A Spatio-Temporal Based Estimation of Sequestered Carbon in the Tarkwa Mining Area of Ghana

The vegetation in the Tarkwa Mining Area (TMA) has experienced changes as a result of population growth, urbanization, mining activities and illegal chainsaw operations and this has led to an increase in temperature over the past years. Therefore, studying its forest biomass carbon (C) stock and its spatio-temporal change is important to the sustainability of forest resources and understanding of the forest C budget in the TMA. In this study, aboveground forest biomass/carbon stock and its changes in the TMA were estimated from three nested-circular plots of horizontal radii 12.62 m, 8 m and 4 m using stratified random sampling from sixty locations in five land use/cover types as well as GIS/Remote Sensing techniques over a 21 year period. An estimated total of 1 250.93 ± 7 Mg/km2 carbon was recorded in the TMA. Carbon in different land-use/cover types ranges from 587.76 ± 4 Mg/km2 carbon in closed canopy to 270.23 ± 2 Mg/km2 carbon in shrubs/herbaceous. The TMA also experienced an average of 412.14 Gg of carbon (equivalent to 19.63 Gg carbon per year) lost between 1986 and 2007 due to the changes in the land use/cover types. The study area is however, considered a net source of carbon.Keywords: Spatio-Temporal, Carbon, Mining, Biomass, GI

Comparative Analysis of Stockpile Volume Estimation using UAV and GPS Techniques

Mining operations involve the extraction of minerals of economic value from the earth. In surface mining operations, overburdens need to be stripped in other to reach the ore. Large volumes of waste as well as ore is stripped in the process. Various technologies have been used to aid in stockpile volume estimation. Notable among them are the Total Stations and Global Positioning Systems (GPS). However, labour, safety and time has challenged the use of these technologies. Unmanned Aerial Vehicle (UAV), commonly known as drone is an emerging technology for stockpile volume computations in the Mine. UAV technology for data collection is less labour intensive, safer and faster. Therefore, this study applied the UAV technology in an open pit to estimate stockpile volumes from a Mine. For the purpose of this study, GPS and UAV data were collected for measuring stockpile volumes of materials mined. The actual volumes of stockpiles A, B, C, D (Case 2), produced differences of 0.05% for A, 0.05% for B, 0.08% for C, 0.07% for D and 0.03% for A, -0.03% for B, 0.03% for C and 0.04% for D, for the GPS-based and the UAV-based techniques, respectively. The GPS-based technique generated moderate accuracies for volume estimation, but was time consuming and labour intensive, compared to the UAV-based technique; which was faster and less labour intensive. The UAV-based technique was the most accurate, safest and is capable of mapping large areas rapidly. It is therefore recommended that UAV survey be incorporated in stockpile volume estimation. Keywords: UAV, GPS, Stockpile, Mine, Total Station

Impacts of global changes on a lowland rainforest region of West Africa.

Doctor of Philosophy in Hydrology. University of KwaZulu-Natal. Pietermaritzburg, 2016.Abstract available in PDF file

Effects of Small-Scale Mining Activities on Fisheries and Livelihoods in the Birim River in Atiwa District, Eastern Region of Ghana

The socioeconomic importance of small-scale mining in Ghana is of great interest to the country. It provides employment, supports the livelihood of poor rural areas, and also contributes to national income. Despite all these importance, it has a great influence on the surroundings, with surface and groundwater bodies being the most affected. The study was conducted to determine the effects of small-scale mining activities on fisheries and livelihoods along the Birim River in Atiwa District of Ghana, and also, to ascertain the nature of mining in the catchment and the current status of fisheries in the river. Data were collected with a help of open and closed ended questionnaire and fishers were sampled using purposive sampling method. Findings revealed that fishing activity was vibrant before 2010, but declined afterward. Respondents indicated that the number of fishers who went fishing daily before 2010 decreased drastically after 2010. Also, before 2010, most of the fishers (62%) used fishing nets compared to that after 2010. About 74% of respondents revealed that they could harvest at most 5 crates of fish a day before 2010, while about 38.6% of respondents indicated they could harvest 5 crates after 2010; thus 25 kg per day before 2010. It was revealed that 34% of respondents indicated they could make above $10 daily before 2010 as against 2$10 per day after 2010. About 90% of the respondents attributed their levels of harvest and average income per day to the effects of mining, with the reason being that miners wash their products in the river (52%), thus polluting the waterbody. Mining in water locally and commonly known as “changfan” was the main type of mining in the study area. From the findings, it is concluded that small-scale mining has detrimental effects on aquatic ecology and has resulted in the creation of deep pits which destroy gears, increasing the costs of gear repairs, fishing effort, and pollutes water (physical and chemical factors). Hence, it is recommended that there should be a ban on all small-scale mining in and around water bodies as this has detrimental effects on water quality and reduce fishing in the area. Keywords: Pollution; small-scale mining; Birim River; Changfan; Atiwa; Ghan

