ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

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

Estimation of vertical plant area density proﬁles in a rice canopy at different
growth stages by high-resolution portable scanning lidar with a lightweight mirror
Fumiki Hosoi, Kenji Omasa ⇑Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku, Tokyo 113-8657, Japana r t i c l e

i n f oArticle history:
Received 30 March 2012
Received in revised form 11 July 2012
Accepted 20 August 2012
Available online 19 September 2012
Keywords:
Laser beam coverage index
Plant area density
Portable scanning lidar
Rice
Three-dimensional imaging
Voxel-based canopy proﬁling

a b s t r a c t
We used a high-resolution portable scanning lidar together with a lightweight mirror and a voxel-based
canopy proﬁling method to estimate the vertical plant area density (PAD) proﬁle of a rice (Oryza sativa L.
cv. Koshihikari) canopy at different growth stages. To improve the laser’s penetration of the dense canopy, we used a mirror to change the direction of the laser beam from horizontal to vertical (0°) andoff-vertical (30°). The estimates of PAD and plant area index (PAI) were more accurate at 30° than at
0°. The root-mean-square errors of PAD at each growth stage ranged from 1.04 to 3.33 m2 m3 at 0°
and from 0.42 to 2.36 m2 m3 at 30°, and those across all growth stages averaged 1.79 m2 m3 at 0°
and 1.52 m2 m3 at 30°. The absolute percent errors of PAI at each growth stage ranged from 1.8% to66.1% at 0° and from 4.3% to 23.2% at 30°, and those across all growth stages averaged 30.4% at 0° and14.8% at 30°. The degree of laser beam coverage of the canopy (expressed as index X) explained theseerrors. From the estimates of PAD at 30°, regressions between the areas of stems, leaves, and ears per unit
ground area and actual dry weights gave standard errors of 7.9 g m2 for ears and 12.2 g m2 for stems
and leaves.
Ó 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
B.V. All rights reserved.

1. Introduction
The plant canopy sustains important roles in cycling materials
and energy through photosynthesis and transpiration, maintaining
plant microclimates, and providing habitats for various species(Monteith, 1973; Jones, 1992; Graetz, 1990; Larcher, 2001). The
vertical structure of crop canopies has been studied to explain
characteristics such as light distribution within the canopy, lightuse efﬁciency, yield, growth rate, and nitrogen allocation (Imaiet al., 1994; Milroy et al., 2001; Takahashi and Nakaseko, 1993;
Yunusa et al., 1993). It is often represented by the vertical proﬁleof leaf area density (LAD), which is deﬁned as the one-sided leaf
area per unit of horizontal layer volume (Weiss et al., 2004). The
leaf area index (LAI) is obtained as the vertical integration of the
LAD values. Plant area density (PAD) and plant area index (PAI),
which encompass all aboveground organs, are used instead of
LAD and LAI when aboveground organs are difﬁcult to separate.
In measuring the structure of crops, it is important to accountfor changes in the vertical structure with canopy growth. Stratiﬁedclipping has been used to obtain LAD and PAD (Imai et al., 1994;
Milroy et al., 2001; Monsi and Saeki, 1953; Takahashi and Nak⇑ Corresponding author. Tel.: +81 3 5841 5340; fax: +81 3 5841 8175.
E-mail address: aomasa@mail.ecc.u-tokyo.ac.jp (K. Omasa).

aseko, 1993). This direct method can provide accurate results,
but its laborious and destructive nature does not permit repeatedmeasurements of the intact crop structure with canopy growth.
The indirect gap-fraction method is widely used for crop measurement with commercially available tools such as cameras with ﬁsheye lenses and optical sensors (e.g. the Li-Cor LAI-2000 plant
canopy analyzer; Bréda, 2003; Grantz et al., 1993; Hanan and
Bégué, 1995; Welles and Cohen, 1996). Although it allows automatic data collection and nondestructive measurement of canopy
structure, the accuracy of measurement is affected by the spatial
distribution of leaves and by sunlight conditions (Chason et al.,
1991; Weiss et al., 2004).
In the last decade, lidar (light detection and ranging) has been
emerged as a powerful tool for three-dimensional measurementsof various purposes, e.g. city modeling (Pu and Vosselman, 2009),
topography mapping (Webster et al., 2006), cultural heritage surveys (Pesci et al., 2012). This technology has been also utilized
for structural measurements of plant canopies (Brandtberg et al.,
2003; Harding et al., 2001; Holmgren and Persson, 2004; Hosoi
et al., 2005, 2010, 2011; Hosoi and Omasa, 2006, 2007, 2009a,b;
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 provide 3D information by calculating the distance between the sensor and the target from the elapsed time between the emission

