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Methods for Mapping Impervious SurfacesAn Exploratory Case Study from the Bassett Creek Watershed

FR5262

Remote Sensing of Natural Resources and the Environment

Josh Dunsmoor

Lucas Winzenburg

Table of Contents

I. Figure Index

II. Abstract

III. Introduction and Background

IV. Heads-Up Digitization

V. Unsupervised Classification

VI. Supervised Classification

VII. LiDAR/HUD Hybrid

VIII. OpenStreetMap

IX. Discussion of Methods

X. Conclusion

XI. Works Cited

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Figure Index

Figure 1. Study AreaFigure 2. .5m Aerial Photography provided by the City of Minnetonka, MNFigure 3. Classification Created from Heads-Up DigitizationFigure 4. Class Breakdown from Heads-UpFigure 5. Accuracy Assessment for Heads-Up DigitizationFigure 6. 1m Color Infrared NAIP Imagery (2008)Figure 7. Classification Created by Supervised ClassificationFigure 8. Class Breakdown from Supervised ClassificationFigure 9. Accuracy Assessment for Supervised Classification

Figure 10. Classification Created by Unsupervised ClassificationFigure 11. Class Breakdown from Unsupervised ClassificationFigure 12. Accuracy Assessment for Unsupervised ClassificationFigure 13. LiDAR Elevation RasterFigure 14. LiDAR Intensity ReturnsFigure 15. LiDAR Building Point Cloud and Derived FootprintsFigure 16. LiDAR Final ClassificationFigure 17. Class Breakdown for LiDAR ClassificationFigure 18. Accuracy Assessment LiDAR ClassificationFigure 19. Comparison of original OSM data and extracted feature classes Figure 20. Final Classification using OpenStreetMapFigure 21. Class Breakdown for OpenStreetMap classificationFigure 22. Accuracy Assessment for OpenStreetMap ClassificationFigure 23. Comparison of Classification Methods

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Abstract

As urban areas continue to grow, the importance of studying impervious surfaces also increases. Many methods for mapping impervious surfaces exist and there is no consensus on the best method for performing this analysis. Determining a method for deriving these areas includes many considerations depending on the study, including size of the study area, type of land cover, data availability, data quality, time constraints, and the technology available to the researchers. Tasked with a study to estimate leaf litter pollution and conveyance to priority watersheds in Minnetonka, MN, we questioned which method would be best to derive impervious surfaces in one of our priority watershed areas. The City of Minnetonka provided us with quality data to perform several methods of analysis regarding impervious surfaces. This paper details our methods, output data, and analyzes the pros and cons of each method with respect to our task. It also provides considerations and alternatives for students, researchers, and analysts who may be tasked with similar exploration in the future.

Introduction

A large urban mall, surrounded by parking lots and office buildings sits aside a small lake with a wetland fringe, abutted by a wooded residential area on the other side. The city wants to study non-point pollution conveyance in the watershed area, specifically leaf litter and road salt pollutants. Non-point pollutants are those that are not sourced from “any discernible, confined and discrete conveyances (EPA).” Impervious surfaces provide a means of transportation for these materials. What is the best method for deriving the impervious areas in this vulnerable environment?

This is the problem leading to our study of deriving impervious surfaces from different methods and data sources. An impervious surface can be defined as “any material of natural or anthropogenic source that prevents the infiltration of water into soil, thereby changing the flow dynamics, sedimentation load, and pollution profile of storm water runoff (Tilley, USGS).” These surfaces, as they relate to our study area, include streets, building and house rooftops, swimming pools, sidewalks, and driveways. In addition, several other types exist. Many different methods for deriving impervious surfaces have been studied in the past and used diverse data sources within the realm of satellite data and aerial imagery. The City of Minnetonka, who tasked a larger group of students with the leaf litter and road salt pollutant conveyance project, provided a wealth of GIS-ready data for our use.

