Spatio-Temporal Land Cover Changes in Wassa Amenfi East and Upper Denkyira East Districts of Ghana

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Spatio-Temporal Land Cover Changes in Wassa Amenfi East and Upper Denkyira East Districts of Ghana

Dr. Saviour Mantey1 and Dr. Michael S. Aduap 1, 2Geomatic Engineering Department University of Mines and Technology

Tarkwa, Ghana

AbstractLand cover changes have become critical element in global environmental studies. Changes in land cover plays a major role in most of the environmental problems seen today. For this reason, modelling and projecting land cover changes is essential for the management and monitoring of our natural resources. The Wassa Amenfi East and Upper Denkyira East Districts in Ghana, have experienced extensive land cover changes for the past eleven years, mostly due to small-scale and illegal mining activities and accelerated urbanisation. This study therefore sought to identify and quantify the land cover changes in the study areas. The procedures used in this study include converting digital numbers (DN) to radiance values and reflectance, classifying satellite images using supervised (maximum likelihood) method. Ground truth observations were performed to check accuracies of the classified land cover classes. Results showed that substantial areas of forest cover vanished during the period of study which may be due to rapid urbanisation, small-scale and illegal mining activities in the study areas. In the Upper Denkyira East District, farmland and urban experienced an increase of 41% and 33% whilst the forest and water bodies decreased by 93% and 8%, respectively. Also, in the Wassa Amenfi East District, the urban and water bodies increased by 130% and 108%, respectively, whilst farmland and forest also decreased by 8% and 11%, respectively.

Keywords Wassa Amenfi East, Upper Denkyira East, Spatio- temporal land cover changes

  1. INTRODUCTION

    Over the years land cover of Wassa Amenfi East and Upper Denkyira East districts of Ghana have undergone significant changes. Human activities such as small-scale and illegal mining (galamsey) activities, farming and rural-urban migration have led to these changes. The study of land cover changes is therefore important for proper planning, utilisation, management and monitoring of the natural resources [1, 2]. Land is the most important natural resource on which all human activities depend on [3, 4]. Land cover change is also regarded as the most important variable of global change affecting ecological systems [3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. The high spatial variability in land cover type, biophysical and socio-economic drivers of land cover change around the world result in the variability in the causes and process of land cover change [13, Literature indicates that demographic changes account for land cover changes more than any other process [15, 16]. Others indicate the superiority of economic factors to be the major contributing factor [11]. Some socio-economic factors of land cover

    change include; poverty, tenure, insecurity and availability of market and credit facilities [17, 18, 19]. Human beings, consciously or unconsciously create pressure on land in their attempt to get the maximum benefit of the land. These pressures may take the form of either conversion or modification of the land cover [8, 11]. Land conversion can more easily be measured and monitored than modification in the composition within the land cover category [8].

    Wassa Amenfi East and Upper Denkyira East districts have undergone changes in land cover over the years due to mainly small-scale and illegal mining (galamsay) activities (Figures 1 and 2) and rural-urban migration [20]. These migration and illegal mining activities put pressure on the land of these districts, because new buildings and infrastructure have to be put up to accommodate the increasing population. Also, illegal miners dig parts of the land in search for gold and other minerals (Figure 3). These adversely affect the land cover in the districts, hence the need to assess the impact of this changes in order to properly manage the environment.

    Fig. 1 Illegal Mining Activity in Wassa Amenfi East

    Fig. 2 Illegal Mining Activity in Upper Denkyira East

    Fig. 3 Ponds created by Illegal Mining Activities

  2. MATERIALS AND METHODS

    Study Area: Wassa Amenfi East district was carved out from Wassa Fiase Mpohor district in 1988. The district forms part of the twenty-two (22) Metropolitan, Municipal and Districts in the Western Region of Ghana. The Administrative capital is Wassa Akropong, which is 6.7 km from the Cape Coast Takoradi main road. The district shares boundaries with Mpohor district to the West, to the East with Twifo/ Heman/ Lower Denkyira and Twifo-Ati Mokwa and to the South with Shama district and Komenda/Edina/Eguafo/Abirem Municipal [20]. The district lies between latitudes 5º 30' N and 6°15' N and longitudes 1° 45' W, and 2°11' W.

