Create and use a mosaic dataset

In this tutorial, you'll focus on the management and storage of large volumes of imagery and remote sensing data in ArcGIS Pro. As a remote sensing and GIS analyst for the Upper Austria government, you have received a collection of orthophotos that you must manage and share effectively with stakeholders. You will explore the challenges of working with multiple images individually and create a mosaic dataset that will allow you to work with the collection of seamless images, making them accessible and turning them into useful information products for both visualization and analysis. Next, you'll enhance the mosaic dataset by applying and incorporating analysis functionality and, finally, add and use a catalog of imagery from an ArcGIS Living Atlas mosaic dataset.

This tutorial was last tested on July 25, 2023, using ArcGIS Pro 3.1 . If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.

Requirements

• ArcGIS organizational account with a Publisher, Facilitator, or Administrator role in an ArcGIS organization (see options for software access)
• ArcGIS Pro (see options for software access)

Work with multiple raster datasets

First, you'll focus on the management and storage of large volumes of imagery and remote sensing data in ArcGIS Pro. Big collections of raster data are being collected and assimilated at a rapid pace, and these datasets have significant value when the information inside them is shared and disseminated. ArcGIS Pro provides extensive enterprise image management capabilities and is used by organizations in a wide range of industries to manage imagery holdings, making them accessible and turning them into useful information products for both visualization and analysis.

Managing imagery and raster data efficiently and correctly is key to ensuring accessibility. For this, ArcGIS Pro uses mosaic datasets, a type of geodatabase structure, to manage imagery. In addition, ArcGIS Pro includes all the technology and associated tools needed to build and maintain mosaic datasets.

As a remote sensing and GIS analyst for the Upper Austria government, you will explore the challenges of working with multiple images individually and create a mosaic dataset that will allow you to work with the collection of seamless images, making them accessible and turning them into useful information products for both visualization and analysis.

Why use a mosaic dataset

This module covers some of the challenges of working with multiple images individually. Even though adjacent images may appear as a single image when displayed in a map, they are separate layers. Working with the individual layers is challenging when project requirements call for the application of any kind of enhancement or analysis because each layer must be handled separately.

1. Download the mosaic dataset tutorial image collection. If prompted, browse and choose a file location to save the downloaded image collection.

   Note:

   The orthophoto image collection is large and consists of 73 individual images that may take some time to download. These images represent a collection of orthophotos that cover the region in and around the historic town of Hallstatt.

2. Locate the Orthophotos.zip downloaded file on your computer. Right-click the file and extract it to a location you can easily find, such as your C drive.
3. In your unzip location, verify the creation of a folder named OrthoPhotos containing the image collection.

   Note:

   The data used in this tutorial comes from the State of Upper Austria (Land Oberoesterreich Open Data), which provides various regional vector and raster data layers under a Creative Commons Attribution 4.0 Austria license.

   If you have questions about this data, use the following contact information:

   Office of Upper Austria. State Government

   Directorate Presidium

   Department Presidium

   Landhausplatz 1

   4021 Linz

   Telephone [phone]

   Fax [phone]

   A list of available data is located on the State of Upper Austria's site.

4. Start ArcGIS Pro. If prompted, sign in using your ArcGIS account.

   Note:

   If you don't have access to ArcGIS Pro or an ArcGIS organizational account, see options for software access.

5. Under Blank Templates, click Map.

   

6. In the Create a New Project window, for Name, delete the default project name and type CreateAndUseMD.

   By default, projects are saved in a new folder; however, you'll save this project to the same folder that contains your data.

7. For Location, browse to and choose your unzipped OrthoPhotos folder.
8. Uncheck Create a new folder for this project and click OK.

   

   The project is created with a default map and associated basemap set to the world extent. The map consists of a map display and a contents pane displaying all map layers. In addition, the Catalog pane is located to the right of the map display and lists all files and folders associated with the project.

9. In the Catalog pane, expand Folders. Verify that the OrthoPhotos folder is included.

   

   Note:

   If the OrthoPhotos folder is not present in your project folders, right-click Folders and add a new folder connection by browsing to and choosing the location containing of your unzipped orthophoto collection.

10. In the Catalog pane, expand the OrthoPhotos folder.

    The folder contains the geodatabase and other items created with the project. It also contains a collection of 73 images in a .jp2 file format.

