GEOM30009 IMAGING THE ENVIRONMENT
Group Assignment 2
Assessing Burn Scars Using Satellite Imagery
Due for submission at 11:55 pm on Friday of Week 6
Value: 15% of Subject Mark
Objective
The aim of this assignment is to learn how to assess bushfire burn scars using Landsat images.
We will first visually compare pre-fire and post-fire images. Then, we compute two burn index
images to highlight burn scars. Finally, we create a differenced normalised burn ratio image,
map fire perimeters, and calculate the total area of burn scars.
Background
In January 2025 California suffered a devastating wildfire season. A series of wildfires burnt
vast areas across Southern California including Altadena and Palisades. Satellite imagery is a
useful resource that can help us assess burn scars and study areas of vegetation regrowth.
Landsat imagery is particularly suitable for assessing burn scars because of its repeated
coverage, ease of access, and spectral wavelengths. In this assignment, we will create burn
severity images using two different burn indices.
Burn area index
The Burn Area Index (BAI) highlights burnt land in the red to near-infrared (NIR) spectrum, by
emphasizing the charcoal signal. The index is computed based on the spectral distance from
each pixel to a reference spectral point resulting in an image where brighter pixels indicate
burnt areas. BAI is computed as:
Normalised burn ratio
The normalised burn ratio (NBR) highlights burnt areas in large fire zones greater than 500
acres. The formula is similar to a normalized difference vegetation index (NDVI), except that
it uses near-infrared (NIR) and shortwave-infrared (SWIR) wavelengths:
Pre-fire, healthy vegetation has a high NIR reflectance and a low SWIR reflectance. In contrast,
recently burnt areas have a relatively low NIR reflectance and a high SWIR reflectance.
Consequently, burnt areas appear dark in the NBR image whereas healthy vegetation appears
bright.
Data
Two Landsat 9 images of Southern California will be used for this tutorial. The first image was
acquired on 13 December 2024 and represents the pre-fire state of the area. The second
image was acquired on 15 February 2025 and represents the post-fire state of the area. Both
images can be downloaded as zip files from LMS under “6.2 - Assignment 2: Group report
submission”. Information about the resolution and wavelength bands of Landsat 9 images can
be found on the USGS website: https://www.usgs.gov/landsat-missions/landsat-9
Software
We use ENVI to open and process the satellite images. ENVI is available in the computer labs,
but you can also use it on your personal computer through myUniApps:
https://unimelb.cloud.com
Additional instructions:
https://studentit.unimelb.edu.au/myuniapps
Tasks
The assignment consists of three main tasks:
1. Visual analysis of burn scars;
2. Creating burn index images;
3. Creating differenced normalized burn Ratio image and mapping the burn scars.
You should be able to complete each task in one lab session. The whole assignment should
be completed within three weeks.
Task 1: Visual analysis of burn scars
In this task, you will first visually analyse the burn scars by comparing the pre-fire and post- fire imagery. Then you will pre-process and calibrate the images to prepare them for the
creation of burn index images.
Steps:
1. Unzip the two datasets. Make sure you unzip the files in two separate folders: one pre- fire dataset and one for post-fire dataset. Each dataset contains 13 bands.
2. Start ENVI. From the menu bar, select File > Open. Navigate to the pre-fire folder and
select the metadata file (*_MTL.xml), then click Open. You might get an error message
warning about some tiles not being displayed properly, please ignore it. Right-click on
the layer name in the Layer Manager and select Zoom to Layer Extent to see the whole
image.
3. From the menu bar, try different histogram stretching options to enhance the
radiometric quality of the image. You can also choose the stretching to be applied to
the whole image or your view extent only. Choose the option that best suits your visual
analysis of the image.
4. Use Zoom and Pan tools to inspect the different parts of the image. Can you find
Bakersfield, Rogers dry lake, and City of Los Angeles in the image?
5. Open the post-fire image set by following the same instructions as above. Choose the
histogram stretching method that best suits you.
