 In this video we'll show you how to access, download, and work with Landsat imagery using ArcGIS Pro. We're starting out in GloViz, which is the USGS Global Visualization Viewer. GloViz is a wonderful online tool that allows you to search different USGS datasets, apply search criteria, and easily download these data so you can bring them into your GIS for further analysis. GloViz makes it really easy to access the data you want. You simply pan or zoom to the location, select the datasets, apply any search criteria you want, and then you can download that data directly from GloViz to your computer. I'm interested in obtaining a Landsat seen over the Presidential Range in New Hampshire, so I'm going to use the jump tool to enter the latitude and longitude coordinates, format Washington, which is New Hampshire's highest peak, and then just zoom in a little bit. Over on the left, for choose your datasets, I'm going to turn on the radio button for Landsat 8 OLI. As soon as I do this, GloViz begins displaying some Landsat scenes. I can browse through those scenes, but this would take a long time, and there's an awful lot of scenes with a tremendous amount of cloud cover. To improve my search, I'm going to go over to the metadata filter and constrain my search to only those scenes with 0-20% cloud cover and those acquired in the months of June, July, and August. Now I'm able to browse through the collection of Landsat 8 scenes that meet my search criteria. I have the ability to exclude certain scenes from my browse by clicking on the hide scene button. This is useful because I clearly have some Landsat scenes in this case that have extensive clouds over the Presidential Range. The whole scene may not be cloudy, but the area around the Presidential Range is, so I'm going to hide those scenes. If at any time I want to show those scenes again, I can go back to the choose your datasets and click on clear hidden scenes. For any scene, I can move over and click on the view metadata button to obtain all the information about that scene. This will include everything from the percent cloud cover, to the acquisition date, to even specific sensor parameters that I may want to use for calibration. Once I've found the Landsat scene that I'm interested in, I simply move over and click on download. Glovis will package up the Landsat scene. I want to make sure that I click the download button for the Level 1 GeoTiff product as this is the full Landsat 8 dataset. Once the download is ready, I'll save the tar.gz file to my computer where I can uncompress it and make use of the Landsat data. You may need a specialized zip utility to uncompress the tar.gz file that Glovis uses to package up your Landsat data. I recommend 7zip. Here I'm going to first uncompress the gz file, then unpack the tar file. Once the data are uncompressed, you'll see that each separate Landsat band is stored as its own individual GeoTiff file. In addition to the individual GeoTiff band files, I also have some metadata and calibration files that may be useful to me. The metadata is stored in the underscore mtl file. The opening it up will allow me to read through the metadata and access key information, particular things like the date the scene was acquired on, the path row, cloud cover, and calibration parameters. Although it's certainly possible to add each individual band and work with it separately within ArcGIS, what I really want to do is combine all these bands together into a single multi-spectral composite image. To make the bands that I'm interested in working with into a single multi-band raster data file, I'm going to use the Composite Bands Geoprocessing tool. The Composite Bands Geoprocessing tool will produce a new raster file containing all of the bands that I enter into the Composite Bands tool. My input rasters are going to be all the bands that I'm interested in. In my case, I'd like to load in bands 1 through 7. These are the 30 meter multi-spectral bands that are commonly used in a multi-band raster data stack. Once I have these rasters selected, it's imperative that I pay attention to their order in the Composite Bands tool. The order they're listed in the Composite Bands tool determines their band order within the multi-band raster output. In this case, you can see that band 1 is accidentally at the bottom, so I'm going to need to reorder the bands by moving band 1 up to the top so that it becomes layer 1 in the resulting output raster data set. I'm going to save my Composite Raster as a geotiff file by navigating to the directory that I want to store it and typing .tif at the end of the file extension. To store it as imagined format, I would replace the .tif with .img, or alternatively, I could save it inside a geodatabase. Running the Composite Bands tool stacks all of these rasters into a new single composite band raster file, and in my case, I've stored it in geotiff format. Once the Composite Bands tool is finished running, it's important to go into the properties of my new layer to confirm that the tool ran as expected. By going into the source tab and raster information, I can access the key source information about this data set. Specifically, I want to check the number of bands, which is 7. I see that the cell size is 30 by 30, which is the resolution of these particular Landsat 8 bands. The format is geotiff, and the pixel depth is 16-bit, just as I expected. The best way to adjust your symbology for imagery is to move over to the appearance tab and go to band combinations. You can see that ArcJS already has two defaults, natural color and color infrared. Unfortunately, these band combinations are for aerial imagery, not for Landsat 8 imagery. Under the appearance tab, I'm going to go to Custom, and I'm going to create custom band combination settings for some common Landsat 8 band combinations, 321, 432, etc., until I have all the band combinations set. This will allow me to quickly and easily symbolize my Landsat 8 multi-band composite image. Now that I have my band combinations set, I can make some other changes to the appearance of my imagery. Activating DRA, or Dynamic Range Adjust, automatically modifies the contrast stretch within my field of view as I pan around the image. You may find that certain stretch types help you pick out particular features of interest, but it's important to keep in mind that there may not be one single stretch type that's optimized for all of the features you're interested in on the landscape. Remote Sensing is an art and a science. Detoning the best symbology takes a bit of trial and error. To make it easier for me to compare the different band combinations, I'm going to copy and paste the raster layer in my table of contents, and adjust the band combination for each one of these layers, and then give each layer a meaningful name for quick reference. I'm interested in doing some vegetation analysis around the presidential range, so I'm going to use the Locate tool to find Mount Washington. Typing in Mount Washington will pull up a list of similar names, and then we'll display the list of those locations that match on the map. Now I'm going to compute in DVI the Raster Function Index. This is very easy to do using the raster function in DVI, so I'm opening up the Raster Functions tab, searching for NDVI, and clicking on it to launch the function. Any one of my layers can be entered as the input raster. It's imperative that I understand my bands. The visible band for this Landsat 8 composite is band 4, and the near infrared band is band 5. Entering the bands incorrectly here will produce invalid NDVI output. I want my NDVI values to have a range from negative 1 to 1, so I'm going to check the box for scientific output, and then when I'm ready to execute the NDVI tool, I'm going to click Create New Layer. The Raster Function NDVI creates a virtual layer pointing back to my original imagery so it executes almost instantaneously. After renaming my layer on the table of contents, I'm going to head over to the Symbology tab. I'm going to make sure I'm not displaying any background values of 0, and then I'm going to adjust the stretch type until I come up with something that I think displays the NDVI values the best. Finally, I'm going to tinker with the color scheme, choosing a ramp that goes from red, meaning no vegetation, to green, which means higher concentrations of vegetation. As I explore my NDVI layer, I can see that areas with high amounts of vegetation, such as the forested areas, have high NDVI values displayed as green, and features that are devoid of vegetation, such as roads, and water bodies, and mountain tops have much lower NDVI values, symbolized by the red color. Clicking on any individual pixel will display the NDVI value in the identify window. One of the advantages of NDVI is that it minimizes the effect that lighting has on my data. As we pan around the presidential range, we see that forested areas in shattered valleys are considerably darker than forested areas that are in unshattered regions. However, when we move over to the NDVI layer, we see that the effect of shadowing has been greatly reduced. This is because lighting has a similar effect on both the near infrared and red bands, and thus when we apply NDVI, which is a ratio between those bands, the effect is minimized. In this video, we showed you how to use GloViz to access the Landsat scene that you're interested in, create a multi-band raster composite within ArcGIS, display the multi-spectral imagery using different band combinations, and then finally compute NDVI.