The document analyzes the burn severity of the 2007 Witch Creek Fire in California using Normalized Burn Ratio (NBR) analysis of pre-fire and post-fire Landsat imagery. The fire burned nearly 198,000 acres and caused two deaths. NBR analysis identified areas of highest burn severity in the northern part of the fire and western sparsely populated areas. However, quantitative analysis of burn severity classes proved difficult due to issues reclassifying the single-band DNBR image. Future analysis could include additional indices and field data to improve accuracy of burn assessments.

1. Introduction:
In 2007, California saw one of the most
devastating fire events in its history; a siege of 30
fires that lasted ten days, burned over a half
million acres, destroyed 3,069 homes and other
buildings, and is connected to 17 deaths
(Overview ). This study focuses on the Witch
Creek Fire, which burned 10 days from the Oct.
21st, to Oct. 31st. The Witch Fire is listed as
California’s sixth largest wildfire recorded(Date
and Structures ). It burned 197,990 acres,
destroyed or damaged 1,727 structures and was
the cause of 2 civilian deaths.
Because high severity wildfires are associated
with erosion and sedimentation, habitat loss or
fragmentation for wildlife, and carbon
sequestration, as well as many other ecological
functions, it is important to analyze the
aftermath of large fires such as the Witch Creek
Fire(Miller et al. 2008). In this project, I will
analyze the area affected by the Witch Creek Fire
using Normalized Burn Ratio (NBR) to look at
burn severity.
Data Identification and Download:
The Witch Creek Fire occurred in San Diego
County, and wrapped around the north west side
of the town of Ramona, California. In order to
locate the area of the fire, I referenced a map
produced by the Governor's Office of Emergency
Services(Allen 2007) (see Figure Number ) as well
as Google Maps. For the DNBR, two images were
required, one taken before the fire, and one
taken after the fire. It is suggested that the two
images should be taken when the vegetation is
healthiest, so I decided that late spring would be
appropriate. The images used are raw scenes
from the Landsat 5 sensor and were taken on
May 6, 2007 and June 1, 2008, respectively. Data
was downloaded from the USGS GLOVIS
website(USGS Global Visualization Viewer ).
Literature review
A Normalized Burn Ratio (NBR) is commonly
used in the analysis of wildfires to highlight the
burn intensity of an area. In an expression similar
to an NDVI (see equation 1), an NBR uses the
Near Infrared and the Mid Infrared bands (bands
4 and 7). These two bands are used because
Band 4, which highly reflects vegetation,
decreases reflectance the most after a fire, while
Band 7 reflects rocks and minerals
efficiently, and so generally increases
reflectance dramatically post burn(Van Driel
). The NBR calculation (see Equation 1) is
performed on an image from before a fire and an
image from after a fire. The pixel values from
the post fire image is then subtracted from the
pixel values of the pre-fire image (see Equation
2). This yields the △NBR, and the resultant pixel
values can be separated into categories, ranging
from High Post Fire Regrowth (△NBR< -0.25 ) to
High Severity Burn (△NBR> 0.66). This △NBR also
acts to isolate the burned area from the
surrounding image(Van Driel ).
Figure 1: Witch Creek Fire map, showing final extent and surrounding
features(Allen 2007).
Analyzing Burn Intensity of the Witch Creek Fire Using Normalized Burn Ratio (NBR)
Emma Carey (Undergraduate)-University of Southern Maine, Dept. of Geography and Anthropology
Firooza Pavri, Ph.D. (Professor of Geography)-University of Southern Maine, Dept. of Geography and Anthropology
Methods
• I created stacks and subsets of pre fire and post fire
images using NEST
• Using the Thematic Land NDVI processor, I created
NBR’s of pre-fire and post-fire images in BEAM to
qualitatively analyze and to create the DNBR
• The DNBR was created in BEAM Using a Band Math
Expression (see Equation 2)
• Breaking the DNBR down into meaningful classes(
see Figure 4) for quantitative analysis proved
difficult. Because burn severity is based on distinct
ranges of pixel values, the K-mean cluster analysis
proved inappropriate.
• I attempted to import the DNBR into ArcGIS to
manually reclassify it, first converting the image
from BEAM-DIMAP to GeoTIFF, but due to an
unknown variable in the conversion process, the
single band image was opened as an RGB image in
ArcMap, disrupting my attempts to classify it.
• I was able to create the visual classes with the color
manipulation tool. Qualitative analysis was then
completed for all images
References
USGS Global Visualization Viewer. (n.d.). Retrieved December 12, 2015, from
http://glovis.usgs.gov/
Allen, D. (2007, November 1). Witch Fire. Governor’s Office of Emergency Services.
Retrieved from
https://w3.calema.ca.gov/Operational/OESHome.nsf/PDF/Fire%20Maps%202007/$file/Wi
tchFire.jpg
Van Driel, N. (n.d.). Burn Severity Overview - Applied Remote Sensing Principles. Retrieved
December 4, 2015, from http://burnseverity.cr.usgs.gov/overview/nbr/index.php
Normalized Burn Ratio []. (n.d.). Retrieved December 4, 2015, from
http://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:normalized_burn_r
atio
Mann, M. E., & Gleick, P. H. (2015). Climate change and California drought in the 21st
century. Proceedings of the National Academy of Sciences of the United States of America,
112(13), 3858–3859.
Escuin, S., Navarro, R., & Fernández, P. (2008). Fire severity assessment by using NBR
(Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from
LANDSAT TM/ETM images. International Journal of Remote Sensing, 29(4), 1053–1073.
Acknowledgements
A great many thanks to Vinton Valentine (Director of USM-GIS), who provided a great
deal of technical support for this project and graciously answered any questions I
though up for him.
Figure 2: Pre-fire NBR (left) and post-fire NBR (right) of Witch Creek Fire
Figure 3: DNBR of Witch Creek Fire area (left) and colored “classified” DNBR,
highlighting areas of highest burn severity (right).
Analysis and Interpretation:
In the pre-fire NBR, you can see from the darker
pixel values that the area was already sparse of
vegetation except in the hills, which catch more
of the moisture brought from the Pacific Ocean.
These are represented by a brighter swath that
runs diagonally from the top left to the lower
right. The perimeter of the Witch Creek Fire is
none the less easily identified in the post-fire
NBR. In the DBNR, we see not only the area
Witch Creek Fire burned, but also the Poomacha
fire to the north and the top of the Harris Fire to
the south. In the Witch Creek Fire (center right in
the image) highest severity burn areas were in
the northern half of the fire where the fire
reached into the Cleveland National forest and
the sparsely populated area on the western side
of the park.
Conclusions:
As mentioned previously, my efforts of quantitative
analysis proved futile. My intent was to find the area
of each class (see Figure 4) to garner some insight on
the overall impact of the Witch Creek Fire.
When using an NBR for burn analysis, it is important to
note that the less healthy vegetation there was at a
burn site, the less accurate the NBR and DNBR will be.
In this case, the study area is Southern California,
which is a fairly dry area to begin with and, at the time
the Witch Creek Fire had been exposed to three years
of drought prior. In addition, the literature notes that
an essential aspect of improving the accuracy of any
burn analysis is to compound any aerial or satellite
imagery with field data, which I did not have access to.
In the future, I would also consider including other
indexes into my analysis, such as an NDVI , Burnt Area
Index, a composite burn index, or a relativized
DNBR(Miller et al. 2009; Escuin et al. 2008).
Figure 4: General classifications of burn severity for a DNBR by
pixel value(Normalized Burn Ratio )
∆
Equation 2: Differenced Normalized Burn Ratio (DNBR)