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

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

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

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

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

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

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

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

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

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)

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

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

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

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)

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

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

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

Case Study Introduction Fire Locations

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

Case Study Data

Case Study Data

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

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

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

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

Case Study Results

Case Study Results

Case Study Results

Case Study Results

Case Study Results

Case Study Results

Case Study Results

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

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

References http://masterweb.jpl.nasa.gov/ http://terra.nasa.gov/ http://www.spot-vegetation.com/vegetationprogramme/Pages/TheVegetationProgramme/spot4.html http://landsat.gsfc.nasa.gov/ http://modis.gsfc.nasa.gov/ http://asterweb.jpl.nasa.gov/ http://modis-sr.ltdri.org/MAIN_RATIONALE/Introduction/sc2-modis%255B1%255D.gif&imgrefurl=http://modis-sr.ltdri.org/MAIN_RATIONALE/WELCOME_MAIN.html 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”

Questions???