Segment the imagery

To determine which parts of the ground are pervious and impervious, you will classify the imagery into land-use types. Impervious surfaces are generally human-made: buildings, roads, parking lots, brick, or asphalt. Pervious surfaces include vegetation, water bodies, and bare soil. Using multispectral imagery for this kind of classification works well because each land-use type tends to have unique spectral characteristics, also called spectral signature.

However, if you try to classify an image in which almost every pixel has a unique combination of spectral characteristics, you are likely to encounter errors and inaccuracies. Instead, you'll group pixels into segments, which will generalize the image and significantly reduce the number of spectral signatures to classify. Once you segment the imagery, you will perform a supervised classification of the segments. You will first classify the image into broad land-use types, such as roofs or vegetation. Then, you will reclassify those land-use types into either impervious or pervious surfaces.

Before you classify the imagery, you will change the band combination to distinguish features clearly.

Download and open the project

To get started, you'll download data supplied by the local government of Louisville, Kentucky. This data includes imagery of the study area and land parcel features.

  1. Download the file that contains your project and its data.
  2. Locate the downloaded file on your computer.

    Depending on your web browser, you may have been prompted to choose the file's location before you began the download. Most browsers download to your computer's Downloads folder by default.

  3. Right-click the file and extract it to a location you can easily find, such as your Documents folder.
  4. Open the Surface_Imperviousness folder.

    Surface_Imperviousness folder

    The folder contains several subfolders, an ArcGIS Pro project file (.aprx), and an ArcGIS toolbox (.tbx). Before you explore the other data, you will open the project file.

  5. If you have ArcGIS Pro installed on your machine, double-click Surface Imperviousness (without the underscore) to open the project file. If prompted, sign in using your licensed ArcGIS account.

    Project file


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

    Default project

    The project contains a map of a neighborhood near Louisville, Kentucky. The map includes Louisville_Neighborhood.tif, a 6-inch resolution image, 4-band aerial photograph of the area, and the Parcels layer, a feature class of land parcels. The Louisville_Neighborhood.tif imagery comes from the U.S. National Agriculture Imagery Program (NAIP).

    Next, you'll look extract specific spectral bands from the imagery.

Extract spectral bands

The multiband imagery of the Louisville neighborhood currently uses the natural color band combination to display the imagery the way the human eye would see it. You will change the band combination to better distinguish urban features such as concrete from natural features such as vegetation. While you can change the band combination by right-clicking the bands in the Contents pane, later parts of the workflow will require you to use imagery with only three bands. So you'll create an image by extracting the three bands that you want to show from the original image.

  1. In the Contents pane, click the Louisville_Neighborhood.tif layer to select it.
  2. On the ribbon, click the Imagery tab. In the Analysis group, click Raster Functions.

    Raster Functions in the Analysis group on the Imagery tab

    The Raster Functions pane appears.

    Raster functions apply an operation to a raster image on the fly, meaning that the original data is unchanged and no new dataset is created. The output takes the form of a layer that exists only in the project in which the raster function was run. You will use the Extract Bands function to create an image with only three bands to distinguish between impervious and pervious surfaces.

  3. In the Raster Functions pane, search for and click the Extract Bands function.

    Extract Bands tool

    The Extract Bands function appears.

    The bands you extract will include Near Infrared (Band 4), which emphasizes vegetation; Red (Band 1), which emphasizes human-made objects and vegetation; and Blue (Band 3), which emphasizes water bodies.

  4. In the Parameters tab, for Raster, choose the Louisville_Neighborhood.tif image. Confirm that Method is set to Band IDs.

    Extract Bands Raster and Method parameters

    The Method parameter determines the type of keyword used to refer to bands when you enter the band combination. For this data, Band IDs (a single number for each band) are the simplest way to refer to each band.

  5. For Combination, delete the existing text and type 4 1 3 (with spaces). Confirm that Missing Band Action is set to Best Match.

    Extract Bands Band and Combination parameters

    The Missing Band Action parameter specifies what action occurs if a band listed for extraction is unavailable in the image. Best Match chooses the best available band to use.

