Fisheries in the Southern Border Zone of Takamanda - Impact ...
Fisheries in the Southern Border Zone of Takamanda - Impact ...
Fisheries in the Southern Border Zone of Takamanda - Impact ...
You also want an ePaper? Increase the reach of your titles
YUMPU automatically turns print PDFs into web optimized ePapers that Google loves.
176 Slayback<br />
(ridge or lowland). On <strong>the</strong> o<strong>the</strong>r hand, a PSF area just<br />
south <strong>of</strong> <strong>the</strong> Obonyi enclave’s sou<strong>the</strong>rnmost po<strong>in</strong>t is<br />
known to be “elephant bush” – an area where extensive<br />
elephant activity has significantly affected <strong>the</strong> forest<br />
strFucture (and <strong>the</strong>reby its reflectance). It is important to<br />
note that <strong>in</strong>clusion <strong>of</strong> this additional class did not<br />
significantly impact <strong>the</strong> change class <strong>of</strong> primary <strong>in</strong>terest<br />
here (forest → secondary forest); <strong>the</strong> PSF is a static class<br />
and only significantly affected <strong>the</strong> areas <strong>of</strong> lowland,<br />
secondary, and ridge forests.<br />
The classification output was <strong>the</strong>n sieve-filtered to<br />
remove isolated <strong>in</strong>dividual or small groups <strong>of</strong> pixels. This<br />
filter<strong>in</strong>g improves <strong>the</strong> appearance <strong>of</strong> <strong>the</strong> results by<br />
remov<strong>in</strong>g what <strong>of</strong>ten appears to be speckle and noise, and<br />
<strong>in</strong>creases <strong>the</strong> accuracy <strong>of</strong> <strong>the</strong> results (see “Accuracy<br />
assessment” below), s<strong>in</strong>ce most <strong>of</strong> <strong>the</strong> landcover types <strong>in</strong><br />
this region naturally occur as fairly homogenous<br />
landscape features. The siev<strong>in</strong>g was applied to isolated<br />
groups <strong>of</strong> pixels with 6 or fewer members<br />
(approximately 0.5 ha); <strong>the</strong>se groups were changed to <strong>the</strong><br />
most common surround<strong>in</strong>g class. The f<strong>in</strong>al landcover<br />
change classification can be found <strong>in</strong> <strong>the</strong> photo gallery.<br />
3.2 Accuracy assessment<br />
In this area, <strong>the</strong>re is a fairly cont<strong>in</strong>uous gradation between<br />
farms, secondary forest, and undisturbed forest. Teas<strong>in</strong>g<br />
out <strong>the</strong>se at-times subtle differences can <strong>of</strong>ten push <strong>the</strong><br />
limits <strong>of</strong> <strong>the</strong> <strong>in</strong>formation available <strong>in</strong> this imagery. For<br />
this classification, <strong>the</strong> pr<strong>in</strong>cipal difficulty was <strong>in</strong> f<strong>in</strong>d<strong>in</strong>g<br />
a balance between a good classification <strong>of</strong> secondary<br />
forest around villages and a good classification <strong>of</strong><br />
undisturbed forest between villages. This f<strong>in</strong>al result is<br />
felt to be <strong>the</strong> best compromise, although a conservative<br />
one; it may be underestimat<strong>in</strong>g to a small extent<br />
secondary forest around villages, <strong>in</strong> favor <strong>of</strong> lowland<br />
forest. However, <strong>the</strong> areas <strong>of</strong> change (forest conversion<br />
between 1986 and 2000) were quite stable <strong>in</strong> all versions<br />
<strong>of</strong> <strong>the</strong> classification due to <strong>the</strong> clear signature produced<br />
by a change, over time, from undisturbed to secondary<br />
forest. The accuracy <strong>of</strong> <strong>the</strong> classification was assessed<br />
both quantitatively, us<strong>in</strong>g test<strong>in</strong>g sites, and qualitatively,<br />
rely<strong>in</strong>g upon personal familiarity with <strong>the</strong> region.<br />
<strong>Takamanda</strong>: <strong>the</strong> Biodiversity <strong>of</strong> an African Ra<strong>in</strong>forest<br />
Along with <strong>the</strong> tra<strong>in</strong><strong>in</strong>g sites, a separate set <strong>of</strong> test<strong>in</strong>g<br />
sites was also selected for each class. As with <strong>the</strong> tra<strong>in</strong><strong>in</strong>g<br />
sites, <strong>the</strong>ir selection was based largely on visible imagery<br />
characteristics and knowledge <strong>of</strong> <strong>the</strong> different landcover<br />
types, and not on ground-truth data. S<strong>in</strong>ce <strong>the</strong>se test<strong>in</strong>g<br />
sites are not <strong>in</strong>put to <strong>the</strong> classification procedure, <strong>the</strong>y can<br />
be used to <strong>in</strong>dependently evaluate <strong>the</strong> classification<br />
output via a confusion matrix; confusion matrices<br />
<strong>in</strong>dicates how <strong>the</strong> pixels <strong>in</strong> each test<strong>in</strong>g site were actually<br />
classified. Given <strong>the</strong> confusion matrix, <strong>the</strong> accuracy <strong>of</strong><br />
each class’ classification can be computed as a<br />
percentage.<br />
For <strong>the</strong> f<strong>in</strong>al output classification (after siev<strong>in</strong>g), <strong>the</strong><br />
average accuracy (for example, <strong>the</strong> average <strong>of</strong> all<br />
accuracies) for <strong>the</strong> landcover classes, exclud<strong>in</strong>g water<br />
and shadow, was 87%, and <strong>the</strong> overall accuracy<br />
(weighted by <strong>the</strong> number <strong>of</strong> pixels <strong>in</strong> each test<strong>in</strong>g site)<br />
was 94%. (The overall accuracy is higher because <strong>the</strong><br />
undisturbed forest sites were relatively large and well<br />
classified). The confusion matrix shows that <strong>the</strong> ma<strong>in</strong><br />
error is a tendency towards classification as lowland<br />
forest; 18% <strong>of</strong> ridge forest, 9% <strong>of</strong> forest conversion, and<br />
6% <strong>of</strong> secondary forest test sites were classified as<br />
lowland forest. Also, 35% <strong>of</strong> PSF test sites were<br />
classified as lowland, but this is less surpris<strong>in</strong>g, s<strong>in</strong>ce we<br />
expect <strong>the</strong> PSF to really be ei<strong>the</strong>r lowland or secondary<br />
forest. Table 1 shows <strong>the</strong> classification accuracies for<br />
each class (for example, <strong>the</strong> percent <strong>of</strong> each test<strong>in</strong>g site<br />
that was classified correctly).<br />
Table 1. Accuracy (percent classified correctly) based on<br />
test<strong>in</strong>g sites<br />
Class Accuracy<br />
Lowland forest 100<br />
Secondary forest 92<br />
Possible secondary<br />
forest (PSF)<br />
65<br />
Forest conversion 91<br />
Ridge forest 70<br />
Mid-elevation forest 99<br />
Montane forest 86<br />
Grassland / bare 100