CSIRO PUBLISHING

Functional Plant Biology, 2009, 36, 998–1005

www.publish.csiro.au/journals/fpb

Detecting seasonal change of broad-leaved woody canopy
leaf area density proﬁle using 3D portable LIDAR imaging
Fumiki Hosoi A and Kenji Omasa A,BA

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku,
Tokyo 113-8657, Japan.
B
Corresponding author. Email: aomasa@mail.ecc.u-tokyo.ac.jp
This paper originates from a presentation at the 1st International Plant Phenomics Symposium,
Canberra, Australia, April 2009.

Abstract. Seasonal change of vertical leaf area density (LAD) proﬁles of woody canopy broad-leaved trees(Zelkova serrata [Thunberg] Makino) was estimated using 3D portable scanning light detection and ranging (LIDAR)
imaging. First, 3D point cloud data for the canopy were collected using a portable LIDAR in spring, summer, autumn andwinter. For data collection, the canopy was evenly scanned by the LIDAR from three positions 10 m above the ground.
Next, the vertical LAD proﬁle in each season was computed from the LIDAR data using the voxel-based canopy proﬁling(VCP) method. For the computation, non-photosynthetic tissues were eliminated using the LIDAR data obtained during
winter. Inﬂuence of leaf inclination angle (LIA) on LAD estimation was corrected by LIA data measured by a highresolution portable scanning LIDAR. The resultant proﬁles showed that LAD values tended to increase at the upper canopy
from spring to summer and decrease at the middle and lower canopy from summer to autumn. Moreover, LIDAR-derived
LIA distributions were compared among different seasons. LIA showed an even distribution in spring but changed to aplanophile distribution in summer. In autumn, the angles in the <30 class decreased and those between the 30 and 40classes
increased.
Additional keywords: Japanese zelkova, leaf area index (LAI), leaf inclination angle (LIA), voxel-based canopy proﬁling(VCP).

Introduction
The plant canopy plays important functional roles in cycling
materials and energy through photosynthesis and transpiration,
maintaining plant microclimates and providing habitats forvarious species (Monteith 1973; Jones 1992; Ehleringer andField 1993; Larcher 2001). Determining the vertical structureof the canopy is very important because the 3D composition of
the canopy helps to sustain these functional roles (Graetz 1990;
Lefsky et al. 2002; Schurr et al. 2006; Omasa et al. 2007).
Moreover, it is important to measure change in the verticalstructure accompanied by canopy growth over time in order tounderstand plant phenological phenomena and long-time scale
plant dynamics.
The vertical canopy structure is often represented by the leaf
area density (LAD) in each horizontal layer, which is deﬁned as
one-sided leaf area per unit of horizontal layer volume (Weisset al. 2004). The leaf area index (LAI) is then calculated byvertically integrating the LAD proﬁle data. To obtain LAD,
stratiﬁed clipping of leaves has been used as a direct method(Monsi and Saeki 1953). Although this direct method offers
accurate results, it is labour intensive and its destructive naturedoes not permit intact and repeated measurements of canopy
 CSIRO 2009

structure as plants change over time with growth. 3D digitising
by ultrasonic or electromagnetic devices has been used
as another direct method where a pointer is located at theposition of each plant component and geometric informationis recorded as 3D spatial coordinates (Sinoquet et al. 1991,
1998, 2007; Thanisawanyangkura et al. 1997; Drouet et al.
1999). Although this technique allows non-destructive
measurement of the detailed 3D structure of plants, it is labour
intensive and is therefore unfavourable to take repeatedmeasurements to capture the temporal changes of the canopy
structure. As an indirect method, the gap-fraction method has
been widely applied to measure canopies (e.g. Li-Cor LAI-2000
plant canopy analyser; Lang and Yueqin 1986; Neumann et al.
1989; Norman and Campbell 1989; Chason et al. 1991; Welles
and Norman 1991; Chen and Cihlar 1995). This method allows
automatic data collection and non-destructive measurement of
the canopy structure by using light transmittance through the
canopy. However, the accuracy of the measurement is affectedby the spatial distribution of leaves and by sunlight conditions(Chason et al. 1991; Jonckheere et al. 2004). As describedabove, measuring temporal changes of vertical canopy
structures remains difﬁcult.

