Transcript UNFCCC Workshop on Reducing Emissions from Deforestation in Developing Countries 30/08-01/9/2006, Rome, Italy Overview of scientific, socioeconomic, technical and methodological issues Sandra Brown [email protected].

UNFCCC
Workshop on Reducing Emissions from
Deforestation in Developing Countries
30/08-01/9/2006, Rome, Italy
Overview of scientific, socioeconomic, technical and
methodological issues
Sandra Brown
[email protected]
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Much progress in the last 10 years….
 Remote sensing data at various scales
readily available and methods and tools have
been developed and for estimating and
monitoring carbon emissions from tropical
deforestation and degradation
 Change in land cover for most tropical regions can
be measured from space with confidence, but
measuring forest degradation from satellites is
more technically challenging
 Peer reviewed tools and methods available using
field measurements to estimate carbon stocks in
forests with high confidence.
 Methods for estimating net and gross emissions
from deforestation/degradation are available in
existing IPCC reports (1996, 2003, 2006)
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10-30%
Definitions
Forest Definition: (annex to decision
16/CMP.1 of Kyoto Protocol)
2-5 m
Minimum forest area: 0.05 – 1 ha
Minimum tree height: 2 – 5 m
0.05-1 ha
Deforestation:
Direct, human-induced
conversion of forested land to
non-forested land
Minimum crown cover: 10 – 30 %
Degradation (from IPCC):
Direct, human-induced, long-term loss
[persisting for X years or more] or at
least Y% of forest carbon stocks since
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time T [not qualifying as deforestation]
Application of definitions
Crown cover
80%
30%
10%
30%
Carbon stocks in t C/ha
120
40
Deforestation ΔC= 80
Degradation
10%
0%
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1
Devegetation
Deforestation ΔC= 108
Degradation
Devegetation
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Drivers of deforestation and degradation
as reported in national communications
to the UNFCCC
Driver
Forest conversion to agricultural uses
Harvesting for fuelwood and charcoal
Improper forest management, including selective
logging and overexploitation
Fires and biomass burning
Population pressure
Development pressure, such as expanding
urbanization, settlements and new infrastructure
(e.g., electricity lines, roads)
Illegal logging
Policies and laws that drive land use conversions
Exploitation of mineral resources, mining
Number
of
Parties
33
25
17
13
13
11
8
7
4
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Steps involved in a C monitoring system for
deforestation
DeFries
et al.
2006.
Forest inventories
In-situ/plot data-projects
Targeted remote surveys—
e.g. Lidar and aerial imagery
FAO statistics
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IPCC-GPG / AFOLU
Monitoring change in forest cover
 Remote sensing data available for many land
cover changes and many developing countries
since 1990s and deforestation can be measured
from space with confidence
 Not all areas covered; cloud cover issues for
some key tropical countries
 Identification of secondary forests—not “easy”
 Identification of degraded forests developing
 Identification of selectively logged forest
developing
 Development of new technology and new
analytical methods in RS field progressing for
addressing these challenges and likely to be
available for future monitoring
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Monitoring carbon stocks
 Need to match estimates of carbon stocks
with changes in land cover to improve
accuracy and precision of emission
estimates
 Current operational optical satellites cannot
remotely sense biomass carbon
 Optical satellites have difficulty in
distinguishing secondary from mature
forests, yet carbon stocks can differ
greatly because of effects of age and
ecological zone
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How are forest biomass C stocks in
the tropics presently assessed?
 Robust tools exist for
converting traditional,
statistically designed forest
inventory data to carbon stocks
in trees; use defaults for other
pools (IPCC GPG Ch. 3; FAO)
 Majority of tropical countries
have no recent national forest
inventories
 Research plots generally
insufficient as not from
population of interest and
designed for other purposes
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Country
Biomass Estimation
Year
Brazil
Scientific studies with small, non-stratified plots.
1988-2004:
Malaysia
Volume to biomass using GPG (2003) and expansion factors from
Brown (1997); extrapolated to Sabah and Sarawak
1993, 2004
Indonesia
Volume (year 2000 estimated for various species) to biomass using
expansion factor from Brown (1997)
1992, 1998,
2000
Peru
Volume inventories, not converted to biomass
1995,2000
Bolivia
Volume (humid forest only) to biomass using expansion factors,
default values for roots and dead wood from IPCC 1996
1999
Dem Rep.
of Congo
Volume to biomass using average wood density for Africa and BEF
from Brown (1997)
1999
Cameroon
1990 volume (assumed constant for 2000 and 2005) to biomass
using density and expansion factors (sources not listed)
1990
Central
African
Republic
Partial inventory only. Volume (constant from 1990-2005) to
biomass using average wood density for Africa and expansion
factors from Brown (1997) and FAO (2004) Directives
1996, 1997
Republic of Volume (assumed constant for 1990-2005) to biomass using
Congo
average wood density for Africa and BEF from IPCC GPG
2004
Tanzania
1999
Volume (by vegetation classes) to biomass using average density
for Africa and expansion factors (sources not listed)
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How to measure carbon stocks ?
 Traditional inventory approach:
 Can be done in smaller countries and
at project scale
 Requires large resources at national
level
 Cost-prohibitive for large countries
and not practical
 Need remote means that are:




Cost-effective
Low uncertainty (high precision)
Transparent and repeatable
Acceptable to policy makers
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Future trends in measuring and
monitoring carbon stocks for DD
 Build on existing techniques—regular
“inventories” done by sampling
 Need remote means
 Not necessary for wall-to-wall mapping
but statistical sampling approach
 New remote technology developing—
 Lidar already shown to measure changes in
forest structure –height is a good indicator
of forest biomass change
 High resolution digital imagery combined
with new field data on key metrics of
forest carbon-crown area and tree height
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Conclusions
 Analysis of airborne or satellite remotely sensed
data is the only practical approach to measure
changes in DD at national and international scales.
 Since the early 1990s, tools and methods exist to measure
changes in forest area from space with confidence.
 Measuring forest degradation from satellites is more
technically challenging but methods are becoming available.
 There are no accepted standard practices for
measuring forest carbon stocks using RS data;
instead they are estimated from traditional forest
inventories or from default data.
 Investments are required to expand inventories of forest
carbon stocks so that reliable carbon estimates can be
applied to deforested and degraded areas interpreted
from RS imagery.
 New technologies and approaches are developing for
monitoring changes in carbon stocks with confidence using
a combination of satellite and airborne imagery.
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Conclusions (cont.)
 Methods for estimating net and gross emissions
from areas with measurable DD are available in
existing IPCC 1996 GHG inventory methods, the
2003 GPG-LULUCF and the pending IPCC AFOLU.
 Reliable and transparent results from application of these
methods are hampered by capacity, availability, and access
to data on both change in forest cover and, more critically,
by change in carbon stocks
 NEXT STEPS
 Development of standard protocols for interpreting and
analyzing remote sensing data at various scales, including
which data to collect and use, how to analyze the data, and
acceptable levels of accuracy to attain, etc (akin to GPG
for C stocks for LULUCF)
 Development of standard protocols for estimating carbon
stocks of forests undergoing change at national scales,
building on existing methods given in IPCC reports, and
decisions on acceptable levels of accuracy and precision to
attain.
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