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CHANGES USING REMOTELY SENSED DATA 1*

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I.J.S.N., VOL.7 (4) 2016: 724-733 ISSN 2229 – 6441

THE EFFECTS OF LARGE-SCALE MINING ON LAND USE AND LAND

COVER CHANGES USING REMOTELY SENSED DATA
1*
Julia Atayi, 2Amos.T. Kabo-Bah, 3Komlavi Akpoti
1
Department of Environmental Management, School of Natural Resources, University of Energy and Natural Resources,
P. O. Box 214, Sunyani, Ghana.
2
Department of Environmental Engineering, School of Engineering, University of Energy and Natural Resources,
P. O. Box 214, Sunyani, Ghana.
3
Earth Observation Research and Innovation Centre, University of Energy and Natural Resources, P. O. Box 214, Sunyani, Ghana.
*
Corresponding author email: atayijulia@gmail.com

ABSTRACT
Land cover is the natural or basic elements of the environment that link and impacts many parts of the local, regional and
global levels of the environment. The study was conducted in two capital districts of the Brong-Ahafo Region which lies
within the green belt of Ghana in the moist semi-deciduous forest zone. This research was to assess the effects of large-
scale mining on the Land Use Land Cover (LULC) using remotely sensed data. Also, this study tries to find out, total area
of the various land use categories, percentage change and annual rate of change of LULC changes as a result of the mining
activities from 2005 to 2015. Iterative Self-Organizing Data (ISODATA) under unsupervised classification showed an
overall accuracy and kappa coefficient of 80.8% and 0.754 for 2005, 92.8% and 0.908 for 2008, 89.2% and 0.861 for 2012
and 87.6% and 0.841 for 2015 respectively. The results from the LULC analysis showed that, Forest Evergreen was the
most dominant land cover type in 2005 with a total area of 1492.93 ha (44.94%), but decreased as the year’s increases with
increasing built-up areas. The built-up areas which consists of mining areas increased from 316.05 ha (9.51%) in 2005 to
1047.27 ha (31.53%) in 2015. We recommend effective management of degraded areas by incorporating tree planting as
this compiles with the 1998 Forest Policy. Concurrent reclamation should be adopted by the mining sectors to achieve a
sustainable and successful post-closure outcome. Also decision makers should adopt the use of remote sensing and GIS
tools as this would enhance identification of areas that are degraded, their rate and extent.

KEY WORDS: Mining, LULC, Remote Sensing, ANOVA, ISODATA, Landsat image, pattern.

INTRODUCTION generates some 5.7% of GDP. During mining activities,

Land cover is the biotic or abiotic features that cover the large vegetation is cleared; huge pits dug to obtain the
earth surface, such as water, grassland, bare soil and the rocks rich in granite and limestone. The continuous
forest while Land use is how the land cover is been extraction of the natural resources leads to the direct loss
modified, example recreational area, built up land and of the forest due to the frequent damage of the forest land,
agricultural land. The direct result of changes in land removal of the fertile top soil layers, thereby resulting in
cover as a result of human activities especially land use, the shortage of fuel woods, grazing area, increase in soil
have changed the physical geographical environment erosion and air pollution. This situation negatively affects
greatly. Land has now become a scarce resource due to the people living within the mining areas (Nzunda, 2013).
increase in population growth and industrialization Dumping of waste rocks in an un-mined environment
(Ahadnejad et al., 2009). Mining which is the extraction of causes disturbances to the surrounding ecosystem thereby
minerals from the earth’s crust makes a great impact on affecting the biodiversity in the area and changing the
the landscape, environment and the surrounding natural topography of the area. The increase in the global
communities of the earth especially surface mining. This demand of these mineral resources such as Gold,
involves the clearing of large area of the forest and Diamond, Bauxite, Coal etc. have stimulated new mining
agricultural land and these results in serious deforestation industries including multinational companies and small-
and land degradation. Rapid growth in the mining sector scale miners throughout the world (Bury, 2004). Gold
has also attributed to the decrease and degradation of the mining activities in the various forest belts is likely to
land, forest cover and the biodiversity, though it serves as increase in Ghana, as the global demand and the prices for
a great economic gain for a country economy (Kumar & them continues to increase. Accurate information is
Pandey, 2013). The mining industry is the second largest therefore needed on the rate and impacts of the mining
industry after agriculture at all scale and regions, and it has activities on the forest since these activities occur within
played a vital role in the development of civilization from the remote forest and this also affects biodiversity
ancient times (Lodha et al., 2009). As an example, (Alvarez-Berríos et al., 2015). Protecting the global
according to (Aryee, 2001), Ghana’s mining sector environment is one of the critical problems the world is
contributes about 40% of gross foreign exchange earnings, facing now and this is due to several factors, such as