In researching a proper method for extraction, one of the few, clear messages we found was that there is no consensus best way to complete this task. We determined that there were many considerations specific to our study. The

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first was study area size. We decided to focus on one of the priority areas identified by the city, the Crane Lake area within the Bassett Creek Watershed. We chose this area given its diverse land cover, including a large concrete area made up by the Ridgedale Mall, which sits to the west of Crane Lake, which is surrounded by a wetland marsh area and a heavily wooded residential area sitting to the south of these two features (Figure 1). This area is approximately 1 sq. mile in area. Existing and easily accessible impervious surface data was derived and made available by the University of Minnesota and the Metropolitan Council. However, that data was derived with LandSat imagery at a resolution of 30m, which is much too coarse for an area and scale we are studying.

Another consideration was data availability. We did not have a budget available to perform this study, however, the city provided us with .5m resolution aerial photography and recently collected citywide LiDAR data. Consideration was also given to the technology available to us. ArcGIS 10.1 and ERDAS Imagine were accessible. ERDAS Imagine is very robust image processing software capable of doing many types classifications. To utilize its capabilities, we decided to capture 1m resolution color infrared (CIR) NAIP imagery from MN Geo GIS data site. Unfortunately, the only imagery readily available was leaf-on data, which would pose issues discussed later in the paper. Unfortunately, CIR leaf-off data was not available for download and could not be used in our classifications. Other consideration impacting our decisions included the knowledge we had of the data and software, and the time we had to complete the study.

After making these considerations, we researched and developed several methods of deriving impervious surfaces with what we had. They included manually (heads-up digitization), supervised and unsupervised classifications, a LiDAR hybrid scheme, and using Open Street Map data. The rest of this paper is devoted to the methods, analyzing our final classifications along with their advantages and disadvantages, and offering up a discussion on lessons learned and future considerations for us or others tasked with similar studies in the future.

Heads-Up Digitization

The first method, and perhaps the most straightforward, was to use heads-up digitization to derive different vector layers for each classification. Simply put this means, “manual digitization by tracing a mouse over features displayed on a computer monitor used as a method of vectorizing raster data” (ESRI).

The City of Minnetonka provided nearly ideal imagery to accomplish this task. We were provided with .5m resolution, leaf-off aerial photography of our study area. Figure 2 shows a zoomed display of a portion of the study area. Although the study area is only approximately 1 sq. mile in area, the assumption was that this would likely be the

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most time consuming of our methods. We decided to vectorize this image into four different classes: streets, buildings, pervious ground, and open water. Had this been for commercial use, these classes could easily be expanded to sidewalks, swimming pools, driveways, parking lots, etc. However, this was not necessary to accomplish our primary objective for this study and would have added additional time constraints to the rest of our project.

Using ArcGIS 10.1, we built separate shapefiles for buildings, streets, and open water. We decided this strategy may save some time as pervious/forested was clearly the largest class. Once the other three classes were draw, we could merge these layers with a copy of our entire study area and perform a clip to remove the overlap, giving us the remaining land area representing the pervious ground areas.

This task is as basic as any type of tracing exercise and with a clear and sharp image, discerning areas was, in general, quite easy. From the onset, we speculated this would likely give us our most accurate classification given the high quality data and a small study area. However, the question remained whether the intensive labor and time commitment, which would be costly in a professional setting, would be worth the effort if we could get a similar accuracy in a fraction of the time using other methods. Our other methods could be performed with software and many parameters tried and tested in the amount of time spent digitizing in ArcGIS, which in this case was an upwards of 10 to 12 man hours.

Once all shapefiles were drawn, they were merged into a single file, which was also converted to a raster TIFF. Converting to a TIFF would allow us to compare in the same unit, pixels, to our other methods.

Figure 3 shows our final classification and Figure 4 shows the breakdown of the total area as a percentage. Combining the pixel totals from Streets and Buildings gives us the total impervious area, and adding the open water and pervious ground pixels gives us the total pervious area.

