Environ Model Assess (2008) 13:473–481
DOI 10.1007/s10666-007-9115-5

Three-Dimensional Modeling of an Urban Park and Treesby Combined Airborne and Portable On-Ground Scanning
LIDAR Remote Sensing
K. Omasa & F. Hosoi & T. M. Uenishi &Y. Shimizu & Y. Akiyama

Received: 26 July 2006 / Accepted: 2 June 2007 / Published online: 3 July 2007
# Springer Science + Business Media B.V. 2007

Abstract In this study, we confirmed the utility of airborneand portable on-ground scanning light detection andranging (LIDARs) for three-dimensional visualization ofan urban park and quantification of biophysical variables oftrees in the park. The digital canopy height model (DCHM)
and digital terrain model generated from airborne scanning
LIDAR data provided precise images of the ground surface
and individual tree canopies. The heights of 166 coniferous
and broadleaf trees of 11 species in the park were estimatedfrom the DCHM images with slight underestimation (meanerror= −0.14 m, RMSE = 0.30 m). Portable on-groundscanning LIDAR provided images of individual trees withdetailed features. Tree height and trunk diameter were
estimated to be within 0.31 m and 1 cm, respectively, fromthe on-ground LIDAR images. We combined airborne andon-ground LIDAR images to overcome blind regions andcreated a complete three-dimensional model of threestanding trees. The model allowed not only visual assessment from all viewpoints but also quantitative estimation of
canopy volume, trunk volume, and canopy cross-sectionalarea.

K. Omasa (*) : F. Hosoi : T. M. Uenishi : Y. Shimizu
Graduate School of Agricultural and Life Sciences,
The University of Tokyo,
Yayoi 1-1-1,
Bunkyo-ku, Tokyo 113-8657, Japane-mail: aomasa@mail.ecc.u-tokyo.ac.jp
Y. Akiyama
Aero Asahi Co.,
Shinsayama 1-18-1 Sayama,
Saitama 350-1331, Japan

Keywords Three-dimensional modeling . FP-mode DEM .
DTM . Composite remote sensing . Scanning LIDAR .
Tree measurement . Urban park . Visual assessment

1 Introduction
Urban parks provide social and psychological benefits tocity dwellers and contribute to the amenity of urban environments. Time spent in parks can reduce stress, enhancecontemplativeness, rejuvenate people, and provide peace
and tranquillity [8, 16, 38]. The vegetation in parks canpurify the air, reduce noise, and stabilize the microclimate[1, 17, 31]. Remote sensing from satellites and aircraft has
been widely used to assess urban parks and functions ofvegetation [10, 27]. While ordinary passive remote sensing
provides useful two-dimensional information for assessments, three-dimensional data may improve understandingof the landscape and tree functions in urban parks [7, 13,
19, 26, 34]. Computer graphics is also capable for visualassessment of urban parks [6, 18]. By joining threedimensional observation and computer graphics, it ispossible to produce detailed three-dimensional visualmodels of landscapes and trees for visual assessment.
Light detection and ranging (LIDAR), which uses a laser
scanner to measure the distance between the sensor andtargets, is one of the most accurate tools for threedimensional measurement. Airborne scanning LIDAR has
been used to acquire three-dimensional information on
landscapes and trees [20, 25, 29]. In particular, the use ofhelicopter-mounted scanning LIDAR with a small footprint
and high pulse frequency allows the production of threedimensional images with a resolution of several tens ofcentimeters and a precise grid [22, 28, 30]. The utility of474

