Journal of Experimental Botany, Vol. 58, No. 12, pp. 3463–3473, 2007
doi:10.1093/jxb/erm203
This paper is available online free of all access charges (see http://jxb.oxfordjournals.org/open_access.html for further details)

RESEARCH PAPER

Factors contributing to accuracy in the estimation of thewoody canopy leaf area density proﬁle using 3D portable
lidar imaging
Fumiki Hosoi and Kenji Omasa*
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo,113-8657 Japan
Received 1 May 2007; Revised 25 June 2007; Accepted 31 July 2007

Abstract

Introduction

Factors that contribute to the accuracy of estimating
woody canopy’s leaf area density (LAD) using 3D
portable lidar imaging were investigated. The 3D point
cloud data for a Japanese zelkova canopy [Zelkova
serrata (Thunberg) Makino] were collected using a portable scanning lidar from several points established on
the ground and at 10 m above the ground. The LAD
proﬁles were computed using voxel-based canopy
proﬁling (VCP). The best LAD results [a root-meansquare error (RMSE) of 0.21 m2 m23] for the measurement plot (corresponding to an absolute LAI error of
9.5%) were obtained by compositing the ground-leveland 10 m measurements. The factors that moststrongly affected estimation accuracy included thepresence of non-photosynthetic tissues, distributionof leaf inclination angles, number (N ) of incident laser
beams in each region within the canopy, and G(um)(the mean projection of a unit leaf area on a plane
perpendicular to the direction of the laser beam at themeasurement zenith angle of um). The inﬂuences of
non-photosynthetic tissues and leaf inclination angle
on the estimates amounted to 4.2–32.7% and 7.2–
94.2%, respectively. The RMSE of the LAD estimations
was expressed using a function of N and G(um).

The plant canopy plays important functional roles incycling of materials and energy through photosynthesis
and transpiration, maintaining plant microclimates, andproviding habitats for various species (Monteith, 1973;
Jones, 1992; Ehleringer and Field, 1993; Larcher, 2001;
Stokes et al., 2006). Determining the vertical structure of
the canopy is thus very important because the threedimensional (3D) composition of the canopy helps tosustain those functional roles (Graetz, 1990; Lefsky et al.,
2002; Schurr et al., 2006; Omasa et al., 2007). Researchers often represent the vertical foliage structure using theleaf area density (LAD) in each horizontal layer, where
LAD is defined as the total one-sided leaf area per unit oflayer volume (Weiss et al., 2004). The leaf area index(LAI), which is defined as the leaf area per unit of groundarea covered by the projected area of the crown, is thencalculated by vertically integrating the LAD profile data.
Although there are several ways to measure LAD and
LAI, these measurements remain difficult. Stratified
clipping of biomass samples is one direct measurement
method. However, its application in the field is limited
because of its destructive and laborious nature. Indirect
methods have thus become a popular alternative. The firstindirect method that was developed is the point-quadrat
method, in which a probe (Warren-Wilson, 1960, 1963;
Groeneveld, 1997) with a sharp point or a laser (Vanderbilt
et al., 1990) is inserted into the canopy at a known inclination and azimuth angle, and the number of times thepoint contacts leaves or stems is counted. LAD and LAI
can then be estimated by calculating the contact frequency,

Key words: Japanese zelkova, leaf area density (LAD), leaf
area index (LAI), portable scanning lidar, three-dimensional(3D) imaging, voxel-based canopy proﬁling (VCP), woody
canopy structure.

* To whom correspondence should be addressed. E-mail: aomasa@mail.ecc.u-tokyo.ac.jp
Abbreviations: G(hm), the mean projection of a unit leaf area on a plane perpendicular to the direction of the laser beam at the measurement zenith angleof hm; LAD, leaf area density; LAI, leaf area index; lidar, light detection and ranging; N, number of incident laser beams in each volumetric region within the
canopy; RMSE, root-mean-square error; SLA, speciﬁc leaf area; VCP, voxel-based canopy proﬁling.
ª 2007 The Author(s).
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which
permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

