Wildfire Imaging with EOIR

STK Premium (Air), STK Premium (Space), or STK Enterprise
You can obtain the necessary licenses for this tutorial by contacting AGI Support at support@agi.com or 1-800-924-7244.

Required Capability Install: For versions 12.10 and earlier of the STK software, this lesson requires the installation of the EOIR capability. For these versions of the software, the EOIR installer is included in the STK Premium software download, but requires a separate installation process. Read the Readme.htm found in the STK software install folder for installation instructions. You can obtain the necessary install by visiting https://support.agi.com/downloads or calling AGI support.

An internet connection is required to complete this tutorial in full.

The results of the tutorial may vary depending on the user settings and data enabled (online operations, terrain server, dynamic Earth data, etc.). It is acceptable to have different results.

Capabilities covered

This lesson covers the following capabilities of the Ansys Systems Tool Kit® (STK®) digital mission engineering software:

  • STK Pro
  • Electro-Optical Infrared Sensor Performance (EOIR)

Problem statement

Emergency management personnel need to be alerted to wildfires before they grow into threats to life and property. As such, early detection is critical, as it allows them to quickly respond with a containment strategy. You are interested in the wildfire-detecting capabilities of an Earth-observing satellite in geosynchronous orbit. The satellite delivers high-resolution visible and infrared imagery and lightning observation data as part of its mission. You need to model what the satellite's imaging camera can see of wildfires on the ground in both the visible and infrared bands.

Solution

Use the STK software’s Electro-Optical Infrared Sensor Performance (EOIR) capability to model the satellite's on-board camera to study a notional wildfire at a site in California. Model what its camera sees from orbit by loading in custom radiance and temperature texture maps. Then, observe the results in the sensor's output.

What you will learn

Upon completion of this tutorial, you will understand how to do the following:

  • Use the EOIR capability to model visible and infrared sensors
  • Load and use both reflectance and temperature texture maps
  • Understand generated EOIR sensor scenes

Creating a new scenario

First, you must create a new STK scenario, and then build from there.

  1. Launch the STK application ().
  2. Click Create a Scenario in the Welcome to STK dialog box.
  3. Enter the following in the STK: New Scenario Wizard:
    4. Option Value
    Name EOIR_Wildfire_Imaging
    Start Default / Set the Time 20:00:00.000 UTCG
    Stop + 12 hrs
  4. Click OK when you finish.
  5. Click Save () when the scenario loads.

    6. The STK application creates a folder with the same name as your scenario for you.

  6. Verify the scenario name and location in the Save As window.
  7. Click Save.

Save () often during this lesson!

Downloading the required files

This lesson uses two texture map files, which you will need to download in order to complete this tutorial.

  1. Download the zipped folder here: https://support.agi.com/download/?type=training&dir=sdf/help&file=EOIR_Wildfire_Imaging.zip

    2. If you are not already logged in, you will be prompted to log in to agi.com to download the file. If you do not have an agi.com account, you will need to create one. The user approval process can take up to three (3) business days. Please contact support@agi.com if you need access sooner.

  2. Navigate to the downloaded zip file.
  3. Right-click on EOIR_Wildfire_Imaging.zip.
  4. Select Extract All...
  5. Set the Files will be extract to this folder: path to be within your scenario folder (for example, C:\Users\<username>\Documents\STK_ODTK13\EOIR_Wildfire_Imaging).
  6. Click Extract.
  7. Go to your scenario folder in Windows File Explorer.
  8. Confirm that the following files are in the EOIR_Wildfire_Imaging folder:
    • ReflectanceMap.csv
    • TemperatureMap.csv

Inserting a ground site

California's wildfire season begins during the hot and dry months of spring and runs through early fall; however, wildfire risks are year-round. You will focus on the area around the Big Basin Redwoods State Park. This area has suffered wildfires in the past. Model the park with a Place object, which models a point of interest on the surface of a central body, using the Search by Address method. You'll use this as a targeting location for the satellite's imaging camera.

  1. Bring the Insert STK Objects tool () to the front.
  2. Select Place () in the Select An Object To Be Inserted list.
  3. Select Search by Address () in the Select A Method list.
  4. Click Insert....
  5. Enter Big Basin in the Enter an address or other search criteria below field when the Insert by Address tool opens.
  6. Select Big Basin Redwoods State Park, CA with Latitude 37.17250 deg and Longitude -122.22250 deg in the Results list.
  7. Click Insert Place(s).
  8. Click Close to close the Insert by Address tool.