airborne scanning LIDAR has been also demonstrated inurban areas [9, 24]. Thus, it could be used for the threedimensional reconstruction of an urban park. However,airborne LIDAR cannot capture some regions, such as thelower parts of trees, because the laser beams can be obscuredby the upper canopy. These blind regions, i.e., the region
that LIDAR cannot capture due to the obstruction of laser
beam penetration by foliage or other tree tissues, might
reduce the quality of the three-dimensional reconstruction
and compromise the assessment of urban parks.
Meanwhile, portable on-ground scanning LIDAR has
been used for three-dimensional reconstruction of objects[33, 35] and for tree measurements [11, 12, 21, 29, 32, 36].
On-ground scanning LIDAR could compensate for blind
regions in airborne LIDAR data because its position on the
ground allows capture of the lower regions of trees thatairborne scanning LIDAR misses, but conversely, it may
not capture some upper regions of trees. Thus, thecombination of data from airborne and on-ground LIDARs
would allow the creation of a complete three-dimensionalmodel of trees without any blind regions.
In this study, we demonstrated the capabilities andlimitations of high-resolution airborne scanning LIDAR andportable on-ground scanning LIDAR for three-dimensional
visualization of an urban park and for quantification ofbiophysical variables of trees growing in the park. In addition,
we produced a complete three-dimensional model of trees andcomputed more descriptive variables of the trees through the
combination of airborne and on-ground LIDARs.
Fig. 1 Aerial photograph of the
Shinjuku Gyoen National Garden in Tokyo. 1 Three Metasequoia glyptostroboides (dawn
redwoods; see Fig. 3). 2 Liriodendron tulipifera (yellow
poplar). 3 Prunus × yedoensis(Yoshino cherry; see Fig. 5).
The broken line shows the
position of the French Formal
Garden (see Fig. 4b)

K. Omasa et al.

2 Materials and Methods
2.1 Study Site and Ground-Truth Data
We selected an urban park, the Shinjuku Gyoen National
Garden in the center of Tokyo, as our study site (Fig. 1).
About 250 species and 2,000 trees grow there. Tree height,
trunk diameter, and canopy diameter were chosen as therepresentative variables to confirm LIDAR’s ability toquantify tree variables in the park. Then, we trigonometrically measured the heights of 166 coniferous and broadleaf
trees of 11 species on the ground in October 2001 with arange finder (Lasertape FG-21-HA, RIEGL, Austria) withan accuracy of ±5 cm. We also measured the trunk diameterand canopy diameter of each tree by tape measure.
2.2 Airborne Scanning LIDAR Data
Figure 2 shows a schematic diagram of three-dimensional
remote sensing by the combination of airborne and onground LIDAR data. High-repetition laser pulses weredirected from a LIDAR mounted on a helicopter (ALTM
1225 special model, Optech and Aero Asahi, Japan [28,30]) onto the canopy and ground surfaces at the study site.
The distance to an object was calculated from the elapsed
time between the emitted and returned pulses. This airborne
LIDAR has two receiving modes: first-pulse mode (FPmode) in which first returned pulses are detected and lastpulse mode (LP-mode) in which the last returned pulses areThree-dimensional modelling of urban park and trees by LIDAR remote sensing

475

Fig. 2 Schematic diagram ofthree-dimensional remote sensing by combined airborne andportable on-ground scanning
LIDAR. a Generation of FPmode DEM, DTM, and DCHM
images from airborne scanning
LIDAR data. b Generation of
the three-dimensional point
cloud image from the on-ground
LIDAR data. c Merging of theairborne and on-ground
LIDAR images

detected. Laser pulses illuminating the canopy surface werereceived as FP-mode data, which were used to generate animage of the woody outer canopy. Pulses that reached theground surface were received as LP-mode data, which wereused to generate a terrain image. The laser wavelength was
1,064 nm, and its repetition frequency was 25,000 Hz. The
scanning frequency was set to 20 Hz. The scanning angle
was 20.0°.
Using this system, we measured the study site (600×700 m)
at a flight speed of 50 km/h and a flight height of 300 m in
November 2001. The range accuracy was within 15 cm. The
beam divergence was 1.0 mrad, and the footprint diameter on
the ground was estimated to be about 30 cm. The footprintinterval on the ground was 21.2 cm in the direction of the scanand 34.7 cm in the direction of flight. By comparing thediameter and interval of footprints on the ground, we could
confirm that the laser pulses covered most of the woody
canopy. The three-dimensional geographic position wasdetermined from a helicopter-borne inertial measurement unit
(IMU) and high-resolution global positioning system (GPS)receivers both in the helicopter and on the ground.

