Individual tree biomass estimation using terrestrial ...

ISPRS Journal of Photogrammetry and Remote Sensing 75 (2013) 64–75

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Individual tree biomass estimation using terrestrial laser scanning Ville Kankare a,⇑, Markus Holopainen a, Mikko Vastaranta a, Eetu Puttonen b, Xiaowei Yu b, Juha Hyyppä b, Matti Vaaja c, Hannu Hyyppä c,e, Petteri Alho d a

University of Helsinki, Department of Forest Sciences, Finland Department of Remote Sensing and Photogrammetry, Finnish Geodetic Institute, Finland c Aalto University, Research Institute of Modelling and Measuring for the Built Environment, Finland d University of Turku, Department of Geography and Geology, Finland e Helsinki Metropolia University of Applied Sciences, Finland b

a r t i c l e

i n f o

Article history: Received 17 April 2012 Received in revised form 1 October 2012 Accepted 2 October 2012 Available online 7 December 2012 Keywords: Terrestrial laser scanning Aboveground biomass Individual tree Features Stem volume Stem curve

a b s t r a c t Determination of stem and crown biomass requires accurate measurements of individual tree stem, bark, branch and needles. These measurements are time-consuming especially for mature trees. Accurate field measurements can be done only in a destructive manner. Terrestrial laser scanning (TLS) measurements are a viable option for measuring the reference information needed. TLS measurements provide dense point clouds in which features describing biomass can be extracted for stem form and canopy dimensions. Existing biomass models do not utilise canopy size information and therefore TLS-based estimation methods should improve the accuracy of biomass estimation. The main objective of this study was to estimate single-tree-level aboveground biomass (AGB), based on models developed using TLS data. The modelling dataset included 64 laboratory-measured trees. Models were developed for total AGB, tree stem-, living branch- and dead branch biomass. Modelling results were also compared with existing individual tree-level biomass models and showed that AGB estimation accuracies were improved, compared with those of existing models. However, current biomass models based on diameter-at-breast height (DBH), tree height and species worked rather well for stem- and total biomass. TLS-based models improved estimation accuracies, especially estimation of branch biomass. We suggest the use of stem curve and crown size geometric measurements from TLS data as a basis for allometric biomass models rather than statistical three-dimensional point metrics, since TLS statistical metrics are dependent on various scanning parameters and tree neighbourhood characteristics. Ó 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved.

1. Introduction Forest biomass mapping and monitoring is a major issue at the global level in cases of forest-bound carbon, bioenergy, biofuel and forest hazards. A major proportion of the total forest carbon storage consists of the growing stock’s carbon reserves. Forest biomass monitoring would also provide the means for detecting changes caused by storm, snow, drought or insect hazards. One of the greatest challenges in programmes that aim at reducing global emissions from deforestation and forest degradation (e.g. U.N. Collaborative Programme on Reducing Emissions from Deforesta-

⇑ Corresponding author. Address: Latokartanonkaari 7, 00014 University of Helsinki, Finland. Tel.: +358 40 9669109; fax: +358 91 9158100. E-mail addresses: ville.kankare@helsinki.fi (V. Kankare), markus.holopainen@helsinki.fi (M. Holopainen), mikko.vastaranta@helsinki.fi (M. Vastaranta), eetu.puttonen@fgi.fi (E. Puttonen), xiaowei.yu@fgi.fi (X. Yu), juha.hyyppa@fgi.fi (J. Hyyppä), matti.vaaja@aalto.fi (M. Vaaja), hannu.hyyppa@aalto.fi (H. Hyyppä), petteri.alho@utu.fi (P. Alho).

tion and Forest Degradation in Developing Countries, REDD) is how to measure and monitor forest biomass and its changes effectively and accurately. Recently, knowledge of forest biomass and its changes has been based on more or less subjective ground measurements and coarse- or medium-resolution satellite images. Therefore, the accuracy of biomass estimations, especially at the local level (e.g. forest stands), is poor. Individual tree level measurements are time-consuming and expensive to conduct. Measurements are done, at the moment, in a destructive manner, because the trees need to be cut down and measured in the laboratory. In recent years, biomass models based on field measurements have been developed, e.g. by Repola (2008, 2009), that could be utilised to estimate single-tree aboveground biomass (AGB) but most of the models do not utilise any information about the canopy or branch size distribution. Therefore more detailed measurement information, especially from tree canopy, is needed as a basis of new biomass models. Forest biomass quantity correlates strongly with tree height, which can be accurately determined by laser scanning-based

0924-2716/$ - see front matter Ó 2012 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.isprsjprs.2012.10.003

