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

IUFRO 2014 Allometric equations Friday Oct 10th 1 / 14

REDD+: Decision 4/CP.15 (CoP 15, Copenhagen, 2009)2006 IPCC Guidelines


Allometric equations in central Africa

5 studies in

central Africa

published since

2010

819 trees

measured

}unpublished data}published datamjhe n trees


Chain of error propagation


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)


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


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)


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


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


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


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


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/)


http://www.globallometree.org/

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


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


Education

Nicolas picard