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

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

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

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

5. Individual Tree Crown (ITC) Delineation Valley following Deep shade threshold 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. Much attribution without specific data. Goal: Goal: DBH’s, total heights, correct species, counts. DBH’s, total heights, correct species, counts. Per-acre statistics: BA, volume, biomass. Per-acre statistics: BA, volume, biomass. Empirical predictions: Empirical predictions: per-acre level is common. per-acre level is common. tree level (matched data) as resolutions and technology improve. tree level (matched data) as resolutions and technology improve.

9. Tree Predictions - Data Ground-measured tree crowns. Ground-measured tree crowns. Rough plot alignment Rough plot alignment correlated distributions. correlated distributions. Crown Images and Actual Trees aligned. Crown Images and Actual Trees aligned. Research: 100% mapped, special locators. Research: 100% mapped, special locators. Fixed area plots for inventory. Fixed area plots for inventory. Sample plan. 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. Errors in Segmentation Errors in Segmentation One delineated crown = 2 neighboring trees. One delineated crown = 2 neighboring trees. One real tree wrongly divided into 2 crowns. One real tree wrongly divided into 2 crowns. Trees entirely missed. Trees entirely missed. Ground vegetation seen as a tree. Ground vegetation seen as a tree. Understory trees don’t contribute. Understory trees don’t contribute. Technical improvements, but no absolute solution. 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. Traditional forest sampling. Plots are installed on the ground. Plots are installed on the ground. Stand boundaries recognized in field. Stand boundaries recognized in field. Hope the stand area is correct. Hope the stand area is correct. Awkward to use crown information. Awkward to use crown information.

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

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

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

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

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

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

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

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

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

24. Summary Attractive technology. Attractive technology. Best for which forest types Best for which forest types Irregular spatial tree distributions. Irregular spatial tree distributions. Some multi-species situations. Some multi-species situations. Detailed predictions without sampling all stands. Detailed predictions without sampling all stands. Useful spatial information. Useful spatial information. Sampling theory has been under-utilized. 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. Many slides were provided by Francois Gougeon and are courtesy of Natural Resources Canada, Canadian Forest Service. Other slides were provided by ImageTree Corporation. Other slides were provided by ImageTree Corporation. Mike Wulder, Canadian Forest Service. Mike Wulder, Canadian Forest Service. Adam Rousselle, Vesa Leppanen, Olavi Kelle, Bob Pliszka (Falcon Informatics). Adam Rousselle, Vesa Leppanen, Olavi Kelle, Bob Pliszka (Falcon Informatics).

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

27. THANK YOU questions?