Environmental Engineering Reference
In-Depth Information
Data Analysis
was used for plantation agriculture and the people were
forcefully displaced from their homes and herded off onto
strange and unfriendly patches of land around the planta-
tions. In a nutshell, the Germans alienated about 400 square
miles of the most fertile land around the Mount Cameroon
alone and stripped the people of over 200,000 acres of their
most fertile lands. The size of the plantations kept on
growing under British supervision following the defeat of
the Germans after the First World War (Cameroon Devel-
opment Corporation Report
1970
). The spread of planta-
tions slowed during the depression years between 1920 and
1940. However, following the return of peace and favour-
able conditions for international trade and investments after
the end of World War II in 1945, the plantation system
started expanding once again. In January 1947, CDC was
created to take over and manage the plantations. The
expansion continued through the following two decades. It
accelerated during the immediate post-Independence era,
about 1960-1965, which saw direct involvement of the
national governments of the newly independent states in the
establishment and running of plantations. Thereafter, the
expansion slackened, mainly because of a decline in
external investments following the growing political insta-
bility and state control of the national economies and the
attendant erosion of foreign investors' confidence in them
(Halfani and Barker
1984
). Since about 1975, the plantation
system has witnessed rejuvenation as a strategy for stimu-
lating agricultural production. In the south-western region
of Cameroon, the area under plantations increased from
about 20,000 in 1960 to over 73,788 ha in 2006 (CDC
Report
2006
).
GIS and remote sensing were used to determine land cover
change following the establishment of plantations in the
area between 1986 and 2011. The activities that were car-
ried out include satellite image processing and classification
for land cover change detection. Changes in the areal extent
of the plantations were analysed using Landsat imageries of
the site for 1986, 2000 and 2011 to be able to monitor the
extent of deforestation for plantation agriculture that has
taken place in the area. The landsat imageries were geore-
ferenced to the coordinate system of the study area
(WGS84, projection: UTM zone 32). Erdas Imagine 9.2
software was used in processing and analysing the imag-
eries. Visual interpretation of satellite imageries was
enhanced through the use of linear stretching. Clouds and
clouds shadows present on images used for the study were
reduced
through
masking
techniques
in
Erdas
Imagine
Software.
Two main steps were followed in land cover mapping.
First, unsupervised image classification was carried out
prior to field visits, in order to determine strata for ground
truthing. Supervised classification based on maximum
likelihood classifier algorithm was then used in the classi-
fication of the 1986, 2000 and 2011 images. This was based
on 130 training sets or ground control points collected.
Expert knowledge was used in selecting the 130 points on
spot at good distances away. The sample points collected
were used to validate classification results.
Some 25 % of the collected ground control points were
used to train the data and the remaining 75 % were used for
the analysis. The image classification accuracy was assessed
by calculating the Kappa coefficient. The Kappa statistics is
an estimate of the measure of overall agreement between
image data and the reference (ground truth) data. Its coef-
ficient falls typically on a scale of 0 and 1. It is often
multiplied by 100 to give a percentage measure of classi-
fication accuracy. Kappa values are classified into 3
groupings: a value greater than 0.80 (80 %) represents
strong agreement, a value between 0.4 and 0.8 (40-80 %)
represents moderate agreement and a value below 0.4
(40 %) represents poor agreement (Congalton
1996
). Post-
classification method (Lu et al.
2004
) of change detection
was used in analysing the result from the land cover maps.
Data Collection
Relevant data for the study were collected from both primary
and secondary sources. Primary data set was collected from
the field with the use of the global positioning system (GPS).
Secondary data included data collection from documented
sources especially plantation records of the Cameroon
Development Corporation on plantation ages, areal extent,
cultural practices, yields, economics, among others. Infor-
mation on the flora and fauna was obtained from organisa-
tions involved in conservation such as the Mount Cameroon
Project, The German Technical Cooperation (GTZ), Dele-
gations of Forestry and Wildlife in the area.
Landsat satellite imageries for the year 1986, 2000 and
2011 were also obtained from the global land cover facility
(GLCF) website. Other sources include journals, internet
and topics. Due to the fact that Mount Cameroon is most of
the time clouded, the factors that governed the choice of the
satellite imageries were the quality of the imageries avail-
able and time interval.
Results and Discussion
Figures
3
,
4
and
5
show the land cover change between
1986 and 2011. They indicate that the secondary forest,
farmlands and the plantations have considerable overlap in
their spectral reflectance. It was however, difficult to sepa-
rate the dense forest and the plantations based on their
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