Modeling a GOES satellite

The U.S. National Oceanic and Atmospheric Administration (NOAA) operates a constellation of weather-observing and environmental-monitoring satellites, known as the Geostationary Observational Environment Satellite (GOES) series, which supply a continuous stream of data for North American forecasting and research operations. Insert a Satellite object using the Orbit Wizard method to model a GOES satellite in geosynchronous orbit over the Western Hemisphere.

  1. Bring the Insert STK Objects tool () to the front.
  2. Insert a Satellite () using the Select Orbit Wizard () method.
  3. Open the Type drop-down list when the Orbit Wizard opens.
  4. Select Geosynchronous.
  5. Enter GOES in the Name field.
  6. Enter -137.2 deg in the Subsatellite Point field in the Definition panel.
  7. Click OK to confirm your changes, propagate GOES (), and close the Orbit Wizard.

Modeling the satellite's imaging camera with EOIR

The Electro-Optical Infrared Sensor Performance (EOIR) capability models the detection, tracking, and imaging performance of electro-optical and infrared sensors. You can use the EOIR capability to support concept development, design, field-testing, and operations. The STK software takes this modeling to the big picture by considering electro-optical and infrared performance in conjunction with sensor platform dynamics, communications, and other mission architecture elements to assess integrated performance. The EOIR capability has the ability generate performance metrics such as signal-to-noise ratio and sensor images with both Earth and space in the background.

To create an EOIR sensor scene, you first need to insert a Sensor object and specify the EOIR properties for both it and the other objects in the scene. Start with the Sensor object.

Inserting a Sensor object

Attach a Sensor object to the GOES satellite.

  1. Bring the Insert STK Objects tool () to the front.
  2. Insert a Sensor () object using the Define Properties () method.
  3. Select GOES () when the Select Object dialog box opens.
  4. Click OK to confirm your selection and to close the Select Object dialog box.

Defining the sensor's Pointing properties

By default, a sensor is mounted on a satellite pointed along a fixed direction. Set the sensor to target Big Basin Redwoods State Park by updating its Pointing properties.

  1. Select the Basic - Pointing page when the Properties Browser opens.
  2. Open the Pointing Type drop-down list.
  3. Select Targeted.
  4. Select Big_Basin_Redwoods_State_Park_CA () in the Available Targets list.
  5. Move () Big_Basin_Redwoods_State_Park_CA () to the Assigned Target list.
  6. Click Apply to confirm your changes and to keep the Properties Browser open.

Selecting the EOIR sensor type

Select the EOIR sensor type on the sensor's Definition page. The EOIR sensor type is used to model electro-optical and infrared sensors an supports up to 36 bands per sensor. For this analysis, you'll use only a single band.

  1. Select the Basic - Definition page.
  2. Open the Sensor Type drop-down list.
  3. Select EOIR.
  4. Click Apply to confirm your selection and to keep the Properties Browser open.

Setting the sensor's spatial properties

By default, an EOIR sensor is set up with a single band. Select the Spatial tab on the Definition page to specify the spatial properties for each band. The default input setting for the band is Field-of-View and Number of Pixels. Update the sensor's field of view to narrow its focus.

  1. Select the Spatial tab.
  2. Leave Field-of-View and Number of Pixels selected for the Input method.
  3. Enter the following values in the Field of View panel:
    4. Option Value
    Horizontal Half Angle 0.05 deg
    Vertical Half Angle 0.05 deg
  4. Click Apply to confirm your changes and to keep the Properties Browser open.

Reviewing the sensor's spectral properties

The GOES satellite has 16 total spectral bands that look at the visible, near-infrared (NIR), and infrared (IR) bands. Review the sensor's spectral properties to model the visible components first. For simplicity, you will use the default settings and look at the visible waveband (0.4-0.7 microns).

  1. Select the Spectral tab.
  2. Confirm Low is set to 0.400 (um) in the Spectral Band Edge Wavelengths panel.
  3. Confirm High is set to 0.700 (um) in the Spectral Band Edge Wavelengths panel.

Reviewing the sensor's optical and radiometric properties

You will be using many of the default settings to highlight how to get an image quickly. Take the moment now to review the sensor's optical properties and its radiometric properties, but do not make any changes.

  1. Select the Optical tab.
  2. Review the settings but leave the default values.
  3. Select the Radiometric tab.
  4. Review the settings but leave the default values.

Updating the sensor-level properties

You can define certain properties that apply to the EOIR sensor in general, including all its bands. These sensor-level properties all appear below the band-specific tabs (Spatial, Spectral, Optical and Radiometric) on the Definition page.

