Transcript PowerPoint-presentatie - The Forest Carbon Partnership Facility

Module 2.3 Estimating emission factors for forest
cover change: Deforestation
Module developers:
Sandra Brown, Winrock International
Lara Murray, Winrock International
Country example:
Guyana
All data courtesy of Winrock International and
Guyana Forestry Commission
V1, May 2015
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation) 1
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
Creative Commons License
Outline of country example
1. REDD+ development in Guyana
2. Approach to sampling and stratification in Guyana
3. Stratification by threat of deforestation
4. Collecting primary data field data for planning
5. QA/QC measures
6. Carbon stock data and emission factors
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
2
REDD+ development in Guyana
 Guyana is an example of a high forest cover, low
deforestation rate (HFLD) country.
 Since 2009, the Government of Norway has provided
performance-based finance to implement a low-carbon
development strategy (LCDS).
 Guyana is a World Bank FCPF pilot country.
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
3
Drivers of deforestation and degradation in
Guyana
Forest cover change 1990-2010
The national scope
 Deforestation:
● Mining—medium and large scale
● Infrastructure—roads, settlements
● Agriculture—permanent
● Fire from human actions
 Degradation:
Total forest loss by period
● Forestry—for timber production
● Mining—small scale
● Shifting cultivation
2009-2010
2005-2009
2000-2005
1990-2000
Non Forest 2009
● Fire from human actions
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
4
Stratification by threat using spatially
explicit land-use change modeling
Create
deforestation
threat map
Identify areas
with high
suitability for
deforestation
Identify key factors
impacting historical
deforestation patterns
Use spatially
explicit land-use
change model
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
5
Stratifying forest lands in Guyana
Factors in 2000:
●
Roads (main and secondary)
●
Rivers
●
Settlements
●
Townships (markets)
●
Eligible areas (mining and forestry
concessions, protected areas, state
forest, state land, Amerindian
areas)
Elevation
Eligible land
2000
Roa
ds
●
Forest species composition
●
Fire incidents per forest species
type
●
Elevation
●
Slope
●
Soil dominant class
Heuristic factors
Roads
Eligible
land
2000
Townships/
markets
Roads
Empirical factors
Elevation
Roa
ds
Elevati
on
Eligible land
2000
Townships/
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
markets
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
6
Stratification by threat of deforestation



GEOMOD analysis was used to
identify spatial patterns of
change in relation to drivers and
other factors and generate a
“threat map.”
Stratifying by “threat” allows for
estimating carbon stocks of
forests where changes have
occurred and likely to occur in
future.
This reduces sampling effort
while maintaining low
uncertainty in estimates of
emission factors.
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
7
Collection of primary field data for planning
24 Single Plots—mean +/- 95% CI
-1
Carbon Pool
Carbon Stock (t C ha )
% of Total
Aboveground tree biomass
192.4 ± 36.2
73.1
Belowground tree biomass
45.2± 8.5
17.2
Saplings*
7.0 ± 1.5
2.7
1.1 ± 1.2
0.4
17.3 ± 8.5
6.6
263.0 ± 44.6
100
Dead wood (standing)
Dead wood (lying)
#
#
Total
29 Cluster Plots—mean +/-95% CI
Carbon Stock (t C ha )
% of Total
Aboveground tree biomass
190.6 ± 15.5
72.4
Belowground tree biomass
44.8± 3.7
17.0
Saplings*
5.2 ± 0.6
2.0
3.3 ± 1.7
1.3
19.3 ± 3.7
7.3
263.2 ± 19.7
100
Dead wood (standing)
Dead wood (lying)
Total
#
#
No difference in C stocks of main forest types
No further stratification by forest type
300
tC/ha ±90% CI
mean
t C/ha±95%CI
Mean
-1
Carbon Pool
a
a
a
dakama
mixed
wallaba
250
200
150
100
50
0
Forest
type
Forest
type
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
8
Decisions on sampling design for
developing emission factors


