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Assessment of standing wood and fiber quality using ground and

airborne laser scanning: a review

Martin van Leeuwen1*

, Thomas Hilker1

, Nicholas C. Coops1

, Gordon Frazer2

, Michael

A. Wulder2

, Glenn J. Newnham3, Darius S. Culvenor

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1-Faculty of Forest Resources Management, University of British Columbia, 2424 Main

Mall, Vancouver, BC, V6T 1Z4, Canada.

2-Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506

West Burnside Road, Victoria, BC, V8Z 1M5, Canada.

3-CSIRO Sustainable Ecosystems. Private Bag 10, Clayton South 3189, Australia.

(*) corresponding author:

Martin van Leeuwen

Phone: (604) 822 6452, Fax (604) 822-9106, Email: mvanleeu@interchange.ubc.ca

Pre-print of published version. Reference: Van Leeuwen, M., Hilker, T., Coops, N.C., Frazer, G., Wulder, M.A., Newnham, G.J., Culvenor, D.S. 2011. Assessment of standing wood and fiber quality using ground and airborne laser scanning: A review. Forest Ecology and Management. 261: 1467-1478. DOI: doi:10.1016/j.foreco.2011.01.032 Disclaimer: The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation).

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ABSTRACT

Accurate information on the wood-quality characteristics of standing timber and logs is

needed to optimize the forest production value chain and to assess the potential of

forest resources to meet other services. Physical and chemical characteristics of wood

vary with both tree and site characteristics. At the tree scale, crown development, stem

shape and taper, branch size and branch location, knot size, type and placement, and

age all influence wood properties. More broadly, at the stand level, stocking density,

moisture, nutrient availability, climate, competition, disturbance, and stand age have

also been identified as key determinants of wood quality. Such information is often

captured in polygon based forest inventory data. Other terrain-related spatial

information, such as elevation, slope and aspect, can improve assessments of site

conditions and limitations upon plant growth which impact wood quality. Light Detection

And Ranging (LiDAR) is an emerging technology, which directly measures the three-

dimensional structure of forest canopies using ground or airborne laser instruments, and

can provide highly accurate information on individual-tree and stand-level forest

structure. In this paper, we explore the potential of LiDAR and other geospatial

information sources to model and predict wood quality based on individual-tree and

stand structural metrics. We identify a number of key wood quality attributes (i.e., basic

wood density, cell perimeter, cell coarseness, tracheid length, and microfibril angle) and

demonstrate links between these properties and forest structure and site attributes.

Finally, the potential for using LiDAR in combination with other geospatial information

sources to predict wood quality in standing timber is discussed.

Key words: wood quality, fiber properties, lidar, remote sensing

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1. INTRODUCTION

An increased focus on sustainable forestry requires forest managers to consider both

ecological and socio-economic conditions, as well as to anticipate future needs under a

changing global climate (Sayer et al., 1997). This includes recognition of the numerous

competing ecosystem services of the forest resource, such as timber production and

other non-timber forest values, such as carbon sequestration, clean water production,

regulation of various ecological processes, and biodiversity. To comply with sustainable

forest management principles, forest management has seen a requirement to move

towards harvesting trees from younger second-growth forests and plantations grown in

shorter rotations, while conserving mature first-growth forests. As a result mills now

receive and are required to process logs with a wider range of wood fiber properties. To

improve wood utilization and production efficiencies, forest managers and mill owners

need to understand how wood quality varies throughout the stem and across a forested

landscape, so that harvested logs can be matched with requirements (Zobel and Van

Buijtenen, 1989). In addition, wood density is a key input needed for accurate estimation

of forest biomass and carbon storage (Freyburger et al., 2009; Vallet et al., 2006), and

such information has potential to improve carbon accounting under schemes such as

REDD (Reducing Emissions from Deforestation and Degradation) (Herold et al., 2007).

For instance, the value of structural timber is often determined by wood properties, such

as stiffness, straightness, and stability, which are largely governed by wood density and

driven by radial (pith-to-bark) and axial (top-to-bottom) variations in cell anatomical and

chemical properties. Information on these characteristics can thus help optimize the

forest product value chain, as well as aid the decision making process around the

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multiple forest ecosystem services, such as biodiversity conservation and carbon

storage and accumulation.

The physical and chemical characteristics of timber vary with species, tree, stand, and

site characteristics (Wilhelmsson et al., 2002; Väisänen et al., 1989; Zobel and Van

Buijtenen, 1989) and change with tree age and stand development (Downes and Drew,

2008; Kint et al., 2010). Hardwood species have a much higher wood density than

softwood species (e.g., oak species can range from 590 to 930 kg m-3

, while western

redcedar is approximately 380 kg m-3

). At the tree scale, knot size, type and placement

(Duchesne et al., 1997), crown development (Mansfield et al., 2007; Lyhykäinen et al.,

2009), branch size and branch placement (Mäkinen and Colin, 1998), stem diameter,

ring width and cambial age (Amarasekara and Denne, 2002; Wilhelmsson et al., 2002)

of the wood have all been shown to influence wood fiber properties. More broadly, at the

stand scale, stocking density, thinning (commercial and pre-commercial), moisture and

nutrient availability (including fertilization), climate, competition, and disturbance are

also important determinants of wood properties, with responses varying by location,

species and clones (Watt et al., 2006; Wilhelmsson et al. 2002; Ramirez et al., 2009).

These characteristics change over time as the result of stand development, branch

mortality, and occlusion (Kint et al., 2010; Hein et al., 2008). As a result, relationships

between wood fiber properties and individual tree and stand scale structure and its

development over time may provide an efficient way to map wood quality over broad

areas (Duchesne et al., 1997; Wilhelmsson et al., 2002; Lindström, 1997; Zobel and

Van Buijtenen, 1989).

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One technology that can provide information on tree and stand level structure is Light

Detection And Ranging (LiDAR). LiDAR measures the three-dimensional (3-D) structure

of forests using the measured time-of-flight of laser pulses reflected from a target of

interest, while simultaneously recording position and orientation of the scanning device

(Lim et al. 2003). LiDAR instruments can be distinguished into discrete and full

waveform systems. Discrete- return systems record single- or multiple-returns from

objects that are located within the path of a single laser beam (e.g. the forest canopy,

understorey, and ground), while full- waveform instruments record the fully digitized

profile of reflected energy from objects illuminated by the laser. LiDAR data typically

represent forest structure as a 3-D point cloud, from which a range of forest inventory

attributes can be derived such as leaf area index, foliage height profiles, tree diameter,

tree or stand height, and biomass (Coops et al. 2007; Lim et al. 2003; Van Leeuwen et

al. 2010). While LiDAR has been extensively investigated and applied operationally in

the context of forest inventory (Næsset et al. 2004; Wulder et al. 2008), its potential to

derive wood quality attributes through links with tree, stand, and terrain-related indices

is a new field of research.

