Mapping Wildfire Burn Severity in Southern CaliforniaPart 2: Image Processing & Analysis

STUDENT HANDOUT

ObjectivesYou will use remotely sensed data and Esri ArcGIS 10.2 software for the area of the “Old Fire” to:

1. Calculate a radiance, then a reflectance image for dNBR 2. Calculate the wildfire burn severity, using pre-fire and post-fire Landsat images. 3. Clip the burn severity data to the burn perimeter and calculate the number of acres each of low,

moderate and high burn severity. 4. Overlay an ownership layer to calculate the number of acres of Forest Service ownership in the

different burn intensity categories. Fill out a chart showing the results, with the number of acres of land owned by the Forest Service in each of the burn severity categories.

5. Throughout the activity you will answer questions or fill out a chart using the worksheet provided by your instructor.

6. (Optional) Produce a layout of your final results

Data Required: Landsat 5 images : November 19, 2003 and November 16, 2002. You should have these

downloaded from GloVis in Part I, in your Landsat folder. OldFire_perimeter polygon, downloaded and extracted from FRAP website, in Part I, stored in

RS_Fire.gdb AreaOfInterest polygon, created in Part I, stored in RS_Fire.gdb

Problem StatementImagine it is mid-November 2003, and you are a GIS Manager for the United States Forest Service (USFS). You have been asked to deliver a burn severity map of the 2003 “Old Fire” wildfire in the San Bernardino Mountains, which burned over 91,000 acres in less than two weeks, from October 25 to November 2, 2003. Within the final burn perimeter, how many acres have burned in each of the burn severity categories: unburned, low, moderate and high? How many acres in each of these categories were owned by the USFS? What percent of the total burn perimeter was USFS land?

Activity OverviewThese types of issues are very important to Forest Service personnel; the resulting draft burn severity maps are used by Burned Area Emergency Response (BAER) teams in the field as they evaluate the potential hazards created by soil changes and vegetation mortality - such as landslides and flooding. They are also used in research to determine the degree of environmental change caused by wildfire and to understand how different ecosystems respond to and recover after fires. National policies, as stated in the National Fire Plan and Wildland Fire Leadership Council (WFLC), require information about long-term trends in burn severity and recent burn severity impacts for land management planning over large, often remote regions over long time intervals.

Developed by the Integrated Geospatial Education and Technology Training (iGETT) project, with funding from the National Science Foundation (DUE-0703185) to the National Council for Geographic Education. Opinions expressed are those of the author and are not endorsed by NSF. Available for educational use only. See http://igett.delmar.edu for additional remote sensing exercises and other teaching materials. Created 2008; last modified April 2012.

How would you go about finding the answers to these questions and meeting the request for a burn severity map? What is burn severity? What methodology would you use? What kinds of measurements would you need to make? What tools would you need to use? For a remote or large fire, such as the “Old Fire” collecting all the necessary data through field measurement is usually too time consuming and expensive for available funding and personnel. Remote sensing data and techniques provide cost effective alternatives, and when and where possible, field reconnaissance is used to field check the resulting data. In the case of the “Old Fire,” suitable imagery wasn’t available soon enough, so the initial burn severity mapping was done without the remotely sensed burn severity maps, which were produced later.

Background What is burn severity? For this activity we will use the definition that is in the “Glossary of Wildland Fire Terminology:”

A qualitative assessment of the heat pulse directed toward the ground during a fire. Burn severity relates to soil heating, large fuel and duff consumption, consumption of the litter and organic layer beneath trees and isolated shrubs, and mortality of buried plant parts. (http://www.nwcg.gov/pms/pubs/glossary/b.htm#Burning)

The use of remote sensing to support national assessment of burned areas began in the mid-1990’s (http://www.nrmsc.usgs.gov/science/fire/burn_severity). In 2001 a Joint Fire Sciences Program (JFSP) created and disseminated standard fire effects protocols. The program was implemented on a national scale, with the support of the National Park Service (NPS) and United States Geological Society (USGS) Center for Earth Resources Observation and Science (EROS).

Around this time, the US Forest Service and US Department of Interior adopted Remote Sensing techniques for use in Burned Area Emergency Rehabilitation (BAER) assessments, depending on the availability of suitable Landsat satellite data. Satellite images for mapping wildfires are selected based on the availability of a cloud-free scene (image). The USGS Center for Earth Resources Observations and Science (EROS) has generated burn severity spatial data for all NPS fires since 2000, and for some areas as far back as 1983. This is usually combined (where feasible) with field measurements to develop a Composite Burn Index (CBI).

An extension of the NPS-USGS Burn Severity Mapping is Monitoring Trends in Burn Severity (MTBS) (http://www.mtbs.gov). MTBS was established under the National Fire Plan in late 2005. Burn severity products have been developed for most public lands (Federal and State) fires covering over 500 acres in the eastern U.S. and over 1,000 acres for the rest of the U.S., going back to 1982.