3464 Hosoi and Omasa

which is defined as the mean number of contacts per
insertion. However, this method is also very laborious(Norman and Campbell, 1989; Jonckheere et al., 2004).
Another indirect method, the gap fraction method, is widely
applied in field surveys and uses commercially available
tools such as cameras with a fish-eye lens and opticalsensors (e.g. the Li-Cor LAI-2000 Plant Canopy Analyzer;
Lang and Yueqin, 1986; Neumann et al., 1989; Norman
and Campbell, 1989; Chason et al., 1991; Gower andNorman, 1991; Welles and Norman, 1991; Chen and Cihlar,
1995; Eschenbach and Kappen, 1996; Welles and Cohen,
1996; Bréda, 2003; Jones et al., 2003). This method
allows automatic estimation of LAI without destruction of
the plants, and is less laborious. However, it depends on
the assumption that the foliage distribution is random,which leads to errors in LAI when the foliage distribution
is non-random. Although the correction factor, the socalled clumping factor, has been applied to overcome theproblem (Chen and Cihlar, 1995; Leblanc et al., 2005), it
is still a difficult problem.
Recently, lidar (light detection and ranging), which is anactive remote-sensing technique that uses a laser scanner,has been applied to canopy measurements. Airborne largefootprint lidar has been used to estimate the verticalprofile of the forest canopy’s surface based on the
waveforms of the returned pulses (Harding et al., 2001;
Lefsky et al., 2002). However, the image resolution has
been insufficient to provide detailed descriptions ofcanopy structure at the level of individual trees. Meanwhile, current airborne small-footprint lidar has made it
possible to measure canopy structures with fine spatial
resolution (Hyyppä and Inkinen, 1999; Omasa et al.,2000, 2003; Hyyppä et al., 2001; Brandtberg et al., 2003;
Riaño et al., 2003; Holmgren and Persson, 2004; Næsset
et al., 2004; Yu et al., 2004). For example, the LAI
values for a Douglas-fir [Pseudotsuga menziesii (Mirb.)
Franco] forest were estimated from the allometric relationships between tree height distribution derived fromsmall-footprint lidar and actual LAI (Magnussen and
Boudewyn, 1998). Other studies of LAI estimates using
airborne lidar, in which the canopy’s gap fraction wasmeasured and converted into LAI values, were carriedout for maize and sunflower (Houldcroft et al., 2005)
and several coniferous trees (Morsdorf et al., 2006;
Solberg et al., 2006). However, this work provided
insufficient information about the vertical foliage structure, so the authors focused on LAI estimation rather
than estimation of the vertical profile of LAD. Lovell
et al. (2003) tried to estimate the vertical profile of
LAD in eucalyptus forest using airborne small-footprint
lidar, but the profile showed little response to the middle
canopy levels that were present in the actual foliage
profile. In this case, it seemed that the laser beams fromthe lidar could not reach the middle canopy due toobstruction by the upper canopy. Thus, it remains chal-lenging to measure the LAD profile using airborne smallfootprint lidar.
Portable ground-based lidar has been used to provide
more accurate measurements of whole plants and vegetation canopies (Radtke and Bolstad, 2001; Omasa et al.,2002, 2007; Parker et al., 2004; Yoshimi et al., 2004;
Hosoi et al., 2005; Hosoi and Omasa, 2006). Groundbased lidar is expected to compensate for the weak pointsof airborne lidar in LAD estimation because its fine spatial
and range resolutions and its small beam size would
permit measurement of the inner canopies of trees,
allowing accurate estimation of LAD profiles. The opticalpoint-quadrat method is one technique for roughly
estimating LAD profiles from the ground (MacArthur andHorn, 1969). The method is based on measuring heights
to the lowest leaves above a set of sample points
established on the ground beneath the canopy using
a telephoto lens or clinometers combined with trigonometry. LAD profiles have also been measured using
portable ground-based non-scanning lidar (Radtke and
Bolstad, 2001; Parker et al., 2004) and portable groundbased scanning lidar (Henning and Radtke, 2006).
Portable ground-based scanning lidar has also been
applied in the gap fraction method for the measurementof LAI (Lovell et al., 2003; Tanaka et al., 2004) and LAD
profiles (Takeda et al., 2005). In those studies, the gap
fraction within the canopy was measured using laser
beams instead of sunlight. Although both methods
allowed effective data collection, they provided insufficiently accurate estimates because it was difficult toilluminate the full canopy evenly. The results were also
affected by non-uniformity of the actual foliage distribution and the presence of non-photosynthetic tissue.
3D imaging using portable scanning lidar has been
demonstrated to provide accurate estimates of LAD(Hosoi and Omasa, 2006; Omasa et al., 2007). The voxelbased canopy profiling (VCP) method (in which volume
elements—‘voxels’—which are equivalent to the pictureelements—‘pixels’—in two-dimensional images) has been
newly developed to improve estimates of LAD using 3D
portable scanning lidar (Hosoi and Omasa, 2006; Omasa
et al., 2007). In this method, horizontal layers of the tree
canopy are fully and evenly scanned using optimally
inclined laser beams emitted from several points surrounding
the tree, thus solving the problem of uneven penetration of
the canopy by the lidar’s laser beams. From the resulting
data, information about the laser traces can be expressed as
voxels that serve as attributes of a 3D array. Based on thevoxels, LAD profiles and the LAI of individual trees can be
estimated accurately by counting the frequency of contact
between laser beams and foliage of the crown in eachhorizontal layer. In addition, subtraction of voxel-based tree
images obtained with the leafless condition from images
obtained with the leafy condition allows researchers toeliminate non-photosynthetic tissues from the calculations.

Factors contributing to accuracy of lidar estimation of woody canopy LAD 3465

However, the application has not yet been demonstrated forwoody canopies. Moreover, the factors that affect theaccuracy of the LAD estimation have not yet been revealed.
In the present study, 3D portable lidar imaging was applied
to estimation of the LAD of woody canopy using the VCP
method. The LAD estimates were first compared with actual
values to validate the approach. The factors that contributed
to the accuracy of the estimates were then investigated.

Materials and methods
Study site
The experiment was conducted in a mixed plantation in Ibaraki
Prefecture, 40 km northeast of central Metropolitan Tokyo, Japan(3559#N, 14002#E). The topography was nearly flat. The
dominant tree species were Japanese cedar [Cryptomeria japonica(L.f.) D. Don], Japanese red pine (Pinus densiflora Siebold &Zuccarini), ginkgo (Ginkgo biloba Linnaeus), and Japanese zelkova[Zelkova serrata (Thunberg) Makino]. The understorey included
grasses, forbs, and young evergreen trees such as Camellia japonica
Linnaeus, Ilex integra Thunberg, and Ternstroemia gymnantheraSprague. A 438 m measurement plot was established at the site
(Fig. 1A), and the Japanese zelkova canopy within the plot wasused for the experiment (Table 1). The plot was divided into eight
232 m quadrats (Fig. 1B).

Direct measurement of LAD
The actual LAD of the canopy was measured directly by means ofstratified clipping in September 2005. Each vertical region withineach of the quadrats was divided into 16 cells (each 2323 0.5 m)
between heights of 5 and 13 m above the ground, as shown in
Fig. 1C. The entire canopy within the measurement plot was thus
divided into 128 cells. Each of the cells was distinguished by areference number of the form (l, m, n) in which (l, m) corresponds tothe quadrat numbers in Fig. 1B and n is the number of the horizontal
layer, with n¼1 for the layer nearest to the ground (Fig. 1C). The
layers below 5 m were omitted because the leaves of the Japanesezelkova canopy were distributed above this height and the understorey
grew below that height. A cherry picker was brought to the study site
to allow the upper region of the canopy to be reached. A laser beam
was emitted vertically from the canopy to the ground using acommercially available laser pointer with a spirit level to define thecorner of each quadrat for each horizontal layer within the canopy. A
tape measure with a plumb bob was hung from each corner of eachcanopy layer to the ground to identify the height. These measures
allowed identification of cells to which each leaf should be assigned.
When the positions of the leaves had been checked, they wereclipped manually and labelled according to the cell they identified.
Leaves corresponding to 100 g fresh weight were randomly selectedfrom the leaves clipped from the cells located at a given height. The
leaves were then scanned into JPEG images using a commerciallyavailable desktop scanner (FB636U, Canon, Inc., Japan). The
relationship between the number of pixels within the images andtheir actual area (m2) was determined by scanning a ruler together

Fig. 1. The 438 m measurement plot established at the study site. (A) Aerial photograph of the study site. The white rectangle shows the location of
the measurement plot that includes a Japanese zelkova canopy [Zelkova serrata (Thunberg) Makino]. (B) Quadrats established on the ground in themeasurement plot. Each quadrat is 232 m. The black dot shows the centre of the measurement plot. Black arrows show the directions in which lidar
scanning was performed. (C) Illustration of the cells established within the measurement plot from 5 m above the ground to a height of 13 m. In total,
128 cells (each 2323 0.5 m) were measured. hm represents the measurement zenith angle.

