1. Exploiting fullwaveform lidar signals to estimate
timber volume and above-ground biomass of
individual trees
Tristan Allouis1 , Sylvie Durrieu1 Cédric Véga2
Pierre Couteron3
1 Cemagref/AgroParisTech, UMR TETIS, Montpellier, France
2 French Institute of Pondicherry, Pondicherry, India
3 Institut de Recherche pour le Développement, UMR AMAP, Montpellier, France
2011 IEEE IGARSS, Vancouver, Canada
1/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

2. Introduction: Context
Why assessing forest biomass?
Estimating forest productivity and carbon sequestration rate
Defining strategies for sustainable forest management and
climate change mitigation
How?
Through allometric equations using field-measured trunc
diameter at breast height (DBH) → Cost and assess issues
Through remote sensing techniques → Do not give access to
the DBH
2/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

3. Introduction: Background
Lidar technique overview
Light detection and ranging
1 Emission/reception of laser pulses
2 Signal processing
3 Signal and echoes geo-positioning
Advantages:
High resolution products
(several pt/m2 )
Ground echoes under the canopy
3/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

4. Introduction: Background
State of the art
3D information derived from lidar data:
Height, basal area, volume (direct
or indirect methods)
Topography under cover
Scope:
Timber inventory and management
Habitat monitoring
Ecosystem modelling
4/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

5. Introduction: Aim of the study
Questions
Can other tree metrics replace
DBH in allometric equations?
Can full-waveform signals improve
volume/biomass estimates?
What is the accuracy of such
estimates at tree level?
5/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

6. Material: Study site
Study area
Located in the French Alps
(mountainous)
Planted with Black Pine
Field data
6 circular plots of 15 m
radius (61 trees)
Tree DBH, total height,
crown base height
6/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

7. Material: Study site
Reference Volume
Equation by the French Institute for Agricultural Research for
Black Pine within France (C=trunc circonference; H=total height):
Volume = 34111.14 + 0.020833846 · H · C 2 − 1486.2307 · C +
2.2695012·C ·H +15.664201·C 2 −56.250923·H −0.0061317691·H 2
Reference Biomass
Equation by Gil et al. (2011) for Black Pine within Spain:
Biomass = 0.6073 · DBH 2 − 5.0998 · DBH − 23.729
Gil, Blanco, Carballo, Calvo, 2011. Carbon stock estimates for forests in the
Castilla y León region, Spain. A GIS based method for evaluating spatial distribution
of residual biomass for bio-energy, Biomass and Bioenergy, vol. 35, pp. 243-252
7/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

8. Material: Lidar data
Characteristics
Small-footprint size ( 25 cm)
Density = 5 shots/m2
⇒ Sample rate of 98% per surface unit
2 types of lidar data
Canopy Height Model (CHM):
classical lidar data derived from
discrete returns
Full-Waveform lidar signals
8/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

9. Method: Deriving metrics from the CHM
CHM metrics
Segmentation of individual trees
(Véga and Durrieu, 2011) and
extraction of:
Total tree height (HtCHM )
Crown projected area (AcrownCHM )
Tree bounding volume
(BVCHM = AcrownCHM · HtCHM )
Véga, Durrieu, 2011. Multi-level filtering segmentation to measure individual tree
parameters based on Lidar data: application to a mountainous forest with
heterogeneous stands, International Journal of Applied Earth Observations and
Geoinformation 13, 646–656.
9/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

10. Method: Deriving metrics from full-waveform lidar signals
Method
Aggregation of signals falling inside
modeled tree crowns ⇒ One
aggregrated signal corresponds to
one individual tree
Vegetation profile calculation
(correction of signal attenuation,
more details in Allouis et al. 2010 )
Allouis, Durrieu, Cuesta, Chazette, Flamant, Couteron, 2010. Assessment of tree
and crown heights of a maritime pine forest at plot level using a fullwaveform
ultraviolet lidar prototype, International Geoscience and Remote Sensing Symposium
(IGARSS), pp. 1382-1385
10/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

11. Method: Deriving metrics from full-waveform lidar signals
FW metrics
Curve integral (ISIG , IPROF ,
Aggregated waveform Vegetation profile
I2SIG , I2PROF ) Power Density
Ratio beween I and ground
component integral
(RSIG , RPROF )
Maximum signal amplitude
except ground (MaxSIG )
Crown base height
(HcrownPROF )
Height of maximum profile
Range Range
amplitude except ground
(HmaxPROF )
11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

12. Method: Deriving metrics from full-waveform lidar signals
FW metrics
Curve integral (ISIG , IPROF ,
Aggregated waveform Vegetation profile
I2SIG , I2PROF ) Power Density
Ratio beween I and ground
component integral
(RSIG , RPROF )
Maximum signal amplitude
except ground (MaxSIG )
Crown base height
(HcrownPROF )
Height of maximum profile
Range Range
amplitude except ground
(HmaxPROF )
11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

13. Method: Deriving metrics from full-waveform lidar signals
FW metrics
Curve integral (ISIG , IPROF ,
Aggregated waveform Vegetation profile
I2SIG , I2PROF ) Power Density
Ratio beween I and ground
component integral
(RSIG , RPROF )
Maximum signal amplitude
except ground (MaxSIG )
Crown base height
(HcrownPROF )
Height of maximum profile
Range Range
amplitude except ground
(HmaxPROF )
11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

