Short Paper

Journal of Agricultural Meteorology 71 (2): 136-141, 2015

Estimating leaf inclination angle distribution of broad-leaved trees in each part ofthe canopies by a high-resolution portable scanning lidar
Fumiki HOSOI a , and Kenji OMASA a, †（a Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo, 113-8657, Japan）
Abstract
We estimated the distribution of leaf inclination angle (LIA) throughout the canopy of three broad-leaved trees by aportable high-resolution scanning lidar. Each canopy was scanned from three positions surrounding it and the data wereco-registered. The 3-D images were divided into five horizontal layers, and the LIA distribution of each layer was estimated by a fitted plane to each leaf and taking the zenith angle of the plane. The LIA decreased significantly with increasing canopy height. The degree of the LIA decrease was different in each specimen. The mean LIA values of each layer
were 36.7° to 43.8° in Camellia sasanqua, 34.6° to 45.9° in Osmanthus fragrans, and 48.9° to 65.4° in Camellia japonica.
The differences in LIA distribution were explained mainly by the light conditions around the trees.
Key words: 3-D, Broad-leaved tree, Canopy, Leaf inclination angle, Portable scanning lidar.

1. Introduction
Leaf inclination angle (LIA) distribution is one of 3-D structural properties of a plant canopy. LIA distribution has been often
related to light environment within the canopy and photosynthetic
productivity (Muraoka et al., 1998; Valladares et al., 2000; Niinemets and Fleck, 2002; Niinemets, 2010; Utsugi et al., 2006). It
has been also reported that plants decrease the leaf temperature byadjusting the LIA more vertically (Medina et al., 1978; Muraoka
et al., 1998). It has been suggested that leaf inclination angle may
affect the way in which water droplets are channeled off leaf surfaces (Wu, et al., 2014) and more vertical leaf inclination increase through-fall to the soil (Holder, 2012). Thus, it is important to take account of LIA distribution within the canopy forunderstanding the mechanism of plant adaptation to various environmental factors. LIA distribution has been measured by clinometer (Gratani and Ghia, 2002), protractor and compass (Norman and Campbell, 1989), and 3-D digitizer (Sinoquet et al.,1998). These methods, however, are labor-intensive, and thus it ishard to measure many leaves. LIA can be also measured by theindirect gap-fraction method with commercially available tools
such as cameras with fisheye lenses and optical sensors (Welles
and Norman, 1991). Although this method allows quick data collection, it provides mean LIA rather than a detailed spatial distribution. In addition, its accuracy is affected by the leaf spatial distribution and light conditions. Recently, portable scanning lidar(light detection and ranging) instruments have been used tomeasure 3-D structural properties of plants (Hosoi and Omasa,
2006, 2007; Omasa et al., 2007; Côté et al., 2009; Hosoi et al.,2011; Dassot et al., 2011). A portable scanning lidar measures the
distance to the target from the elapsed time between the emission
and return of laser pulses (the time-of-flight method) or by trigonometry (the optical-probe or light-section methods). It can record
Received; October 30, 2014.
Accepted; March 5, 2015.
†

Corresponding Author: aomasa@mail.ecc.u-tokyo.ac.jp

DOI: 10.2480/agrmet.D-14-00049

many 3-D point cloud data of a target quickly and automaticallywith a ranging accuracy of milimeter to centimeter, overcoming
the shortcomings of conventional LIA measurements in both accuracy and data collection efficiency. In particular, a highresolution portable scanning lidar with a range accuracy and resolution of about 1 mm and a beam divergence of a few mrad canclearly capture the 3-D shape of each leaf, and thus is suitable for
LIA measurements. Through the use of such an instrument, the
LIA distributions of some tree species have been extracted (Hosoi
and Omasa, 2007; Hosoi et al., 2014). Its data collection efficiency allows repeated measurements, and so it has been also used tomeasure seasonal changes in LIA distribution in broad-leaved
trees (Hosoi and Omasa, 2009) and the LIA distribution of crops
at each growth stage (Hosoi and Omasa, 2012; Hosoi et al.,2009). These studies have shown the usefulness of highresolution portable scanning lidar for LIA measurements in specific parts of the canopy. However, the LIA distribution changes
depending on the position in the canopy. For instance, it has been
often observed that LIA changes gradually from more vertical in
the upper canopy to more horizontal in the lower canopy (Niinemets, 2010; Utsugi et al., 2006). Such variation improves lightinterception in the lower canopy and enables the upper canopy tointercept less potentially damage from midday solar radiation andto allow more light to reach the lower canopy (Niinemets, 2010).
Thus, it is important to consider differences in LIA in each part ofthe canopy for a clear understanding of canopy function. The difference in LIA can be examined by measuring the LIA distribution of each positon in the canopy and comparing them. Since alarge number of leaves must be measured for obtaining the distribution of each part, the traditional methods like use of a clinometer or protractor are not suitable for the purpose. In terms of accuracy and data collection efficiency, portable scanning lidar might
be able to be used for capturing difference of the LIA distribution
in each part of the canopy. However, the ability of the lidar for the
purpose has not yet been fully demonstrated. In the present study,
we demonstrate the capability of the portable scanning lidar tooffer accurately and efficiently difference of the LIA distribution
in each part of the canopy, where whole canopies of broad-leaved- 136 -

