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Evaluating forest biometrics obtained from ground lidar in complexriparian forestsAlexander S. Antonarakisaa Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA

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To cite this Article Antonarakis, Alexander S.(2011) 'Evaluating forest biometrics obtained from ground lidar in complexriparian forests', Remote Sensing Letters, 2: 1, 61 — 70, First published on: 13 July 2010 (iFirst)To link to this Article: DOI: 10.1080/01431161.2010.493899URL: http://dx.doi.org/10.1080/01431161.2010.493899

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Evaluating forest biometrics obtained from ground lidar in complexriparian forests

ALEXANDER S. ANTONARAKIS*

Department of Organismic and Evolutionary Biology, Harvard University,

Cambridge, MA 02138, USA

(Received 26 March 2010; in final form 13 May 2010)

Terrestrial laser scanning is a technique that has been used increasingly in extract-

ing forest biometrics such as trunk diameter and tree heights. Its potential, how-

ever, has not been fully explored in complex forested ecosystems, especially in

riparian forests, considered among the most dynamic and complex portions of the

Earth’s biosphere. In this study, forest inventory data and multiple ground scans

were obtained in a sparse managed and dense natural riparian forest on the

immediate banks of the mid-lower portion of the Garonne River in Southern

France, dominated by black poplar (Populus nigra) and commercial hybrid poplars

(Populus � euramericana). Overall, the ground-based laser-scanning analysis suc-cessfully estimated trunk diameters, tree heights and crown radii from both man-

aged and natural riparian forests. However, the ground scanner analysis was not as

successful in identifying all of the trunks in the dense natural riparian forest, with

only 141 trunks identified from a total of 234. This also results in allometric scaling

exponents for ground scanning, which are significantly different from field-derived

exponents. This study thus shows that there may be a limit to the number of trees

detected in higher density forests, even with multiple scans.

1. Introduction

Terrestrial-based laser scanning is a new technology with the power to generate rapid

(1–3 kHz) and extremely dense (millimetre) spatial data. This technique is also a

relatively accurate way of capturing size, shape and form of complex physical realities

such as forests, from a large amount of point cloud information. Ground scanning

can potentially be used as a substitute for forest inventory data, as the latter can be

costly and time consuming.

Recent research has investigated the use of terrestrial laser scanning to estimate

trunk and canopy dimensions (Hopkinson et al. 2004, Watt and Donoghue 2005,Henning and Radtke 2006, Tansey et al. 2009) as well as leaf and branch attributes

(Clawges et al. 2007, Moorthy et al. 2008, Straatsma et al. 2008, Strahler et al. 2008,

Antonarakis et al. 2009, 2010). The studies considering trunk and canopy dimensions

have been successful in estimating forest biometrics usually from managed and some

natural conifer or deciduous forests. Yet, further assessing the ability of ground

scanning to determine forest biometrics requires a consideration of a broader range

of forest ecosystems, especially natural forests with different spatial and structural

attributes. A truly heterogeneous and important ecosystem is that of riparian forests.

*Email: [email protected]

Remote Sensing LettersISSN 2150-704X print/ISSN 2150-7058 online # 2011 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431161.2010.493899

Remote Sensing Letters

Vol. 2, No. 1, March 2011, 61–70

Downloaded By: [Antonarakis, Alexander S.] At: 17:35 13 July 2010

mailto:[email protected]://www.tandf.co.uk/journals

Floodplain forests are dynamic forested ecosystems present alongside the margins of

river channels and their floodplains. Natural riparian corridors are associated with

the most diverse, dynamic and complex biophysical habitats on the terrestrial portion

of Earth (Naiman et al. 1993). The riparian forest itself can be a complex mosaic of

different forest communities, reflecting the flooding and deposition regime of theriver. In dynamic river systems, forest communities can be made up of vegetation of

all ages, and of different sizes.

This study seeks to evaluate the strength of terrestrial laser scanning in determining

forest metrics in complex riparian forests located on the immediate banks of a lowland

river system. Trunk diameter at breast height (DBH), individual tree heights and

crown radii were estimated both in the field and using lidar, as well as by counting the

number of trunks and trees. This study provides an evaluation of the strengths and

limitations of applying ground scanning, which may be of interest to forestry, ecology,hydrology and remote-sensing communities.