10.1071/FP09113

1445-4408/09/110998

Detecting seasonal LAD proﬁle using LIDAR imaging

Recently, light detection and ranging (LIDAR) imaging,which is an active remote-sensing technique that uses a laser
scanner, has been used for canopy measurements (Omasa et al.
2000, 2002, 2003, 2007, 2008; Harding et al. 2001; Hyyppä
et al. 2001; Lefsky et al. 2002; Brandtberg et al. 2003; Riaño et al.
2003; Holmgren and Persson 2004; Næsset et al. 2004; Hosoi
et al. 2005, 2008; Hosoi and Omasa 2006, 2007, 2009a; Nakai
et al. 2009). LIDAR imaging can measure the distance between
the sensor and a target based on the elapsed time between theemission and return of laser pulses (the time-of-ﬂight method)or based on trigonometry (the optical-probe or light-section
methods), so that 3D information of the target can be obtained.
Several researchers have attempted to measure vertical foliage
proﬁles using portable ground-based non-scanning and scanning
LIDAR (Radtke and Bolstad 2001; Lovell et al. 2003; Parker et al.
2004; Tanaka et al. 2004; Takeda et al. 2005, 2008; Henningand Radtke 2006; Hosoi and Omasa 2006, 2007, 2009a;
Van der Zande et al. 2006; Omasa et al. 2007). A portableground-based scanning LIDAR has several beneﬁcial featuresthat overcome weak points in conventional ways of measuring
temporal changes of vertical canopy structures. First, the nondestructiveness allows intact canopy measurement over time.
Second, it is an active sensor, which means that measurements are
not affected by the light condition in the ﬁeld. Third, the ability toscan horizontally and vertically allows efﬁcient data collection,
where many 3D data of the canopy can be recorded quickly andautomatically as 3D point-cloud data. Fourth, the high-ranging
accuracy and resolution provide faithful 3D information of the
canopy. Finally, its portability facilitates repeated measurements
over time. Recently, we utilised the above described beneﬁts ofthe portable scanning LIDAR and demonstrated that the vertical
LAD proﬁle of a broad-leaved woody canopy can be measured
accurately by optimising the measurement settings (Hosoi andOmasa 2007). Although the measurement was conducted only
during summer, temporal changes in the vertical LAD proﬁlecould be captured by repeating the measurement over time. In the
study by Hosoi and Omasa (2007), leaf inclination angle (LIA)
distribution was also measured using a high-resolution portablescanning LIDAR and correcting the inﬂuence of LIA on the
LAD estimation. Besides such use, LIA distribution itself can beconsidered as one of the measures to express 3D canopy structures
and thus the temporal change offers useful information aboutplant activities (Comstock and Mahall 1985; Imai et al. 1994;
Gratani and Bombelli 2000; Barchuk and Valiente-Banuet 2006).
Temporal measurement has been difﬁcult using conventionalways, e.g. use of clinometers or 3D digitisers (Norman andCampbell 1989; Sinoquet et al. 1998; Gratani and Ghia 2002)
because of the labour-intensiveness. By repeatedly applying the
LIDAR-based method over time, temporal changes of LIA
distribution could also be captured more easily and accurately.
In the present study, vertical LAD proﬁles of the broad-leaved
canopy of Zelkova serrata [Thunberg] Makino were estimatedover three different seasons using a portable scanning LIDAR. By
comparing the LAD estimation between each season, the abilityof the portable scanning LIDAR to detect seasonal changesof the vertical LAD proﬁle was evaluated. In addition, LIA
distributions were measured during each season using a highresolution portable scanning LIDAR. While they were used
for correcting the inﬂuence of LIA on LAD estimation, their

