1. Remote Sensing – Fire Weather Product Presentation
By Brian Hays & Kyle Zahn

2. Overview Introduction to Burned Area Reflectance Classification (BARC)
Satellites and Sensors Used in BARC
BARC Products
Case Study Verification of BARC
References
Questions?

3. History of BARC
Traditionally, burn severity maps drawn up by hand from observations from helicopters
In 1996, RSAC developed an airborne color infrared camera to develop the maps
BARC started in 2001 as a pilot project to overcome the logistical difficulties involved with the previous methods

4. Satellites
Information is primarily gathered from the Landsat system, which contains the thematic mapper
Additional information is gathered from the Terra system, utilizing the ASTER and MODIS sensors
Though expensive, information is also gathered from the SPOT 4 system

5. Satellite Characteristics
Inclination (Degrees)
Altitude (km)
Repeat (Days)
Period (Min)
Landsat
98.2
705 16 99
Terra
98.8
SPOT 98
832 26
101

6. Sensor Names Decoded
MODIS – Moderate Resolution Imaging Spectroradiometer
ASTER - Advanced Spaceborne Thermal Emission and Reflection Radiometer
MASTER – M(ODIS)ASTER – airborne simulator for the MODIS and ASTER sensors

7. Sensors MODIS Uses 31 bands ranging from .5 to 2 microns
Global coverage on a very short time scale
Coarse resolution

8. Sensors ASTER Uses three types of scanners. SWIR – Shortwave Infrared
TIR – Thermal Infrared
VNIR – Visible and Near Infrared

9. Sensors
Source : Field Validation of Burned Area Reflectance Classification (BARC) for Post Fire Assessment, USDA Forest Service 2004

10. Computing the Values Normalized Burned Ratio – NBR
NBR = (NIR – SWIR)/(NIR + SWIR)
Delta Normalized Burned Ratio – dNBR
dNBR = [NBR(pre-burn) – NBR(post-burn)]
Normalized Difference Vegetation Index – NDVI
NDVI = (NIR – RED)/(NIR + RED)

11. Value Meanings
The NBR is a measure of the difference between the reflected shortwave radiation from healthy vegetation and the emitted longwave radiation from charred vegetation
The higher the amount of emitted longwave radiation, the more severe the burn damage

12. Value Meanings
dNBR is a comparative measurement between the pre-fire spectral data and the postfire data
It is not available for all fires due to pre-fire coverage restraints
In theory, the dNBR should yield better information about the burn severity of a given area

13. BARC Levels BARC products come in two flavors
The basic BARC product is a four level scale including healthy, slight, moderate, and severe
The Adjustable BARC comes in a 256-bit format such that the BAER teams can create their own thresholds

14. Case Study Introduction
Groups Involved
USDA Forest Service Remote Sensing Applications Center (RSAC)
Burned Area Emergency Rehabilitation (BAER) Teams
USGS EROS Data Center (EDC)
Products Involved
Burned Area Reflectance Classification (BARC) & BARC-A (Adjustable)
Normalized Burned Ratio (NBR)
Delta Normalized Burned Ratio (dNBR)
Normalized Difference Vegetation Index (NDVI)

15. Case Study Introduction
Case Study of six 2003 wildfires in Western Montana and southern California.
The main objective of the paper was to asses burned severity remotely and on the ground and compare them
For this they would use locations on the ground to provide ground truth to compare to satellite observations

16. Case Study Introduction
Sensors Used
Default Imagery
Landsat-TM
Secondary Imagery
SPOT 4
ASTER
MASTER
MODIS

17. Case Study Introduction
Landsat-TM is the preferred sensor due to its desired temporal, spatial, and spectral characteristics
SPOT XI is also desirable system because it is pointable
However, its use is limited as SPOT XI is much more expensive then Landsat-TM to use.
The other satellite systems have much coarser imagery then either of these two systems

18. Case Study Introduction
Fire Locations

19. BARC Maps of the Six Fires
Case Study Data
BARC Maps of the Six Fires

20. Case Study Data

21. Case Study Data

22. Case Study Analysis Analysis
Calculated Spectral Indices were divided into:
NBR
dNBR
NDVI
dNBR derived BARC
dNBR derived BARC-A
Measured and derived field variables were divided into four categories:
Overstory
Understory
Surface Cover
Soil Infiltration

23. Case Study Analysis Analysis (cont.)
Correlation matrices between field and image variables were generated in R. Pearson correlation statistics
Correlation values greater then 0.5 were considered meaningful
These were tallied within field and spectral categories already mentioned as well as by sensor type and strength of correlation

24. Case Study Results Results Correlations Instruments
Overstory and Understory produced the highest amount of meaningful correlations with the spectral data(>0.5)
Surface cover variables were lower
Soil Infiltration was the lowest
Instruments
ASTER sensor produced the best correlations followed closely by the MASTER sensor
Only available at Old and Simi Fires
LANDSAT-TM and SPOT 4 were intermediate
MODIS produced the worst results

25. Case Study Results Results (cont.) Indices
NBR and dNBR produced much better correlations then NDVI
dNBR did better then NBR in general except in overstory and surface cover categories
NBR correlates better with field attributes when the satellites capture post-fire effects immediately
dNBR correlated better with field attributes when satellites capture post-fire effects after a few weeks
Fires
Cooney Ridge produced the best correlations followed by the other Montana Fires
Southern California fires produced the worst results
Lack of tree overstory at many of the California sites likely accounts for the difference

26. Case Study Results

27. Case Study Results

28. Case Study Results

29. Case Study Results

30. Case Study Results

31. Case Study Results

32. Case Study Results

33. Case Study Results Temporal Fire Effects Temporal Correlations
Some of the lower correlations can be explained by the temporal nature of the data being collected.
Ash cover for example had a very low correlation rate, however, it is quickly removed from the area by wind and water after a fire
Additionally green vegetation regrowth and new litter are also dynamic and depend upon the specific situation
Temporal Correlations
Specifically these effects can be applied to the two Montana fires
Here satellite data was acquired very soon after the fire and ground sites were also set up quickly.
These reasons may explain why these two fires had the highest correlation values

34. Case Study Conclusions
Results indicate that BARC maps should be considered more indicative of vegetation severity then soil severity
This makes sense since vegetation occludes the soil
Spectral mixture analysis is one way to better observe and estimate the green and nonphotosynthetic vegetation and litter and soil fractions directly from the imagery
The large amount of field data will serve as valuable ground truth to determine what effect the vegetation variables have on moderate and especially low burn severities as opposed to high burn severities

35. References http://masterweb.jpl.nasa.gov/ http://terra.nasa.gov/
Hudak, A., Robichaud, P., Jain, T., Morgan, P., Carter, S., Clark, J. “The Relationship of Field Burn Severity Measures to Satellite-Derived Burned Area Reflectance Classification (BARC) Maps”
Hudak, A., Robichaud, P., Evans, J., Clark, J., Lannom, K., Morgan, P. “Field Validation of Burned Area Reflectance Classification (BARC) Products for Post Fire Assessment”

36. Questions???