22.214.171.124. Determining Rates, Extent, and Locations of Forest Clearing and Regrowth
Estimating sources and sinks of carbon from forest clearing and regrowth using
remote-sensing requires repeated measurements of forest clearing over large
areas, fine spatial and temporal scale analyses to document regrowth, and in
situ data on carbon pool changes that are associated with changes in land
Coarse-resolution optical data (>1 km) are useful in describing broad distributions
of different types of land cover, including different forest types (Belward
et al., 1999). These coarse-resolution data generally are inadequate,
however, for accurately quantifying changes in forest cover, such as clearing
and regrowth. Justice and Townshend (1988) demonstrated a need for spatial resolution
of optical data that is less than 250 m.
High-resolution data from the Landsat, SPOT, and similar series of Earth observation
satellites have been employed to make regular regional measurements of forest
clearing and regrowth (Skole and Tucker, 1993; INPE, 1999). Large amounts of
these data exist in several national archives, dating back approximately 20
years. Thus, a continuous and consistent source of data is available from which
a high-resolution, fine-scale (1:250,000 scale mapping) information system could
be developed. Many countries routinely perform regular assessments of forest
clearing and regrowth over large areas-in particular, Brazil, Thailand, and
Indonesia (INPE, 1999). Accuracies that approach 15-25 percent of the area cleared
have been demonstrated (Houghton et al., 2000).
Because cleared areas may revegetate rapidly, they must be observed frequently-as
close to annually as possible. Frequent measurements make it easier to co-register
areas of clearing and regrowth and reduce the probability of missing clearing
on patches that begin to regrow quickly. Frequent observations will help in
attributing changes to specific activities.
Although traditional optical remote-sensing techniques can distinguish between
cultivated areas, pastures, and secondary growth, they are limited in terms
of mapping various stages of fallow and secondary forests (Sader et al.,
1989). On the other hand, synthetic aperture radars (SARs) operate independent
of solar illumination, cloud cover, and smoke and can detect differences in
forest structure and woody biomass associated with various stages of forest
clearing and regrowth (Rignot et al., 1997; Saatchi et al., 1997).
Use of both optical and SAR data can provide a better characterization of land
cover. For example, a plot of newly cleared and partially burned tropical forest
may contain a significant portion of dead woody debris (slash). Visible and
near-infrared reflectance data will show that this area has been cleared but
will provide little insight into the presence of the slash. The SAR data, however,
would indicate a significant amount of biomass; as a result, this area could
be confused with a secondary growth area with comparable radar cross-section.
Using the optical and SAR data together would reveal that the site was deforested
and not in secondary growth, yet still had a significant amount of residual
woody biomass (Rignot et al., 1997).
The difficulty with these high-resolution satellites is not that they fail
to identify cleared areas or areas where trees are returning but that they may
fail to distinguish such clearing and regrowth from other changes, such as harvests,
natural disturbances (fire, insects, storms), or other changes that are unrelated
to human activity.
Very high-resolution data (1-m panchromatic and 3-m multi-spectral) that are
now available from the commercial IKONOS II satellite may be useful for determining
the actual activities on the ground that have led to forest clearing. Although
such data can detect very small clearings, the scientific community as yet has
very little experience with these data.
To obtain annual estimates of forest clearing and regrowth, a stratified sampling
scheme might be employed to determine deforestation rates between the complete
inventory/census years, spaced 3-5 years apart. The stratification might be
based on the last complete inventory/census, assuming that deforestation is
spatially persistent over intervals of 3-5 years. Research with Landsat- or
SPOT-scale spatial resolution in some areas of the tropics suggests that a sample
of 30 percent or less of the total forest area would be sufficient, but further
research is necessary to determine sampling densities for other regions.
The costs of using remote-sensing data vary greatly on a per-hectare basis.
AVHRR, other coarse-resolution optical sensors, and coarse-resolution radar
sensors typically have very low costs per hectare for access to data. The costs
of acquiring Landsat data vary according to the year in which the data were
originally acquired, as well as whether the Landsat system was under private
or public management. The most recent data for Landsat-7 are also the least
expensive (~US$600 per Level 1 scene) because the system is now operated as
a public resource. SPOT data are somewhat more expensive than Landsat and have
finer spatial resolution but do not have global coverage. Very high spatial
resolution data are only publicly available from IKONOS II, at somewhat higher
costs per hectare, but practical considerations regarding data volume are likely
to inhibit their use for broad-area surveys.
Once the spatial extent and rates of forest clearing and regrowth have been
quantified, accurate calculations of changes in carbon stocks require techniques
for measuring pre- and post-disturbance biomass, rates of secondary growth formation
and turnover, and rates of biomass accumulation in secondary growth. There have
been several attempts to use SAR data to determine aboveground biomass directly,
through the known sensitivity of the radar backscatter to total aboveground
material, its structure, and its dielectric properties. With current techniques
and wavelengths, however, there is little ability to discriminate biomass levels
greater than 50-100 t ha-1. The National Aeronautics and Space Administration's
(NASA) planned Vegetation Canopy Lidar (VCL) mission-to be launched in 2001-is
designed to provide data sets of the vertical distribution of vegetation canopies.
These data may be a good proxy for aboveground biomass for many forested ecosystems,
but full knowledge of the utility of the measurements will necessarily await
the instrument's launch and subsequent experience in the scientific community.