Functional Plant Biology

999

tendencies were compared between three different seasons.
The usefulness of the LIDAR-based seasonal measurements of
LIA distributions was then evaluated.
Materials and methods
Study site
The study was carried out in a mixed plantation with nearly
ﬂat topography in Ibaraki Prefecture, 40 km north-east of central
Metropolitan Tokyo, Japan (35590 N, 140020 E). The dominant
tree species were Japanese cedar (Cryptomeria japonica [L.f.]
D. Don), Japanese red pine (Pinus densiﬂora Siebold andZuccarini), ginkgo (Ginkgo biloba Linnaeus) and Japanesezelkova (Zelkova serrata [Thunberg] Makino). The understory
included grasses, forbs and young evergreen trees such as
Camellia japonica Linnaeus, Ilex integra Thunberg andTernstroemia gymnanthera Sprague. A Japanese zelkova
canopy was chosen for measurement and a 4  4 m
measurement plot was established beneath the canopy (Fig. 1a).
Direct measurement of leaf area density (LAD)
The actual LAD of the canopy was measured directly by stratiﬁedclipping in September 2005, the month following the lightdetection and ranging (LIDAR) measurements of the leafy
canopy. The measurement plot was divided into four 2  2 m
quadrats (Fig. 1b) and the region above the plot was divided into
64 cells (each 2  2  0.5 m high) between the heights of 5 and13 m (Fig. 1c). Each cell was distinguished by a reference number(l, m, n) (Fig. 1c), where l, m and n correspond to the x, y and z
coordinate values, respectively. All leaves of the zelkova canopy
in the measurement plot were clipped manually using a cherry
picker and labelled according to the cell from which they came. It
took approximately 1 month to ﬁnish the clipping. Only leaves
were clipped and attention was paid not to cut the branches orshoots. To obtain the leaf area, part of the clipped leaves wasscanned as JPEG images using a desktop scanner (FB636U,
Canon Inc., Tokyo, Japan) and dried in an oven at 80C for
3 days. The leaf area and corresponding dry weight were used to
calculate the area : dry weight ratio (i.e. the speciﬁc leaf area,
SLA). All other leaves were also dried under the same conditions
to obtain the dry weight of each of the cells. The leaf area in eachcell was then determined by multiplying the dry weight of a cell by
the SLA, and the LAD of each cell was calculated by dividing theleaf area by the cell volume (2 m3). By integrating the LAD values
vertically, the actual LAI values were obtained for each quadrat
and for the whole canopy. Descriptive variables for the Japanesezelkova canopy were: height, 12.5 m; LAI, 4.59 m2 m–2; mean
LAD, 0.57 m2 m–3 ; and mean SLA, 11.9 m2 kg–1. More details
about the direct measurement can be found in Hosoi andOmasa (2007).
Measurements using two types of portablescanning LIDARs
A portable scanning instrument for LIDAR imaging(LPM-25HA, RIEGL, Horn, Austria) was used for LAD
measurement. This instrument was able to obtain the distanceto the surface of any object between 2 and 60 m from the sensor
by measuring the elapsed time between the emitted and returned
laser pulses (the time-of-ﬂight method) with an accuracy of1000

Functional Plant Biology

F. Hosoi and K. Omasa

(a)

(c)

(b)

Centre(Xmin, Ymin, Zmin)

Fig. 1. A 4  4 m plot established at the study site. (a) An aerial photograph of the study site. The white rectangle shows thelocation of the measurement plot that includes a Japanese zelkova canopy. (b) Quadrats established on the ground in themeasurement plot. Each quadrat is 2  2 m. Black arrows show the directions in which LIDAR scanning were performed.
(c) Illustration of cells established within the measurement plot and LIDAR measurement using a cherry picker. Each cell
was distinguished by a reference number (l, m, n), where l, m and n correspond to the x, y and z coordinate values, respectively.
qc represents the central zenith angle.