V. Kankare et al. / ISPRS Journal of Photogrammetry and Remote Sensing 75 (2013) 64–75

methodologies. Laser measurements provide a starting point for deriving growth and biomass models: terrestrial and airborne laser measurements can be utilised for determining accurate information on the tree canopy and spatial arrangement of individual trees that, using traditional field inventory methods, is a laborious task to carry out. It is possible, using laser scanning, to increase the accuracy of biomass estimation at all imaginable levels, ranging from the individual tree to the global forested area. Fixed-position (mounted on a tripod) terrestrial laser scanners offer a high potential for three-dimensional (3D) mapping of smaller areas with high detail. Terrestrial laser scanning (TLS) is an efficient and objective option for acquiring accurate field data. The applications of TLS for forestry have not been widely studied, although its potential for forest-related measurements has been more understood in recent years. TLS is capable of measuring all the important tree characteristics such as diameter, height and location (Hopkinson et al., 2004; Pfeifer and Winterhalder, 2004; Vastaranta et al., 2009 and Liang et al., 2012). TLS can also provide information on canopy-related characteristics and stem form, which has not been achievable before (Moorthy et al., 2008; Hyyppä et al., 2009; Kaasalainen et al., 2010 and Raumonen et al. 2011). The principle of TLS is straightforward: a highly collimated laser beam scans over a predefined solid angle in a regular scanning pattern and measures the time of flight of the laser signal. The scanning range of the midrange terrestrial system allows distance measurements between 2 m and 800 m. TLS measurements can be utilised, e.g. in reconstruction of building models for digital factories, virtual reality, architecture, civil engineering, archaeology and cultural heritage, plant design, automation systems (robotics) and detailed planning and documentation. In forestry, TLS has been used for detailed modelling of individual trees and canopies. Using TLS for plot-level inventories offers means of determining basic tree parameters, such as the number and position of trees, diameter-at-breast height (DBH) and tree height, after automation of the data processing has been solved properly. Sample plots can be measured with single-scan or multiple scan modes. The use of single-scan mode results in lower capability for reconstructing individual tree trunks caused by occlusion. The use of multiple scans results in a highly detailed reconstruction of individual tree trunks but it requires coregistration of the scans. A raw scanning dataset contains a huge number of points, and the recognition of trees in a point cloud is essential for estimating forest characteristics (Liang et al., 2012). The use of TLS data has been studied for the following forest applications: measuring forest parameters (e.g. Maas et al., 2008; Vastaranta et al., 2008 and Moskal and Xheng, 2012), tree location accuracy (e.g. Holopainen et al., 2011 and Liang et al., 2012), stem curve prediction (e.g. Liang et al., 2011), stem reconstruction (e.g. Pfeifer and Winterhalder, 2004), stem mapping (e.g. Maas et al., 2008; Holopainen et al., 2011 and Liang et al., 2012) and biomass (e.g. Hyyppä et al., 2009; Kaasalainen et al., 2010; Yao et al., 2011 and Zhao et al., 2011). Maas et al. (2008) reviewed the TLS scanner systems and their applicability in forestry. Reliability and precision of DBH, tree height and stem profiles were analysed. They used five different study plots with varying scan parameters. A tree detection accuracy of 97.5% was reported. The DBH measurement accuracy determined with callipers ranged from 1.48 cm to 3.25 cm. Tree height was measured from a single tree for each different species in each sample plot. The root-meansquared error (RMSE) was 4.55 m and remained 2.07 m, even with the removal of two outliers. Forest management procedural decisions (silviculture treatments, thinnings and final cuttings) are often made either directly or indirectly from tree DBH measurements, but actually, the interesting characteristic is the stem form because the quality of saw

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wood and log yield are directly related to stem form (Uusitalo, 1997; Uusitalo and Isotalo, 2005; Peuhkurinen et al., 2007; Kilpeläinen et al., 2011). Pre-harvest measurements at the moment are based on manually measured DBH and stem curve or volume models (Laasasenaho, 1982) which are used in wood production planning. Stem form cannot be measured cost-efficiently with traditional methods. In Boreal Forest Zone, 75–85% of the tree biomass is located in the stem (Lehtonen et al., 2004). Traditional stem curve measurements are based on stem curve equations (Laasasenaho, 1982) that utilise tree species, measured diameters (DBH and diameter-at-6 m) and tree height. Errors in stem form prediction lead to inaccuracy in simulation of bucking (Holopainen et al., 2010). TLS measurements are a promising method for acquiring more detailed measurements about tree stem than traditional methods. Pfeifer and Winterhalder (2004), Maas et al. (2008) and Liang et al. (2011) introduced methods for utilising and measuring stem form. Pfeifer and Winterhalder (2004) studied the use of dense TLS point data in reconstructing the stem form and branches. Taper curve and trunk or branch ovality are measures for the shape and quality of the wood. Reconstructing the stem curve with traditional measurements (with callipers) is laborious work. Maas et al. (2008) reported that the largest errors in the stem profile were at the bottom and top of the tree. The RMSE was 1.0 cm between heights from 0.7 m to 7.70 m. Liang et al. (2011) achieved an accuracy of 1.3 cm for the single-scan mode and 1.8 cm for the multiple-scan mode. The stem curve accuracy was estimated for heights from 1.3 m to 12 m for pine. Hyyppä et al. (2009) and Kaasalainen et al. (2010) showed that standing tree biomass changes can be measured with TLS in a laboratory environment. Hyyppä et al. (2009) studied the capability of TLS for deriving changes in the tree biomass and defoliation degree by destructive, consecutive defoliation operations. Biomass changes of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.) trees correlated highly with the number of hits in the TLS point cloud. The relative change in the number of reflected points when the geometry differences of the measurements were normalised correlates under laboratory conditions with the real changes of the biomass with an extremely high coefficient of determination (R2 = 0.95–0.98, number of sample 50). Determination of stem and crown biomass requires accurate measurements of individual tree stems, bark, branches and needles (Repola, 2009). These measurements are time-consuming, especially for mature trees. Accurate field measurements can be done only in a destructive manner. TLS measurements are a viable option for measuring the needed reference information. TLS forms dense point clouds of each individual tree from which statistical and geometrical features can be calculated. As far as we know, this type of field data has not yet been used in TLS-based biomass estimation. The objectives of this study were to: (1) Determine the accuracy of the TLS measurements in determining stem form, DBH, height and volume that are further used in tree-level biomass estimation, (2) Develop linear models for total AGB, stem wood-, living branch – and dead branch biomass, using TLS-measured geometric features and statistical 3D point metrics and (3) Compare the accuracy of the TLS models developed with the results based on existing biomass models.

2. Materials and methods 2.1. Reference data Trees for laboratory analyses were collected from Evo (61.19°N, 25.11°E) and Hyytiälä (61.845°N, 24.287°E) in the summers of 2010 and 2011. In all, 64 trees were measured including 33 Scots pine and 31 Norway spruce. Field-measured trees were felled

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Table 1 Description of sample trees measured in the field. Variable

DBH (mm) Height (m) Stem volume (dm3) Age (year) Crown ratio Liv. branch (kg) Dead branch (kg) Stem mass (kg) Bark mass (kg) Total mass (kg)

Scots pine, n = 33

Norway spruce, n = 31

Min

Mean

Max

Stdev.