Landsat imagery, while not used exclusively, is preferred for burn mapping due to its desirable temporal, spatial and spectral characteristics (Hudak et al. 2002). Landsat also has the advantage of providing a large archive of images, available at no cost, for comparing pre-fire conditions to post-fire conditions. Therefore, it offers a cost-effective way to create a preliminary burn severity map. The techniques and processes are continually being researched and evaluated to increase the understanding of how different ecosystems respond to and recover after fires.

Importantly, the resulting burn severity data are used in a variety of GIS analyses, such as determination of ownership patterns within burn extents, vegetation recovery, soil studies, the delineation of fire perimeters (where GPS data is not available) and determination of landslide vulnerability. Remote sensing information is increasingly available and its use in GIS analysis is increasing rapidly. Therefore, it is

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highly desirable for you to develop familiarity and some experience working with remote sensing technology.

Grand Prix fire (west side of map) and Old Fire (center and east side of map). 2003 Southern California.Burn severity developed using field measurements. Map from USDA, Forest Service.

Project Study AreaThis activity focuses on the fire perimeter of the contained 2003 “Old Fire” in the California San Bernardino Mountains in the SW corner of San Bernardino County.

Old Fire Location Old Fire Burn Perimeter

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Getting StartedStep 1: Open ArcMap and click Cancel in the ArcMap – Getting Started dialog. This will open a new ArcMap document. Click on the Catalog button. When Catalog opens, dock it to right side of the application window. Click on the Connect to Folder button and create a connection to your RS_Fire\Data folder.

Step 2: Double-click on your new connection to open it. Right-click on the RSfire.gdb geodatabase and choose Make default geodatbase.

Click on the plus sign next to the RSfire.gdb to open the geodatabase. Drag and drop the following layers into the ArcMap display area:

AreaOfInterestOldFire_perimter

From the Landsat \PostFire folder add bands 4 and 7. From the Landsat \PreFire folder add bands 4 and 7.If prompted Do NOT build pyramids.

Step 3: Change the symbology of the two polygon layers to look like the ones below.Your ArcMap Table of Contents (TOC) should look like it does in this screenshot (note: the band name may have

changed slightly).

SAVE the Project as RS_Fire_pt2 in your RS_Fire folder.

Image Preprocessing: Convert image bands 4 and 7 from DN to Radiance unitsOverviewIn Part I, you downloaded Landsat 5 Images for November 16, 2002 and November 19, 2003. You created a composite image of each in ArcGIS, then clipped them to the Area of Interest boundary and explored different band combinations. In Part II, you will convert bands 4 and 7 for both the PostFire and PreFire Landsat scenes from a digital number (DN) for each pixel to radiance units and then to reflectance units. These steps will correct for atmospheric effects. When comparing images it is important to normalize them in this way.

Radiance is the amount of energy in watts at the satellite's sensor for each pixel on the ground. The Landsat Sensor records the intensity of electromagnetic radiation at a given location as a DN for each spectral band. Landsat measures radiation on a 0-255 scale. When you convert the DN values to radiance, you are converting the digital number recorded by the sensor back to the actual physical energy units that the digital number represents (Watt, W).

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To convert from DN to Radiance you will need the gain and bias information from of the sensor for the post-fire image. This information is in the metadata file (MTL), which is downloaded with the Landsat scene and stored with the scene files.

You will only work with Landsat TM bands 4 and 7, since they are the best indicators of burned areas for later processing to Normalized Burn Ratio (NBR) and for quantifying the difference between the PreFire and PostFire image NBRs by calculation to dNBR.

The equation to convert from DN to radiance is:

Lλ = ((LMAXλ – (LMINλ))/(QCALMAX – QCALMIN)) *((bBAND# – (QCALMIN)) + LMINλ)

L is the spectral radiance. So LMAX and LMIN represent the highest and lowest possible values of spectral radiance, which varies with gain state.

QCALMAX and QCALMIN are the calibrated maximum and minimum cell values. These values are also listed for each band in the metadata.

bBAND# (or QCAL) is the digital number, or the pixel value to be calibrated.

Step 1: Open Windows Explorer and navigate to your RS_Fire\Data\Landsat\PostFire folder. Locate the LT50400362003323PAC02_MTL.txt file. (It downloaded along with the Landsat image from Earth Explorer.) Right-click on it to bring up the context menu. Select Open with WordPad.

Q1: Double-check the values in the MTL file for all of the numbers listed below for the November 19 2003 Landsat image to be sure they concur. Open the MTL file for the November 16 2002 Landsat image Pre-Fire image and fill out the blanks, using the chart in your worksheet. You will need these numbers in the next steps. Note “LMIN” is “RADIANCE” in the MTL file. If your data is on a different date, be sure to change the dates and values in the table.

