ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey

VOXEL TREE MODELING FOR ESTIMATING LEAF AREA DENSITY
AND WOODY MATERIAL VOLUME USING 3-D LIDAR DATA
F. Hosoi, Y. Nakai, K.Omasa

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku,
Tokyo 113-8657, Japan- ahosoi@mail.ecc.u-tokyo.ac.jp

KEY WORDS: Broad leaf, Leaf area density, Lidar, Point cloud, 3D, Voxel model, Woody material volume

ABSTRACT:In this work, the main focus is on voxel tree modeling using 3-D lidar data for accurate leaf area density (LAD) and woody material
volume estimation. For more accurate LAD estimation, the voxel model was constructed by combining airborne and portableground-based lidar data. The profiles obtained by the two types of lidar complemented each other, thus eliminating blind regions andyielding more accurate LAD profiles than could be obtained by using each type of lidar alone. Parts of the LAD profiles that were
underestimated even when data from both lidars were combined were interpolated by using a Gaussian function, yielding improved
results. A laser beam coverage index, Ω, incorporating the lidar’s laser beam settings and a laser beam attenuation factor, wasproposed. This index showed general applicability to explain the LAD estimation error for LAD measurements using different typesof lidars. In addition, we proposed a method for accurate woody material volume estimation based on a 3-D voxel-based solid
modeling of the tree from portable scanning lidar data. The solid model was composed of consecutive voxels that filled the outersurface and the interior of the stem and large branches. By using the model, the woody material volume of not only the whole target
tree but also of any part of the target tree can be directly calculated easily and accurately by counting the number of corresponding
voxels and multiplying the result by the per-voxel volume.
1. INTRODUCTION
Trees have important functional roles, including the cycling ofmaterials and energy through photosynthesis and transpiration,
the maintenance of microclimates, and the provision of habitats
for various species. To understand the relationship between
these functions and structure, many structural parameters,
including height, stem diameter, leaf area density, volume, andbiomass, have been measured by a variety of methods.
Recently, lidar (light detection and ranging) has been applied totree structural measurements (Hyyppä et al, 2001; Lefsky and
McHale, 2008; Popescu et al., 2003; Næsset et al., 2004; Hosoi
and Omasa, 2006, 2007, Hosoi et al., 2010, 2013). Portablescanning lidar systems can capture the complex shape andstructure of individual trees as a detailed 3-D point-cloud image
on the ground. Thus, 3-D tree models faithfully reproduced
from the lidar-derived 3-D point-cloud image can be used toestimate tree structural parameters. Several 3-D tree modelingtechniques have been employed for the estimation of individual
tree structural parameters from portable scanning lidar data
(Dassot et al., 2011). An approach to 3-D tree modeling basedon lidar-derived point-cloud data is voxel representation (Côtéet al., 2011; Hosoi and Omasa, 2006; Lefsky and McHale,
2008; Schilling et al., 2012). Hosoi and Omasa (2006) recently
proposed a voxel-based method of 3-D modeling that uses
portable scanning lidar data (Voxel-based Canopy Profiling
method). In this method, lidar data points are converted intovoxel elements in a 3-D voxel array to faithfully reproduce thecanopy as a voxel model. This method has been used for
estimating vertical leaf area density (LAD) profiles, offering theaccurate estimates when sufficient numbers of laser beams were
supplied to the canopy. However, the ground-levelmeasurements often showed a tendency to underestimate theupper canopy, when the laser beams are obstructed by the

