Urban Forestry & Urban Greening 12 (2013) 546–553
Contents lists available at ScienceDirect
Urban Forestry & Urban Greening
journal homepage: www.elsevier.com/locate/ufug
Tree mapping using airborne, terrestrial and mobile laser scanning –
A case study in a heterogeneous urban forest
Markus Holopainen a,∗ , Ville Kankare a , Mikko Vastaranta a , Xinlian Liang b , Yi Lin b ,
Matti Vaaja c , Xiaowei Yu b , Juha Hyyppä b , Hannu Hyyppä c,d , Harri Kaartinen b ,
Antero Kukko b , Topi Tanhuanpää a , Petteri Alho e
a
University of Helsinki, Department of Forest Sciences, Finland
Finnish Geodetic Institute, Finland
c
Aalto University, Research Institute of Modelling and Measuring for the Built Environment, Finland
d
Helsinki Metropolia, University of Applied Sciences, Finland
e
University of Turku, Department of Geography and Geology, Finland
b
a r t i c l e
i n f o
Keywords:
Airborne laser scanning
Mobile laser scanning
Tree detection
Terrestrial laser scanning
Urban forestry
a b s t r a c t
We evaluated the accuracy and efficiency of airborne (ALS), terrestrial (TLS) and mobile laser-scanning
(MLS) methods that can be utilized in urban tree mapping and monitoring. In the field, 438 urban trees
located in park and forested environments were measured and mapped from our study area located in
Seurasaari, Helsinki, Finland. A field reference was collected, using a tree map created manually from TLS
data. The tree detection rate and location accuracy were evaluated, using automatic or semiautomatic
ALS individual tree detection (ALSITDauto or ALSITDvisual ) and manual or automatic measurements of TLS and
MLS (TLSauto , MLSauto , MLSmanual , MLSsemi ). Our results showed that the best methods for tree detection
were TLSauto and MLSmanual , which detected 73.29% and 79.22% of the reference trees, respectively. The
location accuracies (RMSE) varied between 0.44 m and 1.57 m; the methods listed from the most accurate
to most inaccurate were MLSsemi , TLSauto , MLSmanual , MLSauto , ALSITDauto and ALSITDvisual. We conclude that
the accuracies of TLS and ALS were applicable for operational urban tree mapping in heterogeneous park
forests. MLSmanual shows high potential but manual measurements are not feasible in operational tree
mapping. Challenges that should be solved in further studies include ALSITDauto oversegmentation as well
as MLSauto processing methodologies and data collection for tree detection.
© 2013 Elsevier GmbH. All rights reserved.
Introduction
Urban forests and parks are important for many reasons:
they are used for recreation, provide scenery value and maintain
biodiversity. In the city of Helsinki, there are 902 ha of managed
parks and 4020 ha of urban forests. The City of Helsinki Park
and Garden Department (HPGD) maintains a digital tree register
(approximately 20,000 trees) that includes data on the species,
height, diameter-at-breast-height (DBH), location and vitality
status of the trees. The tree register data are used in city and
∗ Corresponding author at: Latokartanonkaari 7, 00014 University of Helsinki,
Finland. Tel.: +358 9 191 58181; fax: +358 9 191 58100.
E-mail addresses: markus.holopainen@helsinki.fi (M. Holopainen),
Ville.Kankare@helsinki.fi (V. Kankare), Mikko.Vastaranta@helsinki.fi
(M. Vastaranta), Xinlian.Liang@fgi.fi (X. Liang), Yi.Lin@fgi.fi (Y. Lin),
Matti.Vaaja@aalto.fi (M. Vaaja), Xiaowei.Yu@fgi.fi (X. Yu), Juha.Hyyppa@fgi.fi
(J. Hyyppä), Hannu.Hyyppa@metropolia.fi (H. Hyyppä), Harri.Kaartinen@fgi.fi
(H. Kaartinen), Antero.Kukko@fgi.fi (A. Kukko), Topi.Tanhuanpaa@helsinki.fi
(T. Tanhuanpää), Petteri.Alho@utu.fi (P. Alho).
