1. Methods for Mapping Impervious Surfaces
An Exploratory Case Study from the Bassett Creek Watershed
Josh Dunsmoor and Lucas Winzenburg
December 10th, 2012

2. Outline What are Impervious Surfaces? Our Problem Approach
Methods and Analysis
Conclusion/Discussion

3. Impervious Surfaces
“…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)
Rooftops, Roads/Streets/Highways, Sidewalks, Driveways, Parking Lots, Pools/Patios

4. Impervious Surfaces – Importance
Fish populations
Ground Water Assessment
Flood Effects
Heat Islands
Studying Habitat Fragmentation
Water Quality Assessment

5. Our Problem
Tasked with estimating Leaf Biomass in priority watersheds in Minnetonka, MN
Used to determine nutrient loading, specifically amount of pollutants from Leaf Litter (phosphorus, nitrogen)
Where are the conveyances?
What is the best method of mapping these conveyances?

6. Factors in Determining our Methods
Scale/Study area size – approximately 1 sq. mile study area (existing data is 30m resolution)
Data availability, cost
Data quality (i.e. Leaf-on vs. Leaf Off, how recent was the capture?)
Time constraints
Lack of Experience
Technology Limitations

7. Methods/Data Data Used Method
Heads up Digitization
Unsupervised Classification
Supervised Classification
LiDAR/HUD Hybrid
OpenStreetMap
.5m Aerial photo 2008 CIR NAIP Imagery (Leaf-on) State LiDAR data and manual digitization .osm extract converted into feature classes

8. Study Area

9. Heads-Up Digitization
“Manual digitization by tracing a mouse over features displayed on a computer monitor, used as a method of vectorizing raster data.” (ESRI)
Used ArcGIS 10.1
Pros – Accurate
Cons – Labor Intensive, Costly (Time Consuming)

10. Heads-Up Digitization
Imagery
Final Classification

11. Heads-Up Digitization
Class Breakdown
Total %
% Pervious/Impervious
Pervious Ground
54%
64%
Open Water
10%
Buildings 9%
36%
Streets
27%
Accuracy Assessment
Reference Data
Map Data
Total
Pervious Ground
Open Water
Buildings
Streets
Producer's Accuracy 95
100.00% 17 28 6 21 1
75.00% 60 5 55
91.67%
200
106 56
User's Accuracy
89.62%
98.21%
Total Accuracy
94%

12. Unsupervised Classification
2008 CIR NAIP Imagery- 1m, leaf-on
Used ERDAS Imagine – ISODATA Algorithm
4 Attempts – Used varying amount of classes (4, 6, 10). More classes made classes less distinguishable.
Deliverable used 3 classes, 25 iterations

13. Unsupervised Classification
Imagery
Final Classification

14. Unsupervised Classification
Class Breakdown
Total %
% Pervious/Impervious
Pervious Ground
38%
66%
Open Water
28%
Impervious
34%
Reference Data
Map Data
Total
Pervious Ground
Open Water
Impervious Surface
Producer's Accuracy 82 67 11 4
81.71%
100.00% 57 5 7 45
78.95%
150 72 29 49
User's Accuracy
93.06%
37.93%
91.84%
Total Accuracy
82%

15. Supervised Classification
2008 CIR NAIP Imagery – 1m, leaf-on
Used ERDAS Imagine
4 attempts – used a varying amount of training areas and Max Likelihood and Minimum Distance to Mean
Deliverable was 3 classes – Impervious, Vegetation, Water
10 Training Areas per class
Minimum Distance to Mean

16. Supervised Classification
Imagery
Final Classification

17. Supervised Classification
Class Breakdown
Total %
% Pervious/Impervious
Pervious Ground
46%
67%
Open Water
21%
Impervious
33%
Reference Data
Map Data
Total
Pervious Ground
Open Water
Impervious Surface
Producer's Accuracy 86 69 10 7
80.23% 16 2 14
87.50% 48 8 32
66.67%
150 79 39
User's Accuracy
87.34%
43.75%
82.05%
Total Accuracy
77%

18. LiDAR/HUD Hybrid
Obtained LiDAR data from MN Geospatial Information Office
Used ArcMap for analysis
LAStools third-party toolbox for data management
Python scripting language for geoprocessing
Initially tried to use LiDAR elevation/intensity only
Settled on extracting buildings and using hybrid method

19. LiDAR/HUD Hybrid
Elevation Points
Intensity

20. LiDAR/HUD Hybrid
Extraction of Buildings
Python Script

21. LiDAR/HUD Hybrid
Final Classification

22. LiDAR/HUD Hybrid Class Breakdown Total % % Pervious/Impervious
Total %
% Pervious/Impervious
Pervious Ground
54%
65%
Open Water
11%
Buildings 9%
35%
Streets
26%
Reference Data
Map Data
Total
Pervious Ground
Open Water
Buildings
Streets
Producer's Accuracy
116
108 1 7
93% 15
100% 20 6 14
70% 49 5 44
90%
200
119 16 51
User's Accuracy
91%
94%
86%
Total Accuracy

23. OpenStreetMap Crowd-sourced, openly-editable online map
Extracted underlying data directly from map
Used ArcMap to convert into feature classes in GDB
Buffered roads to 15ft, deleted extraneous layers
Merged/clipped all impervious surface classes
Areas not digitized as impervious/water were considered pervious

24. OpenStreetMap
Web Map
Final Classification

25. OpenStreetMap Class Breakdown Total % % Pervious/Impervious
Total %
% Pervious/Impervious
Pervious Ground
62%
72%
Open Water
10%
Impervious
28%
Reference Data
Map Data
Total
Pervious Ground
Open Water
Impervious Surface
Producer's Accuracy 83 78 5
94% 15
100% 52 13 39
75%
150 91 44
User's Accuracy
86%
89%
Total Accuracy
88%

26. Overall Comparison of Methods

27. Overall Comparison of Methods

28. Pros/Cons of Methods Digitization Supervised Classification
Accurate, Time Consuming for large areas
Supervised Classification
Quick, At mercy of imagery
Unsupervised Classification
Quicker, At mercy of imagery and algorithms
LiDAR/HUD Hybrid
Statewide coverage, Not a fully developed method, large dataset
OpenStreetMap
Fast/free, Sparse/imprecise, generalized data

29. Conclusions Heads-Up digitizzzzzzzz
Leaf-off! Too many shadows with leaf-on
Difficult to map at large scales due to shadows
No “one size fits all” solution for RS problems
Would likely choose LiDAR hybrid to perform task as it relates to our problem w/ this data

30. Further Research Object-based classification
Explore LiDAR with greater depth
High-res satellite data to improve accuracy?

31. Questions?