Supplement 1. Estimating carbon emissions and its associated uncertainty using the Monte
Carlo simulation approach (taken from Lusiana et al., in prep.).

Net carbon loss equation:
Lusiana et. al (in prep) estimated carbon emissions in Tanjabar using the net carbon loss
approach with the following equation:
Δ Ct→t+1 = Σi(Ai,t+1 × Ci,t+1) - Σi(Ai,t × Ci,t) = Σi((Ai,t+1 - Ai,t) × Ci,t+1) + Σi(Ai,t ×( Ci,t+1 - Ci,t))
.......... (S1)
where Δ Ct→t+1 is net change in landscape carbon from time t to time t+1 (Mg), Ai,t+1 is
the area of land use i at time t+1(ha), Ai,t is the area of land use i at time t (ha), Ci,t+1 is
carbon stock density of land use i at time t+1 (Mg.ha-1) and Ci,t is carbon stock density
of land use i at time t (Mg.ha-1).

Calculating carbon emissions
Carbon emissions (in CO2 equivalent) were derived by multiplying net carbon loss with a
conversion factor of 3.67.

Data source
Estimating carbon emission required data of land cover/use maps and plot-level carbon
for each land cover/use category. Plot-level carbon data for Tanjabar were obtained from
the carbon database compiled by and stored at the World Agroforestry Centre, which are
based on various research studies that have been carried out by ICRAF and its partners1.
We used land cover/land use maps for years 2000 and 2009 interpreted from LandsatTM. The interpreted land cover maps have 11 land cover categories and spatial resolution
of 100 m.
1
http://db.worldagroforestry.org/

Methods to estimate uncertainty of carbon emission estimates
The methods to estimate uncertainty followed a Monte Carlo (MC) simulation analysis,
which is a statistical based method using a probabilistic sampling. Figure Supp. 1
describes the basic steps involved in the MC analysis. The overall aim of the MC
technique is to perturb (randomly vary) the land cover map with classification errors and
the plot-level carbon value of each land cover category with its standard error of the
mean and then to produce many realizations of carbon emission maps. Thus, the methods
assumed that errors in land cover classification and variation in plot-level carbon
measurements represent the uncertainty of carbon emission estimates. For the Tanjabar
study case, land use classification errors were generated from users’ accuracy of the 2009
land cover map (Table Supp 1) and were assumed to follow uniform distribution. Plotlevel carbon densities were generated following the normal distribution with variation
based from standard error of the mean (Table Supp. 2).

Outcome of the carbon emission uncertainty analysis in Tanjabar
The outcome of the uncertainty analysis was 10,000 maps of net landscape carbon loss
between 2000 and 2009 perturbed with errors. Using these maps, the average net carbon
loss of Tanjabar district was estimated at 35.4 Mg.C ha-1over the period of 9 years which
is equivalent to 129.8 Mg.CO2 eq.ha-1 or 14.2 Mg CO2 eq.ha-1.year-1.
Figure Supp. 1. Steps in estimating net landscape carbon loss and its associated uncertainty using
the Monte Carlo simulation approach.
Table Supp. 1-1. Error matrix: probability that pixel in classified land cover map of Jambi 2009 is classified as land use i when it is
land use j on the ground.
Users’
Ground truth points+
Classified
accuracy*
Map+
Fu
Fd
Rf
Co
Cf
Ac
Rm
Op
Sh
Ag
Set
Fu
0.78
0.17
0.00
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.78
Fd
0.01
0.93
0.03
0.01
0.00
0.00
0.02
0.00
0.00
0.00
0.00
0.93
Rf
0.01
0.03
0.73
0.01
0.01
0.01
0.08
0.09
0.01
0.01
0.04
0.73
Co
0.00
0.00
0.00
0.25
0.00
0.00
0.38
0.38
0.00
0.00
0.00
0.25
Cf
0.00
0.02
0.02
0.11
0.74
0.00
0.00
0.04
0.04
0.00
0.02
0.74
Ac
0.00
0.00
0.00
0.00
0.00
0.33
0.44
0.11
0.00
0.00
0.11
0.33
Rm
0.00
0.01
0.05
0.01
0.00
0.01
0.85
0.06
0.01
0.00
0.02
0.85
Op
0.00
0.00
0.03
0.00
0.00
0.00
0.06
0.85
0.01
0.00
0.02
0.85
Sh
0.04
0.00
0.02
0.06
0.02
0.00
0.02
0.04
0.75
0.00
0.06
0.75
Ag
0.03
0.09
0.04
0.01
0.06
0.03
0.03
0.16
0.07
0.39
0.10
0.39
Set
0.00
0.00
0.00
0.02
0.02
0.00
0.04
0.11
0.01
0.05
0.75
0.75
Note:
+
Fu: Undisturbed forest, Fd:Disturbed forest, Rf: Rubber agroforestry, Co: coconut/bettlenut agrofrestry, Cf: coffee agrofoerstry,
Ac: acacia plantation, Rm: rubber monoculture, Op: oil palm, Sh: shrub, Ag: agriculture; Set: settlement, cleared land, grass.
*
User’ accuracy refers to probability that classified land cover map of Jambi 2009 matches gorundtruth data.
Table Supp. 1-2. Plot level carbon average versus time average carbon stocks
Average carbon stocks a
Land cover/ use systems
(Mg.ha-1)
a
Undisturbed forest
214.2 (17.6)
Disturbed forest
151.0 (9.8)
Rubber agroforestry
91.2 (9.8)
Coconut agroforestry
64.2 (24.3)
Coffee agroforestry
31.0 (4.2)
Acacia plantation
38.2 (8.3)
Rubber monoculture
38.2 (7.4)
Oil palm monoculture
41.0 (5.9)
Shrub
47.7 (6.6)
Agriculture
3.5 (0.7)
Settlement, cleared land, grass
3.6 (0.7)
Value in brackets refers to standard error of the mean