  6. Click the General tab, and for Name, type Louisville Neighborhood Extracted Bands.

    Name the extracted bands layer.

  7. Click Create new layer.

    The new layer, named Louisville Neighborhood Extracted Bands_Louisville_Neighborhood.tif, is added to the map. It displays only the extracted bands.

  8. In the Contents pane, right-click Louisville Neighborhood Extracted Bands_Louisville_Neighborhood.tif, click Properties, and for Name, type Louisville Neighborhood Extracted Bands, and click OK.

    The yellow Parcels layer covers the imagery and can make some features difficult to see. You will not use the Parcels layer until later in the project, so you will turn it off for now.

  9. In the Contents pane, uncheck the Parcels layer box to turn it off.

    Extract Bands output

    The Louisville Neighborhood Extracted Bands layer shows the imagery with the band combination that you chose (4 1 3). Vegetation appears as red, roads appear as gray, and roofs appear as shades of gray or blue. By emphasizing the difference between natural and human-made surfaces, you can more easily classify them later.


    Although the Louisville Neighborhood Extracted Bands layer appears in the Contents pane, it has not been added as data to any of your folders. If you remove the layer from the map, you will delete the layer permanently.

Configure the Classification Wizard

Next, you will open the Classification Wizard and configure its default parameters. The Classification Wizard walks you through the steps for image segmentation and classification.

  1. In the Contents pane, make sure that the Louisville Neighborhood Extracted Bands layer is selected.
  2. On the ribbon, on the Imagery tab, in the Image Classification group, click the Classification Wizard button.

    Classification Wizard in the Image Classification group on the Imagery tab


    If you want to open the individual tools available in the wizard, you can access them from the same tab. In the Image Classification group, click Classification Tools and choose the tool you want.

    The Image Classification Wizard pane appears.

    The wizard's first page (indicated by the blue circle at the top of the wizard) contains several basic parameters that determine the type of classification to perform. These parameters affect which subsequent steps will appear in the wizard. You will use the supervised classification method. This method is based on user-defined training samples, which indicate what types of pixels or segments should be classified in what way. (An unsupervised classification, by contrast, relies on the software to decide classifications based on algorithms.)

  3. Confirm that Classification Method is set to Supervised and that Classification Type is set to Object based.

    Image Classification Wizard Configure page

    The object based classification type uses a process called segmentation to group neighboring pixels based on the similarity of their spectral characteristics.

    Next, you will choose the Classification Schema. The Classification Schema is a file that specifies the classes that will be used in the classification. A schema is saved in an Esri classification schema (.ecs) file, which uses JSON syntax. For this workflow, you'll modify the default schema, NLCD2011. This schema is based on land cover types used by the United States Geological Survey.

  4. For Classification Schema, click the drop-down arrow and choose Use default schema.

    Classification Schema

    The next parameter determines the Output Location value, which is the workspace that stores all the outputs created in the wizard. These outputs include training data, segmented images, custom schemas, accuracy assessment information, intermediate outputs, and resulting classification results.

  5. Confirm that Output Location is set to Neighborhood_Data.gdb.

    Under Optional, you won't enter anything for Segmented Image, Training Samples, or Reference Dataset because you don't have any of these elements created ahead of time.

  6. Click Next.

    The next page of the Image Classification Wizard focuses on segmentation.

Segment the image

You'll now choose the parameters for Segmentation. The Segmentation process groups adjacent pixels with similar spectral characteristics into segments. Doing so will generalize the image and make it easier to classify. Instead of classifying thousands of pixels with unique spectral signatures, you will classify a much smaller number of segments. The optimal number of segments and the range of pixels grouped into a segment change depending on the image size and the intended use of the image.

The first parameter is Spectral detail. It sets the level of importance given to spectral differences between pixels on a scale of 1 to 20. A higher value means that pixels must be more similar to be grouped together, creating a higher number of segments. A lower value creates fewer segments. Because you want to distinguish between pervious and impervious surfaces (which generally have very different spectral signatures), you will use a lower value.