Total % %

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Pervious/ImperviousPervious Ground 54% 64%Open Water 10%Buildings 9%

36%Streets 27%

Figure 4 - Digitized Classification Breakdown

Total pervious area accounted for 64% of the study area, with impervious making up the remaining 36%. With our classification completed, we could now assess the accuracy of the image. As each class we were exploring was well represented in the study area, we decided to use a simple random scheme using the Create Random Points tool in ArcGIS. Given our four classes created, we decided to create 50 sample points per class created and a point file with 200 random points was generated. Using the Spatial Join tool, we were able to quickly bring in our classification to the point file and then add a new field for the actual classification. Then, one by one, we assessed each point against our original .5m image. We realized this was not ideal, as you do not want to use the image you used to classify the data to also perform the accuracy assessment, however, with our resources it was difficult to find an image of equal or better quality than our original. This accuracy assessment scheme was used for all of our classification methods and will not be discussed in detail in the following sections. Figure 5, below, displays our accuracy assessment.

Reference Data Map Data

Total Pervious GroundOpen Water Buildings Streets

Producer's Accuracy

Pervious Ground 95 95 0 0 0 100.00%Open Water 17 0 17 0 0 100.00%Buildings 28 6 0 21 1 75.00%Streets 60 5 0 0 55 91.67%

200 106 17 21 56User's Accuracy 89.62% 100.00% 100.00% 98.21%

Total Accuracy 94%

Figure 5 - Accuracy Assessment for Heads-Up Digitization

Note the overall accuracy of 94%. The rest of the information by itself may not be entirely meaningful. We’ll revisit this information at the end of this paper as we compare the remaining methods performed.

Supervised Classification

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We performed our supervised analysis using 1m resolution CIR NAIP Imagery provided by the City of Minnetonka with ERDAS Imagine 2011. We began with some quick and exploratory classifications to gauge the overall efficacy of supervised classifications. In our initial attempts we used a varying amount of classes (from 2-6) and a wide range of signatures for each class.

A supervised classification using 10 signatures for each of three classes – impervious, vegetation, and open water – grouped using minimum distance to the mean proved to be the most accurate scheme we developed. Figure 7, above, shows the resulting classified image. Upon first glance it appears to be a reasonably accurate classification, but our accuracy assessment proves otherwise. Figure 9 below shows that the image was in fact poorly classified using our parameters for a supervised classification. Examining figure 8, our class breakdown, of particular note is the gross over reporting of the open water class, largely due to shadowing in the vegetated areas and around Ridgedale Mall.

Class Breakdown

Total %%

Pervious/Impervious

Pervious Ground 46%67%

Open Water 21%

Impervious 33% 33%

Figure 8 – Class Breakdown from Supervised Classification

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Total Pervious Ground Open WaterImpervious

Surface Producer's Accuracy

Pervious Ground 86 69 10 7 80.23%

Open Water 16 2 14 0 87.50%

Impervious Surface 48 8 8 32 66.67%

Total 150 79 32 39

User's Accuracy 87.34% 43.75% 82.05%

Total Accuracy 77%

Figure 9 – Accuracy Assessment for Supervised Classification

Unsupervised Classification

Using ERDAS Imagine, we also performed an unsupervised classification. We again used the same 1m resolution CIR NAIP Imagery. Cambpell and Wynn describe an unsupervised classification “as the identification of natural groups, or structures, within multispectral data.” Although ERDAS’ ISODATA algorithm which performs the classification does much of the heavy lifting in this method, it is still up to the analyst to look at the natural groupings created by the computer and identify what the classes are.

This method does not require many parameters. The algorithm will run once the analyst defines the number of classes that are needed and how many times the algorithm is to iterate over the image. Knowing that our original image was leaf-on, we were aware of the shortcomings that may come out of using this type classification due the number of shadows spread all over the image. None the less, we tried several different runs with ISODATA. Initially,

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we used six classes which overestimated water by a large amount. We then tried ten classes. The resulting image did not pick up the side streets and blended too many buildings and streets together. It again overestimated water, an issue that proved to be challenging to overcome. The final image we settled on actually used half the number of classes we started with. We generated three classes, iterating through 25 times, with results visible in figure 11 below.