6. Compare the pre-fire and post-fire images by turning the top layer on and off. What
are the main differences between the two images? Can you identify burn scars?
7. In the Layer Manager, note that ENVI has automatically assigned the Red, Green, and
Blue bands to the RGB colour channels to create a natural colour visualisation. You can
try different band combinations to create a false colour visualisation. To do so, open
File > Data Manager. Here you can find all wavelength bands, as well as some
additional outputs provided with the data, such as the Aerosol Optical Thickness (AOT)
map and several others. We won’t be using these additional outputs for this
assignment but will only use the bands. You can create different false colour
visualisations by selecting which bands are assigned to Red, Blue and Green channels.
First, open the band selection drop-down on the bottom part of the Data Manager
window. Then, click on the bands on the list above to assign them to different
channels, but make sure the selected bands have the same spatial resolution. Try to
create a false colour visualisation of the post-fire image by selecting band B7 (SWIR 2)
for the Red channel, B5 (Near Infrared) for the Green channel, and band B2 (Blue) for
the blue channel. This is a band combination that is often used for visual analysis of
burn scars.
Save your best visualisations of each image and include these in your report. Make sure all
the images are clear and are accompanied with the necessary additional information (e.g.,
colour bar for pseudo-colour images and band combination for false-colour images). While
the visual observation is helpful for qualitative analysis of burn scars, in most cases a
quantitative analysis of the extent of burn scars is required. In the next task, we will create
burn index images which will enable quantitative analysis of burn scars.
Task 2: Creating burn index images
Before creating the burn indices, we need to convert the pixel values to the top-of- atmosphere reflectance. Water pixels can interfere with this process. Therefore, we first need
to mask water pixels to exclude them from the conversion.
2.1 Creating a Water Mask
To create a water mask, we define a region of interest (ROI) around water bodies in the near- infrared (NIR) band, where water has a very low reflectance.
Steps:
1. Right-click on the Post Fire image
(LC09_L1TP_041036_20250215_20250215_02_T1_MTL) in the Layer Manager and
select New Region of Interest.
2. Change the ROI Name to Water ROI.
3. In the ROI Tool, click the Threshold tab.
4. Click the Add New Threshold Rule button.
5. In the File Selection dialog, select the band B5 (NIR) from the post fire image and click
OK. A histogram of the NIR band is displayed in the Choose Threshold Parameters
dialog. You will identify the water pixels by selecting the range of low pixel values in
the histogram.
6. Click and drag the red line on the left edge of the plot toward the right, covering the
data values from 0 to approximately 10000.
7. Click the Preview option. The pixels that fall within the defined range are highlighted
in red.
8. Some of the pixels in the burn scars also have extremely low NIR values, but we do not
want to mark these pixels. You will need to move the slider in the histogram so that
you highlight water pixels but no other features. To make it easier to move the slider
and to see the histogram in more detail, hold down your middle mouse button to draw
a box to zoom into.
9. Move the right-most red slider, until only water pixels are highlighted in the image
and no other features.
10. Click OK in the Choose Threshold Parameters dialog. You may close the ROI Tool now.
11. In the search window of the Toolbox, type build raster mask and double-click the
Build Raster Mask tool name that appears.
12. In the Build Raster Mask Input File dialog, select the metadata file of the Post Fire
Image, which contains 7 optical bands, and click OK.
13. Click the Options drop-down list in the Mask Definition dialog and select Import ROIs.
14. Select the Water ROI from the list, and click OK.
15. Click the Options drop-down list again in the Mask Definition dialog and choose
Selected Areas "Off". By doing this, the water pixels will have values of 0, and all other
pixels will have values of 1.
16. Enter the output filename PostFireWaterMask.dat.
17. Click OK in the Mask Definition dialog. The mask image is displayed. If you get a
Warning about the mismatch of spectral values in the header, ignore it.
When you apply this mask to the image in the next step, the black pixels (values of 0) will be
excluded from further processing, while the white pixels (values of 1) will be processed.
