Terrestrial laser scanning (TLS) produces 3D data with high detail and accuracy. In this paper we explore the potential of TLS data in combination with a method for reconstruction tree structure to estimate above ground biomass (AGB) in Australian eucalypt forest. Single trees are isolated from the registered TLS point cloud and are used as input for the reconstruction method. We explore the impact of different input parameters on the reconstruction and compare inferred AGB estimates from volume reconstruction and basic density with destructively sampled reference values. Based on a limited number of samples, regression analysis demonstrated R2 of 0.98 to 0.99, with an intercept of 110 kg for unfiltered TLS point clouds and 19.8 kg for filtered point clouds. These initial results demonstrate the potential of tree reconstruction from TLS for rapid, repeatable and robust AGB estimation

Armston, J.

Avitabile, V.

Burt, A.

Calders, K.

Culvenor, D.

Disney, M.

Herold, M.

Murphy, S.

Newnham, G.

Raumonen, P.

Wageningen Yield 

SilviLaser 2013, October 9-11, 2013 –Beijing, China 
 1 
Estimating above ground biomass from terrestrial laser scanning in 
Australian Eucalypt Open Forest 
 
Kim Calders1, Glenn Newnham2, Martin Herold1, Simon Murphy3, Darius Culvenor4, 
Pasi Raumonen5, Andrew Burt6, John Armston7,8, Valerio Avitabile1, Mathias Disney6 
 
1 Laboratory of Geo-Information Science and Remote Sensing; Wageningen University; 
kim.calders@wur.nl 
2 CSIRO Land and Water; Glenn.Newnham@csiro.au  
3 Melbourne School of Land and Environment; University of Melbourne; 
murphys@unimelb.edu.au  
4 Environmental Sensing Systems; darius.culvenor@sensingsystems.com.au 
5 Dept. of Mathematics; Tampere University of Technology; pasi.raumonen@tut.fi 
6 Dept. of Geography; University College London; mathias.disney@ucl.ac.uk 
7 Remote Sensing Centre; Department of Science, Information Technology, Innovation 
and the Arts; John.Armston@science.dsitia.qld.gov.au  
8 Joint Remote Sensing Research Program; School of Geography, Planning and 
Environmental Management; University of Queensland 
 
Paper Number:  SL2013-021 
 
Abstract 
 
Terrestrial laser scanning (TLS) produces 3D data with high detail and accuracy. In this paper 
we explore the potential of TLS data in combination with a method for reconstruction tree 
structure to estimate above ground biomass (AGB) in Australian eucalypt forest. Single trees 
are isolated from the registered TLS point cloud and are used as input for the reconstruction 
method. We explore the impact of different input parameters on the reconstruction and compare 
inferred AGB estimates from volume reconstruction and basic density with destructively 
sampled reference values. Based on a limited number of samples, regression analysis 
demonstrated R2 of 0.98 to 0.99, with an intercept of 110 kg for unfiltered TLS point clouds and 
19.8 kg for filtered point clouds. These initial results demonstrate the potential of tree 
reconstruction from TLS for rapid, repeatable and robust AGB estimation. 
 
1. Introduction 
 
Above ground biomass (AGB) is an important indicator for forest productivity, nutrient cycling 
and calculations of biomass energy, carbon storage and sequestration of forests (Bi et al. 2004).  
Current methods to estimate AGB of native eucalypt forests are generally based on indirect 
relationships with tree structural parameters (Hamilton et al. 2005; Bi et al. 2004). This 
allometric approach can be very effective when applied within the species and productivity 
range of the calibration data, but may lead to large uncertainties in broad scale biomass mapping. 
Van Breugel et al. (2011) studied the uncertainties of allometric biomass models in secondary 
forests and their findings suggest that local allometric models provided a more accurate AGB 
estimate than foreign models. Direct estimates of AGB involve destructive sampling of trees, 
which is often impractical and expensive and as such can only be conducted on a limited basis. 
 
Terrestrial laser scanning (TLS) allows for measuring forest structure with high detail and 
accuracy and has the potential to reduce uncertainties in inferring AGB since it gives a direct 
estimate of tree volume. Here, we infer tree volume directly from tree models reconstructed 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
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from the TLS data, and AGB is derived on the basis of basic density information. A comparison 
of these direct AGB estimates with harvested AGB gives some insight into the potential and 
accuracy of TLS for rapid biomass assessments at the plot level, and how this may be used in 
support of broad scale biomass mapping. In this paper, we present proof of concept based on a 
limited number of trees from two eucalypt species and evaluate the sensitivity of the tree 
reconstruction method. 
 
