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Research Highlights: The present study case investigates the differences occurring when tree’s biophysical parameters are extracted through single and multiple scans. Scan sessions covered mountainous and hill regions of the Carpathian forests. Background and Objectives: We focused on analyzing stems, as a function of diameter at breast height (DBH) and the total height (H), at sample plot level for natural forests, with the purpose of assessing the potential for transitioning available methodology to field work in Romania. Materials and Methods: We performed single and multiple scans using a FARO Focus 3D X130 phase shift terrestrial laser scanner at 122kpts and 0.3:0.15 mm noise compression ratio, resulting in an average point density of 6pts at 10m. The point cloud we obtained underpinned the DBH and heights analysis. In order to reach values similar to those measured in the field, we used both the original and the segmented point clouds, postprocessed in subsamples of different radii. Results: Pearson’s correlation coefficient above 0.8 for diameters showed high correlation with the field measurements. Diameter averages displayed differences within tolerances (0.02m) for 10 out of 12 plots. Height analysis led to poorer results. For both acquisition methods, the values of the correlation coefficient peaked at 0.6. The initial hypothesis that trees positioned at a distance equivalent to their height can be measured more precise, was not valid; no increase in correlation strength was visible for either heights or diameters as the distance from scanner varied (r = 0.52). Conclusions: With regard to tree biophysical parameters extraction, the acquisition method has no major influence upon visible trees. We emphasize the term “visible”, as an increase in the number of acquisitions led to an increased number of detected trees (16% in old stands and 29% in young stands).
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Forests 2020, 11, 392; doi:10.3390/f11040392
Retrieval of Forest Structural Parameters From
Terrestrial Laser Scanning: A Romanian Case Study
Ionuț-Silviu Pascu 1,2, Alexandru-Claudiu Dobre 1,2,*, Ovidiu Badea 1,2 and Mihai Andrei Tanase 3
1 Department of Forest Monitoring, “Marin Drăcea“ Romanian National Institute for Research and
Development in Forestry, 128 Eroilor Blvd., 077190 Voluntari, Ilfov, Romania; (I.-S.P.); (O.B.)
2 Department of Forest Engineering, Forest Management Planning and Terrestrial Measurements,
Faculty of Silviculture and Forest Engineering, “TransilvaniaUniversity, 1 Ludwig van Beethoven Str.,
500123 Braşov, Romania
3 Department of Geology, Geography, and Environment, University of Alcala, 2 C. Colegios,
28801 Alcala de Henares, Spain;
* Correspondence:; Tel.: +40-722-194-887
Received: 24 February 2020; Accepted: 30 March 2020; Published: 1 April 2020
Abstract: Research Highlights: The present study case investigates the differences occurring when
tree’s biophysical parameters are extracted through single and multiple scans. Scan sessions covered
mountainous and hill regions of the Carpathian forests. Background and Objectives: We focused on
analyzing stems, as a function of diameter at breast height (DBH) and the total height (H), at sample
plot level for natural forests, with the purpose of assessing the potential for transitioning available
methodology to field work in Romania. Materials and Methods: We performed single and multiple
scans using a FARO Focus 3D X130 phase shift terrestrial laser scanner at 122kpts and 0.3:0.15 mm
noise compression ratio, resulting in an average point density of 6pts at 10m. The point cloud we
obtained underpinned the DBH and heights analysis. In order to reach values similar to those
measured in the field, we used both the original and the segmented point clouds, postprocessed in
subsamples of different radii. Results: Pearson’s correlation coefficient above 0.8 for diameters
showed high correlation with the field measurements. Diameter averages displayed differences
within tolerances (0.02m) for 10 out of 12 plots. Height analysis led to poorer results. For both
acquisition methods, the values of the correlation coefficient peaked at 0.6. The initial hypothesis
that trees positioned at a distance equivalent to their height can be measured more precise, was not
valid; no increase in correlation strength was visible for either heights or diameters as the distance
from scanner varied (r = 0.52). Conclusions: With regard to tree biophysical parameters extraction,
the acquisition method has no major influence upon visible trees. We emphasize the term “visible”,
as an increase in the number of acquisitions led to an increased number of detected trees (16% in
old stands and 29% in young stands).
Keywords: TLS; single scan; multiple scans; biophysical parameters
1. Introduction
Forest inventories are the main way of deriving information on forest stand characteristics for a
range of fields including biometrics, forest monitoring, physiology and for appraising forest
biodiversity or the success of forest management practices. Over the last decade, technological
advances in terrestrial laser scanning (TLS) have allowed the development of highly portable,
accurate and increasingly cost-effective sensors. Similarly, we can observe an increase in data
processing solutions capable of segmenting and reconstructing accurate individual tree models [1
Forests 2020, 11, 392 2 of 16
3]. This facilitated the use of such close range remote sensing technologies for describing forest stands
structural parameters [46] and biophysical parameters[79].
TLS techniques facilitate virtual revisiting of the same forest stand once it has been scanned. This
allows analyzing and testing hypotheses beyond those which initially led to the acquisition of the
data. In addition, research and knowledge on TLS processing in forest ecosystems can be applied to
predating sets of data [10]. Multiple scans processing[11,12], and several TLS scanning distances [7],
have previously been used to assess the quality of tree parameter extraction, sometimes even using a
species-specific approach [13]. Most of the studies on the extraction of forest biophysical parameters
from TLS data focused either on plantations or on small areas of natural growth forest, and analyzed
various size plots (from sample plot to forest stand level) [14,15].
