In: Bretar F, Pierrot-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8 – Paris, France, September 1-2, 2009
ContentsKeyword index
Author index

ESTIMATING CARBON STOCKS OF CONIFEROUS WOODY CANOPY TREESUSING AIRBORNE LIDAR AND PASSIVE OPTICAL SENSER
Y. Nakai, F. Hosoi, K. Omasa

Graduate School of Agricultural and Life Sciences, The University of Tokyo, Yayoi 1-1-1, Bunkyo-ku,
Tokyo 113-8657, Japan- aomasa@mail.ecc.u-tokyo.ac.jp

KEY WORDS: Airborne LiDAR, Carbon stock, Japanese cedar, Japanese red pine, Passive optical remote sensing, Threedimensional
ABSTRACT:
We confirmed the effectiveness of airborne LiDAR and passive optical remote sensing techniques for estimating carbon stocks ofall tree organs for woody canopy two coniferous trees, Japanese red pine and Japanese cedar trees. Airborne 3-D LiDAR data andaerial photograph provided tree height and canopy area for each of the trees. Then, we examined the relationships between 1)
LiDAR-derived tree height and carbon stock 2) canopy area obtained from aerial photograph and carbon stock. In addition, wemade a multiplicative equation composed of both tree height and canopy area to predict the carbon stock, and examined theaccuracy by comparing between the predicted and field-based values. As a result, good correlations were observed between theLiDAR-derived tree height and carbon stock in both species, while correlations were worse between canopy area obtained fromaerial photograph and carbon stock in both species. The multiplicative equation offered better results than the ones of using treeheight or canopy area alone, where R2 and SE were 0.87 and 15.6 kg for Japanese red pine, and 0.85 and 7.1 kg for Japanesecedar. This shows the effectiveness of the combination of LiDAR and passive optical sensors for the carbon stock estimation.
.
1. INTRODUCTION

2. MATERIAL AND METHODS

Accurate estimation of carbon stocks in plants is important not
only to study photosynthetic characteristics but also toestimate global carbon budget affected by recent increase inatmospheric CO2 concentration that causes global changes.
Field based actual measurements give accurate carbon stock,
but the application is limited due to its laborious ordestructive nature. Recently, remote sensing techniques suchas LiDAR and passive optical sensors have been developed as
indirect methods that are capable of obtaining wide area
information efficiently. They have been also applied toestimating forest resources such as tree biomass, carbon
stocks and structural parameters (Nelson et al., 1988; Omasa
et al., 2000, 2002, 2003, 2007, 2008; Lefsky et al.,
2002;Næsset et al., 2004; Patenaude et al., 2004; Zheng et al.,
2004; Hosoi and Omasa, 2006, 2007, 2009; Nakai et al.,2009). In terms of the carbon stock estimation, theirtechniques have been applied to relatively limited tree species,
and the methods have not yet been sufficient for variousspecies. Thus, the carbon stock estimation method is required
to be applied for the other tree species and to be developed forthe various species. In addition, as measurement methods, thecombination of LiDAR and passive optical sensors is
considered to be effective.

2.1 Study site

In this study, we derived structural variables of Japanese redpine (Pinus densiflora Siebold & Zuccarini) from airborne
LiDAR and passive optical remote sensing images, andexamined the usefulness of the variables for the carbon stock
estimation. The carbon stock of Japanese red pine has hardly
been estimated by remote sensing based method, so thisspecies was selected in this study. In addition, for thecomparison between different tree species, similar
examination was also applied for Japanese Cedar(Cryptomeria japonica [L.f.] D. Don).

The study area was scanned in August 2005 by a LiDAR(ALTM 3100 DC, Optech Co.) mounted on a helicopter (AeroAsahi Co., Japan). The airborne LiDAR calculated thedistance to a target by the time-of-flight method, and had thefirst-pulse and last-pulse modes, in which the first and last
returned laser pulses were detected. The data of canopy
surface were received by first-pulse mode (FP-mode), so thedata of FP-mode were selected to generate an image of thewoody outer canopy at the following process. The laser
wavelength was 1,064 nm, and its repetition frequency was50,000 Hz. The scanning frequency was set to 20 Hz. The
scanning angle, the range and horizontal accuracy were 20.0°,