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 The effects of large-scale mining on the Land use and Land Cover Changes using Remotely Sensed Data

population increase, depletion of natural resources and the natural resources and monitoring the environmental
pollution of the environment (Study & Zanjan, 2009). The changes (Mark & Kudakwashe, 2010). Detection of land
unplanned changes of the land use have become a major cover change has been found applicable in land use change
problem because of the absence of logical planning and analysis especially in the assessment of the extent of
consideration of environmental impacts (Study & Zanjan, deforestation in a particular area (Dale et al., 1993).
2009). For the past decades, Remote Sensing (RS) and Therefore viewing the Earth from space has now become a
Geographic Information System (GIS) technologies have necessity to understand the influence of man’s activities
been vital tools for mapping the Earth’s features, studying on the natural resources over a given period (Zubair,
the environmental changes in time and space, managing 2006). The focus of this research is to assess the effects of
the natural resources. This gives the most accurate means large-scale mining on the land use land cover changes of
of measuring the extent and pattern of the changes at a the Sunyani and Asutifi district in the Brong-Ahafo region
particular landscape over time (Kumar & Pandey, 2013). of Ghana for 2005, 2008, 2012 and 2015.
This technology affords a practical means of analysing the
changes in the land use pattern at the mine sites at METHODOLOGY
inaccessible places. It has also become possible to get a Description of the Study Area
synoptic coverage of a larger area, in a cost-effective and The study was conducted in two districts in the Brong-
in a repetitive way. Ahafo region of Ghana; Sunyani and Asutifi (Kenyasi) as
Assessing land-use and land-cover change has become a indicated in the figure 1 below:
central component in the current strategies for managing

FIGURE 1: Location of the study area

The Sunyani Municipality surface area of 1500 km². The district has a total of 117
Sunyani Municipality is one of the twenty-two (22) settlements in the district and four paramountcy, thus
districts in the Brong-Ahafo Region of Ghana. Its capital Kenyasi No.1, Kenyasi No.2, Hwidiem and Acherensua
is Sunyani and this lies between latitudes 7019’57.43’’N (GoG, 2010).
and 7021’41.81”N and longitudes 2019’40.58’’W and
2020’51.47’’W (PHC, 2014). It shares boundaries with DATA COLLECTION
Sunyani West District to the Dormaa District to the West, Spatial Data Collection and Source
Asutifi District to the South and Tano North District to the In order to determine the effects of large-scale mining on
East the LULC changes of the study area, spatial data-sets were
The Asutifi District obtained from Landsat 7 and Landsat 8 archives from U.S
The Asutifi District is one of the twenty-two (22) Districts Geological Survey (USGS) and ground observations
in the Brong-Ahafo Region of Ghana and its capital is obtained from Google Earth. The Four data sets used for
Kenyasi. It is located between latitudes 6058’8.35’’N and the study, its source and date of acquisition are shown in
6058’7.55’’N and longitudes 2025’13.72’’W and table 1 below. The Landsat data were obtained from the
2026’43.56’’W (GoG, 2010).The district is one of the USGS and Earth Observation database. These imageries
smallest in the Brong-Ahafo Region with a total land were selected based on date of acquisition and its

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I.J.S.N., VOL.7 (4) 2016: 724-733 ISSN 2229 – 6441

availability. To prevent bias in the data, the images were seasonal variation in the spectral reflectance of the land
of the same season free from cloud cover and have the cover data-sets (Nzunda, 2013). Also the data were
same identifiable features. This gives uniform radiometric georeferenced to the coordinate system of the study area
and spectral characteristics which helped reduce or prevent i.e. WGS84 projection; UTM zone 30N.