The aim of our study is to assess the potential of transitioning the theoretical methodologies
(e.g., point cloud classification, segmentation, voxelization, 3D reconstruction) towards a forest
inventory oriented tool, specific for the Romanian forest monitoring activities. What was previously
used for single tree scanning (as a means of analyzing the forest ecosystem in a controlled
environment) needs to be transferred to full-scale fieldwork by overcoming the construction
limitation of the TLS (narrow field of view) in the most productive manner. Besides the limitations
of the technology itself, there is also a reluctance towards assimilating such close-range ground-based
remote sensing approaches in Romanian forestry, that should be taken into account. In order to reach
our aim, and asses the level to which the expert knowledge in terrestrial laser scanning can be passed
to personnel from other areas, we had to restrict the acquisition, processing and interpretation of the
data to preexistent algorithms and software. This is a major factor in the acceptance of the research
as a tool in areas that might benefit from it.
Multiple studies have focused on the detection of biophysical parameters at both stand and
individual scale, with major findings. Heinzel and Huber [16] reached close to 100% identification of
stems position by focusing on close range segmentation; similar results are reiterated in several other
papers [17][14]. With similarly good results, studies on biophysical parameters estimation covered
both diameter [18][19][20] and heights evaluation [21][22]. There are still some factors that constrain
the applicability of the technology, leading to the impossibility of gaining such results for all interest
aspects (e.g., diameter, height, volume, crown area). But even so, research covering the aspect of
combining point clouds from multiple positions [23] or that of pre-designing the acquisition
network[24] as compensating measures. As such, for most of the intended goals, we followed and
extended the previous work addressing both tree and stand level [25,26], forest type influence [27]
and even terrestrial laser best practice guides [28,29].
We investigated and quantified the differences that occur when reconstructing trees at sample
plot level from single scan versus multiple scans in deciduous and coniferous dominated forests. We
also analyzed the influence of the distance variance between the scanner and the furthest targeted
stem in both single and multiple scans. As a result, our case study serves as a good practice guide
with focus on Romanian forest ecosystems, by addressing the subject at country specific reach level.
By evaluating the degree to which the above-mentioned results and approaches can be reached
through pre-existing software[3037] and mid-range field equipment[35], we can facilitate the
acceptance of terrestrial laser scanning as a tool in Romanian forestry. The current study case needs
to answer the following questions: can available tree reconstruction methods be applied at a larger
scale in natural forest stands, by non TLS-centric users? What are the limitations of single and
multiple scanning approaches when increasing the study areacan this limit in any way the extent
to which the technology can be used in the field? Is the extra effort implied by multiple scans
reconstruction translated in an increase in quality or quantity of the results?
2. Materials and Methods
2.1. Study Area
We have conducted field inventory activities in twelve circular plots with a radius of 15 m each,
grouped in two sets. The plots were set in the mountainous and Subcarpathian hills’ forests in central
Forests 2020, 11, 392 3 of 16
and southern Romania (Table 1). The plots were selected by taking into consideration the species and
age class (O-old, Y-young), namely sessile oak (So), Quercus petraea (SoO 1, 2, 3; SoY 1, 2, 3) in the hill
region and norway spruce (S), Picea abies (SO 1, 2, 3; SY 1, 2, 3) in the highlands (Figure 1). We selected
the species for their representativeness within the Romanian forest ecosystem, allowing for the
assessment of the studied methods in both deciduous and coniferous plots of various structures.
Figure 1. Location of the forest stands included in the case study.
Table 1. Forest stand characteristics of the sample plots included in the case study.
52 2 16.1 26 3 8.7 SoO8 190 1
40 2 12.6 27 3 6.8 SO10 150 1
1 Mean diameter at breast height; 2 Orthogonal measurements;3 Standard deviation; 4 Mean height; 5
Measurements along the isoline from distances equal to the height of the tree; 6 Ratio between canopy
area without crowns overlap and plot area; 7 Sessile oak young (Quercus petraea); 8 Sessile oak old
(Quercus petraea); 9 Spruce young (Picea abies); 10 Spruce old (Picea abies).
2.2. Field Data Collection
2.2.1. Reference Measurements
We focused reference field measurements on recording diameter at breast height (DBH), height
(H) and position of each tree (XYZ). The DBH values were computed as an average between two
Forests 2020, 11, 392 4 of 16
caliper measurements taken at right-angles and for the heights a digital hypsometer was used. In
order to determine the position of each tree, FieldMap[38] (Integrated GIS field software coupled
with electronic mapping and dendrometrics sensors) measurements were carried out. These
measurements will underpin the validation of TLS processing results.
2.2.2. Single terrestrial laser scan
We performed a single terrestrial laser scan from the center of each plot with a FARO Focus 3D
X130 phase shift terrestrial laser scanner at the same time with the above-mentioned field
measurements. Allowing 8 µs per scan point for each of the 44.4 million samples of the scan,
increased the signal strength in recording reflectance values. We considered the scan origin as the
central position, as this was also used as a reference in the field measurements.
2.2.3. Multiple Terrestrial Laser Scans
The multiple TLS approach aimed at increasing the number of points whilst simultaneously
standardizing their density through a five-scan composition. This was done to estimate whether the
difference in acquisition method translates to an increase in the number of identified trees and
furthermore in the sample plot reconstruction accuracy. Also, by scanning the same plot from several
directions, shadow cones were mostly eliminated.
Forest inventory procedures regarding height measurement require a viewing distance
equivalent to the height of each tree projected along the local terrain contour. Based on this and the
general aim of running the analysis at the plot level, the position of each scan was set at the 15 meters
mark. This reduced the risk of missing any of the peripheric stems. We ran four scans from radial
positions, alongside a central scan. To compare the results from different plots, we established a
repeatable methodology - to position the scans on the directions of the compass points (Figure 2).
Figure 2. Sampling location in the case of multiple scan approach.
Seven spheres (0.07m radius) were used as coregistration markers, as their shape suffers no warp
with the change of the scan perspective [39]. The positioning at variable heights raised them above
the understory vegetation. Their increased visibility translated to increased coregistration confidence.
Forests 2020, 11, 392 5 of 16
Each sphere was placed so it could be recorded from the central scan and from at least two more
Previous to starting any of the scans, we set all stations to ensure maximum visibility. This
required superficial pruning, especially in young spruce sample plots, increasing the time allocated
to one scan from ~8 min to almost 30 min.