2. Methods 
 
2.1 Study area 
 
The national forest area in Australia is the sixth largest in the world and covers approximately 
49 million ha (19.4% national cover; FAO, 2010). Five plots with a 40 m radius were 
established in a native Eucalypt Open Forest in Victoria, Australia. Three of the plots are 
located in Toombullup State Forest (TOOM) and two in Rushworth forest (RUSH) (Figure 1). 
  
  
Figure 1: (left) Overview of harvested plots [source: Google Earth]. (right) RGB image of RUSH07 
(Rushworth forest). 
 
2.2 Data collection 
 
These five plots were partially harvested in 2012-2013 to acquire accurate estimates of AGB. 
Detailed measurements of tree structure and fresh weight were collected for each harvested tree. 
Dry weight to fresh weight conversion factors and basic density (dry weight per unit green 
volume) for each species were derived from a limited number of samples across a range of 
diameters. Pre- and post-harvest TLS data have been acquired in these five plots using five scan 
locations per plot in a systematic sampling design (figure 2). We used the RIEGL VZ-400 
scanner, which records multiple return LiDAR data (up to four returns per emitted pulse) as 
well as additional waveform data. The wavelength of the instrument is 1550 nm and beam 
divergence is 0.35 mrad. The angular sampling for both zenith and azimuth angle was 0.06°, 
azimuth range was 0° – 360° and zenith range was 0° - 130°. Reflecting targets were distributed 
throughout the plot and were used to register the five scan positions using RIEGL’s RiSCAN 
PRO software (http://www.riegl.com/products/software-packages/riscan-pro/). The average 
standard deviation of the registered point clouds was 0.0137 m with values for individual scans 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
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ranging from 0.0051 m to 0.0226 m. 
 
 
 
Figure 2: Systematic plot sampling design. Scan locations are indicated with a black dot. 
 
2.3 Estimating single tree AGB from TLS data 
 
Tree volume is directly inferred from tree models reconstructed from the TLS data, and AGB is 
derived on the basis of basic density information. Inferring tree volume consists of two stages: i) 
extracting single trees from the registered point cloud; and ii) 3D reconstruction of isolated trees. 
As a proof of concept, the analysis in this paper is limited to only six trees from Rushworth 
Forest, evenly distributed between two tree species: Eucalyptus leucoxylon (Yellow Gum) and 
Eucalyptus microcarpa (Grey Box). The six trees represent a tree AGB range from 102.2 to 
2568.9 kg. 
 
2.3.1 Extracting single trees 
 
The isolation of single trees from the registered point cloud is based on a method described in 
Burt et al. (2013). This method uses the open source Point Cloud Library (Rusu and Cousins 
2011) and i) identifies and segmentates the ground plane; ii) isolates individual crowns through 
a Euclidean cluster extraction; and finally iii) uses a second cluster extraction to remove 
undesired understorey. 
 
2.3.2 Calculating volume of single trees 
 
The point clouds of individual trees are used as input for the tree reconstruction method 
developed by Raumonen et al. (2013). The output of the reconstruction method is a cylinder 
model of the tree structure, which allows for straightforward calculations of tree volume. The 
method is relatively fast and a single tree can be modelled within minutes. 
 
The reconstruction method has two main phases: i) segmentation; and ii) surface reconstruction. 
The method reconstructs the tree structure via an advancing collection of cover sets (i.e. small 
local point clouds) and does not need prior assumptions about tree architecture. The 
reconstruction approach was validated by Disney et al. (2012) using detailed 3D tree models, 
where tree structure is known a priori. Results showed that, with some constraints, original 
length and volume could be reconstructed with a relative error of less than 2%. Burt et al. 
(2013) applied the same approach on TLS data from three different forest types in Queensland, 
Australia. Based on TLS simulation of the reconstructed tree model, they found that total 
volume could be recreated to within a 10.8% underestimate. The accuracy to which single scans 
could be globally registered was identified as the greatest constraint in the method. 
 