In this paper, we review the current state of knowledge of predicting wood quality using

tree and stand structural characteristics. First, a number of the wood fiber properties are

described and discussed in relation to their relevance to timber production and carbon

sequestration. The links between fiber properties and tree and stand level indicators are

then investigated. Finally, the potential for using both airborne and terrestrial LiDAR to

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determine these characteristics is discussed and a suite of possible tree and stand level

structural attributes is proposed.

2. Wood anatomy and its radial and axial variation

Wood (secondary xylem) is primarily composed of cellulose, hemicelluloses, and lignin.

Of these, cellulose is the major component of wood, accounting for up to 50% of the

wood’s constituents (Raven et al. 2005). Cellulose molecules are arranged structurally

into fibrils, which in turn are organized into larger structuring elements that build the cell

wall. Lignin is the second most prevalent constituent of wood and acts as a cementing

agent between individual cells. Lignin content varies most notably between softwoods

(25-33%) and hardwoods (18-25%) and can be found throughout the cell walls (Bowyer

et al. 2007). Hemicellulose provides the critical link between the cellulose and lignin

polymers. Finally, extraneous materials can be divided into organic and inorganic

components. Organic components (e.g. oils, fats, resins, tannins, waxes, gum, and

starch) contribute to color, odor, density, resistance against decay, hygroscopy, and

flammability of wood (Twede and Selke 2005). The most abundant inorganic

constituents are calcium, potassium, nitrogen, phosphorus, and magnesium; however,

trace amounts of sodium, iron, silicon, manganese, copper, and zinc are also commonly

found (Raven et al. 2005).

Throughout the stem, these constituents are highly organized within woody cells to fulfill

a broad spectrum of functions, including mechanical support, water transport,

photosynthate translocation, and ultimately, growth. The cross-section of a stem is

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mainly composed of bark, cambium, sapwood, heartwood, and pith. The cambium is

responsible for secondary (diameter) growth and produces phloem to the outside and

secondary xylem cells to the inside (Zobel and Van Buijtenen, 1989; Raven et al. 2005).

The outer bark protects the tree against disease, damage, and dehydration, and its

thickness can vary widely with species and age. The inner bark acts as a pipeline

carrying photosynthate from leaves to the vascular cambium (secondary meristem) and

other living parts of the tree. In gymnosperms, the secondary xylem is mostly composed

of tracheids, ray cells, and resin canals. Angiosperms are more complex in their

structure, and additionally contain vessels, fibers, and longitudinal parenchyma (Zobel

and Van Buijtenen, 1989; Raven et al. 2005). Although tracheids and fibers differ in

function, both are often considered to be wood fibers (Zobel and Van Buijtenen, 1989),

and this terminology will also be followed in this paper. While most of the transport of

water, nutrients, and chemicals occurs vertically through tracheids and vessels, some

movement of resources takes place radially through the ray cells. In temperate climates,

where there are distinct growing seasons, new wood production results in annual

growth rings, most often with larger cells produced in the spring (known as earlywood)

and smaller cells produced in the summer (known as latewood or summerwood) (Zobel

and Van Buijtenen, 1989). Secondary xylem can be classified as either sapwood or

heartwood. Sapwood is responsible for the transport of water and soluble mineral

nutrients from the roots upwards. As sapwood ages, ray and parenchyma cells die and

sapwood turns into heartwood (Plomion et al. 2001). Heartwood is mainly responsible

for the stability of the tree and its high concentration of extractives provides protection

from crown rot and other forms of decay. Extractives are responsible for the darker color

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of the heartwood when compared to the sapwood (Zobel and van Buijtenen, 1989). At

the center of the stem is the pith, a small core of soft tissue from which primary,

longitudinal growth took place. Xylem cells change markedly in their structure and

function with tree age, producing juvenile wood in the first 20 to 40 years of tree growth,

and mature wood thereafter.

Throughout the stemwood, variations in the relative amounts of cell types, chemical

composition, and cell dimensions occur between earlywood and latewood, between

heartwood and sapwood, and between species. The distribution of cell types, cell

dimensions, and chemical compositions determine wood quality attributes such as

shrink, warp, strength, stiffness and pulp and paper quality such as smoothness, and

tear strength (Cannell, 1988; Downes et al. 2002; Zobel and Van Buijtenen, 1989; Oliver

and Larson, 1996; Gartner, 2006). Genetics and environment act in concert to drive

variability in wood cell anatomical and chemical properties (Zobel and van Buijtenen,

1989; Downes and Drew, 2008; Downes et al. 2002, 2009). Genetic control over

morphological and physiological traits in plants is regulated through environmental

factors that alter plant performance and the final expression of these genetic traits (e.g.,

St. Clair, 1994a, 1994b).

3. Intrinsic and extrinsic indicators of wood quality

Assessment of wood quality can be made either through measurement of intrinsic or

extrinsic indicators. Intrinsic indicators relate to the internal, anatomical structure of

wood, and comprise supra-cellular characteristics, such as wood density, ring width, the

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proportion of juvenile and mature wood, the percentage of sapwood and heartwood.

(Sub-) cellular characteristics, such as cell length, microfibril angle, and cell wall

thickness determine the wood’s utility for various applications. Extrinsic indicators of

wood quality relate to external characteristics of the tree, such as tree height, diameter,

stem taper, branch length, thickness, and number, and dimensions of the live crown.

Endogenous (physiology) and exogenous (ecology and environment) factors influence

wood anatomical structure and tree form. Ecological processes related to light

absorption and photosynthesis, nutrient cycling, water storage and transport,

competition (i.e., shading and crowding), and disturbance all have a profound effect on

tree growth and wood quality. Local and regional scale variations in climate and

physiography are the two primary environmental factors that control the spatiotemporal

distribution of critical resources (i.e., solar radiation, temperature, and moisture) needed

for plant growth.

To assess wood quality in standing trees, studies have explored statistical relationships

among intrinsic and extrinsic indicators, ecological and environmental site factors, and

wood quality attributes (e.g. Zobel and Van Buijtenen, 1989). Findings of these studies

form the basis of many silvicultural prescriptions designed to optimize stand productivity

(growth increment) and wood fiber characteristics and properties. However, these

studies have often been confined to relatively small experimental sites, due to budget

and time constraints; plus, comparison of results among geographical locations and

species have often been found contradictory and lacking of consistent trends (Downes

et al. 2009).

