The Potential for Integration of Lidar into FIA Operations

Joseph E. MeansForest Science Department

Oregon State UniversityKenneth C. Winterberger

PNW Research Station

Talk Outline

Introduction to airborne scanning lidarCapital Forest Lidar StudyOther uses of lidar in forestryA plan for integrating lidar into FIA estimation procedures

Airplane cartoon

Transect 700m Wide

Transect Closer

Footprint Pattern

Footprints Close-up

Point Cloud

Apparent in Point Clouds

TopographyVegetation heightCanopy depthUnderstory or lackIndividual crowns

Multiple Return TechnologyMultiple Return Technology

Dave Harding, Goddard Space Flight Center, Maryland

Capital Forest Lidar Study

Joseph E. Means, Forest Science, OSUKen Winterberger, PNW, Anchorage, AKDavid Marshall, PNW, Olympia, WAHans Andersen, Coll. For., Univ. Wash.

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 $45,000Lidar Data flown by Aerotec, courtesy of Steve Reutebuch, PNW SeattlePlot data from Dave Marshall, PNW, Olympia (92), Ken Winterberger (9), Hans Andersen, UW (6)

Orthophoto Overview

Goals for Plot EstimatesDevelop the capability to estimate plot features using lidar data:

HeightCanopy coverBasal areaCubic volumeTree biomass

Additional equations were developed for:

Stocking densityStand Density Index

Goals for Mean Tree Estimates

Develop the capability to predict means & standard errors:

Height & Lorey heightDBH & Quadratic mean DBHBasal areaVolumeBiomass

Aerotec DEM & DTM Problems

Canopy DEM had too-low elevationsDTM elevations were above many lidar last returns

Aerotec Canopy DEM Hole

New Capital Forest Canopy DEM – No Hole

Aerotec ground DTM too high

Comparison of DTMs: 1st return errors

Average number of negative heights (% in parens)

Average height discrepancy(cm)

Maximum height discrepancy(cm)

New DTM 118 (5%) -8 -20

Aerotec 1999 DTM 388 (17%) -18 -70

New Capital Forest Canopy DEM

New Capital Forest Ground DTM

3_D Capital Forest

Bare Ground/Canopy

Vegetation Height, Capital Forest

Canopy Cover @ 1 m Height

Goals for Plot EstimatesDevelop the capability to estimate plot features using lidar data:

HeightCanopy coverBasal areaCubic volumeTree biomass

Additional equations were developed for:

Stocking densityStand Density Index

Lorey Height

Volume

Tree Biomass

Stocking Density

Stand Density Index

Goals for Mean Tree Estimates

Develop the capability to predict means & standard errors:

Height & Lorey height (same as plot averages)DBH & Quadratic mean DBHBasal areaVolumeBiomass

Height Std. Dev.

Diameter (quadratic mean)

Diameter (Quadratic mean) Std. Dev.

HJ Andrews Lidar Paper – ERDAS Award

ERDAS Award for Best Scientific Paper in Remote Sensing 3rd 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.

Additions to FIA Presentation

LHP-FHP-Tree Characteristics Links

LHP (Laser Height Profile)

FHP (Foliage Height Profile)

Tree & Plot Characteristics

Lidar measures &Multiple regression

Not mechanisticLimited applicabilityRisk of over-fitting

How mult regression with many potential predictors

works

Height percentiles are cumulative upwardsCover percentiles are cumulative downwards

LHP Ht%ile Cov%ile

Mult Regress pulls info out of LHP

LHP -> Tree & Plot CharacteristicsCan be described quantitatively by multiple regression

Interaction of predictors and coefficients (+/-) allows “best” transformation of LHP to be used

LHP-CHP-Tree Characteristics Links

LHP FHP

Tree & Plot Characteristics

Beers Law k=1

Statistical link functionMagnussen, et al 1999height only, distribution

Few places with foliageheight profiles

Lidar measures &Multiple regression

Not mechanisticLimited applicabilityRisk of over-fitting

Moment armMechanistic modelGives bole taperIndividual tree

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 -> FHPIs possible in very few places where have measured vertical distribution of foliage

Understanding relationships between

LHP -> tree characteristicLHP -> 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.