0924-2716/$ - see front matter Ó 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.isprsjprs.2012.08.001

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F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

and return of laser pulses (the time-of-ﬂight method) or from thedifference in the phases of the modulation between them (phaseshift method) or by trigonometry (optical-probe or light-section
methods). Airborne lidars with a large footprint (typically 10–
25 m in diameter) and a large scan width have been developed
for a target of forest remote sensing on large scales (Harding
et al., 2001; Lefsky et al., 2002). These systems include a waveform-recording device that digitizes the power level of the entire
return laser signal from canopy elements and the ground. Although
they are sufﬁcient for large scale measurement, the image resolution has been insufﬁcient to provide detailed descriptions of canopy structure at the level of individual plants. Meanwhile,current airborne small-footprint lidar (the footprint diameter istypically 10–30 cm) has made it possible to measure canopy structures with ﬁne spatial resolution (Brandtberg et al., 2003; Holmgren and Persson, 2004; Hyyppä et al., 2001; Næsset et al., 2004;
Omasa et al., 2000, 2003; Riaño et al., 2003). The lidar system functions as a discrete-return recording device, since it only receives asingle return signal or a small number of return signals from thecanopy and ground. At the use of this system, there was a tendency
for tree height to be underestimated when the laser pulse density
was insufﬁcient to detect the actual tree tops (<1 pulse per m2). Recent advances in lidar technology have increased the pulse density
to >10 pulse per m2, with a pulse repetition frequency of >200 kHz,so that the probability of laser hits on the actual tops of the trees
increases and the magnitude of the underestimation is reduced(Omasa et al., 2003). This type of lidar has been used for estimating
the LAI values of tree species (Magnussen and Boudewyn, 1998;
Morsdorf et al., 2006) and also of crops (Houldcroft et al., 2005).
However, these studies mainly focused on LAI estimation rather
than on the estimation of the vertical LAD or PAD proﬁles, because
they captured insufﬁcient information about the vertical canopy
structure to estimate the latter.
Although portable ground-based nonscanning lidar has beenused in several studies of vertical foliage proﬁles (Parker et al.,
2004; Radtke and Bolstad, 2001), portable ground-based scanninglidar is now more popular for the measurement (Henning and Radtke, 2006; Hosoi and Omasa, 2006, 2007, 2009a,b; 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).
The latter can record much 3D data quickly and nondestructively.
The efﬁcient data collection and portability of this technology areadvantageous for repeated measurements of crops over time. Commonly used portable ground-based scanning lidar systems are discrete-return recording systems at which a single or a small numberof return signals from the canopy are received. In addition to thesystem, the waveform-recording ground-based scanning lidar system has been also utilized for canopy structural measurement(Strahler et al., 2008), by which more structural information canbe extracted from the internal canopy. Thus, those technologies
promise to overcome the shortcomings of the conventional meansof measuring the vertical structure of crops.
Recently, we demonstrated that the vertical PAD proﬁles of awheat canopy at different growth stages can be measured accurately by high-resolution portable scanning lidar with a resolutionof about 1 mm at a range of about 5 m through a voxel-based canopy proﬁling (VCP) method (Hosoi and Omasa, 2009a). Besides awheat canopy, it is signiﬁcant for the method to be applied to othercrops. Rice is one of the main crops in the world and so it is important to understand the characteristics. If the VCP method is applicable to rice canopies, the resultant vertical canopy proﬁles would
give useful information for well understanding of the characteristics. However, a rice canopy is denser than a wheat canopy, so laser
penetration might be restricted. We found that a more vertical laser beam penetrates a dense rice canopy better than a horizontal
beam (Hosoi and Omasa, 2012). However, this is difﬁcult toachieve in the ﬁeld, because the lidar needs to be held above thetarget.
We estimated the vertical proﬁles of a rice canopy at different
growth stages using a high-resolution portable scanning lidar
and a mirror oriented at different angles to achieve vertical penetration, and compared the results with direct measurements. To assess the accuracy of the PAD estimates, we investigated therelationships between an index of laser beam coverage and the errors in PAD estimates. We propose a way to estimate the dryweight of each plant organ from the resultant PAD values.
2. Materials and methods
2.1. Plant material
The experiment was conducted in 2010 in a paddy ﬁeld in Ibaraki Prefecture, 40 km northeast of central Tokyo, Japan (35°560 N,
140°040 E). Rice (Oryza sativa L. cv. Koshihikari) seedlings weretransplanted on 1 May with 30-cm inter- and intra-row spacings.
Four square plots (1.2 m  1.2 m) were established for measurements of four growth stages, on 27 May (early tillering), 17 June(late tillering), 13 July (panicle formation), and 14 August(maturity).
2.2. Lidar measurements
We used a high-resolution portable scanning lidar that calculates distances by trigonometry (a modiﬁed TDS-130L 3-D laser
scanner; Pulstec Industrial Co., Ltd., Japan) to make 3D measurements of the rice canopy. The size is 640  263  175 mm andthe weight is 12.0 kg. The measurable range is 3.5–10 m. The range
and scan resolutions are about 1 and 2 mm, respectively, at a rangeof about 5 m. A rotating mount with a stepper motor and a galvano-mirror within the lidar head automate the horizontal andvertical scanning. The range of scanning angle is ±45° for horizontal and ±20° for vertical. The wavelength of the laser beam is786 nm and the beam diameter is 5 mm at 3.5 m. The maximum
laser beam output power is 30 mW (equivalent to class 3B laser
product according to IEC60825-1). The data sampling rate is
1.25 ms per point.
The lidar was installed on a bund near the measurement plots(Fig. 1A). A thin-ﬁlm mirror measuring 1.0 m  1.5 m (Refex; J.
Front Design & Construction Co., Ltd., Japan) was mounted above
the crop (1.5 m above the ground) on rails to reﬂect the laser’s
beam down onto the canopy. Since the mirror is made of an evaporation–deposition aluminum–plastic ﬁlm, at 3.8 kg it is muchlighter than a glass mirror and thus it was easy to handle in theﬁeld. The mirror’s reﬂectance is about 90% at the laser wavelengthof 786 nm. Since the laser beams are reﬂected twice on the mirror
until they return to the lidar sensor, the returning laser beam
power decreases to about 80% of the power at the normal (no mirror) setting. This might cause the decrease of the number of returning laser beams detected at the lidar sensor. Then the emitted laser
beam power was adjusted to the maximum (30 mW) to keep thereturning beam power enough to be detected at the lidar sensor,
so that the number of detected returning beams did not decreaseby using the mirror.
The rails were arranged on the ﬁeld horizontally along a laser
beam direction of the lidar scan. The mirror could be slid along
the rails at 3.3–6.9 m from the lidar, that was equivalent to 4.8–
8.4 m of the beam path length from the lidar to the ground under
the target canopy with reﬂection on the mirror when the beam
direction into the canopy is vertical (see Fig. 1C). It could also pivot
on its base so that the laser beam could be directed into the canopy
at different angles (deﬁned as the central zenith angle, hc; Fig. 1B

