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ISPRS Journal of Photogrammetry and Remote Sensing 64 (2009) 151–158

Contents lists available at ScienceDirect

ISPRS Journal of Photogrammetry and Remote Sensing
journal homepage: www.elsevier.com/locate/isprsjprs

Estimating vertical plant area density profile and growth parameters of a wheat
canopy at different growth stages using three-dimensional portable lidar imaging
Fumiki Hosoi, Kenji Omasa ∗Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japan

articleinfo

Article history:
Received 4 April 2008
Received in revised form
15 September 2008
Accepted 16 September 2008
Available online 18 October 2008
Keywords:
Carbon stock
Plant area density
Portable scanning lidar
Three-dimensional imaging
Voxel-based canopy profilinga b s t r a c t
Vertical plant area density profiles of wheat (Triticum aestivum L.) canopy at different growth stages(tillering, stem elongation, flowering, and ripening stages) were estimated using high-resolution portablescanning lidar based on the voxel-based canopy profiling method. The canopy was scanned threedimensionally by laser beams emitted from several measuring points surrounding the canopy. At the
ripening stage, the central azimuth angle was inclined about 23◦ to the row direction to avoid obstructionof the beam into the lower canopy by the upper part. Plant area density profiles were estimated, withroot mean square errors of 0.28–0.79 m2 m−3 at each growth stage and of 0.45 m2 m−3 across all growthstages. Plant area index was also estimated, with absolute errors of 4.7%–7.7% at each growth stage andof 6.1% across all growth stages. Based on lidar-derived plant area density, the area of each type of organ(stem, leaves, ears) per unit ground area was related to the actual dry weight of each organ type, andregression equations were obtained. The standard errors of the equations were 4.1 g m−2 for ears and26.6 g m−2 for stems and leaves. Based on these equations, the estimated total dry weight was from 63.3
to 279.4 g m−2 for ears and from 35.8 to 375.3 g m−2 for stems and leaves across the growth stages. Basedon the estimated dry weight at ripening and the ratio of carbon to dry weight in wheat plants, the carbon
stocks were 76.3 g C m−2 for grain, 225.0 g C m−2 for aboveground residue, and 301.3 g C m−2 for all
aboveground organs.
© 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by
Elsevier B.V. All rights reserved.

1. Introduction
The plant canopy plays important functional roles in cycling
materials and energy through photosynthesis and transpiration,
maintaining plant microclimates, and providing habitats forvarious taxa (Ehleringer and Field, 1993; Jones, 1992; Larcher,
2001; Monteith, 1973; Stokes et al., 2006). Determining the verticalstructure of the canopy is very important because the threedimensional (3-D) composition of the canopy helps to sustain
those functional roles (Graetz, 1990; Lefsky et al., 2002; Omasa
et al., 2007a; Schurr et al., 2006). Thus, many studies have been
conducted to measure the vertical structure of plant canopies(Eschenbach and Kappen, 1996; Garber and Maguire, 2005; Hosoi
and Omasa, 2006, 2007; Hutchison et al., 1986; Lefsky et al., 2002;
Omasa et al., 2007a; Parker et al., 2004; Radtke and Bolstad, 2001;
Sinoquet et al., 1998; Strachan and McCaughey, 1996; Wang et al.,
1992; Yang et al., 1993).
In crop canopies, the vertical structure has been investigatedand related to characteristics such as light distribution within the∗ Corresponding author. Tel.: +81 3 5841 5340; fax: +81 3 5841 8175.
E-mail address: aomasa@mail.ecc.u-tokyo.ac.jp (K. Omasa).

canopy, light-use efficiency, amount of yield, growth rate, andnitrogen allocation (Constable, 1986; Imai et al., 1994; Milroy
et al., 2001; Sassenrath-Cole, 1995; Takahashi and Nakaseko, 1993;
Yunusa et al., 1993). As noted in those studies, it is important toaccount for changes in the vertical structure of crops accompaniedwith canopy growth over time. The vertical canopy structure isoften represented by leaf area density (LAD) in each horizontal
layer, which is defined as one-sided leaf area per unit of horizontal
layer volume (Weiss et al., 2004). The leaf area index (LAI) is thencalculated by vertically integrating the LAD profile data. When it isdifficult for leaves and other aboveground organs (e.g., stems, ears)
to be separated, then rather than LAD or LAI, plant area density(PAD) or plant area index (PAI) are used.
To obtain LAD and PAD, stratified clipping of leaves has beenused as a direct method (Imai et al., 1994; Milroy et al., 2001;
Takahashi and Nakaseko, 1993). Although this direct method offers
accurate results, its application to crop measurement is often
limited because it is very labor intensive and its destructive naturedoes not permit the measurement of intact crop structure as plantschange over time with growth, which is an important parameterin crop studies. 3-D digitizing by ultrasonic or electromagnetic
devices has been used as another direct method. A pointer islocated at the position of each plant component and geometric0924-2716/$ – see front matter © 2008 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
doi:10.1016/j.isprsjprs.2008.09.003

