Remote Sensing Study of Land Use / Cover Change in West Africa (original) (raw)
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Geospatial assessment of land use and land cover dynamics in the mid-zone of Ghana
Folia Forestalia Polonica , Series A - Forestry, 2020
Land use and land cover (LULC) terrain in Ghana has undergone profound changes over the past years emanating mainly from anthropogenic activities, which have impacted countrywide and sub-regional environment. This study is a comprehensive analysis via integrated approach of geospatial procedures such as Remote Sensing (RS) and Geographic Information System (GIS) of past, present and future LULC from satellite imagery covering Ghana's Ashanti regional capital (Kumasi) and surrounding districts. Multi-temporal satellite imagery data sets of four different years, 1990 (Landsat TM), 2000 (Landsat ETM+), 2010 (Alos and Disaster Monitoring Constellation-DMC) and 2020 (SENTINEL), spanning over a 30-year period were mapped. Five major LULC categories-Closed Forest, Open Forest, Agriculture, Built-up and Water-were delineated premised on the prevailing geographical settings, field study and remote sensing data. Markov Cellular Automata modelling was applied to predict the probable LULC change consequence for the next 20 years (2040). The study revealed that both Open Forest and Agriculture class categories decreased 51.98 to 38.82 and 27.48 to 20.11, respectively. Meanwhile, Built-up class increased from 4.8% to 24.8% (over 500% increment from 1990 to 2020). Rapid urbanization caused the depletion of forest cover and conversion of farmlands into human settlements. The 2040 forecast map showed an upward increment in the Built-up area up to 35.2% at the expense of other LULC class categories. This trend from the past to the forecasted future would demand that judicious LULC resolutions have to be made to keep Ghana's forest cover, provide arable land for farming activities and alleviate the effects of climate change.
Geospatial Analysis of Land Use and Land Cover Transitions from 1986–2014 in a Peri-Urban Ghana
Geosciences
Recently, peri-urbanisation has led to the transformation of the rural landscape, changing rural land uses into peri-urban land uses, under varying driving factors. This paper analyzes the dynamic transitions among identified land use and land cover (LULC) types in the Bosomtwe district of Ghana, from 1986 to 2014. An integrated approach of geo-information tools of satellite remote sensing in Earth Resource Data Analysis System (ERDAS) Imagine 13 and ArcMap 10.2 Geographic Information System (GIS), with Markov chain analytical techniques were used to examine the combined forest land cover transitions, relative to build-up, recent fallows and grasslands and projected possible factors influencing the transitions under business as usual and unusual situations. Statistical analyses of the classified Landsat TM, ETM+ and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIS) indicated that over the period of 24 years, the Bosomtwe district has undergone a series of land use conversions with remarkable forest losses especially between 2002 and 2010. In 2010 dense forest cover was degraded to low forest by 4040 ha indicating 0.40% transition probability in the future. There was a remarkable increase of built-up/bare and concrete area with a 380% increment in the 1986-2002 transition periods. The application of the Markov futuristic land use dynamics by the years 2018 and 2028, projected from the 2014 LULC indicated a future steady decline in the area coverage of the dense forest to low forest category. This is currently being driven (as at the 2017 LULC trends), by the combined effects of increasing build up bare and concrete surface land uses as well as the expanding recent fallows and grassland. The paper concluded that the health of the ecosystem and biodiversity of the lake Bosomtwe need to be sustainably managed by the Bosomtwe district assembly.
Land Cover Dynamics in Wa Municipality, Upper West Region of Ghana
2012
Land cover change is pervasive in urban areas and can destabilise the ecosystem with negative consequences. To manage land effectively and to protect its cover, there is the need for a reliable inventory. GIS and remote sensing technology has become a standard in producing land cover maps worldwide. Therefore, in this study GIS and remote sensing was used to map the land cover of Wa Municipality of the Upper West Region of Ghana. Two Landsat 5 images of 1986 and 2011 were used. The images were pre-processed, subset to the study area and classified using the maximum likelihood classification algorithm. The map accuracies for the classes of interest; built-up, bare land and vegetation were not less than 70%. The land cover maps generated indicated that built-up area has increased by 34% whiles total size of bare land has increased by 47% from 1986 to 2011.These increases have reduced the total area of vegetated land by 10%. Therefore, if the current rate of degradation is not controll...