The study was carried out in a mixed plantation in Ibaraki
Prefecture, 40 km northeast of central Metropolitan Tokyo,
Japan. The dominant tree species were Japanese cedar(Cryptomeria japonica [L.f.] D. Don), Japanese red pine
(Pinus densiflora Siebold & Zuccarini), ginkgo (Ginkgobiloba Linnaeus), and Japanese zelkova (Zelkova serrata[Thunberg] Makino). From this site, 30 Japanese red pine and15 Japanese cedar trees were chosen for this experiment.
2.2 Direct measurement of tree height and DBH
We measured the heights of the selected 30 Japanese red pineand 15 Japanese cedar trees on the ground by a range finder(Lasertape FG-21-HA, RIEGL, Austria) with an accuracy of±5 cm. Then, Diameter at Breast Height (DBH) of each tree
was also measured directly in the site for calculation of carbon
stocks of trees. Circumferential lengths of each tree weremeasured by survey taping and DBH was obtained by dividing
the length by π.
2.3 Measurement using an airborne LiDAR system289

In: Bretar F, Pierrot-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8 – Paris, France, September 1-2, 2009
ContentsKeyword index
Author index±15 and 13cm, and flight speed and height were 50 km/h and400m, respectively. Together with the 3-D point cloud datacollection, aerial photographs of the area were also captured
as the data of passive optical remote sensing from 400m
above.

derived tree height and carbon stocks were obtained at bothspecies.

2.4 Tree height estimation from 3-D LiDAR data andcanopy areas from aerial photograph
A polygonal 3D-LiDAR image of the woody canopy within
the study area was generated by triangulating and smoothing
the FP-mode 3-D point cloud data. For the selected 30
Japanese red pine and 15 Japanese cedar trees, the heights
were determined detecting the tops. In the aerial photograph,each of the selected tree canopies was segmented along theircontours based on the Watershed algorithm (Wang et al.,2004). In this study, the area of each segment in each tree wasnamed as the canopy area and that was calculated by counting
the corresponding number of pixels in each of the segments.
2.5 Derivation of relationships between carbon stocks andvariables estimated from LiDAR data and aerial
photographFigure 1 Aerial photograph of a part of the study site obtainedby optical remote sensing.

Stem volume of each tree was calculated from the directly
measured DBH and the tree height using an allometricequation in each species (The Forest Agency in Japan, 1998).
The stem volume was converted into carbon stock of all treeorgans (stem, branch, foliage and root) using the stem to total
volume ratio and specific gravity obtained from thedestructive field measurement data in Cannel 1982, and fromthe carbon fraction, 0.5, as described in the report in Ministryof the environment in Japan, 2008. For both Japanese cedar
and Japanese red pine, we examined the relationships between(1) LiDAR-derived tree height and carbon stock (2) canopy
area derived from aerial photograph and carbon stock. In
addition to the above two relationships, we made amultiplicative equation to predict carbon stock using two
variables of canopy area and tree height, as follows.
Cp = a× CA b× H c

20.00

15.00

10.00

5.00

0.00

(1)

Figure 2 3D-LiDAR image generated from FP-mode 3D point
cloud data.

Where Cp is predicted carbon stock of all tree organs, CA is
canopy area obtained from aerial photograph, and H is
LiDAR-derived tree height. The coefficients of a, b and cwere determined by multiple regression analysis.
3. RESULTS AND DISCUSSION
Fig.1 is an aerial photograph of a part of the study site andFig.2 is the corresponding 3D-LiDAR image generated from3D point cloud data obtained by the airborne LiDAR. Theairborne LiDAR could offer precise 3-D model of the studyarea.
Fig.3 is the segmentation result in aerial photograph based on
the watershed algorithm. Each canopy is indicated as green
region and the boundaries of each canopy are represented as
black lines. Although there were some mis-segmented areas,
most of canopies were well segmented.
Fig.4 shows that the relationships between tree height
estimated using airborne LiDAR and the carbon stock of alltree organs of (a) Japanese red pine and (b) Japanese cedar.
Power functional relationships are shown in the figure. R2 andStandard Error (SE) of the relationships were respectively
0.85, and 15.1 kg for Japanese red pine and 0.68, and 21.0 kg
for Japanese cedar. Good correlations between the LiDAR-Figure 3 Segmentation result in aerial photograph based on thewatershed algorithm. Each canopy is indicated as green
region. Boundaries of each canopy are represented as blacklines.