3466 Hosoi and Omasa
Table 1. Descriptive variables for the Japanese zelkova canopy [Zelkova serrata (Thunberg) Makino] within the measurement plot at
the study site
Tree name

Zelkova serrata (Thunberg) MakinoaHeight (m)12.5

LAI (m2 m2)

4.59

LAD (m2 m3)

Specific leaf area (m2 kg1)

Mean

Maximum

Mean

SDa

0.57

1.51

11.9

2.5

SD, Standard deviation.

with the leaves. The number of pixels of leaves within the images
was then converted into the actual leaf area by multiplying thenumber of leaf pixels by the area per pixel. After scanning, theleaves were dried to constant weight in an oven at 80C, and the dryweight was measured. The actual leaf area and corresponding
dry weight of the oven-dried samples were converted into an area to
dry weight ratio (i.e. specific leaf area, SLA) at each height. It wasobserved that SLA decreased as height increased. All other leaves
were also dried under the same conditions so that the total dry
weight could be measured for each cell. Leaf area in each cell was
determined by multiplying the dry weight in a cell by the SLA
corresponding to the height of the cell. Consequently, LAD in eachcell was acquired by dividing the leaf area by the cell volume(2 m3). By integrating the LAD values vertically, actual values of
LAI could be obtained for each quadrat and for the whole canopy.
Measurements using two types of portable scanning lidar
A portable fine-resolution scanning lidar that calculates distances
based on trigonometry (a modified TDS-130L 3D laser scanner;
Pulstec Industrial Co., Ltd, Japan) was used to measure leaf
inclination angle. The lidar’s measurable range is 3.5–10 m. The
range and scan resolutions are about 1 mm and 2 mm, respectively,
at a measurement range of about 5 m. A rotating mount witha stepper motor and a galvano mirror within the lidar head
automated the horizontal and vertical scanning. Leaves within
quadrats 1-1 and 2-1 (see Fig. 1B) were scanned by the lidar froma position on side A, about 5 m from the centre of the measurement
plot, in August 2005. Each leaf was distinguishable from theacquired 3D point cloud image of the leaves because of the fineresolution. After randomly selecting 200 leaves in the image, eachleaf was approximated as a plane, and normals to the planes were
estimated. The distribution of leaf inclination angles was derived
from the angles of these normals with respect to the zenith.
Another type of portable scanning lidar (LPM-25HA, RIEGL,
Austria) was used for the application of the VCP method. The
portable lidar was able to obtain the distance to the surface of an
object between 2 m and 60 m from the sensor by measuring thetime elapsed between the emitted and returned laser pulses (the‘time of flight’ method). A rotating mount driven by built-in stepper
motors with 0.009 accuracy panned and tilted the lidar head. The
lidar had an accuracy of 68 mm when computing the range of eachsample point. The Japanese zelkova canopy within the measurement
plot was scanned by the lidar from several ground positions(positions 1, 2, and 3 on sides A and B; Fig. 1B) in August 2005.
The centre of the angles of the laser beams from the zenith (¼ themeasurement zenith angle, hm; Fig. 1C) was 47.2, 57.8, and 71.3 ateach measurement position except the case of 71.3 at B2, where
the measurement position could not be set because of the presenceof a dense understorey in that position.
The distance between the canopy and the lidar position was
adjusted as laser beams at each measurement zenith angle hit theregion of the canopy at heights between 8 m and 13 m, where leaves
grew most densely, and the scan zenith angle of the laser beam (i.e.
the range of angles on either side of the central zenith angle within

the vertical plane) was chosen so that the laser beam could cover the
canopy from 5 m to 13 m in height. The average distances from thecentre of the measurement plot (Fig. 1B) at the measurement zenith
angles of 47.2, 57.8, and 71.3 were 9.3, 13.6, and 24.4 m,
respectively. The scan zenith angles to the measurement zenith
angles of 47.2, 57.8, and 71.3 were 619.1, 610.0, and 610.6,
respectively. The scan azimuth angle (the range of angles on eitherside of the central zenith angle within horizontal plane) ranged from614 to 647 so that the laser beams could cover the entire width ofthe canopy. The increments of the azimuth and zenith angles were set
at 0.054 for the three measurement zenith angles.
The canopy was also measured from positions 10 m above theground using the cherry picker. Because there were few places at the
study site where the cherry picker could be installed, measurements
were carried out from only three positions at this height, corresponding to position 2 on side A and positions 1 and 3 on side B. The
measurement zenith angle of the laser beams was 90.0 (i.e. horizontal
direction) and the corresponding scan zenith angle at this height was
633.1. The azimuth scan angle ranged from 618 to 628.
The increments of the azimuth and zenith angles were set at 0.054.
The average distance between the three lidar positions 10 m above theground and a point 10 m above the centre of the measurement plot
was 12.8 m. After the measurements during the leafy condition in
August 2005, the same canopy was measured from the ground in
February 2006, during the leafless condition. The measurement
conditions used at this time were the same as those used for the 57.8
measurement zenith angle during the leafy condition.
Voxel-based computation of LAD
LAD computation used the VCP method (Hosoi and Omasa, 2006).
The complete data set for each measurement zenith angle wastypically composed of point cloud data obtained from each of thesix measurement positions, but only five data sets were obtained at
the measurement zenith angle of 71.3 and only three were obtainedat the 10 m height. The individual coordinate systems for these data
were registered into a single point-cloud data set with a common 3D
coordinate system for each measurement zenith angle using theiterative closest-point algorithm (Besl and McKay, 1992). The datameasured in the leafless condition were also registered using thisalgorithm. All points in the registered data set were converted intovoxel coordinates for each measurement zenith angle using thefollowing equations:X  Xmini ¼ Int
þ1
ð1Þ
Di




