landsat image processing for post-fire analysis · landsat image processing for post-fire analysis...

RSAC | Landsat Image Processing for Post-Fire Analysis | October 2015 1

Landsat Image Processing for Post-fire Analysis

Introduction

Landsat multispectral imagery is useful for many types of change detection analysis, including burn severity assessments. This exercise will guide you through the process of estimating wildfire burn severity by analyzing Landsat images acquired before and after the fire. Top-of-atmosphere (TOA) reflectance data for a suitable pair of Landsat 8 scenes have been downloaded from the USGS Earth Resources Observation and Science (EROS) Center via the EROS Science Processing Architecture (ESPA) on demand interface. The Landsat data, the ESPA User Guide and other ancillary materials are provided in the data bundle for this exercise.

Objectives • Create a post-fire normalized burn ratio (NBR) raster • Generate a differenced normalized burn ratio (dNBR) raster from pre-and post-fire NBR rasters • Estimate burn severity class thresholds and produce a 4-class thematic burn severity raster • (Optional) Generate secondary burn severity products (vector layer and KMZ) and rescaled 8-bit

dNBR

Required Data • Myers_Burn_Boundary—shapefile delineating the perimeter of the 2013 Myers fire • Landsat imagery and derivatives

o LC80420282013236LGN00_toa.tif—pre-fire TOA-corrected reflectance layer stack (Landsat 8 bands 1–7)

o LC80420282014255LGN00_toa.tif—post-fire TOA-corrected reflectance (layer stack with bands 1–7, and bands 5 and 7 as separate rasters)

o LC80420282014255LGN00_toa_band*.tif—post-fire TOA-corrected reflectance, bands 5 and 7 as separate rasters

o Myers_80420282013236_nbr.tif—normalized burn ratio from the pre-fire imagery in the vicinity of the Myers fire

• Rain_Myers_SBS4.tif—Field-calibrated soil burn severity

Prerequisites • ArcMap 10.x with Spatial Analyst extension • Google Earth

http://espa.cr.usgs.gov/index/

Post-fire Mapping with Landsat 2

Table of Contents Load Data and Set Up Geoprocessing Environment ............................................................ 2

Create Burn Indices (NBR, dNBR) ......................................................................................... 5

Conduct Burn Severity Analysis and Classification ............................................................... 9

Create Vector and KMZ Versions of Burn Severity (Optional) ........................................... 13

Generate 8-bit Raster (optional) ........................................................................................ 13

Load Data and Set Up Geoprocessing Environment A. Pre-processing Landsat imagery from ESPA is delivered as a compressed data bundle (gzipped tar file). The following pre-processing steps were completed in preparation for this exercise:

• Decompress the Landsat data bundles o This step can be accomplished with any software package that is capable of handling

gzip and tar files • Create a multispectral layer stack (Landsat 8 bands 1–7)

o This can be done in ArcMap using the Composite Bands tool (Data Management Tools | Raster | Raster Processing | Composite Bands)

• Create the pre-fire NBR o You will perform a parallel process to create a post-fire NBR as part of this exercise o Note that ESPA offers an NBR raster dataset; however, it is based on surface reflectance

rather than on the top-of-atmosphere reflectance used in this exercise

B. Start ArcMap 1. Start ArcMap by clicking on the Start button and navigating to All Programs | ArcGIS |

ArcMap. 2. If prompted with a dialog box asking whether you would like to open a new map or an

existing map, click Cancel to start with a blank map document.

C. Add data

1. Click on the Add Data button and navigate to C:\Temp\ARSET\Post_Fire_Burn_Sev in Folder Connections.

i. If neither the exercise folder (C:\Temp\ARSET\Post_Fire_Burn_Sev) nor its root directory (C:\) appears under Folder Connections, connect to it now.

(a) Click Connect to Folder and navigate to C:\Temp\ARSET. (b) Select the Post_Fire_Burn_Sev folder and click OK.


2. Select the burn perimeter (Myers_Burn_Boundary.shp) and click Add.

3. Change the display properties of the burn boundary shapefile.

i. Left-click on the burn perimeter symbol in the table of contents to open the Symbol Selector dialog.

ii. Change the symbology to hollow (no fill color) and set the outline width and color to your

preference. Solar yellow works well for this exercise. iii. Click OK.

4. Add the raster data.


i. Click on the Add Data button and navigate to C:\Temp\ARSET\Post_Fire_Burn_Sev\Rasters.

ii. Add the following layers: • LC80420282013236LGN00_toa.tif • LC80420282014255LGN00_toa.tif • Myers_80420282013236_nbr.tif

NOTE: The file naming convention for the ESPA data includes the sensor (LC8 = Landsat 8 optical and thermal bands), scene location (path 42, row 28 in the World Reference System 2 (WRS2)), image acquisition date (day 236 of 2013), and an identifier for the satellite download site (LGN00). The suffixes provide information about additional processing (toa = top of atmosphere reflectance, as distinguished from surface reflectance; nbr = normalized burn severity).