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information is recorded as 3-D spatial coordinates (Drouet et al.,
1999; Sinoquet et al., 1991, 1998, 2007; Thanisawanyangkura
et al., 1997). Although this technique allows measurement of thedetailed 3-D structure of plants, including the vertical structure,through nondestructive means, this method is also labor intensive
because numerous components must be measured manually, point
by point. Therefore, this method is also unfavorable for repeatedmeasurements to capture the temporal changes of crop structurewith growth. As an indirect method, the gap-fraction method has
been widely applied to crop measurement and uses commerciallyavailable tools such as cameras with fish-eye lenses and opticalsensors (e.g., the Li-Cor LAI-2000 plant canopy analyzer; Behrens
and Diepenbrock, 2006; Bréda, 2003; Grantz et al., 1993; Hanan
and Bégué, 1995; Levy and Jarvis, 1999; Sassenrath-Cole, 1995;
Welles and Cohen, 1996). This method allows automatic datacollection and nondestructive measurement of canopy structureby using light transmittance through the canopy. The shortcomingis that the accuracy of the measurement is affected by the spatial
distribution of leaves and by sunlight conditions (Chason et al.,
1991; Jonckheere et al., 2004).
Light detection and ranging (Lidar), which is an active remotesensing technique that uses a laser scanner, has been applied to
canopy measurements (Brandtberg et al., 2003; Harding et al.,
2001; Holmgren and Persson, 2004; Hosoi et al., 2005; Hosoi andOmasa, 2006, 2007; Hyyppä et al., 2001; Lefsky et al., 2002; Næsset
et al., 2004; Omasa et al., 2000, 2002, 2003, 2007a,b; Riaño et al.,2003). Lidar can measure the distance between the sensor anda target based on the elapsed time between the emission andreturn of laser pulses (the time-of-flight method) or based on
trigonometry (the optical-probe or light-section methods), so that
3-D information of the target can be obtained. Several researchers
have attempted to measure vertical foliage profiles using portableground-based nonscanning lidar (Parker et al., 2004; Radtke and
Bolstad, 2001). At present, portable ground-based scanning lidar
is more often used for the measurement because it allows more
efficient data collection than the nonscanning type (Henning andRadtke, 2006; Hosoi and Omasa, 2006, 2007; Lovell et al., 2003;
Omasa et al., 2002, 2007a,b; Takeda et al., 2005; Tanaka et al., 2004;
Urano and Omasa, 2003; Van der Zande et al., 2006).
Portable ground-based scanning lidar has several beneficial
features. For instance, it is nondestructive and, because it isan active sensor, measurements are not affected by the light
conditions in the field. Many 3-D data for a crop can be recorded
quickly and automatically as 3-D point-cloud data. Finally,
portable ground-based scanning lidar is suitable for repeatedmeasurements over time due to its efficient data collection
and portability. Thus, this technology promises to overcome theshortcomings of the conventional means of measuring the verticalstructure of crops. However, portable scanning lidar has beenused mainly for measurement of forest canopies and hardly has
been utilized for crop measurement, because many crops havesmall canopies and most of the commercially available portable
lidar systems were insufficient for the detailed measurement ofcrop canopy in terms of scan and range resolution (typically∼cm). Recently, we demonstrated that the vertical LAD profileof small individual trees and 3-D shapes of individual vegetable
crops can be measured accurately using high-resolution portablescanning lidar, which has a range resolution of about 1 mm at ameasurement range of about 5 m (Hosoi and Omasa, 2006; Omasa
et al., 2007a). These findings suggest that high-resolution portablescanning lidar would provide accurate estimation of the verticalstructure of crops.
In this study, the vertical canopy profiles of a crop (wheat,
Triticum aestivum L.) were estimated at different growth stages
using high-resolution portable scanning lidar. PAD was the variable
used to express the vertical canopy profile. The estimation results