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Recently, rapid acquisition of intrinsic wood fiber properties has become possible

through the SilviScan™ system that combines image acquisition, X-ray densitometry

and X-ray diffraction to measure wood cellular anatomy in radial and tangential planes

(Downes et al. 2002; Downes and Drew, 2008; Evans et al. 2000; Evans and Ilic, 2001).

SilviScan measures variations in wood anatomy and fiber properties from tree cores

and other wood samples, and has allowed the development of large databases of wood

anatomical properties. The wide scale collection of wood anatomical characteristics

from trees grown under a broad range of stand and site conditions facilitates research

on the statistical modeling and prediction of wood fiber properties from tree, stand, and

site measurements derived from remote sensing technology and geospatial databases.

In the following sub- section, we identify and briefly describe a number of key intrinsic

and extrinsic indicators of wood and fiber quality. Exhaustive overviews beyond the

scope of this communication can be found in Zobel and Van Buijtenen (1989) and

references therein.

3.1. Intrinsic Indicators of Wood Quality

Five intrinsic wood fiber characteristics have been widely regarded as key indicators of

wood quality in the wood science literature: wood density, cell perimeter, cell

coarseness, fiber length, and microfibril angle.

3.1.1. Basic wood density (kg/m3), defined as the ratio of dry mass to green volume

(Swenson and Enquist, 2007; Zobel and Van Buijtenen, 1989), is determined by cell

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wall thickness, tracheid size and shape, and the ratio of latewood to earlywood. Wood

density is strongly influenced by tree age (juvenile vs. mature), the seasonal timing,

rate, and duration of secondary growth, and plays a key role in plant-level hydraulic

efficiency, protection from cavitation during drought, and mechanical support (Baas et

al. 2004; Hacke et al. 2001; Roderick and Berry, 2001; Gartner, 2006; Swenson and

Enquist, 2007). Basic wood density has been shown to vary within and among species,

geographically, and with both short- (daily) and long-term (seasonal) variations in

temperature and precipitation (Swenson and Enquist, 2007). The within-tree variability

of wood density can be observed between earlywood and latewood, where latewood is

characterized by smaller, denser wood cells (e.g. Reme and Helle, 2002), and between

juvenile and mature wood, where mature wood generally has higher density (Mansfield,

2009). Wood density is one of the most common indicators (Zobel and Van Buijtenen,

1989) of solid-wood strength and stiffness, elasticity, rupture and pulp yield (Lindström,

1996a, 1996b, 1996c; Downes and Drew, 2008).

3.1.2. Cell perimeter (μm) is calculated from the radial and tangential xylem cell

diameters (μm). Cell perimeter has a strong influence on water transport (i.e., xylem

conductivity and vulnerability to cavitation under water stress) and mechanical support

(Dean and Long, 1986; Gartner, 2006; Baas et al. 2004) and correlates with basic wood

density. Reme and Helle (2002) found that cell wall perimeters of Scandinavian grown

Scots pine and Norway spruce increased with distance from the pith, with most of the

increase occurring during the first 20 years of age, and that the perimeter gradually

decreased from earlywood to latewood. Fibers with larger perimeters and thinner walls,

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collapse more readily and therefore make denser, stronger, and smoother paper (Wang

and Aitken, 2001; Zobel and Van Buijtenen, 1989).

3.1.3. Cell coarseness (μg/μm) is defined as the ratio of cell wall mass (μg) to cell unit

length (μm) (Gartner, 2006). Theoretical models indicate that cell coarseness is tightly

coupled to drought resilience (Hacke et al. 2001; Gartner, 2006). Cell coarseness is a

strong determinant of paper strength and stiffness (Via et al. 2004), with improved paper

qualities associated with low cell coarseness. However, a decrease in cell coarseness

correlates with lower basic wood density and improvements in paper quality attained

through lower cell coarseness comes at the expense of pulp yield (Via et al. 2004).

3.1.4. Microfibril angle (°) is defined as the angle of the cellulose microfibrils with

respect to the long axis of the individual cells (Wimmer et al., 2002), and is largely

influenced by the S2 layer of the secondary cell wall. This layer accounts for 75 - 85% of

the total cell wall thickness (Donaldson, 2008; Gierlinger and Schwanninger, 2006).

Microfibril angle (MFA) normally ranges from 5° up to 20° in outer growth rings and is

higher near the pith and in reaction wood that is formed when the tree is subjected to

mechanical stress such as wind and gravity (Donaldson, 2008; Huang et al. 2003). MFA

strongly influences the shrinkage behavior of wood (Burdon et al. 2004; Huang et al.

2003). Evans and Ilic (2001) found that MFA accounted for 86% of variation in modulus

of elasticity (a measure of the ability of wood to resist deformation under load) for

Eucalyptus Delegatensis R.T. Baker. In conifers, MFA varies both radially and vertically,

with the highest angles in juvenile wood (Donalson, 2008; Gartner, 2006). A negative

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relationship is also found with stem height, where there is often observed variability in

the lower and upper parts of the stem, and constant MFA for the middle section

(Donaldson, 2008). Wimmer et al. (2002) found that MFA was greater in earlywood and

juvenile wood and lower in latewood and mature wood, and that longer growth periods

and higher growth rates were associated with larger MFA.

3.1.5. Fiber length (μm) is the length of the wood cell which in turn influences paper and

timber strength and stiffness. Longer fiber length, together with reduced cell

coarseness, leads to improved bonding by increasing the number of cell to cell contacts

(Via et al. 2004). Fiber length is also important attribute for timber products, since longer

fibers result in greater resistance against buckling of wood beams; however, the trait is

considered of lesser importance than wall thickness and wood density (Zobel and Van

Buijtenen, 1989). Fiber length has been found to correlate with MFA, with smaller

angles (microfibrils more aligned with cell longitudinal axis) related to longer fiber

lengths and this trend of elongating of the fibers can be found from the pith outwards to

the bark (Sheng-zuo et al. 2004).