  1. For Spectral detail, replace the default value with 8.

    The next parameter is Spatial detail. It sets the level of importance given to the proximity between pixels on a scale of 1 to 20. A higher value means that pixels must be closer to each other to be grouped together, creating a higher number of segments. A lower value creates fewer segments that are more uniform throughout the image. You will use a low value because not all similar features in your imagery are clustered together. For example, houses and roads are not always close together and are located throughout the full image extent.

  2. For Spatial detail, replace the default value with 2.

    The next parameter is Minimum segment size in pixels. Unlike the other parameters, this parameter is not on a scale of 1 to 20. Segments with fewer pixels than the value specified in this parameter will be merged into a neighboring segment. You do not want segments that are too small, but you also do not want to merge pervious and impervious segments into one segment. The default value will be acceptable in this case.

  3. For Minimum segment size in pixels, confirm that the value is 20.

    The final parameter, Show Segment Boundaries Only, determines whether the segments are displayed with black boundary lines. This is useful for distinguishing adjacent segments with similar colors but may make smaller segments more difficult to see. Some of the features in the image, such as the houses or driveways, are fairly small, so you will leave this parameter unchecked.

  4. Confirm that Show Segment Boundaries Only is unchecked.

    Show Segment Boundaries Only unchecked

  5. Click Next.

    A preview of the segmentation is added to the map. It is also added to the Contents pane with the name Preview_Segmented.

    Segmentation preview

    At first sight, the output layer does not appear to have been segmented the way you wanted. Features such as vegetation seem to have been grouped into many segments that blur together, especially on the left side of the image. Tiny segments that seem to encompass only a handful of pixels dot the area as well. However, this image is being generated on the fly, which means the processing will change based on the map extent. At full extent, the image is generalized to save time. You will zoom in to reduce the generalization, so you can better see what the segmentation looks like with the parameters you chose.

  6. With the mouse wheel, zoom to the neighborhood in the middle of the image.

    Zoomed segmentation preview

    The preview segmentation runs again. With a smaller map extent, the segmentation more accurately reflects the parameters you used, with fewer segments and smoother outputs.


    If you are unhappy with how the segmentation turned out, you can return to the previous page of the wizard and adjust the parameters. The segmentation is only previewed on the fly because it can take a long time to process the actual segmentation, so it is good practice to test different combinations of parameters until you find a result you like.

  7. In the Contents pane, right-click Preview_Segmented and choose Zoom To Layer to return to the full extent.

    Zoom To Layer

  8. On the Quick Access Toolbar, click Save to save the project.

    Save on the Quick Access Toolbar


    A message may appear warning you that saving this project file with the current ArcGIS Pro version will prevent you from opening it again in an earlier version. If you see this message, click Yes to proceed.


    Saving the project does not save your location in the wizard. If you close the project before you complete the entire wizard, you will lose your spot and have to start the wizard over from the beginning.

You have extracted spectral bands to emphasize the distinction between pervious and impervious features. You also set up the segmentation parameters to group pixels with similar spectral characteristics into segments and simplify the image. Next, you will classify the imagery by perviousness or imperviousness.

Classify the imagery

In this section, you'll set up the classification of the image. A supervised classification is based on user-defined training samples, which indicate what types of pixels or segments should be classified in what way. (An unsupervised classification, by contrast, relies on the software to decide classifications based on algorithms.) You'll first classify the image into broad land-use types, such as vegetation or roads. Then, you will reclassify those land-use types into either pervious or impervious surfaces.

Create training samples

To perform a supervised classification, you need training samples. Training samples are polygons that represent distinct sample areas of the different land-cover types in the imagery. The training samples inform the classification tool about the variety of spectral characteristics that each land cover can exhibit.

First, you'll modify the default schema to contain two parent classes: Impervious and Pervious. Then, you will add subclasses to each class that represent types of land cover. If you attempted to classify the segmented image into only pervious and impervious surfaces, the classification would be too generalized and likely have many errors. By classifying the image based on more specific land-use types, you will create a more accurate classification. Later, you will reclassify these subclasses into their parent classes.