were compared with directly measured values to validate theapproach. PAI was also estimated by integrating the PAD estimatesvertically and was compared with the directly measured value.
Based on our findings, ways to obtain growth parameters related
to the lidar-derived PAD, namely the dry weight of each organ typeand carbon stocks were proposed.
2. Material and methods
2.1. Plant material
The experiment was conducted at an experimental farm in
Ibaraki Prefecture, 40 km northeast of central Metropolitan Tokyo,
Japan (36◦ 03’N, 140◦ 04’E). Winter wheat (T. aestivum cv. Norin 61)
seeds were sown on 2 November 2005 using 90 kg seed grain/haand 50-cm row spacing. Four plots (1 m × 1 m, see Fig. 1A) were
established on the farm for measurements of four different growthstages.
2.2. Direct measurements
To obtain validation data, LAD and PAD values were directly
measured in each horizontal layer by stratified clipping withineach plot on 6 April, 26 April, 16 May, and 5 June 2006 (155,
175, 195, and 215 days after sowing [DAS], respectively), whichcorresponded to the tillering, stem elongation, flowering, andripening stages, respectively. On each measurement date, all theplants within each plot were clipped at each 10-cm thickness
horizontal layer and the clippings were separated into each typeof aboveground organ (leaf, stem, and ear). Areas of leaf, stem, andear in each horizontal layer were measured using a commerciallyavailable desktop scanner (FB636U, Canon, Inc., Japan); each typeof organ in each layer was scanned as a JPEG image (826 ×1165 pixels) and the areas were determined by multiplying thenumber of pixels of each type of organ in each layer by the area
per pixel (Hosoi and Omasa, 2006). PAD of each type of organ ineach layer, which is defined as the projected area of each type oforgan per unit of horizontal layer volume, was derived by dividing
the area of each organ type by the horizontal layer volume (0.1 m3 :1 m × 1 m in horizontal area × 0.1 m in thickness). PAI was
obtained as the vertical integration of PAD of all organs. LAI was
also obtained by vertically integrating PAD of leaves (i.e., LAD). In
addition, to examine the proportion of PAD of each organ type overtime, the PAD values of each organ type were averaged verticallyat each growth stage, and the ratio of the mean PAD of each organ
type to that of all organs was calculated at each growth stage. Afterthe PAD measurement, all of the organs were dried in an oven at
80 ◦ C for 3 days, and the dry weight of each type of organ in eachlayer was measured.
2.3. Lidar measurements
A portable high-resolution scanning lidar that calculates
distances based on trigonometry (a modified TDS-130L 3-D laser
scanner; Pulstec Industrial Co., Ltd., Japan) was used to measure
the canopy structure. The measurement was carried out prior to
the destructive sampling described above. The lidar’s measurable
range is 3.5–10 m. The range and scan resolutions are about 1
and 2 mm, respectively, at a measurement range of about 5 m. A
rotating mount with a stepper motor and a galvano mirror within
the lidar head automated the horizontal and vertical scanning.
Fig. 1A illustrates an aerial view of the wheat canopy
measurement by the portable scanning lidar, with several scanning
positions surrounding the canopy. Arrows A1–A4 and B1–B4 in
Fig. 1A show the directions corresponding to the central azimuth
angles of laser beams (ϕc in Fig. 1A) emitted from each of theAuthor's personal copy

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153

Fig. 1. Schematic view of wheat canopy measurement by high-resolution portable scanning lidar. (A) Aerial view. ϕc represents the central azimuth angle of laser
beams. Arrows show the directions corresponding to the central azimuth angles for beams emitted from each measurement position. The rectangle gray region shows
the measurement plot. ∆L and ∆Le represent row spacing and effective row spacing. α represents angle between the directions of the central azimuth angles (B1–B4) andthe row direction. (B) Side view. θc and ∆θ show the central zenith angle and the zenith scan angle of the laser beams.

