Provide the students printed or digital copies of the worksheet. This includes information from the background information, which is needed to successfully analyse the data.
Install the LEO Works 4.0 software from http://leoworks.terrasigna.com and ensure students can access it on their computers. It is required in order for them to perform this activity.
Introduce the topic by asking the students what they know about observing the Earth. How can we observe the Earth, and what is remote sensing? What information can we collect through remote sensing and what are its uses? The most obvious answers should include weather satellites.
Ask the students if they know where the images in Google Maps or Google Earth come from. The source of the images is mentioned at the bottom of the screen. They might find names like SPOT or Landsat. Ask students to choose one of these satellite campaigns to research. Let them compile information on satellite launch dates, their orbits and countries of origin.
The activity is set up as a step-by-step process for analysing real satellite data. The exercise is interspersed with questions to evaluate the students’ understanding as well as to point to the relevance of the satellite data. Some tasks involve very similar and repetitive procedures that are used to reinforce the steps used in the analysis.
Analysis of satellite imagery data using LEO Works 4.0 This activity introduces the basic tasks for processing and analysing remote sensing satellite data.
The installed version already contains some example datasets that can be used for the purpose of the exercise. They are stored in the leoworks.data folder. When using MS Windows, it can be found in the user directory. From the existing datasets, the one labelled Venice will be used.
Reading the data
After it is launched, the software presents its workspace as shown in Figure 9. Open the file Venice_Landsat_ETM_multispectral.tif by clicking on the first icon in the menu bar or via the menu File\Open\Single File Dataset(s). A window appears from which the file is selected (Figure 10).
Figure 9: LEO Works 4.0 workspace. The menu bar contains procedures and tools for displaying and analysing the data. There are three windows below that provide a list of the loaded data sets and image displays.
The file contains seven individual images obtained in seven bands of the camera “Enhanced Thematic Mapper Plus (ETM+)” of NASA’s Landsat 7 satellite (Table 2) covering the vicinity around the city of Venice in Italy. When the window Specify Subset appears, acknowledge by clicking OK.
Figure 10: Window for file selection.
The data automatically appear in the window to the upper left. The element Bands can be expanded by clicking on it to show the list of the seven images (Figure 11). They are labelled band_1 to band_7 and correspond to the spectral bands of Table 2.
Table 2: List of the seven spectral bands of the “Enhanced Thematic Mapper Plus (ETM+)” camera of the Landsat 7 satellite (Source: NASA; column with colours is not revealed to students).
Landsat 7 | Wavelength (µm) | Resolution (m) | Colour --- | --- | --- Band 1 | 0.450 – 0.515 | 30 | Blue Band 2 | 0.525 – 0.605 | 30 | Green Band 3 | 0.630 – 0.690 | 30 | Red Band 4 | 0.750 – 0.900 | 30 | NIR Band 5 | 1.550 – 1.750 | 30 | SWIR Band 6 | 10.400 – 12.500 | 60* (30) | Thermal IR Band 7 | 2.090 – 2.350 | 30 | IR
Action: Fill in the column labelled “Colour” of Table 2 for bands 1 to 5. Use the information provided with the introduction of the spectral indices.
Figure 11: List of loaded data.
A double-click on the band name issues a command that displays the image.
Action: Do this for band 1 first.
You will see an image of the city of Venice and its surroundings. It consists of different shades of grey, a greyscale display, that correspond to the brightness or intensity measured at a given spot (pixel) in the image. The contrast is quite poor and should be adjusted using the tool Interactive Stretching.
Action: Find the corresponding button or menu item.
You can explore the meaning of the different buttons when moving the mouse pointer above them. After clicking, a new window appears as shown in Figure 12.
Figure 12: Windows for adjusting the contrast levels using Interactive Stretching. The window contains two graphs, one showing the distribution of pixel values in the image and the other used for display. Adjustments can be made by moving the flags. The setting is adopted by clicking Apply. Left: Distribution before adjustment; middle: after adjustments were made; right: the same shown in logarithmic scale, displayed by clicking the bottom left icon to the right.
The scaling of the contrast is accomplished by moving the flags. The window provides additional tools like displaying the data in a logarithmic scale.
Figure 13: Image of band 1 before (left) and after (right) adjusting contrast scaling.
Action: Display the seven images and adjust their scaling.
Creating a realistically coloured image
After having adjusted the contrast settings, a colour picture can be produced by superposing three images. A bad contrast will lead to pale colours. For a realistic impression, the three bands representing blue, green, and red have to be selected.
Action: Find the corresponding bands in Table 2. If you need help assigning colours to wavelengths, research the missing information on the internet.
Select View\New RGB View. A new window appears (Figure 14). Choose the matching bands for red, green, blue and click on OK.
Figure 14: Window for selecting the bands to be used for constructing an RGB image.
A new colour image appears. If necessary, you can adjust the colours with Interactive Stretching.
Actions: Inspect the result and try to identify landscape elements (buildings, water, soil, vegetation).
Find the airport. Figure 15: Three-colour image (RGB) created from satellite data of Venice.