14. Method: Deriving metrics from full-waveform lidar signals
FW metrics
Curve integral (ISIG , IPROF ,
Aggregated waveform Vegetation profile
I2SIG , I2PROF ) Power Density
Ratio beween I and ground
component integral
(RSIG , RPROF ) HmaxPROF
MaxSIG
Maximum signal amplitude
HcrownPROF
except ground (MaxSIG )
Crown base height
(HcrownPROF )
Height of maximum profile
Range Range
amplitude except ground
(HmaxPROF )
11/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

15. Method: Building estimation models
Process
Building volume and biomass estimation models:
1 Selection of significant metrics (stepwise algorithm)
2 Construction of final models (10 subsamples for
calibration/validation)
3 Comparision of model performance (for CHM-only, CHM+FW
and benchmark models)
12/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

16. Results: Replacing DBH in allometric equations
→ Strong relationship
between DBH and crown
projected area.
Perspectives
⇒ Using crown area in
traditional DBH models
⇒ Building new models
with other metrics
West, Enquist, Brown, 2009. A general quantitative theory of forest structure and
dynamics, Proceedings of the National Academy of Sciences of the United States of
America, vol. 106, pp. 7040-7045
13/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

17. Results: Estimation models
Metrics selected in linear models
Benchmark
Volume and biomass: BVtrunkREF , DBHREF , HtREF
CHM-only
Volume: BVcrownCHM , HtCHM , AcrownCHM
Biomass: BVcrownCHM , HtCHM
CHM+FW
Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM
Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF
Volume Biomass
AdjR2 Error AdjR2 Error
Benchmark 1 1% 1 8%
CHM-only 0.93 15 % 0.87 30 %
CHM+FW 0.95 17 % 0.91 25 %
14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

18. Results: Estimation models
Metrics selected in linear models
Benchmark
Volume and biomass: BVtrunkREF , DBHREF , HtREF
CHM-only
Volume: BVcrownCHM , HtCHM , AcrownCHM
Biomass: BVcrownCHM , HtCHM
CHM+FW
Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM
Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF
Volume Biomass
AdjR2 Error AdjR2 Error
Benchmark 1 1% 1 8%
CHM-only 0.93 15 % 0.87 30 %
CHM+FW 0.95 17 % 0.91 25 %
14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

19. Results: Estimation models
Metrics selected in linear models
Benchmark
Volume and biomass: BVtrunkREF , DBHREF , HtREF
CHM-only
Volume: BVcrownCHM , HtCHM , AcrownCHM
Biomass: BVcrownCHM , HtCHM
CHM+FW
Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM
Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF
Volume Biomass
AdjR2 Error AdjR2 Error
Benchmark 1 1% 1 8%
CHM-only 0.93 15 % 0.87 30 %
CHM+FW 0.95 17 % 0.91 25 %
14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

20. Results: Estimation models
Metrics selected in linear models
Benchmark
Volume and biomass: BVtrunkREF , DBHREF , HtREF
CHM-only
Volume: BVcrownCHM , HtCHM , AcrownCHM
Biomass: BVcrownCHM , HtCHM
CHM+FW
Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM
Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF
Volume Biomass
AdjR2 Error AdjR2 Error
Benchmark 1 1% 1 8%
CHM-only 0.93 15 % 0.87 30 %
CHM+FW 0.95 17 % 0.91 25 %
14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

21. Results: Estimation models
Metrics selected in linear models
Benchmark
Volume and biomass: BVtrunkREF , DBHREF , HtREF
CHM-only
Volume: BVcrownCHM , HtCHM , AcrownCHM
Biomass: BVcrownCHM , HtCHM
CHM+FW
Volume: BVcrownCHM , AcrownCHM , I2SIG , HtCHM
Biomass: I2SIG , BVcrownCHM , AcrownCHM , HtCHM , RPROF
Volume Biomass
AdjR2 Error AdjR2 Error
Benchmark 1 1% 1 8%
CHM-only 0.93 15 % 0.87 30 %
CHM+FW 0.95 17 % 0.91 25 %
14/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

22. Results: Estimation models
q q
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Estimation error (%)
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Benchmark CHM CHM+FW Benchmark CHM CHM+FW
Volume estimation Biomass2 estimation
15/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

23. Conclusion
Crown area is a good predictor of DBH
Tree bounding volume (height x crown area) is one of the
most efficient lidar metric for volume and biomass estimation
Slight improvement using FW lidar metrics in biomass
estimation models but no improvement in volume estimations
Approach limited to monospecific and single-storey forests
Future work: evaluating FW metrics worth at plot level
16/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

24. Thank you for your attention
17/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals

25. Exploiting fullwaveform lidar signals to estimate
timber volume and above-ground biomass of
individual trees
Tristan Allouis1 , Sylvie Durrieu1 Cédric Véga2
Pierre Couteron3
1 Cemagref/AgroParisTech, UMR TETIS, Montpellier, France
2 French Institute of Pondicherry, Pondicherry, India
3 Institut de Recherche pour le Développement, UMR AMAP, Montpellier, France
2011 IEEE IGARSS, Vancouver, Canada
18/18 Tristan Allouis, S. Durrieu, C. Véga, P. Couteron Estimation of individual tree biomass using lidar signals