Min

Mean

Max

Stdev.

103.00 13.80 52.19 42.00 0.31 2.70 0.44 19.97 3.00 26.50

206.24 19.54 328.22 50.03 0.50 16.43 3.74 150.17 9.50 179.85

302.00 23.12 746.10 58.00 0.71 43.62 12.16 420.02 20.72 449.22

42.71 2.28 155.36 4.27 0.11 10.68 2.68 78.31 4.49 87.01

102.00 9.03 37.29 30.00 0.06 5.05 0.04 12.76 0.19 28.90

213.71 18.77 390.15 58.00 0.40 36.83 4.78 162.95 13.96 218.52

370.00 26.50 1093.60 92.00 0.87 212.32 40.75 436.63 44.40 568.56

70.37 4.51 301.39 15.88 0.23 39.52 7.40 115.65 11.63 151.59

Fig. 1. Field-measured biomass distributions for different sections (stem, bark, living and dead branches) of the tree for Scots pine and Norway spruce.

and the total heights of the trees (h) and heights of the living and dead crowns (hc, hdc) were measured. The living crown was divided into four equal length sections and from each section one typical and one dead branch were selected for further analyses. Then the felled trees were trimmed and the weights of the branches (including needles), in five classes, were recorded (one class for the dead branches and four for the living branches). The bole was cut into logs and the diameter was measured at the bottom of every log with steel calibers. The first cut was at the stump height, the second at the middle between the stump and breast height, the third at breast height (1.3 m) and then, starting from the 2-m height, cuts were made every 1 m. The weights of the logs were recorded. The moisture content of the bark and stem wood was measured from sample discs from every second log. The bark, stem wood and branches (five samples per tree) were dried in an oven at a temperature of 70 °C for 2–3 days.

2.1.1. Biomass calculation For every bole the following biomasses were estimated: stem wood, stem bark, and living and dead branches. The branch biomass included both branch wood and bark, and the living branch biomass included cones. The biomass of the tree was predicted by applying ratio estimation methods. The measured moisture content of the sample discs for both bark and stem wood were applied separately to the stem mass, together with the estimated proportion of bark. Since we had sample discs from different heights of

each sample tree, we applied the proportions and ratios measured for the logs that were next to the disc. The sample branches were used to estimate the branch dry weight from the fresh weight. Ratio estimates for living branch biomass were calculated first by crown sections. The total living branch biomass was the sum of the crown sections. Constant moisture content, based on the mean moisture content of dead sample branches on the plots, was used for dead branches. Table 1 contains the descriptions of the sample trees measured. In the Boreal Forest Zone most of the tree biomass is in the stem. Fig. 1 represents the average biomass distribution in field measurements for Scots pine and Norway spruce. For Scots pine 83.50% of the total biomass originated from the tree stem and the rest (of the total biomass) was divided into bark (5.28%), dead branches (2.08%) and living branches (9.14%). The stem biomass for Norway spruce covered 74.57% of the total biomass. Living branches had a larger portion (16.85%) of total biomass than with Scots pine. Dead branches covered 2.19% and bark 6.39% of the total biomass for spruces on average. 2.1.2. Biomass models based on tree species, DBH and height The biomass models introduced by Repola (2009) were based on a measurement procedure similar to that used in this study. They measured 908 pine and 613 spruce, but not all the biomass components were measured from all the sample trees, e.g. stem wood measurements were made for 626 pine and for 366 spruce (see Repola, 2009: Table 4).


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Fig. 2. Leica HDS6100 TLS system (left) and specifications (right).

Fig. 3. Left: Multiscan principle. The squares represent different scanning points (Sp) in a single-tree group and green circles represent sample trees. Right: example of the data acquired, using three individual scans.

Models were developed, using multivariate procedures with random parameters for the total biomass of the tree and for the following individual tree components: stem wood and bark, living and dead branches, foliage or needles, stump and roots. The model predictors were tree species, DBH and height. The models are described in further detail in Repola (2008, 2009). The models used in this study were total AGB, stem wood-, living branch- and dead branch biomass. The variables needed for the models (species, DBH, h) were measured in the field. In this study, tree-level biomasses for 64 reference trees were calculated with Repola’s (2009) models for Scots pine and Norway spruce for comparison with TLS-based biomass models and laboratory measurements. The results calculated with models introduced by Repola (2009) will be referred to here as BIOMASSDBH_H. 2.2. Terrestrial laser scanning measurements 2.2.1. Data collection and preprocessing The TLS data were collected with a Leica HDS6100 TLS system (Leica Geosystem AG, Heerbrugg, Switzerland) (Fig. 2). The HDS6100 is a 690 nm phase-based continuous-wave laser scanner

with a 360° 310° field-of-view upwards and its data acquisition rate is 508,000 points per second. The distance measurement accuracy is ±2 mm at a distance of 25 m. The circular beam diameter at the exit and the beam divergence are 3 mm and 0.22 mrad, respectively. The point spacing is 6.3 mm at 10 m (with angular resolution of 0.009°). More detailed specifications are presented below (Fig. 2). TLS measurements for the 64 trees were collected in multiscan mode, as shown in Fig. 3. The objective of the measurements was to obtain the best possible point coverage for each study tree. The study trees were selected in groups of 3–5 trees to reduce the amount of field- and pre-processing work (Fig. 3). The TLS point clouds within each group were coregistered, using reference targets. The reference targets were divided around the study area for point cloud registration. In all, 15 tree groups were measured with 5–7 separate TLS scanning positions. Coregistration was done with Leica’s Cyclone software (Leica Geosystems). Mean absolute error in registration varied from 2 mm to 15 mm. Point extraction for each individual tree was done, using Terrascan software (Terrasolid, Helsinki, Finland).