TABLE 1: Landsat images used in the analysis of land-cover change

Landsat Satellite WRS Date of Spatial Spectral Source
Sensor Path/Row Acquisition Resolution Resolution
1 Landsat 7 195/055 15thJanuary 30m 8 bands glovis.usgs.gov
TM 2005
2 Landsat 7 195/055 1stJanuary 30m 8 bands glovis.usgs.gov
TM 2008
3 Landsat 7 195/055 2ndJanuary 30m 8 bands glovis.usgs.gov
TM 2012
4 Landsat 8 195/055 18thJanuary 30m 11 bands glovis.usgs.gov
TM 2015

FIGURE 2: Flow chart of the image analysis

DATA ANALYSIS from each detector causing distortion in the imagery.

The acquired Landsat imagery were analysed using Some of these distortions are striping, scan line drop-out
ArcGIS 10.2, ENVI 4.7, Google earth and MS and atmospheric haze. The data-sets that were mostly
Excel. The above flow chart shows a summary figure for affected by these distortions were all the Landsat 7
the analysis imageries (i.e. 2005, 2008, and 2012). Land 7 TM
Image Processing developed a faulty scan line corrector in May, 2003 which
The methods used in processing the imagery for this study resulted in scan line drop-out mostly seen as parallel lines
was image restoration/ pre-processing, image in the imagery (O'Neill, 2006). The scan lines in the
enhancement and image classification. imagery were removed using the gap-fill method in ENVI
Image Pre-processing version 4.7. Atmospheric corrections were also performed
For mapping or analysing the change in the land cover, on these imageries to minimize the atmospheric haze
radiometric and geometric restorations are essential in caused by variations in the atmospheric conditions
every remotely sensed data analysis. Geometric restoration between the dates. Atmospheric haze was not completely
gives the accurate orientation of the satellite images, thus removed from the imagery due to limited resources but
geo-referencing of the imagery. The imageries acquired was minimized using ENVI version 4.7.
were already geo-referenced from the World Geodetic Image Enhancement
System (WGS84); they were re-projected to the coordinate This technique deals with modification or improving the
system of the study area, i.e. Universal Transverse quality of the imagery, making it more suitable as
Mercator (UTM) zone 30 North for Ghana using ArcGIS perceived by humans. In order to improve the visibility of
version 10.2. Radiometric restoration removes or the imagery, a colour composite for the imageries were
suppresses the degree of spectral differences emanated established using Landsat TM bands 4, 3 and 2 (i.e. Near-

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 The effects of large-scale mining on the Land use and Land Cover Changes using Remotely Sensed Data

infrared, Red and Green) and this gave a false colour soils vary from dark to light brown, and water appears
composite. False colour composite was chosen because; very dark (Ned, 2004). Figure 3 and 4 shows the
vegetation mostly reflects in an infra-red colour, thus, they composite Landsat imagery of the study area in the false
appear in shades of red. Forest evergreen reflects in deep colour composite and they were carried out using ArcGIS
red, forest deciduous in light red, urban areas in cyan blue, version 10.2.