    The Upper Denkyira East district is one of the twenty (20) Administrative districts of the Central Region. The Administrative Capital is Dunkwa-On-Offin. It lies within latitudes 5º 30' N and 6º 02' N of the Equator and longitudes 1º W and 2º W of the Greenwich Meridian. It shares common boundaries with Adansi South in the North, Assin North Municipal in the East and Twifo Atti-Mokwa district in the West and Upper Denkyira West district in the North-West [20].

    Materials: In this study, data used include; Landsat 7 satellite images from the United States Geological Surveys (USGS) website using the Global Visualisation Viewer (GloVis), a shapefile of Wassa Amenfi East and Upper Denkyira East districts (Table 1) and ground truth coordinates were obtained from the field using a Garmin handheld GPS receiver.

    Table 1 Characteristics Dataset Used

    Data Type

    Path and

    Row

    Date of Acquisition

    Spatial Resolution/Scale

    Landsat 7 ETM+

    194/056

    2018-01-27

    30

    Landsat 7 ETM+

    194/056

    2007-01-13

    30

    Shapefile of

    study area

    2010

    1:50000

    Before image data can be processed and analysed, various pre-processing routines appropriate to the desired output must be applied to the imagery. These pre-processing routines enhances the quality of the image data by reducing various radiometric and geometric errors that arises due to both internal and external factors [21]. Geometric correction procedures are applied to rectify errors in the relative position of pixels due to factors such as variation in altitude, attitude and velocity of the sensor platform, earth curvature, panoramic distortion, relief displacement and non-linearities in the sweep of a sensor [21]. Radiometric correction on the other hand is performed to correct the data for sensor irregularities and unwanted sensor or atmospheric noise.

    Methods: In standard comparative analysis of multi-temporal images acquired on different dates and by different sensors, radiometric calibration is important. Every sensor has its own calibration parameters, data acquired from the Landsat ETM+ sensor were subjected to various routines. The various bands were extracted and converted from Digital Numbers (DN) to Radiance. The output feature classes (Radiance) were then converted to Reflectance for further analysis. Figure 4 shows a summary of the various steps undertaken.

    Equation 2

    where:

    = reflectance as a function of bandwidth

    d = Earth-sun distance correction

    = radance as a function of bandwidth

    = mean solar exo-atmospheric spectral irradiance

    s = solar zenith angle in degrees

    In order to extract the satellite images of the study area, a shapefile of the study area was used as a mask. With fore knowledge of the study area, homogeneous areas called training samples were digitised into classes. These training samples were then used to perform supervised classification using the maximum likelihood algorithm, as shown in Table 2.

    Land cover classes

    Detailed composition

    Urban / Barren Land

    These includes all Residential, and Commercial Complexes. Transportation, Communications, and Utilities Mixed Urban or Built-up Land, Bare exposed rock, and disturbed ground at building sites

    Farmland/Grass Land

    These includes Cropland, Pasture, Other Agricultural and Grass Land

    Forest/Dense Shrubs

    Evergreen, Deciduous, and mixed forests

    Water Bodies

    Lakes, Streams

    Land cover classes

    Detailed composition

    Urban / Barren Land

    These includes all Residential, and Commercial Complexes. Transportation, Communications, and Utilities Mixed Urban or Built-up Land, Bare exposed rock, and disturbed ground at building sites

    Farmland/Grass Land

    These includes Cropland, Pasture, Other Agricultural and Grass Land

    Forest/Dense Shrubs

    Evergreen, Deciduous, and mixed forests

    Water Bodies

    Lakes, Streams

    Table 2 Land Cover Classes

    Fig. 4 A Flow Chart showing Methods Used

    The Landsat Enhanced Thematic Mapper plus (ETM+) satellite sensor records reflected energy from the surface of the earth and stores them in digital numbers. This raw data encoded in 8-bit format (corresponding to 256 DN levels) cannot be used directly for analysing the land covers or surfaces. In order to make spatio-temporal analysis digital numbers of the data were converted to their corresponding radiance [22, 23, 24, 25]. Digital numbers were converted

    using Equation 1.

    = (() / ())

    () +

    Equation 1

    where:

    L = Spectral Radiance at the sensor's aperture.

