Fisheries in the Southern Border Zone of Takamanda - Impact ...

Landcover change
an operator with knowledge of the imagery manually
delineates sites (groups of pixels) of each class on the
imagery. Based on the spectral character of these training
sites, the maximum likelihood algorithm then makes
probability-based class assignments of all image pixels.
To generate landcover change classes using these
classification methods, the training sites were selected to
include both change classes (such as “forest to nonforest”)
and non-change classes (“unchanged lowland
forest”), and the spectral bands from both dates of
imagery were input to the classification procedure
simultaneously. This is sometimes termed composite
multi-date classification, since images from both dates
are composited and used together, as if from a single date.
The preliminary orthorectification step makes this
approach possible as it ensures the precise overlay of
images from different dates. After running the
classification, some post-classification processing and
manual editing were conducted to clean up and finalize
the classification map.
Various statistics were calculated from this final
classification map to quantify changes in landcover. To
provide better insight into where changes might be
occurring most rapidly, these measures were calculated
for several regions: the entire 1600 x 1740 image subset;
the area within the TFR boundaries (excluding enclave
communities); the areas of the two enclave communities
(Obonyi and Kekpane); and the area within a 5-km buffer
zone surrounding the TFR.
3 Results
3.1 Change Classification
Training sites for nine different classes were initially
defined (see Sunderland et al. this volume) to include
eight static classes (lowland forest, ridge forest, midelevation
forest, montane forest, grassland/bare,
secondary forest/farms, water, shadow), and one change
class indicating forest conversion (forest → secondary
forest/farms). Note that in a static interpretation of the
output classification (e.g. for a year 2000 landcover
map), the forest conversion class would be added to the
secondary forest/farms class. No distinction was made
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between secondary forest and farms because these two
landcover types are fluid and difficult to distinguish;
farms usually have a scattering, or more, of larger trees,
and due to the agricultural practices in the region, areas
of secondary forest will be farmed again after several
years of forest regrowth. The four different types of
undisturbed forest (lowland, ridge, mid-elevation, and
montane) were included in the classification both
because we wanted to map the extent of these forest types
(see Sunderland et al. this volume), and because lumping
them together produces an overly broad and poorly
defined forest class, which becomes confused with
secondary forest in the classification results. Several
other types of change might have also been included, but
were not, such as grassland → burned grassland, and
forest regrowth (farms → secondary forest); these were
either not of interest (the former) or too difficult to
consistently differentiate (the latter). In either case, we
verified that these areas were satisfactorily classified with
the existing scheme. For example, the grassland →
burned grassland areas were routinely classified as
grassland, and the areas of possible regrowth (farms →
secondary forest) were classified as secondary forest. A
class for shadow was necessary because the poor and
variable illumination on the shadowed sides of hills
makes differentiation of different landcovers much more
difficult.
The resulting classification was then visually
inspected, and in an iterative process, minor adjustments
were made to the training classes and certain
classification parameters. Despite many such
adjustments, it became apparent that an additional class
would be useful to indicate areas of “possible” secondary
forest. These areas can typically be classified as either
lowland forest, secondary forest, or ridge forest,
depending on the set of training sites and classification
parameters. However, as these areas do not appear to be
separable solely from image reflectances, it was decided
to create a separate “possible secondary forest” (PSF)
class. Generally, firsthand knowledge of the area is
required to assign definitive labels to these regions, but
many can be labeled based on a closer inspection of the
imagery; for example, the areas on hillsides not in the
vicinity of villages are most likely undisturbed forest
SI/MAB Series #8, 2003