1. Accuracy Assessment of Thematic Maps
Thematic Accuracy

3. Learning Objectives
What is accuracy assessment and why is it crucially important?
What is the typical procedure for assessing thematic accuracy of maps?
What are reference data, and how are they collected?
What is contingency table analysis, and how do you calculate the common accuracy metrics?
As a map maker, what can you learn from accuracy assessment?

4. What is Accuracy Assessment?
Map Accuracy -- The proportion of agreement between a classified map and reference data assumed to be correct. Thematic Precision – The level of detail that is mapped. A map distinguishing lodgepole pine, Douglas fir, Ponderosa, etc. is more precise (but probably less accurate!) than a map just showing Forest. Accuracy and precision are DIFFERENT!

5. Spatial Accuracy vs. Thematic Accuracy
Thematic accuracy is how well the class names on the map correspond to what is really on the ground.
Spatial accuracy quantifies errors in the locations of boundaries.
Thematic and spatial accuracy are related but are usually treated separately.

6. Steps for performing thematic accuracy assessment:
Develop a sampling scheme
Collect reference data
Compare reference data to classified map
Compute accuracy metrics and deliver to map users

7. Sampling Schemes Reference locations must be unbiased
Reference locations must be large enough to find with certainty on your classified image
You must ensure that you can correctly identify the types at your reference sites
You must visit LOTS of reference locations

8. Reference data collection sites for 2001 NLCD map, with ecoregions.

9. Collecting Reference Data
Field collection
Use GPS to find pre-determined reference sites
Use explicit definitions of the classes to insure that reference data are consistent (should have done this with map classes too)
Develop efficient plan for visiting sites and contingencies for unreachable sites

10. National Park Service image (nps.org)

11. Collecting Reference Data (cont.)
Double Sampling – Using high resolution remotely sensed data to assess the accuracy of low resolution data
Air photos, Air videography, higher resolution satellites, etc.
Requires some ground work to “train” yourself to accurately interpret the reference data
May negate the assumption of accurate reference data!
Relatively inexpensive, good for inaccessible areas, fast, etc.

12. Air Videography

13. Quantifying Accuracy – Comparing Mapped Types to Reference Types
Contingency Tables = Error Matrices = Confusion Matrices
Traditional Accuracy Statistics are calculated from the error matrix

14. Contingency Table or Error Matrix
Error matrix consists of n x n array where n is the number of classes
Rows: reference (“correct”) data (n rows)
Columns: mapped classes (n cols)

15. Error Analyses - Example
Create a map with 3 thematic classes (water, forest and urban)
Collect 95 ground reference data
(Water 33, Forest 39 and Urban 23)
Compare those locations to those places in the map
Generate Error Matrix.

16. Contingency Table Reference data Classified image Forest Urban Total
Water
Forest
Urban
Total 21 5 7 33 6 31 2 39 1 22 23 27 37 95

17. Classification Accuracy
Lots of ways to look at the thematic accuracy of a classification
Overall accuracy
Errors of omission
Errors of commission
User’s accuracy
Producer’s accuracy
Accuracy statistics (e.g., Kappa)
Fuzzy accuracy

18. Overall Accuracy
Of all of the reference sites, what proportion were mapped correctly?
Easiest to understand but least amount of information for map users and map producers (us).

19. Overall Accuracy Correctly classified: 21 + 31 + 22 = 74
Reference data
Classified image
Water
Forest
Urban
Total 21 5 7 33 6 31 2 39 1 22 23 27 37 95
Correctly classified:
= 74
Total number reference sites = 95
Overall accuracy
= 74 / 95 = 77.9%

20. Off-diagonal Elements
The off-diagonal elements of a contingency table tell us the most about how to improve our remote sensing classification!
Should spend lots of time examining ERRORS to figure out what went wrong

21. Errors of Omission
The type on the ground is not that type on the classified image – the real type is OMITTED from the classified image.

22. Omission Error For water: 5 + 7 = 12 12 / 33 = 36% For forest:
Reference data
Classified image
Water
Forest
Urban
Total 21 5 7 33 6 31 2 39 1 22 23 27 37 95
For water:
5 + 7 = 12
12 / 33 = 36%
For forest:
6 + 2 = 8
8 / 39 = 20%
For urban:
0 + 1 = 1
1 / 23 = 4%

23. Errors of Commission
A type on the classified image is not that type on the ground – the type is COMMITTED to the classified image.

24. Commission Error For water: 6 + 0 = 6 6 / 27 = 22% For forest:
Reference data
Classified image
Water
Forest
Urban
Total 21 5 7 33 6 31 2 39 1 22 23 27 37 95
For water:
6 + 0 = 6
6 / 27 = 22%
For forest:
5 + 1 = 6
6 / 37 = 16%
For urban:
7 + 2 = 9
9 / 31 = 29%

25. Producer’s Accuracy
Map accuracy from the point of view of the map maker (PRODUCER).
How often are real features on the ground correctly shown on the map?

26. Producer’s Accuracy
Can be computed (and reported) for each thematic class Producer accuracy = 100 – omission error Water: 100 – 36 = 64% Forest: 100 – 20 = 80% Urban: 100 – 4 = 96% Or PA = #correct/row total

27. User’s Accuracy
Accuracy from the point of view of a map USER (not a map maker).
How often is the type the map says should be there really there?

28. User’s Accuracy
Can be computed (and reported) for each thematic class User accuracy = 100 – commission error Water: 100 – 22 = 78% Forest: 100 – 16 = 84% Urban: 100 – 29 = 71% Or UA = #correct/column total

29. Kappa Statistic
A measure of how accurate your map is above and beyond the accuracy expected by chance alone
Considers omission, commission, and overall accuracy
Frequently reported in the literature and used as a common way to compare maps

30. Kappa Statistic
Kappa = (observed – expected)/(1 – expected) Observed is the overall accuracy from your contingency table (the sum of the diagonal)/(Total reference pixels) Expected is calculated from a matrix of “marginal products” Create a matrix by multiplying marginal totals. Expected = diagonal sum/total sum (from the marginal table) (See Campbell for calculation details)

31. Fuzzy Accuracy
Recognizes that binary accuracy assessment (right vs. wrong) discards important information
Can use scale of “rightness” or “wrongness” to get more nuanced accuracy assessment (e.g. absolutely right, good, reasonable, mostly wrong, absolutely wrong)
There are many fuzzy metrics that can capture this information numerically.

32. Accuracy Assessment -- Summary
Absolutely critical to any remote sensing based mapping project
Expensive – should be included in the budget from the start
Requires collection of accurate reference data either in the field or from higher resolution data
Analysis should include many aspects of accuracy to give users more information about the product.