Long-Range Plan

Mechanistic models estimate FHP and Tree & Plot characteristicsWhen needed, estimate species groups with limited ground plot data and multi-temporal ETM+

LHP-CHP-Tree Characteristics Links

LHP FHP

Tree & Plot Characteristics

Statistical link functionMagnussen, et al 1999height only, distribution

Use foliage height profiles to estimate FHP with extinction

coefficient that varies with depth

Lidar measures &Multiple regression

Not mechanisticLimited applicabilityRisk of over-fitting

Moment armMechanistic modelGives bole taperIndividual tree

By species groupdistribution of crown shapes

Lidar Uses: Stand StructureAccurate inventories at the stand level:

HeightDBHVolumeSite index, with knowledge of stand ageForm factor *

Parameterize stand growth modelsDiameter distributions, Height distributions ** = Work is needed

Leaf Area (r2 = .8 to .9)

Cougar Reservoir StandsYoung Stand Thinning and Diversity

Study

Cougar Reservoir StandsYoung Stand Thinning and Diversity Study

Vegetation height [0 – 80 meters]

Cougar Reservoir StandsYoung Stand Thinning and Diversity StudyCover percent at 15 meters above ground

Cougar Reservoir StandsYoung Stand Thinning and Diversity

StudyWood volume [0 – 1000 m3/ha]

More work needed for first draft from here on

Lidar Uses: Streams & WatershedsRiparian forest structure:

Stream shading -> stream temperature modelingInput to models of woody debris input to streams*Inventory in riparian zones

Valley floor topographyChannel width, bank incision*, stream gradient, terraces, fans, side channels

Fine-scale watershed structureDepth 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

Aerial PhotographAerial Photograph

SBG NW FORETRY PROJECT - FLOWN JULY, 1996 - 1:32 000 SCALE

TIN of First SurfaceTIN of First Surface

TIN of Understory & Bare TIN of Understory & Bare EarthEarth

3-D Fuels Mapping

Live fuels mappingCanopy heightCanopy 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

Weyerhaeuser Springfield Tree Farm Lidar Study

DTM [650-1010 m elevation]

Weyerhaeuser Springfield Tree Farm Lidar Study

Vegetation height [0 - 40 meters]

Weyerhaeuser Springfield Tree FarmLidar Study, South Site

Weyco Lidar Study, South Site

Wildlife Habitat Applications

Vegetation cover, height, tree size, density at different levels above ground, canopy depthUnderstory plant composition based on

Stand structure, light environment, topographyDistance from water, roads

For gaps and patches of dense vegetationSizes, shapes, height above ground, connectedness, location

The View Inside: Transects

Show vertical canopy profiles

Show understory gaps in canopy & fuels

A Progressive Study:Can Lidar be Profitably Used by

FIA? Winter & Spring:

Slope correctionPublish slope & Capital Forest papersIf slope correction successful then plan for lidar flight in summerStand-level estimates and SE’s

Summer: lidar flight to start study to answer questions:

Species groups & species separations:Conifer & broadleaved separation using intensity of 1st returnsUnderstory tree characteristics from canopy height profile

Statistics of lidar-based stand-level & sub-regional level estimatesDiam, ht & vol distributions

Cougar Reservoir Stands,Transect Locations

Cougar Res. StandsTransect Northwest, N-S

Cougar Res. StandsTransect Northeast, N-S

Cougar Res. StandsTransect Southeast, E-W

The View Inside: Layers

Show relative density of canopy layers

Cougar Res. StandsTransect Southeast, N-S

Cougar Res. StandsLayer 0-10 m

Cougar Res. StandsLayer 10-20 m

Cougar Res. StandsLayer 20-30 m