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Table 2 Features extracted from TLS point clouds.

Table 4 Manual TLS estimation accuracy compared with field measurements for basic singletree parameters, DBH, height (H), stem curve based volume (stemVol) and volume calculated with Laasasenaho’s (1982) volume models (modelVol).

Feature

Description

PD0. . .1 NCD10. . .90 NHQ10. . .90 Skewness Kurtosis maxH meanH cvH crownH crownP crownA treeSurf treeVol D02. . .D10 D02L. . .D10L stemVol

Point density from normalised height (0, 1) Density over certain height level Percentiles for canopy height distribution Skewness Kurtosis Max height Mean height Coefficient of variation of height Crown height Crown perimeter Crown area Tree surface area Tree volume Stem curve (manual) Stem curve Laasasenaho (1982) Stem volume

Table 3 Extracted point density (PD) height fractions from normalised single-tree height (hN) that describes the laser hits found within a specified fraction of the tree. PD, PD, PD, PD, PD, PD,

hN < 0.33 0.33 < hN < 0.67 hN > 0.67 0.1 < hN < 0.2 0.2 < hN < 0.3 0.3 < hN < 0.4

PD, PD, PD, PD, PD,

0.4 < hN < 0.5 0.5 < hN < 0.6 0.6 < hN < 0.7 0.7 < hN < 0.8 0.8 < hN < 0.9

2.2.2. Feature extraction In all, 83 TLS based features were extracted from individual tree point clouds (Table 2). Features consisted of 31 statistical 3D point features and 52 measured geometric features. Feature extraction is presented in more detail in Puttonen et al. (2010, 2011) and Næsset and Gobakken (2005). The derived statistical 3D features were point density from normalised height (PD) that describes the laser hits found within a specified fraction of the tree (Table 3), point densities (NCD) from under a certain normalised height level

DBH, (mm) H (m) stemVol (m3) modelVol (m3)

Bias

Bias%

RMSE

RMSE%

7.05 0.51 0.00 0.01

3.36 2.65 0.67 2.72

14.81 1.54 0.05 0.06

7.06 8.05 15.34 16.65

(0.1–0.9), percentiles calculated from 10% to 100% of the canopy height distribution (NHQ), skewness and kurtosis of the height distribution. The geometric features measured were maximum height (maxH), mean height (meanH) calculated as the arithmetic mean of the laser heights, height coefficient of variation (cvH) calculated as the standard deviation of the individual tree point cloud height divided by its mean height and crown height (crownH) calculated as the difference between tree height and lowest living branch (manually identified), crown perimeter (crownP) and area (crownA), tree surface (treeSurf) area and volume (treeVol), stem curve and stem volume (stemVol). The crownP and crownA were calculated using Delaunay triangulation with the following phases (more detailed description in Puttonen et al., 2010): (1) The point cloud was manually cleaned, (2) duplicate points were removed in the xy-plane, (3) two-dimensional (2D) Delaunay triangulation was calculated for the remaining points, (4) all triangles with a side length exceeded a threshold value (50 cm) were removed from the triangulation, (5) crown area was calculated as the sum of all the areas from remaining triangles, (6) edge point of the triangulation and the edges connecting them were searched, and (7) sum of the lengths of the connecting edges was calculated to obtain the tree crownP. The treeSurf and treeVol were calculated with the same principle but with a 3D Delaunay triangulation. The stem curve was measured manually from point clouds, using Terrascan software (Terrasolid). The diameters were measured from the following heights along the stem: 0.2 m, 0.5 m, 1.0 m, 1.3 m, 1.5 m and from 2.0 m to 10 m in 0.5-m steps. The

Fig. 4. Average success rate in measuring diameters at different points along the stem. Success rate displays the amount of trees (in per cent) that diameter at specific height could be measured divided by total amount of trees.


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Fig. 5. TLS-measured stem diameters (circles), spline stem curve predicted from TLS (blue line), second spline stem curve (Spline2) predicted from TLS (dashed black) and Laasasenaho’s (1982) stem curve predicted from DBH (dashed grey) for a Scots pine with DBH of 206 mm and height of 17.58 m. Second spline function (Spline2) displays poorer extrapolation for stem curve when the last diameter (from 10 m height) is not measured. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 6. TLS measurements for DBH, height, stemVol and modelVol compared with laboratory measurements. Laasasenaho’s (1982) stem curve equation (stemVol) and volume models (modelVol) were used to estimate tree volume.

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stem curve was also formed, using Laasasenaho’s stem curve models (Laasasenaho, 1982), due to the occlusion in manual measurements. The stemVol was then calculated based on the stem curve. 2.2.3. Biomass models and estimation The objective of the model formulation was to develop linear models to estimate total AGB, stem wood-, living branch- and dead branch biomass. The model predictors were analysed based on biological plausibility as well as statistical significance, using correlations and preliminary modelling results. The final predictors were selected, using a combination of two automatic feature selection methods: Lasso regression (Tibshirani, 1996) and stepwise regression. Full set of explanatory variables (TLS-derived features) were the input in Lasso regression. Then the selected features were used in model development in R (R Development Core Team, 2011) and stepwise regression was used to find the final set of explanatory features. Feature selection process was done separately for each biomass compartment of interest. The models were developed and tested with the same dataset (64 field-measured trees). The linear model form is presented below (model 1).