F. Hosoi and K. Omasa：Estimating leaf inclination angle in each part of the canopies by a portable scanning lidar
trees are scanned by a high-resolution portable scanning lidar andour developed method to retrieve LIA from lidar image is applied.
Then, through analysis of resultant LIA distributions, it is shown
the usefulness of the lidar-derived LIA distributions of each partof the canopy in understanding of canopy function.

2. Material and Methods
The experiment was conducted at a mixed plantation in Ibaraki
Prefecture, 40 km northeast of central Metropolitan Tokyo, Japan(35°59′N, 140°02′E), that belongs to a temperate region with theaverage temperature of 14.1℃ and the annual precipitation of
about 1373 mm (in 2008). We chose three broad-leaved trees as
specimens: Camellia sasanqua Thunb. (2.05 m tall), Camellia
japonica L. (2.53 m), and Osmanthus fragrans Lour. var. aurantiacus Makino (2.56 m). Since these specimens were different
species with different structure, they seemed to be suitable for
evaluating the applicability of the proposed method. The heightsof the specimens were favorable to the measurable range of thepresent lidar instrument (3.5 to 10.0 m, described later). Each tree
was surrounded by open space with good light and thus positions
to set up a portable scanning lidar could be easily found.
A portable high-resolution scanning lidar (a modified TDS130L 3-D laser scanner; Pulstec Industrial Co., Ltd., Japan) wasused to scan the specimens. The lidar calculates distances by trigonometry, and has a range resolution of ~1 mm and a scan resolution of ~2 mm at a measurement range of ~5 m with the measura-

ble range of 3.5 to 10.0 m. The resolution becomes coarse as the
distance to a target becomes long. A rotating mount with a stepper
motor and a galvano mirror within the lidar head automate thehorizontal and vertical scanning. To capture the whole canopy, we
set three equidistant measurement positions around each tree at
about 4.6 m from each tree. The distance to the targets allowed toobtaining the lidar images with the spatial resolution fine enoughto estimate LIA. The central zenith angle of each laser scan was
87° for the C. sasanqua, 84° for the C. japonica, and 85° for the O.
fragrans. Those angles near 90° were chosen for more laser beam
penetration into the internal canopy based on the finding reported
in our previous study (Hosoi and Omasa, 2007). Each position
was about 120° apart from each other towards azimuth direction.
The measurement dates were 10 October for C. sasanqua,
21 October for C. japonica, and 26 November for O. fragrans, in
2013, with fine weather condition. Since the daytime was windy,
the measurements were conducted at night when wind became
weak. It took about twenty minutes per one scan. The lidar dataacquired from the three positions per tree were co-registered using
the iterative closest-point (ICP) algorithm (Besl and McKay,
1992) to generate 3-D point cloud images of the whole canopies.
To capture differences of LIA in each part of the canopy, we
divided the canopy within each 3-D image into five horizontal
layers with thicknesses of 0.29 m in C. sasanqua, 0.39 m in C.
japonica, and 0.38 m in O. fragrans. Since the resolution of theobtained lidar images was fine, each leaf shape was distinguisha-Fig. 1. Estimation of LIA from the high-resolution portable scanning lidar data. (a) An example of a set of points corresponding toa leaf. (b) A plane fitted to a point set of a leaf.  is the LIA, derived from the angle of the normal with respect to the zenith. (c)An example of planes fitted to each of 500 leaves within a 3-D point-cloud image. Shading effect by top light is added. (d) Closeup view of c, showing many planes fitted to leaves.