Use stratified two-stage list sampling design with clustered plots


No need to stratify by forest type in high threat zone

Preliminary data used to estimate number of cluster plots to be
installed in the more accessible and less accessible strata
Include the following C pools: aboveground biomass of trees to 5 cm
minimum DBH, standing and lying dead wood, litter, and soil to 30
cm depth

Divide sampling into three phases: phase 1 = high threat area, phase
2 = medium threat area, phase 3 = low threat area
Need to stratify by accessibility for cost-effective sampling—divided
into more accessible (area of buffer of 5 km width on each side of all
roads) and less accessible (area outside buffer)
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
9
Sampling design for obtaining estimates of C
stocks for developing EFs
• The country is divided systematically into 10 km x 10 km blocks (primary sampling units,
PSUs) (left).
• The forest is divided into accessibility strata and phases (middle).
• PSUs within each stratum are selected using a stratified two-stage list sampling design for
carbon measurement (right).
• Secondary
sampling
units emission
(SSUs)factors
designed
as L-shaped
clusters
are established
within each
Module
2.3 Estimating
for forest
cover change
(deforestation
and forest degradation)
10
PSU, and
carbon
measurements
are
obtained.
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
QA/QC: SOPs and tools to automate
calculations for all field data
Worksheet links to
data collected using
standard operating
principles (SOPs)
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank11
FCPF
11
Development of emission factors (EFs) for
deforestation
EF developed = from field measurements of 40 cluster plots (overall precision 95%, CI = 12% of mean)
Carbon stocks in all pools
Stratum
AG Tree
(t C/ha)
More Accessible (MA)
Less Accessible (LA)
C stock change in soil
Stratum
More Accessible (MA)
187.2
284.8
C stock (t
C/ha)
Conversion to permanent agriculture
Mining
Conversion to unpaved roads
FLU
FMG
C stock at
20 yr (t
C/ha)
FI
Change
in Soil C
(t C/ha)
105.50
Conversion to permanent agriculture
Mining
Conversion to unpaved roads
Less Accessible (LA)
Standing
Lying Dead
Sum Carbon
Saplings (t
Litter (t
Dead Wood
Wood (t
Pools (t
C/ha)
C/ha)
(t C/ha)
C/ha)
C/ha)
44.0
1.2
1.7
10.2
5.6
249.7
66.9
1.3
2.0
12.8
5.6
373.3
BG Tree
(t C/ha)
0.48
0.82
0.82
1.00
1.00
1.00
1.00
0.92
0.92
50.64
79.59
79.59
54.86
25.91
25.91
0.48
0.82
0.82
1.00
1.00
1.00
1.00
0.92
0.92
42.18
66.30
66.30
45.70
21.58
21.58
87.88
These values were
used to develop EFs
for deforestation.
Post deforestation
stocks for biomass
pools = 0.
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
12
Development of emission factors for
deforestation
EFs including all pools and changes in soil stocks by stratum and driver for high threat area
Stratum
More
Accessible
(MA)
Less
Accessible
(LA)
Drivers
Forestry infrastructure
Agriculture
Mining (medium and large scale)
Infrastructure
Fire-Biomass burning
Forestry infrastructure
Agriculture
Mining (medium and large scale)
Infrastructure
Fire-Biomass burning
Emission Factors
t CO2e/ha
1,010.6
1,116.8
1,010.6
1,010.6
744.6
1,448.0
1,536.5
1,368.9
1,448.0
1,108.6
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
13
Recommended modules as follow-up
 Module 2.4 to learn about involving communities / local
experts in monitoring changes in forest area and carbon
stocks.
 Module 2.5 to estimate carbon emissions from
deforestation and forest degradation
 Modules 3.1 to 3.3 to proceed REDD+ assessment and
reporting.
Module 2.3 Estimating emission factors for forest cover change (deforestation and forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
14