13A

MirrorB
Rail

Mirrorα >45 °

Lidar

VerticalF. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

Laser beam

α

45°

Lidar

θc

MarkerC

α = 45 °D
3.3 - 6.9 m

α <45 °

α

α
45°

45°

θc
1.5 m

Fig. 1. Measurement of the rice canopy by high-resolution portable scanning lidar and adjustable mirror. (A) Field set-up. (B–D) Relation of beam direction and mirror angle.
a: Zenith angle of the normal to the mirror surface. hc: Central zenith angle of laser beam.

and D). Measurements were taken at hc = 0° and 30° on each occasion. To achieve a hc of 0° (vertical illumination), the zenith angle ofthe normal to the mirror surface, a, was set at 45° (Fig. 1C). To
achieve a hc of 30° (off-vertical illumination), a was set both at>45° (Fig. 1B) and at <45° (Fig. 1D), where laser beams were towards the lidar position and towards the opposite side of the lidar
position, respectively. Since laser beams could illuminate the different sides of the canopy by these settings, more laser beam penetration into the interior was expected. The mirror was not big
enough to cover the plot, so it was moved along the rails to cover
it in two scans in each of the settings. Thus, the total numbers of
the scans were two for hc of 0° and four for hc of 30°. Through thosemeasurements, six cubic markers that were attached to poles andplaced around the plot (see Fig. 1A) were also scanned together
with the target canopy. The markers within the lidar data wereused as reference points for registration of each set of the lidar
data.