Creating a false colour image
You have just produced an RGB image that corresponds to the natural impression of colours how humans see it. It consists of the colours red, green, and blue. Imagine other species like bees or snakes. They can see other parts of the electromagnetic spectrum like the ultraviolet (UV) or the infrared (IR). We can simulate such kind of vision skills by combining different spectral bands than red, green and blue. The resulting colours do not match the natural ones we can see with our eyes, but they can help making interesting details visible.
Use the knowledge that the chlorophyll in green plants absorbs red light but reflects infrared radiation.
Actions: Produce a three-colour image from the near infrared (ca. 0.8 µm), red (ca. 0.65 µm), and green (ca. 0.5 µm).
What are the corresponding bands?
Put the infrared band in the red channel, the red band in the green channel and the green band in the blue channel of the RGB image.
Compare this image with Figure 15. Where do you find green vegetation?
Can you distinguish between green crops and green water (algae)?
What does uncultivated land look like?
Figure 16: False colour image produced by combining the green, red and infrared bands.
Analysis via NDVI
You have already seen in the information section that the NDVI is a colour or spectral index
NDVI = (NIR - R) / (NIR + R)
that is particularly sensitive to green vegetation. The index provides a number that objectively reflects the degree of vegetation. Remember that there is a jump in the spectrum of green vegetation between the red (R) and the infrared (NIR) range (Figure 5). You will now construct a map that contains the NDVI for every image pixel. LEO Works provides a tool for this.
Action: Find the NDVI tool.
After activating that tool, a new window pops up (Figure 17). You select the dataset at the top. The next line contains the name of the image to be constructed and how it appears in the list of data. A name is already suggested. Select the suitable bands in the following rows below.
Action: What are the bands to be selected here? The answer can be found in the section about the NDVI and Table 2.
The formula is shown below. In the beginning, the variables show “null” as long as no band is selected. It is automatically updated as soon as you select the band corresponding to the NIR and the R bands. The NDVI map is created by clicking OK. A suitable false colour representation is chosen automatically, which helps identify green vegetation. However, the scaling of the colour table must be adjusted.
Figure 17: Window of the NDVI computing tool.
The tool Color manipulation is used for this. Move the flags of maximum value to the upper end of the distribution histogram. Then move the flag of the minimum value until the first green coloured flag reaches a value of 0.2 (Figure 18). The new setting is adopted after clicking Apply.
Figure 18: Window that allows adjusting
The result should look similar to Figure 19. You see large white zones with alternating yellow and green areas in between.
Action: Compare the NDVI map with the previously produced images. What can you say about the degree of vegetation in the green and yellow areas?
Would you be able to detect a seasonal change, if the images were taken at a monthly rate?
What would be the situation during a draught?
Figure 19: Map of the NDVI in the vicinity of Venice, based on Landsat 7 satellite data.
Analysis via MNDWI
You will now use the satellite data to identify open wetland with the MNDWI. MNDWI = (G - SWIR) / (G + SWIR)
Especially small ponds and narrow rivers are not easily found on naturally coloured images. The MNDWI can theoretically be constructed using the NDVI tool. However, the correct assignment of the corresponding bands can be confusing. LEO Works provides a generic tool to does all kinds of mathematical operations with the spectral bands. The procedure is called Band arithmetic.
Action: Find the tool in the tool bar or in the menu and open it.
Similar to the tool for calculating the NDVI, you first select the dataset and the name of the image to construct (Figure 20, left). Then click Edit expression … for opening a new window (Figure 20, right). This is where you enter the formula for calculating the spectral index.
Figure 20: Window for doing mathematical operations on the spectral band images.
Action: Find out what bands are needed to calculate the MNDWI.
From the formula of this index you see that you divide the difference of the intensities of the reflected light measured in two spectral bands by their sum. Be careful with placing operators and brackets according to the formula.
After confirming the formula, it also appears in the first window. The procedure is executed by clicking OK.
The resulting image presents the values of the index in greyscale. To improve the readability of the map, you can change colours to certain values via the Color manipulation tool. A colour table is assigned by clicking on the symbol “Import palette” as shown in Figure 21.
Figure 21: Colours can be assigned to image values to improve the readability of the map.
Action: Select the file gradient_red_white_blue.cpd.
Adjust the flags such that the values are well covered and the central flag represents the value 0.
What is the colour coding of water?
Compare the MNDWI map with the previously produced images. Would you be able to find wetland also on the naturally coloured image?
Can you imagine situations for which the identification of water levels can be important or even life-saving?
What would the image look like, if the water level rises?
If you have time, produce a map of the NDMI. Compare it with the other results.
Figure 22: Map of the MNDWI in the vicinity of Venice based on Landsat 7 satellite data.
For advanced students
Two additional datasets are provided that show the same area in January and July 2002. The already analysed dataset is from August 2001.
Action: Load the two additional datasets like the previous one.
Produce naturally coloured RGB images.
Produce images of the NDVI distributions.
Compare the results from the three datasets obtained at different dates during the year. Indicate how the vegetation changes.
In light of the results, describe and explain the advantage of satellite remote sensing.