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  7. Lab 12 - Landsat 8 Imagery

Lab 12 - Landsat 8 Imagery

This lab is a gratefully modified version of lab 11 from Bradley A. Shellito’s Introduction to Geospatial Technologies p814

Learning Objective

This chapter’s lab builds on the remote sensing basics of Chapter 10 and returns to using the MultiSpec program. In this exercise, you’ll be starting with a Landsat 8 scene and creating a subset of it with which to work. During the lab, you’ll examine the uses for several Landsat 8 band combinations in remote sensing analysis. The goals for you to take away from this exercise:

  • Familiarize yourself further and work with satellite imagery in MultiSpec
  • Create a subset image of a Landsat 8 scene
  • Examine different Landsat 8 bands in composites and compare the results
  • Examine various landscape features in multiple Landsat 8 bands and compare them
  • Apply visual image interpretation techniques to Landsat 8 imagery

Outline:

Submission requirements

Materials (click to download)

Data Name Description
GEOG111_Lab2Questions.docx Handout to turn in
You are answering the questions (laid out in the word doc above and also included in the tutorial below) as you work through the lab. Use full sentences as necessary to answer the prompts and submit it to blackboard. Copy the folder Chapter 11, which contains a Landsat 8 OLI/TIRS satellite image (called LandsatJuly) of northeastern Ohio from July 18, 2018, which was constructed from data supplied via EarthExplorer. The Landsat 8 image bands refer to the following portions of the electromagnetic (EM) spectrum in micrometers (µm): * Band 1: Coastal (0.43 to 0.45 µm) * Band 2: Blue (0.45 to 0.51 µm) * Band 3: Green (0.53 to 0.59 µm) * Band 4: Red (0.64 to 0.67 µm) * Band 5: Near infrared (0.85 to 0.88 µm) * Band 6: Shortwave infrared 1 (1.57 to 1.65 µm) * Band 7: Shortwave infrared 2 (2.11 to 2.29 µm) * Band 8: Panchromatic (0.50 to 0.68 µm) * Band 9: Cirrus (1.36 to 1.38 µm) * Band 10: Thermal infrared 1 (10.60 to 11.19 µm) * Band 11: Thermal infrared 2 (11.50 to 12.51 µm) Bands 1–9 are sensed by OLI, while bands 10 and 11 are sensed by TIRS. Keep in mind that Landsat 8 imagery has a 30-meter spatial resolution (except for the panchromatic band, which is 15 meters).

Open raster data

Display in 5-4-3

11.2 Using Landsat 8 Imagery Bands The Landsat OLI/TIRS image has several different bands, each with its own use. (See Table 11.1 for information on which band represents which wavelengths.) For instance, looking at the entire Landsat scene now (the 5-4-3) combination, you have a broad overview of a large slice of northern Ohio using the near-infrared, red, and green bands.

  1. Vegetated areas (such as grass or trees) are reflecting a lot of near-infrared light in the red color gun, causing those areas to appear in shades of red. However, there are a lot of other things in the image as well. Examine the Landsat scene and zoom in on some of the cyan areas on the lakeshore of Lake Erie and then answer Question 11.1. Question 11.1 The features on the image in cyan are largely urbanized and developed areas. Why are they displayed in cyan on this image with the 5-4-3 band combination?
  2. Open the LandsatJuly image again—but this time use band 10 in the red color gun, band 10 again in the green color gun, and band 10 again in the blue color gun. These settings use band 10 in all three guns, so you see only this band in grayscale. This version of the LandsatJuly image loads in a separate window.
  3. Arrange the two windows (LandsatJuly in the 5-4-3 combination and LandsatJuly in the 10-10-10 combination) side by side so you can see both of them together. Keep in mind that band 10 in the Landsat 8 imagery is one of the thermal bands sensed by TIRS. Examine both of the Landsat scenes and answer Question 11.2. Question 11.2 What do the brighter places on the 10-10-10 image correspond with? Why do these places mostly appear brighter than their surroundings in the 10-10-10 image?
  4. Close the 10-10-10 version of LandsatJuly.5. Open the LandsatJuly image again, this time using a 9-9-9 combination (i.e., load the image with band 9 in the red gun, band 9 in the green gun, and band 9 in the blue gun). A new window opens with this image, which is band 9 in grayscale. Place this 9-9-9 image side by side with your original 5-4-3 image.
  5. Band 9 in the Landsat 8 imagery is designed for detecting cirrus clouds in imagery. Answer Question 11.3. Question 11.3 Where are the cirrus clouds in this section of northern Ohio in the image? Why are they so hard to see in the regular 5-4-3 image?
  6. Close the 9-9-9 image when you’re done so you’re only working with the 5-4-3 image. 11.3 Subsetting Images and Examining Landsat Imagery Right now, you’re working with an entire Landsat 8 scene, as shown on the next page, which is an area roughly 170 kilometers ×183 kilometers. For this lab, you will focus only on the area surrounding downtown Cleveland (i.e., path 18, row 31). You will have to create a subset; in essence, you will “clip” out the area that you’re interested in and create a new image from that