  1. Scroll down below the band-specific tabs.
  2. Open the Processing Level drop-down list.
  3. Select Radiometric Input.

    4. EOIR uses this type of calculation to generate a full sensor scene, including both geometric and radiometric aspects. However, it does not apply optical or detector effects. This output is often called entrance aperture radiance.

  4. Click OK to confirm your changes and to close the Properties Browser.

Renaming the sensor

Rename the sensor to be more descriptive.

  1. Right-click on Sensor1 () in the Object Browser.
  2. Select Rename in the shortcut menu.
  3. Rename Sensor1 () EOIR_Visible.

Opening the EOIR toolbar

Before you get started using the EOIR capability with your sensor, you must first display the EOIR toolbar. You can use the EOIR toolbar to access the EOIR configuration and EOIR sensor scene for a particular sensor.

  1. Select View in the Menu Bar.
  2. Select Toolbars in the View menu.
  3. Select EOIR in the Toolbar submenu to show the EOIR toolbar ().

Generating an EOIR sensor scene

Generate an EOIR sensor scene to review what the sensor scene sees. The EOIR Line of Sight and Field of View are synchronized to the STK Sensor object.

  1. Select EOIR_Visible () in the Object Browser.
  2. Click EOIR Sensor Scene... () on the EOIR toolbar.

Coarse Gray Scale color map

This first EOIR Sensor Scene image is not particularly meaningful; it has two colors: black for water and white for land. By default, the EOIR capability only calculates a coarse scene detail to the spatial resolution of the tessellations.

Updating the sensor scene

You can dial up the fidelity by setting the sensor scene detail to Fine by updating the EOIR Scene Visual Details.

  1. Right-click on the EOIR sensor scene.
  2. Select Details... in the shortcut menu.
  3. Move the EOIR Scene Visual Details dialog box when it opens so that it's not sitting on top of the sensor scene.
  4. Note the Gray Scale option is selected in the Color Map panel by default.

    5. Selecting a color map enables various "false color" mappings of the display. The EOIR capability uses false color to bring out details in the image data that are often lost when displayed on a monitor with less resolution than the EOIR sensor. For instance, a typical monitor can display only 256 levels of grayscale, whereas an EOIR sensor might have 4096 levels of grayscale resolution. Color mapping is only for visual effect and does not change any of the internal data values.

  5. Select the Fine option in the Scene Detail panel.

    6. Fine Gray Scale color map

    This level of detail overlays the tessellations of an object with a detailed surface map of materials, if such a map exists for the object. The Earth, for instance, has a surface material map with a 0.93-kilometer resolution.

  6. Click on the land mass' pixels around the vicinity of the state park.

    7. You should see a variety of surface materials (for example, OpenShrubLands, MixedForest, EvergreenNF, and so on) noted in the Material field in the Scene Pick information panel. The EOIR capability simulates spectral reflectance for the 17 International Geosphere Biosphere Program (IGBP) Global Land Cover types plus Tundra as type 18. The data utilized to create these spectral response functions (SRFs) came from the spectral reflectance libraries of the United States Geological Survey (USGS) and Johns Hopkins University. The various land cover types were created as linear combinations of simpler materials. As you can see, the area's wooded terrain is ripe for the formation of wildfires.

  7. Click OK to confirm your selection and to close the EOIR Scene Visual Details dialog box.
  8. Leave the EOIR Sensor Scene window open.

Using EOIR texture maps

The mission thus far uses the built-in globe imagery. You can dial up the fidelity even further by adding EOIR texture maps and specifying their associated parameters. The custom reflectance and temperature maps you downloaded at the beginning of this lesson have already been formatted for use with the EOIR capability. The empirical line method (ELM) was utilized to calculate and create a reflectance data file (in the form of a CSV file) from Microsoft Bing Maps™ data, which only contain red, green, and blue channels. The script normalized the texture map across each of the three channels. can will use the data you have available and extend it to the wavebands used in this study.

You can use the Snap Frame tool in the STK application to create your own texture maps using the MATLAB programming platform. Refer to the Sensor Calibration and Ground Imaging with EOIR tutorial for information on creating your own texture maps.

Updating the EOIR configuration

You can import texture maps by updating the sensor's EOIR configuration.

  1. Click EOIR Configuration... () on the EOIR toolbar.
  2. Click Atmosphere and Textures... when the EOIR Configuration dialog box opens.
  3. Select the Texture Maps page when the EOIR Atmosphere, Clouds, and Texture Maps dialog box opens.