5. Set display properties of the two multispectral layers to SWIR-NIR-Red. i. Left-click on the Red color patch and select Band_7. ii. Left-click on the Green color patch and select Band_5. iii. Left-click on the Blue color patch and select Band_4.

NOTE: This band combination displays healthy vegetation as green and burned areas as varying shades of red.

6. Compare the three images visually. i. Right-click on the burn perimeter layer in the table of contents and choose Zoom to Layer.

(This probably already happened automatically when you added the burn perimeter to the map document.)

ii. Toggle between the pre-and post-event images and observe the differences, particularly inside the burn perimeter.

iii. Toggle between the pre-fire NBR and the pre-fire reflectance (the 2013 236 images) and observe the correspondence. Areas of sparser vegetation (browns and reds in the reflectance image) are darker in the NBR.

7. Save the map document to a folder of your choice (Ctrl + S or File | Save from the main menu).

8. If desired, open Properties of the raster layers and observe the metadata under the Source tab. The raster datasets are in signed 16-bit integer format with -9999 as the NoData value.

D. Set up ArcMap 1. Enable the Spatial Analyst extension.

i. Click Customize | Extensions… from ArcMap’s main menu. ii. Place a checkmark next to Spatial Analyst listed in the Extensions dialog. iii. Click Close to dismiss the Extensions dialog.


2. Set up the geoprocessing environment (see graphic below). i. Click Geoprocessing | Environments… from the ArcMap main menu. ii. Expand Workspace.

iii. Use the Browse button to set the Current Workspace and Scratch Workspace to the Rasters folder in the exercise folder.

iv. Expand Processing Extent. v. Set Extent to “Same as layer Myers_80420282013236_nbr.tif”. vi. Set Snap Raster to “Myers_80420282013236_nbr.tif”.

vii. Scroll down and expand Raster Analysis. viii. Set Cell Size to “Same as layer Myers_80420282013236_nbr.tif”. ix. Click OK to close the Environment Settings dialog.

Create Burn Indices (NBR, dNBR) Indices are commonly used in remote sensing analysis to highlight desired characteristics and to improve standardization. The normalized burn ratio (NBR) is defined as the ratio of the difference between the NIR and SWIR bands to the sum of the same bands:


NBR = (NIR – SWIR)/(NIR + SWIR)

The differenced NBR (dNBR) is simply the difference between two NBR rasters; in our case, pre- and post-fire NBR:

dNBR = (pre-fire NBR) – (post-fire NBR)

Actual reflectance is a number between 0 and 1. However, reflectance, NBR and dNBR are often rescaled for convenient integer representation. ESPA Landsat-8 reflectance is scaled by 10,000 and stored in 16-bit signed integer format. Thus, a reflectance value of 0.500 is represented as 5000 in the ESPA data. In this exercise, we follow the convention of rescaling by 1000, which means that NBR values will lie in the range of ±1000 and dNBR values in the range of ±2000.

A. Create the post-fire NBR 1. Add the individual post-fire SWIR and NIR bands in the Rasters folder to your ArcMap

document. • LC80420282014255LGN00_toa_band5.tif (NIR) • LC80420282014255LGN00_toa_band7.tif (SWIR)

NOTE: Although these bands are included in the layer stacks, it is convenient to use individual bands in raster calculations.

2. Open the ArcToolbox window. 3. Create the NBR.

i. Open the Raster Calculator (Spatial Analyst Tools | Map Algebra | Raster Calculator). ii. Copy and paste the following expression into the tool (see graphic below):

Int(Float("LC80420282014255LGN00_toa_band5.tif" -

"LC80420282014255LGN00_toa_band7.tif") / ("LC80420282014255LGN00_toa_band5.tif" + "LC80420282014255LGN00_toa_band7.tif")*1000)

NOTE: You can build the map algebra expression using the calculator GUI, but it is provided here for convenience. The expression ensures division is performed as a floating point operation and the result converted to an integer value in the range [-1000, 1000].

iii. Save the results (Output raster) as “C:\Temp\ARSET\Post_Fire_Burn_Sev\Rasters\Myers_80420282014255_nbr.tif”.


iv. Click OK to run the process. It may take a couple of minutes. The new raster will be

added automatically to the map document.