3.2. Extrinsic tree-level indicators

Wood fiber attributes vary in response to individual tree characteristics such as age,

crown architecture, growth rate, stem lean and taper, and in response to site attributes

such as climate, topography, stand structure, and disturbance history. As a result,

variations in wood fiber properties occur within and between individual trees, species,

and regions (Zobel and van Buijtenen 1989; Burdon et al. 2004; Lindström, 1997). At

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the tree scale, age, growth rate, crown size and shape, and branchiness have been

used as indicators of wood quality, and many of these factors have been controlled

operationally through silvicultural prescriptions such as planting, thinning, pruning, and

fertilization in an attempt to improve wood quantity and quality (Jack and Long, 1996;

Oliver and Larson, 1996).

3.2.1 Age. As trees grow older, axial and radial variations in wood fiber characteristics

change markedly as a result of the maturation of woody tissue. A number of studies

have examined the relationship between basic wood density and tree social status, and

have shown that suppressed and intermediate tree classes have higher wood densities

than co-dominant and dominant tree classes within the same stand (Lindström, 1996a,

1996b, 1996c; Duchesne et al. 1997). In Lindström (1996b), variations in basic density

of Picea abies were explained using variables related to crown development, tree

height, stem taper and mean ring width, and stand variables such as stocking density

and thinning regime. At the tree level, tree height, diameter and thinning procedure were

found good explanatory variables describing the variation in basic wood density (r2 =

0.68). At the stand level, basic density was explained using solely crown related

parameters volume production, stand density and thinning procedure, yielding an

adjusted r2 of 0.99. Increases in wood density and stem straightness at older age have

been demonstrated by Suarez et al. (2010) using model simulations. Such modeling

approaches allow for estimating wood formation and quality attributes from inventory

data at fixed times and allow interpolation between, and extrapolation beyond, forest

inventories to help assess the quality of timber formed over time. This greatly aids forest

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management and planning to optimize efficiency of timber production to meet the

required end-use properties. Similar modeling approaches are used to model stand

development and growth of individual trees. As trees develop, occlusion of older

branches by new formed branches affects photosynthetic capacity and branch mortality.

Competition among trees and mechanical stress (e.g. windiness or mechanical effects

from neighboring trees) further influences the mortality and formation of branches,

hence affect knottiness and portions of dead knots posing important implications on the

quality of formed wood (Kint et al. 2010). Hein et al. (2008) found higher portions of

sapwood for trees of younger age and closer towards the tree apex.

3.2.2. Growth rate (m3/ha/year). The rate at which an individual tree grows has an

impact on wood quality. Primary production changes daily, seasonally, and throughout

stand development due to spatiotemporal changes in solar insolation, temperature,

moisture and nutrient status, competition, disturbance, and community composition (i.e.,

mutualism) (Ryan et al. 1997; Watt et al. 2006; Lindström, 1997). Seasonal changes in

the timing, rate, and duration of secondary growth influence the characteristics and

proportions of earlywood and latewood, and the distribution of fiber properties

throughout the stem (Downes and Drew, 2008). Faster growth rates, desirable for

volume yield optimization, may be accompanied with greater earlywood portions and

hence lower overall wood densities, thus affecting the quality of the timber. Growth rates

generally decline after a certain age and with increasing tree size. This longer term

reduction in growth has implications for wood formation, production, and quality. Two

competing theories (see Mencuccini et al. 2005 for summary) have been proposed to

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explain the apparent 'age-related growth trend’: (1) ‘cellular senescence’ theory

suggests there is a genetically controlled decline in physiological function (i.e., slowing

down of cell division, cell differentiation, and formation of secondary and tertiary cell

walls, loss of protoplasts, incomplete lignification, etc.) and (2) ‘meristem-extrinsic’

theory which suggests that declining physiological function is not genetically controlled,

but rather due to the physiological constraints imposed by tall and architecturally

complex trees (i.e., respiratory losses needed to maintain increasing stem, branch, and

root mass, as well as the physical limitations of transporting water and nutrients to the

top of a tall canopy). Effects of competition and cambial age on growth rate and fiber

properties in Picea abies were investigated by Lindström (1997) who found good

agreement between earlywood tracheid length with ring width and cambial age, while

diameter of the earlywood tracheids was correlated with cambial age, ring width and site

quality. In an earlier study, Lindström (1996b) explained variations in mean ring width

from variables as tree height and diameter, volume production and thinning regime, with

a high degree of significance (r2 = 0.95). These findings motivate research to explore if

variations in earlywood tracheid length and width may be explained using simple tree

and stand level variables.

3.2.3. Crown and Canopy Shape and Size (m). Crown shape, as determined by the

location and number of branches up the stem, as well as total branch mass, is indicative

of the number of knots present in the timber and therefore its potential use for various

wood products (Kantola et al. 2008). Dimensions of the living crown - sometimes

substituted by simpler measures as tree height, age and BDH - are widely used in

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modeling wood formation and predicting quality attributes (Houllier et al. 1995). Crown

attributes were also found important in modeling wood density and a number of related

quality parameters in Lindström (1996a, 1996b, 1996c, 1997). Crown dimensions are

regulated by stand density and determine the photosynthetic capacity of individual trees

as well as competitive strength. Amarasekara and Denne (2002) found significant

differences in ring with between suppressed, co-dominant and dominant Corsican pines

(Pinus nigra var. maritima) with dominant trees having the widest rings. Crown depth

and variations in leaf densities within the crown determine self-occlusion and hence

mortality and productivity at different canopy strata (Kint et al. 2010). Strong

relationships between living branch area and sapwood portions have been found for

example by Hein et al. (2008) and others. Janse-Ten Klooster et al. (2007) found that

crown width was positively correlated with basic wood density over a range of species

and site conditions, and suggested that increasing wood density serves the function of

supporting the larger crowns. Wood density was also correlated with crown depth and

the number of leaders in the crown; however, this correlation was not significant for all

light conditions and soil types present in the study. The correlation between wood

density and crown architecture is generally explained by increased cambial activity and

the wider ring spacing produced by large, live crowns. Light demanding, faster growing

species also tend to have lower wood densities (Janse-Ten Klooster et al. 2007; King et

al. 2006) and this is often explained by the lower cost per unit growth (Sterck et al.

2006). Amarasekara and Denne (2002) found a higher percentage of latewood for

suppressed Corsican pine than for co-dominant and dominant individuals. Crown size,

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competition for light, and disturbance are considered major factors explaining trends in

tree growth and stem form (Hynynen, 1995).

