3.4.5. Data for Verification
Verifiability of stock changes from ARD activities as required in Article 3.3
will require the ability to verify that the activities have taken place, as
well as the ability to verify stock changes from those activities. The primary
difficulty involves verification of the historical status of a resource that
may no longer exist.
If an existing monitoring system uses accepted quality assurance/ quality control
procedures and reports statistics about the quality of on-site estimates, independent
validation of each estimate may be simplified. Certifying that the methodology
used includes quality assurance and quality control within some specified standards
may be sufficient.
An advantage of remotely sensed data is that archives of images collected over
time are frequently available. This archiving enables third parties, without
on-the-ground inspections, to verify land-cover status at prior points in time
(to the extent that land-cover types are accurately identifiable on the imagery).
Even with extensive archives of imagery, however, weather conditions (cloud
cover), sensor operating status, and other factors may preclude observation
of large portions of the earth at various times. Hence, there is no guarantee
that imagery will be available on or near desired points in time. In addition,
although remote sensing may have a role in verification, use of imagery still
requires ground verification in areas where there is doubt, as well as verification
or causes of perceived ARD.
Even in situations in which historical imagery of acceptable quality is available,
verification will require assessment of carbon stocks, which will rely primarily
on sample data taken at appropriate times and the ability to spatially reference
the sample locations. When such data are not available, stock changes cannot
be accurately verified. For example, a non-reported deforestation activity might
be detected through the use of archived remotely sensed imagery. Yet the only
recourse to estimate the carbon stock loss (in the absence of site-specific
field data prior to deforestation) would be to use averages for carbon stock
by forest type and/or size class. Conversely, an afforested area could be sampled
to obtain forest carbon stock estimates, but previous stocks (such as soil carbon)
would have to be estimated without the use of field data. Thus, stock-based
forest definitions (e.g., Flexible scenario) will result in unique difficulties
With good analysis of error or uncertainty and reliable quality control procedures,
verification could be limited to confirmation that methods were applied correctly.
Most forest inventories include quality control and sometimes report statistics
on data quality. This procedure could be extended to carbon inventory.
Establishing institutional procedures may be necessary to verify that reported
estimates were made using a transparent methodology that includes quality assurance
and control procedures. The methodology could be monitored (or certified) by
an independent authority.
Remote sensing would be useful as one among other data sources in establishing
the initial land use/land cover, to ensure that results are verifiable. It also
helps in detecting, delineating, and measuring area changes and can provide
objective information on whether land-use and forestry activities are human-induced.
The Kyoto Protocol anticipates that Parties will have in place national and,
where appropriate, regional, forest inventory systems for annual estimation
and reporting of human-induced emissions by sources and removals by sinks from
ARD activities in a transparent and verifiable manner. We briefly reviewed the
applicability of remote sensing and forest inventory techniques for establishing
the data and for monitoring and verifying changes in ARD land and carbon stocks
in a statistically reliable manner.
A National Forest Inventory system that is based on continuous forest inventory
concepts is an important component for obtaining and verifying information on
changes in carbon stocks. In addition, transparent and verifiable reporting
of land-use changes calls for use of interdependent remote sensing techniques.
Further gains in precision and reductions of costs might result from integrating
successive surveys for forest area and volume in a common statistical framework.
Models and special studies are needed to estimate carbon above and below ground.
Finally, institutional capacity-building is vital to ensure a high degree of
consistency between successive survey and interpretation procedures, as well
as imagery products (sensors, season, quality, etc.).