j ¼ Int

Y  Ymin
Dj

þ1

ð2Þ

k ¼ IntZ  Zmin
þ1
Dk

ð3Þ

where (i, j, k) represent the voxel’s coordinates within the voxel
array, Int is a function that rounds off the result of the calculation toFactors contributing to accuracy of lidar estimation of woody canopy LAD 3467
the nearest integer, (X, Y, Z) represent the point coordinates of theregistered lidar data, (Xmin, Ymin, Zmin) represent the minimum
values of (X, Y, Z) (Fig. 1C), and (Di, Dj, Dk) represent the voxel
element size. In this experiment, the voxel element size, which
depended on the range and scan resolution of the lidar, was53535 mm. Non-photosynthetic tissues such as trunks andbranches and understorey were excluded by extracting the corresponding points between the 3D voxel data in the leafless condition
and the one in the leafy condition.
To apply the VCP method, voxels corresponding to coordinates
converted from points within the registered data set were assigned
an attribute of 1 (Hosoi and Omasa, 2006). A voxel with an
attribute of 1 represents a voxel in which at least one laser beam
was intercepted by leaves. All laser beams emitted from the lidar
positions were then traced within the voxel array in accordance withthe actual laser beam angles. If voxels that did not have an attributeof 1 were intersected by at least one laser beam trace, the voxel was
assigned an attribute of 2. A voxel with an attribute of 2 therefore
represented a voxel through which one or more laser beams passed
without touching a leaf. Other voxels that were assigned neither
attribute value were omitted from the LAD computation. Based on
the attribute values, LAD was computed in each of the cells within
the measurement plot using the following equation:
LADlmn ¼

coshlmn 1 mhþDH
nI ðkÞ
:+Gðhlmn Þ DH k¼mh nI ðkÞ þ nP ðkÞ

ð4Þ

where (l, m, n) represent the cell reference number, hlmn is the mean
zenith angle for all laser beam incidences within a cell, and nI(k) andnP(k) are the numbers of voxels with attributes of 1 and 2 in the kth
layer of a cell, respectively. Thus, nI(k)+nP(k) represents the total
number of incident laser beams that reach the kth layer of a cell.
G(hlmn) is the mean projection of a unit leaf area on a plane
perpendicular to the direction of the laser beam at hlmn (Norman andCampbell, 1989; Welles and Norman, 1991; Jonckheere et al., 2004;
Weiss et al., 2004). The term cos(hlmn)[G(hlmn)]1 is a correction
factor for the influence of leaf inclination angle and laser beam
direction. DH is the vertical thickness of a cell (¼0.5 m), and mh and
mh+DH are the voxel coordinates on the vertical axis and areequivalent to the height of the bottom of cell h and the top h+DH in
orthogonal coordinates (h¼mh Dk). Equation (4) is analogous to theequation of radiation transfer through canopy in the case of neglecting the scattering term (Ross, 1981; Anisimov and Fukshansky,1993). nI(k)+nP(k) and nI(k) in Equation (4) correspond to theradiation intensity and the attenuation of the radiation intensity in theradiation transfer equation. cos(hlmn)[G(hlmn)]1 was determined following the method of Hosoi and Omasa (2006) using the distributionof leaf inclination angles acquired by the fine-resolution portable
lidar, as described above. A constant value of 1.1 was chosen forcos(hlmn)[G(hlmn)]1 for the measurement zenith angle of 57.8,which is near the particular angle of 57.5 at which the value can beconsidered nearly independent of leaf inclination (Warren-Wilson,
1960; Weiss et al., 2004).
From the LAD estimates computed for each of the cells, the LAD
profile in each quadrat was obtained for each measurement zenith
angle. By vertically integrating the LAD estimates within the same
quadrat, an LAI estimate for each quadrat was obtained. By
averaging the LAD profiles horizontally, the LAD profile in themeasurement plot (eight quadrats) was also obtained. The vertical
integration offered an LAI estimate for the measurement plot. The
influence of non-photosynthetic tissues on the results in themeasurement plot was assessed for each measurement angle bycomparing LAD estimates with and without these tissues. When the
distribution of leaf inclination angle cannot be obtained and then theinfluence of leaf inclination is not corrected, it causes an estimation
error except at a laser zenith angle around 57.5. The error at eachmeasurement zenith angle was assessed from the mean value ofcos(hlmn)[G(hlmn)]1 for all cells in the measurement plot.
Derivation of the relationship between the error of LAD
estimation and the number of incident laser beams
The relationship between the number of incident laser beams ineach cell (N) and the error of the LAD estimates was also
investigated. Laser beams were traced again as described above forthe voxel-based computation of LAD, and N was calculated bycounting the number of laser beam traces that passed through eachcell. Each cell was then sorted into classes based on N. The rootmean-square error (RMSE) of the LAD estimates in each class wascalculated from the errors of the LAD estimates for the cells
included in each class. This enabled the establishment of a relationship between the RMSE of the LAD estimates and N. These
relationships were derived and compared for all measurement zenith
angles. After this comparison was complete, the value of G(hm) for
each of the angles was calculated and the RMSE at each angle wasexpressed as a common function of N and G(hm).

Results
Figure 2 shows 3D lidar images of the zelkova canopy
after registration of the images measured at a measurement
zenith angle of 57.8 from the six ground positions
indicated in Fig. 1. Trees in the measurement plot wereextracted as shown in Fig. 2B(a) from the overall image
shown in Fig. 2A, and then the non-photosynthetic
tissues, including undergrowth (Fig. 2Bb), and thephotosynthetic tissues (Fig. 2Bc) were separated. Eachtissue was clearly distinguishable because of the fineresolution of the images. The percentages of nonphotosynthetic tissue area to total surface area at heights
above 5 m were 4.2, 15.7, 32.7, and 4.4% for themeasurement angles of 47.2, 57.8, 71.3, and 90.0,
respectively, based on the estimates derived from the lidar
data. These non-photosynthetic tissues cause errors in theestimation of LAD.
Figure 3 shows the distribution of leaf inclination angles
within the zelkova canopy in the measurement plot in Fig.
1B using the fine-resolution portable scanning lidar. The
values ranged from 0 to 65, with a mean value of 18.6,as most angles were less than about 30. The distributionof leaf inclination angles allowed calculation of the valuesof cos(hlmn)[G(hlmn)]1 in each cell. The mean values ofcos(hlmn)[G(hlmn)]1 for all cells at measurement angles of
47.2, 71.3, and 90.0 were 1.08, 0.90, and 0.80, respectively. With no correction for leaf inclination, theerrors in LAD estimation were estimated as 7.2, 14.2, and94.2% on average for all cells at measurement angles of
47.2, 71.3, and 90.0, respectively. The results showed thesignificant level of the error at the measurement angle of90.0. The value of cos(hlmn)[G(hlmn)]1 at around 90.0
changes very sensitively according to the leaf inclinationand laser zenith angle when the distribution of leaf
inclination angle is planophile as is in the present study.