2. Study area

Data sets were obtained for managed (43�4903000N 1�1502500E) and natural(43�5303500N 1�1202200E) riparian forests along the Garonne River (SW France) from6 to 12th June 2006. The managed forests were from 100 to 250 m from the bankfull

river channel, and the natural forest was within 35 m of the bankfull river channel.

The Garonne managed floodplain consisted mostly of commercial hybrid poplars (of

Populus � euramericana) with many clones (same genetic stock) in homogeneousplantations, and the natural woodland consisted of black poplar (Populus nigra) inheterogeneous sized patches (Muller et al. 2002). The managed poplar forest consisted

of 110 mature trees within an area of 0.5 ha, and 56 younger trees within an area of

0.15 ha, making the tree density of the managed forest 255 trees/ha. The natural

poplar forest consisted of 95 trees within an area of 0.1 ha, making the tree density 950

trees/ha. Each natural poplar crown, though, contained one or more trunks, which

resulted in a trunk density of 2340 trunks/ha. Trunks greater than 10 cm in circum-

ference and tall enough to enter the leafy crown and contain leafy elements were

measured. The planted poplars were almost evenly spaced, whereas the naturalpoplars were randomly spaced (figure 1) as would be expected in an immediate

riparian zone of natural woodland, with stinging nettles.

3. Methods

3.1 Forest inventory collection

3.1.1 Diameter at breast height. DBH was measured for all trees at breast height(1.4 m), by first measuring the circumference of the trunk with a metre tape. The

accuracy of the measuring tape was millimetric, but the accuracy of the circumference

measurement could have been less, as many of the trunks were rough or undulating.

For all of the planted poplars each crown had a single trunk, yet for many of the

natural riparian forests, the maximum number of trunks supporting each crown was

11. In some cases in the natural forest, two or more different crowns belonged to the

same single tree that had re-sprouted into multiple branches after being buried by a

flooding event.

3.1.2 Tree height. The total tree heights of all trees were measured as the difference

between the apex of the tree canopy and the lowest point of the visible trunk. The

62 A. S. Antonarakis


measurements were performed with the LaserAce 300 instrument (Measurement

Devices, Ltd., York, UK). This is a hand-held reflectorless laser measurement system

that can measure distances and heights of up to 300 m using an integrated digital

inclinometer. The instrument’s laser has an accuracy of approximately �0.19 m at adistance of 30 m. Each tree height was taken as the average of three measurements.Detecting tree heights in the natural riparian forest was challenging at times, but three

clear measurements were always sought and achieved.

3.1.3 Crown radius. The average crown radius was established for each tree from

four perpendicular radii using the trunk as the centre of the canopy. This created

horizontal crown radii that were more representative than a simple circular crown

representation. The lengths were measured using a 30-m tape in four directions from

the trunk, that is the positive and negative x and y directions. From the ground, the

termination of each radius was defined by the furthest overhead branch or leaf.

Figure 1. Side-view photographs (1) of the managed (a) and natural (b) riparian forests adjacentto the Garonne River in Southern France. Bird’s eye views of the mature managed and naturalforest scans (dark shading) are also presented (2), and are underlain by small footprint airbornelidar (light shading). The extent of the ground scanning at the natural site is much larger than thatwhich was considered for tree biometrics. The scanner positions are circled.

Ground lidar and complex riparian forests 63


3.2 Terrestrial laser scanning

A Leica Geosystems High Definition Surveyor (HDS 3000 scanner head, Leica

Geosystems Ltd., Milton Keynes, UK) was used to collect data. The laser data were

analysed using Leica Cyclone 5.5 software. This instrument has a distance range of

0.5–300 m and a field of view of 360� in the horizontal and 270� in the vertical. Theeffective scan distance was approximately 50 m for the managed riparian forest and

20 m for the natural riparian forest. Further details of the technique can be found in Frei

et al. (2005).

At each site chosen, multiple scans were performed to have the maximum three-dimensional representation of the vegetation. This is because one scan can recover data

from a very large area, but there will be occlusion of both the branches and the leafy

canopy. Therefore for the scanning of the full forest stands, the instrument was posi-

tioned at each edge and the centre of the stand (resulting in five scans each). Resolutions

of 10 cm were chosen for the two managed stands. The natural forest site was chosen to

have a resolution of 5 cm from each of the five scanning positions. This finer resolution

was chosen because of the density of trunks and foliage in the planted section, resulting in

the desire for a better point cloud representation of this forest type. Here, as with the twomanaged stands, five scans were performed and registered in the 0.1-ha area.