3.3 Extrinsic stand-level indicators

With confidence, one can state that anything affecting tree growth can also affect wood

properties, and this becomes increasingly evident for species planted outside their

natural geographic range (Zobel and Van Buijtenen, 1989). However, differences in

environmental conditions that regulate tree growth and wood formation can occur due to

topographic position, and local variations in climate (Littell et al. 2008). While detailed

examination of these influences on wood quality is difficult to undertake due to the

extensive cost of field work and subsequent wood fiber analysis, and in some cases

destructive sampling techniques, existing provenance trials and long term forestry plot

installations can provide significant insight into these factors at single plot and single

stand locations. In addition, measurements from these trials can provide unique

calibration and validation data for model development (Steward, 2006; Smithers, 1957).

Remote sensing provides an additional data source which may contain valuable

information on the spatiotemporal patterns of tree growth and wood quality over broad

geographic areas.

3.3.1. Stand / stocking density (trees/ha). Low stocking densities have been related to

shorter fiber lengths, while higher stocking densities have been found to result in lower

juvenile portions due to decreased diameter growth rates at juvenility and faster onset of

mature wood formation (Watson et al. 2003). Higher stocking densities are also related

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to faster longitudinal (primary) growth as a result of increased competition for light.

Seeling (2001) found better drying behavior of Norway spruce grown at denser

spacings, and noted that wider spacings resulted in lower bending strength, more spiral

grain, and compression wood, and larger knot diameters. The effects of tree spacings

on growth and branch properties have also been studied for Norway spruce and

Douglas-fir by Makinen and Hein (2006) and Hein et al. (2008) respectively. Both

studies found improved branch properties at denser spacings (e.g. 1200 ha-1). In Hein

et al. (2008), decrease in branch diameters was found to be more pronounced lower in

the canopy especially maximum branch diameters which were significantly different

between 100, 200 and 1200 ha-1 stocking plots. These high stocking density plots also

showed a significantly lower portion of living branches at lower strata in the canopy and

showed lower portions of sapwood area. Sites with higher stocking densities showed a

lower cumulative branch area per unit sapwood area, compared to sites with lower

stocking densities. This may benefit the prediction of sapwood area through model

simulations, using leaf area profiles or living branch characteristics and stand density as

explanatory variables (Hein et al. 2008).

3.3.2. Nutrition and site quality. Nutrient availability has an affect on diameter growth

rate and, therefore, wood fiber structure and quality (Zobel and Van Buijtenen, 1989).

Interactions between temperature and rainfall influence seasonal moisture balance and

affect growth rate. Also, water surplus or deficit in one year may affect plant growth in

the subsequent year (Littell et al. 2008). The combined effect of these temporal and site

specific factors on growth rate have been demonstrated to alter wood quality properties

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as basic density (Lindström 1996b; Downes and Drew 2008). In Lindström (1996b), for

example, variations in site nutrition affected growth rates and stem diameter

distributions for Norway spruce, while volume production, tree height, and stem

diameter were found significant variables and were used to describe basic density at the

tree and stand level.

3.3.3. Climate and seasonality. Continentality, physiography, and topography all

influence local to regional patterns of climate (i.e. daily, seasonal, and annual cycles of

temperature and precipitation) (Littell et al. 2008). Spring and winter temperatures,

duration of snowpack, growing-season length, and seasonal water balance

(evapotranspiration) influence ring width and earlywood and latewood proportions (Littell

et al. 2008), and the distribution of wood fiber characteristics (Downes and Drew, 2008).

Seasonal variations in precipitation have major impact on wood density and cell

dimensions. The seasonal variation in growth rate affected by climate and weather

alters fiber properties throughout the wood formed. For example, Downes et al. (2002)

explain that trees experiencing drought early in the growing season and good growth

over the summer and autumn may show a positive relationship between wood density

and ring width, arguing that seasonal distribution of growth is an important driver of

wood density and a range of fiber characteristics. In addition, Suarez et al. (2010) found

exposure to wind can be an important determinant of stem straightness which in turn

influences wood quality and yield of sawn timber.

21

4. Potential application of ground and air-based laser scanning

Recent and continuing developments in ground-based (terrestrial) and airborne laser

ranging scanners have allowed the direct measurement of 3-D forest canopy structure

at fine spatial scales. These active sensors typically employ a near-infrared laser light

source and detector, precise timing instruments, and GPS to determine the 3-D location

of reflecting targets such as stems, branches and foliage (Aschoff and Spiecker, 2004;

Wehr and Lohr, 1999; Baltsavias, 1999).

4.1 Terrestrial laser scanning

Terrestrial laser scanning (TLS) support very high laser angular shot densities, and is

capable of capturing very detailed 3-D digital models of vegetation canopies. TLS exist

as tripod mounted, or mobile scanning instruments and as point-scanning devices with

a partial or complete hemispherical field of view or frame-scanning devices. TLS

instruments are able to capture detailed structure out to a radial distance of up to 100m

(Jupp et al. 2009) and in the case of monitoring glacial movements up to several

kilometers (Maas et al. 2008b; Schwalbe et al. 2008). To date, the commercially

available TLS scanners record one, two or several discrete returns per emitted laser

pulse. Discrete-return TLS has been widely used to measure vegetation structure in a

variety of forest ecosystems. For example, discrete-return TLS has been used to

measure plot-level leaf area and gap fractions (Zheng and Moskal, 2009; Danson et al.

2007). A technique to estimate leaf area at the tree-level that takes into account non-

uniformity of the foliage distribution has been demonstrated by Huang and Pretzsch

(2010) using co-registered TLS scans. TLS has also been used to derive stem

22

diameters with root mean square errors ranging between 1.5 – 3.3 cm from field

measurements (Hopkinson et al. 2004; Maas et al. 2008a; Tansey et al. 2009) and has

been used to derive stem taper (Bienert et al. 2007), sweep and lean (Thies et al.

2004). Other areas of application include modeling radiative transfer through forest

canopies (Côté et al. 2009, 2010), the parameterization of water flow resistance models

(Antonarakis et al. 2009), and the estimation of tree volume (Hopkinson et al., 2004;

Lefsky and McHale, 2008).

In contrast to these discrete return instruments, full- waveform TLS is able to fully

digitize the returned energy of an emitted laser pulse, thereby making the instrument

more sensitive to small return intensities and allowing the modeling of secondary return

obscuration (Lovell et al. 2003; Strahler et al. 2008; Jupp and Lovell, 2007). Full-

waveform TLS has also been successfully used to estimate stand structural parameters

(Jupp et al. 2009; Strahler et al. 2008) and parametrize and validate radiative transfer

models (Ni-Meister et al. 2008). Full-waveform recording of multiple TLS wavebands,

while not commercially available, could allow future applications to go beyond the

classical analyses of vegetation structure (Lichti et al. 2008), and may provide a vital

link between plant structure and function.