8 mm. A rotating mount driven by built-in stepper motors with
0.009 of accuracy panned and tilted the LIDAR head. The
Japanese zelkova canopy within the measurement plot wasscanned from three positions 10 m above the ground (position
1 on side A and positions 2 and 3 on side B; Fig. 1b) using thecherry picker in August 2005 and May, August and November
2006 (Fig. 1c). On each date, it took several hours to measure thecanopy from the three positions. For all four measurements, thecentral angle of zenith laser scan (= the central zenith angle, qc;
Fig. 1c) was 90.0 (i.e. horizontal direction) and thecorresponding scan zenith angle was 33.1. The azimuth
scan angle ranged from 18 to 28. The average distance
between the three LIDAR positions 10 m above the ground and apoint 10 m above the centre of the measurement plot was 12.8 m.
Those measurement settings were determined based on the resultsin Hosoi and Omasa (2007), which offered the most accurate LAD
estimation. In addition to the measurements of the leafy canopy,
the same canopy was measured from the six ground positions in
February 2006, when it was leaﬂess. The central zenith angle andthe scan zenith angle were 57.8 and 28. More details about themeasurement can be found in Hosoi and Omasa (2007).
Another portable ﬁne-resolution scanning LIDAR that
calculates distances based on trigonometry (a modiﬁed TDS130 L 3D laser scanner; Pulstec Industrial Co. Ltd, Hamamatsu,
Japan; in which the laser beam wavelength for ranging waschanged from 656 to 785 nm) was used to measure LIA(Hosoi and Omasa 2007, 2009b). The LIDAR’s measurable
range is 3.5 to 10 m. The range and scan resolutions are~1 and 2 mm, respectively, at a measurement range of ~5 m.
A rotating mount with a stepper motor and a galvano mirror within
the LIDAR head automated the horizontal and vertical scanning.
Leaves within quadrats 1 and 2 (see Fig. 1b) were scanned by the
LIDAR from a position on the ground at side A, ~5 m from thecentre of the measurement plot, in August 2005. The
measurement took up to 1 hour so windless conditions werechosen to avoid disturbance of the canopy. The same scanning
was repeated in May, August and November 2006, whichcorresponded to 17, 118 and 194 days after bud break. In
the LIDAR-derived 3D point cloud images, each leaf wasdistinguishable because of the ﬁne resolution. After randomly
and interactively selecting 250 leaves in each of the images, eachleaf was approximated as a plane and normals to the planes were
estimated. LIAs were derived from the angles of these normals
with respect to the zenith and the distributions were obtained foreach of the measurement dates. In the present zelkova tree,
approximation of the leaf surfaces as planes was valid during
the three seasons.
Voxel-based computation of LAD
LAD computation followed the voxel-based canopy proﬁling(VCP) method (Hosoi and Omasa 2006). LIDAR-derived
datasets for each measurement date were composed of threepoint-cloud data obtained from each of the three measurement
positions. The individual coordinate systems for these data wereregistered into a single point-cloud dataset with a common 3D

Detecting seasonal LAD proﬁle using LIDAR imaging

Functional Plant Biology

coordinate system for each measurement date using the iterative
closest-point algorithm (Besl and McKay 1992). The datameasured from the ground when the trees were leaﬂess in
February 2006 were also registered using this algorithm. Nonphotosynthetic tissues such as trunks, branches and understory
were excluded by subtracting points corresponding to dataobtained in the leaﬂess canopy from the data obtained in theleafy canopy. All points in the registered dataset were converted
into voxel coordinates for each measurement date using thefollowing equations:X  X minþ1
ð1Þi ¼ IntDiY  Y min
þ1
Dj