middle and lower parts of the target canopy itself. Suchunderestimation should be improved. The improvement requires
the appropriate determination of the measurement configuration(e.g. laser beam settings, lidar position) according to the canopy
structure. Thus, a criterion to determine the appropriate
configuration is needed, but such a criterion has not yet been
proposed. In addition, although voxel modelling can be used toestimate other structural parameters besides LAD, the potential
has not yet been fully enhanced for retrieving other important
structural parameters such as woody material volume.
Based on the above, we propose a method to improve theaccuracy of LAD estimation of a canopy based on 3-D voxel
tree modeling by compositing airborne and portable scanninglidar systems. In addition, we propose a criterion to explain the
LAD estimation error and to be usable for determining
appropriate lider configurations. Moreover, a method toestimate woody material volume from a lidar-derived 3-D voxel
tree model is proposed.
2. METHODS
2.1 3-D data acquisition and registration
The target trees are scanned from several measurement points
surrounding them and laser beams are optimally inclined tofacilitate full laser beam illumination of whole target up to theinternal. Lidar data obtained in leafy and leafless conditions arerespectively used for estimating LAD profiles and woody
material volume. The complete data set is composed of several
point cloud data, one obtained from each of the several
measurement positions. These data with their individual
coordinate systems are registered into a single-point cloud data
set with a common coordinate system.

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-115-2013
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
2.2 Voxelization
All points within the registered data are converted into voxel
coordinates. All points within the registered data are converted
into voxel coordinates by the following equations.
 Y  Ymin  X  X min   1i  Int   1 , j  Int i j 

 Z  Z min   1 (1) k 

, k  Int 

where (i, j, k) represents the voxel’s coordinates within thevoxel array, Int is a function that rounds off the result of thecalculation to the nearest integer, (X, Y, Z) represents the point
coordinates of the registered lidar data, (Xmin, Ymin, Zmin)
represent the minimum values of (X, Y, Z) and (i, j, k)represent the voxel element size. Voxels corresponding tocoordinates converted from points within the registered data areassigned 1 as the attribute value. A voxel with the attributevalue of 1 represents a voxel in which at least one laser beam isintercepted by leaves or woody material. For retrieving woodmaterial volume, these voxels with the attribute value of 1 areused for the subsequent processes (details are explained in 2.4).
On the other hand, for retrieving LAD, additional voxels thatare assigned as the other attribute value are required for thecomputation. For this reason, all laser beams emitted from thelidar positions are then traced within the voxel array inaccordance with the actual laser beam angles. If voxels that do
not have an attribute value of 1 are intersected by at least one
laser beam trace, the voxel was assigned 2 as the attribute value.
A voxel with an attribute value of 2 therefore represents a voxel
through which one or more laser beams passed without
touching a leaf. .
2.3 Derivation of LAD and laser beam coverage index
2.3.1 LAD computationn I (k )cos θ 1 mh  H
G (θ) H k  mh n I (k )  n P ( k )  Abeam  N exp(  K  LAI cum )(3)

The index Ω is defined as the total horizontally projected areaof the incident laser beams per unit area of a horizontal planewithin the canopy at certain height. This is also translated as the
proportion of the area of a horizontal plane within the canopy
covered by laser beams at certain height. Abeam and N are
determined from the laser beam settings. K∙LAIcum is calculated
for each height by using the voxel attributes, as follows:
K  LAI cum nI (k )mh

 n ( k )  n (k )k  mbottom

I

(4)

p

where nI (k) is the numbers of voxels at which laser beams areintercepted and nP (k) is the ones through which laser beams
passed. mh, and mbottom are the voxel coordinates on the verticalaxis equivalent to height h and the heights of the canopy bottom.
Eq.(4) is for ground-based portable lidar measurements, but it
can be used for airborne lidar measurements, by replacing mhwith mtop (equivalent to height of the canopy top) and mbottom
with mh .
2.4 Derivation of woody material volume

Based on the attribute values of 1 and 2, LAD is computed ineach horizontal layer of the canopy using the following
equation:
LAD(h, H ) beams per unit area of a horizontal plane (N), and a laser beam
attenuation factor (K∙LAIcum). In the laser beam attenuation
factor, K is a parameter for the influence of the leaf inclination
angle and the laser beam direction on laser beam attenuationand is equivalent to the inverse of the correction factorcos()[G()]-1 described in eq.(2). LAIcum is the lidar-derived
cumulative LAI at a certain height. K and LAIcum reflect thestructural attributes of the canopy, while Abeam and N relate to
the laser beam settings. The laser beam coverage index Ω ismade in relation to these three factors as follows:(2)