  1. In the Image Classification Wizard pane, right-click each of the default classes and click Remove Class. For each class, click Yes in the Remove Class window.

    Remove Class

  2. Right-click NLCD2011 and choose Add New Class.

    Add New Class

  3. In the Add New Class window, for Name, type Impervious. For Value, type 20, and for Color, choose Gray 30%. Click OK.

    To see the name of a color, point to the color in the color palette selector and the color name will appear.

    Settings for Impervious class

    The value 20 is the number that will be attributed to all segments identified as impervious through the classification process. It is more of a numeric label and is not intended to be used in any calculations.

  4. Right-click NLCD2011 again and choose Add New Class. Add a class named Pervious with a value of 40 and a color of Quetzal Green. Click OK.

    Settings for Pervious class

    Next, you'll add a subclass for gray roof surfaces.

  5. Right-click the Impervious parent class and choose Add New Class. Add a class named Gray Roofs with a value of 21 and a color of Gray 50%. Click OK.

    In this tutorial, you won't create other roof types. However, in a project with more diverse buildings represented in the imagery, you might consider creating a red roof land-use type, since their spectral characteristics are different from gray roofs.

    Next, you'll create a training sample on the map using this class.

  6. Click the Gray Roofs class to select it. Then, click the Polygon button.

    Polygon button

  7. Zoom to the cul-de-sac to the northwest of the neighborhood.

    You can enable navigation tools while the Polygon tool is active by holding down the C key.

    Northwest neighborhood

  8. On the northernmost roof in the cul-de-sac, draw a polygon. Double-click to finish the drawing. Make sure the polygon covers only pixels that comprise the roof.

    Training sample


    The polygon does not need to cover the entire roof. It just needs to be a sample of the roof, but most importantly, it should only include roof material.

    A row is added to the wizard for your new training sample.

    Row added to wizard

    When creating training samples, you want to cover a high number of pixels for each land-use type. For this tutorial, having about six samples for each land-use type will be enough, but for a real project, where the imagery covers a much larger extent, you might need significantly more samples.

    You'll create more training samples to represent the roofs of the houses.

  9. Draw more rectangles on some of the nearby houses.

    Training samples

    Every training sample that you make is added to the wizard. Although you have only drawn training samples on roofs, each training sample currently exists as its own class. You'll eventually want all gray roofs to be classified as the same value, so you will merge the training samples that you created into one class.

  10. In the wizard, click the first row to select it. Press Shift and click the last row to select all the training samples.
  11. Above the list of training samples, click the Collapse button.

    Collapse button

    The training samples collapse into one class. You can continue to add more training samples for gray roofs and merge them into the Gray Roofs class. The optimum strategy is to gather samples throughout the entire image.

    Next, you'll add more land-use types.

  12. Right-click Impervious and choose Add New Class to create two more impervious subclasses based on the following table (the colors don't have to be a perfect match):

    Subclass Value Color



    Cordovan Brown



    Nubuck Tan

    Impervious subclasses

  13. Right-click Pervious and choose Add New Class to create four pervious subclasses based on the following table:

    Subclass Value Color

    Bare Earth


    Medium Yellow



    Medium Apple



    Leaf Green



    Cretan Blue



    Sahara Sand

    Pervious subclasses


    These eight classes are specific to the land-use types for this image. Images of different locations may have different types of land-use or ground features that should be represented in a classification.

    Shadows are not actual surfaces and cannot be either pervious or impervious. However, shadows are usually cast by tall objects such as houses or trees and are more likely to cover grass or bare earth, which are pervious surfaces.

  14. Draw about six training samples for each land-use type throughout the whole image. Zoom and pan throughout the image as needed.

    You can also turn off and on the Preview_Segmented layer to see the Louisville Neighborhood Extracted Bands layer to make better sense of the landscape.

    Training samples drawn throughout the map

  15. Collapse training samples that represent the same types of land use into one class.

    Collapsed classes

  16. When you are satisfied with your training samples, at the top of the Training Samples Manager pane, click the Save button.