^ ¼ f ðxi ; bÞ þ ei y

ð1Þ

î is the tree biomass to be estimated and f is a known funcwhere y tion of known predictor variables and some unknown parameters b. The biomass estimation results, based on TLS, will be referred to in the following with BIOMASSTLS abbreviation. The accuracies of the biomasses estimated were evaluated by calculating the bias and RMSE:

Pn BIAS ¼

i¼1 ðyi

î Þ y

;

ð2Þ

BIAS ; y

ð3Þ

n

BIAS% ¼ 100

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi Pn ^ i¼1 ðyi yi Þ ; RMSE ¼ n RMSE% ¼ 100

RMSE ; y

ð4Þ

ð5Þ

where n is the number of plots, yi the value estimated from the field î the predicted value for plot i, and y i the mean of the data for plot i, y variable in the validation plots. 3. Results 3.1. Accuracy of TLS in determination of DBH, height and stem volume One problem in measuring the stem curve from TLS data is occlusion of the entire stem. We tested the ability to measure the stem curve manually from the TLS point cloud from the stump height to the 10-m height, which includes most of the AGB. The ability to measure diameters along the stem (success rate,%) from TLS-point clouds is shown in Fig. 4. The average success rate for pine and spruce were 86.91% and 48.24%, respectively. The success rate decreased more rapidly in spruce already after breast height. For pine, the results were much better, because the lowest part of the tree crown was between 5.5 m and 6.5 m on average. The ability to form stem curves for biomass calculation was also studied. Fig. 5 represents the various methods tested in this study. Here is shown a sample tree and three different methods for measuring the stem curve: (1) manual measurements from the TLS point cloud, (2) two cubic smoothing spline functions (Spline and Spline2) fitted to manually measured diameters and (3) stem curve

Fig. 7. DBH is highly impacted by stem form due to its noncircular from. Here is an example of measured DBH from TLS point cloud from two different directions.

based on Laasasenaho’s (1982) stem curve equations. Diameter at 10-m was first measured and used in fitting the first spline function and afterwards excluded from Spline2-function to represent the effect of occlusion. It can be noted that fitting the spline function to the diameters measured works when we have the last (highest) diameter successfully measured. The accuracy of the TLS-measured traditional tree parameters is shown in Table 4. TLS derived DBH was manually measured with high accuracy (RMSE of 14.81 mm, Table 4) using Terrascan compared to steel calibers. The height accuracy (RMSE%) was 8.05%. The stem volume was calculated with Laasasenaho’s (1982) volume models (modelVol) and stem curve equations (stemVol). The parameters used were tree species, TLS-measured DBH and height. The volume estimation accuracy was slightly better with stem curve calculations than with volume models. Fig. 6 shows that there is high correlation between TLS- and field-measured DBH, height and stemVol. Correlations of 0.97, 0.91, 0.97 and 0.97 and adjusted R2 values of 0.95, 0.95, 0.83 and 0.94 for DBH, height, stemVol and modelVol, respectively, were found. The accuracy of the DBH measurements is highly affected by stem form, because tree stem shape is not fully circular. Fig. 7 shows an example of this case measured from the TLS point cloud. 3.2. Accuracy of biomass models based on tree species, DBH and height Biomass estimates were calculated with the biomass models introduced by Repola (2009). Biomasses were estimated for stem Table 5 BIOMASSDBH_H estimation accuracy for Scots pine for different biomass compartments and for total biomass.

Stem wood Liv. branch Dead branch Total

Bias (kg)

Bias%

RMSE (kg)

RMSE%

20.31 2.58 4.27 9.60

13.52 15.68 113.96 5.34

44.27 10.21 4.94 38.77

29.48 62.13 132.07 21.56

Table 6 BIOMASSDBH_H estimation accuracy for Norway spruce for different biomass compartments and for total biomass.

Stem wood Liv. branch Dead branch Total

Bias (kg)

Bias%

RMSE (kg)

RMSE%

35.20 5.14 4.78 11.99

21.60 13.96 100.00 5.49

56.60 37.25 8.71 64.69

34.74 101.14 182.14 29.60

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Fig. 8. Laboratory-measured total and stem wood biomass compared to BIOMASSDBH_H for Scots pine.

Fig. 9. Laboratory-measured total and stem wood biomass compared to. BIOMASSDBH_H for Norway spruce.

Table 7 Correlation between biomass compartments and overall best geometric features for BIOMASSTLS modelling. TLS feature

maxH meanH D13L crownH crownP crownA treeSurf treeV stemVol

Scots pine

Norway spruce

Bark mass

Stem mass

Liv. Branch

Dead branch

Total

Bark mass

Stem mass

Liv. Branch

Dead branch

Total

0.61 0.39 0.60 0.41 0.70 0.72 0.73 0.71 0.78

0.65 0.40 0.64 0.50 0.63 0.60 0.71 0.70 0.80

0.10 0.14 0.10 0.35 0.60 0.55 0.51 0.57 0.33

0.42 0.22 0.41 0.31 0.64 0.56 0.78 0.73 0.73

0.64 0.40 0.64 0.52 0.69 0.66 0.77 0.76 0.82

0.73 0.47 0.70 0.73 0.79 0.87 0.85 0.76 0.92

0.76 0.51 0.74 0.78 0.64 0.68 0.77 0.73 0.92

0.45 0.20 0.42 0.38 0.43 0.64 0.52 0.29 0.44

0.41 0.16 0.39 0.37 0.41 0.56 0.56 0.34 0.47

0.77 0.49 0.75 0.77 0.68 0.78 0.82 0.71 0.91

wood, living and dead branches and total. The results were compared with laboratory-measured biomasses. The accuracies (RMSEs) for stem wood, living branches and total biomass were 29.48%, 62.13% and 21.56% for Scots pine and 34.74%, 101.14% and 29.60% for Norway spruce, respectively. The living branch biomass was estimated more accurately for Scots pine than for Norway spruce. The highest RMSE% values were for dead branch biomass. The results are shown in Table 5 and Table 6. The results for total and stem wood biomasses are plotted in Fig. 8 for Scots pine and in Fig. 9 for Norway spruce. Correlation between the estimated total biomass for Scots pine was 0.90 and for Norway spruce 0.91. Respectively, the estimations for stem wood biomass showed correlations of 0.87 for Scots pine and 0.94 for Norway spruce.