Class Breakdown

Total %% Pervious/Impervious


Open Water 28%

Impervious 34% 34%

Figure 11 – Class Breakdown from Unsupervised Classification

A quick look at Figure 11 shows similar numbers to those in using manual digitization. Pervious surfaces accounted for 66% of the derived image. However, this does not tell the whole story. Looking at the image in Figure 10, it’s clear that, like in our supervised classification, open water is being overestimated due to similar brightness values from the tree shadows, as demonstrated by viewing the north side of the mall, as well as all the blue speckles in the southeast neighborhood. Further, streets and houses are likely hidden by the leaves of the trees. The lost visibility of streets and homes under the dense canopy is being offset by bright areas around Crane Lake that are not impervious in reference data. This is also reflected in our accuracy assessment (Figure 12).


Total Pervious Ground Open Water Impervious SurfaceProducer's Accuracy

Pervious Ground 82 67 11 4 81.71%Open Water 11 0 11 0 100.00%Impervious Surface 57 5 7 45 78.95%

150 72 29 49User's Accuracy 93.06% 37.93% 91.84%

Total Accuracy 82%

Figure 12 – Accuracy Assessment for the Unsupervised Classification

LiDAR

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LiDAR data were obtained from the MN Geospatial Information Office’s statewide LiDAR flight data. LiDAR files were downloaded in compressed .LAZ format and were unpackaged and manipulated using the third party LAStools toolbox extension and the LAS toolbar in ArcMap 10.1. Figure 13 at right shows complete LiDAR coverage of the area by elevation.

Our initial intent was to extract impervious surfaces directly from the LiDAR, but upon exploration of various avenues for achieving this – given our limited experience with LiDAR data and the tools available to us – we resorted to using a hybrid approach to integrate LiDAR data with manually digitized features.

In our first attempt to extract impervious surfaces, we filtered out the LiDAR data to show only ground returns. Unfortunately, ground returns spanned the entire gamut of land uses in our study area, from marshland to manicured lawns to parking lots. We attempted to reclassify these land uses based on subtle changes in the elevation by generating a DEM, but found no conclusive patterns, largely due to the considerable imposition of built features on the natural landscape and topography of the study area.

After browsing scholarly articles on extraction of impervious surfaces from LiDAR and consultation with experts at the university, we then took an exploratory approach to extracting impervious features based on the intensity of their LiDAR returns. It quickly became clear that there was little to no correlation between intensity of returns and impervious surfaces in our study area. Figure 14, above, shows the uniform intensity signature of water and marshland in blue and all other returns in red. Because of the lack of relationship between intensity and surface type for surfaces other than water/mash, we decided to abandon this method as well.

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In the end, we settled upon extracting building features from the LiDAR point cloud because they were the easiest class to distinguish among the points in the cloud. Extracting building footprints proved to be a challenging, multi-step process, and was eventually accomplished by way of a Python script in ArcMap. Figure 15, below, shows the buildings as raw points extracted from the clouds and as derived footprints.

The procedural steps for extracting building footprints via Python were:

1. Merge the four .LAS tiles that covered the study area2. Convert from .LAS to multipoint features3. Convert from multipoint to single point features4. Aggregate all points within 3 meters of one another into polygon features5. Simplify the polygons using the simplify building tool6. Define the projection for the polygon building layer to match the study area’s projection7. Clip the polygon features to the study area8. Delete temporary features created throughout the script

The resulting feature class contained imperfect but reasonably accurate footprints of buildings within the study area. The feature class containing polygonal building footprints derived from LiDAR was then integrated with the features we had previously digitized manually. We accomplished this using ArcMap. The existing layers we had digitized were merged into a single file and overlapping areas were clipped out. We were then able to calculate the percent that each class accounted for in the study area.

