The project appears in ArcGIS Pro.

The project's initial map has only one image displayed, Hallstatt_Image.tif, as well as the basemap layers. However, you downloaded many more layers, which are stored in the project folder on your local drive. You'll review the structure and content of that folder.

The Catalog pane appears.

Review the contents and folder organization and note how the source images and rasters are categorized and stored, especially in the DEM, DSM, Imagery, and ScannedMap subfolders. You'll process and explore these items in the tutorial, and you'll also add new derived raster datasets to some of these folders.

It lists three bands that are currently displayed: Red: Band_1, Green: Band_2, and Blue: Band_3. Band_4 (near infrared) is not currently displayed.

You'll now inspect some of the raster dataset properties of this image.

The Layer Properties window appears.

The Source properties report various details for the layer source, such as the data location on disk and the number of rows and columns of pixels.

This shows how many pixels are in the x direction and y direction, respectively. You can multiply the columns and rows to find how many pixels are in the entire image.

As described earlier, there are four bands.

Cell size tells you the pixel size, or resolution, of the raster; in this case, the value is 0.2. Usually, pixel size is the same for x and y, meaning it is a square pixel. But what is the unit of the 0.2 value? You'll check the Linear Unit property under Spatial Reference to determine the unit.

The Linear Unit property for this raster is Meters (1.0). Therefore, each pixel represents a surface on the ground of 0.2 by 0.2 meters, or 20 by 20 centimeters.

The pixel type is unsigned char, which means that each band of the raster contains only positive pixel values. The pixel depth is 8 Bit, which means that a pixel can hold 256 different values. Since the type is unsigned, the values can range between 0 and 255.

Statistics are reported for each of the four bands in the raster dataset.

The Projected Coordinate System property is set to MGI Austria GK Central, a coordinate system commonly used for this region of Austria. As an analyst, for the Upper Austria government, you know that this is the preferred choice for the project, so you won't have to change it. Later in the tutorial, you'll encounter other data layers that use a different coordinate system and that you'll need to reproject.

Next, you'll explore your composite image further with the Image Information pane.

The Image Information pane displays contextual information about the raster layer.

• The Location section shows geographic information about the current location of your pointer.
• The Spectral section shows the image's spectral information at the location of your pointer. This means that it shows the value of the current pixel for each band.
• The Quick View section shows information about the sensor that captured the image, if that metadata is available.

In the spectral information, the Band 1 (red) and Band 4 (near infrared) values are low, as is typical for water.

Notice that the near infrared band value is particularly high. This is because the cell structure of healthy vegetation strongly reflects near infrared light.

The map zooms to the extent of the town and some of the surrounding forest and lake.

Next, you'll add to the map a duplicate of the Hallstatt_Image.tif image. This will allow you to compare various appearance updates you'll make to the original layer.

For now, the two layers are identical.

Next, you'll try out different types of image stretches.

By default, both Hallstatt_ Image.tif and Hallstatt_Duplicate.tif are set to the Percent Clip stretch, which is a good default stretch for imagery. You'll compare that stretch to no stretch.

You'll use the Swipe tool to compare the two images.

You can use the Swipe tool to pull back the layer that is selected in the Contents pane to reveal what is below. In this case, the Swipe tool pulls back the Hallstatt_image.tif layer to reveal what the Hallstatt_Duplicate.tif layer looks like. This allows you to compare the two layers.

You can see that the image with no stretch applied is less vivid and has less contrast.

You'll now turn your attention to the resampling type.

You can see individual pixels and where the village buildings are located.

Use the Swipe tool to compare the Bilinear type and the default type (Nearest Neighbor).

You can see that the Bilinear resampling type has a smoother look, so you don't see the actual pixels. Instead there is a smooth transition between pixels. This is achieved by averaging the value of neighboring cells. In general, the Bilinear resampling method is most commonly used for imagery. You'll keep Bilinear as the resampling type.

Currently, the band combination displayed is Natural Color (red, green, and blue bands), which is closest to what is seen by the human eye. Next, you'll change the band combination to include the near infrared band, which is good to highlight vegetation.

Since you have a near-infrared band, you can create a Color Infrared (or false color) composite, in which the near-infrared band is displayed as red, the red band is displayed as green, and the green band is displayed as blue. Notice how color Infrared highlights vegetation (in red) and water (in black).

For instance, in Natural Color, it was hard to distinguish whether there was any vegetation inside the village. With Color Infrared, the small vegetated areas between the buildings pop up clearly in bright red.

Notice that most of the tasks you have performed to change image appearance, such as setting the Stretch type, are also accessible from the Symbology pane.

