代写GEOM30009、代写c++,Java编程

日期：2023-04-03 09:28

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 bush fire burn scars using Sentinel images.

We will first visually compare pre-fire and post-fire Sentinel images. Then, we compute two

burn index images to highlight burn scars. Finally, we create a difference normalised burn

ratio image, map fire perimeters, and calculate the total area of burn scars.

Background

In December 2019 and January 2020 Australia suffered a devastating bush fire season.

Hundreds of fires burnt vast areas across New South Wales, Victoria, and Australian Capital

Territory. Satellite imagery are a useful resource that can help us assess burn scars and study

areas of vegetation regrowth. Sentinel 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 Sentinel 2B images of Kosciuszko National Park will be used for this tutorial. The first

image was acquired on 26 December 2019 and represents the pre-fire state of the park. The

second image was acquired on 4 February 2020 and represents the post-fire state of the park.

Both images can be downloaded as zip files from LMS under “6.2 - Assessment Task 2: Group

report submission”. Information about the resolution and wavelength bands of Sentinel 2B

images can be found on the Sentinel Copernicus website:

https://sentinels.copernicus.eu/web/sentinel/user-guides/sentinel-2-

msi/resolutions/spectral

Software

We use ENVI to open and process the Sentinel 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. Image calibration and 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 Sentinel 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 (MTD_MSIL2A.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

Cooma, Mount Kosciuszko peak, and southern suburbs of Canberra 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 13 bands (B1-B13), as well as some

additional outputs provided with the Sentinel 2B 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 B12 (SWIR)

for the Red channel, band B8A (Narrow NIR) 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: Image calibration and creating burn index images

Before creating the burn indices, we need to calibrate the images by applying atmospheric

correction to convert the pixel values to the top-of-atmosphere reflectance. Water pixels can

interfere with calibration and atmospheric correction. Therefore, we first need to mask water

pixels to exclude them from the atmospheric correction.

2.1 Creating pre-fire and post-fire layer stacks

You might have noticed in the previous task that Sentinel data comes in three different spatial

resolutions, 10 m, 20 m and 60 m. In the following steps we will need to use some bandss

with a 10 m spatial resolution and some bands with a 20 m spatial resolution. For example,

we will be calculating a NBR, which from the equation on page 2 uses WNIR band (B12, spatial

resolution 20 m) and NIR band (B8, spatial resolution 10 m).

In order to use bands with different spatial resolutions, we must first stack them together in

a layer stack which will resample and reproject the bands to a common spatial grid.

Steps:

1. In the search window of the Toolbox, type build layer stack. Double-click the Build

Layer Stack tool name that appears.

2. In the Build Layer Stack dialog, click the Input Rasters Browse (…) button.

3. Use the Ctrl key to select the files 10m-S2MSI2A 2020-02-04 and 20m-S2MSI2A 2020-

02-04. Click OK.

4. The order of input rasters matters. The first item in the list defines the pixel size of the

output, and the bottom layers with different pixel sizes will be either up-sampled or

down-sample to match it. You are free to choose the order, which will determine the

spatial resolution. Include this decision in the report and explain why you made it.

5. Keep all of the remaining parameters at their default settings.

6. Enter an output filename of PostFireStack.dat, and click OK.

7. Repeat steps 1-7 for the pre-fire image and name the output PreFireStack.dat.

2.2 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 PostFireStack 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 B8 (NIR) 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 1,000.

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 PostFireStack, 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.

2.3 Calibrating OLI Bands 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 Reflectance from the Calibration Type

drop-down list.

4. Keep the default selections for all other settings. Do not click the Apply FLAASH

Settings button.

5. Enter an output filename of PostFireReflectance.dat and click OK. Wait for the

Radiometric Calibration process to complete.

2.4 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.

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

(Normalized Burn Ratio: PostFireNBR.dat).

11. Select B2 - [undefined].

12. Click Layer (Normalized Burn Ratio: PreFireNBR.dat).

13. Enter an output filename of DifferencedNBR.dat 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 DifferencedNBR.dat layer in the Layer Manager and select New

Raster Color Slice.

2. Select the Band Math band name under DifferencedNBR.dat, 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.

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. How are the calibrated images different from the original images?

iii. Which of the two burn indices highlight burn scars better? Why?

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 research. You may use

any of the referencing styles commonly used at the University, but be consistent

(https://library.unimelb.edu.au/recite/referencing-styles).

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.