It was necessary to provide accurate registrations of multiple scans. In terrestrial laser

scanning, this is achieved using special blue targets provided by the instrument. Around

15–20 targets for each site were placed on stable media from the ground to the bottom

of the canopy, so as to produce the highest accuracy possible when registering multiple

scans, and to detect them from multiple positions. The targets were detected using

inbuilt high-resolution photography and the human eye, and subsequently the registra-

tion of multiple scans automatically ties in different point clouds according to theautomatically acquired targets with the same name identifiers. The targets were also

tied into the Universal Transverse Mercator (UTM) coordinate system through the use

of global positioning system (GPS) equipment, to best compare individual trees with

the forest inventory data. These were done with a Leica Total Station instrument at

three points for each site, calculating a position for up to an hour each to minimize

location error. The final error of the point clouds in the coordinate domain was only 3.8

and 4.3 mm for the managed and natural riparian forests, respectively. The final mean

registration errors (i.e. target position discrepancies) for managed and natural riparianforests were 7.2 and 5.5 mm, respectively. Final registered point clouds are shown in

figure 1 and overlain on small footprint airborne lidar (not described in this study).

3.3 Terrestrial lidar data collection

Individual trees were identified from the UTM coordinates obtained from the forest

inventory campaign, and each was tagged accordingly. Trunk diameter and tree

height measurement techniques were similar to Hopkinson et al. (2004).

3.3.1 Diameter at breast height. Trunks were separated from the full scans, by

including only the ground returns up to approximately 1.5–2 m. Each individual tree

trunk was identified, tagged and fitted with a cylinder centred at 1.4 m from the ground.

This was similar to the cylindrical least squares regression performed by the software in

Hopkinson et al. (2004). Stems that had too few points for the DBH cylinder regression

were omitted from the stem selection. This was the case when the tree had less than

around 20–30 points, and an arc segment coverage of less than a quarter.



3.3.2 Tree height. The horizontal area around each tree trunk was clipped from the

point cloud. The minimum elevation was the lowest visible portion of the tree trunk.

The highest elevation was the highest point in the extracted point cloud representing

the specific tree. If the tree crown was not distinguishable from overhead, in the x–y

plane (usually a crown having fewer than around 500 points), then no tree heightvalue was recorded.

3.3.3 Crown radius. Each tree crown that was distinguishable in the x–y plane was

considered also for recording the crown radius. This was done by taking four radii from

each tree from the centre of the trunk, or from the centre position of the multiple trunks.The radii was measured in the north, south, east and west from the centre trunks to

either the furthest crown return or the point of inflection between two crowns.

4. Results and discussion

The detection of individual tree attributes depends largely on the density of the

trunks, and the amount of branch and leafy material present at the site. At the

managed riparian forest, where there was little undergrowth, all of the 166 treeswere detected with the ground scanner. The natural riparian forest not only contained

dense nettles in the canopy undergrowth, but the woody vegetation was also signifi-

cant at these and all levels of the forest canopy. Even with five scans in a 0.1-ha area,

not all of the trees were detected. In fact, only 81% of the crowns and 60% of the stems

(considering multiple stems per crown) were detected. This resulted in a stem density

of 1410 trunks/ha determined from the ground-scanning technique, as opposed to the

actual 2340 trunks/ha determined in the field. In comparison, Hopkinson et al. (2004)

reported 95% of trees located in a mature red pine plantation with an original stem densityof 661 stems/ha, and 100% of trees located in a deciduous stand with a stem density of 465

stems/ha. Watt and Donoghue (2005) reported that 10 out of 12 tree trunks in a 0.02-ha

plot were clearly visible from two scans placed 30 m away from the plot.

4.1 Diameter at breast height

The agreement of the two methods determining DBH, tree height and crown radius

will be assessed using the method described by Bland and Altman (1986), with

resulting plots shown in figure 2, and plot statistics in table 1. The results indicatethat there is a very good correspondence between field-measured and lidar-derived

DBH for both the managed and the natural riparian forests. There is a very small

positive mean bias shown in figure 2 and table 1, of a few millimetres, and a range of

2–4 cm. Bias in DBH differences are similar for all size classes (figure 2(a)). It should

be noted that the gaps between 0.15 and 0.25 show a lack of trees in the intermediate

size group. Hopkinson et al. (2004) also reported little systematic bias between field-

and lidar-derived DBH, with a regression slope of 1.01, and an R2 of 0.85 for 128

trunks. Watt and Donoghue (2005) derived 12 DBH measurements from groundscanning, and reported an average difference of 1.5 cm and an R2 of 0.92. Henning

and Radtke (2006) derived 28 DBH measurements from ground scanning with a

reported mean bias of around 5 cm. Tansey et al. (2009) determined DBH from

Corsican pines in a stand of 1031 stems/ha, with an mean error of 19–37 cm.

