Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection

1. Phase I Forest Area Estimation Using Landsat TM and Iterative Guided Spectral Class Rejection Randolph H. Wynne, Jared P. Wayman, Christine Blinn Virginia Polytechnic Institute and State University Blacksburg, Virginia John A. Scrivani, Rebecca F. Musy Virginia Department of Forestry Charlottesville, Virginia Support from Southern Research Station FIA NCASI

2. Goals • Phase I forest area estimation • Production of forest/non-forest maps • of acceptable resolution and accuracy • enabling stratification and spatial analyses

3. Criteria for Classification Methodology • Objective and repeatable • across operators, regions and time • Quick and low-cost • repeatable at 3-5 years • Provides a binary forest/non-forest landcover/landuseclassification • Usable to estimate Phase 1 forest land use • adjusting map marginals with ground truth

4. Virginia Work To Date • Iterative Guided Spectral Class Rejection method developed • Applied to three physiographic provinces • Compared to MRLC • Comparison with GAP in progress • IGSCR for areas of more rapid change in progress

5. Traditional PI Method • Operational SAFIS program used for comparison • Phase 1 PI • 1994 NAPP photography (1991-96) • ground truth performed late 1997-2000 • Estimates via Li, Schreuder, Van Hooser & Brink (1992)

6. Phase I Estimates from Image Classifications • Map marginal proportions from classification used as large “sample” • Phase II (and III) FIA ground plots and intensification points used as “small sample”, or ground truth, to adjust map marginal proportions • Standard errors estimated via Card (1982) formulae

7. Study Areas Mountains 2,435,000 ac Piedmont 732,000 ac Coastal Plain 1,499,000 ac

8. Imagery used in IGSCR • Landsat TM • Coastal (Path 14 Row 33) 5/14/98 • Piedmont (Path 16 Row 34) 9/26/98 • Mountains (Path 18 Row 34) 11/11/98 • Registration • Used GCP’s from Virginia DOT roads coverage • Obtained ~15 m RMSE • However, subjective adjustments needed, especially in Mountains

9. Reference Data for IGSCR • Any source of know forest and non-forest can be used • Need to sample range of spectral variability • Need to sample “confused” spectral classes • Need to sample proportionally within confused classes

10. Raw Image Iterative Guided Spectral Class Rejection Reduced Image ISODATAClustering • unsupervised ISODATA clustering into 100-500 spectral classes • reference data used to “reject” relatively “pure” spectral classes (e.g. 90% pure) • “pure” classes removed from image and remaining pixels enter into next iteration Remove Pixels Apply Rejection Criteria More pure classes? yes no

11. Iterative Guided Spectral Class Rejection Signature File from “pure” classes ML Classification 3x3 Scan Majority • Iterations continue until no further pure spectral classes are extracted • Identified “pure” spectral classes used as signatures for a ML classification • 3x3 scan majority filter used to assign final pixel classification

12. Raw TM Image

13. After First Iteration

14. After Pure Classes Removed

15. IGSCRIterationSummary

16. Cumulative Pure Spectral Classes

17. Validation • Used FIA permanent plots located with DGPS, Phase 1-3, 5-10 meter accuracy • Intensification plots digitized from SPOT 10m pan imagery • Since FIA plot locations only used for validation, confidentiality maintained

18. MRLC Comparison • EPA Region 3 MRLC classification (1996) • 1991-93 multi-date TM imagery • +/- 1 pixel registration • Collasped 4, or 5, classes into forest • Applied same validation and estimation method

20. Phase I Estimates - Piedmont 75.1% 72.8% 1992 71.2% 68.1% 63.4% 1.91% 2.55% 2.73% 2.88%

21. Results Summary

22. Accuracy Statistics - Piedmont

23. Accuracy Statistics - Coastal

24. Accuracy Statistics - Mountains

25. Forest From 2-ft Orthophotography

26. IGSCR Classification

27. Recoded MRLC Classification

28. Recoded Virginia GAP Classification

29. Reference Data • Any source of know forest and non-forest can be used • Need to sample range of spectral variability • Need to sample “confused” spectral classes • Need to sample proportionally within confused classes

30. Possible Reference Data Protocol • Training reference data • 500-1,5000 acre maplet at each Phase 3 plot from high resolution imagery (photo, video, IKONOS) • would provide 40-80,000 acres per TM scene • Validation reference data • Phase 2 plots • Larger intensification samples (e.g. 16 pt cluster at each Phase 2 plot)

31. Spectral Class Representation100 ISODATA Classes - 30,000 acres reference data

32. Conclusions • IGSCR is an objective, repeatable and low cost process for a given rectified imagery and reference data • Accuracy is as good or better than MRLC • Further work needed on standardized rectification, reference data protocols, and optimal iteration parameters

33. Conclusions • Acceptable Phase I estimates can be obtained from either ISGSCR or MRLC • Lower accuracy of satellite image classifications requires larger ground truth sample compared to traditional PI method

34. Further work • Standardized rectification • IGSCR Parameters • ISODATA parameters • Number of ISODATA classes • Minimum Pixel Count Per Class • Homogeneity Criteria • Stopping Criteria • Multinomial classifications • Reference data protocol • Trials in other regions

35. Thank you, Questions ?