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Village savings and loans has become a prominent financing option for micro and small enterprises in the Lilongwe District, Malawi. Despite this, its impact on the performances of micro and small e...

Floods are hazard which poses immense threat to life and property. Identifying flood-prone areas, will enhance flood mitigation and proper land use planning of affected areas. However, lack of resources, the sizable extent of rural settlements, and the evolving complexities of contemporary flood models have hindered flood hazard mapping of the rural areas in Ghana. This study used supervised Random Forest (RF) classification, Landsat 8 OLI, and Landsat 7 ETM + images to produce flood prone, Land Use Land Cover (LULC), and flood hazard maps of the Nasia Watershed in Ghana. The results indicated that about 418.82 km2 area of the watershed is flooded every 2–3 years (normal flooding) and about 689.61 km2 is flooded every 7–10 years (extreme flooding). The LULC classification produced an overall accuracy of 92.31% and kappa of 0.9. The flood hazard map indicated that land areas within hazard zones of the river include the Nasia community, Flood Recession Agricultural (FRA), rainfed and woodlands. When compared with a Modified Normalized Difference Water Index (MNDWI), the RF supervised classification had an edge over the MNDWI in estimating the flooded areas. The results from this study can be used by local administrators, national flood disaster management and researchers for flood mitigation and land use planning within the watershed.

Punitive measure against anyone is for correctional purpose and making individuals become better citizens. In this regard, incarceration is supposed to lead to the rehabilitation of individuals to make them socially fit. However, ex-convicts face challenges in society including employment. Therefore, this study explores the willingness of an employer to employ an ex-convict. To achieve the objectives, a survey research design was adopted, and the responses obtained from a sample of 283 SME owners in the Western Region of Ghana. The findings show a high level of un-willingness of SME business to employing ex-convict. Again, employers and owners of SMEs have a negative inclination toward hiring ex-convicts. However, individuals with high levels of education and skills were found to have a better chance of being gainfully employed. To help the situation, education, training, tax reliefs and development of legal framework toward employing ex-convict would help deal with this situation.

Artisanal and Small-Scale Mining (ASM) landscapes form integral part of the Land use land cover (LULC) in the developing worlds. However, the spatial, spectral, and temporal footprints of ASM present some challenges for using most of the freely available optical satellite sensors for change analysis. The challenge is even profound in tropical West African countries like Ghana where there is prolonged cloud cover. Whiles very few studies have used Sentinel-2 data to map change analysis in ASM landscape, none examined the contribution of individual S2 bands to the ASM classifications. Also, despite the capabilities of Machine Learning (ML) and Deep Learning (DL) models for LULC classifications, few studies have compared the performances of different classifiers in mapping ASM landscape. This study utilized Sentinel-2 data, four ML and DL models (Artificial Neural Network –ANN, Random Forest – RF, Support Vector Machines –SVM, a pixel-based Convolutional Neural Network-CNN) and image segmentation to examine the performance of S2 bands and ML and DL algorithms for change analysis in ASM landscape, with the Birim Basin in Ghana as a study area. The result of the change analysis was used to assess changes in LULC during the recent ban on the expansion of ASM in the country. It was found out that ANN is a better classifier of ASM achieving the highest overall accuracy (OA) of 99.80% on the segmented Sentinel-2 bands. The study also found out that the Band 5 Vegetation Red Edge (VRE) 1 contributed most to classifying ASM, with the segmented VRE 1 being superlative over the other predictors. In terms of expansion, ASM increased by 59.17 km2 within the period of the study (January 2017 to December 2018), suggesting that ASM still took place under the watch of the ban. The classification results showed that most of the peripheral of forest and farmland have been converted to ASM with little disturbance within the interior of the forest reserves. The study revealed that, the ban was yielding very little or no results due to a number of policy deficiencies including low staff strength, lack of logistics and low remuneration. Enforcement of legal instruments against ASM and farming activities within the forest reserves, improvement in the monitoring systems and intensification of public education on the value of forest and the need to protect it are some of the major recommendations that could control encroachment on the forest reserves.

Unsustainable anthropogenic activities such as indiscriminate logging of trees, mineral exploitation, conversion of forest into agricultural lands are known to cause major environmental changes, thereby triggering a chain of irreversible forest depletion. This has called an urgent need by government and private agencies to institute policies and programs to curtail the destruction of the ecosystem due to the pressure on the available land. In this study, the Land use/land cover changes between the period of 1986 and 2020 in the tropical rainforest of Ghana was considered. A combination of machine learning and Markov chain approach was adopted to project future LULC for 2040 and 2060.The results showed that area covered by Open Forest declined from 21,531.87 km2 to 14,518.82 km2 and Dense Forest also declined from 14,313 km2 to 8202.98 km2 over a period of 34 years. The CA-Markov model was used to predict the future land use land cover, and it was observed that the total forest cover could decline to 15,551.79 km2 in 2040 and further decrease to 13,401.79 km2 in 2060. It was also found that settlement, mining and agricultural land, which is be driven by rapid population increase, has contributed significantly to the rapid declining forest cover. The results of this study have demonstrated the impact of unsustainable use of natural resources in these three regions. It also highlights the need for concerted effort to develop comprehensive environmental policies to encapsulate sustainable conversion and utilisation of natural resources by focusing on water-energy-food nexus.

Population growth coupled with economic, housing and environmental factors have significantly contributed into accelerated land use change in the New Juaben Municipality of Ghana. These factors have caused destruction of natural habitat and increased natural hazards such as flooding in the Municipality. Monitoring land use/land cover change is essential in respect to the dynamics of both human and natural factors that affect the biophysical and biochemical properties of the land surface. This research investigates the transitions among the major land use/land cover categories in the Municipality as a highly populated urban region that is facing some environmental challenges such as deforestation and degradation of the environment. Random Forest was adopted for the classification of 1985, 1991, 2002 and 2015 land cover maps while the analysis of the dynamics was conducted using intensity analysis. The unique contribution of this article is the combine usage of machine learning algorithm and intensity analysis to assess the changes in land use/land cover. The results showed that 1985–1991 and 2002–2015 periods experience fast change and the land use transformation has been accelerating over the whole period. The major changes were caused by the Built-up and Agricultural activities constituting 21.24 % and 13.19 % respectively in the category level. It is recommended that, authorities should consider several structural transformation measures within Ghana, including inter-sectoral land use harmonization policies (e.g. the Land Use and Spatial Planning Act 2016), land use planning and legal reforms to help address the underlying drivers of urban led deforestation.