These results show that the semi-automated method of extracting the trunk dia-

meters from ground scanning is successful from diameters as low as 3 cm. One reason

why the field- and lidar-derived DBH measurements have such high consistency is



that the scanner was not affected by registration errors that may have plagued otherscanner point collection studies. This was explicitly taken care of by rigorous milli-

metre precision in acquiring the target positions, resulting in few discontinuities in the

cylinders of the measured trunks.

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Figure 2. Difference plots of field-measured and lidar-derived estimates of DBH (a), treeheights (b) and crown radii (c), for the managed (1) and natural (2) riparian forests. The meanbias is given in the plots (m) as well as the second standard deviation limits (2s).

Table 1. Statistics of the difference between field- and ground-scanning derived DBH, treeheight (H) and crown radius (CR).

Mature Natural

DBH (m) H (m) CR (m) DBH (m) H (m) CR (m)

Mean 0.004 1.61 0.28 0.003 1.82 0.58Standard error of mean 0.001 0.26 0.05 0.001 0.36 0.05Two standard deviations 0.012 3.93 0.69 0.019 5.35 0.75

The mean and the standard deviation are graphically represented in figure 2.



The basal areas determined for the managed riparian forest were similar for ground

scanning (26.2 m2/ha) and forest inventory (27 m2/ha) data sets. This is because the

number of trees detected was the same for both methods, and the correspondence

between the two was high. Determining the basal area for the natural riparian forest

was less successful using the ground-scanning method, mainly because of its inabilityto identify all the trunks in the site. This resulted in a basal area of 71.8 m2/ha from the

ground scanning, as opposed to the true value of 123.2 m2/ha.

4.2 Tree height

Resulting tree height differences obtained from forest inventory data and from the

ground-scanning technique is presented in figure 2(b) and table 1. Here there is also a

strong correspondence between the methods, but there is also an evident positive bias

in heights estimated for both the managed and the natural riparian forest, with a

stronger mean bias in the natural forest (1.82� 0.32 m), and a range in errors of 10 m.This is more likely because of the ground-scanning method underestimating the total

tree height. There is also a clear evidence of a positive bias for larger trees, especially

those greater than 20 m in height.

The variation in the height correspondence may first be attributed to the random

error in measuring tree heights in the field using the inclinometer. This error may have

been slightly higher in the natural forest as a result of the increased difficulty in

determining the tops of the crowns. The tree heights may be underestimated from

the ground scanner and can be attributed to the nature of the pulse reflection. Becauseof the fact that the scanner is on the ground shooting upwards, there is inevitable

obstruction from the foliage in the lidar’s field of view. This results in more points

being returned from the bottom of the canopy, with less making it to the top.

Therefore, the taller the tree, and the denser the canopy, the less information will be

returned from the upper canopy, and the lower the probability of determining the

crown tips. Hopkinson et al. (2004) also reported an underestimation of lidar-derived

tree heights of around 7–8% using ground scanning.

4.3 Crown radius

Crown radius comparisons between field- and lidar-derived values have not been

explicitly made in previous studies, and are presented in figure 2(c) and table 1. These

results also show a notable correspondence between the two data collection techni-

ques, although again with evidence of positive bias, with a mean bias for the managed

and natural forest of 0.28� 0.5 and 0.58� 0.5 m, respectively. Again there is evidenceof a larger bias for larger crown radii. The ground-scanning method may under-

estimate crown radii, and this may lie in the difficulty in determining individual crown

edges, especially when the crowns encroach deep into another crown.

4.4 Allometric relationships

Allometric relationships in forestry and ecology are important results from any forest

inventory data set. Allometric power laws were determined for both field forest

biometrics and for lidar-derived biometrics, and are shown in table 2. It is clear that

for the managed riparian forest, the scaling exponents (the slope) as well as the R2

values are not much different for the field- and lidar-derived regressions. The crown

radius and tree height power law showed the largest differences for the managed



forest, with a 5% smaller scaling exponent, and 0.04 smaller R2. The scaling exponents

for the natural forest are significantly different for field- and lidar-derived regressions.