measurement positions. In this study, the azimuth angle wasdefined as the angle between azimuth direction of a laser beam androw direction. The central azimuth angle refers to the central angleof the azimuthal laser beam scan. Fig. 1B shows a side view of thewheat canopy measurement. A lidar head was placed at a height of
2.5 m and the central zenith angle (θc ), which is the center of theangle of the zenith laser beam scan, and its scan angle (∆θ ) were
set to 57.5◦ and 5◦ , respectively.
On 6 April, 26 April, and 16 May, directions A1 to A4, whichwere parallel and perpendicular to the row direction, were applied.
On 5 June, directions B1–B4, which inclined about 23◦ to the row
direction (corresponding to α = 23◦ in Fig. 1A), were applied toincrease the number of laser beams that reached the lower layersof the canopy.
2.4. Computation of plant area density
PAD computation used the Voxel-based Canopy Profiling
method (VCP method; Hosoi and Omasa, 2006). The complete
data set for each date was composed of point-cloud data obtained
from each of the four central azimuth angles of laser beams. The
individual coordinate systems for these data were registered into asingle point-cloud data set with a common 3-D coordinate systemfor each measurement date using the iterative closest-point
algorithm (Besl and McKay, 1992). All points in the registered data
set were converted into voxel coordinates for each measurement
date. In this experiment, voxel element size, which depended on
the range and scan resolution of the lidar, was set to 1 mm.
The selection of the voxel element size comparable to the lidar
resolution (about 1 and 2 mm) is necessary for the lidar data to befaithfully reproduced as voxels in the voxel array. Voxels converted
from points within the registered data set were assigned 1 as
the attribute value. Next, all laser beams emitted from the lidar
positions were traced within the voxel array in accordance withthe actual laser beam angles. Voxels through which one or morelaser beams passed without touching the canopy were assigned 2
as the attribute value (see Hosoi and Omasa (2006)).
Based on the attribute values, PAD was computed in eachhorizontal layer using the following equation:
PAD =cos θcG(θc )·mh+∆H

nI (k).
∆H k=m nI (k) + nP (k)
1

X

h

(1)

Eq. (1) is derived from the equation for LAD in Hosoi and Omasa(2006). In Eq. (1), LAD was replaced with PAD and θc is the central
zenith angle of laser beams,nI (k) and nP (k) are the numbers ofvoxels with attributes of 1 and 2 in the kth horizontal layer of thevoxel array, respectively. nI (k) and nP (k) represent the number oflaser beams intercepted by canopy in the kth layer and the numberof laser beams that passed through the kth layer, respectively.
Thus, nI (k) + nP (k) represents the total number of incident laser
beams that reach the kth layer. ∆His the horizontal layer thickness(=0.1 m), and mh and mh+∆H are the voxel coordinates on thevertical axis equivalent to the height h and h + ∆H in orthogonal
coordinates (h = mh × ∆k). G(θc ) is the mean projection of aunit leaf area on a plane perpendicular to the direction of the laser
beam at θc (Jonckheere et al., 2004; Norman and Campbell, 1989;
Weiss et al., 2004; Welles and Norman, 1991). Eq. (1) is analogous
to the equation of radiation transfer through canopy in the case ofneglecting the scattering term (Anisimov and Fukshansky, 1993;
Ross, 1981). nI (k) + nP (k) and nI (k) in Eq. (1) correspond to theradiation intensity and the attenuation of the radiation intensity
in the radiation transfer equation. The term cos(θc )[G(θc )]−1 is acorrection factor for the influence of leaf inclination angle and laser
beam direction. Normally, measurement of the distribution of leaf
inclination angles is required to determine the correction factor ofcos(θc )[G(θc )]−1 . However, the central zenith angle of 57.5◦ usedin this study was the particular angle at which cos(θc )[G(θc )]−1
can be considered nearly independent of leaf inclination, so thecorrection factor was approximated by the constant value of 1.1(Jonckheere et al., 2004; Warren-Wilson, 1960; Weiss et al., 2004).
From the PAD estimates computed for each horizontal layer, thevertical PAD profile for each measurement date was obtained andcompared with the directly measured one. The PAI estimate at eachgrowth stage was obtained by vertical integration of PAD estimatesand compared with the directly measured value.
2.5. Estimation of dry weight and carbon stock from lidar-derived
plant area density
Ears had developed in the upper layers on 16 May and 5 June.
By multiplying the lidar-derived PAD at the upper layers by theratio of ear to total surface area of each layer on each date obtained
from direct measurements, the PAD estimates corresponding toears were separated. The PAD was converted into area of ears perunit ground area in each horizontal layer by multiplying the PAD
values by layer thickness (=0.1 m). The lidar-derived area of ears