1618-8667/$ – see front matter © 2013 Elsevier GmbH. All rights reserved.
http://dx.doi.org/10.1016/j.ufug.2013.06.002
environmental planning, in locating hazardous (for citizens) old
trees and in biodiversity monitoring. Trees in the register are
located on city-planning maps. The requirements for up-to-date
tree data in city parks and forests are increasing, and an important
question is how to keep the digital databases up-to-date for the
various applications. Traditional updating procedures, such as
visual interpretation of digital aerial images or field measurements using tachymeters, are either inaccurate or very expensive.
Recently, the development of laser-scanning technology has
opened new opportunities for tree mapping.
Small-footprint airborne laser scanning (ALS) is a method based
on LiDAR (light detection and ranging) range measurements from
an aircraft and the precise orientation of these measurements
between a sensor (the position of which is known, using a differential global positioning system (GPS) technique and inertial
measurement unit (IMU)), and a reflecting object, the position of
which (x, y, z) is defined. The ALS gives the georeferenced point
cloud, from which it is possible to calculate digital terrain models
(DTMs), digital surface models (DSMs) corresponding to treetops
and three-dimensional (3-D) models of an object (e.g. canopy
M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
height model (CHM), normalized DSM), which are the main products used for laser-assisted forest measurements. The two main
approaches to deriving forest information from small-footprint ALS
data have been those based on laser canopy height distributions
(Næsset, 1997, 2002) and individual tree detection (ITD) (Hyyppä
and Inkinen, 1999). In the former method, the percentiles of the distribution of laser canopy heights are used as predictors to estimate
forest characteristics. By increasing the number of laser pulses per
m2 , individual trees can be recognized. By analysing the canopy
model and using pattern recognition methods, it is possible to
locate individual trees, determine individual tree heights, crown
diameters, and tree species. Using these data, it is also possible to
derive the stem diameter, age, development class, basal area and
stem volume for each individual tree (e.g. Næsset, 1997; Hyyppä
and Inkinen, 1999; Hyyppä et al., 2001; Falkowski et al., 2006;
Vauhkonen et al., 2010; Vastaranta et al., 2012). Few studies are
available on estimation of the above-mentioned tree parameters or
change detection in urban areas, e.g. Shrestha and Wynne (2012)
and Xiao et al. (2012).
Terrestrial laser scanning (TLS), based on LiDAR range measurements from a scanning system mounted on a tripod, is feasible,
e.g. for the reconstruction of building models, for digital factories,
virtual reality, architecture, civil engineering, archaeology and cultural heritage, plant design, automation systems (robotics), and
detailed planning and documentation (Vosselman and Maas, 2010).
TLS is feasible for detailed small-area surveys having a typical
radius for a high-density scan of less than a few tens of metres.
Processing of TLS is time-consuming for large areas having more
than hundreds of scans. A raw scanned dataset contains a huge
number of points, and the recognition of trees in point clouds is
essential for estimating forest characteristics. TLS is an efficient and
objective option for acquiring accurate field data. The applications
of TLS for forestry, e.g. in forest biomass estimation, have not been
widely studied, although its potential for forest-related measurements has been better understood in recent years. TLS is capable
of measuring all important tree characteristics such as diameter,
height, location and stem curve (e.g. Hopkinson et al., 2004; Pfeifer
and Winterhalder, 2004; Watt and Donoghue, 2005; Tansey et al.,
2009; Liang et al., 2011a,b, 2012, 2013). TLS can also provide information on canopy-related characteristics and stem form that has
not been achievable before.
Mobile laser scanning (MLS, mobile LiDAR, VLS, vehicle-based
laser scanning) is a modification of ALS. It resembles ALS in that it
has a laser scanner, a GPS receiver, an IMU and preferably cameras,
but it is operated from the top of a moving ground vehicle, such as
a car or harvester, and is used for shorter distances. Due to these
shorter operating distances, it can more easily have a higher pulse
rate than an ALS. Currently, the data collected with MLS can be
processed, using programs and methods developed for processing
data from TLS or ALS. But due to the various scanning geometries
and changing point density (as a function of range), data-processing
methods should also be developed, based solely on MLS data. MLS
provides new applications for mapping and monitoring urban trees
and forests.
