In this exercise, we look at using PALSAR imaging radar to identify tropical deforestation for agricultural expansion in Liberia. PALSAR is a radar sensor on the Japanese Space Agency (JAXA) satellite called ALOS. With longer wavelengths than Sentinel 1 it is much better suited to mapping forests.
From 2007 to 2010, and then annually from 2015, JAXA have stitched together all the images from the PALSAR to create a seamless global image, and we use this in this Earth Blox example workflow.
This example workflow can be found in the workflow library. It is called “observe deforestation in Liberia”.
How to see the impacts of deforestation
TABLE OF CONTENTS
Step 1: Import the area
- Attached to this article is a file called Liberia-deforestation.geojson. Download it.
- This is a file that describes a rectangular area near Greenville in Liberia.
- Use the Upload Area button on the right hand side of the map to upload this file into Earth Blox, which will automatically give it an Area Number (which will be Area 1, unless you have another area already on the map).
- It should look like this:
Step 2: Set up the radar data block
- From the toolbox, go to INPUT->SATELLITE IMAGERY and choose the radar block. It will already have the visualisation block in place.
- Select the PALSAR Yearly Mosaic from the dropdown menu.
- Select Area 1 from the Area dropdown.
- For the From and To entries, choose 2007 (for both).
- Leave everything else as default. (See below if you want an explanation of each item).
- Give the layer a meaningful name, like "2007".
- Your block should look like this:
Step 3: Duplicate the workflow
- Use a right-mouse-click to see the block menu and choose Duplicate.
- This creates a duplicate of the workflow above.
- For this new workflow, simply change the dates to the latest year available (we are using 2018 for this example).
- Remember to change the Layer name to the new year you have chosen.
- You now have two workflows that are identical except for the year. When you run these workflows, two layers will appear on the map: one for each year.
Step 4: Interpreting the map output
- Use the Layers button on the map to adjust the visibility of the layers.
- By using the slider to change transparency, or by toggling the layer on and off, you will be able to see the difference between the two years.
- In this kind of radar, the brighter the image the more forest there is. Note how some darker patches have appeared in the 10+ year period signifying that the forest has been cut down in these areas.
- The later image should look like this:
Step 5: Investigate what is causing the deforestation
- Zoom right into one of the dark patches (we suggest the area within the red circle as a good location).
- Switch the background map (Basemap) from ROADMAP to SATELLITE.
- Now use the eye icon to toggle the visibility of the radar data off. If you have zoomed in far enough, you will start to see a hexagonal pattern that is typical of oil palm plantations. It will look like this:
Over the past 50 years palm oil has been a dominate cash crop in Southeast Asia, but it is now being adopted elsewhere. Liberia is well known as one of the African countries that is embracing oil palm to support economic development, but at notable risk to its forests.
Final Note: What do the other values refer to in the radar block?
- The option to choose between dB and DN refers to the way the image is scaled. DN=digital number, which is the linear scaling of the radar data. dB=deciBels, which is a logarithmic scaling of the data and often makes it easier to see the features in the image.
- The HH and HV options in the RGB output refer to the polarisation channel. Radar signals can be transmitted or received as either horizontal (H) or vertical (V) waves. The HH channel is both transmitting and receiving in H, whereas the HV channel is transmitting H and receiving in V. (the "cross-polarisation").
- The Composite option allows you to combine multiple dates together to create a new image. In the above example, you could choose, say, 2007 to 2011 in one block, and using the Mean option create an image layer that is the average of those five years. The other options would give you the minimum or maximum value, or the median.
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