Step 2: You will use the Raster Calculator in ArcToolbox to make the conversions. Open ArcToolbox and Right-Click on the ArcToolbox Icon at the top of the list. In the context menu, select Environments. In the Environment Settings dialog box, make the following changes to the settings:

Workspace : Double-check that the current and Scratch Workspace are set for RSfire.gdb. Processing Extent: Set to AreaOfInterestRaster Analysis: Set Cell Size to Same, choosing one of the Landsat bands in your project. Click OK

Step 3: In ArcToolbox open Spatial Analyst tools Map Algebra Raster Calculator

In the Raster Calculator dialog, use the numeric and operator buttons to enter the formula below in the raster toolbox work area, substituting L5040036200323PACO2_B4.TIF, the bBAND# and the other numbers from the chart for the 2003 band 4. Be sure to Double-click on the ..B4 layer to add it to the formula – don’t keyboard it in.

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Lλ = ((LMAXλ – (LMINλ))/(QCALMAX – QCALMIN)) *((bBAND# – (QCALMIN)) + LMINλ)

((221 - (-1.510))/(255 - 1)) * (("LT50400362003323PAC02_B4.TIF" - 1) + (-1.510))

Be sure to that you enter the parenthesis exactly as shown since this will determine the order in which the parts of the formula are calculated.

Save the Output raster in the Landsat Folder as PostFire_B4_rad. Click OK to run. The new layer will be added automatically to your TOC. Notice that it was created to the extent of the AreaOfInterest layer, since you set this in the Environment Settings dialog.

Note: If you get an error, carefully look at the formula and enter it again.

Step 4: Run the formula again in the Raster Calculator, this time for the 2002 Band 4 layer. Since the numbers are the same, you can use the same formula that you just used, except that you must remember to use the 2002 Band 4 layer. For Output Raster, save as PreFire_B4_RAD.

Step 5: Use the Raster Calculator to convert the 2003 Band 7 layer from DN to Radiance. Your formula for the 2003 Band 7 should look like the one below:

((16.5 - (- 0.150)) / (255 - 1)) * (("LT50400362003323PAC02_B7.TIF" - 1) + (-0.150))

For Output Raster, save as PostFire_B7_RAD

Step 6: Repeat for the PreFire band 7 image. For Output Raster, save it as: PreFire_B7_RAD.

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Step 7: Remove the original 2002 Band 4 and Band 7 layers and the 2003 Band 4 and Band 7 layers. You should only have the two polygon layers and the four .RAD images left in your TOC.

SAVE your project. Now you are ready to convert the Radiance Layers to Reflectance values.

Image Preprocessing: Convert the Radiance values to Reflectance valuesThe next preprocessing step to prepare your data for later analysis is to convert the PreFire and PostFire images from radiance to reflectance values at top-of-atmosphere. This is because of the illumination differences caused by the sun angle and earth-sun distance, which vary during different seasons of the year. Top-of-atmosphere reflectance is a normalized, unit-less measure of the ratio of the amount of light energy reaching the earth's surface to the amount of light bouncing off the surface and returning to the top of the atmosphere, to be detected by the satellite's sensors. The formula and explanations are below. Read through the explanations and then follow the steps to convert the image to reflectance.

π * Lλ * d2

Reflectance: p =

ESUNλ * cosθs

Step 1: Lλ is the spectral radiance at the sensor's aperture, the radiance value, which you calculated in the previous step for each cell in the band. For this value, you will enter the radiance band you are converting as input into the formula. Examine the formula below as this is how it will be constructed for input into the Raster Calculator. The following steps will explain the other values that are required.

(π * bRADIANCE BAND# * d * d) / (ESUNλ * cos(θs))

Step 2: The variable d is the distance from the earth to the sun in astronomical units (AU). To find the value of d, you will first need to know the Julian day (also known as day of year) that the scene was taken. Visit this site for a table of Julian days: http://amsu.cira.colostate.edu/julian.html. Since neither 2002 or 2003 was a leap year, you will use the table for non-leap years. November 16, 2002 (PreFire): ____________November 19, 2003 (PostFire): ____________

Now that you have the Julian day for each of the images, use Table 11.4 from the Landsat 7 Science Data Users Handbook, below to find the value of “d”. Interpolate as necessary.

Source: http://landsathandbook.gsfc.nasa.gov/data_prod/prog_sect11_3.html

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Note: If you are able to access the link for the Landsat Calibration article referenced in Step 3 below, you will find a chart with the Earth-Sun Distance for each day of the year. Otherwise, you will need to interpolate the values.