B. Create the dNBR 1. Open the Raster Calculator as before. 2. Copy and paste the following expression into the tool:

"Myers_80420282013236_nbr.tif" - "Myers_80420282014255_nbr.tif"

3. Save the output raster as “C:\Temp\ARSET\Post_Fire_Burn_Sev\Rasters\Myers_80420282013236_80420282014255_dnbr.tif”.

4. Click OK to run the process. 5. Change the image “stretch” to improve visualization of the dNBR.

i. Open the Layer Properties dialog for the dNBR by double-clicking on the layer in the map table of contents.

ii. Under the Symbology tab, change the Stretch Type to Minimum-Maximum. iii. Click OK.


6. Visually compare the dNBR to the pre-and post-event NBR by toggling between the three

layers.

7. Use the Identify tool to compare pixel values in the two NBR rasters with those in the dNBR. Brighter pixels in the dNBR represent greater change in NBR (higher burn severity). You can also confirm the mathematical relationship between the three layers.

C. Rescale the dNBR 1. Open the Raster Calculator again. 2. Copy and paste the following expression into the tool:

("Myers_80420282013236_80420282014255_dnbr.tif " + 275) / 5

NOTE: This transformation converts the 16-bit data into a range of values that can usually be represented reasonably well as an 8-bit integer (0 to 255). Extreme values may fall outside the 8-bit range. If the rescaled raster is saved in 8-bit unsigned format, extreme values will be collapsed into the valid range—negative values become zero and values greater than or equal to 256 become 255.

3. Set the output raster to “C:\Temp\ARSET\Post_Fire_Burn_Sev\Rasters\ Myers_80420282013236_80420282014255_dnbr_scaled.tif”.

4. Click OK to run the process. 5. Change the image “stretch” to improve visualization of the rescaled dNBR.

i. Double-click on the new scaled dNBR in the map table of contents. ii. Under the Symbology tab, change the Stretch Type to Minimum-Maximum. iii. Click OK.


Conduct Burn Severity Analysis and Classification

The dNBR displays degrees of change between the pre-fire NBR and the post-fire NBR. This information is helpful when estimating burn severity and setting the breakpoints between the burn severity classes (unburned, low, moderate and high). In addition to dNBR values, there are many factors to consider in determining burn severity thresholds, including land cover type and vegetation density. For this exercise, we will rely largely on general information about the vegetation and patterns in the dNBR.

A. Assess vegetation type and density 1. Open the burn perimeter in in Google Earth by double-clicking on

Myers_Burn_Boundary.kmz in the exercise folder. 2. Observe the vegetation in Google Earth’s high resolution imagery and compare it to the

dNBR. Fairly dense evergreen forest is present in much of the brighter areas in the dNBR. We would expect areas with dense forest and large dNBR values to have high severity.

B. Determine burn severity thresholds 1. Duplicate the scaled dNBR layer using the Copy and Paste Layers commands in the Edit

menu. We will modify the symbology of the copy and use the original for reference. 2. Open Properties for the new layer and move the dialog box so that both it and the burned

area are visible. 3. Under the Symbology tab, change the Show type to Unique Values. 4. Change the Color Scheme to the black-to-white color ramp (high values are white).

5. Modify the color used for each raster value to produce four plausible burn severity classes.

The following colors are recommended: • Unburned: Fir Green • Low severity: Tourmaline Green • Moderate severity: Solar Yellow • High severity: Mars Red

NOTE: “Thresholding” is somewhat subjective. This part of the exercise is intended to give you a general feel for the process.

i. Identify the unburned/low class boundary. (a) Select the first few dozen entries in the Symbol table—click the first row, then scroll

down, hold the Shift key and click on value 60. (b) Right-click on one of the selected rows and choose Properties for selected Colors…


(c) Select a color to represent unburned pixels (Fir Green).

(d) Click Apply and observe the change in the raster. (e) Repeat the process, changing a few values at a time until the boundary between the

area inside and outside the fire is clear. It is not necessary to have every pixel outside the perimeter included.

(f) Write down the highest value in your “unburned” class. This is the unburned/low threshold value. (It will be around 70.)

NOTE: It may be helpful to display the greyscale dNBR in a separate map document so that you can compare it with your classified version as you work.

ii. Identify the moderate/high class boundary. (a) Note the brightest areas within the dNBR—these will be identified as high severity. (b) Scroll to the bottom of the values table (highest dNBR values).


(c) Working your way from the highest values, apply the high burn severity color (Mars Red) to successive values until the bright areas in the dNBR have been colored.

(d) Write down the highest value that you didn’t assign to the “high severity” class. This is the moderate/high threshold value.

iii. Identify the low/moderate class boundary. (a) Observe the patterns in the remaining (uncolored) pixels—are there visual breaks

between darker and lighter areas? (b) Continuing from the high end of the unassigned values, apply the moderate burn

severity color (Solar Yellow) to successive values until the brighter remaining areas have been colored.

