Introduction
The purpose of this remote sensing analysis is to determine the effects of the Tubbs Fire that occurred in California in 2017 using binary change, normalized difference vegetation index (NDVI), and normalized burn ratio (NBR). The Tubbs Fire, named the most destructive California wildfire, started on October 8th around 9:43 PM near Calistoga, California (Griggs et al., 2017). Due to high winds from the northeast, the fire quickly spread. The damage from the fire was felt strongest in Santa Rosa, a city southwest from the fire’s origin. During the time this fire was occurring, two other major fires, the Atlas and Nuns Fires, were burning areas to the southeast of Santa Rosa. By the time the fire was contained 32 people were killed and about 5,500 structures were destroyed (Jansen, 2017, Krishnakumar et al., 2017). As of now the cause of the fire is suspected to be a downed power line, but investigations are still continuing.
The Tubbs fire occurred in the central coast bioregion of California. The ecosystems in the area consist of grassy savannas, oak woodlands, and chaparral (Forests Forever, 2018). Due to previous fire suppression in much of the western United States, the majority of these ecosystems have a build-up of dead plant material that would normally be burned away each year in low-intensity fires. This was also the case when the Tubbs Fire occurred. In addition, the fire picked up in speed and intensity once it met the town of Santa Rosa and was able to burn many of the homes.
Two other major factors played a role in the Tubbs Fire: humidity and winds. If the winds are high, as was the case during the Tubbs Fire, then more fuel (oxygen) is brought to the fire which helps it continue to burn. High winds can also carry embers over a mile causing the fire to spread faster and out-pace fire barriers. When the humidity is low, fuel sources such as dead vegetation are drier and thus easier to ignite. The night of October 8th the humidity was 15% (Funke, 2018). The Tubbs Fire was a combination of low humidity, high winds, dry air, and plenty of fuel sources that caused it to be the most destructive fire in California wildfire history.
In this analysis a subset of the Tubbs Fire perimeter from a Landsat 8 satellite image was created to focus the following three functions to determine areas affected by the fire. Binary change determines which areas in an image experienced a change in reflectance and then creates an output image of those areas. In addition to the function in Erdas, two models of binary change were completed as well to create outputs that could be used in ArcMap. NDVI is a function that uses the near infrared (NIR) and red wavelengths of the electromagnetic spectrum to determine which areas have healthy vegetation. NBR uses NIR and short-wave infrared (SWIR) wavelengths to determine which areas had the highest burn intensity. The combination of all these functions highlighted which areas were most heavily affected by the Tubbs Fire (figure 1).
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| Figure 1. Area burned by the Tubbs Fire in 2017 located in the coastal mountain region of California. |
Methods
Data
The two primary images for this analysis were obtained from the United States Geological Survey (USGS) GloVis site. Both images were from the Landsat 8 satellite with one image taken October 1st, 2017 and the other December 7th, 2017 correlating to before the fire started and after it was contained (United States Geological Survey, 2018).
Remote Sensing Analysis
The first step in the analysis was to obtain the data from the USGS. Two images were imported: an image of Sonoma, Napa, and Lake Counties before the Tubbs Fire (October 4, 2017) and an image of the same area after the Tubbs Fire was contained (December 7, 2017).
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| Figure 2. GloVis site with the Landsat 8 images to be downloaded for analysis. |
Next, bands 2 through 7,
or blue through short-wave infrared (SWIR) were imported from TIFF files to IMG
files in Erdas Imagine (figure 3).
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| Figure 3. Import function in Erdas Imagine used to bring in TIFF files and convert them to IMG files. |
Once the images were imported, the layer stack function was run which took bands 2 through 7 and stacked them to make one composite image (figure 4).
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| Figure 4. Layer stack function in Erdas Imagine. |
A subset of the Tubbs Fire from the images were obtained using a shapefile of a polygon digitized from a Tubbs Fire feature class (figure 5).
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| Figure 5. Subset of the October and December Landsat 8 images using the subset tool in Erdas Imagine. |
The subset of the images was analyzed further using binary change detection, NDVI, and Normalized Burn Ratio Area.
Binary Change Detection
In the binary change detection function, the difference between the two subsetted images was computed using subtraction and specifically with band 5 which correlates to the near infrared (NIR) (figure 6).
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| Figure 6. Binary change output showing areas that had a significant change after the Tubbs Fire. The darker areas are those with more change. |
This image is the
corrected version of the original output. When the process was first run, a
pansharpened image was used. This in itself is not a bad process to run.
However, when the pansharpen function was used, the multiplicative method was
employed. This changes the radiometric properties of the image from the
original. When the subsequent models were run, they resulted in almost no areas
with significant changes. Once this error was recognized, a non-pansharpened
image was used and the process ran correctly.
To look more closely at
the areas that changed, two models were run on the NIR bands of each image. In
model one, the pixel values were subtracted from each other and a constant of 127
was added to remove any negative values (figure 7).
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| Figure 7. Model one in model builder. The model is the same as binary change but it adds a constant to remove any negative values. |
Another model was created to show the pixels that changed between October 4th and December 7th, 2017 using the mean pixel value and the standard deviation from the first model’s results (figure 9). In this case, the mean was 42.856 and the standard deviation was 64.651. The threshold for which pixels changed between the two time periods and which did not was defined as the mean pixel value plus three times the standard deviation. This was the threshold because in model one a constant of 127 was added to remove any negative values in the histogram. The mean plus three times the standard deviation was used to determine the threshold because data beyond three standard deviations is significantly different from the mean, approximately 1% of the data (figure 8).