The use of TLS to predict wood fiber attributes has not been broadly applied. However,

studies have applied TLS to estimate standing timber volume using estimates of bole

size and taper (Murphy, 2008; Maas et al. 2008a) and using allometric approaches

based on DBH and tree height (Hopkinson et al. 2004). Estimates of branch size,

23

arrangement, and leaf density have also been extracted from TLS (Bucksch and

Lindenberg, 2008; Bucksch and Fleck, 2009) as well as whorl heights and number of

branches per whorl (Klemmt et al. 2010). In a more physiological based approach

Samson et al. (2000) utilized a multi-wavelength LiDAR to discriminate nitrogen and

sulfur deficiencies through laser-induced fluorescence.

4.2. Airborne LiDAR (ALS)

LiDAR data used in forestry applications are commonly acquired from aircraft or

helicopter platforms. Airborne laser scanning (ALS) have been widely used for

generation of bare-earth digital elevation models (DEM) and the estimation of forest

inventory attributes, and are rapidly being adopted in operational forest management.

Typically, ALS are discrete-return scanning instruments with a footprint size of 0.1 m to 2

m (Lim et al. 2003; Wulder et al. 2008), and can achieve sub-meter accuracy of terrain

surface heights (Blair et al. 1994; Lefsky et al. 2002). The error for measuring the height

of individual trees from ALS is typically less than 1.0 m (e.g. Persson et al. 2002), and

varies with canopy height and height distribution (Hopkinson et al. 2006). When

estimating maximum or mean canopy height within plots and at full canopy closure this

error is often less than 0.5 m (Magnussen and Boudewyn, 1998; Næsset and Økland,

2002). ALS estimates of tree height are considered by some to be more accurate than

manual, field-based measurements (Næsset and Økland, 2002; Coops et al. 2007).

Full-waveform ALS capture the vertical profile of light penetrating the plant canopy, by

producing a fully digitized return intensity (energy) vector for every outgoing laser pulse.

This allows for the computation of radiation budgets within the canopy or - by

24

accounting for the effects of occlusion - vertical plant canopy profiles (Jupp et al. 2009;

Strahler et al. 2008). The range resolution of a full-waveform scanner is determined by

the sampling rate with which the sensor is able to record energy levels of the incoming

energy.

ALS has been successfully used in a variety of forestry applications for the retrieval of

crown and canopy structural attributes, such as crown width, length, height-to-first-living

branch, and biomass and biomass change over time (Chasmer et al. 2006; Riano et al.

2004; Morsdorf et al. 2004; Andersen et al. 2005; Solberg et al. 2006; Bollandsås &

Næsset 2010). An overview of retrieved structural attributes from ALS, together with

attributes derived from TLS is listed in table 1. As these extrinsic properties have been

linked to variations in wood properties, ALS holds promise for landscape-scale mapping

of wood quality. Suarez et al. (2010) demonstrate the use of airborne LiDAR combined

with tree growth modeling to analyze the hierarchy of dominance in forest stands that is

subsequently linked to a model estimating changes in wood properties as wood density

and stem straightness. These results show strong relationships between tree

dominance and site index and wood quality parameters as wood density and stem

straightness.

25

Table 1: Studies employing airborne and terrestrial scanning LiDAR for the assessment of tree level attributes.

Variables

Ground or Airborne

LIDAR

Laser pulse distance or density (++)

Accuracy / goodness of fit

Error (+) Source

Stand Structure / Stocking

Stand height A 11 canopy hits per

100 m² r = 0.8 Within 6%, mean of 3%

Magnussen and Boudewyn, 1998

Mean height Dominant height

A PD = 0.9 m

r² = 0.82-0.95 r² = 0.74-0.93

SD = 0.61-1.17 m 0.70-1.33 m

Næsset, 2002

Height A

6 points/m² Mean ± SD = –0.73 ±

0.43 m

Andersen et al. 2006

Tree line migration

A

7.7 points/m²

Detection of trees < 1m tall = 5-73%

Detection of trees > 1m tall = 91%

Næsset and Nelson, 2007

Canopy height A

PD 0.6 – 2.0 m r² = 0.95 RMSE = 1.8 m Hopkinson et al.

2006

Height

A

G

38 – 71 points/tree

4000 – 87,000 points/tree

Bias = -1.1 m; SD = 0.8m

Bias = -1.2 m; SD =

1.5m Chasmer et al. 2006

Base of live crown

A

G

38 – 71 points/tree

4000 – 87,000 points/tree

Bias = 1.4m; SD = 2.4m

Bias = -6.0m; SD =

1.2m

Fractional cover A

PD = 0.5-1.2 m r² = 0.75-0.78 Hopkinson and Chasmer, 2009

Stand Stocking G FWF 40 % Strahler et al. 2008 Crown

Characteristics

Crown bulk density Crown volume

Foliage biomass

A PD = 1.73 m

r² = 0.80 r² = 0.92 r² = 0.84

Riano et al. 2004

Crown diameter A

10 points/m² r² = 0.20 RMSE = 0.47 m Morsdorf et al.

2004 Canopy fuel weight

Canopy base height

A 3.5 points/m²

r² = 0.86 r² = 0.77

Andersen et al.

2005

Crown height Crown base height

Crown diameter

A

5 points/m² RMSE = 0.8-3.3 m RMSE = 2.7-3.7 m RMSE = 1.1-2.1 m

Solberg et al. 2006

Foliage Profile

G 65,000 - 800,000 points per tree,

depending on set-up geometry

2 – 51% deviations from reference profile, depending on set-up

geometry

Van der Zande et al. 2006

LAI G FWF Jupp et al. 2009 LAI

Height

G

8% of field measured

Lovell et al. 2003

Crown base height A

7.4 points/m² RMSE = 1-4.3 m Breidenbach et al.

2010 Growth Rate

Growth of Crown

A

10 points/m²

39-70% match of individual trees

between data sets (61 of 83 harvested

Precision of 0.05 m at stand level,

0.10-0.15 m at plot level.

Yu et al. 2004

26

trees detected) LAI Increment A 3.1-9.8 points/m² r² = 0.87-0.93 Solberg et al. 2006

Age / Volume / Biomass

Forest successional stage

A 0.26 points/m²

Class accuracies 73-100%

Falkowski et al.

