FOREST INVENTORY BASED ON INDIVIDUAL TREE CROWNS

1. FOREST INVENTORY BASED ON INDIVIDUAL TREE CROWNS Jim Flewelling Western Mensurationist Meeting June 18-20, 2006

2. OUTLINE • Perspective • Crown Segmentation • Tree predictions • Sample Frame • Estimation • Summary

3. Perspective • Aerial Surveys date from 1920’s and 30’s • Images for stand boundaries and attribution. • Individual crown locations and delineation since late 1980’s. • Attribution – training process. • Research process – match trees and Images. • Lidar – huge improvement. • Limited use of sampling theory at tree level.

4. Crown Segmentation, Delineation & Attribution • Identify individual crowns. • Locate center points. • Delineate crown boundaries. • (non-overlapping) • Attribute species. • Attribute height.

5. Individual Tree Crown (ITC) Delineation Deep shade threshold Valley following Rule-based system 1995 Courtesy of Canadian Forest Service

6. Delineated Individual Tree Crowns At ~30 cm/pixel, 81% of the ITCs are the same as interpreted crowns Courtesy of Canadian Forest Service

7. Delineated and Classified ITCs Courtesy of Canadian Forest Service

8. Predictions • Much attribution without specific data. • Goal: • DBH’s, total heights, correct species, counts. • Per-acre statistics: BA, volume, biomass. • Empirical predictions: • per-acre level is common. • tree level (matched data) as resolutions and technology improve.

9. Tree Predictions - Data • Ground-measured tree crowns. • Rough plot alignment • correlated distributions. • Crown Images and Actual Trees aligned. • Research: 100% mapped, special locators. • Fixed area plots for inventory. • Sample plan.

10. Matched Trees & Crowns The tree points are then matched up with the tree polygons to create regressions used for the inventory calculation. ©ImageTree Corp 2006

11. Matched Trees and Crowns

12. Matched Trees and Crowns • Errors in Segmentation • One delineated crown = 2 neighboring trees. • One real tree wrongly divided into 2 crowns. • Trees entirely missed. • Ground vegetation seen as a tree. • Understory trees don’t contribute. • Technical improvements, but no absolute solution.

13. Sample Frame - Ground or Map? Individual stand on LiDAR image after tree polygon creation. A polygon now surrounds every visible tree crown. ©ImageTree Corp 2006

14. Sample Frame - Ground • Traditional forest sampling. • Plots are installed on the ground. • Stand boundaries recognized in field. • Hope the stand area is correct. • Awkward to use crown information.

15. Sample Frame - Crown Map • Data-rich environment. • Fixed-area plots. • New or different challenges: • sample locations • tree & crown matching • stand boundaries • edge bias

16. CROWN BASED SAMPLE FRAMEREQUIREMENT • Trees linked to segmented crowns. • Linkage must be independent of sampling. • BUT • Linkages need not be physically correct. • Suppressed trees need not be linked if sampled another way.

17. TREE MATCHING SCHEMES • Subjective • potential for significant bias • Crown Captures ALL in tessellated area. • Expand crown area. • Trees compete to be captured. • Consider DBH, height, species … • Ground plot size > crown plot size.

18. Sample Locations(Crown Map as Sample Frame) • Select fixed-area plot centers • Ground • - usual compromises plus boundary issues. • Crown map • rigorous random selection process. • Difficult to find on the ground. • Both: • Unequivocal tree & crown matching?

19. Crown-based sampling scheme • Crown delineation, all stands, all crowns. • Select Sample Stands (in strata) • Randomly locate 2 plot centers on map. • GPS to those locations. • Install stem-mapped ground plots.

20. Challenges - plot location. • Map error + GPS error of several meters. • Process to find ground plot center on crown map. • (x, y) plus angular shift. • Force ground plot to include selected pt? • Accept the random deviation? • Ground plot center outside of stand? • Altered probability density.

21. Challenges - Edge Effects • Edge bias correction SIMPLE • “Tree concentric method.” • Computer finds area of “tree-center plot” within stand boundary. • More efficient than field-based methods. • Plot location - random error. • Minor alteration in probability density. • Computer can correct.

22. Estimation • Research focus is deterministic. • Attempt to remove uncertainty. • Alternative is stochastic modeling. • Each crown has multiple outcomes: trees and species. • DBH, heights vary with outcome. • Stand prediction = sum of expectations.

23. Estimation (continued) • Approximate Unbiasedness (strata). • Model-assisted survey estimators (regr.) • DBH distributions NOT unbiased. • “Regression towards the mean” • Can correct for unbiased width. • Could use data from sampled stands to improve those stands.

24. Summary • Attractive technology. • Best for which forest types • Irregular spatial tree distributions. • Some multi-species situations. • Detailed predictions without sampling all stands. • Useful spatial information. • Sampling theory has been under-utilized.

25. Acknowledgements • Many slides were provided by Francois Gougeon and are courtesy of Natural Resources Canada, Canadian Forest Service. • Other slides were provided by ImageTree Corporation. • Mike Wulder, Canadian Forest Service. • Adam Rousselle, Vesa Leppanen, Olavi Kelle, Bob Pliszka (Falcon Informatics).

26. Resources • 2005 Silviscan http://cears.fw.vt.edu/silviscan/ • 2004 ISPRS Laser-Scanner for Forest and - http://www.isprs.org/commission8/workshop_laser_forest/ • ImageTree Corp. www.imagetreecorp.com • Pacific Forestry Center http://www.pfc.forestry.ca/index_e.html • Precision Forestry Coop (U.W.) • http://www.cfr.washington.edu/research.smc/

27. THANK YOU questions?