Previous studies have shown that ALS, TLS and MLS have similar
problems in tree detection caused by visibility. Ground-based measurements are highly affected by understorey shrubs and airborne
methods by canopy closure. The ALSITD methodology has been
widely studied, but is not generally used, due to problems related
to the previously mentioned tree detection (Falkowski et al., 2008;
Kaartinen and Hyyppä, 2008; Vastaranta et al., 2011) and the
higher costs of ALS data. Kaartinen and Hyyppä (2008) reported
an international experiment in which various ITD methods were
tested for tree detection in the same study area. They reported that
tree detection condition accuracies in Nordic forests varied from
25% to 90%. Maltamo et al. (2004) reported an overall tree detection
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accuracy of 39.5%, but dominant trees were detected with 83%
accuracy. Tree detection accuracies for TLS have been studied, e.g.
by Maas et al. (2008) and Liang et al. (2012). Maas et al. (2008)
reviewed TLS scanner systems and their applicability in forestry.
The reliability and precision of DBH, tree height and stem profiles
were analyzed. Maas et al. (2008) used five different study plots
with varying scan parameters. A tree detection accuracy of 97.5%
was reported. Liang et al. (2012) reported an overall stem-mapping
accuracy of 73% on nine dense plots, using an automatic algorithm
and single-scan mode. The results of MLS application for the detection of trees or pole-like objects have been studied, e.g. by Jaakkola
et al. (2010), Lehtomäki et al. (2010) and Rutzinger et al. (2010).
The detection rates of tree and pole-like objects varied from 69.7%
to 90% and the correctness of detection varied from 83% to 93%.
In urban environment, the use of laser scanning methods has
previously been studied in vegetation mapping and in the estimation of green volume. Many of the studies also utilize other data
sources, e.g. high resolution or hyperspectral images with the LiDAR
data in urban vegetation mapping (e.g. Zhang and Qiu, 2012; Huang
et al., 2013). Omasa et al. (2008) utilized airborne and portable
ground based scanning LiDARs to visualize the park and to measure
the physical tree parameters. Urban green volume (the sum of individual tree and grassland object volumes) has been studied, e.g. by
Hecht et al. (2008) and Huang et al. (2013). Individual tree species’
classification have been studied, e.g. by Zhang and Qiu (2012). They
used airborne scanning LiDAR and hyperspectral imagery to classify
tree species and achieved an overall accuracy of 68.8%.
The HPGD commissioned the University of Helsinki Department
of Forest Sciences to evaluate the accuracy and efficiency of laser
measurement methods that can be utilized in city tree mapping and
monitoring. To the best of our knowledge, this type of evaluation
has not been previously published. In addition, urban parks and
forests provide test fields for the development of laser-scanning
methodologies that could also be utilized in future operational
forestry applications.
Our objective was to compare the accuracy and efficiency of
ALS, TLS and MLS measurements in tree mapping in heterogeneous
park forests. The tree detection rate and location accuracy were
evaluated, using ALSITD and manual or automatic measurements of
TLS and MLS (TLSauto , MLSauto , MLSmanual ).
Materials and methods
Study area
The study area, Seurasaari, is a popular outdoor recreation area,
located approximately 5 km from the Helsinki city centre. It was
made a public park in 1890 and quickly became a popular place for
recreational activities. Seurasaari receives hundreds of thousands
of visitors per year and is a wooded island with rocks, hills, wetlands and herb-rich forests covering about 46 ha. Our study area in
Seurasaari comprised two parts, approximately 2.7 ha in all (Fig. 1).
The northern part is a well-managed urban park comprised mainly
of widely separated old oaks and only grass as understorey vegetation, while the southern part more resembles a natural unmanaged
park forest with varying understorey vegetation. In the area, there
is a dense network of artificially constructed outdoor paths that can
also be used by vehicles.
Field measurements
A predefined TLS tree map was used to identify each tree. The
DBH and tree species were determined for 438 trees. Steel callipers
and a talmeter were used for the DBH measurements. The average
DBH for the entire study area was 302 mm and varied between 50
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M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
Fig. 1. Study area and mapped trees in Seurasaari.
and 960 mm. Descriptive statistics were also calculated for two separate areas: the northern part, which is a well-managed urban park
forest with only grass as understorey vegetation, and the southern
part, which is denser, unmanaged forest with varying understorey
vegetation. The average DBH was 476 mm in the northern part and
283 mm in the southern part. The distribution of tree species was
diverse and consisted of 11 different species. The percentages of
the various tree species are presented in Table 1.
