Applicability of personal laser scanning in forestry inventory
Applicability of personal laser scanning in forestry inventory
Shilin ChenID 0 1
Haiyang Liu 0 1
Zhongke Feng 0 1
Chaoyong Shen 0 1
Panpan Chen 0 1
0 Editor: Claudionor Ribeiro da Silva, Universidade Federal de Uberlandia , BRAZIL
1 Precision Forestry Key Laboratory of Beijing, Forestry College, Beijing Forestry University , Beijing , China
Light Detection and Ranging (LiDAR) technology has been widely used in forestry surveys in the form of airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning (MLS). The acquisition of important basic tree parameters (e.g., diameter at breast height and tree position) in forest inventory did not solve the problem of low measurement efficiency or weak GNSS signal under the canopy. A personal laser scanning (PLS) device combined with SLAM technology provides an effective solution for forest inventory under complex conditions with its light weight and flexible mobility. This study proposes a new method for calculating the volume of a cylinder using point cloud data obtained by a PLS device by fitting to a polygonal cylinder to calculate the diameter of the trunk. The point cloud data of tree trunks of different thickness were modeled using different fitting methods. The rate of correct tree trunk detection was 93.3% and the total deviation of the estimations of tree diameter at breast height (DBH) was -1.26 cm. The root mean square errors (RMSEs) of the estimations of the extracted DBH and the tree position were 1.58 cm and 26 cm, respectively. The survey efficiency of the personal laser scanning (PLS) device was 30m2/min for each investigator, compared with 0.91m2/min for the field survey. The test demonstrated that the PLS device combined with the SLAM algorithm provides an efficient and convenient solution for forest inventory.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Funding: This research was funded by the
Fundamental Research Funds for the Central
Universities [2015ZCQ-LX-01] and the National
Natural Science Foundation of China grant number
[U1710123] to ZF.
Competing interests: The authors have declared
that no competing interests exist.
Forest resource inventory is a key basic task of ecological construction, forestry development
and forest resource management. Tree location and diameter at breast height (DBH) are the
crucial parameters in forest inventory. The spatial distribution information of tree location is
the fundamental parameter for the calibration of the individual-tree-based inventory, and also
the main matching criterion between reference data and measured data [
]. By measuring the
DBH of the trees, we can obtain the diameter distribution which describes the forest structure
], as well as the log yield and stem quality [
]. The traditional methods, based on field
inventory work for tree location calculation and DBH measurement, are labor intensive, time
consuming, and limited in their spatial extent. Therefore, laser scanning technology, including
airborne laser scanning (ALS), terrestrial laser scanning (TLS), and mobile laser scanning
(MLS), has been widely investigated for applications in forest inventory [
Airborne laser scanning (ALS) is an effective way to retrieve biophysical variables and
update forest investigation maps. Many scholars and research organizations have put
tremendous efforts into developing methods for the application of ALS in forest surveying. With a
strong penetrating power, the laser can penetrate the canopy, foliage, understory, etc., and
obtain detailed tree parameter information, which is very important for fine modeling.
Consequently, ALS is widely used in forest investigations for tree height estimation [
], stem volume
], tree crown volume estimation [
], tree species classification [
measurement of forest growth [
]. Nevertheless, the application of ALS in forest investigations
largely depends on the quality and quantity of field reference data, especially with the currently
applied ALS-based inventory technique, which is an area-based inventory in which field
references determine the output of each raster cell based on non-parametric estimates [
failure to obtain more detailed structural parameters of trees due to canopy occlusion and low
density point clouds per unit area are major factors limiting the further development of
airborne laser scanning in forestry surveys. In practice, high-accuracy data acquisition and
appropriate measurement methods are always preferred.
