Value Added Data Analysis with ArcGIS Pro

Introduction

ArcGIS Pro will be used through an online tutorial through Esri and I will be calculating impervious surface area. This tutorial will allow me to classify an aerial image to determine surface types.

Methods

Lesson 1. Segment the Imagery

First, download the data into the designated folder for saving purposes and open an aerial image into ArcGIS Pro. This map is a neighborhood near Louisville, Kentucky and has 6-inch resolution, and is a 4-band aerial photograph. Next you will open up the tasks folder under the project pane in order to get the steps on how to calculate surface imperviousness (figure 1).

 Figure 1. Project pane showing where to open up the tasks window

Next to do is extracting the spectral bands while setting specific parameters to create a new image with only three bands. Then I clicked create new layer down at the bottom and a layer with three bands extracted is shown on the map (figure 2).

Figure 2. Extracted spectral bands of the new layer

Next is segmenting the image where you’ll group similar pixels into segments. First I clicked the segment mean shift raster function and input all the correct parameters and once again click create new layer. This will enable the image to be simplified and classify broad land-use types more accurately (figure 3).

Figure 3. Segmented image where it has been simplified

Lesson 2. Classify the Imagery

Right away I cannot create the training samples in ArcGIS Pro so ArcMap must be opened and the training samples are exported to a shape file of your desire. First connect to the desired folder in ArcMap and turn on Image Classification toolbar to create training samples. Bring into ArcMap the Louisville Segmented and Louisville Neighborhood images and makes sure the Louisville Segmented is in the Image Classification Layer. Next select the Training Sample Manager to open up the window as shown in figure 4.

Figure 4. Training Sample Manager window

Next I created seven classes of types of land use in the image by merging multiple colors of the same class into one. These classes consisted on Gray Roofs, Roads, Driveways, Bare Earth, Grass, Water, and Shadows (figure 5). Once the seven classes were finished, I saved the training samples as a shapefile into my designated folder.

Figure 5. Classifying each type of land use (Gray Roofs was the first class)

Next I went back to ArcGIS Pro and opened up the train the classifier task. The parameters window opens up and you input the raster and training sample file while saving it to the correct place (figure 6). Once the parameters are all correct, click finish and go on to classifying the imagery.

Figure 6. Train the Classifier parameters window

Go to Classify the Imagery in the tasks pane and input the parameters. After the parameters are set, run the tool and a map with those color classes are created. Next you change the value of the fields by inputting 0 for gray roofs, driveways, and roads while bare earth, grass, shadows, and water are set to 1 (figure 7).

Figure 7. Value of the fields are changed to 0 (impervious) and 1 (pervious)

Lesson 3. Calculate Impervious Surface Area

In this last lesson, an accuracy assessment on the classification to determine if it is within an acceptable range of error will be performed. After that includes calculating the area of impervious surfaces per land parcel so the local government can assign storm water fees.

First, create accuracy assessment points by going under the tasks pane and selecting the correct inputs and click run. After its run, open up the attribute table in the contents pane under My Accuracy Points. This shows all of the fields that the points have on the map (figure 8). Then for the first ten accuracy points you’ll change the GrndTrth to either a 0 or 1, whatever the class it falls into on the map. After that is finished, Click run, Save edits, and Finish the task.

Figure 8. Table of My Accuracy Points

Next is compute a confusion matrix using the points from before.  Again you’ll click the task of Compute Confusion Matrix, input the parameters, and Finish the task. This shows the ability to give an estimate on how accurate the data truly is (figure 9).

Figure 9. Table which shows the accuracy of the point data

After that is completed close the table and open the Tabulate the Area task. Input the correct parameters and run the tool. Once the new table is created, a table join with parameters must be filled out as well in order to join the tables together. Once the parameters are entered, hit Finish and the tools run and the task ends.

Lastly, the parcels need to be symbolized by impervious surface area to depict the area attribute on the map. First, Click the Clean up the table and symbolize the data task and select the Parcels layer, then click run. Edit the names in the correct rows and columns as shown in figure 10.  

Figure 10. Parcel layer attribute table that has been edited

Following the edits, Run the Parcels layer by making sure it is checked and highlighted in the Contents tab before running. Then set the correct parameters in the symbology tab to the right side of the project. The symbology of the map shows that the highest area of impervious surfaces appear to red and the low area of impervious surfaces appear to be yellow (figure 11).

Figure 11. Symbology map of the most impervious parcels to the least impervious parcels

Conclusions

In this lab I classified an aerial image of a neighborhood to show areas that were pervious and impervious to water. Then I assessed the accuracy of my classification and determined the area of impervious surfaces per land parcel.