ð2Þ



Z  Z minþ1
k ¼ Int
Dk

ð3Þ

j ¼ Int

where (i, j, k) represents a voxel’s coordinates within the voxel
array, Int is a function that rounds off the result of the calculation
to the nearest integer, (X, Y, Z) represents the point coordinates of
the registered LIDAR data, (Xmin, Ymin, Zmin) represent theminimum values of (X, Y, Z) (see Fig. 1c) and (Di, Dj, Dk)
represent the voxel element size. The voxel element size wasdetermined according to the range and scan resolution of theportable ground-based LIDAR and was thus set to 5  5  5 mm.
Voxels converted from points within the LIDAR dataset wereassigned an attribute value of 1. Next, all laser beams emitted
from the LIDAR positions were traced within the voxel array inaccordance with the actual laser beam angles. Voxels throughwhich one or more laser beams passed without touching the
canopy were assigned the attribute value of 2. LAD wascomputed for each of the cells within the measurement plotusing these attribute values and the following equation:
LADlmn ¼h þDH
cosqlmn 1 mX
nI ðkÞ
:Gðqlmn Þ DH k¼m nI ðkÞ þ nP ðkÞ

ð4Þhwhere (l, m, n) represents the cell reference number, qlmn is themean zenith angle for all laser beam incidences within a cell, andnI(k) and nP(k) are the numbers of voxels with attribute values of
1 and 2, respectively, in the kth layer of a cell. DH is the vertical
thickness of a cell (0.5 m), and mh and mh+DH are the voxel
coordinates on the vertical axis and are equivalent to the height ofthe bottom of cell h and the top h + DH in orthogonal coordinates(h = mh Dk). G(qlmn) is the mean projection of a unit leaf area
on a plane perpendicular to the direction of the laser beam at
qlmn (Norman and Campbell 1989; Welles and Norman 1991;
Jonckheere et al. 2004; Weiss et al. 2004). cos(qlmn) [G(qlmn) ]–1
is a factor that corrects for the inﬂuence of the LIA andlaser beam direction. cos(qlmn)[G(qlmn)]–1 was determined foreach of the measurement dates using the LIA distributionsacquired by the ﬁne-resolution portable LIDAR, as describedin the section ‘Measurements using two types of portablescanning LIDARs’.
The LAD estimates computed for each of the cells wereaveraged horizontally and the LAD proﬁle for the whole1001

measurement plot (including four quadrats) was obtained ineach of the measurement dates. The vertical integration offered
LAI estimates for the measurement plot in each of the dates.
Results
Figure 2 shows seasonal change of 3D LIDAR images of
the Japanese zelkova canopy in 2006 after registration of
LIDAR images measured from three 10 m above groundpositions (or six ground positions in the case of February).
The areas enclosed by the broken white line correspond to themeasurement plot. Each type of tissue is distinguishable within
the images due to the ﬁne resolution. In February, defoliation
had completed and thus leaves were not observed in Fig. 2a
except in the undergrowth. In May, leaf growth progressed asshown in Fig. 2b. In August, leaf growth progressed even further
and densely distributed leaves were observed in Fig. 2c. In
November, defoliation had already begun and a reduction ofleaves was observed in Fig. 2d.
Figure 3 shows LIA distributions of the zelkova canopy inMay, August and November 2006 obtained by a high-resolution
portable scanning LIDAR. In May 2006, the angles evenly
distributed and ranged through almost all angle classes. Ninety
percent of the angles were included in classes <65. The meanvalue and the standard deviation were 34.1 and 20.0,
respectively. In August 2006, the inclination angles shifted tosmaller classes compared with the ones in May 2006 and a peak
was observed at the smallest class (including those <5) so thatplanophile distribution was obtained. The distribution ranged
from the 0 to 60 class and 88% of the angles were included in
the <40 class. The mean value and the standard deviation were18.0 and 12.9. In November 2006, the angles in the <30 classdecreased and those between the 30 and 40 classes increased
compared with those in August 2006, so that the angle classes<45 became more even. The range extended up to the 85 classbut 88% of the angles were included in classes <45. A peak was
observed at the 10 class and the mean value and the standard
deviation were 22.8 and 15.4, respectively.
To evaluate the accuracy of the LAD estimates, the LIDARderived LAD proﬁle for the measurement plot in August 2005 wascompared with the corresponding actual stratiﬁed clipping values(Fig. 4a). Although the LAD was a little underestimated at around
11 m and overestimated at below 10 m and around 12 m, overall
the LIDAR-derived LAD agreed with the actual proﬁle. The root
mean square error (RMSE) was 0.26 m2 m–3. The LIDAR-derived
LAI in August 2005 was 4.24 with an absolute percent error of
11.8%.
Figure 4b shows the LAD proﬁles for the measurement plot in
May to November 2006. In May 2006, a peak was observed at aheight of 11.0 m with a value of 1.09 m2 m–3 and mean LAD of
0.29 m2 m–3. In August 2006, LAD values increased at almost
all heights. The peak was at a height of 11.5 m with a value of
1.39 m2 m–3 and mean LAD of 0.52 m2 m–3. In November 2006,
LAD values decreased at heights mainly between 8.5 and 12.5 m.
The peak was at a height of 11.5 m with a value of 1.18 m2 m–3 andmean LAD of 0.31 m2 m–3. Over the three seasons, the LAD
proﬁles were tailed towards a lower height.
Based on the LAD proﬁles in Fig. 4b, the changes in LAD overthe seasonal course in 2006 were examined (Fig. 5). The positive