Terrestrial laser scanning (TLS) has been demonstrated as an effective technique for
acquiring detailed information on tree attributes in forest sample plots during the last two decades
]. Compared with ALS, the point cloud data acquired from TLS are dense enough to
determine the spatial distribution of the trees [
] and to extract almost the whole geometry of each
tree with high precision. Moreover, the TLS in the plot can also easily capture the information
on basic tree attributes, such as the DBH and the tree height, at the plot level [
reconstructing the stem model, other variables of tree structure, such as stem volume, stem
curvature, stem quality and biomass, can be estimated, and the results of the accuracy evaluation are
comparable to the best national allometric models [
]. Three data acquisition approaches
have been reported in TLS-based field measurements: single-scan, multi-scan and
multi-single-scan. The single-scan (SS) mode uses a laser scanner that is placed at the plot center to
obtain a single full field-of-view (e.g., 360??310?) scan of point cloud data of targets in the
surrounding environment. The multi-scan (MS) mode is typically implemented by placing the
scanner inside and outside of the sample plot to obtain more detailed point cloud data of the
sample targets. The multi-single-scan (MSS) mode combines a single-scan mode at multiple
scanning sites to preform sample plot observation [
]. The three scan approaches can be
used to acquire high-quality point cloud data. However, the single scan mode has serious
occlusion problems, and the multi-scan mode is time-consuming but provides the best data
In practice, the TLS instrument needs to be statically placed in the sample plot for scanning
to acquire point cloud data, which greatly limits the mobility of data acquisition. For
largescale sample-plot investigation, this methodology is time-consuming and requires many
reads to build sufficient point clouds for describing the forest environment [
]. Although the
algorithms have been researched to improve the TLS point cloud precision and accuracy
], there are still some algorithms and methods that need further improvement, such
as extracting forest attributes from TLS data and data acquisition protocols [
drawbacks that cannot be eliminated are still limiting the processing efficiency and accuracy of
extracting forest attributes. The use of a mobile laser scanner (MLS) would reduce the
drawbacks, especially in terms of occlusion and mobility [
A mobile laser scanner (MLS) system offers a powerful tool to solve the problem of trees
occlusion and inability to move within the TLS, and greatly reduces the required time and
costs. An MLS system typically combines one or several laser scanner(s) with an inertial
measurement unit (IMU) and a Global Navigation Satellite System (GNSS) tracker that provides
real-time position information. The data acquired by MLS is less precise than TLS point cloud
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data due to the propagation of positioning errors within MLS point cloud data [
systems are predominantly mounted on vehicles for urban mapping and forest survey in flat areas
]. Liang et al. [
] have conducted in-depth research on the application potential of
MLS in forest inventory and the evaluation of the accuracy of extracted tree parameters. Yang
et al. [
] have done significant research and analysis on MLS point cloud data,
developing many effective methods for extracting objects of interest from point clouds. MLS has
certain terrain constraints when conducting forest surveys in the field, such as steep terrain,
dense undergrowth, and barriers such as branches. In order to reduce the terrain constraints,
Liang et al. [
] developed an MLS device mounted onto an all-terrain vehicle to conduct
forest plot mapping, which has great potential for the wider application of MLS in forestry.
Nevertheless, the lack of GNSS signals or weak GNSS signals under the canopy has become the
greatest challenge for the application of MLS in forestry surveys [
Recently, Personal laser scanning (PLS), as an emerging concept, was introduced by Juha
et al. [
], as well as Liang et al [
]. The introduction of PLS started in 2013, and the first
system prototype was large in size and weighed approximately 30 kg, which limited its operability
and mobility. To our best knowledge, the PLS equipment was first used in forest surveys in
]. Subsequently, Ryding et al. [
] and Cabo et al. [
] also introduced PLS technology
and extracted DBH and tree height information from point cloud data. PLS has the potential
to improve mapping efficiency compared with conventional field measurements, and to
compensate for the limitations of other laser scanning techniques, such as having to transport the
scanner and associated equipment from site to site, which is one the major disadvantage of
TLS, in addition to the need for certain terrain conditions and GNSS signals, which limit the
application of MLS [
In this study, we compared the results obtained using PLS equipment with those of field
measurements, and proposed a new method for calculating the DBH. The objectives of this study
were (1) to estimate the accuracy of PLS point cloud data extraction DBH, tree location and trunk
detection; (2) provide an effective method to solve the problem of forest inventories when there is
no GNSS signal or weak GNSS signals under the forest canopy; (3) analyze the advantages and
challenges of PLS; (4) explore the application potential of the PLS in forest inventory.
Materials and methods
The study was carried out in an arbor forest sample plot located in Haidian District (116.20?E,
40.00?N), Beijing, China, in the summer of 2018. The dominant tree species in the study area
is Styphnolobium japonicum (L.) SCHOTT. (syn. Sophora japonica), followed by Birch and
Chinese pine, and the sample plot had a stem density of approximately 1100 stems/ha as shown
in Fig 1. The study area is a typical artificial woodland from northern China with sparse
understory vegetation under the canopy, which is favorable for the scanner to obtain more accurate
3D spatial point cloud data of the targets with less noise.
The individual in this manuscript has given written informed consent (as outlined in PLOS
consent form) to publish these case details.
The ZEB-REVO-RT equipment developed by GeoSLAM Ltd. (UK) is a lightweight personal
mobile laser-scanner (PLS), which consists of a laser scanner, a data logger, a camera, a
low3 / 22
Fig 1. Study area in Haidian District, Beijing, China.
cost IMU and accessories. It weighs approximately 3.5 kg. The laser scanner containing an
eye-safe laser giving 43200 measurements per second is lightweight (1.0 kg) and small
(86?113?287mm), making it more convenient for hand-held movement in forest surveys.
With a maximum laser range of 30 m, the PLS is designed as an area scanner and works
continuously for up to 4 hours. The ZEB-REVO-RT equipment can be used in a variety of ways,
such as handheld, pole-mounted, or attached onto a mobile platform such as a vehicle or
UAV. Instead of using GNSS within the navigation module, the PLS makes full use of
simultaneous localization and mapping (SLAM) technology, which was developed by the machine
vision and robotics community [
]. The concept of SLAM technology relies on the ability to
place a robot at an unknown location in an unknown environment and then have it build a
map, using only relative observations of the environment, and then to use this map to
simultaneously navigate, which makes such a robot autonomous [
]. Currently, the SLAM
technology has been widely used in large-scale mapping of urban structures , mine mapping
], landslide investigation [
] and urban transport [
]. The problem of no GNSS signal or
poor signal under the forest canopy can be solved by using PLS combined with SLAM
technology in forest sample plot surveys.