(c) Write down the highest value that you didn’t assign to the “moderate severity” class. This is the low/moderate threshold value.

(d) Apply the low burn severity color (Tourmaline Green) to the remaining values. 6. Click OK to close the Properties dialogue box. 7. Compare the classified version of the dNBR to the greyscale version. If you see any areas that

prompt a change in class breaks, modify the symbology by switching the color of values near the breakpoints.

C. Reclassify dNBR to thematic burn severity 1. Open the Reclassify tool (Spatial Analyst Tools | Reclass | Reclassify). • Input raster: Myers_80420282013236_80420282014255_dnbr_scaled.tif • Reclass field: Value • Output raster: Myers_80420282013236_80420282014255_dNBR_4class.tif

NOTE: You can use either the greyscale or colored version of the scaled dNBR layer for this step. The symbology applied during the previous step affects the way the raster is displayed but does not change pixel values. In contrast, this step—Reclassify—creates a new raster dataset with new pixel values.

2. Setup the classification. i. Click Classify... ii. Set Classification Method to Equal Interval. iii. Set Classes to 4. iv. Change the first three Break Values to the burn severity thresholds determined above by

clicking on each value and typing the new value. Use the “unburned/low” threshold for the first break value, the “low/moderate” threshold for the second break, and the “moderate/low” threshold for the third break. Leave the highest value unchanged .


v. Click OK to close the classification dialog box.

3. Click OK to run the tool.

D. Symbolize the burn severity 1. Right-click on each of the color patches to change the four class colors (1 = Fir Green, 2 =

Tourmaline Green, 3 = Solar Yellow, 4 = Mars Red). If desired, change the value labels, as well (“Unburned”, “Low”, “Moderate”, “High”).

E. Compare to field-calibrated soil burn severity You have created a reasonable representation of the Myers burn severity based on Landsat imagery. The soil burn severity raster included in this exercise has been calibrated based on field measurements. It is instructive to compare our estimate with the field-calibrated data. 1. Add the field-calibrated soil burn severity raster (Rain_Myers_SBS4.tif) from the Rasters

folder. It also includes the nearby Rain fire. 2. Change the symbology (class colors) to match that of your burn severity layer.


3. Compare the two layers by toggling between them. For example, did you estimate more or less high severity than the field-calibrated version?

Congratulations! You have successfully completed the primary portion of the exercise. If you have time, continue with the following optional parts.

Create Vector and KMZ Versions of Burn Severity (Optional)

If a vector version of the burn severity is desired, it is easy to convert the raster dataset into shapefile in ArcMap. For users who don’t have access to ArcGIS or a similar application, it may be helpful to supply a KMZ version, which can be easily displayed in Google Earth.

A. Convert the thematic burn severity raster to a vector layer 1. Open the Raster to Polygon tool (Conversion Tools | From Raster | Raster to Polygon). • Input raster: Myers_80420282013236_80420282014255_dNBR_4class.tif • Reclass field: Value • Output polygon features:

C:\Temp\ARSET\Post_Fire_Burn_Sev\Myers_80420282013236_80420282014255_dNBR_4class.shp

• Simplify polygons: Unchecked 2. Click OK. 3. If desired, symbolize the shapefile based on the GRIDCODE attribute so that it matches the 4-

class raster.

B. Create a KMZ 1. Open the Layer to KML tool (Conversion Tools | To KML | Layer to KML). • Layer: Myers_80420282013236_80420282014255_dNBR_4class.tif • Output File:

C:\Temp\ARSET\Post_Fire_Burn_Sev\Myers_80420282013236_80420282014255_dNBR_4class.kmz

• Leave all other default settings. 2. Click OK. 3. You can view the KML in Google Earth by double-clicking the resulting KML file.

Generate 8-bit Raster (Optional) The raster creation tools we have used in this exercise produce output with a pixel depth of 32 bits. If desired, we convert the output to rasters with a different pixel depth.

A. Convert raster from 32-bit signed to 8-bit unsigned format 1. Open the Copy Raster tool (Data Management Tools | Raster | Raster Dataset | Copy

Raster).


• Input Raster: dNBR_80420282013236_80420282014255_scaled.tif • Output Raster Dataset:

C:\Temp\ARSET\Post_Fire_Burn_Sev\Rasters\Myers_80420282013236_80420282014255_dnbr_256.tif

• NoData Value: 0 • Pixel Type: 8_BIT_UNSIGNED • Leave other default values unchanged

2. Click OK to run the process. 3. Check the High and Low values shown for the new layer in the Table of Contents. The low

value should be at least 0 and the high value at most 255.

Congratulations! You have successfully completed this exercise.

landsat image processing for post-fire analysis · landsat image processing for post-fire analysis...

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