The tables below show the results of our areal calculations and accuracy assessment. Figure 16 at right shows the LiDAR footprints integrated with the manually digitized data. Interestingly, the difference between the total areas for the buildings class was less than 1% between the manually digitized buildings and footprints derived from LiDAR.

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The subtle differences in total area calculations between manual digitization and LiDAR for features in the streets are due to the numerous small shifts (less than 1%) in the overlapping areas between the streets and buildings classes. It is unclear why the open water class changed subtly between the two methods, though the difference was less than 1%.

In figure 18, below, our accuracy assessment shows that using a LiDAR/HUD Hybrid classification was one of the most accurate classification methods, though using the LiDAR generated footprints rather than manually digitizing them resulted in a lower accuracy for the buildings class and thus a lower overall accuracy than strictly digitizing.

Class Breakdown

Total % % Pervious/Impervious


Open Water 11%

Buildings 9%

35%Streets 26%

Figure 17 – Class breakdown for LiDAR Classification

Accuracy Assessment


TotalPervious Ground Open Water Buildings Streets

Producer's Accuracy

Pervious Ground 116 108 1 0 7 93%

Open Water 15 0 15 0 0 100%

Buildings 20 6 0 14 0 70%

Streets 49 5 0 0 44 90%

200 119 16 14 51

User's Accuracy 91% 94% 100% 86%

Total Accuracy 94%

Figure 18 – Accuracy Assessment for LiDAR Classification

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OpenStreetMap

OpenStreetMap is a collaborative, crowd-sourced, openly-editable map available to anyone free of charge. OpenStreetMap has, at least in part, global coverage. Maps and data from OSM are viewable online through a web browser and are also available for download in multiple formats. OSM data for the Ridgedale mall area are somewhat sparse, though the principal structures and water bodies have been digitized by OSM users as of December 2012.

Crowd-sourced data were exported directly from openstreetmap.org in XML format. Using ArcMap’s data interoperability extension and the quick import tool, OpenStreetMap data in the .osm format were converted into features classes in a geodatabase.

With the OpenStreetMap data converted and imported, the next step was to assign a projection to the entire dataset using the batch project tool. All data files in the geodatabase were assigned the NAD 1983 CORS96 UTM Zone 15N projection to match the projection of the reference imagery and other existing ancillary files.

Several extraneous feature classes were carried over from the OSM extracts (e.g. nodes and duplicate point layers). These features were removed from the working ArcMap document and were consider in further analyses. The remaining feature classes included polygon layers of shops, amenities, retail/commercial land use, and a line layer of roads.

The line layer, called “highway,” but sub-classified into categories such as tertiary, residential, path, and service, was quite rigorously digitized in relation to the other OSM layers. Nonetheless, there were numerous digitization errors visible to the unaided eye. After initial examination and sample measurements to calculate an average width using reference imagery, a 15-foot buffer was applied to all layers in the highways class. Next, the newly-buffered roads were dissolved into a single feature class. Buffering and dissolving the highway layer made it easier to extract areal measurements from the data in later procedural steps.

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All of the feature classes containing impervious surfaces were clipped to the study area using the clip tool and were then clipped to one another in an edit session to avoid overlap. Next, all of the impervious surfaces were merge into a single feature class for ease of areal calculation and visualization. Similarly, open water was extracted from the “natural” land use category and then clipped to the study area and merged into a single feature class.

Examining the final output and subsequent class breakdown table (figure 21), it is clear that the OpenStreetMap data are not a reliable source for areal calculations. Large swaths of impervious surfaces were omitted at the time of digitization. Conversely, several areas were overgeneralized as impervious, where the reference data clearly show landscaped areas and vegetation. The open water class was the most accurately digitized by OSM users, but entire ponds remain missing from the data.

