wood density presentation at forest day (cop13)

Where does uncertainty stem from?

The role of wood density in carbon pool assessments of tropical forests

Johannes Dietz 1 , Christian Wirth 1, Annette Freibauer 1, Joe N. Pokana 2

1 Max-Planck-Institute for Biogeochemistry, Jena, Germany2 Papua New Guinea Forest Research Institute, Lae, Papua New Guinea

Forest Day, COP 13, BaliDecember 8, 2007

Max-Planck-Institutefor Biogeochemistry Wood density implications for carbon pool assessments

ScopeScope

Accurate accounting of carbon stocks from tropical forests is also limited by uncertainties in estimates of carbon stock in forest

Crucial factor in the conversion of volume to mass

Highly variable important source for uncertainty

Look-up in databases if floristic data is available

http://www.worldagroforestrycentre.org/sea/Products/AFDbases/WD/ http://www.prosea.nl/

m3 tρ



MotivationMotivation

Errors may be caused by:

− Inadequate protocols for wood density determination− Inconsistency with standard terminology (incompatibility)− Low reliability of species identifications (e.g. common names)− Gaps in the data structure (missing values)− Issues in upscaling to forest stand or regional level

Recent studies reveal a substantial bias in upscalingprocedures to regional levels. It is suggested that the emission from the Brazilian Amazon alone could have been overestimated by at least 23 million tons of CO2eq. annually.

(Nogueira et al. 2007)



Related studiesRelated studies

Region Country

Borneo Malaysia King et al. 2005

Borneo Indonesia Slik 2006

Amazonia Brazil Fearnside 1997

Amazonia Baker et al. 2004

Central Amazonia Brazil Nogueira et al. 2005

Amazonia Brazil Nogueira et al. 2007

Neotropics Chave et al. 2006

Reference



Case studyCase study

Collaboration with the PNG Forest Research Institute

93 permanent 1 ha sample plots maintained by FRI offer a wealth of information on their forests

Full and repeated inventories of all trees Ø ≥ 10 cm

Floristic data available

Modeling approach to compensate for data gaps- Hierarchical model drawing on Bayesian inference



Papua New GuineaPapua New Guinea

Sour

ce:

JRC

/ M

PI-B

GC

2006

LowlandRainforest

PermanentSample Plot

Forest cover:76 %(34.6 million ha)

Lowland RF:58 %

93 PSPs activepredominantlyin lowlands

Source: FAO FRA 2005



Modeling approachModeling approach

Model drawing on Bayesian inferenceInherits predictive power from hierarchical structureSuitable for hierarchical structure of taxonomy

http://www.mobot.org/MOBOT/Research/APweb/welcome.html



Available dataAvailable data

Species

Genus

FamilyNo data

30,000 Trees

Species

GenusFamily Order

466 Species

Wood density known for the majority of speciesData gaps on species level through:- Missing species wood density data- Identification of trees only at genus level



Modeling effectModeling effect

Input of variable records from different databasesLinkage to superior taxonomic structure solidifies the estimate already on species level

Species

Measured Wood Density (kg m-3)

Mod

eled

Woo

d D

ensi

ty(k

g m

-3)



Modeling effectModeling effect

The effect of higher precision reduces uncertainty also on genus and family levelOnly extreme values appear critical

Genera

Averaged Wood Density (kg m-3)

Mod

eled

Woo

dD

ensi

ty(k

g m

-3)

Families



Genus levelGenus level

Model performance for all genera with > 3 speciesWith modeling, the precision of species level data predicted from genus improves while the effect on residuals is minimal.

R2 = 0.73

0

200

400

600

800

1000

0 200 400 600 800 1000

Species Wood Density (kg m-3)

Aver

age

Gen

us W

ood

Den

sity

(kg

m-3

) R2 = 0.71

0

200

400

600

800

1000

0 200 400 600 800 1000


Mod

eled

Gen

us W

ood

Den

sity

(kg

m-3

)



R2 = 0.43

0

200

400

600

800

1000

0 200 400 600 800 1000


Mod

eled

Fam

ily W

ood

Den

sity

(kg

m-3

)R2 = 0.52

0

200

400

600

800

1000

0 200 400 600 800 1000


Ave

rage

Fam

ily W

ood

Den

sity

(kg

m-3

)Family levelFamily level

Prediction from family level is less accurate but still significantly more precise.On family level average approach preferable?

R2 = 0.43



Forest type levelForest type level

Average vs. modeling approachMean (93 lowland rainforest plots): + 0.6 %Conservative lower limit: + 4.6 %

0.0

0.5

1.0

1.5

2.0

2.5

0 2 4 6 8 10

Average Approach Error (%)

Mod

elin

g Ap

proa

ch E

rror

(%

)



ConclusionsConclusions

Challenges− Dynamic taxonomy (e.g. synonyms)− Databases with inconsistent protocols and standards− Discrepancies between density measures (e.g. air dry density,

wood specific gravity, oven dry weight of green volume)

Perspectives− Knowledge on wood density can reduce the error of carbon pool

estimates from > 20 % even to < 2 %.− Smaller error bars generally result in higher conservative values.− Modeling approach to filling the gaps in wood density data

clearly preferable on the genus level.− Only basic capacities required and efforts very manageable.



• Johannes Dietz Joe N. Pokana− MPI-BGC, Jena, Germany PNG FRI, Lae, Papua New Guinea− [email protected] [email protected]

Thank You for Your AttentionThank You for Your Attention

wood density presentation at forest day (cop13)

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