1. The Pagami Creek Wildfire
Adam Burger and John Habib
Remote Sensing, FNRM 3262
December 8, 2014

2. Stats Large August - September 2011
Unprecedented speed and coverage due to unique conditions
Took place within the Boundary Waters Canoe Area Wilderness
John

3. The BWCAW Popular wilderness area
Forest fires have ecological benefits
Pagami Creek Fire historically large
100,000 acre fire every half-century
Adam
bwca important bc it is one of the most popular wilderness areas in the country.
Picture is of Pagami Creek fire and some of the loss of vegetation that occured. This pic was taken this year
although some might be sad about destruction, forest fires natural ecological process
Many tree species need forest fires to reproduce and fires are also important for nutrient cycling
Fires are actually fairly common in the boundary waters although not necessarily fires as large as the pagami creek fire
historical data from tree cores in the boundary waters indicated that 100,000 acre forest fire occurs every half-century, 90,000 acres

4. Pagami Creek Fire
Map on the left shows the BW and the fire’s location within it.
You can see the size and shape of the fire in the second map. Also how fast it spread.
See smoke from Chicago

5. Goals
Goal 1: To determine the extent of forest cover change caused by the fire
Goal 2: To assess the forest recovery of the area affected by the fire
John

6. Imagery Before: May 2011, Landsat 4 After: October 2011, Landsat 4
Present: October 2014, Landsat 8
Adam
3 images came from USGS glovis website
Minimal amount of cloud cover and from the same sensor. we chose Landsat
Before image is from May 2011, After from October 2011
Before and after images were used to assess the total extent of the fire
the present day image from Oct 2014.
After and present images used together to assess the recovery of the forest
We wanted the after and present images to be from same time of year since we were trying to analyze change in vegetation
If images were from different times of year, we may get skewed results based on temporal variation
Landsat 4, Landsat 8
Prior to stacking the Landsat 8 layers, we eliminated 4 of Landsat 8’s bands that least matched with wavelengths of Landsat 4

7. Unclassified Images Pre-fire Post-fire May 15th 2011 October 6th 2011
In the post-fire image, you can clearly see where the fire is
May 15th 2011
October 6th 2011

8. Supervised Classification
4 classes
Fire
Forest
Water
Other
10 training sites per class
Minimum-distance algorithm
Minimum distance algorithm to assign pixels to classes. Originally we used maximum-likelihood, but found that it result in much more classification error

9. Supervised Classification
As I previously mentioned, we used 4 classes but decided to make the other class the same color as the forest class because we only really cared about the burned forest area
The burned forest area is fairly apparent in the After image and not present in the Before
If you look closely you can see some small dots of red in the before image where classification error occured
Before
After

10. Summary by Zone
Post-classification change detection: from-to change of pixels
Produced a summary report
Count, %, and total hectares of pixels changed from the zone name class in the ‘before’ image’ to the ‘class name’ classes in the after image
For example if we look at the fire class

11. Summary by Zone
For example if we look at the fire class, hectares were classified as fire in the before image and also classified as fire in the after image
These represent error because there should be no fire area in the before image
23,613 pixels falsely classified as fire
Next step is to calculate the total area that was burned

12. Summary by Zone
To calculate total area of the fire, took total number hectares that changed to the fire class
with exception of the wrongly classified fire pixels
Added hectares that changed from forest to fire...

13. Goal 1 Results Total area 33,453 hectares
Reported area by USFS around 37,000
This is our final image created showing the burned area
An image like this can potentially be used by foresters to create a map for visitors or the public who are interest in knowing the extent or areas affected by the fire
A lot of people may want to avoid burn areas when visiting the BWCA and it can also be used for post-fire management so the ability to produce this kind of information is pretty important

14. NDVI Processing “Before” Image NDVI Image Difference Subset
Statistical Analysis
“After” Image
NDVI
Image Difference
Subset
Statistical Analysis
“Present” Image
NDVI
John

15. NDVI Processing
John

16. NDVI Processing
John

17. Goal 2 Results 4%
John

18. Goal 2 Discussion
% of 2010 NDVI values
BUT...
John
Years since 2010

19. Errors Classification Seasonal Spectral Image Extent Clouds Lakes
The list goes on...
Also we could talk about mitigating these errors with subset, a Lakes shapefile, etc, and/or ways to improve the project.

20. Questions?

21. References