3.3. Accuracy of aboveground biomass models based on TLS features The objective of the model formation was to develop linear models for estimating total AGB, stem wood-, living branch- and dead branch biomass. The overall highest correlations between TLS-based features and biomass compartments are presented in Table 7. More detailed correlation matrices can be found in Appendix A. The results showed that overall the best correlations were achieved with measured geometric features that are less dependent on scanning parameters than statistical parameters. The best TLS features for the separate models were selected, using a combination of two methods: Lasso regression (Tibshirani, 1996) and stepwise procedure. The features selected for the models are presented in Table 8 and model coefficients in Table 9.

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Table 8 Features selected for the biomass models by compartments for each tree species (Norway spruce and Scots pine). Features were selected with Lasso regression and stepwise procedure. Features

Scots pine Total

PD4 PD6 PD7 PD8 PD10 PD11 Ncd10 Ncd20 Ncd90 Kurtosis Nhq10 Nhq30 Nhq40 crownH crownP crownA TreeSurf TreeV d80L stemVol

Norway spruce Stem wood

Liv. branch

Dead branch

Total

Stem wood

Liv. branch

Dead branch

Table 9 Calculated coefficients of the linear models. Model

b0

Scots pine Total Stem wood Liv. branch Dead branch

10.83 154.48 56.70 1.20

12.33 13.28 208.11 1.29

20.87 0.11 103.18 0.41

0.12 0.91 4.98 0.48

401.69 324.93 15.12 0.06

Norway spruce Total Stem wood Liv. branch Dead branch

505.03 580.59 112.92 0.11

6.13 479.42 219.05 8.18

9.23 253.11 179.41 0.11

29.77 401.64 170.46 0.55

32.35 1052.46 10.61 0.01

b1

b2

b3

b5

b6

b7

1.96 0.04

1.90 2.42

0.49 2.54 0.25

501.35 6.35 1.84

18.56

492.43 50.95

The correlations between the estimated and measured stem wood biomasses were 0.94 for pine and 0.97 for spruce.

Table 10 BIOMASSTLS estimation accuracies for AGB compartments. RMSE

RMSE%

Total Pine Spruce

22.12 26.00

12.93 11.90

Stem wood Pine Spruce

21.27 26.69

15.04 16.38

Liv. branch Pine Spruce

3.84 14.04

23.36 38.13

1.20 5.85

31.12 122.21

Dead branch Pine Spruce

b4

The accuracy of the BIOMASSTLS models is shown in Table 10. The most accurate result was achieved with total and stem wood biomass models for spruce. The RMSE% for total biomass was 11.90% and for stem wood 16.38%. The highest relative error in biomass estimation was with dead branch biomass for spruce (RMSE% was 122.21%). The results for total and stem wood biomasses are plotted in Fig. 10 for pine and in Fig. 11 for spruce. The correlations between the estimated total biomass for pine was 0.95 and for spruce 0.98.

4. Discussion The requirement for accurate and cost-efficient methods for mapping and monitoring forest biomass and its changes is growing. The use of TLS measurements could provide a more accurate reference data acquisition method for forest biomass inventories. The main objective of this study was to estimate AGB-based features extracted from TLS-point clouds and evaluate the modelling results with laboratory measurements. We hypothesised that TLS measurements would increase the accuracy of biomass measurements compared with existing biomass models in Finland. Most of the tree biomass in the Boreal Forest Zone is located in the tree stem (see Fig. 1). TLS measurements provide data from the tree stem in high detail, but occlusion caused by branches is a large issue. Traditionally, tree stemVol and stem curve measurement are based on tree species, height and DBH (Laasasenaho, 1982). We tested the ability to measure the stem curve manually from TLS data from the stump height to 10-m height. Results showed that the success rate of measuring the diameters manually from TLS point clouds varied between tree species, depending on the branching. TLS was used to measure DBH, height and stemVol with high accuracy. DBH was measured with an RMSE of 14.81 mm. DBH


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Fig. 10. Laboratory-measured total and stem wood biomass compared to. BIOMASSTLS for Scots pine.

Fig. 11. Laboratory-measured total and stem wood biomass compared to BIOMASSTLS for Norway spruce.

measurements are highly affected by the stem form (as shown in Fig. 7). DBH can vary, depending on the side that the measurements are done, due to the noncircular shape of the trunk (West, 2009). In comparison to Yao et al. (2011), our DBH estimation accuracy was significantly better which was expected because we used multiple scan-data compared to single scan-data used in the previously mentioned study. They reported an accuracy (RMSE) of 7 cm for conifers and 8 cm for deciduous trees at individual tree-level. Vastaranta et al. (2008) reported more accurate DBH estimates using TLS data. They achieved a DBH estimation accuracy (standard error) of 8.31 mm. Compared with Maas et al. (2008), the results obtained here were similar. Repola (2008, 2009) introduced AGB models that are based on easily field-measurable tree parameters: DBH, height and tree species. The models, on the other hand, use no information on the canopy size as an input parameter. When the models were tested in our data, the estimation accuracies of living and dead branch biomasses were poor. However, the models worked quite well with total and stem wood biomass estimation. TLS measurements provide dense point clouds, from which descriptive features can be extracted for stem form and canopy size. Existing models do not use canopy size information and therefore, at least in theory, TLS-based estimation methods should improve the accuracy of biomass estimation. We developed biomass models based on TLS features for total-, stem wood-, living and dead branch biomasses. Features used in the models were selected with a combination of Lasso regression and stepwise procedure.