Next, you'll create a layer file to persist the stretch, resampling, and band combinations you have applied to the layer.

A window appears that allows you to choose where to save your layer. The saved layer file has a .lyrx extension.

The layer is added to the map. You can see that the layer file saved all the appearance and symbology parameters that you had set.

Only the Hallstatt_Image.tif and the basemap layers remain.

There are two DEMs in the folder: EastDEM40.tif and WestDEM50.tif.

The two rasters appear on the map, side by side.

Unlike multispectral imagery, they are not composed of several bands, and each one contains a single raster. The darker areas represent low elevation and the lighter areas represent high elevation. The symbology seems to change abruptly from one DEM to the other. That's because for now, each raster has its own default stretch applied to it.

You'll now review the properties of the two rasters.

The Raster Dataset Properties window appears.

EastDEM40.tif has a pixel size of 0.4 meters (or 40 centimeters) and WestDEM50.tif has a pixel size of 0.5 meters (or 50 centimeters).

For these DEMs to be part of the same analysis, they need to have the same resolution. The best practice is to resample the smaller pixel size (here, 0.4 meters) to the larger pixel size (here, 0.5 meters). In the next section, you'll use the Resample tool to resample EastDEM40.tif to 0.5 meters.

But first, you'll finish the inspection of the DEM properties by checking the spatial reference.

Both images use the same coordinate system, GK_M31; however, this does not match the coordinate system used for Hallstatt_Image.tif, which is MGI_Austria_GK_Central and the ideal coordinate system for the project. So, you'll need to reproject the DEM data to MGI_Austria_GK_Central.

After inspecting the two DEM files, you determine that you'll need to do three things:

• Resample EastDEM40.tif to 0.5 meters, so that both DEMs are at the same resolution.
• Mosaic (assemble) the two DEMs together, so you only have a single larger DEM raster to work with.
• Reproject the DEMs so that their spatial reference (coordinate system) matches the one from Hallstatt_Image.tif. You can do that in the step when you mosaic the two DEMs.

The Geoprocessing pane appears.

• For Input Raster, choose EastDEM40.tif in the drop-down list.
• For Output Raster Dataset, click the Browse button. Browse to and open the DEM folder, and for Name, type EastDEM50.tif. Click Save.
• For Output Cell Size, choose WestDEM50.tif in the drop-down list. The X and Y fields update to 0.5.
• For Resampling Technique, choose Bilinear.

For elevation data, the Bilinear resampling technique is preferred.

The Snap Raster parameter ensures that the new output raster and WestDEM50.tif will align perfectly.

The new layer EastDEM50.tif is added to the Contents pane. It is similar to EastDEM40.tif, but the cell size has been resampled from 0.4 to 0.5 meters. Next, you'll combine the rasters to create a mosaic using the Mosaic to New Raster tool. In that step, you'll also reproject the mosaic to the desired spatial reference.

• For Input Rasters, choose EastDEM50.tif and WestDEM50.tif.
• For Output Location, browse to and choose the DEM folder. Click OK.
• For Raster Dataset Name with Extension, type Hallstatt_DEM.tif.
• For Spatial Reference for Raster, choose Hallstatt_Image.tif (MGI_Austria_GK_Central).
• For Pixel Type, choose 32 bit float, because your elevation has decimal precision.
• For Number of Bands, type 1, since there is only one band in the elevation datasets.

The new Hallstatt_DEM.tif layer appears. It is a single seamless elevation raster and has been reprojected to the correct spatial reference.

You need to make a small improvement to the raster, so you'll remove it from the map for now.

• Under Raster Information, the Cell Size X and Cell Size Y values are 0.5 (meters).
• Under Spatial Reference, the Projected Coordinate System value is MGI_Austria_GK_Central.

Choosing the appropriate source type ensures that the raster will be rendered properly by default when displayed. When Source Type is set to Elevation, the default stretch type is Minimum Maximum and the default resampling type is Bilinear, which are the best defaults for elevation data.

Your elevation raster is now ready and can be used for analysis.

Because you declared the raster type as Elevation, these defaults are set up optimally for that raster type. You'll now look at a smaller area of the DEM.

Your display is mainly dark gray, which is not very informative.

DRA stands for dynamic range adjustment. It adapts the stretch display by only taking into account the pixel values within the display extent. When you turn on DRA, you can see more shades of gray each time you zoom in on any areas of your raster.

This is an improvement; however, it would be even better to display the elevation with a more appropriate set of colors.

You'll choose a more appropriate color scheme.

Now you have some green colors for low elevations, and yellow and brown colors for the higher elevations.