Airborne laser scanning
The ALS data were acquired in 2011 with an Optech 3100 laser
scanner (Optech Inc., Vaughan, Ontario, Canada). The flying altitude
was 400 m. The density of the pulses returned was approximately
10 points per m2 .
Table 1
Relative tree species distribution in the study area.
Species
%
Acer platanoides
Alnus sp.
Betula sp.
Picea abies
Pinus sylvestris
Populus tremula
Quercus robur
Salix caprea
Sorbus aucuparia
Tili cordata
Ulmus sp.
2.64
9.13
7.30
25.96
19.88
9.94
6.69
0.81
14.60
2.43
0.61
The ALS data were first classified as ground and nonground
points, using the standard approach of the Terrascan-based method
(Terrasolid Oy, Helsinki., Finland) explained in Axelsson (2000).
Low-point classification was also used to improve the accuracy
of the ground level. A DTM was developed from classified ground
points, and laser heights above the ground (normalized height or
canopy height) were calculated by subtracting the ground elevation from the laser measurements. Canopy heights close to zero
were considered ground returns and those greater than 2 m as vegetation returns. Only the returns from vegetation were used for
ITD. ITD was carried out automatically, as explained in Yu et al.
(2011), and the method is referred to as ALSITDauto . The automatic
results were also checked manually, using Terrascan software to
remove oversegmentation errors, and this method is referred to
as ALSITDvisual. ALSITDvisual was first introduced in Vastaranta et al.
(2012) in plot-level forest parameter retrieval.
Terrestrial laser scanning
Leica HDS6100
The TLS data were collected with a Leica HDS6100 TLS system (Leica Geosystem AG, Heerbrugg, Switzerland) in September
2010. The HDS6100 is a 690-nm phase-based continuous-wave
laser scanner with a 360◦ × 310◦ field-of-view (FOV) upward and
its data acquisition rate is 508,000 points per second. The distance measurement accuracy is ±2 mm at a distance of 25 m.
The circular beam diameter at the exit and the beam divergence are 3 mm and 0.22 mrad, respectively. The point spacing is
6.3 mm at 10 m. Further detailed specifications are presented below
(Table 2).
M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
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Table 2
Leica HDS6100 TLS system and specifications.
Leica HDS6100
Field of view
Range
Speed points/s
Spot size
Distance measurement accuracy at 25 m
Max resolution Hor × Ver
Max points 360◦ Hor × Ver
Laser wavelength
Laser power
Weight
Operating temperature
310◦ × 360◦
79 m
508,000
3 mm + 0.22 mrad
±2 mm
0.009◦ × 0.009◦
40,000 × 40,000
690 nm
30 mW
14 kg
−10 to 45 ◦ C
TLS measurements for the study area were collected in
multi-scan mode. The park areas were scanned as-is. Pre-scan
preparations, e.g. removal of low vegetation, were not done, since it
was not permitted in the city forest. The objective of the measurements was to obtain good point coverage. The data were collected
in five to seven scans per group; in total, 52 scans were performed
to cover the entire study area. We positioned the centre scan station
and at least one reference target ball of each scan group, using
a GPS virtual reference station (VRS) and a tachymeter. The centre scans were placed so that the canopy layer did not block the
GPS satellite visibility. Subsequently, we transformed the scans into
global coordinates according to the scanner and sphere target locations measured. The reference targets were placed on the forest
ground for point cloud registration. The TLS point clouds within
each group were coregistered, using reference targets. Coregistration was done with Leica’s Cyclone software (Leica Geosystems),
in which the reported overlap root-mean-squared error (RMSE) of
the scans ranged between 2.3 and 6.3 cm.
Tree detection
Tree detection and location measurements were done manually,
using TerraScan and automatically, as described in detail in Liang
et al. (2012). The methods are referred to as TLSauto and TLSmanual .