2.3. Terrestrial Laser Scans Pre-Processing
Prior to the reconstruction stage, the raw scans were coregistered. We connected individual
scans using a semi-supervised target-based approach, using the spheres as references, followed by a
cloud to cloud control stage[35]. This extra level of control implied refining of the rough positioning
by semi-supervised sphere identification, based on onboard inclinometer and compass data. The
cloud to cloud optimization was done on a cropped, evenly sub-sampled point cloud (0.08m density
threshold). Coregistration precision values were within tolerances (0.02 m/pair of scansstrong
coregistration; 0.02-0.04 meters–medium-strength coregistration; >0.04 meters–weak
coregistration)[35], averaging between 0.004 and 0.018 m. Any error exceeding the accepted tolerance
for biophysical parameters (0.02m) required supervised coregistration (Table 2).
Table 2. Error values in the case of multiple scans processing.
Max.D. Error [m]
Max.Hz. Error [m]
Max.V. Error [m]
SoY 1
SoY 2
SoY 3
SoO 1
SoO 2
SoO 3
SY 1
SY 2
SY 3
SO 1
SO 2
SO 3
1 Minimum number of connections for each scan; 2 Maximum error for distances computed two ways;
3 Maximum error for horizontally normalized distances; 4 Maximum error for vertically normalized
distances; 5 Young sessile oak stands; 6 Old sessile oak stands; 7 Young spruce stands; 8 Old spruce
After exporting the coregistered data as a coherent point cloud, the ground defining points were
separated. This simplified subsequent processes by concentrating only on the vegetation. The process
was an iterative one, with a three-dimensional (3D) window of 0.2 m height and a footprint of 0.8
square meters. A threshold of 128 points per grid cell (for the selected grid size) was chosen[40]. Noise
was removed from the ground cloud by analyzing the normal orientation of each point, based on six
nearest neighbors. The filtered cloud was then interpolated to generate a digital terrain model (DTM)
of 0.1 m resolution.
The last step before reconstruction was cloud segmentation or classification of the point cloud
into clusters that define individual trees. To facilitate the segmentation stage, several cleanup
procedures have been undertaken. A simple way to bypass the understory vegetation, and the low-
lying branches was to create a section of defined height (0.3 m) at a constant elevation above the DTM
(2 m). This spatial sub-sample aimed to simplify the stem identification stage. Noise was removed
from the resulting slice using the before mentioned method by changing the reference surface from
a theoretically horizontal ground to a surface closer to vertical for the trunks. A fast spatial clustering
based on the work of Hackenberg et al.[7] was applied to segment trunk seeds. We defined a seed as
Forests 2020, 11, 392 6 of 16
any cluster of points containing at least 0.1% of the total point cloud, but not less than 400 points. We
separated the output into two classes through Euclidean clustering, based on the distance between
points (0.10m threshold). Out of the two resulting classes, one was characterized by a lower number
of clusters with a higher percentage of representation in the original point cloud, whilst the other had
a significantly higher number of clusters, with a reduced percentage of the total. Starting with these
seeds, the clusters were expanded in the original vegetation point cloud in a vertical direction. Slices
were generated at 0.10 m intervals and the same Euclidean clustering was applied. Instead of
expanding the clusters based solely on the distance between points, the distance to the previous seed
was also taken into consideration. A cylinder was fitted to each newly created cluster. If the projected
center of the fitted cylinder fell inside the original seed footprint and its specific radius was a solution
of equation 1 (n = 1.2 - as an average of the minimum/maximum values provided by Bitterlich, 1978),
then the cylinder was considered as being part of the same tree. Clusters with less than 10 points or
with a radius exceeding the tolerances were dropped and merged in a residual point cloud to be
reprocessed in the following repetitions. The iteration continued until it reached the end, by either
hitting the lowest Z value or by not generating any clusters that met the conditions above.
r = x
× h
2 (1)
rhradius at height h
n—geometry type coefficient
x—surface of revolution generator
Equation 1 resulted from the initial assumption that a tree trunk can be reduced to a 3D primitive
(the simplest geometric object that can represent the stems). But the structure of a stem, with a
concave base and a slight convex middle accentuated towards the top, better resembles a neiloid. By
not considering this taper, cylindrical representations would generate unrealistically large volumes
and tolerances; a paraboloid would be the next best geometry to represent the volume of a stem, with
cones midway between the tapered shapes. Neiloids would yield the least excess-volume of all the
considered shapes (Figure 3). A theoretical n value would vary between 0 for a cylinder and 3 for a
neiloid. However, these values do not lead to realistic results. The literature recommends an n value
between 0.9 and 1.5 to best define the real shape of a stem [41].
Figure 3. Comparison of stem shape to theoretical models (cylinder-a, paraboloid-b, cone-c, neiloid-d).
Two filters have been applied, addressing spatial extent and density. In the first case, clusters
with a height of below 3 m or above 60 m have been removed. For point density, clusters with less
than 15,000 points, were removed. Both processes were performed using CloudCompare (scalar
fields - compute stat. param)[30] and Quantum GIS tools (vector geometry - bounding boxes)[37].
Down-sampling of the voxel grid reduced the size of the resulting clusters to facilitate the
extraction of biophysical parameters. To limit the information loss, for each 3D cell or voxel (0.01m)
the centroid of the contained points was extracted and used, rather than the centroid of the voxel
itself. The mean distance to the nearest points was computed and based on the condition in equation
2, some points were excluded. The voxel grid was implemented to reduce the processing power
Forests 2020, 11, 392 7 of 16
needed for the subsequent stages by using lidR and lasvoxelize packages for R [36,42]. This step
implied reducing the number of points through the intersection of the point cloud with a 3D grid of
the same extent.
x × | < d
, (2)
mean distance
standard deviation
x—threshold for angle between neighboring normals
Previous research showed that d
± (x = 1) is enough to identify outliers [43].
