Mining in Ghanaian Forest Reserves: Impacts on Forest Cover, Biodiversity and Carbon Stocks

Mining in Ghanaian Forest Reserves: Impacts on Forest Cover, Biodiversity and Carbon Stocks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Mining in Ghanaian Forest Reserves: Impacts on Forest Cover, Biodiversity and Carbon Stocks Simon Abugre, Michael Asigbaase, Samuel Kumi, George Nkoah, Austin Asare This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259522/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Ghana recently legalized mining in forest reserves but the impacts of this policy shift on forest cover, biodiversity and carbon stocks are not well documented. We analysed forest cover dynamics between 2018 and 2023 in the Oda River Forest Reserve and inventoried data from 12 plots in non-mined and mined (low, moderate and heavily) sites for its consequences on biodiversity and carbon stocks. Forest cover declined by 5.9%, shrinking from 16,959.89 ha in 2018 to 15,952.82 ha in 2023, while illegal mining expanded astronomically by 1,917.6%, increasing from 52.78 ha to 1,059.85 ha, with the most rapid expansion occurring between 2022 and 2023. The study revealed significant reductions in plant species richness and diversity across trees, shrubs, and climbers in mined areas, with heavily mined zones exhibiting a complete absence of vegetation. The Shannon diversity index and structural attributes such as tree height and diameter also significantly declined, reflecting the widespread ecological disruption caused by mining activities. Non-mined areas demonstrated higher biodiversity (S = 13.33, H = 2.41), greater structural complexity, and maintained the highest carbon stocks (689.11 Mg C ha − 1 ), emphasizing their role in mitigating climate change. In contrast, heavily mined areas exhibited complete carbon loss, resulting in substantial potential CO 2 emissions (2,522.15 tCO 2 e). Our results demonstrate the urgent need for effective land management policies, enforcement of mining regulations, and restoration efforts, including reforestation with native species. Addressing mining in forest reserves is critical to preserving biodiversity, mitigating climate change, and ensuring the resilience of forest ecosystems. mining forest reserves biodiversity carbon emission land use and land cover changes gold Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1.0 Introduction Forests are among the most valuable ecosystems globally, providing a wide range of ecosystem services essential for human survival and biodiversity conservation (Ankomah, 2012 ; FAO, 2020; Hendriks et al., 2021 ; Amponsah et al. 2022 ). Globally, forests cover about 31% of the Earth's land area, accounting for approximately 4.06 billion hectares (FAO, 2020). These ecosystems serve as critical carbon sinks, sequestering around 7.6 billion metric tons of carbon annually (Pan et al., 2011 ), and house over 80% of terrestrial species of animals, plants, and fungi (Malhi et al., 2020 ). Beyond their role in mitigating climate change, forests support the livelihoods of over 1.6 billion people worldwide and contribute significantly to global food security and water regulation (Ankomah, 2012 ; Amponsah-Tawiah et al. 2016; Chazdon et al., 2016). Locally, forest reserves in Ghana are pivotal for sustaining biodiversity, regulating microclimates, and providing essential resources for rural communities. Ghana’s forests account for approximately 9.2 million hectares, comprising both on-reserve and off-reserve areas, and are home to several endemic species of flora and fauna (Ankomah, 2012 ; Forestry Commission of Ghana, 2016). However, despite their ecological and socio-economic importance, forests are increasingly under threat from anthropogenic activities, with gold mining emerging as one of the most significant drivers of deforestation and forest degradation (Angelsen and Kaimowitz, 2001 ; Espejo et al., 2018 ; Amponsah et al. 2022 ). The threats to forests globally are multifaceted, including agricultural expansion, infrastructure development, logging, and mining (Angelsen and Kaimowitz, 2001 ; Ankomah, 2012 ; Bennett-Lartey & Adu-Dapaah, 2015 ). Among these, mining poses a particularly acute threat due to its capacity to cause