object between 2 and 60 m away, without the use of areflector, using the elapsed time between the emitted andreturned laser pulses. The laser beam diameter was 20 to
60 mm when the measuring distance was 20 to 60 m. The
point at which the infrared (905 nm) laser for range
measurement was aimed was determined from a visible
red (∼650 nm) laser pointer. A rotating mount run by a

2.3 Portable On-ground Scanning LIDAR Data
Three Metasequoia glyptostroboides (dawn redwood) trees
growing at the study site were measured from five groundmeasurement positions in October by portable on-groundscanning LIDAR (LPM-25HA range-finder, RIEGL), as
shown in Fig. 3. Liriodendron tulipifera (yellow poplar)and Prunus × yedoensis (Yoshino cherry) were alsomeasured from five and four ground positions, respectively.
The positions were selected to surround the trees. The
LIDAR could measure the distance to the surface of anFig. 3 Three Metasequoia glyptostroboides trees at the study site. The
broken line shows the target area where the M. glyptostroboides grow.
Five crosses and arrows indicate the measurement positions andscanning directions of the portable on-ground scanning LIDAR. Fourwhite circles within the target area show the positions of tooling balls
placed as references for merging the on-ground LIDAR data. The inset
shows a close-up of the tooling ball. The huge tree on the left of thethree trees is a Liriodendron tulipifera

476

built-in step motor with 0.009° accuracy that could pan andtilt the LIDAR head allowed angular movement of the
instrument. The LIDAR had an accuracy of ±8 mm incomputing the range of each sample point.
2.4 Generation of Digital Models from Airborne Scanning
LIDAR Data
Figure 2a shows the steps in generating the FP-mode digital
elevation model (DEM), digital terrain model (DTM), anddigital canopy height model (DCHM) of the study site.
These models were produced with modified software madeby TopScan, ERDAS IMAGINE (Leica Geosystems GIS &Mapping) and our own software from the airborne scanning
LIDAR data. The point coordinates of both FP-mode andLP-mode data were determined from the GPS data
coordinates recorded in the helicopter and on the ground.
The DEM of the canopy surface was generated from FPmode data. The DTM of the ground surface was generated
by interpolating the extracted ground-level data from theLP-mode data. The DCHM was generated by subtracting
the DTM from the FP-mode DEM [28, 30]. To remove
spike noise, the image was pre-processed with a medianfilter with a mask size of 3×3 pixels.
The visual quality of the FP-mode DEM and DTM
images was assessed by checking the shapes of constructions, trees, and the ground surface within the images. To
confirm the airborne LIDAR’s ability to quantify tree
variables, we extracted the heights of 166 trees in 11
species by manually selecting treetops from the DCHM andcomparing them with ground-truth data.
2.5 Merging of Portable On-Ground Scanning LIDAR Data
Figure 2b shows the steps in merging the on-ground
LIDAR data. To remove spike noise, the data were preprocessed by a median filter with a mask size of 10×10 pixels,
as determined according to the noise level. The data werethen merged into a single coordinate system using theiterative closest point (ICP) algorithm [5]. This algorithm
starts with an initial estimate of corresponding points
between two LIDAR datasets measured from different
positions. Based on the corresponding points, the data are
co-registered through rigid-body transformation. The transform was then iteratively refined by alternately choosing
corresponding points in the LIDAR data and finding thebest translation and rotation matrices that minimize an error
metric based on the distance between them. This procedure
was used for all pairs of LIDAR data. Finally, LIDAR datafrom all positions were merged as a three-dimensional point
cloud image, which is a set of points measured in threedimensional space. A good initial estimate of the rigid-bodytransform is necessary to obtain an accurate point cloud