Importing the reflectance texture map

First, import the reflectance texture map.

  1. Click New Texture Map () on the toolbar.
  2. Enter Reflectance in the Name field.
  3. Open the Type drop-down list.
  4. Select Reflectance.
  5. Open the Value drop-down list.
  6. Select File:.
  7. Click the File: ellipsis ().
  8. Navigate to your scenario folder (for example, C:\Users\<username>\Documents\STK_ODTK13\EOIR_Wildfire_Imaging) when the Open dialog box appears.
  9. Select ReflectanceMap.csv.
  10. Click Open to confirm your selection and to close the Open dialog box.

The values in ReflectanceMap.csv form an n × m grid. The STK application applies the first row of m values as a percentage reflectance, distributed evenly from west to east across the northern boundary of the map. It will apply each row evenly in descending latitude, with row n across the southernmost boundary. You can view the contents of ReflectanceMap.csv in the spreadsheet editor of your choice.

Defining the texture map's boundary

Specify the latitude and longitude for each of the four corners to define the map boundary.

  1. Set the corner points using the values from the table below:
    2. Point Latitude (deg) Longitude (deg)
    NW Corner 37.69591 -122.88801
    NE Corner 37.69591 -121.46507
    SW Corner 36.66721 -122.88801
    SE Corner 36.66721 -121.46507
  2. Click OK to confirm your changes and to close the EOIR Atmosphere, Clouds, and Texture Maps dialog box.
  3. Click OK to close the EOIR Configuration window.

Revisiting the EOIR sensor scene

The EOIR sensor scene should automatically update. Examine it for changes.

  1. Bring the EOIR Sensor Scene window to the front.
  2. If you do not see any changes, close the sensor scene window and click EOIR Sensor Scene... () on the EOIR toolbar.
  3. Review the changes.

Reflectance texture map

You can see a change in the sensor scene, you now have more detail on of the mountains and the region as a whole.

Copying the existing texture map

Next, load in another temperature map for temperatures. Start by copying the existing Reflectance temperature map, then build from there.

  1. Click EOIR Configuration... () on the EOIR toolbar.
  2. Click Atmosphere and Textures... when the EOIR Configuration dialog box opens.
  3. Select the Texture Maps page when the EOIR Atmosphere, Clouds, and Texture Maps dialog box opens.
  4. Click Copy Texture Map () on the toolbar.
  5. Click Paste Texture Map () on the toolbar.

The texture maps list displays the maps in priority order from top down, as you can see by the priority number on the left. This means that a map will take precedence over other maps of the same type with lower priority, which the STK application needs to know if the maps overlap.

Importing a temperature texture map

For the purposes of this lesson, you are provided with an example temperature map. It was created by generating "hot points" specifying wildfires on the texture map. This was done with a script that used a Gaussian blob generator and the MATLAB script referenced above to generate another CSV file. Although the wildfire generator script is not available, you can use the file generated by the script. Load and examine the temperature texture map.

  1. Ensure the Reflectance - Copy map is selected in the list.
  2. Enter Temperature in the Name field.
  3. Open the Type drop-down list.
  4. Select Temperature.

    5. This will show values in degrees Kelvin.

  5. Click the File: ellipsis ().
  6. Navigate to your scenario folder (for example, C:\Users\<username>\Documents\STK_ODTK13\EOIR_Wildfire_Imaging) when the Open dialog box opens.
  7. Select TemperatureMap.csv.
  8. Click Open to confirm your selection and to close the Open dialog box.
  9. Click OK to confirm your changes and to close the EOIR Atmosphere, Clouds, and Texture dialog box.

    10. Since you copied the Reflectance texture map, you already have the corner points entered in. You do not need to set them again.

  10. Click OK to close the EOIR Configuration dialog box.

Reviewing the sensor scene

Review the resulting sensor scene.

  1. Bring the EOIR Sensor Scene window to the front.
  2. Review the changes.

    3. Temperature texture maP

    You've loaded in a temperature map; however, you're looking at visible light. It's daytime and it's bright outside, so any fires are not easy to see. You need a sensor band with a different spectral response in order to view the fires effectively during daylight hours.

  3. Close () the EOIR Sensor Scene window.

Viewing the wildfires in the LWIR band

The GOES satellite's camera is also sensitive to infrared light. Broadly examine the fires as they appear in the thermal infrared spectrum by modeling the camera's long-wave infrared (LWIR) imager, which responds to radiant heat.

Duplicating the EOIR Sensor

Rather than configuring an entirely new band in the existing EOIR Sensor object, to simplify things, copy your existing sensor and make adjustments to its spectral properties.