2009

Canopy gap dynamics

A 0.03 & 0.19 points/m²

Delineation accuracy for gaps

> 5m² = 96%

Vepakomma et al. 2008

Timber volume A

10 points/m² RMSE = 16-25% Bias = 8.2-24.3%

Maltamo et al. 2004

Biomass Volume

A

PD = 0.7 m r² = 0.32 -0.82 r² = 0.39-0.83

RMSE = 29-44 Mg/ha RMSE = 48-53 m

3/ha

Popescu et al. 2004

Aboveground biomass

A

PD = 3-5 m r² = 0.83 RMSE = 26.0 t/ha Hyde et al. 2007

Biomass change A

3.4-4.7 points/m² R²=0.87 RMSE=4.4 Bollandsås & Næsset 2010

DBH Biomass

A

2.6 points/m² r² = 0.87 r² = 0.88

RMSE = 18% RMSE = 47%

Popescu 2007

Volume

A PD = 0.9 m r² = 0.8-0.93 18.3-31.9 m

3 ha

-1 Næsset 2002

Basal Area

G FWF r² = 0.86-0.97 Strahler et al. 2008

DBH G SD = 2.3 cm Tansey et al. 2009

Volume

G r² = 0.75–0.90 Lefsky et al. 1999

(+) RMSE = Root Mean Square Area; SD = Standard Deviation (++) PD = Point Distance; FWF = Full Waveform

4.3 Complementarily and integration of ground-based and airborne LiDAR systems

ALS and TLS systems have their own inherent strength and weakness, and should be

considered complementary technologies. While ALS can estimate structural

parameters, such as stand height, gap probability and canopy volume, a number of

limitations exist for characterizing vegetation structure lower in the canopy (Hopkinson

et al. 2004; Lovell et al. 2003). First, the ability of ALS to accurately characterize forest

vegetation canopy height profiles depends on the point density of laser returns (Lovell et

al. 2003). Consequently, the vegetation understorey is often under-represented by

airborne LiDAR due to the top-down perspective and resultant bias towards the upper

part of the canopy. The infrequency of laser returns from mid and understorey layers is

particularly apparent in forests with extremely dense overstory canopies. Second, ALS

27

is often restricted to near-nadir view angles (±20°), which are less suitable for the

estimation of canopy clumping and leaf angle distribution (Chen et al. 2003; Ni-Meister

et al. 2008) both of which have important ecological and economical implications

(Zheng and Moskal, 2009). Finally, ALS is limited in its ability to characterize the woody

component of vegetation, because the vertical projection of the stem contains very

limited information on bole size (diameter) and shape.

One of the key limitations of TLS is the restricted range within which tree and stand

structural characteristics can be obtained. Depending on stand density, the typical range

of TLS acquisitions is less than 100 m from the sensor position and due to decreasing

point densities with distance, processing TLS data may be limited to range distances

not exceeding 10 – 30 m (Liang et al. 2009; Maas et al. 2008a). Also, since static TLS

features a radial field of view, hit densities decline markedly with distance from the

sensor due to near field obscuration. This bias has to be taken in account when trying to

derive stand-level estimates from TLS data (Hilker et al. 2010). Whereas mobile TLS

improves sampling compared to static TLS (Rutzinger et al., 2010), the issue remains

that TLS-returns are typically biased towards lower parts of the canopy, and as a result

the upper crown structure and tree heights are often difficult to assess (Hilker et al.

2010; Chasmer et al. 2006). In contrast to ALS, most TLS are able to describe

vegetation structure from a broad range of view angles and perspectives (Côté et al.

2009, 2010; Jupp et al. 2009; Strahler et al. 2008), which allows for the analysis of

canopy clumping (Chen, 1996) and the effects of variations in the project function of

different canopy elements.

28

Recent work of Chasmer et al. (2006), Lindberg et al. (2010) and Hilker et al. (2010)

show that integration of TLS and ALS data to a common coordinate frame enhances the

detail of structural information obtained and overcomes challenges mentioned above

with respect to either technique. Such co-registration successfully enables the study of

tree-level structure by combining the complementary values of ALS and TLS in one data

set and thus facilitates studies on tree allometry using LIDAR remote sensing or

enhances leaf area estimates at various canopy strata (Huang and Pretzsch, 2010).

Another important advantage gained through the integration of TLS and ALS data is that

TLS allows calibration and validation of ALS data in an accurate and rapid manner (e.g.

Lindberg et al. 2010).

4.4. Modeling of wood fiber properties from LiDAR data

Tree-level parameters with demonstrated explanatory power for wood physical

properties include DBH, tree social status, light competition, tree height, and growth,

crown dimensions, and branch free bole sections as well as number of branches,

branch angle and diameters (see also Table 2). These parameters have been the focus

of various modeling approaches to estimate wood quality attributes ranging from gross

defects such as knots and spiral grain to cellular level properties. Using height-over-age

growth curves, several branchiness parameters – such as number and size and

insertion angle – can be derived from simple forest inventory parameters, while canopy

characteristics are widely used to model growth and between tree interactions (Houllier

et al. 1995). An important advancement is yet to be made through the application of

29

LiDAR remote sensing derived stand and tree-level attributes to parametrize wood

quality and tree growth models. Recent contributions include those of Suarez et al.

(2010) who demonstrate the use of the Canadian TASS model in combination with the

Timber Quality for Conifers (ConTQ) model to derive estimates of timber properties from

LiDAR data. In addition, a variety of recent studies have focused on quantifying

branchiness, crown depth, and base height, and whorl heights from LiDAR data

(Breidenbach et al. 2010; Klemmt et al. 2010; Lindberg et al. 2010).

30

Table 2: Use of LiDAR for measurement of extrinsic indicators of wood quality and stand and site conditions.