K. Omasa et al.

image. Therefore, we placed four tooling balls made fromstyrene foam around the standing trees for reference asshown in Fig. 3.
The visual quality of the images was assessed bychecking the shapes of L. tulipifera and P. × yedoensis
trees. For confirmation of the on-ground LIDAR’s ability toquantify tree variables, we compared the LIDAR-derived
heights and trunk diameters of three M. glyptostroboides
trees with ground-truth data.
2.6 Merging Airborne and Portable On-Ground Scanning
LIDAR Data
Figure 2c shows the steps in merging the airborne and onground LIDAR data. Three M. glyptostroboides trees werechosen for the merging because images of these trees
obtained from both LIDARs included blind regions. We
selected the portion of DCHM data that included the stand.
To focus on the Metasequoia trees alone, we eliminated ahuge L. tulipifera tree covering part of these trees (seeFig. 3). The DCHM data were then triangulated by the
Delaunay triangulation method [2, 3] to produce a threedimensional polygon image. This was processed by a
Laplacian smoothing filter [15, 37].
The point cloud image generated from the on-ground
LIDAR data was registered and overlapped on the polygon
image generated from airborne scanning LIDAR data by
taking corresponding points between the two LIDAR
images as the reference points (e.g., treetops or intersections of the trees). The blind regions within the polygon
image of the airborne scanning LIDAR were identified bycomparing the two images. The point cloud data equivalent
to the blind regions, which were the canopies hidden by the
Liriodendron and the understructure as shown in Fig. 6aand b, were extracted from the point cloud image tocomplement the blind regions. After the resolution of theextracted point cloud data was adjusted to that of theairborne LIDAR data, the point cloud data were threedimensionally triangulated by Delaunay triangulation andthe ball-pivoting algorithm [4] to produce three-dimensional
polygon images. Finally, the polygon images from theairborne and on-ground LIDARs were merged.
2.7 Computation of the Biophysical Variables of Trees
from the Final Three-Dimensional Model
The final three-dimensional model of M. glyptostroboides
trees still included the ground surface. The height of theground surface was identified from the height histogram of
all point cloud data within the model. The histogram ofpoint cloud data corresponding to the ground surface
formed a large sharp peak including the lowest height.
The trees and ground surface were separated on the peak.

Three-dimensional modelling of urban park and trees by LIDAR remote sensing

Then, we computed the individual tree height, trunk
diameter, and maximum canopy diameter and comparedthem with ground-truth data to evaluate the accuracy of themodel. Tree trunks were defined as the portion from groundlevel to 1.3-m height, corresponding to the height of thelowest foliage, and the maximum canopy diameter was
defined as the maximum external diameter on a horizontal
cross-section at a certain height. We also computed more
descriptive tree variables: canopy volume, trunk volume,and cross-sectional area.

3 Results
Figure 4a shows a three-dimensional view of the FP-mode
DEM image of the study site where trees, buildings, streets,and railways are reproduced. The close-up image in Fig. 4bshows a detailed view of the French Formal Garden (whitebroken line in Fig. 1) where distinctive features such asrectangular flowerbeds, shrubberies surrounding the beds,
and rows of trees arranged on either side can be seen.
Although the canopy of each tree is reproduced properly,
the image contains no information about the understructureof each tree, as shown by the cup-shaped image of each tree
and shrub, because the measurement was made from the air.
Figure 4c shows a three-dimensional view of the DTM
image of the study site. This image yields ground-surface
information, although the ground was covered with many
trees. Ponds appear flat. Figure 4d shows the DCHM image
constructed by subtracting the DTM image from the FPmode DEM image. The DCHM gives the net canopy heightof each tree without the influence of ground slope. The RMSE
(root-mean-square error) of the heights of 166 trees extracted
Fig. 4 Three-dimensional viewsof FP-mode DEM, DTM, andDCHM images of the study site. a
FP-mode DEM image. b Closeup of FP-mode DEM image of
the French Formal Garden. c
DTM image. d DCHM image

