For this exercise, student's accessed on online tutorial made available by ESRI titled Calculate Impervious Surfaces from Spectral Imagery. Using ArcGIS Pro, students will determine the amount of impervious surfaces of a set of parcels in a neighborhood in Louisville, Kentucky. Impervious surfaces are ground surfaces that are impenetrable to water and can present serious environmental issues such as storm water runoff contaminating rivers and lakes. Many local governments will enact fees on land parcels that contain a high amount of impervious surfaces. From this exercise, students will build skills in the following areas:
- Following a workflow with an ArcGIS Pro task
- Performing a supervised classification
- Assessing classification accuracy
- Calculating land-use area per feature
Once the tutorial is complete, students are to create a map with proper elements of the impervious surfaces.
Methods
The online tutorial was broken up into three short lessons:
- Segment the imagery
- Classify the imagery
- Calculate impervious surface area
The outline of the methods for each lesson will be featured below.
Segment the imagery
- Download and open the project: Download data supplied by the local government of Louisville, Kentucky that includes imagery of the study area and land parcel features. Open an ArcGIS Pro task to go through the workflow step by step.
- Extract spectral bands: The multiband imagery of the neighborhood currently uses the natural color band combination. Change the band combination to better distinguish urban and natural features.
- Segment the image: Group adjacent pixels with similar spectral characteristics into segments which will generalize the image and make it easier to classify.
Classify the imagery
- Create training samples: Training samples are polygons that represent distinct sample areas of the different land-cover types in the imagery. They signify that segments with certain spectral characteristics should be classified together to represent the same land-use type. The training sample will then be saved as a shapefile.
- Train the classifier: Use the created training samples to create an ESRI classifier definition file (.ecd) which can then be used during the classification to ensure that features are classified accordingly.
- Classify the imagery: Use the classifier definition file (.ecd) to classify the imagery into the seven land-use classes designated with the creating of the training samples. Then, reclassify that image to have only two classes: pervious and impervious.
Calculate impervious surface area
- Create accuracy assessment points: Create randomly generated accuracy assessment points throughout the image and then compare the classified value of the image at the location of each point with the actual land-use type of the original image.
- Compute a confusion matrix: A confusion matrix is a table that compares the Classified and GrndTruth attributes of accuracy assessment points and determines the percentage of accuracy between them.
- Tabulate the area: Determine the area of impervious surfaces within each parcel of land in the neighborhood. First calculate the area and store the results in a stand-alone table; then join the table to the Parcels layer
- Symbolize the parcels: Replace the field names with shorter aliases and symbolize the parcels by impervious surface area to depict the area attribute on the map.
Results/Discussion
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| Figure 1: Map of impervious surfaces of parcels |
Figure 1 illustrates the impervious surfaces that were the result from the tutorial. Yellow surfaces represent the lowest amount of impervious surface area while the red surfaces represent the highest amount of impervious surface area. The parcels are broken up into seven classes where the lowest begins at 0 square feet and the highest ends at 99345 square feet. Many of the road sections are highly impervious, which makes sense since roads are made of of concrete and asphalt. The area with a very low amount of impervious surfaces are those that made up of mostly or all grass/vegetation. However, most parcels fall in between the low and high extremes due to there being houses with driveways (impervious surfaces) that make up most of the parcel while there is only a small amount of yard (pervious surface) within the same parcel.
Conclusion
Analyzing UAS data in the way that is was in the online tutorial Calculate Impervious Surfaces from Spectral Imagery has the potential to add value to any set of UAS data. By analyzing the imagery of the Louisville, KY neighborhood step by step using the tasks that was set up in ArcGIS Pro, students were able to make a map that illustrated impervious surfaces of parcels that local government officials can then actually use to enact fees on said parcels. This type of approach could also be used for a number of other applications including perhaps comparing the growth of impervious surfaces temporally or analyzing changes in land-use over a certain period of time.
Sources
Information from the online tutorial: https://learn.arcgis.com/en/projects/calculate-impervious-surfaces-from-spectral-imagery/
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