To interpolate: Since Day of Year for the PostFire is 323 and it falls between 319 and 335 (16 days in between), the calculation is as follows:

.98916AU-.98608AU/16 = .0001925 (daily distance closer to sun)

.0001925 x 4 (323 is 4 days into the 16 days) = .00077

.98916 - .00077 = .98839 AU (this is your value for d for the PostFire image)

Q2: The Julian day for the PreFire 11/16/2002 Landsat scene is 320. What is the Earth-Sun distance for the PreFire image? (Answer in your worksheet!)Hint: As this is only one day past 319, you will only subtract the daily value from .98916.

Step 3: ESUNλ is the mean solar exoatmospheric irradiance. More simply stated, it is the mean amount of light in a particular band that makes its way into the sensor from space, without passing through the atmosphere. You could think of it as ambient light around the satellite that is picked up by the sensor. This value doesn't change over time and is constant for each band. Use the chart below from the journal, “Remote Sensing of Environment” 113 (2009) 893-903. http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat_Calibration_Summary_RSE.pdf

TM spectral range, post-calibration dynamic ranges, and mean exoatmospheric solar irradiance ESUNλ

From the chart you can see that the ESUNλ value for band 4 is 1031

Q3: What is the ESUNλ calibration value for Band 7?

Step 4: θs is the solar zenith angle. This is the angle between the sun and the satellite, which depends on how high the sun is above the horizon, or on the sun's elevation. To find this value, find the sun's elevation in the metadata for the scene (you did this for Q1), and subtract the sun's elevation from 90o.

The sun’s elevation for the PostFire image is 31.6412359o. Therefore, the solar zenith angle is:

90o - 31.6412359o = 58.3587641o.

Q4: What is solar zenith angle for the PreFire image? (Show the formula and your answer on the worksheet.)

90o - ______________ = _____________.9

Q5: Complete the chart, which you will also find in your worksheet, with the numbers that you generated above and will need for the formula in the Raster Calculator:

Band Lλ D (in AU) ESUNλ Θs (in degrees)PreFire Band 4 PreFire_B4_RAD 1031PreFire Band 7 PreFire_B7_RADPostFire Band 4 PostFire_B4_RAD .98839 1031 58.3587641PostFire Band 7 PostFire_B7_RAD .98839 58.3587641π = 3.14159

Once again, here is the formula to convert from Radiance units to Reflectance Units:

(π * bRADIANCE BAND# * d * d) / (ESUNλ * Cos(θs))

Below is that same formula in the format needed for the Raster Calculator. This formula requires an additional operation to convert θs from degrees to radians: The numbers for the PostFire band 4 are plugged in:

(3.14159* "PostFire_B4_RAD"*.98839 * .98839)/(1031* (Cos(58.3587641*3.14159/180)))

Open the Raster Calculator and input the expression (above), using the buttons in the Raster Calculator dialog box. For Output Raster, name it PostFire_B4_REF and save it to your Landsat folder. Click OK.

Note: While you could attempt to copy and paste, this sometimes introduces error. But, if you want to try it that way first, go for it. Then if you get an error, input with the buttons.

IMPORTANT! The Raster Calculator is case sensitive, so be sure that you enter all alphanumeric characters as they are above. It is also critical that the Environment settings are correct, particularly defining the extent and the cell size. You did this in an earlier step, but if you continue to get error messages, this along with checking parentheses and case, is another avenue of troubleshooting.

REMEMBER! The value for ESUNλ will be different for each band, and you will need to look these up in Table 1 for each calculation. Remember to replace the input band with the correct one. Also, remember that band 6 in your layer stack is actually Landsat band 7.

Step 5: In the Raster Calculator tool, enter the following expression to convert the PostFire radiance band 7 to reflectance:

For band 7 in the 2003 PostFire image(3.14159*"PostFire_B7_RAD"*.98839 * .98839)/(83.44* (Cos(58.3587641*3.14159/180)))

For Output Raster, name it PostFire_B7_REF and save it to your Landsat folder. Click OK.

Step 6: Repeat, using the formula in the Raster Calculator for the two PreFire Radiance images. Be sure to modify the numbers in the formula to match what is in the Q5 chart, above.

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Step 7: Take the time to explore the reflectance bands, comparing the pixel values to the radiance band pixel values and observing the minimum and maximum pixel values.

Q6: Before moving on, use the Identify tool to compare values of some of the pixels between the original band 4 that is in DN, the Radiance image and the Reflectance image. Fill in this chart on your worksheet. Add an Esri base map, such as Bing Maps Hybrid, if you need help identifying features.

DN Radiance ReflectanceLayer L5040036_0362001116_B40.TIF PreFire_B4_RAD PreFire_B4_REF9 Pixel Value (lake)

9 5.6854 .0323

Pixel Value (vegetation)31

Which feature pixel has the highest reflectance value? Do you notice that all of your reflectance values are less than 1.0? If they are not, you may need to go back and re-run, making sure that the parentheses are all in the correct locations.