The PLS allows several smartphones or tablets to seamlessly connect to the scanner via the
Wi-Fi interface of the device, which enables the field data to be collected in the mobile
terminal, allowing for real-time data visualization and eliminating the need for post-processing.
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Along with the direction of the investigator?s movement, the PLS equipment captures a strip of
the point cloud data at the speed of movement and generates a movement trajectory of the
]. The manufacturer claims a measurement range of up to 30 meters. However, in
practical field investigation, the measurement range of the ZEB-REVO-RT equipment is
reduced to 15?20 m due to the influence of solar radiation [
]. The scanner performs a
270?360? auto-rotation scan in space during the process of measurement, enabling fast and
easy 3D mapping without loss of detail. The manufacturer stated that the relative accuracy of
measurement is?15 mm within 30 m [
]. Table 1 shows the detailed technical specifications
of the ZEB-REVO-RT equipment.
Tree position and DBH data were acquired from the field data and PLS point cloud data. The
data acquired by field measurements served as the reference measurements, and the data
derived from the PLS point cloud served as measurement values.
Field data acquisition. The field data were collected in a 300 m2 (15?20m) rectangular
study area in early July of 2018. The collected data encompassed the species, DBH and
locations of all the trees within the sample plot. The DBH of each tree of more than 5 centimeters
was manually measured by a steel tape to the nearest millimeter at DBH height (1.3 m vertical
above the ground from the base of the tree). The position information of each tree (X, Y, Z
coordinate values) was recorded using the independent coordinate system established by the
total station. In order to convert the coordinate system established by the total station into the
coordinate system established by ZEB-REVO-RT equipment, the four marker plates were used
to attach to four different tree trunks (the center of the four marker plates was not present in
any plane of space at the same time). The total station was used to record the spatial coordinate
information of the centers of the four marker plates.
PLS (ZEB-REVO-RT) data acquisition. To ensure maximum coverage of all trees and
acquire high resolution data in the test area, we used a method of serpentine scanning and
walked slowly to acquire the point cloud, as shown in Fig 2(B). The setting of the scanning
path mainly considers the following four main factors: (1) a suitable data acquisition path; (2)
avoiding the occlusion problem among trees, and guaranteeing a better scanning coverage for
the trees in the study area; (3) avoiding the problems associated with drift, which can arise
once the SLAM algorithm fails to execute the alignment correctly; (4) reducing the scanning
range noise to generate reliable point cloud data.
Time of flight
30 m (15m outdoors)
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Fig 2. Field data collection and roadmap. (a) Data acquisition in the field using the ZEB-REVO-RT device; (b) the dotted line refers to the field scan path and spatial
distribution of tree trunk point cloud data within the sample plot in the black point.
The ZEB-REVO-RT equipment firstly needs to initialize the IMU for establishing the
reference coordinate system before the data acquisition starts. After approximately 15 seconds of
initialization, the survey is executed by moving at walking speed whilst gently oscillating the
ZEB-REVO-RT laser head forward and backward to capture data from the full 3D
environment. The survey path should form a closed loop, so that the same region is covered at the
beginning and the end of the path. The IMU integrated into the scanning head is capable of
measuring angular velocities and linear accelerations that can be used to calculate the sensor?s
]. The SLAM algorithm used to calculate the position of laser data adopts a
linearized model to minimize the error in the IMU, and then generate reliable position and
The recording of the entire plot (15?20m) takes approximately 5 minutes. The survey data
will be processed automatically within the ZEB-REVO-RT equipment while the data are being
collected. Furthermore, since registration is conducted in real time, the results are available
almost immediately on completion of the survey. The real-time local processing service offered
by GeoSLAM Ltd. can be used to check the survey data instantly on site using a smartphone or
tablet. Real-time feedback enables us to see exactly what we have and haven?t captured before
the survey has even finished so nothing is missed. The ZEB-REVO-RT field measurement is
shown in Fig 2(A).
Pre-processing of point cloud data
When the field data collection is completed, the data logger processes the collected data in real
time and generates a scan point cloud, which is displayed on a device such as a mobile phone
or a tablet. The point cloud data generated by the ZEB-REVO-RT equipment is more accurate
in a small range of acquisition areas. However, if the area is very large and the characteristics
of the area are not distinctive enough, the accuracy of the SLAM algorithm will decrease, and
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the displayed result will be misaligned. In order to obtain more accurate processing results, it
is necessary to re-process the collected scan data using the GeoSLAM desktop software which
is an all-in-one solution for 3D point cloud manipulation.
Due to the influence of tree branches and understory vegetation during data collection,
filtering represents a crucial part of point cloud data preprocessing. We selected the sample plot
and used CloudCompare software to further process the point cloud data within the sample
plot. The hybrid filtering de-noising method was adopted as a noise filter. In this method, the
sphere radius value was set to 0.0466 m, and its function was roughly the same as the radius
filtering. The default value of 1 was selected for the maximum relative error, and the method for
removing outliers was similar to that of statistical filtering. Fig 3 shows the laser point
preprocessed by CloudCompare software.
