Factors enhancing and restricting the success of SRA compliance on the FLEGT/VPA initiative in Ghana. Insights from Juaso forest district in Ghana.

This study examines the Voluntary Partnership Agreement (VPA) between Ghana and the European Union (EU) within the Forest Law Enforcement Governance and Trade Initiative (FLEGT). The VPA aims to enhance forest governance, reduce deforestation, combat illegal practices, and improve livelihoods of forest fringe communities. The research focuses on the implementation of social responsibility agreements (SRAs) under the VPA framework and identifies factors contributing to their success or presenting challenges. Data collection involved mixed methods, including literature review and a survey of individuals involved in SRAs. Descriptive and inferential statistical analyses, including exploratory factor analysis, were conducted. Principal component analysis revealed that accountability, monitoring of implementation and progress of SRAs, and documentation of SRA agreements were key factors contributing to the success of negotiated SRAs, explaining about 68.36 % of success variance. Challenges and constraints were categorized into two main factors: weak community capacity to negotiate SRAs and weak community capacity to enforce compliance, explaining about 71.4 % of challenge variance. The study found that the exclusion of the local SRA committee (LSRAC) from certain decision-making processes affected trust and transparency in calculating SRA benefits. Elite capture of benefits was identified as an issue, as the LSRAC did not conduct sufficient consultations with community members before negotiations. The findings emphasize the importance of including local communities in all forest management activities and call for increased awareness of SRAs, particularly for the LSRAC. The study highlights the need for proper representation of community interests during negotiations and their inclusion in forest management plans.

Land use and land cover changes implications on biodiversity in the Owabi catchment of Atwima Nwabiagya North District, Ghana.

This paper examined land use and land cover (LULC) change and implications to biodiversity in the Owabi catchment of Atwima Nwabiagya North District in Ghana from 1991 to 2021 using remote sensing, and geographic information systems (GIS), with participatory methods such as interviews and questionnaires with a sample size of 200 participants. The use of supervised classification with maximum likelihood algorithm in QGIS was employed to generate LULC maps of 1991, 2001, 2011, and 2021. Molusce Plugin in QGIS was applied to predict probabilities of LULC changes in 10 years (2021-2031). The results showed that high-density forest has disappeared from 1991 to 2021 while built-up has increased and remained the most dominant LULC from 2011 to 2021. There is a continual decline in the number of plant and animal species in and around the Owabi catchment. This can be attributed to the decline of high-density forests and increased built-up in the study area through human actions. The study identified the influence of human activities as the key forces of LULC change to biodiversity loss. This problem stemmed from the taste for housing and trading activities in the Kumasi Metropolitan Area which has resulted in an increasing demand for settlement because of its closeness to Kumasi and its environs. The study recommends that stringent preventive measures should be developed and enforced by various stakeholders including the Forestry Commission, Ghana Water Company Limited, Environmental Protection Agency, as well as the District/Municipal Assemblies to safeguard the forest from human activities. This recommendation will help these agencies to keep abreast with changes in LULC in the various communities and factors such as changes during the planning of the communities.

Monitoring the Extent of Reclamation of Small Scale Mining Areas Using Artificial Neural Networks.

Small scale mining is mainly widespread in developing and underdeveloped countries. Although it is a source of livelihood for several people, it causes environmental degradation. Reclamation is needed to restore mined areas to an acceptable condition. This study uses ANN to monitor reclamation activities in small scale mining area. Landsat satellite images of study area (2007, 2011 and 2016), ground truth data and ESRI shapefile of the study area were used for the analyses. Two ANN classification methods, Unsupervised Self - Organized Mapping (SOM) and Supervised Multilayer Perceptron (MLP), were used for the classification of the satellite images. Normalized Difference Vegetation Index (NDVI) change maps were generated in order to help confirm where actual change had occurred and to what extent it had occurred. The results show disturbance and revegetation in the study area between 2007 and 2016. The Barelands/mined areas class increased by 60.4% and a decrease in the vegetation class by 18.7% from 2007 to 2011. There was revegetation from 2011 to 2016 with the Barelands/Mined Area decreasing by 51.7% and the vegetation increasing by 3.9%. The study shows an increase in the settlement class by 87.3%. The research concludes that the application of ANN be strongly encouraged for image classification and mine reclamation monitoring in the country due to the size and quality of training data, network architecture, and training parameters as well as the ability to improve the accuracy and fine tune information obtained from individual classes as compared to other classification methods.