FIGURE 3: Landsat 7 TM of 2005 and 2008 scene of the Study Area

FIGURE 4: Landsat 7 TM & Landsat 8 of 2012 and 2015 scene of the Study Area

Image Classification Data (ISODATA) or Iterative Self-Organizing Cluster

The imageries were classified using unsupervised (ISOCLUSTER) algorithm. This groups similar pixels into
classification method prior to the little knowledge of the a unique cluster of specified classes according to statistical
study area available at hand. This method involves determined criteria. The grouped pixels were re-labelled
extracting land cover information from the imagery and it and combined with spectral clusters for informed classes.
is mostly referred to as clustering. Clustering of the land The main objective for classification was to produce a land
cover was achieved using an Iterative Self-Organizing cover classes that resemble the actual land cover types of

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I.J.S.N., VOL.7 (4) 2016: 724-733 ISSN 2229 – 6441

the earth surface (Kashaigili & Majaliwa, 2010). The imageries were grouped into five (5) land cover class prior
unsupervised classified imageries also known as thematic to the knowledge of the area and this is as shown in table 2
map was generated using IsoCluster in ArcGIS version below;
10.2 and Google Earth as the reference. The classified

TABLE 2: Description Land Use Land Cover Class

Land Cover Class Name Description
Forest Evergreen These are areas covered with trees that do not shed its leaves periodically.
Bare Land These are areas with less vegetation cover or no vegetation cover.
Forest Deciduous These are areas covered with Shrubs, Agricultural, Grasslands, and Herbaceous
perennials, i.e. those that shed their leaves periodically and are mostly in patches.
Built-Up Areas These are areas with residential or commercial structures i.e. roads, institutions,
mining areas, villages and towns.
Water body These are areas covered by rivers, ponds, dam etc.

Accuracy Assessment Excel to determine the accuracy. The data exported were
The ideal knowledge for accuracy assessment was from used to determine the error matrix i.e. the kappa
the fact that it is essential for every classified imagery coefficient (k), overall accuracy, commission error (user’s
results, due to the fact that classified imageries are deemed accuracy) and omission error (producer’s accuracy), of the
inaccurate to be used for its intended purpose or as a images classified. Overall accuracy is the total accuracy of
decision tool. This helps in determining the feasibility of the classified images. Commission error (user’s accuracy)
the classified image depending on the acceptable level of is the probability of a specific class to be incorrectly
error in the imagery. The accuracy level of a map is classified on the map, whiles Omission error (producer’s
determined by selecting reference points identified in the accuracy) is the probability of a specific class is
imagery which are evenly distributed and by comparing it incorrectly classified on the ground. Kappa coefficient (K),
with the test pixel or corresponding reference location of a gives a discrete multivariate technique used in accuracy
ground observation. Equal number of text pixels selected assessment, thus K>0.80 gives a strong accuracy or
for the reference point is not advisable as some classes agreement of the class assessed, 0.40-0.80 is average and
may have larger number than the others, hence the larger <0.40 is poor (Maps & GIS Library, 2014). The formulae
the class, the more the test pixels. The reference points given below were used to determine the kappa coefficient,
were randomly distributed in the imagery and generated overall accuracy, user’s and producer’s accuracy
using ArcGIS version 10.2 and further exported to MS respectively;
r r
N  x ii   ( x i  X x  i )
K  i 1
r
i 1
.........(4)
N   ( xi  X x  i )
2

i 1

Where; N is the total number of observations in the matrix

r is the number of rows in the matrix
xii is the number of observations in row i and column i
x i is the total for row i

xi  is the total for column I (Jensen, 2014).

Total number of individual pixels correctly classified
Overall accuracy  x 100.................(5)
Total number of classified cells
Total number of correctly classified individual cell(row)
User's accuracy= x 100..................(6)
Total number of pixel in a given class (row)
Total number of correctly classified individual cells(column)
Producer' s accuracy= x 100.....(7)
Total number of classified pixel(column)

Change Detection and Analysis rate of change between the imageries i.e. 2005-2008,
Change detection measures the changes that have occurred 2008-2012 and 2012-2015. These were done by using Ms
in a particular area over a period of time. In this study, Excel and the rate of change for the different land covers
post classification method was used to determine the cover estimated based on the following formulae used by
change, annual rate of change and the percentage annual (Kashaigili & Majaliwa, 2010);

A reai year x+ 1  A reai year x

P e r c e n ta g e C h a n g e = n
x 1 0 0 ................. ..................(8 )
i 1
A reai year x

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 The effects of large-scale mining on the Land use and Land Cover Changes using Remotely Sensed Data

Areai year x+1  Areai year x

Annual rate of change= ........................................(9)
t years
Areai year x+1  Areai year x
%Annual rate of change= n
x 100...................................(10)
 Area
i 1
i year x x t years

Where; Areai year x+1 = area of land cover (i) for the second date or the following date
Areai year x = area of land cover (i) for the first date/ initial date
n

 Area
i 1
i year x = the sum of the land cover area for the first date

t years = the number of years between the first and second imagery date.