imcenter

  1. Zoom to the part of the LandsatJuly image that shows Cleveland (as in the following graphic):

imcenter 2. In the image, you should be able to see many features that make up downtown Cleveland —the waterfront area, a lot of urban development, major roads, and water features. 3. In order to create a new image that shows only Cleveland (a suggested region is shown in the graphic above), from the Processor pull-down menu, choose Reformat and then choose Change Image File Format. 4. You can draw a box around the area you want to subset by using the cursor, and the new image that’s created will have the boundaries of the box you’ve drawn on the screen. However, for the sake of consistency in this exercise, use the following values for Area to Reformat: a. Lines: ► Start 9015 ► End 10261 ► Interval 1 b. Columns: ► Start 847 ► End 2143 ► Interval 1 Leave the other defaults alone and click OK

  1. In the Save As dialog box that appears, save this new image in the Chapter 11 folder with the name clevsub.img. Choose Multispectral for the Save as Type option (from the pull-down menu options next to Save as Type). When you’re ready, click Save.

  2. Back in MultiSpec, minimize the window containing the LandsatJuly image.

  3. Open the clevsub image you just created in a new window. (In the Open dialog box, you may have to change the Files of Type that it’s asking about to All Files to be able to select the clevsub image option.)

  4. Open the clevsub image with a 5-4-3 combination (band 5 in the red gun, band 4 in the green gun, and band 3 in the blue gun).9. Use the other defaults for the Enhancement options: stretch set to Linear and Min-max set to Clip 2% of tails.

  5. In the Set Histogram Specifications dialog box that opens, select the Compute new histogram method and use the default Area to Histogram settings.

  6. Click OK when you’re done with the settings. The new subset image shows that the Cleveland area is ready to use.

  7. Zoom in on the downtown Cleveland area, especially the areas along the waterfront. Also open Google Earth Pro and compare the Landsat image to its very crisp resolution imagery. Answer Questions 11.4 and 11.5. Question 11.4 What kinds of features on the Cleveland waterfront cannot be distinguished at the 30-meter resolution you’re examining in the Landsat image? Question 11.5 Conversely, what specific features on the Cleveland waterfront are apparent at the 30-meter resolution you’re examining in the Landsat image? 11.4 Examining Landsat Bands and Band Combinations

  8. Zoom in on the Cleveland waterfront area in the clevsub image, so you can see FirstEnergy Stadium, home to the Cleveland Browns, and its immediate surrounding area.

  9. Open another version of the clevsub image using the 4-3-2 combination.

  10. Arrange the two windows on the screen so that you can examine them together, expanding and zooming in as needed to be able to view the stadium in both three-band combinations.Question 11.6 Which one of the two band combinations best brought the stadium and its field to prominence? Question 11.7 Why did this band combination best help in viewing the stadium and the field? (Hint: You may want to do some brief online research into the nature of the stadium and its field.)

  11. Return to the view of Cleveland’s waterfront area and examine the water features (particularly Lake Erie and the river). Paying careful attention to the water features, open four display windows and then expand and arrange them side by side so you can look at differences between them. Create the following image composites, one for each of the four windows: a. 4-3-2 b. 7-6-5 c. 5-3-2 d. 2-2-2 Question 11.8 Which band combination(s) is best for letting you separate water bodies from land? Why?

  12. Return to the view of Cleveland’s waterfront area. Focus on the urban features and vegetated features. (Zoom and pan where necessary to get a good look at urbanization.) Open new windows with the following new band combinations and note how things change with each combinations: a. 7-5-3 b. 2-3-4 c. 5-4-3

Question 11.9 Which band combination(s) is(are) best for separating urban areas from other forms of land cover (i.e., vegetation, trees, etc.)? Why?

Submission

All you have to turn into blackboard for this week is the final image you created above.