Note: Landsat Data User's Handbook, Chapter 11.3 includes explanations and look-up tables for many of the parameters. http://landsathandbook.gsfc.nasa.gov/pdfs/Landsat_Calibration_Summary_RSE.pdf

Step 8: Before continuing, clean up your project by removing all of the images that end with RAD from the TOC.

Image Analysis – Calculate the Normalized Burn Ration (NBR) for the PreFire and PostFire imagesThe Normalized Burn Ratio (NBR) calculates the severity level of a burned area, using images acquired before and after a fire. Band 4 acquires near-infrared reflection and band 7 acquires mid-infrared reflection. They are the two bands most useful for this calculation. Recall the differences in the spectral signatures that you collected in Part 1.

To calculate the NBRs you will use the following formula in the Raster Catalog for the PreFire image and then for the PostFire image:

NBR = (Band 4 – Band 7) / (Band 4 + Band 7)

Step 1: In ArcToolbox, open the Raster Calculator. Use the Raster Calculator buttons to enter the following:

("PreFire_B4_REF" - "PreFire_B7_REF") / ("PreFire_B4_REF" + "PreFire_B7_REF")

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Save the Output raster to your RSfire.gdb as PreFire_NBR. Click OK. The processed image will be added automatically to the TOC. Turn on the OldFire_perimter. Examine the image and notice how well two older burn scars stand out; one to the west of the perimeter and one in about the middle.

Step 2: Now, you will create an NBR from the PostFire reflectance band 4 and band 7. Repeat Step 1 above, using the PostFire bands instead. Save the Output raster to your RSfire.gdb as PostFire_NBR. Click OK. Examine this image and compare it to the PreFire_NBR image.

Q7: Where do you see the most recent burn scars? Can you still see the burn scars that stood out so well on the 2002 image?

Step 5: Remove the images that end in REF from the TOC. You should only have the Pre and Post fire NBR images and the OldFire_Perimter and AreaOfInterest polygons in the TOC.

Image Analysis – Normalized Burn Ratio (NBR) to Differenced Normalized Burn Ratio (dNBR)The Differenced Normalized Burn Ratio is calculated by subtracting the PostFire NBR from the Prefire NBR. This differencing is used to detect the extent and degree of change from the fire.

dNBR = NBR_PreFire – NBR_PostFireStep 1: Open the Raster Calculator and enter the following expression:

"PreFire_NBR" - "PostFire_NBR"

For Output raster, save to your RSfire.gdb and name it: dNBR_ OldFire. Click OK. After processing, the calculated dNBR_ OldFire image will be added to the TOC. Your TOC and Display area should looks like the screenshot, below.

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Step 2: Next, you will convert this grid from a floating point to an integer grid (whole numbers), so that you can use the Spatial Analyst post-processing tools, such as the Majority Filter, to reduce the speckling. At the same time, you will scale the numbers by multiplying by 1000 to match the integers in the chart below from the FIREMON document Landscape Assessment (LA): Sampling and Analysis Methods, by Carl H. Key and Nathan C. Benson. (USDA Forest Service Gen. Tech. Rep. RMRS-GTR-164-CD. 2006. http://www.fs.fed.us/rm/pubs/rmrs_gtr164/rmrs_gtr164_13_land_assess.pdf)

Open the Raster Calculator. Enter the following expression:

Int ("dNBR_OldFire" * 1000)

Save it to your RSfire_gdb as dNBR_int.

To better view the data, open Layer Properties Symbology tab and choose a Cold to Hot Diverging Color ramp (blue to red). The orange and reds will represent areas with a higher burn severity.

Step 3: Use the Identify tool to explore the dNBR_int image pixel values. Compare to the values of the NBR image. Focus particularly on areas where there are clusters of pixels that are similar rather than areas with a lot of speckling. How does your dNBR_int data Compare to the five categories of Burn Severity Classes; Unburned, Low, Moderate-low, Moderate-high, High, based on the dNBR ranges listed?

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Note: If your values differ substantially from what you expect, check with your instructor. You most likely will need to retrace your steps from the creation of the radiance images of bands 4 through to producing the dNBR to see if you made a math error earlier on.

Q8: Within the OldFire_perimeter boundary, where are the least burned areas and where was the fire the most severe? Add an Esri Terrain or Topographic base map and discuss how topography might have been a factor in the most severely burned areas.

Step 4: Remove the dNBR_OldFire, PostFire_NBR and PreFire_NBR images from the TOC. In the next step you will use post-processing procedures to remove some of the speckling by generalizing the image through the use of a Majority Filter and the Boundary Clean Tool. Finally, you will remove isolated regions of pixels using the Nibble tool. Zoom into an area where there is a lot of speckling, such as the one shown here. Notice that you are able to make out individual pixels.

The Majority Filter tool will be used to clean these up, so that these pixels instead are assigned the value of the majority of pixels surrounding them.

Step 5: Open ArcToolbox and click on Spatial Analyst tools Generalization Majority Filter.

