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Land use and land cover change (LULCC) is a general term for the human modification of Earth's terrestrial surface. These modifications come by direct and indirect activities of human actions when securing essential resources. Therefore, it is necessary to analyze these land changes for the best management of natural resources and to ensure better livelihood of the inhabitants. The influx of population from the Kumasi metropolis and peripheral areas coupled with urbanization is causing rapid changes to LULC in the Atwima Nwabiagya District. This has put pressure on the land cover especially in the forest cover areas and has affected the socioeconomic activities in the district. In view of this problem, this research sought to map and analyze the spatio-temporal LULCC patterns using multi-temporal satellite images from 1986-2019 and to identify the socioeconomic factors and their impact of LULC on the study area. Seven LULC classes were identified including; bare land, closed forest, cropland, open forest, savannah, settlement, and water. The results showed that during the period under review (1986-2019) there have been losses of 4178.5 hectares (7%) in closed and open forest and bare land whilst crop land and settlement have seen an increase of 21685.3 hectares (37%). In all, settlement establishment as a result of population increase, commercial activities, and porous land tenure system was identified as the main factors for LULCC in the Atwima Nwabiagya District.

The objective of this paper is to analyze the land surface temperatures (LST) derived from three satellite images as a proxy for urban heat island potential, through a peri-urban heat troughs (PuHT) to rural cool troughs (RuCT) continuum, concepts largely overlooked in the literature, in the Bosomtwe district of the Ashanti region of Ghana. Four Landsat satellite images from 2002, 2008 Enhanced Thematic Mapper+ (ETM+) and 2014 Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) were geo-referenced and processed for classification using the maximum likelihood classifier algorithm in ERDAS Imagine 13. Land Use and Land Cover (LULC) transition analysis was performed in ArcMap for ArcGIS 10.2. Results indicate that, in order of importance, recent fallows and grasslands along with built up/bare land and concrete surfaces have been increasing in terms of coverage. A corresponding surface reflectance translated into LST values ranging between a minimum of 24oC (297K) to a maximum of 53oC (326K). Changing LULC types correlated with the land surface temperature fluxes, creating the RuCT and PuHT. This result explains the relatively substantial peri-urban land use dynamics in the district. Future studies should develop threshold values for RuCT and PuHT temperatures.

A wall-to-wall Earth observation (EO) data is required, as recommended by the Intergovernmental Panel on Climate Change, private sector organizations and major development partners, to allow for the implementation of forest monitoring commitments, and also to monitor commodity-led deforestation. However, a major limitation associated with the use of optical EO data in the high forest zone of Ghana is the presence of persistent cloud cover and the spectral limitations of segregating agroforestry cocoa (AFC) from open canopy forest (OCF) cover. The aim of the study was to investigate the synergistic use of Sentinel-1 (S1) and Sentinel-2 (S2) EO data to produce a Land Use Land Cover map which shows AFC and OCF as different land cover classes. It was hypothesized that, a hybrid method of spectral, radar and image objects will accurately segregate the different cocoa system from forest and other land use classes. The research was conducted in the Bia-Juaboso REDD + Hotspot Intervention Area in the cocoa-forest mosaic landscape within the High forest zone of Ghana. The S1 and S2 datasets were freely acquired for the periods January to March 2018. The S1 datasets were pre-processed from backscatter intensity values to the VV and VH bands. The S2 datasets were corrected for atmospheric effects, and cloud pixels were masked and filled using a temporal gap-filling method. Six vegetation indices (VIs) were extracted, and the Multiresolution Segmentation algorithm was used to derive image objects (IOs). The S2 bands, the six VIs, the S1 VV + VH data, and the IOs were stacked into 3 different multi-layer image dataset denoted with D (i.e. D1 = S2 + VIs; D2 = S2 + VIs + S1; and D3 = S2+VIs + S1+IOs). The three datasets were classified using Random Forest and 1228 training points. Overall accuracy (OA) and kappa (k) were calculated for the classification outcome using 615 independent validation points. McNemar's test ( χ ) was used to access the statistically significant difference between D1, D2 and D3. The results of the study show that, D3 significantly improved the overall classification output (OA = 89.76%, k = 0.877) compared to D1 (OA = 79.02%, k = 0.748; χ = 5.56, p-value = 0.018) and D2 (OA = 80.49%, k = 0.765; χ = 5.50, p-value = 0.019). Combining spectral pixels with image objects increases overall classification accuracy and specifically, the accuracy of isolating AFC from OCF. This research is significant because it will provide an improved decision support to government-led monitoring and the private sector's commitment to halt cocoa-driven deforestation. Furthermore, and most importantly, the map shows agroforestry cocoa separated from monoculture cocoa, which provides a tremendous boost to monitoring landscape-level improvements associated with the promotion and adoption of agroforestry in cocoa landscapes, as a climate-smart practice and also for monitoring various off-reserve landscape forest restoration activities.

The unfavorable effect of human disturbances in major river basins resulting in extensive variations in the ecosystem has necessitated this research within the Pra River Basin of Ghana. Knowledge about how individual land use influences water balance components will massively improve natural resources planning, management and sustainable development. Land use/cover change in the basin was evaluated by the assessment of six historical and one future land use/cover maps relating to the land use/cover stages in 1986, 2000, 2004, 2008, 2013, 2016 and 2025. The land use/cover maps were deployed to generate seven land use/cover scenarios relating to diverse states of land use/cover change in the basin which were then deployed to simulate the hydrological components. The Soil and Water Assessment Tool was deployed for hydrological simulation. The results revealed that there was a continuous increase in cropland, settlement and mining at the detriment of forest areas from 1986 to 2016, which is expected to continue into 2025. The variations in land use/land cover brought about increment of surface runoff (124.51%) and water yield (40.13%), and reduced baseflow (30.08%) and ET (13.248%). Conversely, the results showed a reduction in surface runoff and water yield during dry season and increased baseflow and evapotranspiration during wet season. These trends are expected to further increase in 2025. Moreover, settlement and mining are the major contributors to changes in water balance components. These results show how important control of land use/cover is in order to maintain water balance components to guarantee water accessibility for all generations.