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| Figure 8. Model two in model builder. This model creates two values: a '1' if the pixel changed based on the threshold calculated in model one or '0' if the pixel did not change. |
Then the results from model two were used to create a map in ArcMap showing which areas had a significant change based on the NIR wavelength.
Normalized Difference Vegetation Index
The NDVI function was relatively simple. The function was run two times, once on the October 4th subset image and once on the December 7th subset image. The parameters were set to Landsat Multispectral for each run (figure 9).
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| Figure 9. NDVI function in Erdas Imagine. |
Normalized Burn Ratio
The NBR ran the same as the NDVI with the same parameters but only the post-fire image was run through the function (figure 10).
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| Figure 10. NBR function in Erdas Imagine. |
Results
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| Figure 11. Results of the binary change detection. |
Figure 11 shows the results of the binary change detection function. The Tubbs Fire caused the most significant changes on the slopes of the mountainous terrain areas. The concentration of changes in these areas could be due to the winds which pushed the fire southwest. The changed areas also follow the main path of the fire and are less prevalent on the perimeter of the fire where conditions were probably less severe than in the center of the fire’s path.
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| Figure 12. Results of the NDVI function before and after the Tubbs Fire. The left map is before the fire and the right map is after the fire. |
Figure 12 shows that
there was a significant decrease in vegetation health after the Tubbs fire. The
decrease is visible as an increase in red or yellow areas (unhealthy or very
unhealthy vegetation) and a decrease in green areas (healthy vegetation). The
main area of decrease was the center of the area of interest in the mountainous
region. This function was performed on band 5 (NIR) because healthy vegetation
reflects NIR radiation and unhealthy vegetation does not. Another smaller area
of change is the very top of the perimeter. In the left map, the area is mostly
healthy vegetation. In the right map, the area is more red and yellow
indicating unhealthy or very unhealthy vegetation.
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| Figure 13. Burn Severity map of the area affected by the Tubbs Fire. |
Figure 13 shows which areas had the highest burn severity in the area affected by the Tubbs Fire denoted as red. The areas with the highest burn severity are in the mountainous terrain of the area of interest along the main path of the fire. These results mirror the findings in the NDVI and the binary change outputs.
Conclusions
Overall the binary change, NDVI, and NBR functions proved useful in identifying areas that were affected by the Tubbs Fire. The binary change detection showed changes in the landscape within the Tubbs Fire perimeter which isn’t surprising considering it has been labeled the most destructive wildfire in California history.
The NDVI function indicated the highest losses in healthy vegetation cover were in the center of the fire perimeter starting in the north and moving south. The NBR function mirrored the spatial patterns found in the binary change and NDVI image indicating fire intensity was highest in the center of the mountainous region where the main path of the fire was.
These methods do have some limitations. Both NDVI and NBR used the NIR band which is most useful in detecting changes in vegetation. The most destructive aspect of the Tubbs Fire was its effect on Santa Rosa, a city in the southwest corner of the Tubbs Fire perimeter. Because cities tend to have less vegetation, these processes did not pick up on the changes that occurred in the area. Different bands would have to be employed to assess changes the fire caused in Santa Rosa.
This analysis shows that basic remote sensing processes such as binary change, NDVI, and NBR are helpful in determining areas affected by wildfires. However, these processes used the NIR band to assess vegetation changes. This meant that urban areas were not assessed nearly as well as vegetated areas. In future studies other bands should be studied using different processes to assess changes in urban areas caused by wildfires. Despite this shortcoming this analysis has proved remote sensing to be a useful tool in hazard monitoring, especially regarding wildfires.
References
California Department of Forestry and Fire Protection. (2018, January 12). Top 20 Most Destructive California Wildfires. Retrieved from http://www.fire.ca.gov/communications/downloads/fact_sheets/Top20_Destruction.pdf
Forests Forever. (2018). Central Coast Bioregion. Retrieved from http://www.forestsforever.org/archives_resources/cabioregions/centralcoastbioregion.html
Griggs, T., Lai, R.K.K, Park, H., Patel, J.K., & White, J. (2017, October 12). Minutes to Escape: How One California Wildfire Damaged So Much So Quickly. New York Times. Retrieved from https://www.nytimes.com/interactive/2017/10/12/us/california-wildfire-conditions-speed.html
Humboldt State University. (2017). Normalized Burn Ratio. [Website page]. Retrieved from http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson5-1/NBR.html
Jansen, B. (2017, October 13). California Fires Have Killed 32 People; here’s a Look at Some of the Victims. USA Today. Retrieved from https://www.usatoday.com/story/news/2017/10/13/california-fires-victims- killed/762312001/
Krishnakumar, P., Fox, J., & Keller, C. (2017, October 23). Here’s Where More than 7,500 Buildings were Destroyed and Damaged in California’s Wine Country Fires. LA Times. Retrieved from http://www.latimes.com/projects/la-me-northern-california-fires-structures/
S Funke. (2018, January 9). Fire Ecology of Non-Scientists: The Fire Triangle & Fire Behavior. [Blog Post]. Retrieved from https://www.pepperwoodpreserve.org/2018/01/09/fire-ecology-for-non-scientists-the-fire-triangle-fire-behavior/
United States Geological Survey. (2018). Landsat 8 Satellite Imagery [satellite image]. Retrieved from https://glovis.usgs.gov/
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