477

from the DCHM was 0.30 m. The standard deviation was
0.26 m, and the mean error was −0.14 m. This result indicatesthat the tree heights were slightly underestimated.
Figure 5a and b shows three-dimensional point cloud
images of an L. tulipifera and a P. × yedoensis produced bymerging on-ground LIDAR data. The images show detailed
features, including ramifications of branches, shapes oftrunks, and distribution of leaves, without limitation ofviewing angle.
Figure 6a shows three-dimensional views of the DCHM
images of the three M. glyptostroboides trees (Fig. 3) after
triangulation and smoothing (Fig. 2c). Regions A and B in
Fig. 6a represent the blind regions in the airborne scanning
LIDAR data. Region A is canopy covered by the huge L.
tulipifera tree (see Fig. 3), and region B is understructure.
Figure 6b shows a three-dimensional point cloud image
obtained by merging on-ground LIDAR data measured fromfive measurement ground positions (see Figs. 2b and 3).
Regions A and B in Fig. 6b correspond to those in Fig. 6a.
The two regions within the three-dimensional point cloud
image were extracted and triangulated. Then, they weremerged with the helicopter-borne three-dimensional polygon
image as shown in Fig. 6c and d (see Fig. 2c). An entire
three-dimensional model of the three trees was thus producedby complementing the blind regions.
Table 1 shows biophysical variables of the three M.
glyptostroboides trees obtained from the on-ground LIDAR
image after merging, the final three-dimensional modelobtained by combining data from both LIDARs, andground-truth data. Tree height errors ranged from −0.13 to0.31 m for the on-ground LIDAR image and from −0.05 to−0.73 m for the final model. The error of the trunk diameter
was within 1 cm for both the on-ground LIDAR image and478

K. Omasa et al.

Fig. 5 Three-dimensional point
cloud images (side view and topview) of isolated trees in the
study area obtained by merging
portable on-ground scanning
LIDAR data. a Liriodendron
tulipifera. b Prunus × yedoensis.
Regions corresponding to leaves
are colored green and others arecolored brown

three-dimensional model. The final three-dimensional model was sliced at different heights and scales as shown in
Fig. 7 to compute the trunk diameter, maximum canopy
diameter, and canopy cross-sectional area. The error of the
maximum canopy diameter ranged from −0.58 to 0.28 m.

Fig. 6 Combining airborne andportable on-ground scanning
LIDAR images. a Airborne LIDAR images produced from theportion of DCHM data including three Metasequoia glyptostroboides trees. Regions A and B
are blind regions. b Combined
three-dimensional point cloud
image of the M. glyptostroboides trees measured by onground LIDAR from fiveground measurement positions.
Regions A and B correspond tothe blind regions in the airborne
scanning LIDAR data. c and d
Final three-dimensional modelof the M. glyptostroboides trees(side and top view, respectively)

The canopy volume, trunk volume, and canopy crosssectional areas at 1.5 and 10 m heights of each tree ranged
from 289.0 to 616.8 m3, 0.29 to 0.61 m3, 37.42 to
54.73 m2, and 17.42 to 24.33 m2, respectively. The total
volume of the three trees was 1,464.5 m3.

Totalvolumeb (m3)

–
–
–
289.3
617.4
557.8
–
–
–
–
–
–
0.29
0.57
0.61
–
–
–

Trunk
volume (m3)

45.0
55.6
57.4
45.0
55.6
57.4
45.2
55.6
57.5
51.1
65.8
67.6
51.1
65.8
67.6
50.9
65.7
67.4
Ground measurements

Model

Heights h1 to h4 correspond to those in Fig. 7.
ah1–h4: height from the groundb
Total volume: canopy volume + trunk volume

–
–
–
37.42
54.73
53.81
–
–
–

–
–
–
17.42
24.33
22.54
–
–
–
–
–
–
5.29
6.63
6.02
5.01
7.15
6.07
–
–
–
7.86
9.50
9.40
7.82
9.79
9.98
19.87
20.37
19.73
19.27
20.01
19.25
20.00
20.06
19.88
1
2
3
1
2
3
1
2
3
On-ground scanning LIDAR
image

h2a =
10.00 mh1a =1.50 mh2a =10.00 mh1a =1.50 mh3a =0.50 mh4a =0.75 m

–
–
–
289.0
616.8
557.2
–
–
–

Canopy volume(m3)
Canopy cross-section area(m2)

Trunk diameter (cm)479

4 Discussion

Maximum canopy
diameter (m)Height(m)
TreeTable 1 Biophysical variables of three Metasequoia glyptostroboides trees obtained from the portable on-ground scanning LIDAR image, the final three-dimensional model, and ground-truth
measurements