2.2 Converting pixel values to reflectance
To create spectral index images such as Burn Area Index and Normalized Burn Ratio, the
source images should be calibrated to top-of-atmosphere (TOA) reflectance, where pixel
values range from 0 to 1.0 or 0 to 100.
Steps:
1. In the search window of the Toolbox, type calibration. Double-click the Radiometric
Calibration tool name that appears.
2. In the File Selection dialog, select the post-fire multispectral image set, then click
Mask … and select your PostFireWaterMask file, and click OK.
3. In the Radiometric Calibration dialog, select Top-of-Atmosphere Reflectance from
the Calibration Type drop-down list.
4. Keep the default selections for all other settings.
5. Enter an output filename of PostFireReflectance.dat and click OK. Wait for the
Radiometric Calibration process to complete.
2.3 Computing burn indices
To create the burn index images we will use ENVI's Spectral Indices tool. You must run this
tool each time you create an index image.
Steps:
1. In the search window of the toolbox, type spectral indices. Double-click the Spectral
Indices tool name that appears.
2. In the File Selection dialog, select the file PostFireReflectance.dat, and click OK.
3. In the Index list, select Burn Area Index.
4. In the Output Raster field, enter a filename of PostFireBAI.dat and click OK.
5. Repeat Steps 1-4 for the Normalized Burn Ratio (output filename: PostFireNBR.dat).
Notice that the brighter pixels in the Burn Area Index image indicate burnt areas, while darker
pixels indicate burnt areas in the Normalized Burn Ratio images. Use the Zoom and Pan tools
in the toolbar to further explore the images. How are the BAI and NBR images different from
each other? Does one separate burnt areas better than the other? Save each index image and
include these in your report.
Task 3: Creating differenced normalized burn Ratio image and mapping the burn scars
A differenced normalized burn ratio (ΔNBR) is created by subtracting the post-fire NBR image
from the pre-fire NBR image. It highlights burn-severity as brighter pixels represent larger
differences between the post-fire and pre-fire NBR images. To create a ΔNBR image, you will
need to apply all the pre-processing and calibration steps for the pre-fire image that you did
for the post-fire image and create a PreFireNBR.dat image file.
Steps to create a ΔNBR image:
1. Load PreFireNBR.dat and PostFireNBR.dat if they are not loaded already (using the
File > Open).
2. Before you can subtract one image from the other, both must be in the same spatial
grid. While both images are in the same projection, they might be offset by a few
pixels. Layer stacking will ensure that they are in a common grid. In the search window
of the Toolbox, type build layer stack. Double-click the Build Layer Stack tool name
that appears.
3. In the Build Layer Stack dialog, click the Input Rasters Browse (…) button.
4. Use the Ctrl key to select the files PreFireNBR.dat and PostFireNBR.dat. Click OK.
a. IMPORTANT: Note the order the above layers appears in the window. If
PreFireNBR.dat is above PostFireNBR.dat, that means Band 1 will be PreFire
and Band 2 will be PostFire in step 10 below.
5. Keep all of the remaining parameters at their default settings.
6. Enter an output filename of NBRLayerStack.dat, and click OK.
7. In the ENVI Toolbox, expand the Band Algebra folder and double-click the Band Math
tool.
8. In the Enter an expression field, enter float(b2 - b1).
9. Click Add to List, then click OK.
10. With B1 - [undefined] selected in the Variables used in expression dialog, click Layer
(NBRLayerStack.dat > Band (PostFireNBR.dat from Step 4 above).