2.3. Direct measurements
To validate the lidar-derived estimates, we directly measured
LAD and PAD in horizontal layers by stratiﬁed clipping within eachplot. On each date, all plants within each plot were clipped in layers 5 cm thick (27 May) or 10 cm thick (17 June–14 August), andthe clippings were separated into leaves, stems, and ears. The areasof each were measured with a commercially available desktop
scanner (ScanBit MFS-A3; Exemode, Inc., Japan; details in Hosoi
and Omasa, 2009a). The PAD of each organ in each layer was calculated as the projected area of each organ (see Section 2.4) per unitof horizontal layer volume (0.07 m3 for 27 May and 0.14 m3 for 17
June–14 August). The PAI was obtained as the vertical integrationof the PAD of all organs. The LAI was obtained as the vertical integration of the LAD. After the PAD measurement, all of the organs
were dried in an oven at 80 °C for 3 days and weighed.

2.4. Computation of plant area density
PAD was computed by the VCP method (Hosoi and Omasa,
2006). The complete point-cloud data set for each of the laser beamincidence angles was registered into a single point-cloud data set
with a common 3D coordinate system for each measurement dateusing the iterative closest-point algorithm (Besl and McKay, 1992)
with the reference points of six cubic markers (described in Section 2.2). All points in the registered data set were converted intovoxel coordinates. The voxel size was set to 1 mm in recognitionof the lidar’s resolution (1–2 mm). Voxels converted from points
within the data set were given an attribute value of 1. All laser
beams emitted from each lidar position were traced within thevoxel array in accordance with the actual beam angles and the mirror angles. Voxels through which one or more beams passed without touching the canopy were given an attribute value of 2. From
the attribute values, PAD in each horizontal layer was computedas (Hosoi and Omasa, 2006):

PAD ¼

mhþDH
cos hc 1 X
nI ðkÞ
:Gðhc Þ DH k¼m nI ðkÞ þ np ðkÞð1Þhwhere hc is the central zenith angle of a laser beam; nI(k) and nP(k)are the numbers of voxels with attributes of 1 (‘‘intercepted’’) and 2(‘‘passed through’’), respectively, in the kth horizontal layer of thevoxel array (thus, nI(k) + nP(k) represents the total number of incident beams that reach the kth layer); DH is the horizontal layer
thickness (=0.05 m for 27 May and 0.10 m for 17 June–14 August);
and mh and mh+DH are the voxel coordinates on the vertical axis
equivalent to heights h and h + DH in orthogonal coordinates(h = mh  Dk). G(hc) is the mean projection of a unit leaf area on aplane perpendicular to the direction of the laser beam at hc (Norman
and Campbell, 1989; Weiss et al., 2004; Welles and Norman, 1991).
The term cos hc [G(hc)]1 corrects for the inﬂuence of leaf inclination
angle and laser beam direction; the distribution of leaf inclination

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F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

angles is required to determine its value. Each leaf was distinguishable in the lidar images of each growth stage because of the ﬁne resolution. After each leaf was extracted from the images, it was
divided lengthwise into 15-mm segments, and 200 segments wererandomly selected. Points within each of the pieces were ﬁtted by aplane based on a least square method and normals to the planes
were estimated. The distribution of leaf inclination angles at eachgrowth stage was derived from the angles of these normals with respect to the zenith. From the PAD estimates computed for each horizontal layer, the vertical PAD proﬁles at hc = 0° and 30° on eachmeasurement date were obtained and compared with the directly
measured proﬁles. In addition, PAI values at each growth stage were
estimated as the vertical integration of the PAD estimates and theaccuracy was examined.
2.5. Derivation of the laser beam coverage index
A laser beam coverage index, X, can be used to predict the errorof LAD estimates obtained by different types of lidar and with different beam settings (Hosoi et al., 2010):

X ¼ Abeam  N0 expðK  LAIcum Þð2Þ

where Abeam is the horizontally projected area of the laser beam; N0
is the initial number of incident laser beams just before entry into
the canopy per unit area of a horizontal plane; K accounts for theinﬂuence of the leaf inclination angle and the angle of incidenceof laser beams (the inverse of the correction factor cos hc [G(hc)]1
in Eq. (1)); and LAIcum is the lidar-derived cumulative LAI at a certain height (equivalent to cumulative PAI in this study). X indicatesthe proportion of the area of a horizontal plane within the canopy
covered by laser beams. The index is adapted here to PAD estimatesas:

X ¼ Abeam  N

ð3Þ

100

where N is equivalent to the term N0exp(KLAIcum) in Eq. (2) but
means the number of incident laser beams per unit area of a horizontal plane at a given height. Since N could be derived directly together with PAD by the VCP method in Section 2.4, X was calculated
by Eq. (3) rather than by Eq. (2). Then we investigated the relationships between X and the absolute errors of the corresponding PAD
estimates at hc = 0° and 30°.
2.6. Correlation between lidar-derived areas of aboveground organs
and dry weights
The errors of the lidar-derived PAD proﬁles at hc = 0° and 30°
showed which angle gave more accurate results. The more accurate values were used in the following calculations. The lidar-derived total areas of stems and leaves per unit ground area at eachgrowth stage were obtained by multiplying the lidar-derived PAD
by the layer thickness and summing up the values vertically. The
area corresponding to ears was subtracted from the total as described below to give the total areas of stems and leaves on 14 August. We investigated the correlation of the total areas with thecorresponding actual stem and leaf dry weights.
The PAD estimates corresponding to ears on 14 August wereseparated by multiplying the lidar-derived PAD above 50 cm by
the ratio of ear to total surface area obtained from direct measurements of each layer. The PAD was converted into area of ears perunit ground area in each layer by multiplication with the layer
thickness (=0.1 m). We investigated the correlation of the area ofears with the corresponding actual ear dry weights.
3. Results
The directly measured PAD values were larger in lower layers
on 27 May (Fig. 2A). The distribution spread to higher layers as

A

B
leaf

leaf

stem

stem

Height (cm)

80
60

25

40
15

20
5
60.0
-60.0

100

40.0
-40.0

20.0
-20.0

0.0
0.0

C

2.00
20.0

4.00
40.0

6.00
100.0 -60.0
60.0 20.0
60.0-140.0 140.0 -100.0
-20.0

leaf

2.00
20.0

6.00 100.0
10.00 14.00
60.0

D

289.0

stem

Height (cm)

80
60

leaf

25

stem
ear

40
15

20
5

-140.0140.0 -100.0
100.0 -60.0
60.0 -20.0
20.0
Dry weight (g m-3)

20.0
2.00

60.0
140.0
-140.06.00 100.0
10.00 14.00

PAD (m2 m-3)

-100.0100.0 -60.0
60.0 -20.0
20.0
Dry weight (g m-3)

20.0
2.00

60.0
6.00 100.0
10.00 14.00

PAD (m2 m-3)

Fig. 2. Directly measured vertical PAD and dry weight distributions of leaf, stem, and ear within a rice canopy obtained by stratiﬁed clipping. (A) 27 May, tillering; (B) 17 June,tillering; (C) 13 July, panicle forming; (D) 14 August, maturing.

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F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

Fig. 3. Image of the rice canopy on 17 June obtained by co-registration of images
measured by lidar at hc = 30°. Shading effect is added to this image by changing
brightness of each point.

the crop grew, along with the peaks in the distribution (Fig. 2B–D).
Leaves dominated in the middle to higher layers and stems in thelower layers. Ears became dominant in the upper layers on 14 August. The PAI values were 0.44 (27 May), 2.10 (17 June), 6.82 (13July), and 6.94 m2 m2 (14 August), and the LAI values were 0.34,
1.67, 5.20, and 4.35 m2 m2, respectively. The dry weight in eachlayer increased with canopy growth (Fig. 2). Stems contributed
greatly to the dry weight in lower layers on 27 May and 17 Juneand in lower to middle layers on 13 July and 14 August. Ears contributed greatly to the dry weight in the upper layers on 14 August.
The total dry weights were 20.1 (27 May), 106.8 (17 June), 451.2(13 July), and 962.8 g m2 (14 August). The total ear dry weighton 14 August was 403.1 g m2.