    Gain =Re-scaled gain (the data product "gain" contained in the Level 1 product header or ancillary data record) or (LMAX LMIN) 255

    Bias =Re-scaled bias (the data product "offset" contained in the Level 1 product header or ancillary data record)

    QCAL = the quantized calibrated pixel value in DN LMIN is the spectral radiance that is scaled to QCALMIN in mWcm-2sr-1

    QCALMIN is the minimum quantized calibrated pixel value (corresponding to LMIN) in DN

    QCALMAX is the maximum quantized calibrated pixel value (corresponding to LMAX) in DN

    The reflectance value can be obtained by converting radiance to Top-of-Atmosphere (TOA) reflectance using Equation 2

    = (2) / ( )

    Accuracy Assessment: A set of ground control points were obtained from ground truthing. These control points though obtained using selective sampling method, adequately represented the diverse land cover classes within the study area. The points were plotted and were assigned their respective classes. Upon performing the supervised classification, a statistical accuracy assessment was performed. The overall classification accuracy of the study was 85.45% as presented in Table 5. Anderson et al [26] stated that accuracies of 85% are required for land use data for resource management. The overall accuracy was calculated using Equation 3 [7, 18, 22].

    Equation 3

    The training pixels with the ground truth data was measured using kappa coefficient technique. The kappa values are in range of +1.0 to -1.0, high positive value indicates high accuracy and vice versa. A value of zero kappa coefficient indicates no correlation in the classification. The kappa coefficient is calculated from Equation 4 [7, 18].

    where:

    Equation 4

    = total number of pixels p = total number of classes

    = total number of elements in confusion matrix

    o = sum of row i

    o= sum of column i

    from 2007 to 2018, while the forest area decreased by 11% from 2007 to 2018.

    Table 3 Land Cover Changes for Upper Denkyira East

    Areas in Kilometers Square

    Land cover classes

    2007

    2018

    Diff.

    Increase (%)

    Decrease (%)

    Urban

    39.523

    92.049

    52.526

    32.899

    Farm land

    295.934

    418.720

    122.786

    41.491

    Forest

    186.666

    13.177

    -173.489

    92.941

    Water

    22.842

    21.019

    -1.823

    7.981

    Total

    544.965

    544.965

    Areas in Kilometers Square

    Land cover classes

    2007

    2018

    Diff.

    Increase (%)

    Decrease (%)

    Urban

    39.523

    92.049

    52.526

    32.899

    Farm land

    295.934

    418.720

    122.786

    41.491

    Forest

    186.666

    13.177

    -173.489

    92.941

    Water

    22.842

    21.019

    -1.823

    7.981

    Total

    544.965

    544.965

    District

    The kappa coefficient obtained from this study was 0.92 which shows high accuracy.

    Table 5 Classification Accuracy Table for 2018

    Class

    Names

    Reference Total

    Number

    Correct

    Users Accuracy

    (%)

    Urban

    18

    14

    77.78

    Farmland

    14

    12

    85.71

    Water Bodies

    11

    9

    81.82

    Forest

    12

    12

    100

    Total

    55

    47

    Overall Accuracy=85.45%

  3. RESULTS AND DISCUSSION

    Results: Four land cover classes were identified. The total areas for each class are shown in Tables 3 and 4. Figures 5 and 6 show the land cover classes for Upper Denkyira East in 2007 and 2018, respectively. Also, Figures 7 and 8 show the land cover classes for Wassa Amenfi East in 2007 and 2018 respectively. From Table 3, in the Upper Denkyira East district, the study revealed an increase in the urban area from

    39.523 km2 in 2007 to 92.049 km2 in 2018 and decrease of forest cover from 186.666 km2 in 2007 to 13.177 km2 in 2018. From Table 4, in the Wassa Amenfi East district, the study also observed an increase of 130% in the urban area

    Table 4 Land Cover Changes for Wassa Amenfi East District

    tr>

    Areas in Kilometers Square

    Land cover classes

    2007

    2018

    Diff.

