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 A 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 Topography Vegetation height Canopy depth Understory or lack Individual crowns

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

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.

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)

Orthophoto Overview

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

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

Aerotec DEM & DTM Problems Canopy DEM had too-low elevations DTM 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: 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

New Capital Forest Canopy DEM

New Capital Forest Ground DTM

3_D Capital Forest

Bare Ground/Canopy

Vegetation Height, Capital Forest

Canopy 1 m Height

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

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 DBH Basal area Volume Biomass

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 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 Predicting forest stand characteristics with airborne scanning lidar. Photogrammetric Engineering & Remote Sensing 66(11):

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 mechanistic Limited applicability Risk of over-fitting

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

LHP Ht%ile Cov%ile

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

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

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 We cannot describe quantitatively: LHP -> FHP Is possible in very few places where have measured vertical distribution of foliage

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.

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+

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

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)

Cougar Reservoir Stands Young Stand Thinning and Diversity Study

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

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

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

More work needed for first draft from here on

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

Aerial Photograph SBG NW FORETRY PROJECT - FLOWN JULY, : SCALE

TIN of First Surface

TIN of Understory & Bare Earth

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

Weyerhaeuser Springfield Tree Farm Lidar Study DTM [ m elevation]

Weyerhaeuser Springfield Tree Farm Lidar Study Vegetation height [ meters]

Weyerhaeuser Springfield Tree Farm Lidar Study, South Site

Weyco Lidar Study, South Site

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

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 correction Publish slope & Capital Forest papers If slope correction successful then plan for lidar flight in summer Stand-level estimates and SE’s

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

Cougar Reservoir Stands, Transect Locations

Cougar Res. Stands Transect Northwest, N-S

Cougar Res. Stands Transect Northeast, N-S

Cougar Res. Stands Transect Southeast, E-W

The View Inside: Layers Show relative density of canopy layers

Cougar Res. Stands Transect Southeast, N-S

Cougar Res. Stands Layer 0-10 m

Cougar Res. Stands Layer m

Cougar Res. Stands Layer m