    

    Note:

    A .jp2 file is a compressed bitmap image created using JPEG 2000 (JP2) Core Coding. It supports color bit depth and image metadata and may be compressed with lossy or lossless compression and is typically used for storing digital photos and images.

11. In the Orthophotos folder collection, expand 4727-08.jp2.

    The image consists of four bands. While they are not named as such, these represent red, green, blue, and infrared bands.

12. In the Orthophotos folder, collapse 4727-08.jp2
13. From the OrthoPhotos folder, press the Shift key and select 4727-08.jp2, 4727-16.jp2, and 4727-23.jp2. Right-click the selection and choose Add To Current Map.

    

14. In the Calculate Statistics pop-up, click Yes.

    After the statistics have been calculated, the selected images are added to the map as raster layers.

    

    In the map display, adjacent images are rendered seamlessly so they appear as one image. However, as you pan and zoom, each individual image will be refreshed and displayed as a separate layer.

    

15. In the Contents pane, collapse the layers. Select 4727-08.jp2 and 4727-16.jp2.

    

16. On the ribbon, on the Raster Layer tab, in the Rendering group, click the Stretch Type drop-down menu and verify the current stretch type applied is Percent Clip.

    

17. On the Map, zoom to the 4727-08.jp2 and 4727-16.jp2 layers.

    The layers overlap and vary in brightness and contrast.

    

18. On the Raster Layer tab, in the Rendering group, click the Stretch Type drop-down menu and choose Minimum Maximum.

    

    On the map, the overlap between the layers is less pronounced and the stretch type has adjusted the brightness and contrast.

    

19. In the Enhancement group, experiment using the Brightness, Contrast, and Gamma sliders to apply interactive adjustments to the imagery.

    

    If you have overlaps between your images, you must adjust the order of the layers manually to control the overlap. The more images you have, the harder they are to manage as a collection.

20. When you're finished, in the Contents pane, select 4727-08.jp2, 4727-16.jp2, and 4727-23.jp2. Right-click the selection and choose Remove.

You have you added separate images to a map and explored enhancing them. While this is a good way to work with a few images, working with larger numbers such as your collection of 73 images can be challenging. In the next module, you'll explore creating a mosaic dataset and adding and working with your entire collection of orthophotos.

Create a mosaic dataset

A mosaic dataset is a well-defined geodatabase structure optimized for working with large collections of imagery and rasters. Mosaic datasets are stored in either a file geodatabase or an enterprise geodatabase. The imagery and raster data do not need to reside in the database. Most organizations store their imagery as files on disk, enterprise, or cloud storage. A single mosaic dataset can reference millions of images and make them appear as a single virtual dataset or enable quick access to any individual image or collection of images. With mosaic datasets, the large volume of pixel data (contained in the imagery and rasters) are not loaded into the database and are instead referenced. The metadata about the data sources, as well as information on how to process the imagery into different products, is stored in the mosaic dataset. When a request for imagery is made, the mosaic dataset is used to determine what images are required and what processing is to be applied. Only the required imagery is read, processed, and returned.

Manage the image collection

First, you'll create and populate a mosaic dataset to efficiently manage and display your orthophoto image collection.

1. In the Catalog pane, expand Folders and expand the OrthoPhotos folder.
2. In the Catalog pane, right-click CreateAndUseMD.gdb, point to New, and choose Mosaic Dataset.

   

   The Create Mosaic Dataset geoprocessing tool appears.

3. In the Create Mosaic Dataset window, for Mosaic Dataset Name, type HallStatt.
4. For Coordinate System, choose Current Map [Map].

   The MGI_Austria_GK_Central coordinate system appears because this is the coordinate system used by the orthophotos.

5. Click Run.

   

   The tool executes and creates a new mosaic dataset in the project geodatabase. In addition, it adds a mosaic dataset group layer to the contents pane of the map.

   Next, you'll associate your imagery to the mosaic dataset.

6. In the Catalog pane, in the CreateAndUseMD.gbd item, right-click the HallStatt mosaic dataset and choose Add Rasters.

   

   The Add Rasters To Mosaic Dataset tool appears. In the tool, mosaic dataset parameters are already filled in based on your context. Since the imagery you are working with is simple imagery without any metadata, you can use the default Raster Dataset raster type.