For the height and DBH power law, the scaling exponent decreases from 0.71 (field)

to 0.49 (lidar). This means that the lidar-derived allometric relationship no longer

follows elastic similarity but a constant stress (see McMahon (1973)). Elastic similar

allometric relationships have a scaling exponent of around 2/3, and are resistant totree buckling. Constant stress is where the scaling exponent reaches around 1/2. The

crown radius and height power law also shows a significant change in the scaling

exponent from 0.62 (field) to 0.79 (lidar). The R2 values are also lower for the lidar-

derived allometries, with decreases of up to 0.27. This difference in the lidar-derived

allometric regressions and scaling exponents at the natural riparian forest shows the

inability to accurately develop regressions from lidar data. This may mainly be due to

the number of trees detected with the scanner, as discussed previously. The study

by Henning and Radtke (2006) is one of the few to derive tree height and DBHallometries from field and ground-scanning measurements of Liriodendron tulipifera

and to calculate a difference in slope of 11% between the scan- and field-derived

allometries.

5. Conclusion

Terrestrial laser scanning is a relatively new technique that has seen recent use in

extracting forest structural parameters such as trunk diameter and tree height. Its

potential, however, has not been fully explored in forested ecosystems, especially in

riparian zones. This study has sought to further demonstrate the application of

ground-scanning techniques, by attempting to extract forest biometrics from thisimportant ecosystem.

Ground-based laser scanning has been successful at determining trunk diameters,

tree heights and crown radii from both managed and natural riparian forests on the

banks of the Garonne River in Southern France. Lidar-determined trunk diameters

were very close to the forest inventory values, with mean biases of 0.3–0.4 cm and a

range of 2–3 cm. Lidar-determined tree heights and crown radii were also close to the

forest inventory values, with some bias most likely on the part of the lidar-derived

Table 2. Allometric relationships fitted to the field measurements and lidar measurements inthe managed and natural riparian forests, for total tree height (H), diameter at breast height (D)

and crown radius (CR) (in m).

Field Lidar

a b R2 a b R2

Managed riparian forest H ¼ bDa 0.88 64.12 0.98 0.84 56.83 0.98CR ¼ bDa 0.60 7.81 0.94 0.63 7.55 0.96CR ¼ bHa 0.68 0.46 0.91 0.75 0.36 0.95

Natural riparian forest H ¼ bDa 0.71 33.88 0.62 0.49 27.93 0.58CR ¼ bDa 0.44 5.12 0.79 0.41 4.22 0.53CR ¼ bHa 0.62 0.58 0.53 0.79 0.30 0.42

For each allometric regression, the scaling exponent (a) and the intercept (b), as well as R2 areoffered, and are calculated using the reduced major axis regressions (Niklas 2004). All regres-sions have p , 0.0001, except for the two lidar-derived crown radius natural forest allometries,which have p ¼ 0.0005.



metrics. This was because of the difficulty in determining the apex of trees from

upward-looking scans, and the inability to determine the true edge of a crown.

However, mean biases were less than 2 m for the tree heights, and less than 0.6 m

for the crown radii. However, the ground scanner has not been as successful in

detecting all of the trees present on the natural riparian forest site. In fact, onlyaround 60% of the individual trunks were detected, and around 80% of the trees.

This affects the overall estimate of the forest, such as the stem density and basal area.

This has also affected the allometric regressions determined from the field and from

the scanner for the natural riparian forest. More research needs to be done in complex

forests of high stem densities to determine whether there is a systematic underestima-

tion of trees captured for a certain scanning resolution.

Acknowledgements

This research was supported by the British Society for Geomorphology (BSG) and

from the William Vaughan Lewis and Phillip Lake Funds. This study was made

possible by the valuable help of Pr. Keith Richards, Dr. James Brasington and

Dr. Etienne Muller. This research was also made possible by the use of the Leica

Geosystems HDS 3000 scanner belonging to the Geography Department of the

University of Cambridge.

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Documents

Remote Sensing Letters Evaluating forest biometrics …aantonar/Antonarakis2010b.pdfEvaluating forest biometrics obtained from ground lidar in complex riparian forests Alexander S