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JARLATH O'NEIL-DUNNE: In this video, we'll take a look at pixel-based image differencing change detection techniques in our ArcGIS, using the Yosemite Rim Fire as an example.
The Yosemite Rim Fire started on August 17, 2013. It wasn't fully contained until late October of that year. It burned over 250,000 acres in the Sierra Nevada mountain ranges in California.
Landsat satellite imagery, with its 30-meter resolution and multi-spectral capabilities, is an excellent data source for change detection. The USGS Globalization Viewer, Glovis, makes it easy to browse through landsat scenes. We see the landsat 8 image was acquired pre-fire on August 15. And that we have another image on September 16 that shows the majority of the burned area. We'll download both of these images and use them in our example.
After creating composite band images for both landsat scenes, I loaded them into ArcGIS. In both cases, I've displayed the images as 654 band composites, meaning that short-wave infrared, near infrared, and red light are assigned to the red, green, and blue color guns, respectively. These band combinations are excellent for vegetation analysis.
Using our image analysis tools and the swipe capability, we can swipe away the August 15 scene and clearly see the effects of the fire. To better illustrate the change that's occurred between these two scenes, we use the difference tool, also in the Image Analysis window.
The first step is to select both scenes in the Image Analysis window. Then scroll down and click on the button for Differencing. The output of the Image Difference function is a new image that contains the band-by-band differences between the pixel values from the August 15 scene and the September 16 scene.
I'll rename this new image, Difference, in the layer tree, and then go in and adjust the symbology, so that it, too, has a 654 band combination. It's important to note that the output of image analysis functions, such as the difference tool, only exist within the ArcMap document. And you'll need to save your image, if you want to make it permanent.
The output of the Difference function clearly displays the area that's burned between August 15 and September 16, 2013. In order to get an estimate of the burned area, we'll need to classify the image. We will do so using an unsupervised pixel-based technique.
We'll start off by going over to our toolbox and opening the ISO Cluster Unsupervised Classification tool. This tool applies the ISO data unsupervised classification to the input image. The output raster layer will contain a specified number of classes. We're going to set this as 10. This means that each one of the 10 classes is spectrally similar, based on the difference image.
Next, we'll give our output raster layer a name, using the .img extension to specify it as imagined format. And then click OK to run the tool.
As expected, our output raster layer consists of 10 classes. Once again, each one of these classes especially distinct from the other nine classes, based on the ISO data algorithm. In order to isolate those classes that actually reflect the burned area, we'll have to do some data exploration. As we've done before, we'll make use of the Image Analysis tools and the swipe function, to compare our output classified image to our input difference layer, and also our original Landsat scenes.
It appears that most of the change is contained within the first two classes, Classes 1 and 2. So we'll use the raster calculator to create a brand new image, containing the result of only these two classes.
The expression we will enter into the raster calculator says that the ISO data classification is equal to 1, or that the ISO data classification is equal to 2, producing a new raster image. The output raster image will consist of cells that have values of either 0 or 1. Cells with a value of 0 means they don't meet the criteria. Cells with a value of 1 means that they've met the criteria, that is, their original cell values were either 1 or 2.
In our new output raster layer, those areas that correspond to change, Classes 1 and 2, a have a cell value of 1. So we'll make the cell values of 0 transparent.
Using our Swipe tools, we see that we've done a fairly decent job of capturing change through the combination of image differencing and unsupervised classification.
We can now use the Reclassify tool to create a new raster layer that removes the cells that have a value of zero, that is, all 0 cells will have a value of No Data, and will only retain the change class, Class 1.
Converting the output of the Reclassify tool into Polygon format will allow us to do two things. First of all, we could manually edit the polygon layer, to deal with errors and inconsistencies. Second, it will allow us to more easily compute the actual area of change so that we can quantify the effect of the Yosemite Rim Fire.
Once our raster layer is finished converting to polygon format, we will adjust the symbology, so that we can more easily view those areas of change due to the fire. Because this particular feature class is stored in the geodatabase, we can go into the attribute table and use the Shape Area field, to get an estimate of the area that is burned.
In this video, we shared an example of a simple and straightforward approach to change detection, using image differencing and unsupervised classification. It's important to note that change direction can be a very complex process. And it may be important to take into account radiometric differences between your input scenes and also apply a host of post-processing techniques, in order to improve the results of your classification.