University of Minnesota.

The Pagami Creek Wildfire

Adam Burger

John Habib

Remote Sensing of Natural Resources and EnvironmentFNRM 3262

December 7, 2014

INTRODUCTION

Wildfires are some of the most prolific examples of nature’s fury. Destroying life, habitat

and property, they are at the same time an essential component of forest ecosystems. Remote

sensing provides us with powerful tools we can use to learn more about wildfires: where they

are, and how they change the areas that they affect. The Pagami Creek Wildfire was started by a

lightning strike approximately 13 miles east of Ely, MN and was first detected on August 18,

2011 [1]. Due to low humidity, dry conditions, and shifting winds, the fire had grown to 93,000

acres by September 13 [1], spreading so fast that fire shelters were deployed for the first time in

Minnesota.[2] This fire was particularly notable in that it took place in the Boundary Waters

Canoe Area Wilderness (BWCAW). The BWCAW is a 1,000 acre wilderness area located

within the Superior National Forest, containing over 1,200 miles of canoe routes and

approximately 2,000 designated campsites [3]. It is a popular destination for Minnesotans and

visitors alike, and contains some of the most treasured natural landscapes in the state. Thus the

Pagami Creek Wildfire was not only a threat insomuch as it might escape the BWCAW and

threaten private property, but the very fuel it burned was an immensely valuable natural and

recreational resource.

The goals of this project are to determine the extent of land cover change caused by the

Pagami Creek Wildfire (Goal 1), and to assess the progress of recovery in the affected area

which has occurred since the fire subsided (Goal 2). No man-made efforts, no matter how

intensive, can cause land cover to change as quickly over a large area as a wildfire can, so it is

expected that drastic change will be observed over the duration of the fire. The ability to use

remote sensing to assess the extent of a wildfire and also recovery of vegetation is very important

for forest managers as they make important decisions regarding forest restoration. Limited

though it may be, remote sensing offers a far more cost-effective toolbox to complete our

objectives than boots-on-the ground field observation of several thousand acres, to be sure.

MATERIALS

In order to assess the parameters which define the goals set forth in this project it was

first necessary to obtain imagery of the area affected by the Pagami Creek Wildfire. Landsat data

is available for all of Minnesota for many decades and is available at no cost, se we chose to use

Landsat imagery to perform our analysis. The USGS Global Visualization Viewer

(glovis.usgs.gov) offers a user-friendly way to browse satellite imagery available for a particular

place and time. Using the general location of the fire and an image of its footprint provided by

the US Forest Service, we were able to locate the affected area in the Glovis browser, and we

procured three images, one before the fire, one immediately after, and one of the present day.

The “before” image was taken on May 15th, 2011, by Landsat 4, and had 0% cloud cover. The

“after” image was taken on October 6th, 2011, by Landsat 4, and had 0% cloud cover. The

“present day” image was taken on October 22nd, 2014, by Landsat 8, and had 0% cloud cover.

For image processing we chose to use ERDAS Imagine 2014, due to its powerful and diverse set

of tools, and also its widespread availability and industry-recognized reputation.

METHODS

The USGS Glovis browser delivers each image in a compressed format. In order to

process each image using ERDAS Image it was first necessary to unzip each image into

individual TIFF files, each representing one band, and then use the Layer Stack function in

Imagine to compile each image into a single, multiband IMG file, recognizable by Imagine. The

difference in number of bands between Landsat 4 and Landsat 8 (Landsat 8 has more) made it

necessary to eliminate some of the bands available when stacking the “present” image in order to

ensure spectral homogeneity in the image pool.

After the images were rendered, the task of accomplishing the first goal of determining

what percentage of forest cover was eliminated by the fire was at hand. This involved classifying

the images using a supervised classification. Advantages of supervised classification over

unsupervised include the ability of the user to specify classes of interest prior to classification,

which eliminates the need for classes which are not of consequence to the task at hand. This

method is useful if the classes in the images are known and apparent, which was the case here.

Disadvantages include the time and manpower needed to train the classifier, but given the small

amount of images and the small area they cover that was hardly a concern for our project. We

used ERDAS Imagine software to classify the images. In order to perform a supervised

classification it was first necessary to train the classifier to recognize the classes of interest. We

chose four classes which comprised the bulk of the imagery: forest, burned forest, water, and

other. It was essential to have water classified as there are several small lakes interspersed in the

imagery as well as a large portion of Lake Superior. We created an AOI layer and drew polygons

to identify 10 training sites for each class on the October 6th (after) image, and then ran a

supervised classification on the ‘before’ and ‘after’ images using a minimum-distance algorithm.