RESULTS & DISCUSSION matrix using the formulas in equation 1-9. The results
Accuracy Assessment obtained are summarised in table 3 below for the four
The accuracy of the classified imageries was determined periods of the study area.
by generating 250 reference points to obtain an error

TABLE 3: Summary of Overall Accuracy and Kappa coefficient (k)

Year Classified Image Overall Accuracy (%) Overall Kappa coefficient (k)
2005 Landsat 7 TM 80.8 0.754
2008 Landsat 7 TM 92.8 0.908
2012 Landsat 7 TM 89.2 0.861
2015 Landsat 8 87.6 0.841

As shown in table 3 above, the highest overall accuracy lowest accuracy was obtained in 2005 image classification
for the four period of the study was 92.8% in 2008 because of the low quality of the imagery as a result of
whereas the lowest was 80.5% in 2005. The highest kappa clouds and the scan lines in the imagery and this led to
coefficient was 0.908 and the lowest was 0.754. The misclassification of the LULC.

FIGURE 5: Land Use Land Cover map, 2005

Land Use Land Cover Classification from 2005-2015 Five (5) land cover classes i.e. Forest Evergreen, Forest
The Landsat images for the study area were classified in Deciduous, Bare land, Built-up areas and Water Body
order to identify the changes in the cover between the four were identified. The land use land cover analysis is
periods, i.e. 2005, 2008, 2012 and 2015 respectively and presented in maps and tables as shown in figures 5, 6 and
this yielded four LULC maps from the satellite images. tables 4, 5 and 6 respectively.

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I.J.S.N., VOL.7 (4) 2016: 724-733 ISSN 2229 – 6441

FIGURE 6: Land Use Land Cover map, 2008

LULC Change between 2005-2015 0.15% respectively whiles built-up areas continued to
The various land use land cover classes for 2005, 2008, increase by 5.70%. The results are also presented in annual
2012 and 2015 were analysed to determine the area rate of change (ha/yr) and percentage annual rate of
covered by each class quantitatively. The LULC area, area change (%/yr) in table 4, 5 and 6, these were determined
of change and the rate of change between 2005-2008, using the formula in equation 8, 9 and 10 in the previous
2008-2012, 2012-2015 and 2005-2015 that have occurred section. In 2005-2008, forest deciduous, bare land and
in the study area were analysed and presented in table 3, 4 built-up areas increased by 240.75ha/yr (7.25%/yr),
and 5 respectively for the four (4) classified maps as 38.86ha/yr (1.17%/yr) and 13.51ha/yr (0.41%/yr) and
shown in the previous section. From 2005-2008 as shown forest evergreen and water body decreasing by
in table 4, the forest evergreen and water body decreased 289.14ha/yr (8.70%/yr), 3.99ha/yr (0.12%/yr). For 2008-
by 26.11% and 0.36% respectively, whiles forest 2012, most of the land covers of the study area decreased
deciduous, bare land, built-up areas and increased by whiles land use (built-up areas) was increasing. Thus
21.74%, 3.51% and 1.22% respectively. For the second forest evergreen, forest deciduous, water body, bare land
period (2008-2012) shown in table 5 above in the previous were decreasing in rate by 61.41ha/yr (1.84%/yr),
section, forest evergreen and water body still by decreased 52.15ha/yr (1.57%/yr), 1.34ha/y (0.04%/yr), 10.72ha/yr
by 7.36%, 0.61% and built-up areas increasing by 15.09%. (0.32%/yr) respectively whiles built-up areas were
During the second period, forest deciduous and bare land increasing by 125.34ha/yr (3.77%/yr). From 2012-2015
which increased in the first period decrease tremendously result, there was a continuous reduction in the forest
by 6.28% and 1.29% respectively. For the third period evergreen, forest deciduous, bare land, water body by
(2012-2015) shown in table 6 above in the previous 14.19ha/yr (0.43%/yr), 40.56ha/yr (1.22%/yr), 1.64ha/yr
section, forest evergreen, forest deciduous, bare land and (0.05%/yr), 6.71ha/yr (0.20%/yr) whiles built-up areas
water body still decreased by 1.28%, 3.66%, 0.61% and continued to increase by 63.10ha/yr (1.90%/yr).