where  is the zenith angle of a laser beam, H is the horizontal
layer thickness, and mh and mh+H are the voxel coordinates on
the vertical axis equivalent to height h and h+H in orthogonal
coordinates (h = k × mh). nI(k) and nP(k) are the numbers ofvoxels with the attribute values of 1 and 2 in the kth horyzontal
layer of the voxel array, respectively. Thus, nI(k) + nP(k)represent the total number of incident laser beams that reach thekth layer and nI(k)/(nI(k) + nP(k)) represents contact frequencyof laser beams on the canopy. G() is the mean projection of aunit leaf area on a plane perpendicular to the direction of thelaser beam at ). The term cos()[G()]-1 is a correction factor
for the influence of leaf inclination angle and laser beam
direction. cos()[G()]-1 is determined using the distribution ofleaf inclination angles.
2.3.2 The laser beam coverage index
The laser beam coverage index Ω is an indicator of the
proportion of the area of a horizontal plane within the canopy
covered by laser beams. The index can be used as a criterion toestimate accuracy of lidar-derived LAD estimation and to
appropriately determine laser beam settings. Three factors that
relate to the coverage are selected: the horizontally projectedarea of the laser beam (Abeam ), the number of incident laser

For estimating woody material volume, a voxel solid model ofthe entire tree is produced and used, at which consecutive
voxels fill the outer surface and the interior of the stem andlarge branches. Although this is categorized as voxel treemodelling, the procedures are different from the ones used in
LAD estimation. The procedures of the model production andvolume estimation are the following three steps.
2.4.1 Separating the stem and large branches from small
branches
After the initial voxelization of the target (section 2.2), thevoxels of the stem and larger branches is separated from thoseof the small branches. First, several voxels corresponding to thestem or large branches are manually picked up from thevoxelized 3-D model. These are used as starting points forseparating the stem and large branches from small branches.
Then, voxels neighbouring these initial voxels within a certain
distance are categorized as stem or large-branch voxels. The
categorized voxels then become new initial (starting) points andthe above searching procedure is repeated. This procedure isiterated until no more voxels can be categorized as stem or large
branches. Then, all of the remaining voxels are categorized as
small-branch voxels (see Hosoi et al., 2013 for the details).
2.4.2 Surface generation
After the above separation, a surface generation procedure isapplied. The surface generation procedure interpolates
additional voxels between the initial voxels of each one-voxelthick horizontal layer of the voxelized model to obtain acontinuous closed curve representing the exterior surface of theThis contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
stem and each large branch.
To generate the surfaces, the voxels within a horizontal layer
are classified into sets, each one corresponding to the stem or toa large branch. To do this, a grid is first superimposed on eachhorizontal voxel layer. The grid size is chosen to be larger thanboth the original voxels and the laser beam pitch (Fig. 1B).
Then, each cell of the grid that included at least one voxel isfilled (labeled) and Moore's neighbor-tracing algorithm is used
to connect adjacent filled cells to trace the approximate contourof the exterior surface of the stem or large branch (Ghuneim,2009). In this method, cells in each row of the grid, startingfrom the top row and proceeding to the bottom row, are scanned
from the leftmost to the rightmost. Then, the first-encounteredfilled cell is treated as a starting cell and the eight neighbouring
cells are examined in a clockwise direction until another filledcell is encountered; this encountered cell is then treated as thenext cell in the contour. These steps are repeated until thestarting cell is again encountered. This procedure yields a
closed curve outlining the stem or a large branch. During thisprocedure, the starting cell and each selected cell are labeled (acertain value is given to each cell as its attribute value), thus
producing a set of labeled cells corresponding to the stem or toa large branch. Next, scanning of the grid is repeated, skipping
already labeled cells, and the first-encountered filled andunlabeled cell is treated as the next starting cell. The sameprocedure is applied to the other filled cells, with different
labels being used to discriminate the stem and each large branch(Fig. 1B: (a), (b), and (c)). In this way, the voxels included in
the cells are classified into sets, each of which corresponded
either to the stem or to a specific large branch.