7. In the Add Rasters To Mosaic Dataset tool, verify the Raster Type is set to Raster Dataset.

   Next, you'll specify input data type and location.

8. Click the Input Data drop-down menu and choose Folder.
9. Click the Browse button. Browse to and choose your OrthoPhotos folder.
10. Expand the Raster Processing options. For Minimum Rows or Columns, type 10.

    By specifying a value of 10, you are allowing the mosaic dataset access to all the pyramids from the source images that have 10 or more rows or columns. Now you are ready to run the tool and associate the images to the mosaic dataset.

    

11. Click Run.

    Adding images to the mosaic dataset does several things, including adding references to the images on disk (not copies) to the attribute table of the mosaic dataset and setting the field values that control at which scale images are displayed based on the pyramids in the image and which images overlap them. Once the tool is finished, the mosaic layer in the Contents pane updates to reflect the number of bands in the first image added to the mosaic.

    The HallStatt layer is a group layer containing three sublayers:

    • Boundary
    • Footprint
    • Image

    

12. In the Contents pane, right-click Footprint and click Zoom To Layer.

    

13. In the Contents pane, uncheck the Image and Footprint sublayers and check the Boundary sublayer.

    

    The Boundary sublayer is a feature class that represents the combined footprints of images added to the mosaic dataset.

14. In the Contents pane, uncheck the Boundary sublayer and check the Footprint sublayer.

    

    The Footprint sublayer is a feature class that represents the footprint (minimum and maximum extent) of each image added to the mosaic dataset.

15. In the Contents pane, right-click the Footprint sublayer and choose Attribute table.

    

    The attribute table has fields that not only tell you the source name of the image or scene but also the pixel sizes of the images (MinPS and MaxPS) and the scales at which they will be displayed (LowPS and HighPS).

    

    Note:

    If your mosaic dataset consists of images with metadata and you specify the appropriate raster type, the attribute table will be populated with far more attribute fields with values derived from the metadata.

16. Close the table. In the Contents pane, check the Image sublayer.

    

    This layer represents a seamless mosaic of all the images added to the mosaic dataset. Not all 73 images added to the mosaic dataset are displaying and that the band names in the contents pane for the mosaic dataset do not reflect the colors. Next, you'll correct this.

17. In the Catalog pane, right-click the HallStatt mosaic dataset and click Properties.

    

    In the Mosaic Dataset Properties pane, there are two tabs: General and Defaults. In the General tab, you'll find information regarding the mosaic dataset such as source information, location, and whether it has statistics or not.

18. Verify the General tab is selected and expand Raster Information.

    

19. In the expanded Raster Information properties, locate Product Definition and click the Edit button next to NONE.

    In Product Definition, you can specify the type of imagery the mosaic dataset represents, resulting in the setting of several preset default values for various display and other properties applicable to that raster type.

20. In the Product Definition window, click NONE and choose NATURAL_COLOR_RGBI.

    The product definition settings update to display band information that correctly identifies band names and the minimum and maximum wavelengths for each raster band.

    

21. In the Product Definition window, click OK.
22. In the Mosaic Dataset Properties pane, click the Defaults tab. Expand Image Properties.

    

    The Defaults tab allows you to control properties specific to the images comprising the mosaic dataset. Currently, your mosaic dataset is only displaying 10 images.

23. In the Image Properties list, for Maximum Number of Rasters Per Mosaic, type 100 and press Enter.

    This property sets how many images are displayed by the mosaic dataset. Since your mosaic dataset consists of 73 images, setting the maximum value to 100 ensures all source images will be displayed.

    

24. In the Mosaic Dataset Properties pane, click OK.

    

25. In the Contents pane, uncheck the Footprint sublayer.

    

    The band names in the Contents pane under the Image sublayer have updated to display the correct band names.

    The mosaic layer in the contents pane updates and now displays all 73 images in the mosaic dataset.

    

In this module, you created a mosaic dataset and added the images from your orthophoto collection. Now you are ready to work with the images as a collection and enhance the mosaic dataset by applying and incorporating analysis functionality.

Use a mosaic dataset as a dynamic image

Next, you will work with the mosaicked image collection and enhance the dataset by applying and incorporating analysis functionality.