    Save button

    Your customized classification schema is saved in case you want to use it again.

  17. Click Next.

Classify the image

Now that you have created the training samples, you will choose the classification method. Each classification method uses a different statistical process involving your training samples. You will use the Support Vector Machine classifier, which can handle larger images and is less susceptible to discrepancies in your training samples. Then, you'll train the classifier with your training samples and create a classifier definition file. This file will be used during the classification. Once you create the file, you will classify the image. Lastly, you will reclassify the pervious and impervious subclasses into their parent classes, creating a raster with only two classes.

  1. In the Image Classification Wizard pane, in the Train page of the wizard, confirm that Classifier is set to Support Vector Machine.

    For the next parameter, you can specify the maximum number of samples to use for defining each class. You want to use all your training samples, so you will change the maximum number of samples per class to 0. Changing the maximum to 0 is a trick to ensure all training samples are used.

  2. For Maximum Number of Samples per Class, type 0.

    Settings for Classifier and Maximum Number of Samples per Class

    Next, you'll train the classifier and display a preview.

  3. Click Run.

    The process may take a long time, as multiple processes are run. First, the image is segmented (previously, you only segmented the image on the fly, which is not permanent). Then, the classifier is trained and the classification performed. When the process finishes, a preview of the classification is displayed on the map.

    Classification preview

    Depending on your training samples, your classification preview should appear to be fairly accurate (the colors in the dataset correspond to the colors you chose for each training sample class). However, you may notice that some features were classified incorrectly. For instance, in the example image, the muddy pond south of the neighborhood was incorrectly classified as a gray roof, when it is actually water. Classification is not an exact science and rarely will every feature be classified perfectly. If you see only a few inaccuracies, you can correct them manually later in the wizard. If you see a large number of inaccuracies, you may need to create more training samples. Also, in this case, roads and driveways are both impervious, so it won't change the final classification into pervious and impervious land cover.

  4. If you are satisfied with the classification preview, click Next.

    The next page is the Classify page. You will use this page to run the actual classification and save it in your geodatabase.

  5. For Output Classified Dataset, change the output name to Classified_Louisville.tif. Leave the remaining parameters unchanged and click Run.

    The process runs and the classified raster layer Classified_Louisville is added to the map. It looks similar to the preview.

    The next page is the Merge Classes page. You will use this page to merge subclasses into their parent classes. Your raster currently has seven classes, each representing a type of land use. While these classes were essential for an accurate classification, you are only interested in whether each class is pervious or impervious. You will merge the subclasses into the Pervious and Impervious parent classes to create a raster with only two classes.

  6. Click Next.
  7. For each class, in the New Class column, choose either Pervious or Impervious.

    New Class column

    When you change the first class, a preview is added to the map. The preview shows what the reclassified image will look like. When you change all of the classes, the preview should only have two classes, representing pervious and impervious surfaces.

  8. Click Next.

Reclassify errors

The final page of the wizard is the Reclassifier page. This page includes tools for reclassifying small errors in the raster dataset. You will use this page to fix an incorrect classification in your raster.

  1. In the Contents pane, uncheck all layers except the Preview_Reclass and Louisville_Neighborhood.tif layers. Click the Preview_Reclass layer to select it.
  2. On the ribbon, click the Raster Layer tab. In the Compare group, click Swipe.

    Swipe in the Compare group on the Raster Layer tab

  3. Drag the pointer across the map to visually compare the preview to the original neighborhood imagery.

    Swipe map

    One inaccuracy that is present in the example image is that some bare earth patches south of the neighborhood were misclassified as impervious. These patches are not connected to any other impervious objects, so you can reclassify them with relative ease.

  4. Zoom to the patches of bare earth area.

    Bare earth patches

  5. In the wizard, click Reclassify within a region.

    Reclassify within a region

    With this tool, you can draw a polygon on the map and reclassify everything within the polygon.

  6. In the Remap Classes section, confirm that Current Class is set to Any. For New Class, choose Pervious.

    Remap Classes

    With these settings, any pixels in the polygon will be reclassified to pervious surfaces. Next, you'll reclassify the bare earth patches.