This symbology gives a good account of the area's elevation. However, since the lake and the nearby coastal land are almost at the same level of elevation, a good supplement to this data can be to display the water bodies as a vector layer on top of the DEM.

The Hallstatt_Lake layer is added to your map and Contents pane.

The Hallstatt_Lake polygon appears in light blue, clearly distinguishing water from land.

You'll save the symbolized DEM layer to a layer file.

You now have the symbolized DEM layer saved so that it can easily be shared. You can also use it to apply the symbology to another layer.

In this section, you changed the appearance of your DEM layer to better show elevation heights. Then you saved it to a layer file that you'll use in the next section to apply the same symbology to another raster.

The Hallstatt_DSM.tif raster has a single band, as is expected for elevation rasters.

This ensures that the default stretch type is Minimum Maximum, and the default resampling type is Bilinear.

The Projected Coordinate System is set to MGI_Austria_GK_Central, which is the preferred coordinate system chosen for the project.

The Hallstatt_DSM.tif raster seems to be well set up and does not need any transformations. You'll now enhance its appearance.

The DSM layer is added to the map, with the lake layer still showing on top.

You'll use the layer file saved for the DEM and apply its symbology to the DSM.

The Symbology pane appears.

The symbology you defined for the DEM is now applied to the DSM.

You'll notice that the DSM is similar to the DEM overall, but its elevations are slightly higher than the ones from the DEM. Also, its surface does not appear as smooth. You'll zoom in to see more details.

Due to the DRA setting, the stretch of the DSM is recomputed to show you more details. You can clearly see the height of the buildings. You can even recognize an occasional tree.

For reference, the image below shows how the village looks from the ground.

You can clearly see some forests on steep slopes.

The Raster Functions pane appears.

The Surface group contains many functions that work with elevation data.

The Slope_Hallstatt_DEM.tif layer representing slope in degrees is added to the map.

The lighter areas correspond to the steepest slopes. The darker areas correspond to the flatter sites. As you can see in the Contents pane, the cell values vary from 0 to 90 degrees. You'll now add a second slope layer, this time in percent rise.

The Slope_Hallstatt_DEM.tif_1 layer representing slope in PercentRise is added to the map.

In Contents pane, the legend for the Slope_Hallstatt_DEM_1.tif raster is visible, showing that the cell values vary from 0 to 2804.54 percent.

Raster functions allow you to create quick, on-the-fly results. This allows you to create a prototype of how you want the result to look. When you are satisfied with the result, you can export the raster to persist the dataset on disk. You'll save the slope generated in degrees.

The Export Raster pane appears.

The new DEM_Slope.tif layer appears. Unlike the preview raster function slope layers, it is a permanent layer that exists on disk.

The Hallstatt_HistoricDrawing.JP2 adds to your map.

This is a standard RGB color image, and it is represented as a three-band raster (red, green, and blue bands).

You'll inspect this layer's properties.

The Raster Dataset Properties window appears.

Due to the accurate georeference, the historical map appears well aligned with the aerial photo. Notice how Hallstatt has not changed much since the creation of the historic map.

Scanned maps generally do not require much processing and are good for background images and visual reference. In this case, the historical imagery of the Hallstatt area offers an opportunity to chart growth and changes that were recorded over time.

In this section, you worked with a scanned image and observed that, as long as it is properly georeferenced, it can be added to an ArcGIS Pro project like any other raster.

Now that you have preprocessed all of the layers, you'll clip them all to the same area of interest.

• Hallstatt_Image.tif
• Hallstatt_DEM.tif
• Hallstatt_DSM.tif
• DEM_Slope.tif
• Hallstatt_HistoricDrawing.JP2

The Contents pane also contains the Hallstatt_Lake feature class and the basemap layers that will not be clipped. You'll add the boundaries of your area of interest.

The feature class layer appears. It contains a single polygon feature, which represents the area of interest (AOI) that you'll use to clip your data.

Since you'll be clipping all raster datasets to the same extent, you'll use the batch geoprocessing mode.

The Batch Clip Raster tool pane appears.

This means that when the Clip Raster tool is run as a batch, a list of rasters will be fed to it to be clipped one after the other.

Make temporary batch tool is selected, as you'll only use the batch process once. If you want to create a reusable tool, you can choose the Save the batch tool option.

The Parameters tab appears.

All the raster layers in your project are added. They will all be clipped, one after the other.

The Output Extent parameter allows you to use the boundary of another layer to define the clipping boundary.

This ensures that the tool uses specifically the extent of the polygon representing the Hallstatt boundary as the clipping geometry, instead of using the extent of the entire feature class, which could be much larger.

After the batch tool has executed, all the clipped layers are added to the map.

