Mike Hoppus & Andrew Lister USDA-Forest Service Northeastern Research Station

1. A Statistically Valid Method for Using FIA Plots to Guide Spectral Class Rejection in Producing Stratification Maps Mike Hoppus & Andrew Lister USDA-Forest Service Northeastern Research Station Newtown Square, Pennsylvania

2. Objectives • Use the large number of high quality – expensive FIA ground plots to classify satellite imagery into a forest/non-forest map. • Use the forest/non-forest map to stratify the ground plots in order to reduce the variance of estimates of “timberland” area and volume.

3. The Challenge • Develop a sampling method that both uses the valuable plots AND doesn’t allow the plots to stratify themselves.

4. CIR Bands of Landsat TM: West Virginia Unit 3 (Southern)

5. Stratification TechniqueSelect FIA Plots for “Training” sites • Identify all “forestland” single condition plots; • Randomly split these plots into 2 groups; • Divide each group into 4 sets – based on basal area; • Select “training” plots from each set.

7. West Virginia FIA Unit 3 – Southern: Counties and Plots

8. Stratification TechniqueMake 2 maps based on the 2 groups of plots • Use FIA plot group “A” as ground truth in the IGSCR classification method to produce a Forest/Non-Forest Map(A)of WV Unit #3; • Use FIA plot group “B” to produce a Forest/Non-Forest Map(B) of WV Unit #3; • Use “A” plots to assess the accuracy of map B, and visa versa.

10. Iterative Guided Spectral Class Rejection Method Unsupervised Classification “Reject” pure classes 2nd Iteration Unsup Class. Maximum Likelihood Class. using spectral training classes from 3 unsup classifications 3rd Iteration Unsup Class. of unrejected classes

11. Unsupervised Classification: 100 Classes

12. Unsupervised Classification-Second Iteration Output: Black Areas Were Rejected as Pure Classes in First Iteration

13. CIR Satellite Image with Corresponding IGSCR Forest Map

14. Stratification TechniqueStratify the Plots • Use Map A to label and group “B” plots into map class strata; • Use Map B to label and group “A” plots into map class strata; • Use the most accurate of the two maps to “weight” each stratum estimate by the % area of each map class strata; • Produce an estimate of “Timberland” area using a stratified random sampling procedure.

15. Two maps made from different random samples of fia plots

16. Two maps made from different random samples of fia plots

18. IGSCR CIR Image GAP MRLC IGSCR (2000), GAP (1993) and MRLC (1991)

19. Accuracy Assessments

20. FIA Ground Plot Geometry vs 30m TM Pixels FIA plot design: a cluster of four 0.017 ha plots. Dark gray circles = area of locational uncertainty due to GPS errors; Larger circles = area of locational uncertainty due to image registration errors.

21. F/NF, 2 Strata CIR Image F/F-edge/NF-edge/NF, 4 Strata F pixel count filter, 4 strata

22. Mean % Timberland per MRLC5 Class

23. Maximum Likelyhood 5X5 Forest Pixel Count Map

24. Results of FIA Phase 1 Inventory West Virginia – Southern Unit

25. The FIA Sampling Error Objective is 3% per Million Acres • of Timberland. • The Sampling Error Required for the Southern Unit of WV • is: 1.5%. • -1989 Timberland Estimate: 4,139,200 ; SE = 0.7 : 3% more

26. --Ground Plot Equivalent of Standard Error Differences One comparison of standard errors is the number of additional ground plots required to bring the less precise estimate to the same level as the estimate provided by the more precise technique. By evaluating: Sampling Error 1- Sampling Error 2=1 - __N1_ Sampling Error 1 N2 1722 FIA Plots required to reduce the variance as much as IGSCR Stratification: An increase of 908 plots @ over $700,000

27. Questions ?