Mapping the expansion of galamsey gold mines in the cocoa growing area of Ghana using optical remote sensing

Artisanal gold mining (galamsey) and cocoa farming are essential sources of income for local populations in Ghana. Unfortunately the former poses serious threats to the environment and human health, and conflicts with cocoa farming and other livelihoods. Timely and spatially referenced information on the extent of galamsey is needed to understand and limit the negative impacts of mining. To address this, we use multi-date UK-DMC2 satellite images to map the extent and expansion of galamsey from 2011 to 2015. We map the total area of galamsey in 2013 over the cocoa growing area, using k-means clustering on a cloud-free 2013 image with strong spectral contrast between galamsey and the surrounding vegetation. We also process a pair of hazy images from 2011 and 2015 with Multivariate Alteration Detection to map the 2011–2015 galamsey expansion in a subset, labelled the change area. We use a set of visually interpreted random sample points to compute bias-corrected area estimates. We also delineate an indicative impact zone of pollution proportional to the density of galamsey, assuming a maximum radius of 10 km. In the cocoa growing area of Ghana, the estimated total area of galamsey in 2013 is 27,839 ha with an impact zone of 551,496 ha. In the change area, galamsey has more than tripled between 2011 and 2015, resulting in 603 ha of direct encroachment into protected forest reserves. Assuming the same growth rate for the rest of the cocoa growing area, the total area of galamsey in 2015 is estimated at 43,879 ha. Galamsey is developing along most of the river network (Offin, Ankobra, Birim, Anum, Tano), with downstream pollution affecting both land and water

Human Exposure Risks Assessment of Heavy Metals in Groundwater within the Amansie and Adansi Districts in Ghana using Pollution Evaluation Indices

Non-carcinogenic risk assessment was done using Hazard Quotient (HQing/derm) and Hazard Index (HIing/derm) following USEPA methodology for a total of 59 boreholes and 12 hand dug wells sampled between July and October 2012. The objective was to assess the potential human health risks caused by exposure to non-carcinogenic heavy metals and estimate the potential environmental risk exposure in order to ensure the health safety of consumers within the Amansie and Adansi Districts. The results shows that, the heavy metal abundance in groundwater within the districts is in the order: Fe > Mn > As > Zn > Cu = Pb > Cd > Hg, for borehole water and Fe > As > Mn > Zn > Cu > Cd > Pb > Hg, for well water. The percentage contributions are: Fe (60%), Mn (20%), As (7%), Zn (5%), Cu (4%), Pb (4%), Cd (0%) and Hg (0%). The results also show that, the potential non-carcinogenic risks of exposure (HQing/derm) posed by Fe, Mn, Cd, Cu, Zn, Pb, As and Hg within a single route of exposure via ingestion or dermal contact is 3.30 x 10-2, 1.40 x 10-1, 5.00 x 10-4, 3.70 x 10-2, 3.00 x 10-1, 3.60 x 10-2, 3.00 x 10-4 and 3.00 x 10-4 respectively for both adults and children, suggesting a decreasing order of Zn > Mn > Cu > Pb > Fe > Cd > As = Hg, for borehole water, and Zn > Mn > Cu > Fe > Cd > As = Hg, for well water. The concerns for potential human health risks caused by exposure to non-carcinogenic heavy metals for Fe, Mn, Cd, Cu,Zn, Pb, As, and Hg are: 6.0 x10-2, 2.56 x 10-1, 9.15 x 10-4, 6.77 x 10-2, 5.49 x 10-1, 6.59 x 10-2, 5.49 x 10-4, 5.49 x 10-4 for boreholes, and 6.46 x 10-2, 2.74 x 10-1, 9.79 x10-4, 7.25 x 10-2, 5.88 x 10-1, 5.88 x 10-4, 5.88 x 10-4 for well water, suggesting that there is no concern for potential human health risks caused by exposure to non-carcinogenic toxic heavy metals in groundwater within the Districts (i.e HQ/HI As > Cd > Pb > Cu > Zn, for borehole water, and As > Cd > Cu > Zn for well water, suggesting that, groundwater within the Districts is potentially threatened by anthropogenic activities primarily, mining activities where, chemicals such as arsenic (As) and mercury (Hg) are used to recover gold from its amalgam. Based on the classification of environmental risk using comprehensive risk factor (CRI), borehole water within the districts could be classified as very high risk, while, well water could be classified as high risk. Generally, the main environmental heavy metals that poses pollution risk in groundwater within the Districts were Hg, As and Cd and contributed mostly to the Risk index factor (Ri)