Next, you'll clean up the Contents pane.

• Hallstatt_Boundary
• Hallstatt_Lake
• Clip_Hallstatt_Image.tif
• Clip_Hallstatt_DEM.tif
• Clip_Hallstatt_DSM.tif
• Clip_DEM_Slope.tif
• Clip_Hallstatt_HistoricDrawing.tif
• World Topographic Map and World Hillshade basemaps

The rendering and symbolization options that were applied to the input rasters did not carry over to the clipped rasters. You'll quickly reapply the layer files you saved earlier in the tutorial to the clipped rasters.

The preferred imagery rendering options are now applied to the clipped imagery.

The Share tab is used to create various packages and files.

The Package Project pane appears.

This option allows you to save the project package as a file stored on your local computer. You can then choose to deliver the package to stakeholders in the way that is most convenient to you.

The project package contains the raster layers prepared to analyze overtourism in the town of Hallstatt. The package includes an aerial image of the town; DEM, DSM, and slope layers; and a historical map.

Now that all the information is entered, the next step is for the tool to analyze the content to verify that it is all set to create the package successfully.

If there are any errors, you can right-click them to see how to fix them. Once errors have been fixed, analyze the package again. The next step is to create the package.

When the package has been successfully created, a green notification appears at the bottom of the Package Project pane.

The spatial reference is Unknown. This means that you need to specify one.

Notice that the extent values are not zero or near zero. This means that the raster has an extent and is thus properly georeferenced: it has all the information needed to locate it properly on a map, if the coordinate system used were known. So, you only need to fix the lack of a coordinate system definition.

Next, you'll display the image on a map. You'll create a map to avoid any interferences with the layers present on the previous map.

A new map appears.

A warning appears, mentioning that the layer has an unknown coordinate system. The layer does not seem to appear on the map.

It is off the coast of Africa.

You'll now define the projection. By reading some external documentation about the scanned map, you determined that it is using the MGI_Austria_GK_Central coordinate system.

On the map, the Hallstatt_HistoricDrawing.JP2 layer now appears properly located in the Hallstatt area.

The spatial referencing has been updated to reflect the projected coordinate system as MGI_Austria_GK_Central.

In this section, you discovered that a raster dataset was missing spatial reference information, and you used the Define Projection tool to fix it.

The coordinate system is set as GK_M31. This spatial reference is valid, but it does not match the one used for other rasters in the project, MGI_Austria_GK_Central. You'll need to reproject the raster.

This tool is used to reproject a raster dataset from one coordinate system to another. You'll match the spatial reference to the one for Hallstatt_HistoricDrawing.jp2, which you just set to MGI_Austria_GK_Central in the previous section.

• For Input Raster, browse to HallstattImagery, SpatialReferenceData, and DSM, and select DSM.tif.
• For Output Raster Dataset, browse to HallstattImagery, SpatialReferenceData, and DSM. For Name, type Hallstatt_DSM.tif and click Save.
• For Output Coordinate System, choose Hallstatt_HistoricDrawing.jp2 (MGI_Austria_GK_Central).
• For Resampling Technique, choose Bilinear interpolation.
• For Output Cell Size X and Y, keep the values 0.5.

The reprojected DSM layer is added to the map.

In this section, you discovered that a raster had a valid spatial reference, but that it was different from the one chosen for the project, and you reprojected it to the preferred coordinate system.

A new map, Map1, appears. You'll add to the new map the same Hallstatt_Image.tif imagery you used earlier in the tutorial.

Notice that Hallstatt_Image.tif is not lining up correctly with the basemap layer.

You'll inspect the image properties to see if you can identify the issue.

Notice that the spatial reference is set to MGI Austria GK Central.

MGI Austria GK Central is the correct projected coordinate system for this region, so the reason Hallstatt_Image.tif is not lining up with the basemap layer must be something else. You'll explore the properties of the map to determine why.

Notice that the map also has the MGI Austria GK Central coordinate system set. This means that the map is currently using the same spatial reference as Hallstatt_Image.tif.

Since everything looks normal, and you have not yet found the problem, you'll inspect the transformation information.

To convert on the fly between the map's coordinate system, WGS 1984 Web Mercator (auxiliary sphere), and the one from Hallstatt_Image.tif, MGI Austria GK Central, an additional transformation called a geographic datum transformation is needed. By default, the transformation is set to a default value, MGI To WGS 1984. The problem is that it is not correct for this region. In this case, the correct transformation is MGI To WGS 1984 3.

The map updates.

You fixed a misalignment between an image and the basemap by changing the geographic transformation of the map. In this module, you learned about three types of spatial reference issues that can occur and you learned how to fix them.