Enhance the mosaic dataset

First, you'll enhance the dataset.

1. In the Contents pane, ensure the Image sublayer is checked and the Footprint and Boundary sublayers are not checked.

   To perform visual enhancements on the mosaic dataset, you must build statistics. When a mosaic dataset is created and images are loaded, statistics are not automatically generated, since calculating statistics for the mosaic dataset may be time consuming. Once the mosaic dataset is built, it is advisable to calculate statistics to improve performance.

2. In the Catalog pane, right-click the Hallstatt mosaic dataset, point to Enhance, and choose Calculate Statistics.

   

3. In the Calculate Statistics geoprocessing tool, verify the following parameters:
   • Confirm that Input Raster Dataset is set to Hallstatt.
   • Confirm that X Skip Factor is set to 1.
   • Confirm that Y Skip Factor is set to 1.

   

4. Click Run.

   Calculating statistics for the mosaic dataset may take many minutes to complete.

   When finished, the mosaic layer in the Contents pane will update and you can then use the updated statistics to improve performance.

   

5. In the Catalog pane, right-click the Hallstatt mosaic dataset and click Properties. On the General tab, expand the Statistics section.

   

   The mosaic dataset statistics have been updated and now include band specific values that are used to enhance display and processing performance.

   Next, you'll use a stretch type to apply additional enhancements to your mosaic dataset.

6. Close the Mosaic Dataset Properties window.
7. In the Contents pane, select the HalStatt mosaic layer. On the Mosaic Layer tab, in the Rendering group, click the Stretch Type drop-down menu.

   

   The options on this menu change the way your mosaic dataset displays.

8. On your own, experiment with various stretch types and explore the imagery. When you are finished, change the Stretch Type back to Percent Clip.
9. On the Mosaic Layer tab, in the Enhancement group, use the Brightness, Contrast, and Gamma sliders to interactively enhance the way the image looks.

   

   Tip:

   Click the Reset buttons next to the sliders to return to the default values.

   Because your mosaic dataset has multiple bands, you can also use the Band Combination drop-down menu and pick a preset to quickly change to a different band combination.

10. On the Mosaic Layer tab, in the Rendering group click the Band Combination drop-down menu and choose Color Infrared.

    

    Since the mosaic dataset images have a near-infrared band, the color infrared band combination creates a false color composite, where the near-infrared band is displayed as red. The red band is displayed as green, and the green band is displayed as blue. The Color Infrared band combination is good to highlight vegetation (in red) and water (in black).

    

    Note:

    Any change to the appearance of a layer, such as band combination, is only for visual display purposes and will not actually change the source data. Changes will not persist unless you save a layer file or save the project. Saving the project will save the state of the layer as you have modified it but only within this project. Saving a layer file will save the layer properties and allow for the layer to be reused in multiple projects.

11. Change the Band Combination value back to Natural Color.

    Next, you'll build overviews or reduced resolution datasets for your dynamically mosaicked image to speed up display.

    The mosaic dataset name in the geodatabase has been modified to Hallstatt to conform to database naming conventions.

    Note:

    You can generate overviews, which are like raster pyramids, for a mosaic dataset. Overviews are reduced resolution datasets that are generated to improve the speed at which the mosaic is displayed. You can allow the default overviews to be generated across the entire mosaic dataset. Alternatively, you can control how they're created by defining specific a down sampling ratio, as well as a defined extent and a specific spatial resolution.

12. In the Catalog pane, right-click the Hallstatt mosaic dataset, point to Optimize, and choose Build Overviews.

    

13. In the Build Overviews geoprocessing tool, ensure that the Define Missing Overview Tiles and Generate Overviews check boxes are both checked.
14. Expand the Overview Generation Options section and ensure that the Generate Missing Overview Images Only and Regenerate Stale Overview Images Only check boxes are both checked.

    

15. Click Run.

    Building overviews may take a few minutes as the reduced resolution datasets are generated to improve the display speed of the mosaic dataset. Once it is finished, the mosaic layer in the Contents pane updates, and you can observe the effect of updated overviews by changing the extent of the map.

16. Use the Explore tool to pan and zoom the map.

    The image display performance is faster as a result of the overviews. Before generating overviews, the entire image was rendered at full resolution each time you navigated the map. Now, based on the extent, only specific overviews at the relevant resolution are displayed and as you navigate the map, the display updates quickly as it no longer needs to render all data, but only that which falls within the new boundary.