is equivalent to the minimum and the last voxel to the
maximum x-coordinate value along the scan line, and all empty
voxels between the starting and ending voxels are labeled. This
procedure is repeated for each voxel along the contour in boththe x and y directions. Then, those voxels that have been labeled
twice (that is, by both the x- and y-direction scans) are regarded
as interior voxels of that contour and filled them. This interior
filling procedure is applied to the stem and all large branches
within each horizontal voxel layer, from the lowest to thehighest horizontal layer. In this way, both the surfaces andinteriors of the entire stem and all large branches are filled withconsecutive voxels. Then a 3-D voxel-based solid model of thewhole target tree is produced by merging the filled stem andlarge branches with the voxels previously categorized into small
branches (Sec. 2.4.1).

(A)
(B)
A
B
(a)

(b)

(C)
C

(c)

(D)
D
Y

Next, a contour interpolation procedure is applied to each set ofclassified voxels (Fig. 1C) within a horizontal layer. Here, thesuperimposed grid cells described above are no longer used. To
generate contours of the exterior surfaces of the stem and eachlarge branch composed of consecutive voxels, gaps (empty
voxels) between voxels in a voxel set are filled by linear
interpolation. First, voxels that should not be included in acontour are excluded as noise (Fig. 1C). To judge whether avoxel is noise, the mean (dm) and standard deviation (σ) of the
distance between each voxel and the centroid of the voxel set is
calculated. Voxels at a distance of more than dm + 3σ areregarded as noise and excluded them from the set. Next, Theangle between the x-axis and each line segment connecting avoxel with the centroid of the voxel set is calculated. The angles
are then sorted in ascending order to determine the tracking
order of each voxel. Then, to generate the contour, a certain
tracked voxel are picked and then the empty voxels between
that voxel and the next filled voxel are filled in the tracking
order by linear interpolation (Fig. 1C). This procedure isrepeated until all voxels composing the contour of the exterior
surface of the stem or large branch have been filled (that is,until a continuous contour composed of consecutively filled
voxels have been generated). This procedure is applied to eachclassified voxel set within a horizontal voxel layer and repeatedfrom the lowest to the highest horizontal layer, thus generatingexterior surfaces of the stem and each large branch composed ofconsecutive voxels.
2.4.3 Internal filling and volume estimation
The interiors of the stem and each large branch are filled byscanning between the voxels of a contour in both the x and y
directions (Fig. 1D). For instance, for a scan in the x direction, astarting voxel near the lower left of the contour is picked and aline is traced in the positive x direction from the starting voxel
to the voxel on the opposite side of the contour. The first voxel

X

Noise

X

Centroid

Figure 1: Generation of the exterior surface contours of thestem and large branches. (A) Original voxels within a horizontal
layer obtained by conversion of the lidar data points. (B) A grid
was overlaid on a one-voxel-thick horizontal layer, and voxel
sets (a), (b), and (c), corresponding to the contours of the stemand each large branch are identified by connecting cells using aneighbor-tracing algorithm. (C) Contours of the stem and oflarge branches are filled by interpolating voxels (light gray)between the original voxels. (D) Interior voxels are identified
and filled by scanning in the x and y directions from voxels on
the contour (Hosoi et al., 2013).
The volume of the woody material is calculated from thegenerated 3-D voxel-based solid model. This model iscomposed of consecutive voxels that filled the outer surfaces
and the interiors of the stem and large branches, and a cloud ofvoxels equivalent to small branches that are discretely scatteredin mainly the upper part of the target. By using the model, we
can easily estimate the woody material volume of any part of
the tree by counting the number of voxels within the part andthen multiplying the number of voxels by the unit voxel volume.

