on ground truthLongest run had the most uniform and lowest intensity fires

Sanity check: airborne imaging achieved 100% agreement with ground sampling for unburned regions

ConclusionsIt appears that prescribed fire intensity (heat release) is strongly spatially correlated to ignition patterns

Patterns generated by point ignition (lightning) may be different form line ignition, and heat release (effects) may be significantly different

We may not be simulating historical fire patterns with prescribed fire using line ignitions

We obtained proof of the utility of time-sequenced images for rapidly obtaining burned/unburned area maps

We have developed reliable in-fire calibration techniques for overhead IR imagery using fire resistant data loggers

Future DirectionsObtain heat pattern from point ignition prescribed fire or freely propagating wildfire

Collect data in many different forest/land cover types

Continue checking ground sensor/airborne method for absolute heat flux measurements

References:WASP- A high performance, multi-spectral airborne imager for wildland fire detection, R. Kremens, D. McKeown, J. Cockburn, J. Faulring, D. Morse, H. Rhody, M. Richardson, presented at the 2nd International Wildland Fire Ecology and Fire Management Congress, Orlando, Fl, November 2003Autonomous field-deployable wildland fire sensors, R. Kremens, J. Faulring, A. Gallagher, A. Seema, A. Vodacek, Int. J. of Wildland Fire, 12, 237-244 (2003)Measurement of the time-temperature and emissivity history of the burn scar for remote sensing applications, R. Kremens, J. Faulring, C. Hardy, presented at the 2nd International Wildland Fire Ecology and Fire Management Congress, Orlando, Fl, November 2003Contact InformationRobert KremensRochester Institute of Technology54 Lomb Memorial DriveRochester NY 14623585.475.7286kremens@cis.rit.edu

We have had to modify the content of the talk as presented in this abstract because we had a slow fire season and uncooperative conditons across the country.

In this talk we will focus on the time-resolved imaging method as a way to measure the total heat released and thus the fire effect.We have devised a method to measure heat release from a fire. We now would like to know whether the heat patterns generated by prescribed fires which are usually ignited to produce the maximum effect in the shortest time using line ignition, is similar to naturally occurring, free running fires which are usually ignited at a point or series of points.We would like to validate our airborne techniques, which include information gathered from sparsely placed ground sensors. (data fusion)

Of course, we learned a lot by mistakes!We fly an aircraft over the fire for the duration of the fire. We also have sensors on the ground to measure heat released and other fire behavior related parameters. Our clocks are synchronized (GPS clocks) so that we observe the same point at the same time.

We have built about 30 fire sensors, but usually deploy 3 5 per fire. The important thing here is that we observe the fire from cold ground to cold ground, for the entire fire cycle. We are then looking at th total heat release from the event. Time integration of the calibrated flux from each image over the fire duration gives the total heat released from any pixel.

We eliminate the effects of atmospheric absorption and smoke and haze by using sensors that provide ground truth.

When we finally get data from point ignition fires in similar forest types, we can use a landscape analysis tool to determine if the patterns of fire are the same in both.

We can also compare our heat measurement techniques with ground measurements (before and after) sampling using conventional techniques.The WASP camera system is a 4-camera visible/SWIR/MWIR/LWIR camera using commercial components. We have a very good inertial management unit and very good internal models of the camera system that enables us to overlay frames from different cameras, and from different time periods (i.e. flight lines) Some example imagery from WASP. The most important thing about the camera is its multispectral high resolution imagery (14 bit) which allows us to capture fire scenes along with some background scene information on a single exposure.A photo of the WASP camera in our Piper Aztec aircraft and the ground based collection units deployed at the Cooney Ridge fire in Montana in 2003.We have developed a series of ground sensors that can measure in-fire parameters that are of interest to fire behavior and fire effects. The units deployed on these experiments measured air temperature, ground temperature, ground IR flux, wind speed, wind direction and relative humidity. In this talk we only discuss the IR flux data as applied to calibration of the airborne instruments.The red plot is the LWIR flux near the surface form a patch about 1 pixel across. The blue dots are the recorded sensor-reaching IR flux in the LWIR band from the same pixel at the same times.

We speculate that the airborne image sees a wider time signature from the fire because of upwelled radiance from hot gases near and above the observed pixel.This is a 5 image sequence from a prescribed fire in Vinton Furnace Ohio. The value of time sequence imaging is apparent in judging fire behavior (we can measure the velocity and intensity of the fire front). We can also time integrate the flux to get the total energy release, as in the sixth frame on the right side of the slide.This is the result of the image analysis technique presented previously. The labels represent field sampling (T,S,R) and ground stations (U). U3 is the station we showed previously.

We can see easily from this image that the heat release shows signatures from the ignition pattern. In this case of low fuel loading, some of the highest heat releases were obtained from the torch path. We dont think this pattern is characteristic of a natural fire disturbance. The total heat released may also not be characteristic of a natural fire.

We await a better fire season to obtain corroborating evidence of these claims. We also have to try these experiments over several different forest/grassland types to see if this is a general statement or just a statement about Eastern fires. A sanity check our method correctly identified all 9 unburned areas in the fire, even though some of the areas were within a few meters of a burned area. There were some 40 ground truth plots in all for this experiment. We need more data, but are convinced that our method is a viable one. These experiments are planned as part of a proposal that was submitted to the USDA Joint Fire Sciences.