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Journal of Agricultural Meteorology 71 (2), 2015
ble in the images. Then by eye we interactively picked out 100
leaves per each layer (500 leaves per tree), using a software attached to the present lidar instrument. The points that composedeach leaf were fitted to a plane by a least-squares method, andnormals to the planes were calculated (Fig. 1). The LIA distribution was derived from the angles of these normals with respect tothe zenith (Hosoi and Omasa, 2007). We compared mean LIAvalues among layers of each tree and among trees. We also combined the upper two layers (“upper region”) and the lower threelayers (“lower region”) to compare LIA between the southern andnorthern sides of each tree (determined from a vertical central
cross-section of a lidar image). For all comparisons we used
Welch’s t-test of all possible combinations at P < 0.05 with the
Bonferroni method.
To evaluate the accuracy of the lidar-derived LIA distribution,
we cut some branches from each tree and took them back to thelaboratory. The branches were laid on a stand and scanned. Thenthe LIA values of 30 leaves of each specimen were estimated asabove. To measure the actual LIA values, we measured the inclination of the same leaves by clinometer (A-150; Shinwa Measuring Tools Corp. Japan), and then compared the values to assess
the accuracy of the lidar estimates.

3. Results and Discussion
The lidar-derived point cloud images allowed each leaf to be
distinguished (Fig. 2). Therefore, LIA could be estimated fromthe plane of each leaf. Comparison with laboratory measurements
gave mean absolute errors of 2.4° for C. sasanqua, 2.3° for C.
japonica, and 2.8° for O. fragrans. Thus, LIA could be estimatedaccurately from the lidar data. In each tree, the mean LIA value
decreased gradually with increasing canopy height (Figs. 3, 4). In
all trees, the differences between the top and bottom layers, aswell as between some other layers, were significant (Fig. 3). The
mean LIA of the C. japonica was significantly larger than those of
the other two trees in all layers (Fig. 3). The mean LIA values ofthe southern (sunward) side of the C. japonica were significantly

larger than those of the northern side in both the upper and lower
regions, but there were no significant differences in the C. sasanqua or O. fragrans (Fig. 4). As shown in the results in Fig.3 and
4, difference of LIA in each part of the canopies could be obtainedby the present method using a high resolution portable scanninglidar. In traditional methods used to measure LIA (e.g., by protractor), it can be both laborious and difficult to measure LIA indifferent parts of the canopy, particularly in the higher part. In
addition, the measurement is often affected by wind, because it
takes long time and windless conditions rarely last long. Thus, themeasurement accuracy may not be maintained due to the disturbance by wind. On the other hand, a high-resolution portable scanning lidar can record quickly the whole canopy (about twenty
minutes per one scan at the present instrument) including thehigher part of the canopy where LIA measurement is difficult bytraditional methods and any part of the canopy can be extracted
from the point-cloud image. Thus, the LIA distribution of any partof the canopy can be efficiently and quickly obtained without
intensive effort. The ranging accuracy of milimeter allows accurate results with the mean absolute errors of less than 3.0°, in thecase of the present lidar instruments. Since the measurement timeis shorter than the traditional methods, the data can be taken
quickly during brief windless condition, resulting in less influenceof wind on the LIA estimation. Thus, in terms of the efficiency
and accuracy, the proposed method to obtain difference of LIA ineach part of the canopy is more advantageous than the traditional
methods.
LIA distribution is affected by environmental conditions, inparticular light. At our site, there was open space around the trees
with good light. In such an open environment, LIA tends to increase (Niinemets and Fleck, 2002; Valladares et al., 2000; Muraoka et al., 1998), because plants decrease light interception by
inclining their leaves more vertically, creating a more uniform
light distribution within the canopy and avoiding excessive radiation and the consequent water stress and photoinhibition of photosynthesis (Muraoka et al., 1998). However, this does not offer

Fig. 2. 3-D point cloud images of (a) Camellia sasanqua, (b) Osmanthus fragrans, and (c) Camellia japonica obtained by highresolution portable scanning lidar. Shading effect by top light is added.