TABLE 4: LULC Change between 2005 and 2008

LULC (2005) LULC (2008) LULCC (2005-2008)
Class Area Area (%) Area Area Area % Cover Annual %Annual
Name (Ha) (Ha) (%) Change Change rate of rate of
(ha) change change
(ha/yr) (%/yr)
FE 1492.93 44.94 625.52 18.83 -867.41 -26.11 -289.14 -8.70
FD 1282.03 38.60 2004.28 60.34 722.25 21.74 240.75 7.25
WB 37.08 1.12 25.11 0.76 -11.97 -0.36 -3.99 -0.12
BL 193.62 5.83 310.21 9.34 116.59 3.51 38.86 1.17
BA 316.05 9.51 356.59 10.73 40.54 1.22 13.51 0.41
TOTAL 3321.71 100.00 3321.71 100.00 0 0 -0.01 0.01
Forest Evergreen (FE), Forest Deciduous (FD), Water Body (WB), Bare Land (BL), Built-Up Areas (BA).

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 The effects of large-scale mining on the Land use and Land Cover Changes using Remotely Sensed Data

TABLE 5: LULC Change between 2008 and 2012

LULC (2008) LULC (2012) LULCC (2008-2012)
Class Area Area (%) Area Area Area % Cover Annual %Annual
Name (Ha) (Ha) (%) Change Change rate of rate of
(ha) change change
(ha/yr) (%/yr)
FE 625.52 18.83 380.95 11.47 -244.57 -7.36 -61.14 -1.84
FD 2004.28 60.34 1795.69 54.06 -208.59 -6.28 -52.15 -1.57
WB 25.11 0.76 19.77 0.59 -5.34 -0.16 -1.34 -0.04
BL 310.21 9.34 267.34 8.05 -42.87 -1.29 -10.72 -0.32
BA 356.59 10.73 857.96 25.83 501.37 15.09 125.34 3.77
TOTAL 3321.71 100.00 3321.71 100.00 0 0 -0.01 0
Forest Evergreen (FE), Forest Deciduous (FD), Water Body (WB), Bare Land (BL), Built-Up Areas (BA).

TABLE 6: LULC Change between 2012 and 2015

LULC (2012) LULC (2015) LULCC (2012-2015)
Class Area Area Area Area Area % Cover Annual %Annual
Name (Ha) (%) (Ha) (%) Change Change rate of rate of
(ha) change change
(ha/yr) (%/yr)
FE 380.95 11.47 338.38 10.19 -42.57 -1.28 -14.19 -0.43
FD 1795.69 54.06 1674.01 50.39 -121.68 -3.66 -40.56 -1.22
WB 19.77 0.59 14.85 0.45 -4.92 -0.15 -1.64 -0.05
BL 267.34 8.05 247.20 7.44 -20.14 -0.61 -6.71 -0.20
BA 857.96 25.83 1047.27 31.53 189.31 5.70 63.10 1.90
TOTAL 3321.71 100 3321.71 100 0 0 0 0
Forest Evergreen (FE), Forest Deciduous (FD), Water Body (WB), Bare Land (BL), Built-Up Areas (BA).