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-115-2013
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
3. EXPERIMENTS
3.1 LAD estimation for broad-leaved woody canopy using a
portable and airborne lidar
The experiment was conducted in a mixed plantation in Ibaraki
Prefecture, 40 km northeast of central Metropolitan Tokyo,
Japan (Hosoi et al., 2010). The topography was nearly flat. A
4×8-m measurement plot was established at the site (Fig. 2 (A)),
and the Japanese zelkova canopy within the plot was used forthe experiment. The plot was divided into eight 2  2-m
quadrats (Fig. 2 (B)) and each vertical region within each of thequadrats was divided into 16 cells (each 2  2  0.5 m)
between heights of 5 to 13 m above the ground. The entire
canopy within the measurement plot was thus divided into 128
cells. The actual LAD in each cell was measured directly bystratified clipping in September 2005, the month following thelidar measurements of the leafy canopy.
(B) 11 Side A

(A)

2

2

2m

2m

Side A

1-1

2-1

1-2

2-2

1-3

2-3

1-4

2-4

3

3

Y

Side B

X

10 m
1

1
Side B2

2

3

Gaussian function. As a result, LAD profile was improved by
the interpolation with the MAE (Mean Absolute Error) of 0.13
m2 m-3. Although a portable scanning lidar used in thisexperiment (LPM-25HA) was an old model, up-to-date lidar
systems that record multiple returns or full-waveforms can get
more information about canopy inside. The use of such
instruments would improve the accuracy of the present method.

3

Figure 3: A 3-D image of the Japanese zelkova canopy at the
study site, obtained by registration of the images measured fromabove with the airborne lidar and from the six ground positions
with portable ground-based lidar (the data obtained from theairborne lidar are colored red: Hosoi et al., 2010) .

Figure 2: Study area. (A): Aerial photograph (B): A
measurement plot established beneath the zelkova canopy.
Arrows in (b) show the directions in which lidar scanning was
performed (modified from Hosoi and Omasa, 2007).

ActualAirborne
PortableInterpolation

12.0

Height (m)10.0

In this experiment, not only ground–based portable scanninglidar but also airborne lidar was used for LAD estimation
because the combination of airborne and portable ground-based
scanning lidar could potentially allow accurate estimation of
LAD by eliminating the blind regions of each lidar.
Measurements using portable ground-based scanning lidar(LPM-25HA, RIEGL, Austria; the range accuracy of ± 8 mm)
were conducted in August 2005 as reported in (Hosoi et al.,2010). The measurement plot and the area around the plot werealso scanned in August 2005 from above by a lidar mounted ona helicopter (ALTM 3100 DC, Optech Co., and Aero Asahi Co.,
Japan). The range accuracy and horizontal accuracy were
within 15 and 13 cm, respectively. The footprint interval, i.e.
the distance between centers of adjacent laser beams on the
ground, was 0.29 m in the direction of the scan and 0.26 m inthe direction of flight.

Figure 4: Comparison of LAD profiles for the measurement plot
estimated from airborne and portable ground-based lidar
measurements and the actual LAD profile (modified form Hosoi
et al., 2010).

3-D point cloud data obtained by airborne and portable groundbased lidars were registered into a common coordinate system
as shown in Fig.3. To facilitate the registration between the datataken by different lidar systems, houses neighbouring the study
area were scanned by the two lidars and used as references forthe registration. Figure 4 shows the LAD profiles for the entire
measurement plot, obtained from actual measurements and fromthe two lidars. The portable ground-based lidar data and theactual ones were cited from (Hosoi and Omasa 2007). LAD
estimates from the airborne and portable ground-based lidar
data agreed well with the actual values for the upper and lower
canopy, respectively, but underestimated the values at around
10 m height. Thus, the profile needed to be interpolated with a

The relationships between the laser beam coverage index, Ω(Eq. 3), and the absolute error of the LAD estimates are shownin Fig. 5. In spite of the different laser beam settings of the two
lidars, the points from both lidars in the scatter plot were foundin the same general region on the plot, indicating that therelationship between the index Ω and the LAD estimation error
was similar between the two lidars. The absolute errors of bothlidars began to increase drastically when Ω decreased to around1.0. Large errors were associated with Ω less than or equal toapproximately 1.0, whereas smaller errors were associated withvalues of Ω increased above 2.0. When the value of Ω exceeds
1.0, the laser beams can illuminate the entire horizontal plane,
but when it is less than 1.0, then some parts of the horizontal