  7. Draw a polygon around the bare earth patches. Make sure you do not include any other impervious surfaces in the polygon.

    Polygon drawn around the bare earth patches

    The bare earth patches are automatically reclassified as a pervious surface.

    Reclassified bare earth patches


    If you make a mistake, you can undo the reclassification by unchecking it in the Edits Log pane.

    While you likely noticed other inaccuracies in your classification, for the purposes of this tutorial, you will not make any more edits.

  8. Zoom to the full extent of the data.
  9. In the Image Classification Wizard, for Final Classified Dataset, type Louisville_Impervious.tif (including the .tif extension).
  10. Click Run. When the tool completes, click Finish.

    Reclassify output

    The tool runs and the reclassified raster is added to the map.

  11. On the Quick Access Toolbar, click Save to save the project.

    There are methods to systematically quantify the level of accuracy of a classification. You can learn about this in the tutorial Assess the accuracy of a perviousness classification, where you will randomly generate accuracy assessment points, ground truth them, create a confusion matrix, and obtain a classification accuracy percentage. The tutorial includes steps to perform the accuracy assessment for the impervious and pervious classification you just completed.

You have classified imagery of a neighborhood in Louisville to determine land cover that was pervious and land cover that was impervious. Next, you'll calculate the area of impervious surfaces per land parcel so the local government can assign storm water fees.

Calculate impervious surface area

Using the results of the classification, you will calculate the area of impervious surface per parcel and symbolize the parcels accordingly.

Tabulate the area

To determine the area of impervious and pervious surfaces within each parcel of land in the neighborhood, you will first calculate the area and store the results in a stand-alone table. Then, you will join the table to the Parcels layer.

  1. On the ribbon, on the Analysis tab, in the Geoprocessing group, click Tools.

    Tools in the Geoprocessing group on the Analysis tab

    The Geoprocessing pane appears.

  2. In the Geoprocessing pane, search for the Tabulate Area tool and open it.

    Tabulate Area tool in the Geoprocessing pane search results

    This tool calculates the area of some classes (in this tutorial, pervious and impervious) within specified zones (in this tutorial, each parcel).

  3. For Input raster or feature zone data, choose the Parcels layer. Confirm that the Zone field parameter populates with the Parcel ID field.

    Tabulate Area parameters

    The zone field must be an attribute that identifies each zone uniquely. The Parcel ID field has a unique identification number for each feature, so you will leave the parameter unchanged.

  4. For Input raster or feature class data, choose the Louisville_Impervious layer.
  5. For Class field, choose Class_name.

    Continued Tabulate Area parameters

    The class field determines the field by which area will be determined. You want to know the area of each class in your reclassified raster (pervious and impervious), so the Class_name field is appropriate.

  6. For Output table, click the text field, confirm that the output location is the Neighborhood_Data geodatabase, and change the output name to Impervious_Area.

    Continued Tabulate Area parameters

    The final parameter, Processing cell size, determines the cell size for the area calculation. By default, the cell size is the same as the input raster layer Louisville_Impervious, which is half a foot (in this case). You'll leave this parameter unchanged.

  7. Click Run.

    The tool runs and the table is added to the Contents pane, in the Standalone Tables section. You'll take a look at the table that you created.

  8. In the Contents pane, under Standalone Tables, right-click the Impervious_Area table and click Open.

    Table for Impervious_Area

    The table has a standard ObjectID field, as well as three other fields. The first is the Parcel_ID field from the Parcels layer, showing the unique identification number for each parcel. The next two are the class fields from the Louisville_Impervious raster layer. The Impervious field shows the area (in feet) of impervious surfaces per parcel, while the Pervious field shows the area of pervious surfaces.

  9. Close the table.

    You now have the area of impervious surfaces per parcel, but only in a stand-alone table. Next, you'll join the stand-alone table to the Parcels layer so that the area information becomes available in the layer. You'll perform the join based on the Parcel ID field, which is common to the Parcel layer and the stand-alone table.

