ArcGIS Pro Demo

Intro

Today will be a dive into ArcGIS Pro demo which can be located at here.  This demo is to teach how to create a permeable/impermeable layer map from an aerial photograph.  This is done by using a few different tools that will be mentioned in the methods section.  ArcGIS pro is the new updated version of Arcmap and ArcScene.  In a few years ESRI will be moving to ArcPro.  The main difference is having more of a ribbon style banner instead of the tool bars everywhere.

Methods

The first step is to open up an already processed aerial image into Arcgis Pro.  This one used for this demo had a resolution of 6 inches and contains 3 bands.  Under the projects tab and then under task there is a calculate surface imperiousness tool.  This is used in conjunction with the bands to determine where there is surface imperiousness (Figure 1). Then the next tool used is called "group similar pixels into segments." This allows the image to be simplified to more accurately classify broad land-use types (Figure 2).

Figure 1: On the right, where it is highlight is under the tasks, and on the left is what the tasks open up into.  This allows the run of the surface imperiousness tool.  

Figure 2:  This is the more simplified image which shows the smooth edges well.  

The Second lesson has the user open Arcmap to create a training sample, which cannot be made in ArcGIS Pro currently.  therefore, a shapefile will be made to open in ArcGIS Pro.  First, open the Neigborhood and segmented images and turn on the image classification toolbar.  To use the image classification tool bar spatial analysis needs to be turned on in the extension window.  Next, on the classification tool bar click the drop down on the draw polygon tool to select the draw rectangle tool. Next, start to draw rectangles on the roofs of the houses found in the cul-du-sac.  Then open up the training sample manager (Figure 3). Next, highlight all of the training ID's and merge them together into one class name.  The next step is to repeat the last step except for roads, driveways, bare earth, grass, water, and shadows (Figure 4).   Finally, to finish this step open it back up into ArcGIS Pro.

Figure 3: Image showing the rectangles on the roofs, and where the class can be found in the training sample manager. 

Figure 4:  This is an image showing the Training sample manager should end up like.

Now, back in ArcGIS Pro open up the train the classifier task.  This opens up the parameters window.  Input the raster and training sample file, and then make sure to save it in the correct place with the extension .ecd (Figure 5).

Figure 5: Train the classifier parameters window.  

The next step is to select the classify the imagery from the tasks tool bar.  Input the correct information into the right spots, and run the tool.  It should create an image that has the colors of the classes to look like Figure 6.

Figure 6: Reclassified image of the original using the classes created from the pixel color.  

Now,  it is now on the reclassify tool which allows the user to change the value of the fields.  For this demo change the gray roofs, driveways, and roads to 0, and change the rest of the fields to 1.  This will create an image like Figure 7 after it has ran the reclassify again.

Figure 7: the final reclassify that distinguishes between roofs, driveways, and roads to bare ground/penetrable ground. 

Finally the last lesson is to calculate impervious surface area.  To start, click on the create accuracy assessment points in the task window.  Then enter the information as described in the tutorial and let it run.  It will create an image that looks like Figure 8 with all of the accuracy points now added into it.

Figure 8:  Accuracy points added to the map.  

The next step is to open the accuracy points in the contents window to allow the modification of the GrdnTruth column.  The 1 means permeable and the 0 means impermeable.  This is required to go through each point to determine this and change as needed (Figure 9).  Then return to the task bar and hit next and run to finish the accuracy assessment points.

Figure 9:  Showing the editing of the GrndTruth. 

The next step is to compute a confusion matrix.  This is done by selecting the compute a confusion matrix under the task window.  Then enter the accuracy points in the input and create a correct output. This generates the ability to give an estimate on how accurate the data is.  In Figure 10, the image is showing an accuracy of 92% under the Kappa heading.

Figure 10: Showing the Confusion Matrix Results. 

Next,  is to fill out the tabulate area parameters with the information found in Figure 11.

Figure 11: Tabulate the area parameters. 

Now, after creating the new table a table join tool opens up next that can filled out exactly like Figure 12.  This joins together the two tables.

Figure 12: Table join.  

Finally the last step up sets is to symbolize the parcels.  The first step is to select the clean up the table and symbolize the data from the tasks window.  This is done by deleting a field and modifying the name of a few of the fields to better suit their needs (Figure 13).

Figure 13: Attribute table being edited. 

Finally after modifying the attribute table go to the symbology window to change it to graduated colors maps with 7 breaks (Figure 14).

Figure 14: Graduated color map showing the impermeability of the land classification with 7 different classes. 

There are a few different patters that can be seen in the image above.  First off, There seems to b a lot more of the yellow (permeable) on the outside of the neighborhood compared to the middle which is due to the houses surrounding the pond.  Therefore, most of the water filling the pond may be coming from the outside of the neighborhood instead of the neighborhood where it most likely just flows off. Also, the pond itself is not the darkest red (impermeable) due to its ability to take water into the ground.  

This data set can be used with UAS by doing a comparison to data collected by UAS using the same type of imagery, and looking for similar spectral patterns.  A UAS platform may even be able to get a more accurate representation of the area by generating a larger amount of pixels.  This type of map can be used as a first look at the data, but a UAS should be able to get more detail if needed.  Also, a UAS would be able to make this faster if using the same method.