3468 Hosoi and Omasa

Fig. 2. 3D images of the Japanese zelkova canopy at the study site. This image shows the results after co-registration of the images measured ata measurement zenith angle of 57.8 from the six positions on the ground shown in Fig. 1B. Leaves are coloured green and the other plant parts arecoloured brown. (A) A section of the Japanese zelkova canopy at the study site. The area enclosed by the white broken line corresponds to themeasurement plot. Arrows show the directions of lidar scanning from side A. (B) Separation of the image into its components: (a) Trees in themeasurement plot. (b) Separation of non-photosynthetic tissues, including undergrowth. (c) Separation of photosynthetic tissues (i.e. foliage).

Fig. 3. Distribution of leaf inclination angles in the Japanese zelkova
canopy derived from a high-resolution portable scanning lidar image.
Mean¼the mean leaf inclination angle.

Thus, the correction based on the actual leaf inclination
angle is particularly essential for the measurement angle
around 90.0. Although a constant value of 1.1 wasapplied for cos(hlmn)[G(hlmn)]1 at a measurement angleof 57.8, the validity of this assumption was tested using
the actual distribution of leaf inclination angles and theactual zenith angles of the laser beams for each cell. The
resulting values ranged from 1.04 to 1.06, depending on
the cell. The difference between the assumed value of 1.1
and the values derived from actual data was <5.5%, andthe choice of a constant value was thus reasonable fora measurement angle of 57.8.
The LAD profiles estimated by eliminating nonphotosynthetic tissues from the lidar data were comparedwith the actual stratified clipping values for an entire
region within the measurement plot (Fig. 4). All LAD

estimates agreed well with the actual values up to a heightof 8.5 m. However, the estimates at measurement anglesof 47.2–71.3 underestimated the actual values above thisheight. The estimate at 90.0 also underestimated theactual value at heights above 10.0 m, but the degree ofunderestimation was less than at the other angles. RMSE
values for all estimates in the LAD profiles were 0.33,
0.40, 0.55, and 0.24 m2 m–3 at measurement zenith anglesof 47.2, 57.8, 71.3, and 90.0, respectively. The LAD
estimates showed that smaller zenith angles had lower
RMSE values, but that the 90.0 angle produced thelowest RMSE. The mean values of N for all cells at
measurement angles of 47.2, 57.8, 71.3, and 90.0 were5.33104, 2.53104, 2.03104, and 1.33 104 incidencesm3, respectively. The LAD estimates also showed that
RMSE decreased with increasing N, except at the 90.0
angle. The absolute errors in LAI for the measurement
plot were 23, 37, 57, and 4.1% at measurement zenith
angles of 47.2, 57.8, 71.3, and 90.0, respectively.
It seemed to be a problem that the largest discrepancies
between the actual and the estimated LAD profiles for an
entire region within the measurement plot (Fig. 4)
occurred in the top layer, because most light interception,energy, and mass exchanges occur in the top layers of the
canopy. However, in the LAD estimation of each quadrat,
good agreements between the actual values and estimates
up to the top layers were obtained in some quadrats. To
provide examples of the LAD profiles for each quadrat,
the estimated LADs in quadrats 1-1 and 2-3 werecompared with the actual values (Fig. 5). The estimatesfor quadrat 1-1 (Fig. 5A), which was close to the lidar
position on side A, showed good agreement with theactual values at all measurement zenith angles. However,the LAD estimates at quadrat 2-3 were overestimates of
the actual values at heights of about 7–9 m, butFactors contributing to accuracy of lidar estimation of woody canopy LAD 3469

Fig. 4. Comparison of LAD profiles for the measurement plot between
the lidar-derived estimates obtained with different measurement zenith
angles (hm) and the actual value. RMSE, root-mean-square error of the
LAD estimates.

Fig. 5. Comparison of LAD profiles in two quadrats between the lidarderived estimates obtained with different measurement zenith angles(hm) and the actual value. (A) Quadrat 1-1. (B) Quadrat 2-3. RMSE,
root-mean-square error of the LAD estimates.

underestimated the actual values at greater heights, except
at a measurement angle of 90.0 (Fig. 5B); this quadrat
was in the middle of the measurement plot. The sametendency was observed in the other quadrats, i.e. thecloser the quadrats were to the lidar positions, the smaller
the underestimation of the actual LAD value, except at the
90.0 measurement angle. Compared with the LAD
estimates at other angles, the estimates at 90.0 offeredless underestimation for quadrats near the middle, butshowed slight overestimation at heights of <10.0 m. The
mean RMSEs of the LAD estimates for each quadrat were0.50, 0.64, 0.78, and 0.44 m2 m3 at measurement zenith
angles of 47.2, 57.8, 71.3, and 90.0, respectively. The
mean absolute errors of LAI for each quadrat were 21, 37,
57, and 12.7% at angles of 47.2, 57.8, 71.3, and 90.0,
respectively.
To improve the error of the LAD estimates, theunderestimated values at a measurement angle of 57.8
were replaced with the estimates derived from themeasurements at a height of 10 m (hm¼90.0). Figure 6
shows the results for the entire region within themeasurement plot (Fig. 6A) and for quadrat 2-3 (Fig.
6B). The measurement zenith angle of 57.8 was chosen
to represent the ground measurements because of theusefulness of the leaf inclination correction in the field
and the fact that the accuracy at this angle was comparablewith the best accuracy (at an angle of 47.2). The RMSEs
were decreased from 0.40 m2 m3 to 0.21 m2 m3 for theentire region within the measurement plot and from0.95 m2 m3 to 0.12 m2 m3 for quadrat 2-3. The mean
RMSE of the LAD estimates for each quadrat improved
from 0.64 m2 m3 to 0.36 m2 m3. The errors of LAI also
improved, from 37% to 9.5% for the absolute error for theentire region and from 37% to 9.6% for the mean for eachquadrat.
Figure 7 shows the relationship between RMSE of the
LAD estimates and N in each cell for the measurement
zenith angles of 47.2 (Fig. 7A), 57.8 (Fig. 7B), 71.3
(Fig. 7C), and 90.0 (Fig. 7D). The slope of the regression
lines increases as the measurement zenith angle increases,
so large measurement angles offer more accurate estimates
than small angles when the value of N exceeds a certain
value (e.g. N >0.33104 incidences m3 for 90.0,
estimated from the intersection points of the regression
lines in Fig. 7A–D). In particular, the rate of decrease of
RMSE with increasing N is higher at the 90.0 angle thanat the other angles, so that better LAD estimates (asshown in Figs 4 and 5) have been obtained even though
the mean value of N for all cells at this angle (¼ 1.33104
incidences m3) was the smallest of all the angles.
Although the 71.3 angle offered the worst result (Fig. 4),
Fig. 7C suggests that this angle offers better results than
the 47.2 and 57.8 angles when a sufficient number oflaser beam incidences is supplied (N >3.23104 incidencesm3).