Add analysis to a mosaic dataset

Next, you'll execute an analysis workflow using the mosaic dataset and associate the analysis workflow with the mosaic dataset so users can apply the same workflow on demand.

1. In the Contents pane, select the HallStatt mosaic layer.
2. Ensure that the Image sublayer is checked on and the Footprint and Boundary sublayers are checked off.
3. On the ribbon, on the Imagery tab, in the Analysis group, click Raster Functions.

   

   The Raster Functions pane displays and shows various functions that you can use to perform analysis on your imagery.

   

4. In the Raster Functions pane, under Analysis, click the NDVI Colorized function.

   NDVI Colorized creates a multiband dataset that represents vegetation health based on the difference between the red and near infrared bands.

   

   The NDVI Colorized Properties pane appears.

5. In the NDVI Colorized Properties pane, choose the following parameter values:
   • For Raster, choose HallStatt in the dropdown list.
   • For Visible Band ID, choose 3 in the dropdown list.
   • For Infrared Band ID, choose 4 in the dropdown list.

   Bands 3 and 4 correspond to the red and near infrared bands respectively.

6. Leave the other parameters unchanged and click Create new layer.

   

   The NDVI Colorized Properties raster function creates and adds a new layer to the map and uses the red and infrared bands from your imagery to highlight areas of healthy vegetation.

   

7. In the Contents pane, select the NDVI Colorized_HallStatt layer.

   

8. On the ribbon, click the Raster Layer tab. In the Compare group, click Swipe.
9. Drag on the map to reveal the natural color imagery under the NDVI Colorized_HallStatt layer.

   Exploring the map with the Swipe tool shows you that areas of healthy vegetation are colored green and areas with snow or shadows and areas where vegetation has been removed are colored lighter. Areas with water are colored orange to brown.

   

   Using NDVI Colorized function is a relatively fast way to highlight healthy vegetation in your imagery. However, it is also temporary and only available in the new layer created in the Contents pane.

   Next, you'll take this analysis and attach it to the mosaic dataset so it can be applied by anyone using the mosaic dataset.

10. On the ribbon, click the Map tab. In the Navigate group, click the Explore button.

    Now you can pan on the map without swiping.

11. In the Contents pane, right-click the NDVI Colorized_HallStatt layer and choose Save Function Chain.

    

    The Raster Function Editor view appears.

12. In the Raster Function Editor window, click the Save button.

    

13. In the Save window, for Name, type NDVI Colorized.
14. For Category, choose Project. Click OK.

    

    You've now saved the analysis as a raster function template that can be associated to the mosaic dataset.

15. Close the Raster Function Editor. In the Contents pane, right-click the NDVI Colorized_HallStatt layer and choose Remove.

    

16. In the Catalog pane, right-click the HallStatt mosaic dataset and choose Manage Processing Templates.

    The Manage Processing Templates pane appears.

17. In the Manage Processing Templates pane, click the Import button.

    

18. In the Select Processing Templates window, click Raster Functions. Double-click Project and Project1. Pick the NDVI Colorized function.

    

19. Click OK.

    The Manage Processing Templates pane updates and displays the attached NDVI Colorized raster function.

    

    Now that you have attached the processing chain to the mosaic dataset, you can reuse the same analysis any time it is required.

20. In the Contents pane, ensure the HallStatt layer is selected. On the ribbon, click the Data tab. In the Processing group, click the Processing Templates drop-down menu and choose NDVI Colorized.

    

    The map display changes to show you the results raster function template applied to the layer.

21. Click the Processing Templates drop-down menu again and choose None.

    Your display changes back to the imagery.

    

You've explored how to enhance your mosaic dataset by applying and incorporating analysis functionality. Next, you'll explore a mosaic dataset hosted as a dynamic imagery layer in ArcGIS Living Atlas.

Use a mosaic dataset as a catalog of imagery

Next, you'll use a catalog of imagery from a ArcGIS Living Atlas mosaic dataset layer and explore how you can use the layer as a catalog of imagery.