  10. Right-click the Parcels layer, point to Join and Relates, and then choose Add Join.

    The Add Join window appears.

  11. In the Add Join window, enter the following:
    • For Input Table, confirm that Parcels is selected.
    • For Input Join Field, choose Parcel ID.
    • For Join Table, confirm the Impervious_Area table is selected.
    • For Join Table Field, confirm Parcel_ID is selected.

    Add Join tool parameters


    You can ignore the warning appearing next to Input Join Field. The number of features in the Parcels layer is not very large, so it is not an issue that the Parcel ID field not indexed.

  12. Accept the default values for the other parameters and click OK.
  13. In the Contents pane, right-click the Parcels layer and choose Attribute Table. In the attribute table, confirm that the attribute table now includes the following fields:

Clean up the Parcels attribute table

Now that the tables have been joined, you will change the field aliases to be more informative.

  1. In the Parcel layer attribute table, click the options button and choose Fields View.

    Fields View

    The Fields view for the Parcels attribute table appears.

    With the Fields view, you can add or delete fields, as well as rename them, change their aliases, or adjust other settings. You'll change the field aliases of the two area fields to be more informative.

  2. In the Alias column, change the alias of the IMPERVIOUS field to Impervious Area (Feet). Change the alias of the PERVIOUS field to Pervious Area (Feet).

    Rename field

  3. On the ribbon, on the Fields tab, in the Changes group, click Save.

    Save button in the Changes group on the Fields tab

    The changes to the attribute table are saved.

  4. Close the Fields view and close the attribute table.

Next, you'll symbolize the parcels by impervious surface area on the map.

Symbolize the Parcels layer

Now that you have impervious area values assigned to each parcel, you will symbolize the parcels to visually compare the parcels by impervious area.

  1. In the Contents pane, ensure the Parcels and Louisville_Neighborhood.tif imagery layers are turned on and all other layers are turned off.

    Parcels and Louisville_Neighborhood.tif layers visible

  2. Click the Parcels layer to select it.
  3. On the ribbon, on the Feature Layer tab, in the Drawing group, choose Symbology.

    Symbology in the Drawing group on the Feature Layer tab

    The Symbology pane for the Parcels layer appears. Currently, the layer is symbolized with a single symbol, as a yellow outline. You will symbolize the layer so that parcels with high areas of impervious surfaces appear differently than those with low areas.

  4. In the Symbology pane, for Primary symbology, choose Graduated Colors.

    Graduated Colors symbology

    A series of parameters becomes available. First, you will change the field that determines the symbology.

  5. For Field, choose Impervious Area (Feet).

    The symbology on the layer changes automatically. However, there is little variety between the symbology of the parcels because of the low number of classes.

  6. Change Classes to 7 and change the Color scheme to Yellow to Red.

    To see a color scheme name, point to the color scheme.

    Symbology parameters

    The layer symbology changes again.

    Final map

    The parcels with the highest area of impervious surfaces appear to be the ones that correspond to the location of roads. These parcels are very large and almost entirely impervious. In general, larger parcels tend to have larger impervious surfaces. But there could also be great variations between smaller parcels, based on the size of the buildings or the choice by the owners to replace an impervious driveway or terrace with pervious ones or perhaps install a green roof on their house.

    While you could symbolize the layer by the percentage of area that is impervious, most storm water fees are based on total area, not percentage of area.

  7. Close the Symbology pane.
  8. Save the project.

In this tutorial, you classified an aerial image of a neighborhood in Louisville, Kentucky, to show areas that were pervious and impervious to water. You then assessed the accuracy of your classification and determined the area of impervious surfaces per land parcel. With the information that you derived in this tutorial, the local government would be better equipped to determine storm water bills.

You can use this workflow with your own data. As long as you have high-resolution, multispectral imagery of an area, you can classify its surfaces.


To go further, consider doing the tutorial Assess the accuracy of a perviousness classification. Building on the results you just obtained, you'll learn how to formally assess the accuracy of your classification. This is an important step to prove the reliability of your results.

You can find more tutorials like this on the Introduction to Imagery & Remote Sensing page.