Processing and modelling of point cloud data
In order to model the tree trunks precisely, we used LiDAR 360 software to further process the
point cloud data. Firstly, we performed secondary denoising on point cloud data to eliminate
the effects of outliers and other types of noise. Then, we filtered the point cloud data and
generated DEM using the filtered ground points. Finally, the point cloud data were normalized to
eliminate the influence of topographic fluctuations. Due to the presence of low-density point
cloud data acquired by ZEB-REVO-RT equipment and the defects in the point cloud matching
generated using the SLAM algorithm, the obtained DBH values may have a large deviation if
the intercepted point cloud slice is directly fitted with circles or cylinders. Therefore, we
proposed a method for solving the DBH values by calculating the polygonal cylindrical volume.
The specific process was as follows: (1) We intercepted the point cloud data at 1.2 m to 1.4 m
Fig 3. Pre-processed point cloud data.
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and 1.1 m to 1.5 m at the trunk (the trees in the sample plot had no branches at 2.0 m and
below.), respectively, and then used the point cloud data fitting polygonal cylinder. (2)
Calculating the volume of the fitted polygonal cylinder; (3) Calculating the DBH values of the trees
using the cylindrical volume formula (1).
where V is the volume of the fitted polygonal cylinder, D is the diameter of the fitted cylinder,
h is the height of the horizontal slice.
The diameter of the trees in the sample plot was analyzed based on the trunk in the
horizontal strip. Two parts (i.e. 1.2?1.4 m and 1.1?1.5 m) of point cloud data were collected from the
tree trunk point cloud as the strip (shown in Fig 4), after which the points between the h(high)
cross section and h(low) cross section were fitted with the cylinder. The definition of the value
of the upper cross-section of the strip avoids the influence of the point cloud generated by the
branches and the upper canopy on the fitted cylinder. At the same time, the definition of the
value of the lower cross-section avoids the influence of the point cloud generated by the surface
vegetation and low shrubs on the fitted cylinder.
Before the cylinder modeling of point cloud data, we need to individualize the point cloud
of the trunk within the strip, and finely model the segmented point cloud one by one, which
helps us timely check which point cloud of the trunk is not successfully modeled and assess the
existing problems. To ensure the convergence of the point cloud data iteration, and that the
point cloud data is successfully modeled, the point cloud data used in our modeling must be
We approximately considered that the part of the trunk below 1.5 m is vertical, and that the
central points of the individual trunk point cloud data in the strip 1.25?1.35 m is the position
of the tree. The trunk points that belong to the same tree were clustered based on location and
distance analysis. All points less than 0.3 m apart were clustered into a group of points and
were considered as belonging to the same tree. Points that were more than 0.3 meters away
Fig 4. Strip on the normalized point cloud. The gray points in the strip are used to fit the cylinder. (h(high) is 1.5 meter and 1.4 meter respectively, corresponding to
h(low) of 1.1 meter and 1.2 meter).
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were considered to belong to different trees. The reason for choosing 0.3 meters is that all the
trees in the sample plot had a breast diameter of less than 30 cm. Therefore, using such an
approach helps uniquely identify each tree. The center position of each point group was
calculated and determined.
Evaluation of the accuracy of the mapping results
To evaluate the accuracy of the mapping results, the reference data measured in the field was
compared with the PLS (ZEB-REVO-RT) data. The criteria for the mapping results included
omission errors, commission errors, trunk detection accuracy and tree location accuracy.
Omission errors refer to trees that exist in the sample plot, but were not accurately detected in
the point cloud data acquired by the ZEB-REVO-RT equipment. Commission errors were
defined as the trunk models mapped from point cloud data for which no corresponding trees
were found in the sample plot. The trunk detection accuracy was the percentage of trees in the
plot that were correctly detected. The tree location accuracy was the degree of deviation
between the tree location in the mapping and the corresponding tree location in the sample
plot, which was reflected by bias and root mean squared errors (RMSE). Table 2 shows the
accuracy requirements of different investigation factors at different survey levels in China?s
The bias, root mean squared error (RMSE), relative bias and relative RMSE were employed
to gauge the accuracy of the DBH estimations, as defined in the following equations:
where yi is the ith measurement value, yj is the jth reference value, yj is the mean of the
reference values, and n is the number of estimations.
The data on the trunks detected and mapped using ZEB-REVO-RT equipment are listed in
Table 3. The total number of trees in the sample plot was 33, all of which were Sophora
japonica, and their diameters were distributed between 5.1 cm and 21.5 cm. The point cloud dataset
obtained using the ZEB-REVO-RT equipment was used to successfully model 30 strunks, with
a detection accuracy of 90.9%. The correct detection rate of the trunks reached 93.3% without
considering the measurement errors at the border of the plot.
Three trunks that were not mapped accounted for 9.1% of the total. One tree was cut off
when the sample plot was split because it was located on the boundary of the sample plot. The
missed trees in the interior of the sample plot were mistaken for noise points and filtered out
due to the thinness of their trunks and partial occlusion of the trees. The commission errors of
two trunks that were located on the border of the sample plot accounted for 6.1% of the total.