3470 Hosoi and Omasa

a ¼ 22:48 ln½GðhmÞ  5:808 R2 ¼ 0:992

ð6Þ

b ¼ 0:183 ln½Gðhm Þ  0:171 R2 ¼ 0:999

ð7Þ

From Equations (5) to (7), the RMSE at each angle wasexpressed as a common function of N and G(hm).

Discussion

Fig. 6. Comparison of LAD profiles between estimates from the groundmeasurements and estimates obtained for composite data from measurements from the ground and from a height of 10 m. Open triangle,
profile from the six ground measurements at a measurement zenith
angle (hm) of 57.8 . Open circle, profile obtained with a composite of
the LAD estimates from the ground measurements at a hm of 57.8  andone from three measurements at a height of 10 m. (A) The measurementplot. (B) Quadrat 2-3. RMSE, root-mean-square error of the LAD
estimates.

The relationships in Fig. 7 were fitted well using
a power function:
RMSE ¼ aN b

ð5ÞThis function is characterized by the coefficient a andthe scaling exponent b. Figure 7 indicates that a increased
and b decreased as the measurement zenith angle increased.
It was hypothesized that the change in a and b for eachmeasurement zenith angle relates to the change in G(hm)that accompanies the increase in the measurement zenith
angle, and G(hm) was calculated for each measurement
zenith angle and the result related to the corresponding
values of a and b. As shown in Fig. 8A for coefficienta and Fig. 8B for the scaling exponent b, the resulting
relationships could be expressed as functions of G(hm) as
follows:

As reported in previous studies (Radtke and Bolstad,
2001; Henning and Radtke, 2006; Hosoi and Omasa,
2006), the presence of non-photosynthetic tissues and the
distribution of leaf inclination angles were responsible for
estimation errors. The influences of non-photosynthetic
tissues in measurements taken from the ground increased
as the measurement zenith angle increased because thetrunks and branches of the zelkova canopy stretched morevertically than the leaves. The laser beam directions
became approximately perpendicular to the trunks andbranches, and parallel to the leaves as the measurement
angle increased, so that the ratio of the number of incidences of the laser striking trunks and branches to theincidences striking the leaves would have increased as
the angle increased. A similar effect that is observed in the
gap fraction method was discussed by Chen and Black(1991), who defined G(h) based on the inclination angles
of both the leaves and the branches. The relatively small
effect at a measurement angle of 90.0 in the measurements at a height of 10 m would result from fewer laser
beams striking the trunks and branches in the inner
canopy due to obstruction by the dense leaves at thisheight.
Measurements in the leafless condition permitted separation of 3D portable lidar data into leaves and nonphotosynthetic tissues. As described above, the ratio of
the number of points corresponding to non-photosynthetic
tissues within the leafy image was different in each of themeasurement angles, but the difference did not affect theeffectiveness of the separation because the image taken
during the leafless season included almost all parts of nonphotosynthetic tissues and the separation was done by
extracting corresponding points between the leafless andleafy images based on the voxel coordinates. Besides thismethod, processing the lidar data with additional information about trees, such as difference in colour,
reflectance, and texture in each of the tree tissues, mayalso be a possible way of achieving the separation.
A laser zenith angle of around 57.5 permitted effective
correction for leaf inclination without angle measurements
for the woody canopy as well for as individual trees(Hosoi and Omasa, 2006; Omasa et al., 2007). Anotherway to correct for leaf inclination, using the mean value ofcos(hlmn)[G(hlmn)]1 calculated from the lidar-derived
distribution of leaf inclination angles, would performFactors contributing to accuracy of lidar estimation of woody canopy LAD 3471

Fig. 7. Relationships between the root-mean-square error (RMSE) of the LAD estimates and the number of laser beam incidences in each cell (N).
The measurement zenith angles are (A) 47.2 , (B) 57.8 , (C) 71.3 , and (D) 90.0 . Error bars are standard deviations.

effectively where the 57.5 angle cannot be used due tophysical restrictions on the allowable angle. The correction on the lidar-derived leaf inclination angle distribution
will be particularly useful at the measurement zenith angle
around 90.0, where the measurement is strongly affectedby leaf inclination.
An error in the LAD estimations (Figs 4, 5) was caused
despite the above-mentioned corrections. This error
depends on the number of incident laser beams withineach cell (N) at each angle (Fig. 7). In measurements fromthe ground, the distance between the canopy and the lidar
increased as the zenith angle increased, while the increments in the azimuth and zenith angles remained constant
at 0.054. Thus, the values of N decreased as the zenith
angle increased, and LAD estimates would become moreaccurate at smaller zenith angles (Figs 4, 5). However, the90.0 angle at a height of 10 m was the exception.
Although the mean value of N was smallest at a measurement zenith angle of 90.0 due to the decreased number ofmeasurement positions, the LAD estimate offered the best
results of all measurement zenith angles. The exception at
90.0 suggests the presence of another factor that affects
the accuracy of LAD estimation.
One possible factor that could explain this result is thedifference in the quantity of obstructed leaves at eachmeasurement zenith angle. In this context, ‘obstructed’refers to leaves that the laser beams cannot reach asa result of obstruction by other leaves. The presence of

these leaves causes an error in LAD estimation, and thedegree of obstruction would differ among measurement
zenith angles. The degree of the obstruction might beassessed by projection of leaf area on a plane perpendicular to the direction of the laser beam. The mean of thisprojection would be expressed as G(hm); thus the degreeof the obstruction should be reflected in G(hm). In thepresent study, the value of G(hm) decreased as themeasurement zenith angle increased; thus it is expectedthat the obstruction at large measurement angles decreases
compared with the value at small zenith angles. This
seems to be supported by the results in Fig. 7, in which
the 90.0 angle offered better results than the other angles
as N increases. These considerations are also supported by
the results in Fig. 8, in which parameters a and b (which
express the differences among the fitting functions for themeasurement angles) were described as functions ofG(hm). These fitting functions to provide a relationshipbetween RMSE and N were expressed as a function of N
and G(hm), as presented in Equations (5) to (7).
Based on this discussion, the results demonstrate thatthe factors that affect the accuracy of LAD estimation
using 3D portable lidar imaging were the presence of nonphotosynthetic tissues, the distribution of leaf inclination
angles, N, and G(hm). By considering these factors in thelidar measurements, more accurate LAD estimates can beobtained. In measurements obtained from the ground,a larger zenith angle will provide more accurate results