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F. Hosoi and K. Omasa：Estimating leaf inclination angle in each part of the canopies by a portable scanning lidar

Proportion

Camellia sasanqua

Osmanthus fragransh =1.90 m
Mean:36.7° a A

h =2.34 m
Mean:34.6° a A

h =1.61 m

h =1.96 m

Mean:38.7° a A

Mean:36.5° a A

h =1.32 m
Mean:37.9° a A

Mean:44.0° b A

h =1.58 m

h =1.20 m

Camellia japonica
h =2.36 m
Mean:48.9° a B

h =1.97 m
Mean:54.7° ab B

h =1.58 m
Mean:59.9° bc B

h =1.19 m

h =1.03 m
Mean:42.4° a A

Mean:41.5° ab A

Mean:59.6° bc B

h =0.74 m
Mean:43.8° a A

h =0.82 m
Mean:45.9° b A

h =0.80 m
Mean:65.4° c B

Leaf inclination angle (degree)
Fig. 3. Lidar-derived LIA distributions at each height in each tree. Small letters mean significant difference of the distribution
between layers in the same tree and capital letters mean the significant difference between trees in the same layer. Means followedby the same letters in each layer or each tree are not significantly different at P < 0.05 by the Bonferroni method. h is height corresponding to a center of a layer.
well explanation from our results because the mean LIA was
smaller in the upper layers than in the lower layers, neverthelessthe available light was more in the upper layers than in the lower
ones. Our sample trees might not have decreased the light interception but increased it. They may have increased the LIA in theupper layer so as to capture solar radiation from all directions. As
the height decreases, leaves become difficult to capture radiationfrom the upper due to the obstruction by the upper canopy andthus they may have increased the LIA in their lower layers so as tocapture more radiation from the sides which is relatively wellavailable in the open space. The differences between layers in the
C. sasanqua were smaller than those in the C. japonica, possibly
because the C. sasanqua had a lower leaf area density, allowing
lower leaves to capture more radiation from above than in the C.
japonica. These results thus suggest that LIA is well explained interms of adaptation to the light environment. Besides light adapta-- 139 -

Journal of Agricultural Meteorology 71 (2), 2015(a)Camellia sasanqua

Osmanthus fragransSouth side

Proportion
Proportion

South side

South side
Mean:37.8°

Mean:54.6°

North side

North side

North side

Mean:39.9°

Mean:33.2°

Mean:49.2°

Leaf inclination angle (degree)

Leaf inclination angle (degree)

Camellia sasanqua

Osmanthus fragrans

Mean:35.3°(b)Camellia japonica*

Leaf inclination angle (degree)

Camellia japonica

South side

South side

Mean:41.0°

Mean:43.4°

South side
Mean:64.2°

North side

North side

North side

Mean:42.2°

Mean:44.1°

Mean:59.1°

Leaf inclination angle (degree)

Leaf inclination angle (degree)*

Leaf inclination angle (degree)

Fig. 4. LIA distributions on the southern and northern sides of each tree in (a) the upper two layers and (b) the lower three layers. An asterisk (*) means significant difference between the southern and northern sides in the same tree at P < 0.05.
tion, the tendency may reflect species specific characteristics of
LIA. Our study did not allow us to discriminate both contributions.
Thus, the results obtained by the proposed method provided detailed information about the LIA distributions of the specimens
and allowed to discuss the relationship between the canopy structure and function. This shows the usefulness of the structural information obtained by the proposed method for understanding of
canopy function. For obtaining more knowledge about canopy
function, it is an useful way to combine the LIA information withother structural information such as leaf area density (Hosoi andOmasa, 2006) or physiological information obtained by thermalor multispectral images (Omasa et al., 2007). In addition, the LIA

information would be also able to be used as input data for plantmodeling (Valladares and Pearcy, 2000; Kobayashi and Iwabuchi,2008). Besides LIA measurements, azimuth directions of leaves
would be also measurable using this method, offering useful information to study canopy function, such as canopy light interception (Barradas et al., 1999) or seasonal change of canopy structure (Gratani and Ghia, 2002).

4. Conclusions
We obtained accurate LIA distributions of the entire canopy ofthree broad-leaved trees by portable high-resolution scanning lidar.
The LIA of individual leaves was estimated by fitting a plane to- 140 -

F. Hosoi and K. Omasa：Estimating leaf inclination angle in each part of the canopies by a portable scanning lidar
each leaf and calculating the zenith angle of each. The LIA decreased significantly with increasing canopy height, depending on
the specimen. The LIA distribution differed significantly between
the C. japonica and the other two trees and between the southernand northern sides of the C. japonica tree. The differences in LIA
distribution were explained by the light condition around the trees.
Our results show that our lidar method can offer effectively andaccurately differences of LIA distributions in each part of the
canopy and the lidar-derived data are useable for improving researchers’ understanding of the relationship between LIA andplant function. Using this method, other species and environmental conditions are desirable to be examined in the additional works.

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