The individual trunks were modeled within the strip (Fig 4) utilizing the Geomagic software.
By keeping 100% sampling of the tree trunk point data, the characteristic information of the
tree trunks was maintained to the maximum extent. In order to obtain a more accurate fitting
diameter, the fitted cylinder of contact feature and the fitted cylinder of non-contact feature
were respectively used on the tree trunk points. The polygonal transformation of the fitted
cylinder and the features of the tree trunk points were used to create a new cylinder composed of
a large number of triangles (e.g. the cylinder in Fig 5C was composed of 8720 triangles with a
Fig 5. The fitted cylinders of non-contact features in the strip of 1.1?1.5m. (A) Diagram of the trunk points in three-dimensional coordinates; (B) cylinder fitted by
trunk points; (C) fitted polygonal cylinder composed of a large number of triangles.
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Fig 6. The fitted cylinders of contact features in the strip of 1.1?1.5m.
maximum deviation of 0.25 mm; the one in Fig 6C was composed of 9426 triangles with a
maximum deviation of 0.27 mm). Subsequently, the volume of the polygonal cylinder was
calculated and the diameter of the trunk was obtained.
In the strip of 1.1?1.5 m, the standard deviation of the polygonal cylinder fitted by the
noncontact features of all trunk points was in the range of 0.66?1.21 cm, among which the
standard deviations of less than 1 cm accounted for about 80% of the total deviation. The shape
deviation was between 3.80?11.00 cm, among which the shape deviations of less than 8 cm
accounted for about 90% of the total deviation. The standard deviation of the polygonal
cylinder fitted by the contact features of all the trunk points was in the range of 0.77?1.24 cm, and
the shape deviation was between 3.80 cm and 10.85 cm. In the strip of 1.2?1.4 m, the standard
deviation of the polygonal cylinder fitted with the non-contact features of the tree trunk point
was within the range of 0.64?1.27 cm, among which the deviations of less than 1 cm accounted
for 86.7% of the total deviation, and the shape deviation was between 2.76?11.06 cm, among
which the shape deviations of less than 8 cm accounted for about 96.7% of the total deviation.
The standard deviation of the fitted cylinder corresponding to the contact features was
between 0.68 cm and 1.4 cm, and the shape deviation varied from 3.06 cm to 11.06 cm. The
results show that the standard deviation and shape deviation of the polygonal cylinders
obtained by different fitting methods of different strips was similar.
Table 4 lists the results of DBH estimation from non-contact fitted cylinders, indicating
that the DBH values for the same trunks fitted in the two strips (i.e. 1.2?1.4 m and 1.1?1.5 m)
were similar. The reference measurements of the DBH were distributed in the range of 5.9?
21.5 cm, and in the strip of 1.1?1.5 m, the DBH values obtained by fitting the cylinder volumes
were distributed between 4.6?20.1 cm. In the strip of 1.2?1.4 m, the values of the DBH
obtained by fitting the cylinder volume were distributed in the range of 5.1?20.1 cm.
11 / 22
Estimation of tree positions
The tree position data in the total station coordinate system was converted to the
ZEB-REVO-RT coordinate system via the marker points. The center position of each point group was
calculated, and the distance from the center of each tree to the tree position measured using
the total station was manually measured in the software. The spatial distribution of trees in the
field assessed using the total station is shown in Fig 7(A), and a comparison of the positions of
the trees obtained by the ZEB-REVO-RE equipment with the observed positions of the trees is
shown in Fig 7(B). A comparison of the measured values of the tree positions with the
reference measurements is summarized in Table 6.
Comparison of the efficiency of PLS survey and field survey methods
The survey time was recorded to analyze and compare the efficiency of the PLS survey and field
survey methods. Both survey methods were applied to the same sample plot (15?20m). The
ZEB-REVO-RT survey time, which includes data collection and data processing, was approximately 10
minutes. The data collection time includes the time spent in automatic calibration at the
beginning of data collection and automatic registration at the end. The field survey time mainly
includes recording the DBH values, tree species and determining the tree locations. At least three
investigators are involved in the field survey, while only one is needed in the ZEB-REVO-RT
equipment survey. In addition, the data collected by the PLS instruments requires additional
processing to obtain the DBH and tree locations, while the field survey method can yield these
parameters at the end of the field survey. The time spent on the two survey methods is shown in Table 7.
Analysis of the mapping results of the PLS data
The point cloud data of the target in the sample plot were obtained using the ZEB-REVO-RT
instrument. They were used to extract the DBH and position information of the trunks.
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Fig 7. Estimation of tree positions. (a) Positions of trees observed using the total station. (b) Positions of trees obtained using ZEB-REVO-RT compared with their
observed positions (red dots indicate observed tree locations, gray dots indicate the position of the trees obtained using the ZEB-REVO-RT equipment).
