1 / 79

The Potential for Integration of Lidar into FIA Operations

The Potential for Integration of Lidar into FIA Operations. Joseph E. Means Forest Science Department Oregon State University Kenneth C. Winterberger PNW Research Station. Talk Outline. Introduction to airborne scanning lidar Capital Forest Lidar Study Other uses of lidar in forestry

betty
Download Presentation

The Potential for Integration of Lidar into FIA Operations

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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.

  46. 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+

  47. 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

  48. 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)

  49. Cougar Reservoir StandsYoung Stand Thinning and Diversity Study

More Related