Accuracy Assessment of Thematic Maps

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 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 gather 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 • Generated a map with 3 thematic classes (Water, Forest and Urban) • Collected 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

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 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: 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: 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 that would be expected by chance alone • Considers omission, commission and overall accuracy simultaneously • 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 number of matches in 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 diagram)

31. 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.