Moving on From Experimental Approaches to Advancing National
Systems for Measuring and Monitoring Forest Degradation Across Asia
Moving on From Experimental Approaches to Advancing National
Systems for Measuring and Monitoring Forest Degradation Across Asia
June 16-18, 2015 Bangkok Logging (Planned and Unplanned): Activity
Data & Emission Factors for an integrated, scalable system Dr.
Sandra Brown Senior Scientist Winrock International
Slide 2
Importance of selective logging to global carbon emissions
Gross emissions from timber producing countries significant but
poorly known at global scale Ranges from about 7% (e.g. Brazil and
Indonesia) to about 40% (e.g. Republic of Congo and Malaysia) of
deforestation emissions Under sustainable forest
managementemissions exceed regrowth for several decades or more and
not C neutral Very difficult to monitor activity data with remote
sensing imagery with a high degree of confidence Can obtain higher
resolution data but at added cost
Slide 3
Important direct drivers of degradation From Hosonuma et al.,
2012 Proportion of forest degradation drivers Tropical Asia:
Commercial timber production about 80-85% of total degradation
Latin America: Commercial timber production > 70% of total
degradation Africa: Fuel wood collection, charcoal production,
followed by timber production
Slide 4
4 Carbon dioxide Damage is not contiguous Timber extraction
decreases the stocks in live biomass and increases the stocks in
dead wood. -Skid trails -Roads -Landing decks Collateral damage
Wood products Estimating emissions from selective logging LIF LDF
ELE
Slide 5
Hybrid system for estimating emissions from logging Developed
an efficient and effective hybrid system based on: field data to
estimate carbon losses and Emission Factors volumes of timber
produced Activity Data high resolution satellite imagery for
infrastructure Activity Data
Slide 6
Estimating emissions for selective logging Logging components
that impact C emissions: Quantity of extracted timber Portions of
timber tree left in forest (crown, stump) Incidental damage to
surrounding trees (the increase in dead wood resulting from felling
and extraction) Logging infrastructure (skids, log landings, and
roads) Relate total selective logging carbon emissions to easily
measurable parameters Volume of timber extracted (m 3 ) > easier
to track Area of roads and decks and lengths of skid trails from
imagery
Slide 7
Strategies for estimating EFs Forest lands undergoing selective
logging activities must be identified. Sampling must take place
very soon after felling timber treesif possible while timber tree
is still in the forest Hard to assess damage if not sampled quickly
Avoids miscounting from regrowth Reduces uncertainty for key
parameters Sampling plans should be designed to meet a targeted or
required level of certainty and to meet international standard of
representative, unbiased, consistent, transparent, and
verifiable.
Slide 8
Gross Carbon losses or emission factor due to selective logging
are estimated as: EF (t C) = (ELE + LDF)*V T + sum[LIF*A i ] Where:
ELE = extracted log emissions (t C/m 3 extracted) LDF = logging
damage factordead biomass carbon left behind in gap from felled
tree and incidental damage (t C/m 3 extracted) V T = total volume
over bark extracted (m 3 ) LIF = logging infrastructure factordead
biomass carbon caused by construction of skid trails, roads, decks
(t C/ha) A i = area of infrastructure skid trails, roads and decks
(ha) Field data are collected from multiple logging gaps and skid
trails, and RS data for roads and decks( refer to Pearson et al.
2014 - http://iopscience.iop.org/1748-9326/9/3/034017/article )
http://iopscience.iop.org/1748-9326/9/3/034017/article Estimating
EF for selective logging
Slide 9
Extracted Log Emissions (ELE) Estimate volume extracted (volume
of a conical frustum) and convert to biomass =volume *species
specific wood density Apply the appropriate allometric equation to
the parameters of the felled tree (e.g. DBH, wood density) to
estimate aboveground biomass Biomass left in forest = biomass of
felled tree-biomass of volume extracted Conical Frustum (cone with
top sliced off)
Slide 10
Measure DBH of all surrounding trees fatally damaged due to
timber tree falling Includes snapped and uprooted trees Measure
broken branches from surrounding trees Estimate damaged biomass by
applying appropriate allometric equations for damaged trees Logging
Damage Factor (LDF)
Slide 11
Sum all dead biomass left in plot: Top+stump+pieces left in
forest (biomass of Timber Tree biomass of log) Incidentally damaged
trees (snapped + uprooted + broken branches) Repeat for all plots
and estimate mean LDF Logging Damage Factor (LDF)
Slide 12
Estimating Logging Infrastructure Factor (LIF) Use high
resolution imagery (e.g. RapidEye) to obtain deforested areas of
roads and landing decks (Activity Data) LIF is the C stock of
adjacent forest For skid trails Direct measurements of a sample of
trails including measures of length and width =Area (Activity Data)
Estimate biomass of damaged trees from DBH & species of damaged
trees that are snapped, broken, uprooted by skidder using
allometric equation
Slide 13
Example of estimating total emissions C emissions, t C/yr =
[vol x ELE)]+[vol x LDF]+[A I x LIF][vol x ELE)][vol x LDF][A I x
LIF] C = [(500*10) x 0.36] + [(500*10) x 1.05] + [23 x 190] +[19 x
110] C = 1,800 + 5,250 + 6,460 C = 13,510 t C ~ 49,537 tCO 2
Concession TBD Inc. constructs 19 ha of skid trails and 23 ha of
roads to harvest 10 m 3 /ha on 500 ha in 2013. No decks are built
as logs are piled alongside wide roads. Assumed factors: ELE= 0.36
t C/m 3 LDF= 1.05 t C/m 3 LIFroad= 190 t C/ha LIFskid= 110 t
C/ha