The Scientific World Journal, 2021
This study aimed to evaluate land use/land cover changes (1987–2017), prediction (2032–2047), and identify the drivers of Majang Forest Biosphere Reserves. Landsat image (TM, ETM+, and OLI-TIRS) and socioeconomy data were used for the LU/LC analysis and its drivers of change. The supervised classification was also employed to classify LU/LC. The CA-Markov model was used to predict future LU/LC change using IDRISI software. Data were collected from 240 households from eight kebeles in two districts to identify LU/LC change drivers. Five LU/LC classes were identified: forestland, farmland, grassland, settlement, and waterbody. Farmland and settlement increased by 17.4% and 3.4%, respectively; while, forestland and grassland were reduced by 77.8% and 1.4%, respectively, from 1987 to 2017. The predicted results indicated that farmland and settlement increased by 26.3% and 6.4%, respectively, while forestland and grassland decreased by 66.5% and 0.8%, respectively, from 2032 to 2047. Eve...
Land Use Land Cover (LULC) Change Analysis of the Akuapem-North Municipality, Eastern Region; Ghana
International Journal of Research and Innovation in Social Science, 2021
Land-use changes are a significant determinant of land cover changes; this is on the grounds that it is human specialists; people, families, and private firms that make explicit moves that drive land-use change. An increment in family size, traveler populace, and abatement in the monetary prosperity of the indigenous area compels agricultural expansion. This paper aimed at analysing the Land-use Land-cover change pattern in the Akuapem-North Municipality and provide experimental record of land-cover changes in the municipality thereby broadening the insight of local authorities and land managers to better comprehend and address the complicated land-use system of the area and develop an improved land-use management strategies that could better balance urban expansion and environmental protection. Land cover change was observed through advanced processing and classification dependent on five multi-temporal medium resolution satellite symbolism (Landsat: 1986, 1990, 2002, 2017) into five classes. From this, precisely arranged pixel data were assigned to decide each land cover class size and the quantity of changed pixels into different classes through spatial change detection. It was discovered that land cover from 1986 to 2017 shows rapid changes in the landscape as there is high growth in built-up area. However, farmland and forest cover areas has reduced. Urban built-up area has extended outwards from the central-eastern part to the rest of the areas and has covered most of the northern, western, and southern parts. If the present growth trend continues, most of the vegetated areas will be converted into built-up areas in the near future, which may create ecological imbalance and affect the climate of the municipality.
Modelling forest loss and other land use change dynamics in Ashanti Region of Ghana
Forest losses amid land use dynamics have become issues of outermost concern in the light of climate change phenomenon which has captivated the world’s attention. It is imperative to monitor land use change and to forecast forms of future land use change on a temporal and spatial basis. The main thrust of this study is to assess land use change in the lower half of the Ashanti Region of Ghana within a 40 year period. The analysis of land use change uses a combination method in Remote Sensing (RS) and Geographic Information System (GIS). Cellular Automata and Markov Chain (Cellular Automata-Markov) are utilized to predict for land use land cover (LULC) change for 2020 and 2030. The processes used include: (i) a data pre-processing (geometric corrections, radiometric corrections, subset creation and image enhancement) of epoch Landsat images acquired in 1990, 2000, and Disaster Monitoring Constellation (DMC) 2010; (ii) classification of multispectral imagery (iii) Change detection mapping (iv) using Cellular Automata-Markov to generate land use change in the next 20 years. The results illustrate that in years 2020 to 2030 in the foreseeable future, there will an upsurge in built up areas, while a decline in agricultural land use is envisaged. Agricultural land use would still be the dominant land use type. Forests would be drastically reduced from close to 50% in 1990 to just fewer than 10% in 2030. Land use decision making must be very circumspect, especially in an era where Ghana has opted to take advantage of REDD+. Studies such as this provide vital pieces of information which may be used to monitor, direct and influence land use change to a more beneficial and sustainable manner.