  1. Select EOIR_Visible () in the Object Browser.
  2. Click Copy () on the Object Browser toolbar.
  3. Click Paste ().
  4. Rename EOIR_Visible1 () EOIR_LWIR.

Updating EOIR_LWIR's properties

Update the sensor's spectral band edge wavelengths to model the LWIR band.

  1. Open EOIR_LWIR's () Properties ().
  2. Select the Basic - Definition page when the Properties Browser opens.
  3. Select the Spectral tab.
  4. Enter the following values in the Spectral Band Edge Wavelengths panel:
    5. Option Value
    High 12.00
    Low 8.00
  5. Click OK to confirm your changes and to close the Properties Browser.

Generating a new sensor scene

Now that your camera has been set up, review what the sensor scene sees.

  1. Select EOIR_LWIR () in the Object Browser.
  2. Click EOIR Sensor Scene... () on the EOIR toolbar.
  3. Right-click on the sensor scene when the EOIR Sensor Scene window opens.
  4. Select Details... in the shortcut menu.
  5. Move the EOIR Scene Visual Details dialog box when it opens so that it's not sitting on top of the sensor scene.
  6. Select the Fine option in the Scene Detail panel.
  7. Select the BGRY option in the Color Map panel.
  8. Click OK to confirm your changes and to close the EOIR Scene Visual Details dialog box.
  9. Bring the EOIR sensor scene window to the front.
  10. Review the resulting sensor scene.

Temperature texture map in LWIR

You should see the bright hotspots of the wildfires. They're clearly visible for a few reasons. For one, you're no longer seeing the bright daytime light because you're not looking at that part of the spectrum. The sensor scene also has automatic gain control enabled; with this selection, the EOIR capability automatically calculates brightness and contrast such that the brightest scene detail fits within the brightness resolution of the monitor.

Viewing the scene in visible light at night

While you can see these wildfires with a LWIR camera, but is there any way you can utilize your earlier built camera that looked at visible light? Reexamine the region, but this time at night. This way you can see how the previously loaded reflectance and temperature maps appear at night.

  1. Close () the EOIR Sensor Scene window.
  2. Change the time to the following day at 05:00:00.000 in the Current Scenario time field in the Animation toolbar.

    3. For example, if your scenario's Analysis Period Start time is 20 Aug 2027 20:00:00.000, change Current Scenario Time to 21 Aug 2027 05:00:00.000.

  3. Select the Enter key.
  4. Save () your scenario.

Generating another sensor scene

Now that your region of interest is experiencing night, review what the sensor scene sees.

  1. Select EOIR_Visible () in the Object Browser.
  2. Click EOIR Sensor Scene... () on the EOIR toolbar.
  3. Right-click on the sensor scene when the EOIR Sensor Scene window opens.
  4. Select Details... in the shortcut menu.
  5. Move the EOIR Scene Visual Details dialog box when it opens so that it's not sitting on top of the sensor scene.
  6. Select the Red 2 option in the Color Map panel.
  7. Click OK to confirm your selection and to close the EOIR Scene visual details dialog box.
  8. Review the resulting sensor scene.

Temperature texture map at night

You're now looking at the scenario at night. Without daylight, the wildfire hotspot will become more visible. Your image may look different than the one above, but it should be similar.

Saving your work

Close out your scenario and save your work.

  1. Close () the EOIR Sensor Scene window.
  2. Close any open reports, properties, and tools.
  3. Save () your work.

Summary

Your goal in this mission was to understand what a GOES satellite would see when looking at wildfires. You modeled the satellite and its on-board imaging camera. You also loaded in texture maps, specifically the reflectance and temperature texture maps, to model the region at a higher resolution and to see the wildfires. You found you can't see the hotspots in the visible light band during the day, but you can at night. You can also see them better in the LWIR band.

On your own

In this lesson, you simplified the bands you looked at. A typical GOES satellite looks at 16 spectral bands that have are sensitive to changes in different aspects of the earth, such as vegetation, water vapor, ozone, and more. Specifications for and details about these bands can be found in the 2018 paper "Applications of the 16 spectral bands on the Advanced Baseline Imager (ABI)" by T. J. Schmit, S. S. Lindstrom, J. J. Gerth, and M. M. Gunshor at https://doi.org/10.15191/nwajom.2018.0604. On your own, model the individual bands and explore what each band would see and how the sensor scene changes. Take the time to explore the radiometric settings and note how your scene changes with different sensitivities.