2.4. Terrestrial Laser Scans Processing
With the original point cloud cleaned and segmented, diameters and heights could be extracted.
Besides these two parameters, the position of each stem had to be recorded in the same reference
system as the field data to identify the correspondences between them.
Each stem position was computed as the average point location (X, Y) within a 0.1 m tall cylinder.
The altimetric position was computed as a mean valueof the points lying inside a best-fitting cylinder
above the ground, with a height of 0.1 m.
The DBH was determined by extracting a section parallel to the DTM, between 1.25 and 1.35 m
and identifying the true stem (no branches) through a randomized Hough transform (weight based
analysis for detecting shapes in 3D point clouds)[44]. Afterward, a circle was fitted using the least
square regression. The height, or vertical projection of the stem, was computed in two ways, as TLS
based height estimation usually leads to erroneous results. This way, two sets of height values were
obtained, allowing for further evaluation of their accuracy against field measurement. The first
approach was based on computing the maximum difference on the Z-axis between its DTM elevation
and the top of the vegetation cluster. In the second approach, the height was computed as the highest
Z value enclosed inside a cylinder of diameter equal to the stem’s DBH, from the unfiltered cloud. In
the case of trees with an obvious lean (either recorded during field observations or identified during
any of the supervised processing stages), the length was used instead of the height. The length was
computed as the maximum distance between any two points in the tree cluster.
3. Results
3.1. Individual Tree Segmentation
The precision of TLS reconstruction was evaluated based on the accuracy of the diameters,
heights, and number of extracted individuals for the two scanning approaches. To assess whether or
not multiple scans led to more accurate results, individual trees have been identified and mapped in
both the TLS point clouds (single and coregistered multiple) and in-field measurements. One to one
join was used to allow comparing them as identical data sets and assess only the quality of the
extracted biophysical parameters (DBH and height). Consequently, both the field and the multiple
scan data sets were subsampled to only display those individuals identified through single scanning
since it provided the lowest number of stems (Table 3).
Forests 2020, 11, 392 8 of 16
Table 3. Differences regarding the number of identified trees between single and multiple scans.
SoO 1
SoO 2
SoO 3
SO 1
SO 2
SO 3
SoY 1
SoY 2
SoY 3
SY 1
SY 2
SY 3
1 Number of trees from single scan; 2 Number of trees from multiple scans; 3 Number of trees from
field measurements; 4 Percentage of extraction from multiple scans in relation to field measurements;
5 Percentage of extraction from single scan in relation to field measurements; 6 Percentage of
increase/decrease between multiple and single scans.
The increase in the number of identified individuals is substantial when switching from a single
central scan to a coregistered point cloud. Also, a relationship between the sample plot’s structure
(age and species) and segmentation quality was observed, as the number of detected trees increased
by 16% for multiple scans in old sample plots and by 29% in young sample plots. By species, multiple
scans detected on average 25% more trees when compared to single scans for sessile oak and only
18% more for norway spruce sample plots.
3.2. DBH Estimation
When analyzing the quality of extraction (diameter and height), the positive influence that the
increased point cloud density had on stem extraction was not replicated in the biophysical
parameters’ evaluation.
A comparison between the trends of the diameter values in relation to a theoretical abline
(constrained by a constant relation between the two data sets) was carried out to better differentiate
the two scanning approaches. Single scans displayed an overall underestimation of large diameters,
regardless of the species or age of the plots (deviation within 0.33.6 cm). Moreover, in old sample
plots, the underestimation of large DBH values was coupled with an overestimation of smaller DBH
values. This effect is reduced when processing multiple scans. Regarding the overestimation of young
trees’ DBH and underestimation of old trees’ DBH, the phenomenon can be explained by the natural
evolution of trees. Young trees tend to maintain a more cylindrical stem shape, whereas old trees,
because of different stress and environmental factors, develop in a more irregulate manner. This
process is called ovalization (Figure 4).
Figure 4. Stem cross section extracted from single scan (a) and multiple scans (b).
In the case of multiple scans, inconsistencies occurred only in spruce sample plots where old
plots did not just maintain an overestimation for small DBH, but also extended the effect slightly
towards the larger values.
Forests 2020, 11, 392 9 of 16
In contrast with the visible DBH underestimation in the single scan results, multiple scans
overestimated DBH values in spruce sample plots. It is worth mentioning here, that spruce plots are
characterized by an uneven DBH distribution. Only when overlooking the spruce sample plots, can
multiple scans approach be considered to have a regulating effect on diameter values, eliminating
over- or under-estimation tendencies (Table 4). Despite the slight under and over-estimation, the
diameter values strongly correlated to the field measurements (Figure 5), with r values reaching
above 0.9. This relation was reflected in the strength of the determination coefficient, r2 which
averaged a value above 0.9. Despite the largely similar results, important differences were observed
at some sites between single and multiple scans. For example, for SO 1 the r2 coefficient (with respect
to the field measurements) changed from 0.33 to 0.98 from single to multiple scans.
Figure 5. Comparison between experimental and theoretical (DBHfield = DBHTLS) DBH values.
Table 4. Evaluation of extracted DBH in relation to real values.
ID S.1 M.2 F.M.3 ΔFM-S4 ΔFM-M5
SoO 1 26.9 26.0 25.6 -1.3 -0.4
SoO 2 19.4 19.9 19.4 0.0 -0.5
SoO 3 10.9 20.1 20.0 9.1 -0.1
SO 1 34.2 31.5 30.8 -3.4 -0.7
SO 2 30.6 32.7 32 1.4 -0.7
SO 3 35.6 32.5 31.5 -4.1 -1.0
SoY 1 10.7 11.3 11.1 0.4 -0.2
SoY 2 10.4 10.5 10.4 0.0 -0.1
SoY 3 11.1 11.7 11.6 0.5 -0.1
SY 1 16.8 17.4 16.4 -0.4 -1.0
SY 2 17.5 16.7 16.5 -1.0 -0.2
SY 3 22.9 21.1 20.8 -2.1 -0.3
1 Mean DBH from single scan; 2 Mean DBH from multiple scans; 3 Mean DBH from field
measurements; 4 Difference between field DBH and single scan DBH; 5 Difference between field DBH
and multiple scans DBH;.