In a co-registered image of the rice canopy on 17 June at
hc = 30°, where shading effect is added to this image by changing
brightness of each point, each leaf is clearly distinguishable because of the ﬁne resolution of the image (Fig. 3).
The lidar-derived distributions of the leaf inclination angle ofthe canopy showed a peak at 65° on 27 May, with the distribution
tailed to lower angles (Fig. 4A). The distribution was approximately
the same on 17 June with a weak peak around 55° (Fig. 4B).
Although the distribution shifted to higher angles with a peak at
80° on 13 July (Fig. 4C), it moved to lower angles with a peak at
15° on 14 August (Fig. 4D).
At hc = 30°, although the actual PAD proﬁles changed greatly as
the canopy grew, the lidar-derived PAD estimates were good except in the lowest layers on 27 May (greatly underestimated)and 14 August (greatly overestimated) (Fig. 5). On 13 July and 14
August, the lidar-derived PAD was slightly overestimated in thelayers corresponding to the peak and below. At hc = 0°, the lidar-derived values underestimated PAD at all growth stages in most layers (except, for example, at 40 cm on 14 August, with anoverestimate). In particular, the underestimation was large on 27
May and 17 June, and in the layer corresponding to the peak of actual PAD on 13 July. At hc = 30°, the root-mean-square errors(RMSEs) of the PAD estimates were 0.69 (27 May), 0.42 (17 June),
0.99 (13 July), and 2.36 m2 m3 (14 August). At hc = 0°, the RMSEs
were 1.04, 3.33, 1.32, and 1.33 m2 m3, respectively. At hc = 30°,
the absolute percent errors of the PAI estimates were 22.9%, 4.8%,
4.3%, and 23.2%, respectively. At hc = 0°, they were 42.9%, 66.1%,
10.9%, and 1.8%, respectively.
Across all growth stages, the relationship between directly measured and lidar-derived PAD values was more accurate at hc = 30°(RMSE = 1.52 m2 m3, absolute percent error = 13.8%) than at
hc = 0° (RMSE = 1.79 m2 m3, absolute percent error = 30.4%;
Fig. 6). As shown in Fig. 5, there was an overall tendency for overestimation at hc = 30° (Fig. 6A) and underestimation at hc = 0° (Fig. 6B).

0.20

A
0.15
Proportion

B

Mean : 45.4
S.D. : 20.8

Mean: 45.4°

Mean: 43.3°

Mean : 43.3
S.D. : 20.7

0.10

0.05

0.00

0.20

C

D

Mean: 64.2°

Leaf inclination angle (degree)
Mean : 26.1
Mean: 26.1°
S.D. : 18.8

Proportion

0.15

0.10

0.05

0.00
5

15

25

35

45

55

65

75

Leaf inclination angle (degree)

85

5

15

25

35

45

55

65

75

85

Leaf inclination angle (degree)

Fig. 4. Distributions of leaf inclination angles in rice canopy derived from lidar images. (A) 27 May, tillering; (B) 17 June, tillering; (C) 13 July, panicle forming; (D) 14 August,maturing. ‘‘Mean’’ indicates mean leaf inclination angle.

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F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

100

A

Height (cm)

80

60

40

40

20

20

100

1.00

2.00

3.00

4.00

C

0
0.00

100

80

80

60

60

40

40

20

20

0
0.00

4.00

8.00

12.00

B

80

60

0
0.00

Height (cm)100

Directly measured LAD
Directly measured PAD(θ cdegree)= 30 °)
Lidar-derived PAD (30Lidar-derived PAD (0(θdegree)c= 0 °)

16.00

PAD or LAD (m2m-3)

0
0.00

4.00

8.00

12.00

16.00

4.00

8.00

12.00

16.00

D

PAD or LAD (m2m-3)

Fig. 5. Comparison of proﬁles among lidar-derived PAD at hc = 0° and 30°, directly measured PAD (modiﬁed from Hosoi and Omasa, 2012), and directly measured LAD at eachgrowth stage. (A) 27 May, tillering; (B) 17 June, tillering; (C) 13 July, panicle forming; (D) 14 August, maturing.