In the TLSauto method, the TLS point clouds merged were divided
into 10-m × 10-m blocks. In each block, the data were sampled to
speed the processing. The number of points after sampling was
no more than one million. In each block, tree detection was conducted, using a robust modelling procedure. The spatial properties
were studied for each point. A local coordinate system was built in
its neighbourhood, using the eigenvalue decomposition. The axes’
directions were defined by the eigenvectors. Possible stem points
were selected if they were of low variance along one direction in
the local coordinate system and had a near-horizontal normal vector in the real-world coordinate system. Point groups were built
such that a point in a group had at least one point from the same
group within a certain distance. A series of 3-D right-circular overlapping cylinders were built in the point group along the axis. The
reconstruction is a growing process. After the first cylinder was
built, the parameters of the current cylinder were used as the initial
estimation for the next cylinder. For further details on the robust
modelling procedure, refer to Liang et al. (2012).
Mobile laser scanning
FGI Roamer
The MLS data were collected in August 2010, using the FGI
Roamer mobile mapping system developed at the Finnish Geodetic
Institute (FGI, Fig. 2) mounted on a car. The Roamer consists of a
Faro LS 880 laser scanner (Faro Technologies, Lake Mary, FL, USA)
with a measurement frequency of 120 kHz and a NovAtel HG1700
SPAN58 INS system (NovAtel Inc., Calgary, Alberta, Canada). With
Fig. 2. MLS measurement unit, Roamer.
slightly modified hardware for the standard FARO LS, it provides
a so-called tunnel mode, or profile measurements, synchronized
with external-positioning and data-logging systems. This information is needed to derive the position and attitude information for
each 3-D point produced by the laser scanner. The mirror rotation
frequency or scanning rate of the scanner on the Roamer can be set
to 3–30 Hz, giving a vertical angular resolution of 0.0096–0.096◦
(0.17–1.7 mrad). The corresponding point spacing at a typical scanning range of 15 m in road mapping is, thus, 2.5–25 mm in the
scanning plane. The MLS data was collected by driving along all the
existing walking paths inside the study area that were driveable
(see Fig. 1).
Tree detection
Tree detection and location measurements were done manually
using TerraScan software and automatically using the algorithm
presented below. The methods are referred to as MLSauto and
MLSmanual . MLS was also processed with a more semiautomatic
method to reduce the estimation error in DBH caused by laser
points coming from understorey vegetation near the tree trunks
and to improve the tree detection. The semiautomatic method
consisted of the manual detection of trunk-points and the automatic extraction of DBH from these tree trunks detected. The DBH
was used as an auxiliary information in matching the MLS-detected
trees to reference. The method is referred to as MLSsemi .
The basic strategy for MLS-based stem location and DBH estimation is rooted in the idea of layering, i.e. a certain layer in the
MLS-collected point cloud is extracted, and the operations planned
are conducted, aimed at this layer. Here, the layer is defined as having several restrictions, i.e. keeping parallel with the DTM, covering
the DBH-related height and occupying an appropriate thickness.
These premises can ensure a compromise between the efficiency
of data processing and the accuracy of parameter retrieval. Among
the entire frame, the key procedure potential of deciding the final
performance refers to DTM extraction, shrub removal and stem
reconstruction. The specific methods respectively appropriate for
these three key tasks are as follows.
For the issue of DTM extraction, the ‘ground’ filter in the
TerraScan software is employed (Terrascan Manual, 2010). An
approximation of the terrain surface is first acquired via block minimum analysis. This routine is interactive, with the user defining the
largest possible horizontal distance between ground points. Then,
the points are triangulated, and the triangulated irregular network
(TIN) is adapted further, with ground points added if they fall within
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M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
Table 3
Detection accuracy. ‘Matched %’ represents the trees correctly detected and matched
to reference measurements.
Method
n
Matched %
TLSauto
MLSauto
MLSsemi
MLSmanual
ALSITDauto
ALSITDvisual
321
118
192
347
287
298
73.29
26.94
43.84
79.22
65.53
68.04
the predefined description length and angle criteria. The maximum terrain angle can also be defined to prevent the unrealistic
commission of ground points over larger areas.