For Input raster, choose dNBR_int. For Output raster, save to your RSfire.gdb and name it dNBR_4_half. Use FOUR for the number of neighbors and Half for the Replacement threshold. Click OK.

Run the Majority Filter tool again, this time using the dNBR_4_half image for the Input Raster. Save as dNBR_4_half2.

Compare the results to the dNBR_int layer. The results won’t be dramatic, but you should see a small reduction in the amount of speckling.

Note: There is no set number of times to run this tool. Instead, you must observe the results to determine what is optimum for both the application and the data.

Step 6: Next you will run the Boundary Clean tool to smooth the ragged edges of class boundaries. This will allow adjacent areas that belong to the same class to be connected. Open ArcToolbox Spatial Analyst Generalization Boundary Clean. For Input raster, use dNBR_4_half2.

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For Output raster save it as dNBR_bc.

Zoom in closely and compare the dNBR_4_half2 and the dNBR layers. You should notice that the dNBR_bc layer displays smoother boundaries between the edges of large areas with similar values.

ArcGIS Help has a section called “An overview of the Generalization toolset” that discusses how the various generalization tools, including Majority Filter and Boundary clean work. There are different ways of approaching generalization of image data, and which tools you use will depend on your particular dataset and subsequent analyses that you have planned.

Q9: By observing the two files above, explain the effects of the post-classification procedures in these particular images.

Step 7: Open the dNBR_bc attribute table and scan down the records for the Value field, noting the number of records and the range of values. Close the table when you are done. To prepare for further analysis, you will reclass the image to group the many values into seven ranges suitable for this analysis. The chart on page 12, “LA-2-Ordinal Severity Levels and Example Range of dNBR (scaled by 103), to the right,” will be used as a guide to establish four new values based on the seven ranges in the table.

Open ArcToolbox >> Spatial Analyst Tools >> Reclass >> Reclassify tool. For Input raster, select dNBR_bc, for Reclass field, accept the default, Value.

Click on the Classify button. Change the Classification to Natural Breaks and the number of classes to 4. Change the Break Values to: 99, 350, 572, 1162 (highest value in this dataset). Click OK.

For the Output polygon features, save to your RSfire.gdb as Reclass_dNBR_bc. Double-check that your dialog box looks like the one below, except that your path to the RS_Fire folder will be different. Click OK. Save your project.

In Layer Properties Symbology, adjust the names of the Recl_dNBR_bc layer categories and the colors to be similar to the ones below:

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Note: The choice in break values was determined using the mid-point for the Moderate Low and Moderate High categories and placing the lower and higher of these categories, respectively into the Low Severity and High Severity classes.

Step 8: Run the Majority Filter tool with the Recl_dNBR_bc layer as input. Save the output as Majorit_Reclass and use FOUR neighbors and a MAJORITY Replacement threshold. When you zoom in on the resulting Majorit_Reclass layer and compare to the Recl_dNBR_bc layer, you should see a significant reduction in the speckling.

Convert Raster to VectorConvert Majority Filtered, reclassed image to a polygon feature class. 1. Open ArcToolbox Conversion Tools From Raster Raster to Polygon tool. For Input raster,

select Majorit_Reclass, for Field, accept the default, Value, for Output polygon features, save to your RSfire.gdb as dNBR_vec. Be sure the default option “Simplify polygons (optional)” is checkmarked. This means the polygons will be smoothed into simpler shapes rather than squared off to conform to the raster cell edges.

2. The results will look similar to what you see below on the left, although likely a different color. Examine the attribute table and notice that there is a field “GRIDCODE”, with values of 1 or 2.

3. Change the symbology to Unique Values with the value field set to grid_code. Use the same color scheme for the four grid codes and change the labels to match the raster above. Your results should look like the one on the left, below. The image on the right is a zoom-in by the lake.

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Q10: Working with a partner, write up a one-two page summary of the work that you have completed so far. This should include a flow chart (overview) of the process. Include a short discussion of the differences you note between the Majorit_Reclass layer and the conversion to the vector layer dNBR_vec.

Calculate Burn Severity Acres Objectives

Display dNBR vector output Create new field named “Acres” in table and calculate acres Create new field named “Burn_Sev” in table. Build attribute queries for each density class and use Field Calculator to populate the Burn_Sev

field for each Burn_Sev type (unburned, low, moderate, high) Create summary statistics for Burn Sev Type and fill in chart Run overlay analysis (Intersect) using the burn severity layer and ownership layer. Update Acres field on burnseverity_Owner layer. Create an attribute query to select USFS records Calculate summary statistics for USFS for each burn severity category. Fill in chart

Step 1: From the TOC, remove all layers except OldFire_perimeter, AreaOfInterest and dNBR_vec, the Save the project.

Q11: Open the dNBR_vec layer attribute table. In what units is the Shape_Area field?

Step 2: Click on the Table Options button. Create a new field for acres as shown here.