When the index of laser beam coverage, X, decreased to around2, the absolute errors of the PAD estimates at both angles began toincrease (Fig. 7). Large errors were associated with X 6 1.0, at
which the errors were larger at hc = 0° than at hc = 30°. Smaller errors were associated with values of X > 2.0.
Lidar-derived areas of stems and leaves were linearly correlatedwith actual dry weights, with R2 = 0.99 and a standard error of
12.2 g m2 (Fig. 8A). Ears also showed a linear correlation, with
R2 = 0.94 and the standard error of 7.9 g m2 (Fig. 8B).
4. Discussion
The angle of incidence of laser beams into a canopy affects thenumber of beams that penetrate the interior and thus relates to theaccuracy of LAD or PAD estimation (Hosoi and Omasa, 2007). To
improve the accuracy of estimation, the angle should depend on
the canopy structure. However, it can be difﬁcult to set the angle
freely in the ﬁeld, because this requires the position of the lidar
to be moved. In particular, setting a near-vertical angle of laser
incidence is often difﬁcult because a heavy weight lidar instrument
must be placed at a higher place above or around the canopy. In thecase of the present study, the lidar whose weight is 12.0 kg must beplaced at least 4.8 m height (equivalent to minimal beam path
length in the vertical beam incidence into the canopy, describedin Section 2.2) above the canopy if the mirror was not used. Useof light weight lidar instruments (e.g. Rosell et al., 2009; van derZande et al., 2006) may ease such setting, but the performance ofsuch systems (e.g. measurable range, scanning performance, range
accuracy, etc.) is not still insufﬁcient to use the accurate measurement of vertical canopy structure. Thus, in the case of a low target
such as rice, the angle of incidence is restricted to near-horizontal
due to the difﬁculty of a way to place the lidar above the canopy ora higher place around the canopy. Our method removes such a limitation through the use of an adjustable lightweight mirror that canbe easily handled and installed in the ﬁeld.
In a previous study (Hosoi and Omasa, 2009a), we used an incidence angle of 57.5° in a wheat canopy, because it allowed for thecorrection factor (cos(hc) [G(hc)]1; see Eq. (1)) to be approximatedas a constant value. Since this angle is more horizontal than vertical, the path of the laser beam within the canopy is longer than thepath at a more vertical angle. A longer path length decreases thelikelihood that a laser beam will reach the middle and lower partFig. 6. Relationship between directly measured and lidar-derived PAD at all growth stages. (A) hc = 30°; (B)=0°. RMSE: root-mean-square error.

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F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

6.00

θc = 0°5.00

θc = 30°

4.00
3.00
2.00
1.00
0.00
0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

Laser beam coverage index Ω
Fig. 7. Relationships between the laser beam coverage index (X) and the absolute
errors of PAD at hc = 0° and 30°.

5. Conclusion
The vertical PAD proﬁle of a rice canopy at different growthstages can be estimated through the use of a high-resolution por300.0

600.0

A

B

400.0

Actual dry weight of ears

y = 65.782x
R² = 0.9884
SE= 12.2 g m-2

500.0(gm-2)

Actual dry weight of stems and leavesof the canopy. This is not a problem for the lower PAD values of awheat canopy, but is likely to be a problem for the higher PAD values of a rice canopy (at maturity, the mean directly measured PAD
value of the rice canopy was 2.1 times that of the wheat canopy in
Hosoi and Omasa, 2009a). The mirror allowed the path length in
the rice canopy to be shortened by selecting more vertical angles.
At both values of hc, X was related to the PAD errors in eachlayer at all growth stages. Theoretically, when X > 1.0, the laser
beams can illuminate the entire horizontal plane, so the PAD error
decreases. When X < 1.0, some parts of the horizontal plane cannot
be illuminated, so the error increases owing to a lack of information about the canopy. In practice, X should be >2.0 (Fig. 7). The results in Fig. 7 show that laser beam coverage within the canopy canwell explain the errors of estimation of the PAD of a rice canopy,
and the value can be used to judge the accuracy of the PAD estimates. As shown in Eq. (2), the index X is composed of the laser
beam settings Abeam and N0, and the canopy structural attributes
K and LAIcum (LAIcum is equivalent to PAIcum in this study). K is expressed by G(hc) (cos hc)1, which depends on the leaf inclination
angle distribution and the angle of incidence of laser beams (equivalent to hc). Thus, factors affecting the accuracy of estimation of the
PAD of a rice canopy are PAIcum, Abeam, N0, the leaf inclination angle
distribution and the angle of incidence of laser beams.
Although our results were collected by a high-resolution portable scanning lidar and relate to rice (a grass), similar results pertaining to LAD estimation have been collected by a moderateresolution, portable scanning lidar and an airborne scanning lidar
in relation to broadleaf trees (Hosoi et al., 2010). This shows thegeneral applicability of X for different plant species and different
lidar instruments.