3472 Hosoi and Omasa

strated in this study would be common to other broadleaved canopies, and the methods used in this study seemto be applicable to them. In coniferous canopies, they
have different structure from broadleaved ones and thus
additional work would be needed.
References

Fig. 8. Relationship between the mean projection of a unit leaf area ona plane perpendicular to the direction of the laser beam at hm [G(hm)]and the parameters a and b in Equation (5). (A) G(hm) versus coefficienta. (B) G(hm) versus scaling exponent b. hm represents the measurement
zenith angle.

when a sufficiently high N can be provided by means offine increments of the laser scan angles from several
measurement positions and when the distribution of leaf
inclination angles can be measured. A measurement zenith
angle of around 57.5 would be useful when it is notpossible to measure the distribution of leaf inclination
angles. If appropriate measurement positions are available,measurements from higher positions would allow more
direct scanning of the dense upper canopy by a sufficientnumber of laser beams and would thus give a moreaccurate estimate. Compositing ground-based measurements and measurements taken from a position higher
above the ground would provide better estimates in thepresence of blind regions both from the ground and fromhigher positions. These various strategies for improving
3D portable lidar imaging for LAD estimation would also
apply to the estimation of LAI, which represents thevertical integration of LAD. However, care should betaken to ensure that the over- and underestimations of
LAD will cancel each other out in order to reduce theerror in the LAI estimate. The factors affecting LAD
estimation error with 3D portable lidar imaging demon-

Anisimov O, Fukshansky L. 1993. Light–vegetation interaction:a new stochastic approach for description and classification.
Agricultural and Forest Meteorology 66, 93–110.
Besl PJ, McKay ND. 1992. A method for registration of 3-D
shapes. IEEE Transactions on Pattern Analysis and MachineIntelligence 14, 239–256.
Brandtberg T, Warner TA, Landenberger RE, McGraw JB.
2003. Detection and analysis of individual leaf-off tree crowns insmall footprint, high sampling density lidar data from the eastern
deciduous forest in North America. Remote Sensing of Environment 85, 290–303.
Bréda NJJ. 2003. Ground-based measurements of leaf area index:
a review of methods, instruments and current controversies.
Journal of Experimental Botany 54, 2403–2417.
Chason JW, Baldocchi DD, Huston MA. 1991. A comparison of
direct and indirect methods for estimating forest canopy leaf area.
Agricultural and Forest Meteorology 57, 107–128.
Chen JM, Black TA. 1991. Measuring leaf area index of plant
canopies with branch architecture. Agricultural and Forest
Meteorology 57, 1–12.
Chen JM, Cihlar J. 1995. Plant canopy gap-size analysis theory
for improving optical measurements of leaf-area index. Applied
Optics 34, 6211–6222.
Ehleringer JR, Field CB. 1993. Scaling physiological processes—
leaf to globe. San Diego: Academic Press.
Eschenbach C, Kappen L. 1996. Leaf area index determination inan alder forest: a comparison of three methods. Journal of
Experimental Botany 47, 1457–1462.
Gower ST, Norman JM. 1991. Rapid estimation of leaf area index
in conifer and broad-leaf plantations. Ecology 72, 1896–1900.
Graetz RD. 1990. Remote sensing of terrestrial ecosystem
structure: an ecologist’s pragmatic view. In: Hobbs RJ, Mooney
HA, eds. Remote sensing of biosphere functioning. New York:
Springer, 5–30.
Groeneveld DP. 1997. Vertical point quadrat sampling and an
extinction factor to calculate leaf area index. Journal of Arid
Environments 36, 475–485.
Harding DJ, Lefsky MA, Parker GG, Blair JB. 2001. Laser
altimeter canopy height profiles. Methods and validation for
closed-canopy, broadleaf forests. Remote Sensing of Environment
76, 283–297.
Henning JG, Radtke PJ. 2006. Ground-based laser imaging forassessing three-dimensional forest canopy structure. Photogrammetric Engineering and Remote Sensing 72, 1349–1358.
Holmgren J, Persson Å. 2004. Identifying species of individual
trees using airborne laser scanner. Remote Sensing of Environment 90, 415–423.
Hosoi F, Omasa K. 2006. Voxel-based 3-D modeling of individual
trees for estimating leaf area density using high-resolution
portable scanning lidar. IEEE Transactions on Geoscience andRemote Sensing 44, 3610–3618.
Hosoi F, Yoshimi K, Shimizu Y, Omasa K. 2005. 3-D
measurement of trees using a portable scanning lidar. Phyton –Annals Rei Botanicae 45, 497–500.
Houldcroft CJ, Campbell CL, Davenport IJ, Gurney RJ,
Holden N. 2005. Measurement of canopy geometry characteristics

