Nicolas picard
Transcript of Nicolas picard
Allometric equations for biomass estimation in centralAfrican rain forests: state of the art and challenges
IUFRO 2014 World Congress
Salt Lake City, USA, October 5�11, 2014
Forests and climate changeQuantifying uncertainty in forest measurements and models
Nicolas Picard∗ ([email protected]), Matieu Henry, Noël Fonton∗,Josiane Kondaoule∗, Adeline Fayolle∗, Luca Birigazzi, Gaël Sola, Anatoli
Poultouchidou, Carlo Trotta, Hervé Maïdou∗
∗ Regional REDD+ project of the Forests Commission of Central Africa
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REDD+: Decision 4/CP.15 (CoP 15, Copenhagen, 2009)2006 IPCC Guidelines
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Allometric equations in central Africa
5 studies in
central Africa
published since
2010
819 trees
measured
}unpublished data}published datamjhe n trees
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Inventory dataM'Baïki permanent sample plots in the Central African Republic
40 ha of permanent plots monitored since 1982
control plots, logged plots, logged + thinned plots, perturbation by
�re
data of 1987 (after all treatments): dbh, species (→ wood density)
twelve 1-ha plots (pseudo-replicates) of undisturbed forest
twelve 1-ha plots (pseudo-replicates) of disturbed forest
Emission factor = (biomass of undisturbed plots)
− (biomass of disturbed plots)
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Biomass equations4 biomass equations with the datasets used to �t the models
Author Type Model n
Chave et al. (2014) pantropical B = f(D,H, ρ)H = f(D,E)
4004
Ngomanda et al. (2014) local
(northeastern
Gabon)
B = f(D, ρ) 101
Djomo et al. (2010) local (southern
Cameroon)
B = f(D, ρ) 71
Henry et al. (2010) local (Ghana) B = f(D) 42
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Error propagation
Monte Carlo method for:I measurement errorI error due to the uncertainty on the model coe�cientsI residual error of the model
Error due to the model choice:1 Models are considered equally likely2 Or Bayesian model averaging (BMA) is used to assign di�erent
weights to the 4 models§ No tree biomass data available at M'Baïkiå Training data set for BMA: African data from Chave et al. dataset
(n = 1429)
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Biomass in the 24 plots at M'Baïki according to 4biomass equations
5 10 15 20
100
200
300
400
500
Plot rank (basal area)
Bio
mas
s (t
onne
ha−1
)
Chave et al. (2014)Henry et al. (2010)Djomo et al. (2010)Ngomanda et al. (2014)
Z large error due to
the model choice
Z if the plot ×model interaction
is null, this error
has no impact on
the estimation of
the emission factor
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The di�erence of biomass between disturbed andundisturbed plots depends on the biomass equation
Source Df Sum Sq Mean Sq F value p-value
model 3 391 484 130 495 175.426 < 0.001plot type 1 512 373 512 373 688.790 < 0.001model × plot type 3 42 415 14 138 19.006 < 0.001residuals 88 65 461 744
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Estimates of the emission factor for the di�erent biomassequations
5010
015
020
025
030
035
0
Biomass model
Em
issi
on fa
ctor
(to
nne
ha−1
)Error
samplingmeasurementcoefficientsresidual
Chave Henry Djomo Ngomanda
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Combining the di�erent biomass equations to get a singleestimate of the emission factor
5010
015
020
025
030
0
Biomass weights
Em
issi
on fa
ctor
(to
nne
ha−1
)
Error
modelsamplingmeasurementcoefficientsresidual
Equal BMA
Weights
Model Equal BMA
Chave et al. 0.25 0.152
Henry et al. 0.25 0.001
Djomo et al. 0.25 0.639
Ngomanda et al. 0.25 0.207
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Conclusions
At M'Baïki, emission factor from intact to degraded forest (logging
+ thinning) is approximately 150 tonne ha−1, but with a very large
uncertainty
The choice of the allometric equation is the largest source of error
(40% of the square error) when estimating the emission factor
1-ha plot sampling (30%) and the uncertainty on the model
coe�cients (20%) are also important sources of errors
Improving the choice of the allometric equation will require
additional tree biomass measurements
Data base on allometric equations: Globallometree
(http://www.globallometree.org/)
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Thanks for your
attention
This study was supported
by the regional REDD+
project of the COMIFAC �
GEF trust fund grant n◦
TF010038 � World Bank
project n◦ P113167
We thank
for access to the M'Baïki
data base
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Studies on allometric equations in central Africapublished since 2010 Map showing the locations of the studies
Djomo et al. (2010) Allometric equations for biomass estimations inCameroon and pan moist tropical equations including biomass data fromAfrica. For Ecol Manage 260:1873-1885
Ebuy Alipade et al. (2011) Biomass equation for predicting treeaboveground biomass at Yangambi, DRC. Journal of Tropical ForestScience 23:125-132
Dorisca et al. (2011) Établissement d'équations entre le diamètre et levolume total de bois des arbres, adaptées au Cameroun. Bois For Trop
65:87-95
Fayolle et al. (2013) Tree allometry in Central Africa: Testing thevalidity of pantropical multi-species allometric equations for estimatingbiomass and carbon stocks. For Ecol Manage 305:29-37
Ngomanda et al. (2014) Site-specic versus pantropical allometricequation: Which option to estimate the biomass of a moist centralAfrican forest? For Ecol Manage 312:1-9
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