Three-dimensional modelling of urban park and trees by LIDAR remote sensing

Airborne scanning LIDAR allowed the generation of
precise visual models of the study area as shown in
Fig. 4. These visual models provided not only precise
surface information of trees and constructions in the park
but also information about undulations of the ground in
spite of the presence of many trees. The result shows thecapability of airborne LIDAR for three-dimensional visualization of an urban park. The heights of 166 coniferous andbroadleaf trees within the study area could be estimatedwith only slight underestimation (RMSE=0.30 m), so thedata would be suitable for managing and studying the park.
The underestimation would have been caused by misdetection of treetops. As a treetop is a very small target,
LIDAR will often miss it, resulting in underestimation oftree heights [20, 25, 30].
Although the airborne scanning LIDAR provided a
precise three-dimensional visual model of the study area,
there existed blind regions, as shown in Fig. 4b, by the cupshaped images of trees and shrubs. These blind regions
reduce the visual quality of the LIDAR-derived image. In
addition, tree variables for the lower parts of trees, such as
trunk diameter and basal area, are not available. This shows
the limitation of airborne LIDAR in the three-dimensional
visualization of an urban park and quantification of tree
variables.
The on-ground LIDAR could capture the lower parts ofindividual trees, as shown in Fig. 5, owing to the measurements from several ground positions surrounding the trees.
Such precise three-dimensional images without blind
regions would be as appropriate for the visual assessmentof urban parks as recent realistic three-dimensional models
of individual trees based on several algorithms such as
texture mapping, AMAP, and the TREE system [14, 23]. In
addition, the LIDAR-derived images are more advantageous than those three-dimensional models in a point thatvariables of real trees can be obtained from the images, as
shown here. On the other hand, some regions of dense trees
may not be captured by on-ground LIDAR. In this study,
parts of the canopies of three M. glyptostroboides were
missed owing to overlapping of trees. The blind region
could affect the visual quality of the LIDAR-derived image
and limit the estimation of tree variables.
Combination of the airborne and on-ground LIDAR data
provided a complete image of trees without blind regions,allowing visual assessment without limitation of viewpoints. In addition, several tree variables that could nothave been estimated from airborne or on-ground LIDAR
data alone were estimated from the complete tree model by
the use of image processing techniques, as shown in Fig. 7.
Any region of the model can be selected and sliced freely.
The estimated variables also include some that are difficult

480

K. Omasa et al.

Fig. 7 Final three-dimensionalmodels of the Metasequoia
glyptostroboides sliced at different heights and separated individually. a Sliced canopy. b
Sliced trunk. c Separated individual trees. Heights h1 to h4 areslice positions

to measure even with actual ground measurements, i.e.,
canopy volume, trunk volume, and canopy cross-sectional
area. These variables obtained through the image processing may provide additional knowledge for understanding
the botanical and environmental functions of urban parks.

5 Conclusions
The present study demonstrates three-dimensional modeling of an urban park and trees using airborne and portable
on-ground scanning LIDARs. First, we confirmed thecapability of these LIDARs for three-dimensional visualization of an urban park and quantification of tree variables.
The DCHM and DTM generated from airborne LIDAR
data provided precise images of the canopy and groundsurfaces. Tree heights were only slightly underestimatedfrom the DCHM images. On-ground LIDAR provided

images of individual trees with detailed features. Treevariables such as trunk diameter and tree height were
estimated accurately from the images. Blind regions withinboth LIDAR images limited the capability of either for
three-dimensional visualization of the park and quantification of tree variables. Then, airborne and on-ground LIDAR
were combined to complement the blind regions, and acomplete three-dimensional model of three standing trees
was created. The model allowed not only visual assessment
from all viewpoints but also quantitative estimation of more
descriptive tree variables. The study demonstrates our basic
concept for composite remote sensing by airborne and onground scanning LIDARs. Only three trees were reproduced in this study; in the future, it will be desirable toimage more trees in parks to enhance the quality of visualand quantitative assessment for planning and managing
urban parks and contributing to a better understanding of
their environmental functions.

Three-dimensional modelling of urban park and trees by LIDAR remote sensing

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