According to the data of the tree trunk points, the methods of fitting the polygonal cylinders
with contact features and non-contact features were used. Fig 8 shows a line graph of the DBH
values obtained from the fitted cylinders and the DBH values measured in two different strips
in the field. It can be seen that the DBH values obtained using the fitted polygonal cylinders
with contact features significantly overestimated the measured DBH values. This is because
when using this method to fit the trunk point data, the farthest point in the point cloud data in
the same plane is used as the boundary of the polygonal cylinder. In Fig 8A, there is a value in
the DBH obtained using the fitted cylinder of the contact feature that is smaller than the
corresponding reference value. It was found that the point cloud data acquired using the
ZEB-REVO-RT instrument only yielded the relative point in one direction of the trunk, and the
number was small. In the process of filtering, a large part of the trunk point data was filtered
out, which resulted in a smaller fitting of the polygonal cylinder than the actual trunk when
fitting the cylinder with the contact features. Fig 8B shows a similar situation.
From Fig 9, we can see that the DBH estimations obtained by fitting the polygonal cylinder
with the point cloud data of the two strips were similar, which significantly underestimated the
reference measurements. We can preliminarily draw the conclusion that the DBH estimations
of the trunk are similar by horizontally intercepting the point cloud data of different thickness
(i.e., 20 cm and 40 cm) at the chest diameter of the trunk to fit the polygonal cylinders. The
deviations of the DBH estimations were -1.26 cm and -1.99 cm, respectively, while the
corresponding root mean square errors (RMSE) were 1.58 cm and 1.62 cm, respectively. The DBH
estimates for the fitted polygonal cylinders obtained for different trunk diameters were not
significantly different from the measured reference values, since in this study, the reference
measurements were within the range of 5.9?21.5 cm.
As can be seen from the different fitting methods, the trunk diameters obtained by fitting
the polygonal cylinders using the non-contact features significantly underestimated the
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reference measurements, which were closer to the measured diameters of the trunks than
those obtained using the fitting method of contact features. Fig 10 shows a comparison of the
DBH estimations and the reference measurements. The comparison of the DBH values
estimates and the reference DBH values revealed that the diameter accuracy of the fitted cylinder
using the non-contact features in the 1.1?1.5 m strip is optimal.
The DBH values estimations obtained by the volume of the fitted polygonal cylinder of
non-contact features were closer to the reference measurements than those obtained using
contact features. The non-contact features method for fitting the polygonal cylinders was
more robust, while the contact features method as poorer in performance and was highly
susceptible to noise, which directly caused a large error in the estimations of the breast diameters.
In this study, a method for calculating the DBH via the volume of a fitted polygonal cylinder is
proposed, which is similar to the method of fitting a cylinder. The DBH values of the trunks
obtained by this method were not significantly different from the reference values of the DBH
values measured in the field. PLS (ZEB-REVO-RT) instruments offer unique investigative
advantages such as the ability to quickly acquire 3D point cloud data without GNSS signals for
positioning, which offers great promise for practical applications in forest surveys. However,
there are still some issues in the processing and mapping results of the point cloud data [
Fig 8. Comparison of the trunk DBH estimates in the two strips with the reference measurements. (A) Comparison of the DBH values of the two differently fitted
cylinders in the strip at 1.1?1.5 m with the reference measurements; (B) Comparison of the DBH values of the two differently fitted cylinders in the strip at 1.2?1.4 m
with the reference measurements. Reference refers to the field measurements; Contact feature refers to the DBH values obtained via the fitted polygonal cylinder of
contact features; Non-contact feature refers to the DBH values obtained via the fitted polygonal cylinder of non-contact features.
14 / 22
Fig 9. Comparison of the DBH estimations of non-contact features with reference values. Reference refers to the field measurements; 1.1?1.5 m estimation refers to
the DBH estimations of the 1.1?1.5 m part of the trunk; 1.2?1.4 m estimation refers to the DBH estimations of the 1.2?1.4 m part of the trunk.
There was significant noise in the point cloud data collected by the ZEB-REVO-RT equipment
]. After the first filtering, there was still noise in the obtained point cloud data (Fig
11A). To further reduce the influence of noise on the fitted trunk diameters, secondary
filtering of the point cloud data with optimal threshold parameters was conducted to improve
the accuracy of the DBH estimations. We performed filtering tests many times by setting
different thresholds and compared the test results. Finally, we set the number of domain
points to 10 and the standard deviation to 5 times for secondary filtering. The point cloud data
after the secondary filtering had less noise (as shown in Fig 11B) and was used for further
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Fig 10. Scatterplots of the DBH measured with a steel tape versus the DBH derived from different strips with different fitting methods. The dashed line shows the
1:1 line and the red solid line shows the trend-line. (a) and (b) represent the DBHs estimations of contact features and non-contact features in the strip 1.1?1.5m
respectively; (c) and (d) represent the DBHs estimations of contact features and non-contact features in the strip 1.2?1.4m respectively.