Forests 2020, 11, 392 10 of 16
3.3. Height Estimation
Significantly weaker correlations (Figure 6) were reached in the case of heights estimation
(r<0.76) with determination coefficients of up to 0.59. Switching from single to multiple scans slightly
improved the results from r2 of 0.31 for single scans to r2 of 0.41 for multiple scans.
Figure 6. Comparison between experimental and theoretical (Hfield = HTLS) height values.
Another analysis was conducted to examine whether the distance to the scanner impacts the
results. The hypothesis was that trees at a distance equivalent to their height can be measured more
precisely[45]. In the same acceptance, the closer one gets to the scanner, the better the diameter can
be represented and measured. To evaluate this supposition, the plot radius of 15 m was divided into
two segments upon which spatial filters have been applied. The filtering consisted in the creation of
a buffer around the centre of the plot with a radius of 7 m and its intersection with the position of the
trees. The results showed no increase in correlation strength for either heights or diameters, as the
distance from scanner increased (r = 0.52 for the entire plot vs. r = 0.62 closer than 7 m and r = 0.59
further than 7 m). Regarding diameters, there is no difference reflected in the correlation coefficients
(r for the entire plot equals that of values subsampled closer than 7 m and that of values sampled
further than 7 m, 0.94).
Forests 2020, 11, 392 11 of 16
By comparing the results of the two methods used for the estimation of height, the quality of the
segmentation and tree reconstruction could be assessed. The old sample plots were characterized by
a bigger residual cloud, whilst reconstruction based on young sample plots point clouds resulted in
sparse residual clouds (represented in Figure 7 as the area between the Digital Surface Models (DSM)
based on the original and the segmented point clouds).
Figure 7. Differences between heights extracted from single and multiple scans, before and after
segmentation (represented as DSM cross sections), in relation to average field values.
As seen from Table 5, no matter the age, the heights are underestimated (mean underestimation
for old sample plots reaches 37% (± 25%) and for young sample plots 14% (± 19%) of the mean real
height of the plot) in the sessile oak study area. For spruce areas the mean underestimation is even
greater, reaching values of 35% (± 31%) for young trees and 44% (± 27%) for old trees. Both height
Forests 2020, 11, 392 12 of 16
estimation techniques (segmenting and maximum height per crown projection) lead to unsatisfactory
results, suggesting that single wavelength TLS in leaf-on conditions is not suitable for extracting
height values at the plot level, because of difficulties in penetrating dense canopies (Figure 8).
Figure 8. Distribution of the segmented point clouds, used for biometric parameters extraction (DBH,
H) and the residual point clouds.
Table 5. Descriptive statistic on heights extracted before and after segmentation in relation to field
SoY 1
SoO 1
SY 1
SO 1
Forests 2020, 11, 392 13 of 16
4. Discussion
The current study case may not have provided significant improvements in the extraction
accuracy for the biophysical parameter, but can be used as a guideline. Comparison and contrast-
based optimization using a mid-class terrestrial laser scanner in natural growth forests, covered the
gap between controlled research and field implementation. Results are therefore similar to some
degree to that of previous research, although our approach focused on sample plot level in natural
When analyzing diameter extraction, we found multiple scans approach to lead to an increase
in precision. This does not invalidate single scan results, as the differences are within measuring
tolerances. Despite this, a significant improvement arising from multiple scans approach is the
number of identified trees, which in terms translates into a more accurate stand level estimation of
all the biophysical parameters. The use of multiple scans in DBH evaluation would be beneficial for
transitioning from circular fitting to an ellipse. Having a more accurate representation of the stem (as
the one resulting from the multiple scans) could yield better values for the semi-major and semi-
minor axis of the ellipse. Since none of the height extraction methods could provide realistic
estimations, investing time in multiple scans approach is not recommended. The shadowing and
occlusion effects could not be fully compensated by multiple scanning. A potential solution could be
the use of a dual-wavelength full-waveform terrestrial laser scanner[46]. The stand structure (density,
species, age) and local topography had an influence upon the height’s estimation, but different
conditions would have not differentiated between the used methods.
Since the method for estimating DBH is based on the least square error when best fitting a circle,
straying away from the circular shape increases the error of radius estimation. A solution might be
found in increasing the number of iterations involved in extracting DBH values, but better still would
be moving to a two dimensional (2D) ellipse fitting [47]. Even so, the DBH values that we obtain were
similar to those highlighted in recent papers (average RMSE of 3 cm)[25,48,49]
The low values observed for the correlation coefficients between heights extracted from
FieldMap[38] and TLS did not improve when the number of scans increased. An increase in the
number of scans proves to not increase the strength of penetration of the canopy. As a result, tree
tops are still not reachable.
In an intra-species comparison, when analyzing the influence of the sample plots’ age, the
residual point cloud displays a rise in both density and coverage with the increase in age. This
translates into a decrease of the vertical reach as age increases. Age variation generates a more
complex structure, and with higher diameters and densities (above a threshold of 0.5 m/0.7
consistency-see Table 1), we expect lower relative height values.
The increase in the proportion of identified stems between single and multiple scans (10%)
observed by Xi et al.-[15] was confirmed but with higher values (16 to 29%). Literature provide overall
detection percentages to exceed 97% [16,17], with a slight decrease for smaller diameters. Although
depended on the structure and number of scans[24], the~10% decrease is similar to our results. Height
estimation remained an issue no matter the species or sample plot structure, similar to Saarinen et al.