Class Breakdown

Total % % Pervious/Impervious

Pervious Ground62%

72%

Open Water10%

Impervious 28% 28%

Figure 21 – Class Breakdown for OpenStreetMap classification

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TotalPervious Ground Open Water Impervious Surface Producer's Accuracy

Pervious Ground 83 78 0 5 94%

Open Water 15 0 15 0 100%

Impervious Surface 52 13 0 39 75%

150 91 15 44

User's Accuracy 86% 100% 89%

Total Accuracy 88%

Figure 22 – Accuracy Assessment for OpenStreetMap Classification

Discussion of Methods

The figure 23, below, show a summary of the percent impervious and total accuracy by method for each of the classification methods used. Though there are no clear outliers among the group, each method had its unique strengths and shortcomings in both categories. Below, each method is examined in detail.

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At 36% and 35%, respectively, manual digitization and LiDAR/Hybrid classifications were nearly identical in their calculations impervious surfaces. Both had a 94% classification accuracy, the highest of any of our classification schemes. This does not come as a surprise to us given the time and attention we devoted to precisely digitizing features. Manual digitization was accurate but time consuming. LiDAR data were readily available free of charge, but large files meant long processing times and we could conceive a fully-developed method for extracting impervious surfaces with LiDAR alone.

With 33% and 34% of the surfaces recorded as impervious using supervised and unsupervised classifications, respectively, these methods proved to be on par with manual digitization. Their faults lie in their accuracy, however. Both methods had extreme difficulty categorizing shadowy areas, and the open water class was largely skewed using both of these methods. Overall accuracy for supervised and unsupervised was 77% and 82%, respectively. This is a significantly lower accuracy than any of the other methods used. Both of these classification methods were extremely quick in relation to using manual techniques, but they do not afford the user as much control and are thus likely to lead to more error.

OpenStreetMap data only accounted for 28% of the impervious surfaces in our study area, the lowest of any method used. Our accuracy assessment found OSM data to be 88% accurate, though this number is misleadingly high as many of the randomly generated points happed to fall in the pervious areas. OpenStreetMap data are free and easy to acquire, but are sparse and imprecise depending on the area because of their crowd-sourced nature.

Conclusions

This project reinforces that determining a best method for mapping impervious surfaces involves a myriad of considerations. Factors including scale, data quality and availability, time, and budget need to

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be carefully weighed. Effectively deriving impervious surfaces at a scale smaller than ours, at a county or statewide level would require an entirely different dataset, perhaps utilizing ASTER or LandSat imagery. Even after completing five separate classifications, it isn’t entirely clear which would be best. However, if we had to make a decision to choose one approach for our purposes in Minnetonka, using our LiDAR hybrid may be best. Given a similar accuracy, the time saved by not having to digitize buildings would make it worthwhile. Yet, had we obtained leaf-off imagery of the area, our decision could easily shift to either a supervised or unsupervised method as it would greatly impact the accuracy of the classification.

Leaf-off imagery would be an obvious improvement to our methods and overall accuracy of our final products. Other improvements in quality and accuracy may be obtained with a budget to purchase high-quality commercial data. Additional time and technology to develop and test methods may also have improved results. Being able to use LiDAR to perform an object-based classification would have been worthwhile to pursue as well.

Although some of our final classifications would fail to meet higher accuracy standards, we hope this project can provide some food for thought when it comes to developing an approach and offer some advantages and disadvantages of several methods being used today.

Works Cited

“What is Non-Point Source Pollution?”. Retrieved from http://water.epa.gov/polwaste/nps/whatis.cfm.

“ESRI GIS Dictionary”. Retrieved from http://support.esri.com/en/knowledgebase/GISDictionary/term/heads-up%20digitizing.

Cambpell, J., Wynn, R. (2011). Introduction to Remote Sensing, 5th Edition. New York, NY. The Guillford Group.

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http://support.esri.com/en/knowledgebase/GISDictionary/term/heads-up%20digitizing

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