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The Accra Metropolis of Ghana has experienced rapid urban expansion over the past decades. Agricultural and forest-lands have been transformed into urban/built-up areas. This study analysed urban expansion and its relationship with the temperature of Accra from 1986 to 2022. Multi-source datasets such as remote sensing (RS) and other ancillary data were utilised. Land use land cover (LULC) maps were produced employing the random forests classifier. Land surface temperature (LST) and selected d(RS) Indices were extracted. Regression techniques assessed the interplay between LST and remote sensing indices. The LULC maps revealed increasing trends in the urban/built-up areas at the expense of the other LULC types. The analysis from the LST and the RS indices revealed a direct relationship between temperature and urban/built-up areas and an inverse relationship between temperature and vegetation. Thus, spatial urban expansion has modified the urban temperature of Accra. The integrated utilisation of RS and GIS demonstrated to be an efficient approach for analysing and monitoring urban expansion and its relationship with temperature.
Accra and Kumasi are the two major cities in Ghana. Spatial urban expansion has been experienced in transforming different non-urban Land Use Land Cover (LULC) types into urban/built-up areas with a potential direct relationship to temperature rise in the cities. Thus, this dissertation aims to establish the relationship between urban spatial expansion and temperature in Accra and Kumasi metropolis. Multi-source datasets such as remote sensing images, different GIS vector layers, reference maps and historical temperature datasets were used for this retrospective study. This research was grouped under three components: environmental science, environmental technology, as well as environmental management and planning. From the environmental science component, LULC maps were produced for different years to assess the trend of temporal change in the various LULC classes in the two metropolises. Remote sensing indices and land surface temperature were retrieved from the remote sensing images to determine their correlations. Temperature time series was analysed by calculating temperature indices and determining temporal trends to reveal changes in air temperature to detect urban warming and its impacts. From the environmental technology, the novel random forest algorithm was utilised to classify the satellite images of both cities since previous works have utilised other traditional classifiers. The satellite imageries were used for point-based estimation of temperature to determine Urban Heat Islands (UHIs) hotspots. For environmental management and planning, spatial urban expansion techniques were utilised to ascertain trends in urban/built-up areas, especially in both cities' sub-metropolitan zones. For prescient purposes, future LULC modelling was implemented to provide insights into the proportions of the various LULC changes in 2025. The analysis identified two salient findings: increased urban/built-up areas at the expense of agricultural and forestlands throughout the study period and the positive correlation between spatial urban expansion and temperature. This indicated warming up of urban temperature in both cities. The major findings in this dissertation provided evidence of how integrated datasets and research techniques can be utilised for LULC changes to determine the relationship between spatial urban expansion and temperature at local scales. Institutions such as metropolitan assemblies and policymakers may adopt the concepts demonstrated in this work to rapidly assess urban environments and investigate the relationship between spatial urban expansion and temperature.
Human influence on the biosphere has transformed natural land cover (LC) into modified LC. Shifting focus from individual species study to large-scale assessment is therefore needed to restore the damaged ecosystem. The hypotheses were: (a) the post-mining landscapes are getting more diverse over time (b) GIS, remote sensing and patch analyst can generate LC information and landscape characterization statistics for assessing habitat diversity and monitoring land cover change (LCC) in a disturbance-dominated area (c) relationship exist between habitat diversity and field based species richness/LCC and environmental conditions/LCC and biodiversity losses. The general aims of this research were (a) to assess the impact of disturbance on LCC and habitat diversity (b) to identify how accurately patterns of habitat diversity, complexity, fragmentation, primary production and LCC can be assessed or predicted with GIS and remotely sensed data. LC maps of 1988, 1991, 1995, 1998, 2000 and 2003 were produced from LANDSAT TM images of Schlabendorf Nord and Schlabendorf Süd. These images were used to survey the changing landscape. After classifying the images based on dominant land cover types, the area and perimeter of all patches were defined. Landscape characterization metrics were generated using patch analyst. LCC statistics were estimated for each LC map year. Change detection extension was used to identify changes among vegetation or land cover types into “negative change”, “no change” and “positive change”. DCA ordination technique (CANOCO) was used to study similarity among the distribution of land cover types. Other relevant analysis made include soil pH analysis and climate data evaluation. The outcome of the research shows that the process of LCC takes place at the interface between environmental and human systems. Land cover transformations in most cases in Schlabendorf were as a result of progressive and reversed plant succession. Overall changes in both landscapes showed increased area of pine afforestation, deciduous trees afforestation, lake and mixed grassland with trees. The LCC s were predominantly caused by harvesting of afforested pine, restoration related construction activities or other land cover management practices, reverse succession, low soil pH especially along the path of the mine strip, loss of soil fertility, human decisions and policies. There was increase in habitat richness, heterogeneity, fragmentation, and shape complexity due to decrease in habitat size, increased land use intensity etc. Lower habitat diversity in Schlabendorf Süd was due to the comparatively large landscape area, high patch number and higher habitat richness in most cases which confirms the claim that relationship between habitat diversity and habitat heterogeneity varies according to scale (Tews et al., 2004). Increase in biomass accumulation particularly in Schlabendorf Süd contributed to the exchanges for greenhouse gases between the forest cover, soil and the atmosphere, hence changing the climatic condition on terrestrial ecosystems, biodiversity and LCC. Unlike Schlabendorf Nord, Schlabendorf Süd still undergoes more active reclamation activities resulting in less similarity between the two post-mining landscapes over time. Within a given ecosystem, any change in habitat diversity can change the number of species in the resulting habitats types, in other words habitat diversity can be used to predict the species richness in a given ecosystem. The approach to this work provides a beneficial trade off between expensive ground vegetation or LCC survey and low-priced image processing analysis. It requires less manpower and less time but greatly reliable for assessing LCC, habitat diversity/richness and heterogeneity in large inaccessible areas such as dunes in post-mining landscape.