The class delineations can be seen in ‘Results’. Since the ‘fire’ class is the main focus of our

analysis, both the ‘forest’ and ‘other’ class are simply represented by one color, which is green

on the map, while ‘water’ is blue and the 'fire' is red.

The next step was to perform a change detection. Post-classification change detection

offers the advantage of knowing what changed, and how much, as opposed to only the

magnitude of change offered by pre-classification, which is particularly important to our

objectives as stated here. We applied the “thematic change” function in Imagine to the classified

images of the burned area before and immediately after the fire, as well as to the images

immediately after the fire and of the present day. To obtain hard statistics describing the

destruction and regrowth of forest cover, we used the “Summary Report of Matrix” function to

obtain percentages and total hectares of class changes between images. This provided us with a

mathematical depiction of the natural processes at play. To calculate the total area affected by the

fire, the total areas that changed to from a class to 'fire' were summed, with the exception of the

area that was classified as 'fire' in the ‘before’ image and also in the ‘after’ image. This area was

considered to be a classification error because the ‘before’ image should not have any 'fire' and

was therefore subtracted from the final total of area affected by the fire.

Next the second goal presented itself: determining how much of the area decimated by

the wildfire has “grown back” between the cessation of the flames and the present day. “Grown

back” is hardly quantifiable, but fortunately a widely used spectral metric presents itself in the

form of the Normalized Difference Vegetation Index, or NDVI. NDVI is the ratio of the

difference between near infrared reflected light and visible reflected light to the sum of near

infrared reflected light and visible reflected light [4]. Mathematically, NDVI = (NIR —

VIS)/(NIR + VIS). Due to the spectral characteristics of healthy vegetation, a higher NDVI value

corresponds to a higher density of plant growth [4]. Hence, a higher NDVI value in the area

affected by the wildfire is an indicator of increased plant growth, affording us a quantifiable way

of determining how much of the area has indeed “grown back”.

ERDAS Imagine offers an NDVI function, which accepts a multiband spectral image and

outputs a single-layer image in which the DN value of each pixel is the NDVI value calculated

for that same pixel on the original image. This function was applied to each image in the pool,

generating three NDVI image files. In order to determine how NDVI has changed between the

three time periods, we applied the Image Difference change detection function in Imagine to the

“before” and “after” images, as well as to the “after” and “present” images. While only the latter

offers progress toward our second goal, the former acts as a sort of crude accuracy assessment:

the “ground truth” of vegetation decrease caused by the fire is as well known to us as it is to the

wildland firefighters who felled countless snags, but does the NDVI-image difference combo

corroborate their testimony?

Image difference simply subtracts the pixel DN values of the second image from the first

image. The result is a quantification of change. For example, a positive DN value for a pixel on

the “‘present’ minus ‘after’” change detection image indicates that the pixel in question had a

greater NDVI value in 2014 then it had in 2011, immediately following the fire. A DN value of

0.30 for that same pixel would indicate that the NDVI value has increased by 30% over that time

period. In this way we were able to quantify change in NDVI, on a pixel-by-pixel basis, for the

entire area of our imagery. This accomplished, we still needed to confine our analysis to the area

of interest, that is, the area affected by the wildfire. This area is visibly distinct on all imagery

following the fire, including that of the present day. Thus it was a small task to digitize an AOI

layer which represented the boundary of the affected area. A perfect match between the area’s

boundary and the layer is impossible, not to mention the fact that the boundary is likely not as

stark as it appears in satellite imagery, so care was taken to ensure that the AOI layer remained

within the area’s visual boundary. More important than including every pixel affected by the fire

in our analysis was the necessity of excluding pixels unaffected by the fire, which could easily

wreak havoc with our results. Finally, we used the Subset function in Imagine to generate new

image difference files composed of only the pixel values of each image contained within the AOI

layer. We then computed the statistics of the DN values contained within these two subset

images. The end result: the average increase in NDVI in the area affected by the Pagami Creek

Wildfire between 2011 and 2014. A single number, from so much data.

RESULTS

Below are the two unclassified Landsat 4 images used for the change detection before and after

the fire with the area of the fire clearly visible in the ‘After’ image.

Unclassified- Before

Unclassified- After

The classified images below show the

‘forest’ and ‘other’ classes as green, the

‘water’ as blue, and the ‘fire’ as red.