[11]. The slight precision increase in close range scans, also mentioned in previously published papers
[45], was considered of no significant value. This conclusion is supported by the Wilcoxon signed
rank test which results in p-values smaller than of 1×e−5 for both close (7m radius) and far (15m radius)
range. Computing the cosine similarity of both close and far range height trends to the normal, once
again confirmed their similitude (average value of 1.5×e−3 ± 2×e−3). Different conditions led to
improved values in Height evaluation in similar research (average RMSE of 7 cm [50,51]).
If the aim is to identify the number of individuals, multiple scans technique is the proper way to
acquire data. As very few investigations require solely the number of individuals and their positions,
this might not represent an advantage of multiple scans over single scans.
5. Conclusions
TLS proved to be a suitable solution in some aspects of forest monitoring and inventory
activities. The wide range of applications confirms TLS’s utility in forestry. No matter the density nor
Forests 2020, 11, 392 14 of 16
the species variability, forest sample plots can be inventoried using a TLS based method. Point
density, increased through multiple scans approach, does not equivalate to an increase in accuracy
since single scans (43.7 million points) are already over-sampling the plot. However, because of the
change in the distribution of data, the shadowing effect is reduced, leading to an increase in the
number of detected individuals.
Multiple scans approach better defines the shape of the stems, with visible effects in the quality
of DBH values. Single scan leads to an underestimation of diameters in all the plots, especially for
large values.
An improvement in the extraction of height values could be the application of the multiple scan
approach in a leaf-off state. As an alternative, if available, the extension of the raw point cloud with
airborne scan data, through TLS-ALS (Aerial Laser Scanning) fusion would be beneficial.
The results show the necessity of repeating all the measurements in leaf-off condition and if
possible, using a dual-wavelength terrestrial scanner. These two improvements upon our tested
methodology are aspects of further research; either one might prove to solve the highlighted
Author Contributions: Conceptualization, I.-S.P.; methodology, I.-S.P. and A.-C.D; software I.-S.P; validation,
I.-S.P. and A.-C.D; formal analysis I.-S.P. and A.-C.D.; investigation, I.-S.P. and A.-C.D; data curation, I.-S.P.;
writingoriginal draft preparation, I.-S.P.; writingreview and editing, I.-S.P., A.-C.D. and O.B.; supervision,
O.B. and M.A.T.; visualization, I.-S.P. and A.-C.D.; project administration, M.A.T.; funding acquisition, O.B. and
M.A.T.; resources, O.B.. All authors have read and agreed to the published version of the manuscript.
Funding: This work was developed under the auspices of the EO-ROFORMON Project (Prototyping an Earth-
observation Based Monitoring and Forecasting System for the Romanian Forest - P_37_651/105058), financed
through the Operational Competitivity Program and managed by the Romanian Ministry of Research and
Innovation (MCI) and the European Funds Ministry.
Acknowledgments: We acknowledge the contribution of the EO-ROFORMON team, especially Leca Ștefan,
Apostol Bogdan, Garcia Duro Juan, and Borlaf Ignacio.
Conflicts of Interest: The authors declare no conflict of interest.
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distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license
... The airborne LiDAR data for the one-hectare plots were collected through the use of a full-wave airborne laser scanner [60]. The discrete points extraction was conducted by Regarding the TLS pre-processing methods for classification and segmentation of the point cloud prior to obtaining the stems and the foliage, an approach proposed by Pascu et al. was followed [24,30,32,59] (Figure 2b). ...
... In our study, the applied methodology was the one proposed by Pascu et al. in the work of [30]. Therefore, the above-ground estimation implied the use of stand volume ...
... In literature, wood products, equated to above-ground biomass, are an important variable that can be estimated through remote-sensing techniques. Between the implementation of biophysical parameters relationships [64,65] to allometric models and direct measurements [30,[66][67][68], the above-ground biomass estimation gained impressive interest in research due to the associated accuracy. ...
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... Moreover, advancements in remote sensing technologies and survey instrumentation have provided new avenues to measure, detect, and analyze spatial changes in landcover [49]. Terrestrial Laser Scanning (TLS) can be used to accurately measure heights, Diameter at Breast Height (DBH), and other tree parameters, providing information to characterize the location, distribution, and maturity of forests [50]. Thermal Imaging can monitor fire risks and human activities in forests and provide support in identifying high-risk areas for forest loss [51]. ...
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Under the backdrop of achieving carbon neutrality and accelerating urbanization, China’s forests face unprecedented pressures. This study explored the spatiotemporal characteristics of forest loss in the urban agglomeration in the middle reaches of the Yangtze River (UAMRYR). The dynamic mechanism of forest loss caused by fire, logging, construction, and pollution was also analyzed using spatial database development, polygon superposition analysis, grid system construction, and coordinate system calculation. The results show that the forest loss in the UAMRYR experienced three stages: continuous acceleration (1990–2010), peak (2010–2015), and slight decline (2015–2020). Rapid urban expansion is the primary cause of forest loss, and the three metropolitan areas had the fastest urban expansion and the most severe forest loss. Due to the success of afforestation efforts, the forest loss caused by fire, logging, and pollution was restored by 80%, while most of the forest losses caused by construction are permanent. Given the current forest loss trends, large expanses of forests in the UAMRYR are at risk of being destroyed and causing serious damage to the region’s ecological environment. Forest losses can be significantly reduced by guiding the rational expansion of cities, supporting afforestation for urban construction projects, strengthening forest fire risk investigation, and implementing ecological reconstruction of polluted areas.
... Laser scanning provides three-dimensional forest information, and has shown to be promising in forestry. The technological advances of the last decade in Terrestrial Laser Scanner (TLS) have allowed the development of accurate and increasingly cheaper sensors (PASCU et al., 2020). ...