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.

An article published by Journal of Scientific Research & Reports 5(1): 16-31, 2015; Article no.JSRR.2015.071

The outcome reveal that as the land use in the catchment areas change from mixed agricultural to cassava cultivation, the simulated loads and concentrations of nitrogen species from cassava land-use scenario recorded reduction. The resultant concentrations of nitrate and nitrite for both current and future land-use scenarios are all below the daily limit suggested by the WHO, (World Health Organization). For the phosphate concentration, an increase of 4.21% was depicted under cassava land-use scenario. The results show that SWAT is a reliable water quality model, capable of simulating accurate information for developing environmental management plans.

Reducing emissions from deforestation and forest degradation with other benefits (REDD+) mechanism is supposed to address the reversal of forest-based land degradation, conservation of existing carbon stocks, and enhancement of carbon sequestration. The Bosomtwe District is predominantly agrarian with potentials for climate change mitigation through REDD+ mechanism among smallholder farmers. The limited knowledge and practices of this strategy among farmers are limiting potentials of mitigating climate change. This paper assesses the REDD+ potentials among smallholder farmers in the district. Using a triangulation of quantitative and qualitative design, 152 farmer-respondents were purposively sampled and interviewed, using snowballing method from 12 communities. Quantitative data gathered were subjected to the tools of contingency and frequencies analysis, embedded in the Statistical Package for Social Sciences (SPSS) v.16. The qualitative data were analyzed thematically. Results indicate that respondents have knowledge of REDD+ but not the intended benefit sharing regimes that can accrue to the smallholder farmers. Farmers’ willingness to practice REDD+ will be based on the motivation and incentive potentials of the strategies. The Forestry Services Division should promote the practice of REDD+ among smallholder farmers through education, to whip and sustain interest in the strategy.

Using Satellite Remote Sensing and Geographic Information System, this paper analyzes the land use and land cover change dynamics in the Bosomtwe District of Ghana, for 1986, 2010 thematic mapper and enhanced thematic Mapper+ (TM/ETM+) images, and 2014 Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIS) image. The three images were geo-referenced and processed for classification, using the maximum likelihood classifier algorithm. A Jeffries-Matusita’s separability check was used in confirming the degree of spectral separation acceptability of the bands used for each of the land use and land cover classes. The best Kappa hat statistic of classification accuracy was 83%. Land Use and Land Cover (LULC) transition analysis in Environmental Systems Research Institute ESRI’s ArcMap was performed. The results of the classification over the three periods showed that built up, bare land and concrete surfaces increased from 1201 in 1986 to 5454 ha in 2010. Dense forest decreased by 2253 ha over the same period and increased by 873 ha by the 2014. Low forest also decreased by 1043 ha in 2010, however, it increased by 13% in 2014. Our findings showed some of the important changes in the land use and land cover patterns in the District. After the urbanization process, coupled with farmland abandonment, between 1986 and 2010, substantial increments in urban land and clear increments in farmland coverage between 1986 and 2014were found to be the reason for vegetation cover decreases. This suggests that major changes in the socio-ecological driving forces affecting landscape dynamics have occurred in the last few decades.

The main aim of this paper is to examine the effects of peri-urbanization on peri-urban land use change patterns, using a binary logistic regression model, in the Bosomtwe district of the Asante region, Ghana. The decision to convert from agricultural land uses to residential and commercial land uses are driven by a myriad of factors, ranging from social to economic in the Bosomtwe District. A triangulation of qualitative and quantitative design was used. Household questionnaires were proportionately administered to 270 respondents in 14 communities, on the basis of population. The data was subjected to the Pearson’s Chi-square, Nigelkerk R2 and Cramer’s V test of strength of association. Astep-wise binary logistic regression modeling was performed using the PASW v.17. Pearson chi-square value of ?2 = 73.546 at 26 degrees was significant at p< .000,athough the Cramer’s V test of the strength of the association was moderate at V = 0.37. The logistic regression model reported an overall significance of the model at p< .000 with ?2 = 24.453, at 4 degrees of freedom. The confidence intervals of (CIs) of (CI: 1.358—4.517) and (CI: 1.039—11.486) for the two main predictors of the outcome, and a B(Exp) values ranging between 2.477 and 3.455 were also reported. This means the odds of respondents being more likely to change their land uses is about 66%. Increasing rate of peri-urbanization is caused by increasing demand for residential, recreational (Hotels and Guest houses) and commercial land uses at the expense of agro-forest land uses. These have some negative implications on local climate and food security. The District assembly should strictly monitor physical development in line with planning schemes.

This paper is a critical review, which synthesizes the theory-application linkage of peri-urban land use and land cover changes (LULCC) using the Bosomtwe District in the Ashanti Region of Ghana as the case. From abstractive thinking to empirical possibility, we conjecture human decisions within agent-based modeling (ABM) perspective. The key question the paper has tried to answer is: what are the probable future land use conversion and modification potentials in the district? LULCC in peri-urban areas respond to social and biophysical dynamics. These control spatial distribution of populations, infrastructure, and the space economy. Under systemic laxity of controls, peri-urban land uses deviate from effective land use plans.