Regarding the issue of shrub removal, a multicriteria method is
assumed. The brief scheme is to conduct the operations of plane
fitting and projection area ratio (PAR) thresholding, and the latter
is based on the layer after vertical rastering. Specifically, the plane
fitting is based on principal component analysis (PCA), and detailed
implementation appropriate for 3-D scattered points (Hyyppä and
Lin, 2012). PAR is sought by calculating the ratio between the rasters
with projected points and the rasters within the roughly fitted circle, and then PAR thresholding can distinguish between stems and
crowns. After all of these acts, the plane-distributed point clusters
capable of characterizing the stems can be acquired, and the layer
is ‘cleaned’, i.e. with the low shrubs and bushes removed.
The last issue of stem reconstruction is implemented by 3-D
cylinder fitting. The nonlinear least-square cylinder fitting is solved,
based on the Gauss–Newton algorithm (Bjork, 1996). After the iterations for estimating the rotation and translation parameters for
the best fit, the cylinder model with the least residual bias can
be achieved. Then, the axis of the fitted cylinder can be used to
manifest the location of the stem, and the radius can represent the
DBH.
Evaluation of tree detection and location accuracy
Traditional field measurements with GPS have poor location
accuracy due to the dense canopy layer. GPS measurements cannot
be used as a reference for tree detection and location accuracy measurements, because tree-level information cannot be matched with
high reliability to laser-based methods. Tree matching was done
with a procedure similar to that of Kaartinen and Hyyppä (2008).
The trees were matched, using spatial location and DBH information. If multiple reference trees had hits on the same study tree,
only the best match according to location and DBH was selected.
The ALS data matching also required location difference limitation,
which was set to ±4 m.
Traditionally, the HPGD produces tree maps, using tachymeter
measurements. The tachymeter measures were collected in
autumn 2011 for 101 randomly selected trees. Real-time kinematic (RTK)-GPS was used to locate the starting point for the
tachymeter measurements. Previous studies (e.g. Mechelke et al.,
2007) and a comparison between tachymeter and TLS measurements (Table 3) showed that the location accuracy of the manual
measurements from the TLS data was comparable to that of the
tachymeter measurements (e.g. Mechelke et al., 2007). Therefore,
manual measurements from TLS data were used as references for
tree location accuracy (models 1–3).
Location error =
dxi2 + dyi2 ,
RMSE =
(Location error)2
n
,
(3)
where dxi is the difference between the x coordinates and dyi the
difference between the y coordinates.
Results
In this study, several mapping and monitoring methods were
tested for mapping urban trees. In an urban park environment,
most of the methods tested resulted in tree detection accuracies
of between 65% and 80% (Table 3). Only MLSauto and MLSsemi produced poor results. ALSITDauto detected too many trees, because the
ITD parameters were more justified for managed forests with relatively small crown areas compared with trees growing in parks.
This oversegmentation error (Fig. 3) was corrected manually.
GPS measurements could not be used as location references,
due to the poor location accuracy of the hand-held GPS device and
because the trees could not be matched correctly with the laserbased measurements. Therefore, the location accuracy of TLS was
determined using 101 tachymeter-measured, randomly selected
trees to validate the use of TLS as a reference. The results (Table 4)
showed that a manually measured tree map could be matched
with 100% accuracy. The location accuracy was favourable, resulting in an average location error of 15 cm with TLSmanual and 20 cm
with TLSauto . The stem location measured, using the automated and
manual methods, gave very similar results in terms of the number
of detections and location errors.
Based on the results in Table 4, the TLSmanual method was
selected as the reference for location accuracy. The mean location
accuracies varied from 0.12 m to 1.27 m (Table 5). The most accurate method was TLSauto , while the MLSauto and MLSmanual methods
were almost as accurate. The wide tree crowns incurred inaccuracy
for ALS-based tree maps. In several cases, there were metre-class
errors stemming from differences in the crown’s highest point and
tree stump location (Fig. 4).
Table 4
Tree matching and location accuracy for TLS methods. Tachymeter measurements
were used as a reference.
(1)
Method
n
Matched %
(2)
TLSauto
TLSmanual
93
101
92
100
Location error, m
Min
Mean location error =
Fig. 3. ALS oversegmentation error. Red squares represent the treetops detected
with ITD. (For interpretation of the references to color in this figure legend, the
reader is referred to the web version of this article.)
dxi2 + dyi2
n
,
0.01
0.00
Mean
Max
SD
0.20
0.15
0.65
0.58
0.15
0.12
M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
Table 5
Tree location accuracies compared with manual TLS tree map.