8.0

6.0

4.0
0.00

0.40

0.80

1.20
2

1.60

2.00

-3

LAD (m m )This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
plane cannot be illuminated by the laser beams. In the former
case, the LAD error decreases because the number of laser
beams incident into the canopy is sufficient, but in the latter
case, the error increases due to a lack of information about the
canopy caused by an insufficient number of laser beams
incident into the canopy. This explains the results shown in
Fig.5, in which the LAD error began to increase drastically atvalues of Ω near 1.0. Thus, for better LAD estimation, the valueof Ω should exceed 1.0. In practice, the value of Ω should be
more than 2.0 to be certain of obtaining accurate results.
Therefore, in practical lidar measurements, the measurement
settings such as the laser beam settings and the lidar positions
should be chosen such that Ω becomes as large as possible
exceeding 2.0. In addition, it is notable that a similar tendency
was observed in the relationship between Ω and LAD error forboth airborne and ground-based lidars in spite of their different
setting values for Abeam and N, as shown in Fig. 4. This meansthat Ω can be used to assess the LAD error of lidar
measurements made by using different settings and alsosuggests that Ω, that is, the proportion of the area covered bylaser beams in a horizontal plane, is an essential and practicalfactor that relates to estimation accuracy in lidar-based LAD
measurements.

Absolute error of LAD (m2 m-3)

1.60

m, most of the total volume was accounted for by the stem andlarge branches, but above 9.00 m height, most of the volume
was in small branches. In the whole target, the volume of thestem and large branches was 0.417 m3, and that of the small
branches was 0.135 m3, for a total volume of 0.552 m3 for thewhole target.
Direct stem and branch measurements and model-estimatedstem and branch volume demonstrated strong agreement (Fig.
8). The mean absolute percentage error, obtained by calculating
the absolute percentage error in each height interval and thenaveraging them, was 6.8%. The error of the total volume of thepart, obtained by summing the volumes of each height interval,
was 0.5%. The estimated percentage error corresponding to thesampled part of the small branches, obtained by comparing theestimated volume with the directly measured value, was 34.0%.
One advantage of this volume estimation method comparedwith other methods that use allometric equations orapproximations of stem and branch shapes is that no allometricequations are needed and shapes are not approximated, so thatthe complex shaped tree volume can be estimated accurately.
Another advantage is that by using the model, the woody
material volume of not only the whole target tree but also of
any part of the target tree can be directly calculated easily andaccurately by counting the number of corresponding voxels andmultiplying the result by the per-voxel volume.

Airborne
Portable

1.20

(A)

0.80

0.40

0.00
0.00

1.00 2.00 3.00 4.00 5.00
Laser beam coverage index 

6.00

Figure 5: Relationship between the laser beam coverage indexΩ (= Abeam × N exp(–K LAIcum); Eq. 3) and the absolute errors of
LAD in all airborne and portable ground-based lidar
measurements (Hosoi et al., 2010).

(B)

(C)

3.2 Volume estimation for a broad-leaved tree
This experiment was conducted in January 2010, during leaf-off
conditions, with a portable ground-based scanning lidar (Hosoi
et al., 2013). The measurement site, portable scanning lidar
system and lidar-set-up were similar to the ones explained in
3.1. Fig.6 B shows the 3-D voxel-based solid model of thezelkova tree generated from the 3-D point cloud data obtainedby the portable scanning lidar system. The voxel size was 0.5
cm × 0.5 cm × 0.5 cm. The small branches were well separated
from the stem and large branches, and a cross-sectional view ofthe stem shows that both the outer surface of the model and its
interior are composed of consecutively filled voxels (Fig. 6C).
Examination of the estimated volume at each height (Fig. 7)
showed that the volume of the stem and large branches
increased as the height decreased, reaching its maximum in thelowest height interval (Fig.7). Conversely, the volume of thesmall branches increased with height and reached its maximum
value at 12.00 m. As a result, between heights of 0.00 and 9.00

Figure 6: The 3-D voxel-based solid model of the zelkova tree
generated from the 3-D point cloud data obtained by portablescanning lidar. (A) A point cloud image of a group of zelkova
trees before voxelization. Area enclosed by dashed lines is thetarget tree within the measurement plot. (B) The 3-D model ofthe target tree: small branches are white. (C) Stem and large
branches only. In the cross-section of a part of the stem, theinterior voxels are colored white (modified from Hosoi et al.,2013).