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Fig. 2. Directly measured vertical PAD and dry weight distributions of leaf, stem, and ear within a wheat canopy by stratified clipping: (A) 6 April, 155 DAS, tillering stage;(B) 26 April, 175 DAS, stem elongation; (C) 16 May, 195 DAS, flowering; (D) 5 June, 215 DAS, ripening.

was correlated with the corresponding actual ear dry weight, and aregression equation and standard error were obtained. By applying
each lidar-derived area of ears to the regression equation, theestimate of ear dry weight in each layer was obtained. Estimatesof total dry weight of ears for 16 May (flowering) and 5 June(ripening) were obtained by summing up the estimated dry weightof each layer for each date.
The lidar-derived total area of stems and leaves at each growthstage was obtained by multiplying the lidar-derived PAD by thelayer thickness and integrating the values vertically at each growthstage. The total area of stems and leaves on 16 May and 5 June
was calculated by subtracting the PAD value of ears from the total
for each date. The lidar-derived area of stems and leaves wascorrelated with the corresponding actual stem and leaf dry weight,
and a regression equation and standard error were calculated. By
applying each lidar-derived total area of stems and leaves to theregression equation, estimates of the total dry weight of leaves andstems at each growth stage were obtained.
The grain dry weight estimate for 5 June (ripening stage) was
separated from the total ear dry weight estimate for that date byusing the ratio of grain to ear, 0.7 (Kernan et al., 1984; Sivakumar
et al., 2001). The carbon stock within the grain was estimated bymultiplying the grain dry weight by the ratio of carbon to dry
weight in wheat grain, 0.39 (Huang et al., 2007). Chaff dry weight
estimate (i.e., ear dry weight minus grain dry weight) and the total
dry weight estimate of leaves and stems on 5 June were summed toobtain the dry weight estimate of aboveground residue. The carbon
stock within the aboveground residue on 5 June was estimated bymultiplying the dry weight by the ratio of carbon to dry weight in
the residue, 0.49 (Huang et al., 2007; IPCC, 2000). By adding thecarbon stocks of grain to that of the residue, the total amount ofaboveground carbon stock on 5 June was obtained.
3. Results and discussion
The directly measured values of PAD and dry weight of eachtype of organ are shown in Fig. 2. PAD was distributed at low

Fig. 3. Change in proportion of directly measured PAD of each organ type over time.
The proportions were derived from the vertical mean values of PAD of each organ
type at each growth stage.

heights on 6 April (Fig. 2A), but the height distribution expanded
upward with stem elongation (Fig. 2B–D). Peaks in the PAD
distribution were observed in the lower part of the canopy on
16 May and 5 June and ears shared the upper part of the canopy
at those stages (Fig. 2C, D). The proportion of PAD of each organ
type changed according to stem and ear development over time
(Fig. 3).While leaves accounted for the greatest proportion at thetillering and stem elongation stages (6 and 26 April), the proportionof leaves decreased and that of stems and ears increased at theflowering and ripening stages (16 May and 5 June). The change ofthe proportion occurred because the development of leaves had
already stopped during the latter two stages but stems and ears
continued to develop. The values of PAI on 6 April, 26 April, 16 May,and 5 June were 0.90, 2.21, 2.50, and 2.94 m2 m−2 , respectively,
and the values of LAI on these dates were 0.83, 1.70, 1.37, and1.38 m2 m−2 , respectively. The dry weight in each layer changed
as the canopy grew (Fig. 2). The contribution of leaves to total
dry weight was less than that of other organs, except on 6 April.
During the flowering and ripening stages, most of the dry weightAuthor's personal copy

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155

Fig. 4. A 3-D image of wheat canopy on 16 May measured by high-resolution portable scanning lidar. This image was obtained after registration of the images measured
by the four central azimuth angles of laser beams (A1–A4 in Fig. 1A). Shading effect was added to this image by changing brightness of each point.