The best features were, in most cases, stemVol and canopy shape and volume features. The results showed that the accuracy, especially that of living and dead branch biomass estimation, can be improved, compared to models using DBH, height and tree species as estimation parameters. The most difficult section of biomass to model was dead branches. It was difficult to find laser-based feature describing dead branches well, because the amount of dead branches is low and they are, in the worst case, scattered all around the canopy and distinction between dead and living branches requires spectral information that was not available in the present study. Features describing canopy size should be important for biomass estimation especially in tropical forest zones where the portion of canopy biomass is higher out of the total biomass than in the Boreal Forest Zone (Bastien-Henri et al., 2010). In this study, we used features describing the distribution of laser points in the canopy and canopy shape and volume features calculated with Delaunay triangulation. Canopy shape and volume features correlated highly, especially, with branch biomass (see Table 7). Another possible method for measuring the canopy size is to use branchsize distributions. Pfeifer and Winterhalder (2004) and Raumonen et al. (2011) measured branch diameters, using TLS data. Popescu (2007) derived forest parameters from ALS point clouds and calculated total AGB estimates for 43 trees with existing biomass models developed for the USA. RMSE values of 162.72 kg were achieved, using a linear model with only laser-derived DBH as an explanatory variable. The results achieved in pres-

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Table A1 TLS feature correlations. TLS feature

PD1 PD2 PD3 PD4 PD5 PD6 PD7 PD8 PD9 PD10 PD11 Ncd10 Ncd20 Ncd30 Ncd40 Ncd50 Ncd60 Ncd70 Ncd80 Ncd90 Skewness Kurtosis Nhq10 Nhq20 Nhq30 Nhq40 Nhq50 Nhq60 Nhq70 Nhq80 Nhq90 maxH meanH cvH crownH crownP crownA treeSurf treeVol d02L d05L d10L d13L d15L d20L d25L d30L d35L d40L d45L d50L d55L d60L d65L d70L d75L d80L d85L d90L d95L d100L stemVol

Scots pine

Norway spruce

Bark mass

Stem mass

Liv. Branch

Dead branch

Total

Bark mass

Stem mass

Liv. Branch

Dead branch

Total

0.07 0.10 0.18 0.00 0.19 0.04 0.00 0.00 0.21 0.19 0.12 0.16 0.11 0.07 0.06 0.06 0.05 0.20 0.14 0.28 0.03 0.06 0.02 0.07 0.15 0.19 0.23 0.31 0.41 0.53 0.58 0.61 0.39 0.09 0.41 0.70 0.72 0.73 0.71 0.58 0.59 0.60 0.60 0.60 0.59 0.59 0.59 0.59 0.59 0.58 0.57 0.57 0.56 0.56 0.55 0.55 0.55 0.55 0.55 0.54 0.54 0.78

0.07 0.14 0.22 0.02 0.27 0.06 0.10 0.08 0.26 0.13 0.30 0.19 0.12 0.07 0.05 0.02 0.07 0.26 0.32 0.43 0.00 0.03 0.01 0.00 0.17 0.23 0.27 0.34 0.43 0.53 0.57 0.65 0.40 0.08 0.50 0.63 0.60 0.71 0.70 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 0.63 0.63 0.63 0.63 0.63 0.63 0.62 0.62 0.62 0.62 0.62 0.80

0.30 0.53 0.38 0.37 0.20 0.28 0.39 0.44 0.39 0.24 0.48 0.29 0.33 0.34 0.26 0.14 0.14 0.42 0.49 0.41 0.25 0.62 0.58 0.53 0.40 0.10 0.09 0.15 0.16 0.14 0.10 0.10 0.14 0.38 0.35 0.60 0.55 0.51 0.57 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.10 0.33

0.07 0.12 0.21 0.02 0.10 0.09 0.12 0.08 0.06 0.27 0.05 0.16 0.09 0.07 0.09 0.13 0.16 0.21 0.06 0.12 0.08 0.03 0.10 0.00 0.05 0.14 0.13 0.15 0.24 0.31 0.38 0.42 0.22 0.09 0.31 0.64 0.56 0.78 0.73 0.39 0.40 0.41 0.41 0.41 0.40 0.39 0.40 0.40 0.39 0.39 0.38 0.38 0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.35 0.73

0.03 0.20 0.26 0.06 0.28 0.02 0.03 0.13 0.30 0.16 0.34 0.15 0.08 0.03 0.02 0.01 0.08 0.30 0.36 0.46 0.02 0.11 0.08 0.07 0.21 0.23 0.25 0.31 0.39 0.49 0.54 0.64 0.40 0.04 0.52 0.69 0.66 0.77 0.76 0.63 0.63 0.64 0.64 0.63 0.63 0.63 0.63 0.63 0.63 0.63 0.62 0.62 0.62 0.62 0.62 0.61 0.61 0.61 0.61 0.61 0.61 0.82

0.19 0.10 0.23 0.20 0.04 0.03 0.11 0.11 0.23 0.14 0.30 0.25 0.25 0.21 0.24 0.23 0.24 0.23 0.34 0.34 0.03 0.06 0.21 0.30 0.33 0.38 0.44 0.45 0.44 0.44 0.50 0.73 0.47 0.13 0.73 0.79 0.87 0.85 0.76 0.66 0.69 0.71 0.70 0.69 0.68 0.67 0.67 0.67 0.65 0.64 0.62 0.61 0.60 0.59 0.59 0.58 0.58 0.57 0.57 0.56 0.56 0.92

0.14 0.06 0.19 0.13 0.07 0.03 0.20 0.01 0.18 0.09 0.29 0.37 0.27 0.18 0.21 0.15 0.19 0.18 0.32 0.30 0.08 0.08 0.07 0.25 0.32 0.41 0.49 0.52 0.50 0.50 0.54 0.76 0.51 0.19 0.78 0.64 0.68 0.77 0.73 0.72 0.74 0.75 0.74 0.74 0.73 0.73 0.73 0.72 0.71 0.70 0.69 0.68 0.67 0.67 0.66 0.66 0.66 0.65 0.65 0.65 0.64 0.92

