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3.4.4. Uncertainty of Afforestation, Reforestation, and Deforestation Activities
The first determination needed for Article 3.3-identification of ARD land-requires
classification of land as forest or non-forest for at least two points in time
or an activity reporting mechanism to identify lands that have been subject
to ARD activities. Regardless of the method chosen to identify where ARD activities
have occurred, some uncertainty is expected to occur. The uncertainty involved
in activity reporting is primarily caused by reporting errors or biases (underreporting
or overreporting). In the land-use/land-cover classification case, for example,
a remote-sensing identification of forest/non-forest areas may include some
inaccuracy in classification, which is also a function of the type of orbital
system used. Mantovani and Setzer (1997) compared the number of deforestation
polygons using the Advanced Very High Resolution Radiometer (AVHRR) sensor (1.1-km
resolution) with the number from the yearly deforestation project conducted
by Instituto Nacional de Pesquisas Espaciais (INPE) using the Thematic Mapper
(TM) sensor (30-m resolution). The comparative results indicated that only 49-57
percent of the total cases were correctly identified with the low-resolution
sensor. This difference in ability to detect deforestation appears to be related
to the size of the sites considered. As observed in the TM images, deforestation
polygons less than 3.1 km2 in size were usually not detected in the AVHRR imagery.
In addition, depending on the season in which the imagery is taken, forested
areas can be confused with other vegetated areas in classifications. Areas in
which forest cover may be sparse or very young may meet certain definitions
of forest yet may appear in the image to be in a non-forest condition. Here,
using definitions of forest that are based on low canopy cover thresholds or
that provide for classification of early growth stages likely will result in
more misclassifications (of forest as non-forest) than definitions that require
high percentage cover. Ground-based inventories are more accurate in this respect
than inventories that are based on remotely sensed data.
Different definitions for forest, afforestation, reforestation, and deforestation
will lead to different levels of difficulty of data collection and different
levels of uncertainty. Definitions that are based on objective and readily measurable
variables will tend to yield lower uncertainties. For example, a forest definition
that is based completely on the proportion of land covered by woody species
(e.g., Land Cover scenario) will lead to more accurate estimates than a definition
that involves purpose or history of land use or functional mechanisms used for
establishing forests (e.g., Land Use scenario).
The uncertainty of stock change estimates will also vary with the magnitude
of the stock in terms of spatial extent and density. Detecting large relative
changes in a small stock is easier than detecting small relative changes in
a large stock. This fact implies that countries with higher carbon density in
forests will have a greater uncertainty for a given magnitude of stock change
than countries with lower forest carbon density. Furthermore, changing the threshold
of forest cover in a definition may influence the uncertainty in detecting ARD
activities.
Because ARD activities are change processes, definitions and methods have to
be consistent on successive occasions. In the framework of the Kyoto Protocol,
it is convenient to group the scenarios presented in Section
3.2 in two broad classes:
- Scenarios for which country statistics and map systems with appropriate
models exist
- Scenarios that require a new set of data (inventories, maps, results from
models) following a common, consistent approach.
If a new data collection system has to be implemented, obtaining the necessary
retrospective information will be difficult regardless of the scenario applied
because, at a minimum, data are required on the status of lands in 1990. Use
of archived remotely sensed data, inventory plots that may have been established
for another purpose, and/or retrospective models may be the only available alternatives.
Evaluating the uncertainty or verifying such estimates would be impossible.
Uncertainties in determining stock changes from ARD activities will be sensitive
to various components of the data collection process. A crucial component is
the sampling scheme used for detection of ARD activities, which will determine
the minimum detectable change in forest condition. Difficulties arise if the
sampling resolution is inconsistent with the assessment unit sizes in the definition
adopted for forest. If a ground-based forest inventory is used to sample ARD
activities, the sampling intensity-expressed as hectares of land represented
by each plot-will give an indication of the ability of the system to detect
ARD activities on small land areas. For assessments that are based on remotely
sensed data, the spatial resolution of the sensor used will be crucial in determining
the minimum detectable change.
Consistency in sampling schemes between assessments is also important: Erroneous
conclusions can be reached if sampling intensity (or sensor resolution) changes
substantially between assessments. For example, suppose that 1-km resolution
data were used for a first assessment, and a 1-km2 area was identified as forest.
At a second assessment, 100-m resolution data might detect non-forest areas
within the original 1-km2 area, leading to an interpretation of deforestation.
That 100-m opening may have existed within the 1-km2 area during the first assessment,
however, in which case the determination of deforestation would be in error.
Timing of measurements will also affect the ability to detect stock changes
from ARD activities. Forest inventories can be time-consuming, multi-year processes,
particularly in large areas. In the United States and many European countries,
for instance, the national forest inventory cycle is about 10 years. With this
type of measurement timing, it is impractical to expect accurate, verifiable
estimates of stock changes during a 5-year commitment period and for a small
but comprehensive subset of the inventory area. Some nations are beginning to
use annual inventory systems, but the transition from periodic national inventories
to annual inventories is costly and difficult. In some cases, new sampling protocols
(timing and intensity of samples) may need to be adopted if national inventory
data are to be useful for the carbon accounting required under Article 3.3.
Although the focus here has been on implications for the first commitment period,
there are considerations for subsequent commitment periods. Errors in classification
of forest/non-forest can have effects in subsequent commitment periods, either
in the determination of areas requiring stock estimates for Article 3.3 or in
assessments of stock changes between the first commitment period and later commitment
periods. For example, consider a parcel that was forested in 1990 and was subsequently
misclassified as non-forest after 1990. This misclassification could lead to
an erroneous classification of deforestation and carbon debits for the assessment
of the first commitment period. The error depends on the definitions applied
and the method used to determine carbon stock changes. If the method involves
some form of carbon stock measurement, the misclassification of ARD land will
have no impact on the reported carbon stock change because there will be none
in the misclassified area. A subsequent (correct) classification of the same
area as forested would produce a second faulty determination-this time of reforestation
and creditable carbon stock increase. Similarly, errors in estimates of stock
changes can have persistent effects after the first commitment period. No matter
what sampling scheme is employed, these types of errors will occur. The key
issue is the identification of systematic problems that will generate biased
estimates of ARD activities and random errors that offset one another, having
little effect on ARD activities determinations.
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