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: 1 st return errors Average number of negative heights (% in parens) Average height discrepancy (cm) Maximum height discrepancy (cm) New DTM118 (5%)-8-20 Aerotec 1999 DTM388 (17%)-18-70

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 ERDAS Award for Best Scientific Paper in Remote Sensing 3 rd Place, 2001 American Society of Photogrammetry & Remote Sensing 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.

38. Additions to FIA Presentation

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

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

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

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 (r 2 =.8 to.9)

50. Cougar Reservoir Stands Young Stand Thinning and Diversity Study

51. Cougar Reservoir Stands Young Stand Thinning and Diversity Study Vegetation height [0 – 80 meters]

52. Cougar Reservoir Stands Young Stand Thinning and Diversity Study Cover percent at 15 meters above ground

53. Cougar Reservoir Stands Young Stand Thinning and Diversity Study Wood volume [0 – 1000 m 3 /ha]

54. More work needed for first draft from here on

55. Lidar Uses: Streams & Watersheds Riparian forest structure: Stream shading -> stream temperature modeling Input to models of woody debris input to streams* Inventory in riparian zones Valley floor topography Channel width, bank incision*, stream gradient, terraces, fans, side channels Fine-scale watershed structure Depth of road prism cuts, headwall basin size-gradient- locations, small gullies* Input to models of soil and regolith depth, modeling of watershed hydrology, canopy water retention & buffering* * = Research is needed

57. Aerial Photograph SBG NW FORETRY PROJECT - FLOWN JULY, 1996 - 1:32 000 SCALE

58. TIN of First Surface

59. TIN of Understory & Bare Earth

60. 3-D Fuels Mapping Live fuels mapping Canopy height Canopy depth * Understory vegetation height, cover * Vertical distribution of ladder fuels * Turn around in a few hours * Distinguish species, live vs. dead when integrate with multi-spectral data * = Work is needed to develop system

62. Weyerhaeuser Springfield Tree Farm Lidar Study DTM [650-1010 m elevation]

63. Weyerhaeuser Springfield Tree Farm Lidar Study Vegetation height [0 - 40 meters]

64. Weyerhaeuser Springfield Tree Farm Lidar Study, South Site

65. Weyco Lidar Study, South Site

66. Wildlife Habitat Applications Vegetation cover, height, tree size, density at different levels above ground, canopy depth Understory plant composition based on Stand structure, light environment, topography Distance from water, roads For gaps and patches of dense vegetation Sizes, shapes, height above ground, connectedness, location

67. The View Inside: Transects Show vertical canopy profiles Show understory gaps in canopy & fuels

68. A Progressive Study: Can Lidar be Profitably Used by FIA? Winter & Spring: Slope correction Publish slope & Capital Forest papers If slope correction successful then plan for lidar flight in summer Stand-level estimates and SE’s

69. Summer: lidar flight to start study to answer questions: Species groups & species separations: Conifer & broadleaved separation using intensity of 1 st returns Understory tree characteristics from canopy height profile Statistics of lidar-based stand-level & sub- regional level estimates Diam, ht & vol distributions

70. Cougar Reservoir Stands, Transect Locations

71. Cougar Res. Stands Transect Northwest, N-S

72. Cougar Res. Stands Transect Northeast, N-S

73. Cougar Res. Stands Transect Southeast, E-W

74. The View Inside: Layers Show relative density of canopy layers

75. Cougar Res. Stands Transect Southeast, N-S

76. Cougar Res. Stands Layer 0-10 m

77. Cougar Res. Stands Layer 10-20 m

78. Cougar Res. Stands Layer 20-30 m