Fig. 5. Comparison of profiles among lidar-derived PAD, directly measured PAD, and directly measured LAD at each growth stage: (A) 6 April, 155 DAS, tillering stage;(B) 26 April, 175 DAS, stem elongation; (C) 16 May, 195 DAS, flowering; (D) 5 June, 215 DAS, ripening.

was composed of ears and stems. The values of total dry weight ofall organs on 6 April, 26 April, 16 May, and 5 June were 33.4, 143.8,
345.0, and 606.6 g m−2 , respectively. Total ear dry weights for 15
May and 5 June were 76.4 and 292.1 g m−2 .
Fig. 4 shows a 3-D lidar image of the wheat canopy on
16 May after registration of the images measured by the four
central azimuth angles of laser beams (A1–A4 in Fig. 1A). In theimage, shading effect was added by changing brightness of eachpoint. Each leaf shape is distinguishable due to the precise image
obtained by high-resolution portable lidar.
Profiles of the lidar-derived PAD estimates and directly
measured PAD and LAD are compared in Fig. 5. Changes in thevertical PAD profiles that accompanied the development of plantorgans (i.e., leaf development, Fig. 5A, B; stem elongation, Fig. 5B,
C; ear development, Fig. 5C, D) was captured well by the accurate
PAD estimates, except for underestimation of the lower layers on 6

and 26 April and 16 May. On 5 June, such an underestimation of thelower part was hardly observed, while a slight overestimation wasobserved in the upper region (>60-cm height). The root-meansquare errors (RMSE) of PAD estimates were 0.38, 0.79, 0.28, and0.35 m2 m−3 for 6 April, 26 April, 16 May, and 5 June, respectively,
and 0.45 m2 m−3 across all the growth stages. Absolute errorsof PAI estimates were 6.5%, 5.6%, 7.7%, and 4.7% for these dates,
respectively, and 6.1% across all the growth stages. Fig. 6 illustrates
the overall relationship between directly measured and lidarderived PAD across all the growth stages; the lidar-derived values
agreed well with the directly measured values (R2 = 0.95).
The following quadratic regression equation shows the relationship between ear dry weight DWe (g m−2 ) and lidar-derived
area of ears per unit ground area Ae (m2 m−2 ):DWe = 4854.4A2e + 202.3Ae(2)Author's personal copy

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values in the lower layers would cause obstruction to laser beam
penetration into the internal canopy. On 16 May, directly measured
LAD values in the lower layers decreased compared with those on
6 and 26 April (Fig. 2C). Although this would facilitate laser beam
penetration into the internal canopy of the lower layers, the upper
part of the canopy began to obstruct laser beam incidences into thelower layers, in particular for the laser beams directed along A2 andA4 (Fig. 1A).
Fig. 8 illustrates the effects of canopy height and row spacing
on laser beam obstruction. At short canopy height, laser beams canreach from the upper to the lower part of the canopy (Fig. 8A). As
the canopy grows taller, however, laser beams cannot reach thelower part of the canopy because the upper part obstructs the beam
(Fig. 8B). The case in Fig. 8A satisfies the following condition:
Fig. 6. Relationship between directly measured and lidar-derived PAD through all
growth stages. RMSE, root-mean-square error.

(R2 = 0.96, SE = 4.1 g m−2 ; Fig. 7A). Using Eq. (2), the total
ear dry weight was estimated as 63.3 and 279.4 g m−2 for 16
May and 5 June, respectively. The following regression equation
represents the relationship between dry weight of stems andleaves DWs+l (g m−2 ) and lidar-derived area of stems and leaves
per unit ground area As+l (m2 m−2 ):DWs+l = 12.714 exp(1.228As+l ).

(3)(R = 0.94, SE = 26.6 g m ; Fig. 7B). From Eq. (3), the total dry
weight of stems and leaves was estimated as 35.8, 164.7, 183.7, and375.3 g m−2 for 6 April, 26 April, 16 May, and 5 June, respectively.
Based on these dry weight estimates, the carbon stock of grain on
5 June was estimated to be 76.3 g C m−2 and that of abovegroundresidue was 225.0 g C m−2 , such that the total amount of carbon
stocks for all aboveground organs on 5 June was estimated to be
301.3 g C m−2 .
We previously reported that several measurement points
surrounding the canopy and optimally inclined laser beams
facilitate full laser beam penetration into the internal canopy,
resulting in accurate estimation of the vertical canopy profiles(Hosoi and Omasa, 2006). This was also the case in the present
study, as shown by the overall agreement between directly
measured and lidar-derived PAD values (Figs. 5 and 6). Having thelaser beams inclined at a central zenith angle of 57.5◦ served well
not only for laser beam penetration but also for effective correctionof leaf inclination without leaf angle measurements (Hosoi andOmasa, 2006, 2007; Jonckheere et al., 2004; Weiss et al., 2004).
At the lowest layers on 6 and 26 April, it appears that there wasan insufficient number of laser beams reaching the stems locatedin the inner canopy; on these dates leaves with the high LAD
2

