 In this video, we'll take a look at pixel-based image-differencing change detection techniques in ArcGIS using the Yosemite Rimfire as an example. The Yosemite Rimfire started on August 17, 2013 and 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, or 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, and in both cases I've displayed the images as 6.54 band composites, meaning that shortwave 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 15th 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 15th scene and the September 16th 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 6.54 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 15th and September 16th of 2013. In order to get an estimate of the burned area, we'll need to classify the image. We'll do so using an unsupervised pixel-based technique. We'll start off by going over to Arc 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 is spectrally distinct from the other 9 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 in 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 will enter into the raster calculator says if the ISO data classification is equal to 1 or if the ISO data classification is equal to 2, produce 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, 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 0. 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'll allow us to more easily compute the actual area of change so that we can quantify the effect of the Yosemite rimfire. Once our raster layers finish converting to polygon format, we'll 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 within a geodatabase, we can go into the attribute table and use the shape area field to get an estimate of the area that has burned. In this video, we showed an example of a simple, straightforward approach to change detection using image differencing and unsupervised classification. It's important to know that change detection 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.