Comparison of measurement results
According to previous studies, we know that ALS, TLS, MLS and emerging PLS are affected by
tree density in the field. The smaller the density of trees in the sample plot, the higher the
detection rate of the trunks, and the greater the density of the trees, and the lower the detection
rate. In sparse forests with a trunk density of 100?200 stems/ha, the detection range of the
stems can reach 70% to 100%. In a forest with a trunk density of more than 1000 stems/ha, the
trunk detection rate is approximately 70% [
]. Maas et al. [
]investigated four plots with a
trunk density of 212?410 stems/ha, and the detection rate of the trunks was in the range of
86.7% to 100%. In Carlos Cabo et al. [
], in three plots with densities of 300 stems/ha, 900
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Fig 11. Point cloud of a single tree. (A) The first filtered tree trunk point cloud. (B) Secondary filtered tree trunk point cloud.
stems/ha and 2100 stems/ha, the completeness of trunk detection was 100% and the
correctness of trunk detection was 98.5?100%. In addition, the detection rate of the trees was also
affected by the laser scanning mode. In the study by Lovell et al. [
], with single-scan (SS)
method using TLS in the plots with densities of 124 stems/ha and 477 stems/ha, the average
detection rate of trees reached 54% and 68%, respectively. In the study by Liu et al. [
different terrestrial laser scanning methods (single center scan, MSS method, matched
multiple scans) were used in 10 plots with trunk densities of 340?1200 stems/ha. The mean
completeness and correctness were 70% and 94.2%, 80.8% and 90.9%, as well as 73.1% and 97.2%,
respectively. In this study, if the measurement error of trees located on the boundary of the
plot is not taken into account, the correct detection rate of the trunks using ZEB-REVO-RT
equipment is 93.3% in the sample plot with a density of 1100 stems/ha. Compared with the
study by Liang et al. [
], which used PLS instruments to achieve an overall trunk detection
rate of 82.6%, the tree detection rate in this study was higher.
The diameter at breast height (DBH) was assessed by comparison with field-measured
breast diameter results. This comparison was performed using different fitting methods for
point cloud slices of different thicknesses. By different fitting methods, the optimal fitting
results of deviation and root mean square error of -1.26 cm and 1.58 cm were obtained. In the
available literature, the breast diameters obtained by different terrestrial laser scanning
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methods were also different. Liu et al. [
] used the SS, MS and MSS methods to obtain tree
parameters in 10 sample plots. For the SS method, the MS method, and the MSS method, the
deviations and the RMSE of the DBH estimation were in the range of -0.30?1.01 cm and 1.1?
3.07 cm, 0.18?1.75 cm and 0.73?2.70 cm, as well as -0.04?0.66 cm and 0.96?4.28 cm,
respectively. Liang et al. [
] used PLS instruments to map the trunks of the sample plot, and arrived
at an RMSE of the DBH values estimation of 5.06 cm, which was significantly lower than the
measurement accuracy in this study (1.58 cm). In the study by Joseph Ryding et al. [
results indicated that there was a significant difference in the fitting accuracy of the trunks
with diameters greater than 10 cm and less than 10 cm. When the DBH was greater than 10
cm, the overall deviation and the RMSE were 0.9 cm and 1.5 cm, respectively. By contrast, the
overall deviation and the RMSE when the DBH was less than 10 cm were 1.6 cm and 3.9 cm.
However, the reference measurements of the DBH values of the trees in this study were
between 5 cm and 22 cm, and the deviation of the DBH estimations was not significantly
different in the range of the reference DBH greater than 10 cm and less than 10 cm. The effect of
diameter size on mapping accuracy needs to be studied further.
The advantages and challenges of PLS
The PLS (ZEB-REVO-RT) instrument has higher mobility than ALS, TLS or MLS, and it can
be used in areas where the mentioned three methods are not applicable. Although airborne
laser scanning (ALS) is less restricted by topographical factors and has high efficiency for
large-area forest surveys, the acquired tree parameters and tree canopy structure information
are limited, especially the trunk diameters and tree heights. Both TLS and MLS are greatly
restricted by terrain factors. They are not applicable in some forest conditions with large
topographic fluctuations and steep terrains, and the PLS may be a very good choice in such cases.
Only one operator can implement all the data collection work using the ZEB-REVO-RT
equipment. The data from the surrounding environment is rapidly acquired by constant scanning
when the operator moves forward, and a synchronous video can be observed in the data
collection process, saving a lot of time compared to TLS and field data collection [
]. Hence, it is
more efficient than the field investigation. When the data collection is completed, the collected
point cloud data can be viewed and checked in the field through a smartphone or tablet to
ensure the integrity of the collected data, and the density and quality of the collected point
cloud data can be checked on-site. The ZEB-REVO-RT equipment automatically processes the
collected data in real time through an online processing service provided by GeoSLAM Ltd.,
which reduces the processing time of the data. The PLS instruments combined with the SLAM
algorithm have a higher registration accuracy in forest surveying than that of the MLS
equipment, and can acquire and process point cloud data under the canopy in real time without
GNSS signals, which can solve the positioning problem in areas without a GNSS signal or
weak GNSS signal under the forest canopy. This again reduces the processing time.