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Application of LiDAR technology has greatly enhanced tree segmentation and phenotypic analysis. There are few studies in urban green spaces using tree segmentation methods. Our aim is to improve the single-plant segmentation accuracy in tree and shrub communities through segmenting algorithm optimisation based on TLS LiDAR data of the urban green space. We developed a multi-round comparative shortest-path algorithm (M-CSP) to achieve the objectives: a) tree and shrub plant layer pre-division (TSPD); b) shrub type classifications (STC) into spherical, cylindrical, and rectangular shapes. The overall detection kappa value using M-CSP is 0.933, which is 18% higher than the CSP value of 0.790. M-CSP-based overall segmentation accuracy value (F-score) is 0.886, which is 13% higher than the CSP value of 0.783. The shrub F-score using M-CSP is 0.817, which is 26% higher than the CSP (0.646). M-CSP should provide a more accurate, faster, and less costly tool to study plant communities in urban green spaces.
Complex canopy cover conditions often challenge the accurate measurement of many individual tree attributes that are pivotal to the sustainable management of forest resources. Advances in drone laser scanning (DLS) and mobile laser scanning (MLS) have enabled the acquisition of high-density point clouds with the potential to better resolve detailed tree structures. Yet, the quality of DLS and MLS data can be limited by occlusions and environmental complexities. To quantify the impacts of canopy cover on the tree attribute estimation, this study investigated the utility of DLS and MLS data both individually and combined. Considering the scanning characteristics, we examined direct fusion and a new strategy using a relative weighting scheme based on the probability density of vertical point distribution. We compared the accuracy of seven tree attributes derived from quantitative structure models (QSMs) based on (1) DLS, (2) MLS, (3) fused, and (4) weighted point clouds under low, moderate, and high canopy cover levels. We found that the weighted data improved the modelling efficiency of QSMs by ∼ 20% on average, compared to fused and MLS data. Across canopy cover levels, the fused and weighted data achieved comparable results and outperformed DLS/MLS data in estimating tree attributes. Specifically, diameter at breast height and crown base height were accurately extracted from the fused, weighted, and MLS data under low canopy cover with the concordance correlation coefficient (CCC) > 0.80. As canopy cover increased, they were best estimated using the fused data (CCC > 0.90, RRMSE < 22%). Height was accurate regardless of canopy cover, which was independent of data collection platforms (CCC > 0.80, RRMSE < 16%). The crown diameter was also well estimated by fused, weighted, and MLS data across canopy cover levels (CCC > 0.82, RRMSE < 19%). The total, stem, and branch volumes could be best modelled by the fused data with increasing canopy cover. Overall, the fusion of DLS and MLS point clouds allowed the retrieval of comprehensive tree-level information. However, forestry practitioners still need to evaluate the trade-offs in selecting the most appropriate platform for laser scanning data based on their needs. Future studies should also enhance the modelling of trees with complex branching structures to strengthen the extraction of diverse attributes.
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Research highlights: In this study, the possibility of developing predictive models for both individual trees and forest stands, based on information derived from digital surface models (DSMs), was evaluated. Background and objectives: Unmanned aerial systems (UASs) make it possible to obtain digital images with increased spectral and spatial resolution at a lower cost. Based on the variables extracted by means of the digital representation of surfaces, we aimed at generating mathematical models that would allow the prediction of the main biometric features of both individual trees and forest stands. Materials and methods: Forest stands are characterized by various structures. As such, measurements may address upper-level trees, but most often are oriented towards those belonging to the mean tree category, randomly selected from those identifiable from digital models. In the case of grouped trees, it is the best practice to measure the projected area of the entire canopy. Tree and stand volumes can be determined using models based on features measured in UAS-derived digital models. For the current study, 170-year-old mixed sessile oak stands were examined. Results: Mathematical models were developed based on variables (i.e., crown diameter and tree height) extracted from digital models. In this way, we obtained results characterized by root mean square error (RMSE) values of 18.37% for crown diameter, 10.95% for tree height, and 8.70% for volume. The simplified process allowed for the estimates of the stand volume using crown diameter or diameter at breast height, producing results with RMSE values of 9%. Conclusions: The accuracy of the evaluation of the main biometric features depends on the structural complexity of the studied plots, and on the quality of the DSM. In turn, this leads to the necessity to parametrize the used models in such a manner that can explain the variation induced by the stand structure.
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Terrestrial laser scanners provide accurate and detailed point clouds of forest plots, which can be used as an alternative to destructive measurements during forest inventories. Various specialized algorithms have been developed to provide automatic and objective estimates of forest attributes from point clouds. The STEP (Snakes for Tuboid Extraction from Point cloud) algorithm was developed to estimate both stem diameter at breast height and stem diameters along the bole length. Here, we evaluate the accuracy of this algorithm and compare its performance with two other state-of-the-art algorithms that were designed for the same purpose (i.e., the CompuTree and SimpleTree algorithms). We tested each algorithm against point clouds that incorporated various degrees of noise and occlusion. We applied these algorithms to three contrasting test sites: (1) simulated scenes of coniferous stands in Newfoundland (Canada), (2) test sites of deciduous stands in Phalsbourg (France), and (3) coniferous plantations in Quebec, Canada. In most cases, the STEP algorithm predicted diameter at breast height with higher R2 and lower RMSE than the other two algorithms. The STEP algorithm also achieved greater accuracy when estimating stem diameter in occluded and noisy point clouds, with mean errors in the range of 1.1 cm to 2.28 cm. The CompuTree and SimpleTree algorithms respectively produced errors in the range of 2.62 cm to 6.1 cm and 1.03 cm to 3.34 cm, respectively. Unlike CompuTree or SimpleTree, the STEP algorithm was not able to estimate trunk diameter in the uppermost portions of the trees. Our results show that the STEP algorithm is more adapted to extract DBH and stem diameter automatically from occluded and noisy point clouds. Our study also highlights that SimpleTree and CompuTree require data filtering and results corrections. Conversely, none of these procedures were applied for the implementation of the STEP algorithm.
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This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate.
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Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.