Land is the most important natural resource on which all activities are based. Land use is seasonally dynamic and indeed is more changing. The knowledge of the Land Use/ Land Cover (LULC) is important for a lot of human activities. The purpose of the study was the mapping of land use / landcover for the years 2000 and 2016 and quantifying the changes in land use through a simple repeatable methodology for land cover change detection using high multi spectral Landsat TM satellite data. The objectives were to delineate the land cover changes spatially and quantitatively and to create a land use/land cover classification scheme. Envi 5.0 and Arc GIS software were the tools for getting out the land use/land cover layers, SOI top sheets and satellite imageries. Change detection was finally performed between the images of 2000 and 2016 to quantify the changes in various classes (dense vegetation, less dense vegetation, settlement, water bodies and settlement) from 2000 to 2016. The results show a positive change in settlement as it gained 134,163 pixel counts on the image representing 120,746,700 square metres. The percentage change in settlement was from 3.8% to 15.10% from 2000 to 2016. Bare land also gained 170,356-pixel counts representing 153,320,400 square metres. Also, dense vegetation and less dense vegetation were the classes loss. Dense vegetation had a negative change of 75,476 pixel counts which represent a loss of 67,928,400 square metres and less dense vegetation loss a total amount of 206,296,200 square metres. The outcome of this research is good for raising concerns of decreased vegetation in the area. It would therefore help in specifying models of land-use change.
Spatio-Temporal Land Cover Changes in Wassa Amenfi East and Upper Denkyira East Districts of Ghana
International Journal of Engineering Research and, 2020
Land 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.
Sensors
Forest loss, unbridled urbanisation, and the loss of arable lands have become contentious issues for the sustainable management of land. Landsat satellite images for 1986, 2003, 2013, and 2022, covering the Kumasi Metropolitan Assembly and its adjoining municipalities, were used to analyse the Land Use Land Cover (LULC) changes. The machine learning algorithm, Support Vector Machine (SVM), was used for the satellite image classification that led to the generation of the LULC maps. The Normalised Difference Vegetation Index (NDVI) and Normalised Difference Built-up Index (NDBI) were analysed to assess the correlations between the indices. The image overlays of the forest and urban extents and the calculation of the annual deforestation rates were evaluated. The study revealed decreasing trends in forestlands, increased urban/built-up areas (similar to the image overlays), and a decline in agricultural lands. However, there was a negative relationship between the NDVI and NDBI. The re...
West African Journal of Applied Ecology, vol. 26(SI), 2018
Kakum Conservation Area which is roughly 1187km2, extends over large portions of forest reserves in the Assin South District of Ghana. The district hosts the remaining biodiversity hotspots within highly fragmented rainforest of West Africa. Although the conservation is gazetted as protected area, it has since been impacted by illegal chainsaw logging, expanding agricultural land use and built construction to meet the housing needs of the rapidly growing population of the district. However, there is paucity of data on the magnitude, rate and types of land cover change occurring in the district. This study seeks to address these by examining the magnitude, the rate and direction of change in land cover between 1991 and 2015. The study objective was achieved using supervised classification and post classification change detection of remotely sensed Landsat satellite imagery of the district taken in 1991, 2001 and 2015. The results show that, within the study period, the population of the area increased by 2.9%, thick forest decreased by 8.2km2, light forest increased by 5.3km2 and built environment increased by 2.9km2 per annum. These results are considered potential hindrance to sustainable development, including biodiversity conservation in the forest reserves and climate change mitigation in general. There is therefore need for measures to end deforestation and stimulate reforestation of the lost forest cover. The district needs to initiate an enquiry into the effectiveness of the current forest reserve management practices and sustainability of land use systems in the district.