Use dynamic imagery hosted online

Dynamic imagery is imagery hosted online that you can add to a project and use for exploration and analysis. For example, all the Landsat imagery used in this section is stored in the cloud through the Amazon Web Services (AWS) cloud. Therefore, you'll access the imagery dynamically through an internet connection without the need to download and store the imagery locally. In addition, visual rendering and image computations are executed on-demand in real time. As a result, everything displayed on your screen is dynamically generated from the source Landsat repository on AWS without the need to precompute and store separate imagery products.

1. In the Catalog pane, click the Portal tab. On the Portal tab, click the Living Atlas button.

   

   Note:

   ArcGIS Living Atlas of the World is a large collection of geographic information from around the globe and includes many dynamic imagery layers.

2. In the search bar, type Multispectral Landsat and press Enter.

   Multispectral Landsat is a dynamic image service provided by Esri that is driven by a mosaic dataset. It is a service containing imagery of the world over multiple years collected by the Landsat satellites.

3. In the search results, right-click the Multispectral Landsat imagery layer and choose Add To Current Map.

   

4. If necessary, in the Contents pane, right-click the Hallstatt layer and choose Zoom To Layer.

   

5. In the Contents pane, right-click the Multispectral Landsat layer and choose Attribute table.

   

   The Multispectral Landsat layer attribute table (footprint table) contains several fields that are different from those added to the table for your local mosaic dataset. These fields contain metadata, such as Acquisition Date, Cloud Cover, Sun Azimuth, Sensor Name, and so on. Additional attributes were added by a technician creating the service and these include a Best field. You can use these fields to review information about the data but also to query and filter images as needed.

6. Close the table. In the Contents pane, right-click the Multispectral Landsat layer and click Properties.
7. In the Layer Properties pane, click Definition Query.

   The layer has a current definition query set. This definition query filters the images displayed using the values from the Best field. The current query does not change the visible imagery for the Hallstatt area.

   

8. On the Definition Query tab, point to Query 1 and click the Remove definition query button.

   

9. In the Remove Definition Query window, click Yes.
10. In the Layer Properties pane, click the Time tab.

    The Time tab uses a time field in your mosaic dataset to allow users to step through time and view the data that corresponds with different times. This is one of the major features of this mosaic dataset. For now, you will turn time off so you can explore other cataloging capabilities of the mosaic dataset.

11. For Layer Time, choose No Time.

    

12. In the Layer Properties pane, click OK.

    The map refreshes, but removing the definition query and the time settings has no effect on the image display.

13. In the Contents pane, select the Multispectral Landsat layer. On the ribbon, click the Data tab. In the Processing group, click the Processing Templates drop-down menu and choose Natural Color with DRA.

    

    It may take a few minutes for the display to update, but when it does, the imagery is shown in natural colors.

    

    Note:

    Images in this mosaic dataset have a lot of overlap not only between images captured at the same time but also between images captured over multiple years. This makes it important to configure your mosaic dataset to display the most relevant imagery for your analysis.

    Next, you'll filter and pick items of interest in the Hallstatt area using the Raster Item Explorer.

14. In the Contents pane, select the Multispectral Landsat layer. On the ribbon, on the Data tab, in the Selection group, click Explore Raster Items.

    

    The Raster Item Explorer pane presents a way to review current images displayed in the mosaic dataset and apply a filter to them based on fields in the attribute table.

    The item explorer displays one item representing the image currently displayed on the map. If the map were displaying multiple images, the item explorer would show more than one item that representing the mosaic of the images in the display. For example, if you were to change the scale of the map to 1:250,000 and refresh the Explore Raster Items pane, you would see two or more items, which means these multiple items are currently being mosaicked together to make up the layer displayed.

    Next, you'll explore all the items that make up the current display extent.

15. In the Raster Item Explorer pane, for By Area Of Interest, choose Display Extent. Check Exclude Overviews and click Apply.

    

    The item list updates and identifies 457 items (items numbers may vary based on your extent) that do not represent overviews and that partially or completely overlap the current extent.

    Note:

    The Multispectral Landsat layer is a dynamic image layer published on ArcGIS Living Atlas and as such is subject to frequent updates that may include additional or more current scenes added or removed. As a result, the number of images selected may differ from those in this tutorial.

    

    The Multispectral Landsat layer has been configured to display imagery that is best on top. The best imagery is determined using the value in the best field. Next, you'll explore how this can affect how images in the mosaic are displayed.