The reconstruction method requires the maximum size of the cover sets to be restricted by 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
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defining its radius (rcov). The other input parameters are the minimum distance between the 
centers of the cover sets (dmin) and the minimum number of points in a single cover set for it to 
be used in the reconstruction (nmin). Optional filtering can be applied before the reconstruction 
to remove isolated points or noise. Filtering happens in two stages and each stage requires the 
the radius of the cover sets to be specified. Additionally, a threshold number is specified for 
each stage. In the first stage, this is the minimum number of points in the cover set to pass the 
filtering stage and in the second stage, it is the minimum number of cover sets forming a 
connected component (cluster). 
 
3. Results 
 
First we will explore the impact of cover set radius (rcov), minimum distance between cover 
sets (dmin) and the minimum number of points in a single cover set (nmin) on the volume 
reconstruction. Next, we look at the impact of prior filtering on the derived tree volumes. 
Finally, we will present the inferred AGB estimates and compare these with the reference values 
from destructive harvesting. 
 
3.1 Impact of the input parameters on the volume reconstruction 
 
Combinations of different input parameters (table 1) are used to make multiple reconstructions 
of each tree. dmin is generally a little smaller or equal to rcov, with typical values for trees 
ranging from 1 to 3 cm (Raumonen et al. 2013). These parameters should be small enough to 
reconstruct the details of the surface, but large enough to infer the different characteristics of the 
tree model. 
 
Table 1: Input parameters for the reconstruction method; each of the rcov – dmin combinations was 
reconstructed with nmin values of 3, 5, 7 and 10. 
Input parameter Different combinations 
rcov [m] 0.01 0.02 0.01 0.03 0.015 
dmin [m] 0.01 0.02 0.02 0.03 0.03 
 
The impact of nmin on the tree reconstruction is illustrated in Figure 3. Larger values of nmin 
lead to smaller estimates of tree volume. This is not unsurprising since the value of nmin is a 
threshold for which cover sets are kept and which ones are discarded in the reconstruction. For 
this example tree, it can be seen that too many cylinders are modelled in the crown when nmin 
is too small (3), but too few are modelled when nmin is too large (10). 
 
 
nmin = 3; reconstructed volume = 1.894 m3 
 
nmin = 5; reconstructed volume = 1.335 m3 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
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nmin = 7; reconstructed volume = 1.045 m3 
 
nmin = 10; ; reconstructed volume = 0.844 m3 
Figure 3: Crown view to illustrate the impact of nmin on the tree reconstruction (no filtering). Example of 
a Eucalyptus leucoxylon, rcov and dmin were 0.03. Reference volume for this tree was 1.079 m3. 
 
Figure 4 illustrates the impact of rcov and dmin on the reconstructed trees. The tree 
reconstruction with rcov 0.03 m results in the best reconstruction, with an estimated volume of 
3.185 m3, which relates well to the reference value from destructive harvesting (3.283 m3). 
Cover sets with radius 0.02 m (nmin = 5) fail to pick up some of the branches in the crown, 
resulting into an underestimate of the tree volume. 
 
 
rcov = 0.02 m; dmin = 0.02 m; reconstructed volume = 1.721 m3 
 
rcov = 0.03 m; dmin = 0.03 m; reconstructed volume = 3.185 m3 
 
rcov = 0.02 m; dmin = 0.01 m; reconstructed volume = 0.251 m3 
 
rcov = 0.03 m; dmin = 0.015 m; reconstructed volume = 1.601 m3 
Figure 4: Crown view to illustrate the impact of rcov and dmin on the tree reconstruction (no filtering). 
Example of a Eucalyptus microcarpa with nmin set to 5. Reference volume for this tree was 3.283 m3. 
 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
 6 
3.2 AGB calculations via volume reconstruction 
 
For all the possible combinations of input parameters in Table 1, the best performing 
combination was visually identified by overlaying the tree model with the LiDAR point cloud. 
Once the optimal parameters (rcov, dmin & nmin) were identified, the reconstruction was run 
again with prior filtering applied to the point cloud. For the first filtering stage we used a radius 
of 2 cm with a threshold of 3 and for the second stage a radius of 3 cm with a threshold of 5. 
Table 2 compares the reference AGB with the inferred AGB from volume reconstruction.  
 