290

In: Bretar F, Pierrot-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8 – Paris, France, September 1-2, 2009
Contents
Keyword index
Author index

(a)

(a)

500

600

4. 19y = 0. 0021x
R2 = 0. 85
SE = 15. 1kg

500
Carbon st ock ( kg)

Carbon st ock ( kg)

600400300200600

Carbon st ock ( kg)500

30. 00y = 5. 13x1. 22
R2 = 0. 63
SE = 29. 8kg

020406080

Canopy Area ( m2)

(b)
600

3. 55

y = 0. 014x
R2 = 0. 68
SE = 21. 0kg

y = 3. 29x1. 39
R2 = 0. 29
SE = 20. 8kg

500

400
300
200
10000. 00

2000

10. 00
20. 00Hei ght ( m)Carbon st ock ( kg)

(b)

300

100

10000. 00

400

400
300
200100

5. 00

10. 00
15. 00Hei ght ( m)

020. 00

0102030

Canopy Area ( m2)

Figure 4 Relationships between the tree height estimated using
airborne LiDAR and the carbon stock of all tree organs.
(a) Japanese red pine and (b) Japanese cedar.

Figure 5 Relationships between the canopy area calculated by
the aerial photograph and the carbon stock of all tree organs.
(a) Japanese red pine and (b) Japanese cedar.

Fig.5 shows the relationships between canopy area calculated
by aerial photograph and carbon stock of (a) Japanese red pineand (b) Japanese cedar. Power functional relationships werealso shown in the figure. R2 and SE of the relationships wererespectively 0.63 and 29.8 kg for Japanese red pine and 0.29
and 20.8 kg for Japanese cedar. In both species, correlations
between canopy areas and carbon stock were worse than thecases in Fig.4. It was also observed that canopy area ofJapanese red pine varied more widely than Japanese cedar.

800(a)

1:1

Fi el d ( kg)

600

400

200

Coefficients a, b and c in Eq.(1) for Japanese red pine wererespectively 10–2.21, 0.35 and 3.42, and for Japanese cedar
respectively 10–2.76, 1.12 and 3.29. Fig.6 shows thecomparisons of carbon stock between the predicted and fieldbased values of (a) Japanese red pine and (b) Japanese cedar.
R2 and SE of stem volume were 0.87 and 15.6 kg for Japanese
red pine, and 0.85 and 7.1 kg for Japanese cedar. In terms ofthe values of R2, it was shown that carbon stock can bepredicted more accurately from Eq.(1) composed of both treeheight and canopy area than the use of tree height or canopy
area alone.

Cp = 10- 2. 21× CA0. 35× H3. 42
R2 = 0. 87
SE = 15. 6kg

00200

800500(b)Cp = 10- 2. 76× CA1. 12× H3. 29
R2 = 0. 85
SE = 7. 1kg

Fi el d ( kg)400

It was shown in Fig.4 that LiDAR-derived tree height alone
can offer accurate estimation of the carbon stock. For details,
some errors were observed above height of 13m in Fig.4,
where the regression curves become steep. Because of thesteep curve, a little difference of tree height would havecaused the error of the carbon stock estimation.

400600Predi ct ed (kg)

1:130020010000100

200300Predi ct ed ( kg)

400500Figure 6 Comparisons of stem volume between the predicted
and field-based carbon stock. (a) Japanese red pine (b)Japanese cedar.

291

In: Bretar F, Pierrot-Deseilligny M, Vosselman G (Eds) Laser scanning 2009, IAPRS, Vol. XXXVIII, Part 3/W8 – Paris, France, September 1-2, 2009
Contents
Keyword index
Author index

Canopy area was chosen as a variable that represents
horizontal canopy structure and it seemed to perform well thecarbon stock estimation. However, even if trees have the sameamount of carbon stock, canopy area can have different values
depending on the 3-D structure, such as blanching pattern or
foliage distribution. This would explain poor correlation
between the canopy areas and the carbon stock. Due to thisreason, it seems to be difficult to estimate the carbon stock
accurately from the canopy area alone. In terms of species
difference, canopy area of Japanese red pine varied more
widely than Japanese cedar. This would mean that Japanese
red pine has more structural variability than Japanese cedar.

Næsset, E., Gobakken, T., Holmgren, J., Hyyppä, H., Hyyppä,J., Maltamo, M., Nilsson, M., Olsson, H., Persson, Å.,
Söderman, U., 2004. Laser scanning of forest resources: theNordic experience. Scandinavian Journal of Forest Research,19, pp.482-499.
Nelson, R., Krabill, W., Tonelli, J., 1988. Estimating forest
biomass and volume using airborne laser data. RemoteSensing of Environment, 24, pp.246-267.
Omasa, K., Akiyama, Y., Ishigami, Y., Yoshimi, K., 2000. 3D remote sensing of woody canopy heights using a scanninghelicopter-borne lidar system with high spatial resolution.
Journal of Remote Sensing Society of Japan, 20, pp.394-406.