Forest cover monitoring in Southwestern Ghana with remote sensing and GIS

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesObuasi is one of the major municipalities in southwestern Ghana, Forest resources play a major significant role in the day-to-day activities of the locals due to their high dependency on it. Despite this contribution, the annual rate of current deforestation in Obuasi is about 50 hectares. At this rate the municipality may lose its substantial forest cover completely in the next 25 years. GIS and remote sensing techniques have proven to be efficient ways to monitor forest cover, especially on a large-scale using satellite imagery. In this study, a post-classification comparison change detection algorithm was used to determine the change in forest cover in the 1991-2021 period. The methodology includes a statistical analysis of rainfall and temperature variability for a period of 30years as well as the analysis of perceptions and knowledge of locals on forest modifications. MOLUSCE plugin in QGIS was used to model and generate maps of forest cover and predict future changes in land use/land cover. The land-use/landcover maps showed that between 1991 to 2000 forest areas decline at the rate of 17.1% while another class such as agricultural, built-up, and mining sites has a significant increase of 14%, 4%, and 2% respectively. Between 2000 to 2021, forest areas and agricultural lands decrease from 67% to 60% and 26% to 20% respectively while built-up and mining areas increase from 4% to 12% and 3% to 7% through forest areas remain the dominant landcover class in the area. During the same study period, there was a fluctuation in climatic conditions. Rainfall between 1991 to 2021 has reduced by an amount of 24 mm while temperature has increased to 0.037°C per annum. The majority of the locals believe that cultivated land expansion and mining are the driving forces of forest cover change in the area and the only solutions to these issues are through enrichment planting, and strengthening forest protection laws and mining regulations. Future prediction on forest cover in the area for 2030 map shows that forest areas will be the major contributor of land to other land use/landcover class, henceforth causing it to decline if no intervention is made. These findings can be used to inform conservation and management strategies to mitigate the impact of forest cover change and protect the ecological integrity of forests in the municipality

Development of Mining Sector Applications for Emerging Remote Sensing and Deep Learning Technologies

This thesis uses neural networks and deep learning to address practical, real-world problems in the mining sector. The main focus is on developing novel applications in the area of object detection from remotely sensed data. This area has many potential mining applications and is an important part of moving towards data driven strategic decision making across the mining sector. The scientific contributions of this research are twofold; firstly, each of the three case studies demonstrate new applications which couple remote sensing and neural network based technologies for improved data driven decision making. Secondly, the thesis presents a framework to guide implementation of these technologies in the mining sector, providing a guide for researchers and professionals undertaking further studies of this type. The first case study builds a fully connected neural network method to locate supporting rock bolts from 3D laser scan data. This method combines input features from the remote sensing and mobile robotics research communities, generating accuracy scores up to 22% higher than those found using either feature set in isolation. The neural network approach also is compared to the widely used random forest classifier and is shown to outperform this classifier on the test datasets. Additionally, the algorithms’ performance is enhanced by adding a confusion class to the training data and by grouping the output predictions using density based spatial clustering. The method is tested on two datasets, gathered using different laser scanners, in different types of underground mines which have different rock bolting patterns. In both cases the method is found to be highly capable of detecting the rock bolts with recall scores of 0.87-0.96. The second case study investigates modern deep learning for LiDAR data. Here, multiple transfer learning strategies and LiDAR data representations are examined for the task of identifying historic mining remains. A transfer learning approach based on a Lunar crater detection model is used, due to the task similarities between both the underlying data structures and the geometries of the objects to be detected. The relationship between dataset resolution and detection accuracy is also examined, with the results showing that the approach is capable of detecting pits and shafts to a high degree of accuracy with precision and recall scores between 0.80-0.92, provided the input data is of sufficient quality and resolution. Alongside resolution, different LiDAR data representations are explored, showing that the precision-recall balance varies depending on the input LiDAR data representation. The third case study creates a deep convolutional neural network model to detect artisanal scale mining from multispectral satellite data. This model is trained from initialisation without transfer learning and demonstrates that accurate multispectral models can be built from a smaller training dataset when appropriate design and data augmentation strategies are adopted. Alongside the deep learning model, novel mosaicing algorithms are developed both to improve cloud cover penetration and to decrease noise in the final prediction maps. When applied to the study area, the results from this model provide valuable information about the expansion, migration and forest encroachment of artisanal scale mining in southwestern Ghana over the last four years. Finally, this thesis presents an implementation framework for these neural network based object detection models, to generalise the findings from this research to new mining sector deep learning tasks. This framework can be used to identify applications which would benefit from neural network approaches; to build the models; and to apply these algorithms in a real world environment. The case study chapters confirm that the neural network models are capable of interpreting remotely sensed data to a high degree of accuracy on real world mining problems, while the framework guides the development of new models to solve a wide range of related challenges