    Increase (%)

    Decrease (%)

    Urban

    17.953

    59.225

    41.272

    129.889

    Farm land

    533.600

    490.072

    -43.528

    8.157

    Forest

    582.609

    520.338

    -62.271

    10.688

    Water

    59.864

    124.391

    64.527

    107.789

    Total

    1194.026

    1194.026

    664920

    Ü

    664920

    Ü

    654920

    654920

    644920

    644920

    634920

    0

    3.5

    7

    14 Kilometers

    Legend

    Farmland Forest

    Water bodies Urban

    634920

    0

    3.5

    7

    14 Kilometers

    Legend

    Farmland Forest

    Water bodies Urban

    624398 632398 640398 648398 656398

    624398 632398 640398 648398 656398

    Fig. 5 Land Cover Classes (2007) for Upper Denkyira East District

    662845

    Ü

    662845

    Ü

    652845

    652845

    642845

    642845

    632845

    0 2.5 5

    10 Kilometers

    Legend

    Urban Farmland Forest

    Water Bodies

    632845

    0 2.5 5

    10 Kilometers

    Legend

    Urban Farmland Forest

    Water Bodies

    624695

    632695

    640695

    648695

    656695

    624695

    632695

    640695

    648695

    656695

    Fig. 6 Land Cover Classes (2018) for Upper Denkyira East District

    662514

    652514

    Ü

    662514

    652514

    Ü

    642514

    642514

    632514

    632514

    622514

    0 3.25 6.5

    13 Kilometers

    Legend

    Forest Urban Farmland

    Water Bodies

    622514

    0 3.25 6.5

    13 Kilometers

    Legend

    Forest Urban Farmland

    Water Bodies

    592122 600122 608122 616122 624122 632122

    592122 600122 608122 616122 624122 632122

    Fig. 7 Land Cover Classes (2007) for Wassa Amenfi East District

    661697

    Ü

    661697

    Ü

    651697

    651697

    641697

    641697

    631697

    631697

    621697

    0 2.755.5

    11 Kilometers

    Legend

    Urban

    Water Bodies Farmland Forest

    621697

    0 2.755.5

    11 Kilometers

    Legend

    Urban

    Water Bodies Farmland Forest

    592232

    600232

    608232

    616232

    624232

    632232

    592232

    600232

    608232

    616232

    624232

    632232

    Fig. 8 Land Cover Classes (2018) for Wassa Amenfi East District

    Discussion: Tables 3, 4 and Figures 5, 6, 7, 8 show changes in the various land covers. In the Upper Denkyira District, the urban areas increased by 33%. This may be due to the increase in the rural-urban migration. Increase in the small- scale mining especially in the district capital might have led to the influx of many people from various towns and villages in search of jobs. Farmlands increased by 41%. Although most of the people are farmers, the farms are mainly subsistence type, which does not generate much revenue for both the local people and the local government. It can be observed that there was a 93% reduction in the forest cover, which may be the result of many factors including the increase in farms, small-scale and illegal mining and timber lumbering. The water bodies also decreased by 8%, which may also be the result of the small-scale mining activities in and around water bodies.

    Wassa Amenfi East District land cover also experienced changes. The urban land cover increased by 130% which may be because of increased in population. Normally it will be suspected that water bodies may decrease due to the extensive nature of small-scale and illegal mining in the district. Water bodies in the district has increase by 108% because of uncovered pits by both small-scale and illegal mining activities. These uncovered pits accumulate large volumes of water forming lakes. In 2010, report from the district indicated farming as the major occupation of the district [20]. It was expected that as the districts major occupation is farming, farmland would increase, however this study shows that farmlands have decreased by 8% because some farmers sold their farmlands for small-scale and illegal mining activities. The largest land cover type in the district is forest. The forest cover also decreased by 11% and this is due to lumbering, and some people clearing the forest cover for small-scale mining activities.

  4. CONCLUSIONS AND RECOMMENDATION

Conclusions: In conclusion, four land cover classes were produced for the study areas as urban, farmlands, water bodies and forest. The studies show that there have been significant changes in the land cover types. In the Upper Denkyira East District the farmland and urban experienced an increase of 41% and 33% respectively due to population growth and farming as the major occupation. Lumbering, small-scale and illegal mining with other factors resulted in the decrease of the forest and water bodies by 93% and 8%, respectively.

Wassa Amenfi East District also experienced land cover changes. The urban and water bodies increased by 130% and 108%, respectively. Farmland and forest also decreased by 8% and 11%, respectively. Some of the driving forces of these land cover changes are small-scale and illegal mining, population growth and lumbering.

Recommendation: This study recommends that, Laws regulating small-scale and illegal mining as well as lumbering should be enforced to ensure proper management of the environment. Afforestation program should also be implemented to conserve the depleting forest.

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