At the result of multiple regression analysis, T-values of treeheight and canopy area were 7.19 and 2.16 for Japanese redpine, and 7.23 and 4.10 for Japanese cedar. T-value of treeheight is higher than that of canopy area for each species, so itshows that the tree height more affects the carbon stock
estimation than canopy area.

Omasa, K., Hosoi, F., Konishi, A., 2007. 3D lidar imaging fordetecting and understanding plant responses and canopy
structure. Journal of Experimental Botany, 58, pp.881-898.

4. CONCLUSION

Omasa, K., Qiu, G.Y., Watanuki, K., Yoshimi, K., Akiyama,Y., 2003. Accurate estimating of forest carbon stocks by 3-D
remote sensing of individual trees. Environmental Science &Technology, 37, pp.1198–1201.

It can be concluded that the proposed multiplicative equation
composed of tree height from 3-D LiDAR data and canopy
areas from aerial photograph is better for estimating carbon
stock of the present two coniferous trees than using the treeheight or canopy area alone. This shows the effectiveness ofthe combination of LiDAR and passive optical sensors forcarbon stock estimation. In the future, more works should beconducted for more species to well utilize the findings
obtained in the present study.

Omasa, K., Urano, Y., Oguma, H., Fujinuma, Y., 2002.
Mapping of tree position of Larix leptolepis woods andestimation of diameter at breast height (DBH) and biomass ofthe trees using range data measured by a portable scanninglidar. Journal of Remote Sensing Society of Japan, 22,
pp.550–557.

5. REFERENCES

Omasa, K., Hosoi, F., Uenishi, T.M., Shimizu, Y., Akiyama,Y., 2008. Three-dimensional modelling of an urban park andtrees by combined airborne and portable on-ground scanning
LIDAR remote sensing. Environmental Modeling andAssessment, 13, pp.473-481.

Cannell, M. G. R., 1982. World Forest Biomass and Primary
Production Data. Academic Press: London, pp.145-166.
Hobbs, R.J., Mooney, H.A. (Ed.), 1990. Remote sensing ofbiosphere functioning. Springer-Verlag, pp.312.

Patenaude, G., Hill, R.A., Milne, R., Gaveau, D.L.A., Briggs,BBJ., Dawson, T.P., 2004. Quantifying forest above groundcarbon content using LiDAR remote sensing. Remote Sensingof Environmet, 93, pp.368-380.

Hosoi, F., Omasa, K., 2006. Voxel-based 3-D modeling ofindividual trees for estimating leaf area density using highresolution portable scanning lidar. IEEE Transactions onGeoscience and Remote Sensing, 44, pp.3610-3618.

The Forest Agency in Japan. Timber Volume Table – EastJapan. 1998: Nihon Ringyo Chousakai, Tokyo.

Hosoi, F., Omasa, K., 2007. Factors contributing to accuracy
in the estimation of the woody canopy leaf-area-density
profile using 3D portable lidar imaging. Journal ofExperimental Botany, 58, pp.3464-3473.

Wang, L., Gong, P., Biging, G.S., 2004. Individual tree-crowndelineation and treetop detection high-spatial-resolution aerialimagery. Photogrammetric Engineering and Remote Sensing,70, pp.351-357.

Hosoi, F., Omasa, K., 2009. Estimating vertical plant area
density profile and growth parameters of a wheat canopy at
different growth stages using three-dimensional portable lidar
imaging. ISRPS Journal of Photogrammetry and RemoteSensing, 64, pp.151-158.

Zheng, D.L., Rademacher, J., Chen, J.Q., Crow. T., Bresee.
M., Le Moine, J., Ryu, S.R., 2004. Estimating abovegroundbiomass using Landsat 7 ETM+ data across a managed
landscape in northern Wisconsin, USA. Remote Sensing ofEnvironmet, 93, pp.402-411.

Lefsky, M.A., Cohen, W.B., Parker, G.G., Harding, D.J.,
2002. Lidar remote sensing for ecosystem studies. Bioscience,52), pp.19-30.
Ministry of the Environment, National Institute forEnvironmental Studies, Japan, 2008. “National greenhouse gas
inventory report of Japan.”
http://www-cger.nies.go.jp/publication/I084/i084.pdf(accessed 4 Apr. 2009), pp.7-10.
Nakai, Y., Hosoi, F., Omasa, K., 2009. Estimation ofconiferous tree biomass using airborne LiDAR and passive
optical remote sensing (in preparation).

292