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Point clouds generated by terrestrial laser scanners (TLS) have enabled new ways to measure stem diameters. A common method for diameter calculation is to fit cylindrical or circular shapes into the TLS point cloud, which can be based either on a single scan or a co-registered combination of several scans. However, as various defects in the point cloud may affect the final diameter results, we propose an automatized processing chain which takes advantage of complementing steps. Processing consists of two fitting phases and an additional taper curve calculation to define the final diameter measurements. First, stems are detected from co-registered data of several scans using surface normals and cylinder fitting. This provides a robust framework for localizing the stems and estimating diameters at various heights. Then, guided by the cylinders and their indicative diameters, another fitting round is performed by cutting the stems into thin horizontal slices and reassessing their diameters by circular shape. For each slice, the quality of the cylinder-modelled diameter is evaluated first with co-registered data and if it is found to be deficient, potentially due to modelling defects or co-registration errors, diameter is detected through single scans. Finally, slice diameters are applied to construct a spline-based taper curve model for each tree, which is used to calculate the final stem dimensions. This methodology was tested in southern Finland using a set of 505 trees. At the breast height level (1.3 m), the results indicate 5.2 mm mean difference (3.2%), −0.4 mm bias (-0.3%) and 7.3 mm root mean squared error (4.4%) to reference measurements, and at the height of 6.0 m, respective values are 6.5 mm (3.6%), +1.6 mm (0.9%) and 8.4 mm (4.8%). These values are smaller compared to most of the corresponding contemporary studies, and outperform the initial cylinder models. This indicates that the applied processing chain is capable of producing relatively accurate diameter measurements, which can, at the cost of computational heaviness, remove various defects and improve the modelling results.
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Quantitative comparisons of tree height observations from different sources are scarce due to the difficulties in effective sampling. In this study, the reliability and robustness of tree height observations obtained via a conventional field inventory, airborne laser scanning (ALS) and terrestrial laser scanning (TLS) were investigated. A carefully designed non-destructive experiment was conducted that included 1174 individual trees in 18 sample plots (32 m × 32 m) in a Scandinavian boreal forest. The point density of the ALS data was approximately 450 points/m2. The TLS data were acquired with multi-scans from the center and the four quadrant directions of the sample plots. Both the ALS and TLS data represented the cutting edge point cloud products. Tree heights were manually measured from the ALS and TLS point clouds with the aid of existing tree maps. Therefore, the evaluation results revealed the capacities of the applied laser scanning (LS) data while excluding the influence of data processing approach such as the individual tree detection. The reliability and robustness of different tree height sources were evaluated through a cross-comparison of the ALS-, TLS-, and field- based tree heights. Compared to ALS and TLS, field measurements were more sensitive to stand complexity, crown classes, and species. Overall, field measurements tend to overestimate height of tall trees, especially tall trees in codominant crown class. In dense stands, high uncertainties also exist in the field measured heights for small trees in intermediate and suppressed crown class. The ALS-based tree height estimates were robust across all stand conditions. The taller the tree, the more reliable was the ALS-based tree height. The highest uncertainty in ALS-based tree heights came from trees in intermediate crown class, due to the difficulty of identifying treetops. When using TLS, reliable tree heights can be expected for trees lower than 15–20 m in height, depending on the complexity of forest stands. The advantage of LS systems was the robustness of the geometric accuracy of the data. The greatest challenges of the LS techniques in measuring individual tree heights lie in the occlusion effects, which lead to omissions of trees in intermediate and suppressed crown classes in ALS data and incomplete crowns of tall trees in TLS data.
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The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources.
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Abundant and refined structural information under forest canopy can be obtained by using terrestrial laser scanning (TLS) technology. This study explores the methods of using TLS to obtain point cloud data and estimate individual tree height and diameter at breast height (DBH) at plot level in regions with complex terrain. Octree segmentation, connected component labeling and random Hough transform (RHT) are comprehensively used to identify trunks and extract DBH of trees in sample plots, and tree height is extracted based on the growth direction of the trees. The results show that the topography, undergrowth shrubs, and forest density influence the scanning range of the plots and the accuracy of feature extraction. There are differences in the accuracy of the results for different morphological forest species. The extraction accuracy of Yunnan pine forest is the highest (DBH: Root Mean Square Error (RMSE) = 1.17 cm, Tree Height: RMSE = 0.54 m), and that of Quercus semecarpifolia Sm. forest is the lowest (DBH: RMSE = 1.22 cm, Tree Height: RMSE = 1.23 m). At plot scale, with the increase of the mean DBH or tree height in plots, the estimation errors show slight increases, and both DBH and height tend to be underestimated.
Forest stands are often parameterized by vegetation indices such as the Leaf Area Index (LAI). However, other indices (i.e. stand denseness, espacement, canopy density, canopy cover, foliage cover, crown porosity, gap fraction) may better characterize forest structure. Terrestrial and airborne active sensor data has been used to describe canopy structural diversity and provide accurate estimates of forest structure indices. This study uses Terrestrial Laser Scanner (TLS) to characterize forest structure through the above-mentioned indices. The relationship between all of them was studied to assess the extent to which they relate and their capability to properly describe forest stands. A strong correlation was visible between LAI and the canopy density index (r = 0.87 to 0.91 depending on the extraction methods) despite the underevaluated values of the first. Even though more precise LAI estimates were expected from using co-registered multiple scans, the LAI variability proved to be low and correlations with the remaining indices weakened when compared to a single scan approach. An exception was canopy cover, a structural index that disregards the three-dimensionality of the canopy, with which the LAI obtained from multiple scans maintained a strong correlation. This suggests that multiple scanning leads to an unweighted oversampling of the scene, overshadowing its advantages in removing tree occlusions. Weak correlations were visible between classic forest structural indices (basal area density index, espacement index, denseness index) and the rest of the descriptors. Despite this exception, most of the forest indices showed average to strong correlations in-between each other. Therefore, we conclude that a better description of forest stands structure may be achieved through unsegmented single scan point cloud processing in both 3D and 2D space, optical data from the incorporated digital camera being a plus, but not an essential requirement.