The automatic co-registration of PLS (ZEB-REVO-RT) equipment would fail if the sample
plots had a lower or higher stem density, especially in case of a dense understory with moving
leaves. The lower stem densities hindered the object recognition in the SLAM algorithm,
leading to a ?slip? of the object recognition algorithm. A higher stem density and dense understory
would affect the co-registration of the ZEB-REVO-RT scan, which resulted in slight offsets of
the point at the stems and presence of many double stems in the point cloud. Due to the
limitations of the ZEB-REVO-RT?s equipment measurement range and the effects of ambient solar
radiation, the farther the measurement distance is, the lower the point density of the laser.
This limits the measurement of tree heights. A laser scanner with a higher range and lower
divergence is therefore needed to better determine tree heights. It is an important factor to
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assess the timber volume and biomass. When there is more vegetation or branches under the
forest canopy, there will be more noise in the collected point cloud data. Thus, it is necessary
to set an optimal threshold to filter the point cloud data. A single scan time using
ZEB-REVO-RT equipment is recommended for no more than 20 minutes. With a single scan time of
more than 20 minutes, there will inevitably be some drift in the collected point cloud data. To
reduce the drift of point cloud data registration, the data collection route should form a closed
loop by starting and ending the scan at the same point.
In this study, the ZEB-REVO-RT equipment was used to achieve a correct detection rate of
93.3% of the trunks within the sample plot. Through the optimal fitting of the non-contact
features, the deviation and the RMSE of the DBH estimations were -1.26 cm and 1.58 cm,
respectively, which meets the C-level accuracy requirements of China?s forestry resources survey.
Nevertheless, the accuracy of the DBH estimations may be significantly influenced by the
quality of the point cloud acquired using the ZEB-REVO-RT combined a SLAM algorithm in the
forest sample plots, as Bauwens et al. [
] noticed. The ZEB-REVO-RT equipment needs to be
further studied to estimate the completeness and correctness of tree trunks in forest areas with
different tree density and at different growth stages. The accuracy of the DBH estimations
measured with ZEB-REVO-RT equipment for different tree diameters also needs to be further
analyzed and studied. The error of tree positions obtained via point cloud data meets the
requirements of precision in actual surveys in China, and the determination of tree positions is
not affected by the GNSS signal. The survey method using ZEB-REVO-RT equipment shows
that the survey coverage area per unit of time is higher than that of the field survey, which
greatly reduces the time spent in the survey and provides a possibility for large-scale forest
surveys. However, due to the limited measuring range and poor penetration of the
ZEB-REVO-RT equipment, it is difficult to measure the top parts of the canopy in heavily occluded
forests to obtain reliable tree height information. However, this is an important factor in the
assessment of timber volume and biomass. At the same time, since the point cloud data of
ZEB-REVO-RT does not include detailed canopy structure information of the upper part of
the trees, the species and status information of the trees cannot be obtained from its point
In this study, personal laser scanning (PLS) equipment combined with SLAM technology
was used to acquire the tree parameters in a forest sample plot (eliminating the influence of
GNSS signals), and a method for calculating the DBH of the trees using a fitted polygonal
cylinder volume was proposed. The point cloud data for slices of different thicknesses (i.e.,
20 cm and 40 cm) was evaluated using different cylinder fitting methods (cylinder fitting of
contact features and cylinder fitting of non-contact features), and the calculated DBH values
of the trees obtained using the fitted cylinder of non-contact features was closer to the DBH
measured manually using the steel tape. The same fitting method was used to fit the data of
the slices of different thicknesses, and the measurement error between the calculated DBH
values of the trunks and reference measurements was similar. The optimal breast diameter
deviation and RMSE of the cylinders fitted using non-contact features were -1.26 cm and
1.58 cm, respectively, which meets the C-level accuracy requirements of China?s forestry
inventory. The accuracy of the point cloud data obtained using the ZEB-REVO-RT equipment
to detect the correct rate of the trunk, the accuracy of the DBH estimations and the tree
positions were evaluated and analyzed. In term of survey time, the PLS instrument had a unique
advantage. The trial showed that the PLS equipment has broad application prospects in forest
19 / 22
In this study, the advantages and limitations of PLS (ZEB-REVO-RT) equipment were
analyzed in detail. The applicability of PLS equipment for forest areas with different densities and
different growth stages needs to be further studied, as well as its use in complex topographies
and areas with understory vegetation with different densities.
S1 Table. Field acquisition of diameter at breast height (DBH) and tree position data.
S2 Table. The time taken to obtain the DBH and tree position information for both
S1 File. The point cloud data of the study area were obtained using personal laser scanning
S1 Text. Tree location information obtained using total station equipment.
Thanks to the equipment support provided by Beijing ONROL Technology Co., Ltd.
Conceptualization: Shilin Chen, Zhongke Feng.
Data curation: Panpan Chen.
Formal analysis: Shilin Chen, Chaoyong Shen.
Investigation: Shilin Chen, Haiyang Liu.
Methodology: Shilin Chen, Chaoyong Shen.
Project administration: Shilin Chen.
Resources: Haiyang Liu.
Supervision: Panpan Chen.
Validation: Shilin Chen, Panpan Chen.
Writing ? original draft: Shilin Chen.
Writing ? review & editing: Shilin Chen.
20 / 22
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