Step 3: To calculate the acres, right click on the new Acres field name and choose Calculate Geometry. Click Yes when the warning dialog comes up. Note: You can ignore this message, as you are working in a new field and there is no danger of corrupting existing data in the field.

When the Calculate Geometry dialog comes up, for Property choose Area. The Coordinate system should be set at Use coordinate system of the data source. For Units choose Acres US [ac]. Click OK.

Step 4: Create another new Field, named Burn_Sev, Type: Text, Length: 9. Click OK.

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Step 5: From the dNBR_vec attribute table, click on Options Select by Attributes. Create the query: “grid_code” = 1 then click Apply. All records with a grid code of 1 will be highlighted in the table.

Right click on the field name, Burn_Sev and select Field Calculator. If the warning comes up, ignore the message and click Yes. Below “Burn_Sev =” type in “Unburned or Regrowth” (be sure to include the quotes). Click OK.

Step 6: Back in the “Select by Attributes” dialog, remove the old query and create a new one for: “grid_code” = 2 then click Apply. All records with a grid code of 2 will be highlighted in the table.Right click on the field name, Burn_Sev and select Field Calculator. Below “Burn_Sev =” type in “Low Severity”. Click OK.

Step 7: Back in the “Select by Attributes” dialog, remove the old query and create a new one for: “grid_code” = 3 then click Apply. Right click on the field name, Burn_Sev and select Field Calculator. Below “Burn_Sev =” type in “Mod Severity”. Click OK.

Step 8: Back in the “Select by Attributes” dialog, remove the old query and create a new one for: “grid_code” = 4 then click Apply. Right click on the field name, Burn_Sev and select Field Calculator. Below “Burn_Sev =” type in “High Severity”. Click OK.

Step 9: Bring up the dNBR_vec Layer Properties dialog. In the Symbology tab, click on Show Categories Unique Values. For value field, choose Burn_Sev. Use the arrows to the right of the dialog box to move the values in the order as shown, below: unburned, Low, Moderate, High. Match the colors as shown. Click OK. Save the project.Your map display should look like the one to the right.

Step 10: Now you will summarize the number of acres for each of the burn severity types. In the dNBR_vec table, right click on the Burn_Sev field name. In the context menu, choose Summarize. When the Summarize dialog comes up do the following: 1. Select Burn_Sev for the field to

summarize

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2. For summary statistics to include, click on the + sign next to the Acres field, then click in the box next to Sum.

3. For Specify output table; Save to your RSfire.gdb with the name: Sum_BurnSeverity_acres. Click Save. Click OK. When prompted to add the result table to the map, click Yes. Close the dNBR_vec Table.

Step 11: Open the Sum_BurnSeverity_acres.dbf table. Right-click on the Sum_Acres. In the Field Properties dialog, click on the button next to Numeric and change the number format to display with two decimal places and to show the thousands separators.

Q12: Using the above information, fill in this chart on your worksheet with the number of acres of each burn severity class.

Burn Severity Class Number of AcresUnburned or RegrowthLow SeverityModerate SeverityHigh SeverityTOTAL ACRES

Step 12: Next you will download data for land ownership from the GIS Data Center for San Diego Fire Recovery Network. In your Internet Browser go to: http://map.sdsu.edu/firenet/ Then click on the Data Download button. Scroll down to the vBase Package and next to Old Fire on Ownership. Save the zipped file to your RS_Fire\Data folder. Then unzip it this shapefile – it will be named Ownership.shp and will also include an ownership Classification Layer (.lyr) file. Please note: this site may or may not be available. So the rest of the unit is optional. If Ownership layer is not available, create a cartographically appropriate display of burn serverity.

Step 13: Add the Ownership Classification.lyr to the project. It will come in with a red exclamation mark, so you will need to right-click on it and choose Data Repair Data Source. In the Data Source dialog window, select Ownership.shp and click Add. You should now see the Ownership layer displayed classified by Agency. Change the name in the TOC to Ownership.

Notice that it is clipped to the boundary of the Old Fire, although due to the use of a slightly different burn perimeter files it won’t line up exactly with the Old Fire Perimeter file that you have. Next, you will use this ownership layer to determine the number of acres of each burn severity type. Save the project.

Step 14: Open the ownership.shp attribute table and examine the fields. Notice the Agency field. Close the table.

Step 15: Open toolbox Analysis tools Overlay Intersect toolbox. When the Intersect dialog box comes up enter the following:

Input features: dNBR_vec, then Ownership. Output Feature Class (to your RS_Fire geodatabase): Burnseverity_Owner.shp Click OK.

Step 16: Use Select by Attribute on the BurnSeverity_Ownership layer with the following query: "Acres" <= .25.