11. Select B2 - [undefined].
12. Click Layer (NBRLayerStack.dat > Band (PreFireNBR.dat from Step 4 above).
13. Select Output Result to: “Memory” and click OK.
Save your differenced NBR image and include it in your report. Notice how burnt areas are
highlighted by brighter pixels in the image. To create a map of fire severity we use the burn
severity categories recommended by the U.S. Geological Survey FIREMON program as listed
in the table below:
ΔNBR Values Burn Severity
< -0.25 High post-fire regrowth
-0.25 to -0.1 Low post-fire regrowth
-0.1 to 0.1 Unburned
0.1 to 0.27 Low-severity burn
0.27 to 0.44 Moderate- to low- severity burn
0.44 to 0.66 Moderate- to high-severity burn
> 0.66 High-severity burn
Steps to create a burn severity map:
1. Right-click on the Memory layer in the Layer Manager and select New Raster Color
Slice.
2. Select the Band Math band name under Memory, and click OK.
3. In the Edit Raster Color Slices window that pops up, click the Clear Color Slices button.
4. Click the Add Color Slice and create 7 colour slices corresponding to the 7 burn
severity categories. For each colour slice enter the minimum and maximum value from
the burn severity table and choose a suitable colour. Then click OK.
5. In the Layer Manager, you can deselect some of the colour slices to display the
different levels of burn severity. If you turn on the post-fire image layer the selected
burn severity map will be overlaid on the post-fire image.
6. Right click on Slices and select Statistics for All Color Slices. Note down the statistics
for each burn severity category.
7. Important: Note that the above steps on Colour Slice will not be saved to the ENVI
session, so it is important to finalise them and record the results (or take screenshots)
as part of the report discussion.
Save your burn severity maps and include these in your report. Also compute the total burnt
area based on the statistics you obtained above and include that in your report.
Submission
This is a group assignment. Each group submits one group report. All group members are
expected to contribute to the assignment and the report.
We expect you to write a 1500 words scientific report and include the following content. You
can add more words if necessary but please keep it below the word limit, which is 2500 words.
1. Provide a proper introduction. Describe the purpose of this tutorial and the role of satellite
images in assessing bushfire burn scars.
2. In the Methods section describe briefly the process you performed to complete each of the
three tasks.
3. In the result section include the result of each process and provide an analysis of your
results. When including images, make sure each image is clear and is accompanied with the
necessary additional information (e.g., colour bar for pseudo-colour images and band
combination for false-colour images).
4. In the Discussion section address the following questions:
i. How does vegetation appear in your visualisation of pre-fire and post-fire images in
Task 1? How do burn scars appear in the image? Why?
ii. Which of the two burn indices highlights burn scars better? Why?
iii. Are there alternative indices for identifying burn scars? How do these compare
(advantages/disadvantages) to the indices you created in this assignment? You may
support your answer by citing reliable scietific sources.
iv. How realistic is your burn severity map? How can the accuracy of your map be
verified?
v. How realistic is your estimate of the total area of burn scars? How do you verify your
estimate?
5. Provide a clear and concise conclusion summarizing your findings.
6. Provide a reference list if you used external sources to support your arguments in the
report. You may use any of the referencing styles commonly used at the University, but be
consistent (https://library.unimelb.edu.au/recite/referencing-styles).
7. Statement of contribution – include a statement describing the contribution of each
group member to the assignment tasks and the report.
Submit a digital version of your report via LMS and in pdf format only.
Evaluation
The assessment of group assignments consists of two parts: the group report mark, and the
peer evaluation mark. Through the peer evaluation, each group member will anonymously
rate the contribution of their other group members to the project. After the peer evaluation
(PE), each group member receives an individual mark for the assignment calculated as:
Individual assignment mark = group report mark * (total PE mark/ average PE total)
Group report marking rubric
Appropriate length and proper formatting
5%
Proper introduction
5%
Proper Method
10%
Two burn index images
20%
The differenced NBR image
10%
Map of burn severity
10%
Calculation of fire’s acreage
10%
Questions answered and properly discussed 25%
Logical conclusions
5%
Peer evaluation (PE)
Peer evaluation ensures that your individual assignment mark reflects your contribution to
the group report, whether it was average, below average, or above average. After the group
report is submitted, each group member will provide feedback on the other members of their
group. More details about the PE process and the rubric used can be found on LMS.
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