1002

Functional Plant Biology(a)F. Hosoi and K. Omasa(b)Side A(c)

Side A(d)Side ASide A

Fig. 2. Seasonal change of 3D LIDAR images of the Japanese zelkova canopy in the study site. Each image shows the result after registration of LIDAR images
obtained from several measurement positions in each measurement date: (a) February, (b) May, (c) August and (d) November 2006. The areas enclosed by thebroken white lines correspond to the measurement plot. Close-up views of a potion of the canopy are on the right side in each ﬁgure (leaves are coloured green andthe other plant parts and soil are coloured brown in the online version of this manuscript).

and negative signs of the change in Fig. 5 correspond to theincrease and decrease of leaves, respectively. From May toAugust 2006, the leaves increased at almost all heights andthree peaks were observed at 9.0, 10.5 and 12.0 m heights. The
maximum change was observed at 12.0 m. At 11.0 and 11.5 m,
which correspond to each peak of the LAD proﬁles in May andAugust 2006, respectively (Fig. 4b), a small increase of LAD was
observed ( Fig. 5). From August to November 2006, the leaves
decreased at almost all heights due to defoliation. The peaks
observed were similar to the heights from May to August 2006,
but the maximum peak height was 10.5 m, which was lower thanthe period from May to August 2006. The heights at 11.0 and
11.5 m showed a small decrease of LAD where the height of
11.5 m corresponded to peaks in the LAD proﬁles of August andNovember 2006 (Fig. 4b).
Discussion
LIDAR-derived LIA distributions clearly showed distinctive
features in each measurement date. In August 2006, leaves had
already matured and the planophile LIA distribution appeared,
where low LIA leaves are most frequent. In contrast, it iscommonly observed that leaves of Japanese zelkova trees
incline with high LIA at the early leaf growth stage just after
bud break. According to the tendencies of LIA at the two growthstages, the LIA distribution in May 2006, which includes both low
and high values, would show the transition of LIA from early tomature leaf growth stages. Such changes in LIA from growth canbe translated as adaptation to the environment as reported inprevious studies on different species and environments(Comstock and Mahall 1985; Imai et al. 1994; Gratani andBombelli 2000; Barchuk and Valient-Banuet 2006). In
November 2006, the distribution shifted to higher angle
classes compared with those in August 2006. In November
2006, defoliation had already started, so the increase of higher
angle classes would have been due to leaf senescence. The present
study has shown that a high-resolution portable scanning LIDAR
can make effective and accurate measurements of seasonal LIAchange. In future studies, it would be useful if more regions of the
canopy are measured using this method to obtain spatial
variations of LIA distributions.
Comparing LIDAR-derived LAD proﬁles between August
2005 and August 2006 (Fig. 4), the shapes of the proﬁles were
somewhat different from each other. This may resulted fromthe annual change of foliage distribution accompanied by theenvironmental change in each year. In addition, stratiﬁedclipping in 2005 may have affected the foliage distribution in2006. Although only leaves were clipped, leaving branches orshoots in 2005, the sudden removal of leaves in the growing
season may have affected the tree activity; i.e. the reduced amountof assimilates stored in the artiﬁcially defoliated canopy may haveaffected the dynamics of foliation in the following spring. In theseasonal comparison of the proﬁles, it was shown that a portablescanning LIDAR can detect the difference in LAD proﬁles in eachseason (Fig. 4b). This enables us to discuss the changes in LAD
accompanied by seasonal course (Fig. 5). From May to August,Detecting seasonal LAD proﬁle using LIDAR imaging