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To remove sliver and other very small polygons from the BurnSeverity_Ownership layer, run the Eliminate tool in ArcToolbox: ArcToolbox Data Management Tools Generalization Eliminate. For Input Layer, choose BurnSeverity_Ownership; for Output Feature Class, save to your RSfire.gdb as BurnSeverity_Ownership_Elim. Click OK.

Step 17: Remove the BurnSeverity_Ownership and the Ownership layers from the project and Save.

Step 18: It is necessary to update the Acres field after the Intersect Overlay. Right-click on the BurnSeverity_Ownership_Elim layer in the TOC and choose Edit Features Start Editing. Open the BurnSeverity_Ownership_Elim layer attribute table. Right-click on the Acres field and choose Calculate Geometry: For Property: Area, Use coordinate system of the data source, Units: Acres US [ac]. When done, from the Edit menu choose Stop Editing, and Save Edits when prompted.

Open Layer Properties for the BurnSeverity_Ownership_Elim layer. Go to the Symbology tab and under categories, choose Unique values. For Value, choose Agency. Adjust the fill colors so that USFS is Green and all of the other categories are light gray. Click OK.

Step 19: From the Selection Menu, choose Select by Attribute. Create a query to select all of the USFS records in the table.

Step 20: Open the BurnSeverity_Ownership_Elim attribute table. Right click on the Burn_Sev field and click Summarize. For the summary statistics to include, click on Acres and checkmark Sum.For “Specify Output Table”, save to your RS_Fire.gdb with the name Sum_BurnSeverity_acres_USFS. Add the resulting table to the project. Use the “Clear Selected Features” button to clear the map display of selected features.

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Step 21: Open the Sum_BurnSeverity_acres_USFS table. Format the Sum_Acres field to show 0 decimal places and to show the thousands separators.

Q13: Fill in this chart on your worksheet with the number of acres in each burn severity class owned by the USFS.

Ownership Burn Severity Class Number of AcresUSFS UnburnedUSFS LowUSFS ModerateUSFS High

TOTAL ACRES

Q14: What percentage of the total acres within the burn perimeter was owned by the USFS?

Note: This exercise will come up with different calculations than were actually arrived at by the agencies involved due to the use of different data sets and methods, but they should not be vastly different.

Because of cloud cover, there was a lack of a suitable Landsat images soon enough after the Old Fire. The on-the-ground BAER team analysis was therefore done before a BAER remote sensing map was completed.

Step 22: Save and then close ArcMap.

Evaluation & Deliverables1. Turn in the separate worksheet (not the entire activity), with the answers to all questions in this

activity.2. Write a 1-2 page summary of the processes taken in ArcGIS.3. Using the tools in MS PowerPoint or Word, create a short flowchart of the overall steps you took

to complete this activity.

ReferencesAllen, Jeannie. “How People Use Remote Sensing.” iGETT-NASA.Drake, Vicki. “Severity and Post-fire Chaparral Recovery.” iGETT Learning Unit, 2009.Hudak, Andrew T., Peter R. Robichaud, Jeffrey S. Evans, Jess Clark, Keith Lannom, Penelope Morgan,

Carter Stone (2004) Field Validation of Burned Area Remote Classification (BARC) Products for the Purpose of Rapid Response. http://www.fs.fed.us/rm/pubs_other/rmrs_2004_hudak_a001.pdf

Key, C. and C. Benson. Remote Sensing Measure Of Severity: The Normalized Burn Ratio. FIREMON Landscape Assessment, 4, 1-16 2004.

Key, C. H., Remote Sensing Sensitivity To Fire Severity And Fire Recovery. Proceedings of the 5th International Workshop on Remote Sensing and GIS Applications to Forest Fire Management: Fire Effects Assessment: 29-39, 2005.

Key C., and C. Benson. Landscape Assessment (LA): Sampling and Analysis Methods. FIREMON General Tech. Rep. RMRS-GTR-164-CD, 2006. http://www.fs.fed.us/rm/pubs/rmrs_gtr164/rmrs_gtr164_13_land_assess.pdf)

Lillesand, T.M., R.W. Kiefer and J.W. Chipman. Remote Sensing and Image Interpretation, 5th ed. 2005.

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WebsitesArcGIS http://www.esri.com/ Monitoring Trends in Burn Severity (MTBS) http://www.mtbs.gov USGS Post-fire Burn Assessment on Federal Lands. http://www.nrmsc.usgs.gov/science/fire/burn_severity USGS Global Visualization Viewer http://glovis.usgs.gov NASA “How are Satellite Images Different From Photographs?”

http://landsat.gsfc.nasa.gov/education/compositor Landsat Handbook, http://landsathandbook.gsfc.nasa.gov/handbook/handbook_htmls/chapter11/chapter11

.html#section11.3

TutorialsThe Canada Course for Remote Sensing: http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/index_e.php Esri Virtual Campus courses – courses vary from free to fee-based.

http://training.esri.com/gateway/index.cfm

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