−2

h≤

∆L

(4)

tan θcwhere h is the canopy height, ∆L is the row spacing, and θc is thecentral zenith angle of the laser beams. In this case, the zenith
angles of all incident laser beams are approximated by θc . Thus,
underestimation of PAD in the lower layers on 16 May (Fig. 5C)
would be due to obstruction of the beams by the upper canopy.
However, obstruction by the upper canopy can be avoided if therow spacing is expanded such that condition (4) is satisfied (seeFig. 8C). In the present study, instead of expanding row spacing, on
5 June the directions of the central azimuth angles were inclined
about 23◦ with respect to the row direction (B1–B4 in Fig. 1A). In
this configuration, effective row spacing ∆Le (Fig. 1A) is expressed
as follows:∆ Le =

∆L

(5)

sin α

where α is the angle between the laser beam direction and therow direction. ∆Le was estimated to be 2.6 times longer than
∆L when α = 23◦ , as in the present experiment. That is,changing the azimuth inclination of the laser beam with respect
to the row direction would have the same effect as expanding
the row spacing. This change likely led to the improvement of
PAD estimates in the lower layers on 5 June. The condition of α
necessary to avoid obstruction by the upper canopy (αc ) is given
by replacing ∆L in Eq. (4) with ∆Le in Eq. (5), as follows:α ≤ αc = arcsin

∆L
h tan θc



.

(6)

In the measurement on 5 June, αc was calculated as 21◦ . The
measurement positions and angular setting of lidar on 5 June were
determined based on the criterion that α be as close as possible
to αc .

Fig. 7. Relationships between lidar-derived area per unit ground area and actual dry weight. (A) Ears (B) Stems and Leaves. SE, standard error.

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Fig. 8. The effects of canopy height and row spacing on laser beam obstruction. (A)At short canopy height, laser beams can reach from the upper to the lower part ofthe canopy. ∆L is the row spacing, h is the canopy height, and θc is the central zenith
angle of laser beams. (B) At tall canopy height, laser beams cannot reach the lower
part of the canopy. (C) By expanding row spacing, laser beams can reach the lower
part of canopy.

4. Conclusion
The present findings indicate that the dry weight of ears, leaves
and stems can be estimated from the regression equation between
the dry weight and lidar-derived area of each type of organ.
Dry weight estimates can provide useful information about thegrowing conditions of crops, and lidar-derived data could be used
for more effective agricultural management.
Accurate estimation of carbon stocks in plants is important not
only to study photosynthetic characteristics but also to estimate
global carbon budget affected by recent increase in atmospheric
CO2 concentration that causes global changes. Thus far, lidar
has been used mainly for the estimation of forest carbon stocks(Kotchenova et al., 2004; Lim et al., 2003; Omasa et al., 2002, 2003;
Patenaude et al., 2004). However, the present study showed thatportable scanning lidar also can be used to estimate carbon stocks
in a crop canopy. Thus, lidar-derived data sets can be utilized forthe estimation of carbon budgets in agricultural regions.
This study demonstrated that vertical PAD profiles of a crop(wheat) canopy at different growth stages can be estimated byhigh-resolution portable scanning lidar based on the VCP method.
In addition, the dry weight and carbon stocks of each organ typecan be estimated based on the PAD-derived area of each organ
type and the relationship between dry weight and area. To enhance
the applicability of portable scanning lidar on estimation of PAD
and other growth parameters, additional studies that apply thepresent method to different growing conditions in other fields anddifferent kinds of crops are needed. Moreover, processing lidar datawith additional information, such as shape, color, and texture, may
allow lidar data to separate out each organ type so that PAD may be
estimated for each organ type. In particular, ears have a distinctive
shape and color compared with other organs, so the use of thisadditional information might allow the efficient separation of ears
from other plant organs.
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