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-5/W2, 2013
ISPRS Workshop Laser Scanning 2013, 11 – 13 November 2013, Antalya, Turkey
14.00

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12.00
Small branches

Height (m)10.00

Stem and large branches
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8.00

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6.00
4.00
2.00
0.00
0.00

2.00

4.00

6.00

8.00

10.00

Volume (10-2 m3)


Figure 7: The estimated volume of the stem and large branches,
the estimated volume of the small branches, and the estimatedtotal volume, derived from the voxel-based solid model bycounting voxels in each 1.0-m height interval (Hosoi et al.,2013).
8.00

6.00
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Hosoi, F., Omasa, K., 2006. Voxel-based 3-D modeling ofindividual trees for estimating leaf area density using highresolution portable scanning lidar. IEEE Transactions on
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Hosoi, F., Nakai, Y., Omasa, K., 2010. Estimation and error
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2.00
1.00
0.00

0.00

Ghuneim, A.G., 2009. “Moore-Neighbor Tracing Idea”.
http://www.imageprocessingplace.com/downloads_V3/root_do
wnloads/tutorials/contour_tracing_Abeer_George_Ghuneim/moore.html (8 June, 2013).

Hosoi, F., Omasa, K., 2007. Factors contributing to accuracy inthe estimation of the woody canopy leaf-area-density profile
using 3D portable lidar imaging. Journal of Experimental
Botany, 58 (12), pp.3464-3473.

Directly measured
Estimated

7.00

Height (m)Côté, J. F., Fournier, R.A., Egli, R., 2011. An architecturalmodel of trees to estimate forest structural attribute using
terrestrial LiDAR. Environmental Modeling & Software, 26 (6),pp.761-777.

0.50

1.00

1.50

2.00

2.50

Volume (×10-2 m3)

Figure 8: Comparison of the woody material volume of acertain part of the target tree estimated from the voxel solid
model and the directly measured volume for each 0.30-m heightinterval (modified from Hosoi et al., 2013).
4. CONCLUSIONS
Accuracy of lidar-derived LAD estimation using the voxel treemodel of a broad leaved canopy were improved by combining
airborne and portable ground-based lidar data in conjunction
with Gaussian interpolation. A proposed laser beam coverageindex Ω could explain the LAD estimation error even indifferent types of lidars. This index is useful for knowing LAD
estimation accuracy without destructive measurements anddetermining the appropriate laser settings for more accurate
LAD estimation. Voxel solid modeling using lidar data could
offer accurate estimation of woody material volume. Thismodeling method utilizes almost all spatial information
contained in the lidar-derived 3-D point cloud data to faithfully
reproduce the complex shape of the target without any tree
shape approximation. Thus, this method is suitable for woody
volume estimation of complex shaped trees. Future studies
should investigate the applicability of the voxel modeling using
lidar data to other regions with different conditions and species,
and to different tree structural parameters.

Hosoi, F., Nakai, Y., Omasa, K., 2013. 3-D voxel-based solid
modeling of a broad-leaved tree for accurate volume estimation
using portable scanning lidar. ISRPS Journal of
Photogrammetry and Remote Sensing, 82, pp.41-48.
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segmentation-based method to retrieve stem volume estimatesfrom 3-D tree height models produced by laser scanners. IEEE
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Lefsky, M., McHale, M., 2008. Volume estimates of trees withcomplex architecture from terrestrial laser scanning. Journal ofApplied Remote Sensing 2; doi: 10.1117/1.2939008.
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Popescu, S.C., Wynne, R.H., Nelson, R.F., 2003. Measuring
individual tree crown diameter with lidar and assessing its
influence on estimating forest volume and biomass. Canadian
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representation from TLS point clouds using depth-first search invoxel space. Photogrammetric Engineering and Remote
Sensing, 78(4), pp.383-392.

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
doi:10.5194/isprsannals-II-5-W2-115-2013120