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Nicolas picard
- 1. Allometric equations forbiomass estimation in central African rain forests: state of the art and challenges IUFRO 2014 World Congress Salt Lake City, USA, October 511, 2014 Forests and climate change Quantifying uncertainty in forest measurements and models Nicolas Picard∗ (nicolas.picard@cirad.fr), 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
- 2. REDD+: Decision 4/CP.15(CoP 15, Copenhagen, 2009) 2006 IPCC Guidelines IUFRO 2014 Allometric equations Friday Oct 10th 2 / 14
- 3. Allometric equations incentral Africa 5 studies in central Africa published since 2010 819 trees measured }unpublished data }published data mjhe n trees IUFRO 2014 Allometric equations Friday Oct 10th 3 / 14
- 4. Chain of errorpropagation IUFRO 2014 Allometric equations Friday Oct 10th 4 / 14
- 5. Inventory data M'Baïki permanentsample 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) IUFRO 2014 Allometric equations Friday Oct 10th 5 / 14
- 6. Biomass equations 4 biomassequations 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 IUFRO 2014 Allometric equations Friday Oct 10th 6 / 14
- 7. Error propagation Monte Carlomethod for: measurement error error due to the uncertainty on the model coecients residual error of the model Error due to the model choice: 1 Models are considered equally likely 2 Or Bayesian model averaging (BMA) is used to assign dierent weights to the 4 models No tree biomass data available at M'Baïki ¯ Training data set for BMA: African data from Chave et al. dataset (n = 1429) IUFRO 2014 Allometric equations Friday Oct 10th 7 / 14
- 8. Biomass in the24 plots at M'Baïki according to 4 biomass equations 5 10 15 20 100200300400500 Plot rank (basal area) Biomass(tonneha−1 ) Chave et al. (2014) Henry et al. (2010) Djomo et al. (2010) Ngomanda et al. (2014) large error due to the model choice if the plot × model interaction is null, this error has no impact on the estimation of the emission factor IUFRO 2014 Allometric equations Friday Oct 10th 8 / 14
- 9. The dierence ofbiomass between disturbed and undisturbed plots depends on the biomass equation Source Df Sum Sq Mean Sq F value p-value model 3 391 484 130 495 175.426 0.001 plot type 1 512 373 512 373 688.790 0.001 model × plot type 3 42 415 14 138 19.006 0.001 residuals 88 65461 744 IUFRO 2014 Allometric equations Friday Oct 10th 9 / 14
- 10. Estimates of theemission factor for the dierent biomass equations 50100150200250300350 Biomass model Emissionfactor(tonneha−1 ) Error sampling measurement coefficients residual Chave Henry Djomo Ngomanda IUFRO 2014 Allometric equations Friday Oct 10th 10 / 14
- 11. Combining the dierentbiomass equations to get a single estimate of the emission factor50100150200250300 Biomass weights Emissionfactor(tonneha−1 ) Error model sampling measurement coefficients residual 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 IUFRO 2014 Allometric equations Friday Oct 10th 11 / 14
- 12. Conclusions At M'Baïki, emissionfactor 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 coecients (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/) IUFRO 2014 Allometric equations Friday Oct 10th 12 / 14
- 13. Thanks for your attention Thisstudy 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 IUFRO 2014 Allometric equations Friday Oct 10th 13 / 14
- 14. Studies on allometricequations in central Africa published since 2010 Map showing the locations of the studies Djomo et al. (2010) Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For Ecol Manage 260:1873-1885 Ebuy Alipade et al. (2011) Biomass equation for predicting tree aboveground biomass at Yangambi, DRC. Journal of Tropical Forest Science 23:125-132 Dorisca et al. (2011) Établissement d'équations entre le diamètre et le volume total de bois des arbres, adaptées au Cameroun. Bois For Trop 65:87-95 Fayolle et al. (2013) Tree allometry in Central Africa: Testing the validity of pantropical multi-species allometric equations for estimating biomass and carbon stocks. For Ecol Manage 305:29-37 Ngomanda et al. (2014) Site-specic versus pantropical allometric equation: Which option to estimate the biomass of a moist central African forest? For Ecol Manage 312:1-9 IUFRO 2014 Allometric equations Friday Oct 10th 14 / 14