2500

2000

LULC,2005
Area (ha)

1500
LULC, 2008
1000 LULC, 2012
LULC,2015
500

0
FE FD WB BL BA
FIGURE 7: The trend of the Land Use Land Cover Changes

Considering the trend for Forest Evergreen over the years This shows a sharp increase from 2005-2008 and a gradual
between 2005 and 2015 with respect to the percentage area decrease from 2008-2015. The percentage area covered by
covered as shown in figure 8, the trend shows a strong water body over the years between 2005 and 2015 showed
negative correlation of r2=0.88 as there was a gradual a strong negative correlation of r2=0.967.
decrease in the percentage area coverage over the years. Generally, from the results, forest evergreen, forest
Therefore, as the years increase, the percentage area deciduous, and water bodies were decreasing as the built-
coverage of the Forest Evergreen decreases. However, the up areas increased. The increased depletion rate of the
Forest Deciduous shows a weak positive correlation i.e. general land cover indicated that, human population and
r2=0.385 over the years. The results also indicate a strong mining activities were immensely destroying the
positive correlation r2=0.969 over the years between 2005 vegetation cover. However, the mining activity is the main
and 2015 in relation to the percentage area covered by reason for the tremendous changes in the study area. This
built-up areas. The trend shows that as the year’s increase, is because, these activities are linked to both direct and
the percentage area covered by the built-up areas indirect changes of the land cover especially in areas
increases. Comparatively, the trend of Bare land was where the operations are being carried out. These have
weakly positive r2=0.285 in correlation over the years. substantial effects on the land cover land use of which the

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forest is the greatest victim because these mineral come to settle at the location of the mining operation for
resources are embedded within the soils in which they are work and trade purposes. Though, mining activities boosts
found. The loss of the forest cover was due to the random a country’s economy, it leaves a negative impact on land
and rampant clearing of the forest for mining activities cover because it is a function of time that results in direct
(surface mining) especially those mostly found on the and indirect effects on different land use and land covers.
mountainous areas in the Asutifi district. This indirectly This however, confirms the studies conducted by (Opoku-
has affected the water bodies as most of them had their Ware, 2010; Pandit, 2011; Kumi-Boateng et al., 2012 and
source from these mountainous and forested areas leading Nzunda, 2013) who concluded that, increase in
to their drying up as these vegetation covers are cleared. populations and mining activities and the changes in the
Built-up areas were increasing because of increase in land use/land cover were mutually related to each other.
population and also immigrants from far and near have

FIGURE 8: Forest Evergreen with respect to the percentage area covered

CONCLUSION increasing built-up areas. The built-up areas which

The study revealed that considerable portion of the land consists of mining areas increased from 316.05 ha (9.51%)
cover especially the forest evergreen area was converted to in 2005 to 1047.27 ha (31.53%) in 2015. Bare lands also
forest deciduous, built-up areas, bare lands during the decreased from 310.21 ha (9.34%) in 2008 to 247.20 ha
period of 2005-2015 at the Sunyani Municipality and the (7.44%) in 2015. The water body also decreased from
Asutifi district. Unsupervised classification method was 37.08 ha (1.12%) in 2005 to 14.85 ha (0.45%) in 2015.
used to delineate five (5) LULC classes (forest evergreen, This is attributed to the decline in the forest area, as most
forest deciduous, water bodies, bare lands and built-up of the water bodies find their source from these areas. The
areas). The highest overall accuracy for the classified forest deciduous areas were increasing because of the
imageries for the four periods was 92.8% with a kappa inability of the native tree species to regenerate and most
coefficient of 0.908 in 2008 whiles the lowest was 80.8% of these areas were colonised by herbaceous species. The
with a kappa coefficient of 0.754 in 2005. From the land use/land cover types keep changing at an alarming
results, Forest Evergreen areas were decreasing as the rate in the study area as the population and mining activity
years increased showing a strong negative correlation of increases. However, if the current trend of degradation of
r2=0.88 whiles Forest Deciduous areas showed a weak the environment continues without putting measures to
positive correlation of r2=0.385. Built-up areas increased curb the situation at hand, there could be an imbalance in
as the years pass-by showing a strong positive correlation the ecosystem.
of r2=0.969 whiles Bare-land areas showed a weak
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