Supervised Classification- Before

Supervised Classification- After

Summary Report

The summary by zone report shows

the pixel count, percentage, and

total area in hectares of the class changes before and after the fire. The ‘Zone name’ represents

the classification in the ‘before’ image and data for each class that changed from that

classification is shown in the table. For example, in the first table below, 8009 pixels, or 720.81

hectares, were classified as ‘fire’ in the ‘before’ image and then classified as ‘fire’ in the ‘after’

image. The total area affected by the fire was found to be 33,453 hectares.

NDVI Processing

After Layer Stack

After NDVI

After Image Difference

“Highlight Change” file: Pixels with more than 10% increase are green, pixels with more than

10% decrease are red

Statistics for subset of image difference file generated from “after” and “present” NDVI files.

The “meat” of NDVI analysis is the Mean Value.

Statistics for subset of image difference file generated from “before” and “after” NDVI files.

Nonessential to NDVI analysis, rather a “crude accuracy assessment.”

DISCUSSION

As seen in the unclassified ‘before’ and ‘after’ images, the area of the burned forest is

relatively apparent. Once the supervised classification was applied, the area of the burned forest

is very well defined. Initially, the supervised classification was run using ‘maximum likelihood’

as the algorithm. This produced images with significantly more classification error than

‘minimum-distance’ and thus it was not used. However, some error is still apparent in both the

‘before’ and ‘after’ images. In theory, the ‘before’ image should not have any area classified as

'fire'. However, this is not the case as some small areas, especially near the margins of the photo

are seen as red. It is assumed that the error that occurred along the margins of the images are

likely due to sensor error. Classification error is also seen in the ‘after’ image where areas are

classified as 'fire' that are certainly outside the extent of the fire. These errors, however, were

mitigated by subtracting the areas misclassified in the ‘before’ image from the total area

calculation. While some misclassification still exists in the classified ‘after’ image, it is relatively

small with respect to the total area of the fire. The total area of the fire was calculated to be

33,453 hectares, which is fairly comparable with the 37,636 hectares reported by the US Forest

Service [1]. Some possible ways to reduce the amount of misclassification error in the future

would be to include more classes or to subset the image to focus more closely on the area of

interest.

The mean value for NDVI increase was 4%. What is the significance of this? To analyze

this statistic we can turn to what was previously referred to as a “crude accuracy assessment”: the

decrease in NDVI values caused by the fire. This number was 17%. On the surface, we can

conclude our analysis here: the reduction in NDVI values caused by the fire validates our

method, so we have confirmation that the area is indeed growing back, although slowly. But let

us go deeper. 4% increase, over three years, is an increase of approximately 1.3% per year. In

theory, 17% increase would constitute that area having achieved its previous level of vegetation.

Mathematically, this translates to 10 years from 2014, assuming a constant growth rate. Less

superficially, then, we could say: in 10 years, the acres affected by the Pagami Creek wildfire

will have regained their recreational and ecological value. Caveats, however, abound. First, the

data itself is questionable. The Inquire cursor in Imagine allows the user to browse pixel values

in an image. Browsing the values in the 4% file, we found an “observational average” of around

20% . The statistics reported a maximum of 40%. Why such a low number? Lakes, of which

there are no shortage, typically had values around zero, occasionally below. A more accurate, but

far more time-consuming analysis, would have involved eliminating lakes from the imagery.

Furthermore, with the countless factors affecting forest regeneration, how can we assume a

constant growth rate? We only measured two points on the line: in between, all sorts of things

may have happened. Even if the data is sound, however, can we trust it entirely? NDVI indicates

“greenness”. In 10 years we may have an area as “green” as it was before the fire, but it will not

be old growth forest. “Greenness” doesn’t translate directly to a resurgence in ecological and

recreational value. How much of the area’s value is dependent on its pure age? This is a question

for biologists and foresters.

REFERENCES

1. US Forest Service. Pagami Creek Wildfire. Available at

http://www.fs.usda.gov/Internet/FSE_DOCUMENTS/stelprdb5346343.pdf

2. James Wilson, MN-DNR Forester

3. US Forest Service. The Boundary Waters Canoe Area Wilderness. Available at

http://www.fs.usda.gov/detail/superior/specialplaces/?cid=stelprdb5202169

4. NASA Earth Observatory. Measuring Vegetation (NDVI & EVI). Available at

http://earthobservatory.nasa.gov/Features/MeasuringVegetation/printall.php

DIVISION OF WORK

● Location of imagery, initial processing trials, and research: Adam and John

● Processing, report, and presentation formation related to Goal 1: Adam

● Processing, report, and presentation formation related to Goal 2: John