Although the PAD estimation errors were large when X < 2.0,
the errors were larger at hc = 0° than at hc = 30° (Fig. 7). The points
with the largest errors correspond to the low PAD estimates at
hc = 0° on 27 May and 17 June (Fig. 5A and B). The differences in
the PAD estimates on these dates between the two angles affected
the overall accuracy at all growth stages, resulting in the overall
better estimates at hc = 30° than at hc = 0° (Fig. 6). A possible causeof the difference is the difference in the quantity of obstructedleaves (blocked by other leaves) at each angle (Hosoi and Omasa,2007). The presence of these leaves causes an error in PAD estimation, even if the same number of laser beams intersect the canopy
at each angle. The degree of obstruction can be reﬂected in G(hc) in
Eq. (1) (Hosoi and Omasa, 2007). The degree of obstruction increases as the value of G(hc) increases. In our rice canopy, the meanvalues of G(hc) were 0.65 at hc = 0° and 0.59 at 30° on 27 May, and0.67 and 0.60 on 17 June. Thus, the obstruction is a possible causeof the difference in accuracy between the two angles.
Although the PAD values at hc = 30° were well estimated overall,they were greatly overestimated in the lowest layer in August. Thisoverestimation would relate to the distribution of leaf inclination
angle, which changed greatly from erectophile to planophile from
July to August with lodging. The planophile distribution increased
the probability of the laser beams’ hitting the leaves at the nearvertical beam angle, reducing the number of beams reaching thelowest layer. In the lowest layer, X = 4.7  103: this very low value explains the large error.
As in the wheat canopy (Hosoi and Omasa, 2009a), the dryweights of ears, leaves, and stems in the rice canopy could be also
estimated from a regression between the dry weight and lidar-derived area of each organ. Lidar-derived dry weight estimates canoffer useful information on growing conditions and for agricultural
management and carbon budgeting.
In the present study, use of an adjustable lightweight mirror removed a limitation of the allowable laser beam angle of incidence,
so that measurements at more vertical angles appropriate for aricecanopy could be effectively conducted. Consequently our proposed
method with the mirror greatly contributed to obtaining good results of the PAD estimation and useful knowledge about the structural features of a rice canopy and the measurement method. In the
future works, by selecting a long range lidar system and considering the setting points of the mirror and the lidar, this method may
be useable to mitigate the limitation of the laser beam angle ofincidence for other plants with tall canopies.

300.0
200.0
100.0

y = 614.14x
R² = 0.9406
SE= 7.9 g m-2

250.0
200.0
(gm-2)

Absolute error of PAD (m2 m-3)

7.00

150.0
100.0
50.0
0.0

0.0

0.00

2.00

4.00

6.00

8.00

10.00

Lidar-derived area of stems and leaves
per unit ground area (m2m-2)

0.00

0.10

0.20

0.30

0.40

Lidar-derived area of ears per unit ground area
(m2m-2)

Fig. 8. Relationships between lidar-derived area per unit ground area and actual dry weight. (A) Stems and leaves; (B) ears. SE, standard error.

18

F. Hosoi, K. Omasa / ISPRS Journal of Photogrammetry and Remote Sensing 74 (2012) 11–19

table scanning lidar together with a lightweight mirror and the
VCP method. The angle of incidence of the laser beam into the canopy needed to be nearly vertical to cope with the high PAD valuesof the canopy. This was achieved with a simple mirror installed
above the crop to deﬂect the laser beam. The PAD proﬁles at eachgrowth stage were computed by the VCP method from co-registered data. They were well estimated overall at the incidence angleof 30°, with a few exceptions (e.g. the lowest part on 14 August). It
was shown that the laser beam coverage within canopy expressedas the index X can well explain the errors of PAD estimates andthus the index can be used to judge the accuracy. Since the index
is composed of structural attributes of canopy and lidar settings,
the attributes and the settings were shown to be factors affecting
the accuracy of the PAD estimates in the rice canopy. X could be
used to estimate the accuracy of estimation in a rice canopy as wellas the other plant reported in previous works. These and previous
results show the general applicability of X to various plant species
and lidar instruments. The degree of obstruction of leaves could explain the difference in the PAD errors between the angles of incidence. The lidar-derived PAD could be used to estimate the dryweight of ears, leaves, and stems.
The use of a lightweight mirror released the limitation on theangle of incidence of the laser beam in the ﬁeld. In spite of the highdensity of the rice canopy, the PAD of different growth stages could
be well estimated by a near-vertical laser beam. To use the method,
it is essential to conﬁgure the lidar and mirror to maximize X andminimize G(hc). Future work should investigate the applicability of
our method to other ﬁeld conditions and species.

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