Factors contributing to accuracy of lidar estimation of woody canopy LAD 3473
using LiDAR laser altimetry: a feasibility study. IEEE Transactions on Geoscience and Remote Sensing 43, 2270–2282.
Hyyppä J, Inkinen M. 1999. Detecting and estimating attributes
for single trees using laser scanner. Photogrammetric Journal of
Finland 16, 27–42.
Hyyppä J, Kelle O, Lehikoinen M, Inkinen M. 2001. A
segmentation-based method to retrieve stem volume estimatesfrom 3-D tree height models produced by laser scanners. IEEE
Transactions on Geoscience and Remote Sensing 39, 969–975.
Jonckheere I, Fleck S, Nackaerts K, Muys B, Coppin P,
Weiss M, Baret F. 2004. Review of methods for in situ leaf areaindex determination. Part I. Theories, sensors and hemisphericalphotography. Agricultural and Forest Meteorology 121, 19–35.
Jones HG. 1992. Plants and microclimate, 2nd edn. Cambridge:
Cambridge University Press.
Jones HG, Archer N, Rotenberg E, Casa R. 2003. Radiation
measurement for plant ecophysiology. Journal of Experimental
Botany 54, 879–889.
Lang ARG, Yueqin X. 1986. Estimation of leaf area index fromtransmission of direct sunlight in discontinuous canopies. Agricultural and Forest Meteorology 37, 229–243.
Larcher W. 2001. Physiological plant ecology, 4th edn. Heidelberg:
Springer.
Leblanc SG, Chen JM, Fernandes R, Deering DW, Conley A.
2005. Methodology comparison for canopy structure parameters
extraction from digital hemispherical photography in boreal
forests. Agricultural and Forest Meteorology 129, 187–207.
Lefsky MA, Cohen WB, Parker GG, Harding DJ. 2002. Lidar
remote sensing for ecosystem studies. Bioscience 52, 19–30.
Lovell JL, Jupp DLB, Culvenor DS, Coops NC. 2003. Using
airborne and ground-based ranging lidar to measure canopy
structure in Australian forests. Canadian Journal of Remote
Sensing 29, 607–622.
MacArthur RH, Horn HS. 1969. Foliage profile by verticalmeasurements. Ecology 50, 802–804.
Magnussen S, Boudewyn P. 1998. Derivations of stand heightsfrom airborne laser scanner data with canopy-based quantile
estimators. Canadian Journal of Forest Research 28, 1016–1031.
Monteith JL. 1973. Principles of environmental physics. London:
Edward Arnold.
Morsdorf F, Kötz B, Meier E, Itten KI, Allgöwer B. 2006.
Estimation of LAI and fractional cover from small footprint
airborne laser scanning data based on gap fraction. Remote
Sensing of Environment 104, 50–61.
Næsset E, Gobakken T, Holmgren J, Hyyppä H, Hyyppä J,
Maltamo M, Nilsson M, Olsson H, Persson Å, Söderman U.
2004. Laser scanning of forest resources: the Nordic experience.
Scandinavian Journal of Forest Research 19, 482–499.
Neumann HH, Hartog GD, Shaw RH. 1989. Leaf area measurements based on hemispheric photographs and leaf-litter collection
in a deciduous forest during autumn leaf-fall. Agricultural andForest Meteorology 45, 325–345.
Norman JM, Campbell GS. 1989. Canopy structure. In: Pearcy
RW, Ehleringer J, Mooney HA, Rundel PW, eds. Plant
physiological ecology: field methods and instrumentation.
London: Chapman and Hall, 301–325.
Omasa K, Akiyama Y, Ishigami Y, Yoshimi K. 2000. 3-D remotesensing of woody canopy heights using a scanning helicopterborne lidar system with high spatial resolution. Journal of Remote
Sensing Society of Japan 20, 394–406.
Omasa K, Hosoi F, Konishi A. 2007. 3D lidar imaging fordetecting and understanding plant responses and canopy structure.
Journal of Experimental Botany 58, 881–898.

Omasa K, Qiu GY, Watanuki K, Yoshimi K, Akiyama Y. 2003.
Accurate estimation of forest carbon stocks by 3-D remotesensing of individual trees. Environmental Science and Technology 37, 1198–1201.
Omasa K, Urano Y, Oguma H, Fujinuma Y. 2002. Mapping oftree position of Larix leptolepis woods and estimation of diameterat breast height (DBH) and biomass of the trees using range datameasured by a portable scanning lidar. Journal of Remote Sensing
Society of Japan 22, 550–557.
Parker GG, Harding DJ, Berger ML. 2004. A portable LIDAR
system for rapid determination of forest canopy structure. Journalof Applied Ecology 41, 755–767.
Radtke PJ, Bolstad PV. 2001. Laser point-quadrat sampling for
estimating foliage-height profiles in broad-leaved forests. Canadian Journal of Forest Research 31, 410–418.
Riaño D, Meier E, Allgöwer B, Chuvieco E, Ustin SL. 2003.
Modeling airborne laser scanning data for the spatial generationof critical forest parameters in fire behavior modeling. Remote
Sensing of Environment 86, 177–186.
Ross J. 1981. The radiation regime and architecture of plant
stands. The Hague: The Netherlands Dr W. Junk.
Schurr U, Walter A, Rascher U. 2006. Functional dynamics ofplant growth and photosynthesis—–from steady-state to dynamics—
from homogeneity to heterogeneity. Plant, Cell and Environment 29,
340–352.
Solberg S, Næsset E, Hanssen KH, Christiansen E. 2006.
Mapping defoliation during a severe insect attack on Scots pineusing airborne laser scanning. Remote Sensing of Environment
102, 364–376.
Stokes VJ, Morecroft MD, Morison JIL. 2006. Boundary layer
conductance for contrasting leaf shapes in a deciduous broadleaved
forest canopy. Agricultural and Forest Meteorology 139, 40–54.
Takeda T, Oguma H, Yone Y, Yamagata Y, Fujinuma Y. 2005.
Comparison of leaf area density measured by laser range finderand stratified clipping method. Phyton – Annals Rei Botanicae
45, 505–510.
Tanaka T, Park H, Hattori S. 2004. Measurement of forest
canopy structure by a laser plane range-finding method. Improvement of radiative resolution and examples of its application.
Agricultural and Forest Meteorology 125, 129–142.
Vanderbilt VC, Silva LF, Bauer ME. 1990. Canopy architecture
measured with a laser. Applied Optics 29, 99–106.
Warren-Wilson J. 1960. Inclined point quadrats. New Phytologist
59, 1–8.
Warren-Wilson J. 1963. Estimation of foliage denseness andfoliage angle by inclined point quadrats. Australian Journal of
Botany 11, 95–105.
Weiss M, Baret F, Smith GJ, Jonckheere I, Coppin P. 2004.
Review of methods for in situ leaf area index (LAI) determination. Part II. Estimation of LAI, errors and sampling. Agriculturaland Forest Meteorology 121, 37–53.
Welles JM, Cohen S. 1996. Canopy structure measurement by gap
fraction analysis using commercial instrumentation. Journal of
Experimental Botany 47, 1335–1342.
Welles JM, Norman JM. 1991. Instrument for indirect measurement of canopy architecture. Agronomy Journal 83, 818–825.
Yoshimi K, Hosoi F, Shimizu Y, Yamada H, Omasa K. 2004.
3-D measurement of terrain and woody canopy height using
portable scanning lidar. Eco-Engineering 16, 203–207.
Yu X, Hyyppä J, Kaartinen H, Maltamo M. 2004. Automatic
detection of harvested trees and determination of forest growthusing airborne laser scanning. Remote Sensing of Environment
90, 451–462.