16. On the ribbon, click the Data tab. In the Image Display Order group, click the Sort drop-down menu and choose North-West.

    

    The layer updates but does not look as good. This is because the images that are most northwest in the mosaic dataset are being displayed regardless of whether they are covered by clouds or not.

    

17. On the Sort drop-down menu, choose By Attribute.

    Sorting by attribute requires you to specify an attribute field to sort images in the mosaic dataset. As a result, the Layer Properties pane appears and you can select an order field.

18. In the Layer Properties pane, for Order field, ensure that Best is selected.

    

19. Click OK.
20. In the Raster Item Explorer pane, click the Configure Results button.

    

21. In the Configure Results window, check Show Thumbnails and click Apply.

    

    The item list updates and displays thumbnails for the items, which simplify identification and allow for a visual comparison.

    

    Next, you'll filter out the most current items for the year 2022.

22. In the Raster Item Explorer pane, click the By Attribute drop-down menu and choose Field. For Field name, choose Acquisition Date.

    

23. Click the Start Date drop-down menu and click More.

    

24. Click the Start Date drop-down menu again and choose the first 2022 date, 1/9/2022 9:52:02 AM. Click the End Date drop-down menu and choose the latest 2022 date.
25. Click Apply.

    The item list updates and has been filtered to show only those views that were captured in 2022.

    By default, these items are sorted by date, so the last item is the latest date. Ideally, you want to work with the latest imagery of the area; however, not all images may be of the same quality.

26. In the Raster Item Explorer pane, in the Item list, scroll to the last item. Click Add to Current Map.

    

    A new layer named is added to the map. It is named Multispectral Landsat followed by a long number.

27. In the Contents pane, right-click the new layer and click Zoom To Layer.

    

    The map zooms out. The new layer shows only a single view, unlike the original Multispectral Landsat layer underneath, which shows a composite of views covering the entire earth.

28. In the Contents pane, right-click the single-view Multispectral Landsat layer and choose Remove.

    In addition to adding items as new layers, you can lock one or more items to filter the original Multispectral Landsat layer.

29. In the Raster Item Explorer pane, click two of the images. Confirm that their check boxes are checked on. On the item list, on the toolbar, click the Lock Item(s) button.

    

    The Multispectral Landsat layer filters to only show the selected views.

    

    The two images display as one. This is an important way to combine multiple items seamlessly for a specific area of interest.

    Note:

    The Multispectral Landsat layer is a dynamic image layer and as such is subject to frequent updates that may include additional or more current scenes added or removed. As a result, your images may differ from those displayed in this tutorial.

30. On the toolbar, click the Unlock Items button.

    The full Multispectral Landsat layer reappears on the map.

    Next, you'll sort items based on cloud cover.

31. In the Raster Item Explorer pane, click the Sort By button, and choose CloudCover.

    

    The item list refreshes and is sorted based on cloud cover.

32. In the item list, locate the item with the least cloud cover (the top item) and click the Add to Current Map button.

    The image has little cloud cover.

33. In the Contents pane, drag the Hallstatt layer to the top of the layers list. Turn on the Footprint sublayer.

    

    Now your map displays your area of interest with detailed high-resolution local imagery on top of less detailed dynamic imagery hosted online.

34. Zoom to the Hallstatt layer.
35. Save the project.

In this tutorial, in your role as a remote sensing and GIS analyst for the Upper Austria government, you received a collection of orthophotos that you needed to manage and share effectively with stakeholders. You explored the challenges of working with multiple images individually, and created a mosaic dataset to allow you to work with the collection of seamless images, making them accessible and turning them into useful information products for both visualization and analysis. Finally, you enhanced the mosaic dataset by applying and incorporating analysis functionality. Lastly, you added a dynamic image layer hosted online in ArcGIS Living Atlas to your map and explored how overlapping imagery is handled dynamically, how you can use the fields in the mosaic dataset to query items, and how the mosaic dataset is time aware.

You can find more tutorials in the tutorial gallery.

Acknowledgements

• Orthophoto images provided by Land Oberoesterreich Open Data.
• Multispectral Landsat image accessed from the ArcGIS Living Atlas of the World - credits to Esri, USGS, NASA, and AWS.