Visualizing Landsat Data in ArcGIS Pro

Of the hundreds of remote-sensing satellites launched in the past half century, the Landsat program has proved especially valuable for civilian use.

This tutorial will cover the basic visualization of Landsat data in ArcGIS Pro.

Landsat

Landsat 1 was launched on 23 July 1972 and subsequent satellites have provided continuous satellite imagery of the Earth. This is arguably one of the most important scientific enterprises of our time, and if you work with remote sensing, you will probably use Landsat data on multiple occasions.

Landsat 7 (launched 1999) and Landsat 8 (launched 2013) are currently operational. Landsat 9 is scheduled for launch in 2021 and will replace Landsat 7 (NASA 2020).

Landsat VII (NASA 1999)

Landsat satellites complete just over 14 orbits a day, covering the entire earth every 16 days. This gives a temporal resolution of 16 days. Landsat satellites travel in a near-polar, sun-synchronous orbit so they spend as much time as possible looking at sunlit area.

Landsat Orbit Coverage (NASA 2011)

Landsat data is publicly available as scenes (images) that cover an area around 100 miles square. Scenes are designated by a path number (scanning path west to east) and row number (scene location on the path south to north).

The scene below was acquired from Landsat 8 on 10 November 2015. It is path 34, row 32, covering North Central Colorado with Denver in the lower right-hand corner.

Landsat Image of Denver, 10 November 2015 (USGS)

One issue with Landsat is that since it only passes over an area every 16 days, if there happen to be clouds on that day, the ground may be totally or partially obscured and the next availble data will not be for 16 more days. Therefore, depending on the climate in your particular area of interest, you may need to plan for missing data when you plan your research.

Cloudy Landsat Image of Denver, 24 May 2020 (NASA)

Landsat 8 captures data in 11 different bands using cross-track swath, giving Landsat 8 a spectral resolution that includes the visible light, near-infrared, short wave infrared, and thermal infrared bands (USGS 2017).

The diagram below shows the relationship of Landsat bands 1 - 9 to the visible light spectrum. Bands 10 and 11 captured by the Thermal Infrared Sensor (TIRS) are much lower frequencies and are not shown.

Landsat Bands

Landsat 8 spatial resolution varies from 15 meters with panchromatic data (grayscale) to 30 meters with multispectral data, to 100 meters with thermal data (USGS 2017).

Landsat Pixels: Downtown Chicago, IL, 5 July 2020

Landsat data is available in three different levels (USGS 2020).

Downloading Landsat Data

The USGS makes recent and historic Landsat data freely available via the EarthExplorer website.

In order to download data, you need to register (for free). Click the Login button and select Create New Account.

Registering On USGS Earth Explorer

Once you have registered, you can log in and download scenes.

  1. Log in
  2. To get data for a specific location, select a location that you want to explore
  3. For the data set, use
    • Landsat
    • Landsat Collection 1
    • Landdsat Collection 1 Level-1
    • Landsat 8 OLTI/TIRS C1 Level-1
  4. View scene footprints that are in the time frame of interest, that cover your area of interest, and are not covered in clouds. Be aware that for vegetation analysis, summer scenes are better.
  5. Click Download and you will be given multiple options.
    • LandsatLook Images with Geographic Reference are RGB scenes that have been enhanced to be suitable for simple visualization.
    • The Level-1 GeoTIFF Data Product is a file containing all Landsat bands for the scene that is useful for more-sophisticated analysis, like NDVI. Accordingly, the data is much larger than the simple RGB scenes.
  6. Uncompress the data files.
    • The LandsatLook image files are made available in a .zip archive that reduces the size and speeds up download time. Extract the data files from these .zip archive files.
    • The multiband GeoTIFF data product is a .tar archive which contains another .tar archive file. You can extract these using a program like 7-zip.
Finding and Downloading Landsat Data from Earth Explorer

Visualizing LandsatLook Images

LandsatLook images are enhanced scenes created with the three visible light bands (RGB) to form a false-color image suitable for simple visualization. The earth never actually looks like a LandsatLook (or Google Earth) image from space, but the false-color enhancements make areas of vegetation, urban development, water, and bare earth appear the way we imagine the earth to look like. (USGS 2020).

  1. Create a new map.
  2. Add Data and go to the directory where the files were extracted from the .zip archive.
  3. There should be three GeoTIFF (.tiff) files: the image file, a quality file (with QB in the name) and thermal image (with TIR in the file name). Use the image file.
  4. If you zoom in closely you can see the individual square pixels. To smooth out the edges, in the Appearance tab, change the Resampling Type to Bilinear or Cubic.
  5. Create a layout and export as needed.
Visualizing LandsatLook Data

Visualizing Composite RGB Images

To show the scene more-closely to the way it appears from space, you can use the individual red (band 4), green (band 3), and blue (band 2) files from the Level-1 GeoTIFF Data Product data.

  1. Create a new map.
  2. Add Data with the red band (B4), green band (B3), and blue band (B2) GeoTIFF files. They have "B4," "B3," and "B2" in their names, respectively.
  3. Rename the layers so the names are more meaningful.
  4. In the View Tab and Geoprocessing open the Composite Bands tool. This will combine the different file bands into a single layer.
  5. Add your three color bands as Input Rasters. Add B4 (red), then B3 (green), and finally B2 (blue) in that order so the software knows which band is which.
  6. Give the Output Raster a meaningful name (rgb_composite).
  7. The composite raster should be added to the map as a color image.
  8. Hide the individual band layers.
  9. Create a map layout, if needed.
Creating and Visualizing a Composite RGB Layer

Normalized Difference Vegetation Index

Bands other than visible light are useful for analyzing a variety of phenomena. For example, biogeographers commonly use a combination of red and near-infrared bands called normalized difference vegetation index (NDVI) to determine levels of vegetation in a particular area.

NDVI is based on a characteristic that photosynthetic green plants tend to reflect infrared light to avoid overheating. They also reflect green light, which is why they appear green to our eyes. However, they absorb red light to power the process of photosynthesis.

Normalized Difference Vegetation Index

The range of the index is negative one to positive one.

When near infrared is high and red is low, that is when plants are reflecting infrared and absorbing red, that's when NDVI is high and closer to one.

1 - 0
----- = 1
1 + 0

When near infrared is low and red is high, such as with bare ground or water, NDVI is low and closer to negative one.

0 - 1
----- = -1
0 + 1

This phenomena can be used with the Landsat 8 near infrared band (band 5) and the red band (band 4) to calculate an index that is highest in areas with large amounts of vegetation, and lower in areas of low vegetation.

  1. Create a new map.
  2. Add Data with the red band (B4) and near-infrared band (B5) GeoTIFF files.
  3. Rename the layers so the names are more meaningful.
  4. In the View Tab and Geoprocessing open the Raster Calculator tool. This will allow you to calculate an NDVI layer from the red and near-infrared bands.
  5. In the Map Algebra Expression box, enter the formula for NDVI using the raster layer names:

    ("Infrared" - "Red") / ("Infrared" + "Red")
    
  6. Give the Output raster a meaningful name (ndvi).
  7. The layer will automatically be added to the map as a grayscale layer with lighter colors of higher NDVI values (more vegetation) and darker colors of lower NDVI values (less vegetation).
  8. You can use a false color symbology to make the areas of vegetation stand out more clearly.
  9. Although the base map is not visible, you can use it to identify what is located at areas of high NDVI (high vegetation).
Creating and Visualizing an NDVI Layer

Save Your Project

You can save your project as a Project Package to ArcGIS Online in case you need it in the future.

Note that Landsat raster scenes are quite large, so saving the package may take a few minutes.

Saving a Project Package