1. An Assessment of Repeatability for Crown Measurements Taken on Conifer Tree Species James A. Westfall William A. Bechtold KaDonna C. Randolph USDA Forest Service Forest Inventory and Analysis

2. FIA QA/QC Data Collection Hot Checks Cold Checks Blind Checks Independent Plot Remeasurement Randomly Chosen Plots Experienced Personnel Target 3%

3. Conifer Data FIA Tree Crown Indicator (Phase 3) Uncompacted Crown Ratio (nearest 1%) Light Exposure (6 categories) Crown Position (4 categories) Crown Vigor Class (saplings – 3 categories) Crown Density (nearest 5%) Dieback (nearest 5%) Foliage Transparency (nearest 5%)

4. Analysis Data Matching (i.e., trees) No one-to-one correspondence (independent remeasure) Manually expensive (large # observations) Automate most matches –2-pass approach –Weighted distance = f(dbh, horz. distance, azimuth) –‘Conservatism’ via a decision rule MUST review unmatched trees and add legitimate matches into analysis data set

5. Analysis MQOs and Tolerances Tolerance A range of acceptable variation Can be specific value or percentage Example: ± 0.1 in. for dbh Measurement Quality Objective (MQO) The desired percentage of measurements that fall within the tolerance range Example: 95% of the time

6. Analysis Computations Obtain differences between field and QA crews for matched observations Determine percentage of total observations where difference is within the tolerance range Compare with MQO to see if standard is met Optional: compute percentages across range of tolerance values

7. Results *

8. Results

9. Results

10. Uncompacted CR

11. Vigor Class

12. Crown Density

13. Crown Dieback

14. Foliage Transparency

15. Conclusion Crown Light Exposure, Crown Dieback, and Foliage Transparency measurements met the stated repeatability standard. Uncompacted CR, Crown Position, Crown Vigor, and Crown Density measurements did not meet the repeatability standard.

16. Conclusion With few exceptions, levels of repeatability are similar across geographic regions. The poorest repeatability statistics were generally associated with relatively rarer crown characteristics. For some variables, improved training and/ or re-evaluation of the tolerance/MQO may be needed.

17. Conclusion Quality assurance data are important for: Evaluating training effectiveness Employee performance feedback Evaluating measurement protocols Identifying significant sources of error for computed attributes, model projections, etc.

18. Conclusion Further reading: Westfall, J.A., ed. 2009. FIA national assessment of data quality for forest health indicators. USDA For. Serv. Gen. Tech. Rep. NRS-53. Pollard, J.E., et al. 2006. FIA national data quality assessment report for 2000-2003. USDA For. Serv. Gen. Tech. Rep. RMRS-181.

19. Questions ??