Functional Plant Biology

(a)

0.20

1003

(a)
Actual
Lidar-derived

12.0

Mean : 34.1°°
s.d.: 20.0°

Height (m)Proportion

0.15

0.10

10.0

8.0

0.05
RMSE = 0.26 (m2 m–3)

6.0

0.00
0.20

(b)

(b)

0.15

May 2006
Aug. 2006

Mean : 18.0°
s.d.: 12.9°

12.0

Nov. 2006

0.10

Height (m)Proportion

4.0

0.05

10.0

8.0
Mean LAD (m2 m–3)

0.00

May 2006: 0.29

6.0

0.20

(c)

Aug. 2006: 0.52
Nov. 2006: 0.31
4.0
0.00

Proportion

0.15
Mean : 22.8°
s.d.: 15.4°

0.50

1.00

1.50

2.00

2.50

LAD (m2 m–3)

0.10

Fig. 4. LAD proﬁles for the measurement plot. (a) Comparison of LAD
proﬁles in August 2005 between LIDAR-derived estimates and the actual
stratiﬁed clipping values. (b) Comparison of LIDAR-derived LAD proﬁlesamong three different dates in 2006. RMSE, root mean square error of the
LAD estimates.

0.05

0.00
5

15

25

35

45

55

65

75

85

Leaf inclination angle (°)
Fig. 3. Leaf inclination angle distributions of the zelkova canopy obtainedby a high resolution portable scanning LIDAR. (a) May, (b) August and(c) November 2006. s.d., standard deviation.

leaves grew steadily at the upper canopy above 11 m. Leaves areable to capture more light at the upper canopy than the middle or
lower canopy; thus more leaves would have grown there. In
contrast, the period from August to November showed that
defoliation progressed at the canopy below 10.5 m comparedwith the upper canopy. This may suggest that leaves that cancapture more light remain longer on the tree at defoliation. In
previous work, such differences in the progress of defoliation
dependent on leaf position was also observed in an alder (Alnus
glutinosa (L.) Gaertn.) canopy where defoliation started earlier
in leaves within a shaded region (Eschenbach and Kappen

1996). Fewer changes in LAD at around 11.0 m in the periods
May–August and August–November may be attributed to theunderestimation of LAD in August 2006 because the LAD
proﬁle in August 2005 showed a little underestimation ataround the same height (Fig. 4a). Similar underestimation also
possibly occurred in August 2006. Recently, we showed that theunderestimation around the peak was improved by interpolating
the underestimated part using ﬁtting functions (Nakai et al.
2009). Although study of this method is still in progress withregard to the applicability to various situations, it may contributeto a more reliable estimation around the peak of the LAD
proﬁle. Overall, it has been shown that seasonal trendsof LAD change can be captured clearly by our proposed
method of using a portable scanning LIDAR. In future studies,
the applicability of the method to be expanded to other ﬁelds
and species using not only ground-based portable scanning
LIDAR, but also airborne scanning LIDAR, needs to beinvestigated.

1004

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F. Hosoi and K. Omasa

12.0

Height (m)

10.0

8.0
May to Aug.
Aug. to Nov.

6.0

4.0
–1.00

–0.50

0.00

0.50

1.00

Change in LAD (m2 m–3)
Fig. 5. Changes in LIDAR-derived LAD proﬁles over the seasonal course
in 2006. The positive and negative signs of the change correspond to increase
and decrease of leaves, respectively.

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Manuscript received 15 May 2009, accepted 16 September 2009http://www.publish.csiro.au/journals/fpb