Method
Tree
count
Mean location
diff., m
Stdev, m
RMSE, m
TLSauto
MLSauto
MLSsemi
MLSmanual
ALSITDauto
ALSITDvisual
321
118
192
347
287
298
0.12
0.42
0.36
0.38
1.27
1.27
0.43
0.28
0.25
0.30
0.90
0.93
0.45
0.50
0.44
0.49
1.55
1.57
Fig. 4. Inaccuracy in ALS-based tree mapping. Lines are for the tree’s highest point
and stump. Reference location was recorded from the centre of the trunk at a height
of 1.3 m.
Fig. 5 shows tree maps created with various laser methods compared with the reference. The reference is marked as green circles
on the map. The map shows that the TLSauto and MLS methods
accurately provide the locations of the trees.
Discussion
Several laser scanning-based tree mapping and monitoring
methods were tested in this study. Tree detection was tested by
551
comparing the number of trees found and correctly matched by
each method to a reference. The tree matching was done with a
procedure introduced by Kaartinen and Hyyppä (2008). The best
methods were TLSauto and MLSmanual , which detected 73.29% and
79.22% of the reference trees, respectively. Tree detection by TLS
could be improved, using a grid larger than 10 m × 10 m, because
the trees near the edge of the grid may not be detected with the
automatic algorithm because only part of the trunk is visible inside
of the processed grid. MLS methods are greatly affected by visibility
near the edges of roads and road network coverage. An important
research subject would be to further analyze the effect of target
distance, from the scanner, to tree detection. Coregistration of the
MLS scan blocks is an important factor in data processing. The correct location and position of the MLS device are needed to register
point clouds together accurately. Poor location accuracy will result
in point mismatch for the same tree trunk between different data
blocks.
Tree mapping with MLS was done with the push-broom vertical
scanning setting, which influences the geometric characteristics of
the data acquired, and thus the tree detection. The data acquired
in driving only the relatively straight pathways/tracks of the study
area reduce the probability of hitting tree trunks behind the first
line of trees and bushes next to the track with the laser beam.
Hence, the data acquisition principle could partially explain the
low trunk detection rates. Means for improving the data acquisition
with MLS may include a multi-pass approach from different sides,
i.e. increasing the data reach, and by making the survey trajectory
have more non-linear platform movement by making extra turns
so that the laser profile makes a back-and-forth pattern. Improvement would be obtained by employing a backpack MLS (Kukko
et al., 2012) for the data acquisition. This increases the mobility
and allows more variable trajectory, but reduces the speed of data
collection.
To improve the detection rates of MLS, some of the tilted scanner positions available with the ROAMER could be used in the
extraction of points from the trees. Tilted scanning planes produce
multiple hits even from narrow vertical structures such as trees,
but also traffic signs, light poles and bridge pillars. This is achieved
with high scanning frequency, high angular resolution, and due
to the fact that the wide FOV of the scanner makes it possible to
acquire multiple hits from several sequential profiles of an object
Fig. 5. Tree detection and location accuracy for various laser-based methods.
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M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
Fig. 6. Tree trunk shape.
as the MLS unit passes by, as illustrated in Kukko (2009). Such an
approach allows higher platform speeds than those obtained with
vertical scanning profile orientations that could miss tree trunks
completely, if the platform speed is too high. With multiple point
arcs, the positioning and modelling of the object become more reliable, since tilted scanning provides more localization information
on the object along the track direction than the vertical scanning,
because the front portion of the swath/profile sees the top of a tree,
whereas the lower part of the trunk is seen in the back section of
the profile. The most obvious drawback with the use of tilted scanning planes is thus that turning of the vehicle reduces the detection
of complete tree trunks. On the other hand the turning rate (deg/s)
affects the profile spacing in the vertical scanning case, leading to
sparsening of profiles on the outer side of the bends in the survey
trajectory.
Tree location accuracy was calculated for the laser-based
method, using manual tree location measurements from TLS point
clouds. GPS measurements were also collected in 2010, but the
location accuracy of the hand-held GPS device was too low and the
data matching was unreliable. In all, 101 randomly selected trees
were measured with a tachymeter to validate the use of manual
TLS measurements as a reference for location mapping. The results
showed that 100% of the trees could be found manually by TLS
and that the location error was 15 cm. Other laser methods were
compared with TLSmanual for 438 trees.