Measurement Source of information (+) Related extrinsic attributes

Related wood anatomical and wood quality characteristics

Branchiness, branch diameter

TLS Knots Heterogeneity among all fiber properties and wood density, impacts on strength, sapwood portions

Crown dimensions

TLS/ALS Social status, vigor, growth rate, branchiness

Branch free bole section, wood density

Stem diameter & growth

TLS Stem volume, stem biomass, aboveground biomass

Ring width, wood density, sapwood vs. heartwood portions, mature vs. juvenile portions, fiber length, cell coarseness, cell perimeter

Tree height & growth

ALS/TLS Stem volume, stem biomass, aboveground biomass, site quality

Ring width, wood density, sapwood vs. heartwood portions, mature vs. juvenile portions

Vertical distribution of foliage (foliage profile)

ALS/TLS Biomass, crown length, and branchiness

Branch free bole section, heterogeneity among fiber properties, uniformity of timber

Gap fraction ALS/TLS Stocking density, growth rate, availability of sunlight with canopy depth (branchiness), biomass

Heterogeneity among all fiber properties and wood density, impacts on strength, ring width

Stocking density ALS/TLS Stocking density Ring width, wood density, juvenile vs. mature portions

Terrain: slope & aspect

ALS Local climate, growth rate, site quality, hydrology

Ring width, formation of reaction wood, variations in wood density

Precipitation & temperature

Weather data/ GIS

Seasonal distribution of growth Ring width, and proportions of earlywood and latewood, wood density

Site nutrient availability

Soil sampling/ GIS

Growth rate, site quality Ring width

(+) TLS = Terrestrial Laser Scanning; ALS = Airborne Laser Scanning; GIS = Geo-Information Systems

5. Summary and outlook for future research

This paper reviews some of the key indicators of wood quality, discusses the main

endogenous and exogenous controls on plant growth and wood anatomy, and explores

the potential of TLS and ALS remote sensing technologies to capture a suite of forest

and terrain-related metrics needed to predict local and regional variations in wood

quality (Table 2). TLS allows tree-level observations of individual stems and crown

attributes at a high level of detail. For instance, TLS observations have been used to

measure the diameter at breast height and taper of individual tree boles (Strahler et al.

2008; Thies et al. 2004), plus canopy and crown properties (Jupp et al. 2009; Hilker et

31

al. 2010). These types of measurements currently allow for the accurate estimation of

stem volume, above ground biomass, and many crown attributes such as branching

structure, branch diameter, and crown volume and shape. We expect that the

integration of LiDAR measurements with other ancillary data sources related to local

and regional variations in climate and species composition may provide a strong

foundation for predictive modeling of plant growth rates and wood properties at tree,

stand, and regional scales.

ALS provides opportunities for much broader spatial characterizations of wood quantity

and quality at the stand and landscape level. TLS, on the other hand, provides

unparalleled spatial descriptions of forest canopy structure at local scales, and may

ultimately be used to calibrate and validate the lower density point clouds, structural

metrics, and wood quality predictions obtained from ALS. Among the most important

quantities measured by LiDAR is the vertical distribution of foliage within the canopy.

The vertical distribution and density (cover) of foliage within a stand are strong

indicators of stand age, height, density, successional status, stand developmental

stage, site productivity, competition (shading and crowding), and disturbance history, all

of which are expected to influence tree- and stand-level growth responses and wood

properties. Cumulative foliage area on a per-tree basis has been found to correlate with

sapwood cross-sectional area (Waring et al., 1982). Another parameter of interest that

can be routinely obtained from ALS is the canopy gap fraction. Several studies (e.g.

Danson et al. 2007; Ni-Meister et al. 2008) have demonstrated the negative correlation

between stocking density and LiDAR measurements of canopy gap fraction. Stocking

32

density determines the potential growing space available to each individual tree, and is

known to influence wood density, juvenile wood portions, and cell perimeter and

coarseness. Finally, bare-earth or digital elevation models constructed from LiDAR

ground returns allow for the extraction of topographical indices (e.g., slope, aspect,

elevation), which capture local and regional variations in critical plant resources, such

as solar radiation, moisture, and temperature. Resource availability is critical to plant

growth and the development of wood anatomical structure.

In addition to single-date acquisitions, multi-temporal LiDAR datasets may prove useful

for the assessment of vegetation dynamics (i.e., growth, mortality, and disturbance), and

can therefore be a valuable addition to traditional canopy characterizations. For

instance, daily, seasonal, and annual cycles of secondary growth influence radial and

axial patterns of ring width, proportions of earlywood and latewood, wood density, fiber

size and shape, and many other wood anatomical characteristics (Downes and Drew,

2008). Using multi-temporal LiDAR acquisitions, information on the deposition of fiber

throughout the tree's life spans can be derived and may be used as input in wood

quality models and can be used to compute, for example, sapwood portions, branch

size or the fraction of living branches interpolated between the individual LiDAR

acquisitions. This yields important information to forecast the wood quality formed

through growth, or to estimate the effects of sylvicultural interventions on wood

formation (Hein et al. 2008; Kint et al. 2010). To these means, formulation of

standardized acquisition methodologies as well as protocols are needed that enable

comparison of data from scanners from different manufacturers and model updates. In

33

addition, better insight into technical considerations behind scanning technology is of

critical importance though often unavailable due to proprietary knowledge involved

(Næsset, 2009).

Investigated trends between tree- and stand-level mensurational data and wood

anatomical structure, together with the proven results of LiDAR remote sensing to

retrieve a broad range of forest inventory parameters, motivate research that seeks to

explore the potential of LIDAR to gain an understanding of the spatial distribution of

wood fiber quantity and quality. This knowledge can be used to support decision

making, and to evaluate competing forest functions and management scenarios. The

link between wood density and other wood characteristics and properties holds potential

to address issues of accounting and accountability of harvested timber resources, and

can be used for monitoring carbon offsets attained through forest growth. Where sites

are managed for timber production, such information can be further used to facilitate in-

stand sorting of timber according to its fiber properties to improve processing efficiency

and to better meet end-use requirements.

However, management of several competing forest functions requires quantification of

their societal- and intrinsic values. Therefore, in parallel to research on the quantification

and spatial mapping of wood quality attributes, research on the measurement of other

forest values is needed together with forest management policies that balance socio-

economic aspects of forest use with ecological constraints in order to assure sustained

resource utilization, conservation of biodiversity, and sustained ecosystem functioning.

34

This includes considering costs of maximizing current yield and growth placed on future

generations.

This review was intended to help guide future research on the application of LiDAR

remote sensing to derive forest structure metrics that relate to wood quality and

quantity. We anticipate that LIDAR derived structure may be linked to supra-cellular

characteristics and include juvenile and mature wood portions, ring width, and wood

density. Since many wood anatomical characteristics, including wood density, correlate

well with tree size, growth rate, stand structure, and site conditions, we expect that

LIDAR-derived measurements of canopy height, density, and topography will furnish

strong predictor variables for modeling the spatial distribution of wood quality at tree,

stand, and landscape scales.

35

Acknowledgements

Components of this research were partly funded by a NSERC Accelerator grant to Prof.

Dr. Nicholas C. Coops and by the Government of Canada through the Lodgepole Pine

Partnership Project funded by the Canadian Forest Service and the Canadian Wood

Fibre Centre. The authors would like to acknowledge Prof. Dr. Shawn D. Mansfield and

two anonymous reviewers for their helpful comments.

36

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