The Potential for Integration of Lidar into FIA Operations

1. The Potential for Integration of Lidar into FIA Operations Joseph E. Means Forest Science Department Oregon State University Kenneth C. Winterberger PNW Research Station

2. Talk Outline • Introduction to airborne scanning lidar • Capital Forest Lidar Study • Other uses of lidar in forestry • A plan for integrating lidar into FIA estimation procedures

3. Airplane cartoon

4. Transect 700m Wide

5. Transect Closer

6. Footprint Pattern

7. Footprints Close-up

8. Point Cloud

9. Apparent in Point Clouds • Topography • Vegetation height • Canopy depth • Understory or lack • Individual crowns

10. Multiple Return Technology Dave Harding, Goddard Space Flight Center, Maryland

11. Capital Forest Lidar Study • Joseph E. Means, Forest Science, OSU • Ken Winterberger, PNW, Anchorage, AK • David Marshall, PNW, Olympia, WA • Hans Andersen, Coll. For., Univ. Wash.

12. Capital Forest Lidar Study • South of Olympia, Site Class 1 & 2 Douglas-fir • At Blue Ridge Site of Silvicultural Options Study • Lidar research cooperatively supported by FIA $38,000, RSAC $10,000, OSU $45,000 • Lidar Data flown by Aerotec, courtesy of Steve Reutebuch, PNW Seattle • Plot data from Dave Marshall, PNW, Olympia (92), Ken Winterberger (9), Hans Andersen, UW (6)

13. Orthophoto Overview

14. Goals for Plot Estimates • Develop the capability to estimate plot features using lidar data: • Height • Canopy cover • Basal area • Cubic volume • Tree biomass • Additional equations were developed for: • Stocking density • Stand Density Index

15. Goals for Mean Tree Estimates • Develop the capability to predict means & standard errors: • Height & Lorey height • DBH & Quadratic mean DBH • Basal area • Volume • Biomass

16. Aerotec DEM & DTM Problems • Canopy DEM had too-low elevations • DTM elevations were above many lidar last returns

17. Aerotec Canopy DEM Hole

18. New Capital Forest Canopy DEM – No Hole

19. Aerotec ground DTM too high

20. Comparison of DTMs: 1st return errors

21. New Capital Forest Canopy DEM

22. New Capital Forest Ground DTM

23. 3_D Capital Forest

24. Bare Ground/Canopy

25. Vegetation Height, Capital Forest

26. Canopy Cover @ 1 m Height

27. Goals for Plot Estimates • Develop the capability to estimate plot features using lidar data: • Height • Canopy cover • Basal area • Cubic volume • Tree biomass • Additional equations were developed for: • Stocking density • Stand Density Index

28. Lorey Height

29. Volume

30. Tree Biomass

31. Stocking Density

32. Stand Density Index

33. Goals for Mean Tree Estimates • Develop the capability to predict means & standard errors: • Height & Lorey height (same as plot averages) • DBH & Quadratic mean DBH • Basal area • Volume • Biomass

34. Height Std. Dev.

35. Diameter (quadratic mean)

36. Diameter (Quadratic mean) Std. Dev.

37. HJ Andrews Lidar Paper – ERDAS Award Means, J.E., S.A. Acker, B.J. Fitt, M. Renslow, L. Emerson, and C. Hendrix. 2000. Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering & Remote Sensing 66(11):1367-1371. ERDAS Award for Best Scientific Paper in Remote Sensing 3rd Place, 2001 American Society of Photogrammetry & Remote Sensing

38. Additions to FIA Presentation

39. LHP-FHP-Tree Characteristics Links LHP (Laser Height Profile) FHP (Foliage Height Profile) Lidar measures & Multiple regression Not mechanistic Limited applicability Risk of over-fitting Tree & Plot Characteristics

40. How mult regression with many potential predictors works • Height percentiles are cumulative upwards • Cover percentiles are cumulative downwards

41. LHP Ht%ile Cov%ile

42. Mult Regress pulls info out of LHP • LHP -> Tree & Plot Characteristics • Can be described quantitatively by multiple regression • Interaction of predictors and coefficients (+/-) allows “best” transformation of LHP to be used

43. LHP-CHP-Tree Characteristics Links Beers Law k=1 LHP FHP Few places with foliage height profiles Statistical link function Magnussen, et al 1999 height only, distribution Moment arm Mechanistic model Gives bole taper Individual tree Lidar measures & Multiple regression Not mechanistic Limited applicability Risk of over-fitting Tree & Plot Characteristics

44. Understanding relationships between LHP <-> tree characteristic • We can describe quantitatively: • LHP -> Mean height for Douglas-fir in B.C. Applicable to other monocultures. Magnussen, et al. 1999 • We cannot describe quantitatively: • LHP -> FHP • Is possible in very few places where have measured vertical distribution of foliage

45. Understanding relationships between LHP -> tree characteristic • LHP -> FHP • Cannot describe quantitatively or mechanistically except at a very few places where know vertical foliage distribution • LHP -> Tree & plot characteristics (DBH, BA, volume, biomass, TPH, SDI) • Cannot describe mechanistically except for individual trees with complete foliage distribution using moment arm model. Potential to expand to all spp.

47. Long-Range Plan • Mechanistic models estimate FHP and Tree & Plot characteristics • When needed, estimate species groups with limited ground plot data and multi-temporal ETM+

48. LHP-CHP-Tree Characteristics Links Use foliage height profiles to estimate FHP with extinction coefficient that varies with depth LHP FHP Statistical link function Magnussen, et al 1999 height only, distribution By species group distribution of crown shapes Lidar measures & Multiple regression Not mechanistic Limited applicability Risk of over-fitting Moment arm Mechanistic model Gives bole taper Individual tree Tree & Plot Characteristics

49. Lidar Uses: Stand Structure • Accurate inventories at the stand level: • Height • DBH • Volume • Site index, with knowledge of stand age • Form factor * • Parameterize stand growth models • Diameter distributions, Height distributions * • * = Work is needed • Leaf Area (r2 = .8 to .9)

50. Cougar Reservoir StandsYoung Stand Thinning and Diversity Study