All the TLS and MLS methods had location accuracies that were
better than 0.50 m. For the ALSITD methods, the location accuracy
was poorer, because the location of a tree was selected from the
highest point of the canopy. The inaccuracy in ALSITD methods
caused by this was demonstrated in Fig. 4. One factor that affects
tree location accuracy for all the various methods in urban park
areas is the unique shapes of the trunk; an example is shown in
Fig. 6.
The efficiency of the laser methods was also reviewed to
determine which were the most suitable for tree mapping in
heterogeneous park forests. ALS-based ITD provided rapid measurements with relatively small amounts of data processing,
compared with other laser-scanning methods. If only tree maps
are desired, ITD combined with some fieldwork, as basically
demonstrated in Korpela et al. (2007), would be the most costefficient approach. TLS is suitable for areas in which the positional
accuracy of the tree maps needed is a high priority and additional
information from the trees is needed. Large TLS campaigns are justifiable when intensive data are needed for several applications. It
is difficult to justify using TLS measurements only for tree mapping,
although it is still more cost-efficient than traditional tachymeter
measurements. MLS provides a means of monitoring trees growing
near roads or paths. Change detection and tree health mapping
would be the main application for MLS in urban parks and forests.
Overall, the automation of data processing and calculations is an
important factor to be addressed and developed further before
these methods can be used at their full potential in practice.
The two most important factors affecting ALSITD tree detection are the detection algorithm used and the canopy structure
(Kaartinen and Hyyppä, 2008; Vauhkonen et al., 2012). Tree detection, especially in park areas, tends to overdetect the number of
trees because of the wider canopy shapes. This could be avoided to
some extent using an ITD-algorithm fitted to park-trees, but this
would require field data. Field measurements could be used to calibrate ITD, but it would cause the method to lose its practicality. In
the present study, we used a practical approach to reduce oversegmentation using visual correction of the ITDauto (Vastaranta et al.,
2012). The ALSITD and TLSauto tree detection accuracies in this study
were in line with the findings of previous studies (e.g. Kaartinen and
Hyyppä, 2008; Maas et al., 2008; Brolly and Kiraly, 2009; Kaartinen
et al., 2012; Liang et al., 2012; Vauhkonen et al., 2012).
The MLSauto and MLSsemi tree detection accuracies were lower
than those in Jaakkola et al. (2010), Lehtomäki et al. (2010) and
Rutzinger et al. (2010). However, in these studies, detection was
done mostly for pole-like objects. The tree location accuracy for
ALSITD was similar to that previously reported (Kaartinen and
Hyyppä, 2008; Kaartinen et al., 2012). The tree location accuracies for TLS and MLS have not been previously studied, as far as we
know.
To introduce these methods into the practical mapping of
urban forests, further development and research are needed and
are already underway. Data collection and processing are major
subjects when these methods are used, and the automation of
processing is improving continually. MLS is a viable method for
monitoring trees near road paths in city centres. One of the most
interesting technical research topics for forestry applications of
MLS and TLS is the coregistration accuracies in TLS (between scanning groups and inside groups) and MLS scan blocks and also the
minimum TLS point resolution needed to detect and measure tree
location accurately.
Conclusions
This study showed the high potential of ALS, TLS and MLS
measurements in heterogeneous urban park tree mapping. We
conclude that the accuracies of TLS and ALS were applicable for
operational urban tree mapping. The use of MLS in operational
urban tree mapping requires still more development, especially
in more difficult park forest structures. MLSmanual showed high
potential in tree mapping but manual methods are not feasible
for use in operational applications. TLSauto was the best method
among automated methods and gave results quite similar to
TLSmanual . Our results also showed the potential of ALSITD for use
in updating existing tree maps and monitoring the urban forest
environment.
Acknowledgments
This study was made possible by financial aid from the Finnish
Academy for the project entitled ‘Science and Technology towards
Precision Forestry’. We also thank the City of Helsinki Park and
Garden Department for their cooperation.
M. Holopainen et al. / Urban Forestry & Urban Greening 12 (2013) 546–553
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