0.32 0.28 0.20 0.38 0.10 0.14 0.23 0.20 0.21 0.11 0.23 0.27 0.36 0.32 0.29 0.23 0.21 0.18 0.27 0.34 0.24 0.11 0.04 0.06 0.05 0.07 0.10 0.15 0.20 0.23 0.28 0.45 0.20 0.30 0.38 0.43 0.64 0.52 0.29 0.38 0.41 0.43 0.42 0.41 0.40 0.39 0.39 0.39 0.38 0.36 0.35 0.34 0.33 0.33 0.32 0.32 0.31 0.31 0.31 0.30 0.30 0.44

0.23 0.17 0.20 0.34 0.06 0.04 0.14 0.12 0.21 0.12 0.22 0.24 0.33 0.23 0.23 0.20 0.20 0.18 0.27 0.32 0.14 0.03 0.04 0.01 0.02 0.03 0.06 0.13 0.17 0.20 0.24 0.41 0.16 0.26 0.37 0.41 0.56 0.56 0.34 0.38 0.39 0.40 0.39 0.39 0.38 0.38 0.38 0.38 0.37 0.37 0.36 0.35 0.35 0.35 0.34 0.34 0.34 0.34 0.33 0.33 0.33 0.47

0.22 0.14 0.22 0.23 0.03 0.01 0.23 0.07 0.22 0.11 0.32 0.38 0.33 0.25 0.26 0.20 0.23 0.21 0.36 0.36 0.01 0.03 0.08 0.23 0.29 0.36 0.44 0.48 0.48 0.49 0.53 0.77 0.49 0.25 0.77 0.68 0.78 0.82 0.71 0.71 0.74 0.75 0.75 0.74 0.73 0.73 0.73 0.72 0.71 0.70 0.68 0.67 0.66 0.66 0.65 0.65 0.64 0.64 0.63 0.63 0.63 0.91

ent study for tree biomass were more accurate than those of Popescu (2007). As far as is known, this was the first study in which TLS was used in biomass estimation and models were developed and evaluated with laboratory-measured trees. It should be noted that there was no external testing dataset for the models, because this type of reference data is slow and expensive to produce. TLS-based tree-level biomass estimation aggregated at the plot level could be

used as reference data in large-area biomass mapping and inventory. However, to develop robust TLS-based biomass models is challenging, because it would require large treewise laboratorymeasured reference datasets and the laser features used as explanatory variables should be nondependent on the scanning parameters. We suggest the use of automatically extracted diameter and crown size measurements from TLS as a basis for allometric biomass models. One direction of future development would be the


use the TLS-based biomass models as a part of calculation of the embedded-carbon of forests (Heinonen and Junnila, 2011). 5. Conclusion The main objectives of this study were to determine the accuracy of the TLS measurements in tree parameter acquisition and to develop linear models for tree-level biomass estimation. Accuracy of the models was compared to laboratory measurements and to existing biomass models. TLS measurements were used to estimate tree-level parameters, DBH, height and stemVol. Results showed that TLS could be used to measure tree DBH, height and stemVol accurately. DBH measurements were highly affected by stem form. Current biomass models, based on DBH, height and species, do not use canopy size information in estimation of AGB. Our hypothesis was that individual tree-level biomass modelling could be improved using TLS data especially for branch biomass. Current biomass models worked rather well for stem- and total biomass but there was large estimation errors in branch biomass. TLS-based models improved estimation as hypothesised, especially that of branch biomass. Based on modelling results, we suggest the use of stem curve and crown size geometric measurements from TLS data as a basis for allometric biomass models rather than statistical 3D point metrics, since TLS statistical metrics are dependent on various scanning parameters and tree neighbourhood characteristics. In the current state, operational TLS measurements are not fully applicable under various forest conditions. If TLS processing becomes more automatic and reference data could be, collected even with the use of mobile platforms, it could also provide savings in reducing fieldwork. However, the strength of TLS so far is the feasibility of accurately measuring crown and stem characteristics that are laborious to measure by traditional means. Acknowledgements This study was made possible by financial aid from the Metsämiesten säätiö and the Finnish Academy projects ‘Improving the Forest Supply Chain by Means of Advanced Laser Measurements’ (L-impact) and ‘Science and Technology Towards Precision Forestry’ (PreciseFor). Appendix A See Table A1. References Bastien-Henri, S., Park, A., Ashton, M., Messier, C., 2010. Biomass distribution among tropical tree species grown under differing regional climates. Forest Ecology and Management 260 (3), 403–410. Heinonen, J., Junnila, S., 2011. Implications of urban structure on carbon consumption in metropolitan areas. Environmental Research Letters 6 (1). http://dx.doi.org/10.1088/1748-9326/6/1/014018. Holopainen, M., Vastaranta, M., Rasinmäki, J., Kalliovirta, J., Mäkinen, A., Haapanen, R., Melkas, T., Yu, X., Hyyppä, J., 2010. Uncertainty in timber assortment estimates predicted from forest inventory data. European Journal of Forest Research 129 (6), 1131–1142. Holopainen, M., Vastaranta, M., Kankare, V., Hyyppä, J., Liang, X., Litkey, P., Yu, X., Kaartinen, H., Kukko, A., Kaasalainen, S., Hyyppä, H. Vaaja, M., Jaakkola, A., 2011. The use of ALS, TLS and VLS measurements in mapping and monitoring urban trees. In: Stilla, U., Gamba, P., Juergens, C., Maktav, D. (Eds.) JURSE 2011 – Joint Urban Remote Sensing Event – Munich, Germany, April 11–13, pp. 29–32. Hopkinson, C., Chasmer, L., Young-Pow, C., Treitz, P., 2004. Assessing forest metrics with a ground-based scanning lidar. Canadian Journal of Forest Research 34 (3), 573–583. Hyyppä, J., Jaakkola, A., Hyyppä, H., Kaartinen, H., Kukko, A., Holopainen, M., Zhu, L., Vastaranta, M., Kaasalainen, S., Krooks, A., Litkey, P., Lyytikäinen-Saarenmaa, P.,

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