Table 2: Summary of the optimal input parameters per tree, reference AGB (derived from destructive 
harvesting) and inferred AGB via volume reconstruction. 
 rcov [m] dmin [m] nmin Reference AGB [kg] Inferred AGB  
(no filtering) [kg] 
Inferred AGB  
(filtering) [kg] 
Yellow Gum 1 0.03 0.03 5 2568.9 2726.9 2619.9 
Yellow Gum 2 0.03 0.03 5 1906.6 2135.0 1819.7 
Yellow Gum 3 0.03 0.03 5 717.6 868.6 635.2 
Grey Box 1 0.03 0.03 5 2493.2 2418.9 2031.7 
Grey Box 2 0.02 0.02 7 1871.9 1902.9 1793.7 
Grey Box 3 0.02 0.02 7 102.2 157.5 150.4 
 
Regression analysis shows a strong relationship between the reference and inferred AGB 
(Figure 5). Volume reconstruction without prior filtering of the point cloud relates with an R2 of 
0.99 and with prior filtering the R2 is 0.98. The estimates from the filtered point cloud are 
consistently lower compared to estimates from the raw LiDAR point cloud. The intercept of the 
regression from the filtered data is also smaller: 19.8 kg versus 110 kg. 
 
 
Figure 5: Regression plot of reference AGB vs. inferred AGB (from TLS) 
 
4. Discussion & Conclusions 
 
Varying the input parameters (rcov, dmin and nmin) mainly influenced the reconstruction of the 
crown constituents (Figure 3 and Figure 4). The stem and main branches are generally well 
sampled by the LiDAR instrument and are less susceptible for variations in the reconstruction 
method input. This is important since the main part of tree biomass is located in the stem and 
SilviLaser 2013, October 9-11, 2013 –Beijing, China 
 7 
the main branches. In this study, we used visual assessment to determine the optimal input 
parameters. Future work will include a more automated assessment to achieve this. For the six 
trees in this study, only two different optimal combinations of input parameters were identified 
(Table 2). 
AGB inferred from the filtered point cloud were consistently lower (Figure 5). Filtering 
removes isolated points in the LiDAR data, so it is not surprising that estimates are lower. 
Isolated points lead to erroneously modelled cylinders, which increase the AGB estimate. 
 
As a proof of concept, the work in this paper was limited to only six trees (from two species) 
from Rushworth Forest. Further analysis will include all the harvested trees from the five plots. 
Approximately 150 trees from multiple eucalypt species were harvested and cover the whole 
range of single tree AGB for these species. We will also compare the reconstruction approach 
with indirect estimates of AGB derived from allometric equations that depend on structural 
parameters such as diameter at breast height (DBH) and tree height. We will use both field 
inventories and TLS derived parameters as input for these allometric equations. A comparison 
of these three methods with the harvested AGB will give some insight into the potential and 
accuracy of TLS for rapid biomass assessments at the plot level, and how this may be used in 
support of broad scale biomass mapping. 
 
In conclusion, we have used TLS point clouds of single trees to infer AGB via volume 
reconstruction and basic density. These estimates were compared with destructively sampled 
AGB and regression analysis demonstrated R2 of 0.98 to 0.99, with an intercept of 110 kg for 
unfiltered TLS point clouds and 19.8 kg for filtered point clouds. With a limited number of 
samples, we have demonstrated the potential of tree reconstruction from TLS for rapid, 
repeatable and robust AGB estimation. 
 
References  
 
Bi, H.,Turner, J. and Lambert, M. J., 2004. Additive biomass equations for native eucalypt 
forest trees of temperate Australia. Trees, 18, 467-479. 
Burt, A., Disney, M.I., Raumonen, P., Armston, J., Calders, K. and Lewis, P., 2013. Rapid 
characterisation of forest structure from TLS and 3D modelling. Proceedings of IEEE 
International Geoscience and Remote Sensing Symposium (IGARSS 2013). 
Disney, M., Lewis, P. and Raumonen, P., 2012. Testing a new vegetation structure retrieval 
algorithm from terrestrial lidar scanner data using 3D models. Proceedings of Silvilaser 
2012. 
FAO, 2010. Global forest resources assessment 2010, Main report, FAO Forestry Paper 163. 
378p. 
Hamilton, S. D., Brodie, G., and O'Dwyer, C., 2005. Allometric relationships for estimating 
biomass in grey box (Eucalyptus microcarpa). Australian Forestry, 68, 267-273 
Raumonen, P., Kaasalainen, M., Åkerblom, M., Kaasalainen, S., Kaartinen, H., Vastaranta, M., 
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Estimating above ground biomass from terrestrial laser scanning in Australian Eucalypt open forest

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Estimating above ground biomass from terrestrial laser scanning in Australian Eucalypt open forest

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