irreversible ecological damage (Amankwah, 2013 ; Espejo et al., 2018 ). Mining activities often lead to deforestation, habitat fragmentation, and soil erosion, thereby compromising the structural and functional integrity of forest ecosystems (Sonter et al., 2017 ). On a global scale, mining accounts for 7% of annual forest loss in tropical regions (Alvarez-Berríos & Aide, 2015 ). In Ghana, the situation is no different, with gold mining emerging as a primary driver of forest loss and ecosystem degradation. Gold mining, particularly in forest reserves, has escalated in recent years, exacerbated by the government’s legalization of mining in these protected areas (Yoda, 2024 ). This policy shift has intensified forest encroachment, with approximately 1.5% of Ghana's forest cover lost annually, significantly impacting biodiversity and ecosystem services (Obenga et al., 2019 ; Acheampong et al., 2019; Ankomah et el., 2020). The environmental impacts of mining extend beyond deforestation. The removal of forest cover leads to the destruction of habitats, resulting in the loss of flora and fauna diversity (Asiedu, 2013 ; Attuquayefio et al. 2017 ; Obenga et al., 2019 ). Forest reserves in Ghana, which serve as biodiversity hotspots, are particularly vulnerable to mining activities. These reserves host a rich array of plant species, including economically valuable timber and non-timber species. However, mining activities disrupt seed dispersal, regeneration processes, and soil nutrient cycles, leading to a decline in plant diversity and the degradation of ecosystem health (Asiedu, 2013 ; Bach, 2014 ; Mensah et al., 2015 ; Attuquayefio et al. 2017 ; Boamah, 2020 ). At the regional scale, the loss of biodiversity diminishes the resilience of ecosystems to climate change and other environmental stressors, while at the global scale, it contributes to the ongoing biodiversity crisis, with over 1 million species at risk of extinction (IPBES, 2019; Kuffour et al. 2020 ). Another critical consequence of mining in forest reserves is its impact on carbon stocks and greenhouse gas emissions. Forests act as major carbon reservoirs, storing approximately 662 gigatons of carbon in their biomass and soil (Pan et al., 2011 ; Houghton, 2012 ; Global Forest Watch, 2024 ). The destruction of forest cover through mining releases significant amounts of carbon dioxide into the atmosphere, contributing to global warming. In Ghana, deforestation and land-use changes, primarily driven by mining, account for 24% of the country’s total greenhouse gas emissions (Forestry Commission of Ghana, 2016; Global Forest Watch, 2024 ). The loss of carbon stocks not only undermines Ghana’s commitments to the Paris Agreement but also threatens the global goal of limiting temperature rise to 1.5°C above pre-industrial levels (IPCC, 2018 ). Furthermore, the legalization of mining in forest reserves in Ghana (Environmental Protection (Mining in Forest Reserves) Regulation 2022 (L.I. 2462) highlights critical gaps in policy and research; it is unclear how this policy shift impacts on biodiversity, carbon stocks and emissions. Moreover, while the economic contributions of gold mining are undeniable, the ecological costs have received limited attention (Sobeng et al., 2018 ; Yoda, 2024 ). Existing studies have largely focused on deforestation trends, with minimal emphasis on the impacts of mining on biodiversity and carbon stocks (Pan et al., 2011 ; Sobeng et al., 2018 ). Therefore, the study sought to address these gaps by providing a comprehensive assessment of the impacts of gold mining on forest cover dynamics, plant diversity, and carbon stocks in Ghana. By integrating ecological, spatial, and carbon accounting approaches, the study aims to generate robust evidence to inform sustainable land-use policies and practices. The findings of this research have broader implications for global environmental goals and development agenda. The study aligns with the African Union’s Agenda 2063, which emphasizes sustainable resource management and biodiversity conservation as pillars for achieving a prosperous and environmentally sustainable Africa. It also contributes to the United Nations Sustainable Development Goals (SDGs), particularly Goal 13 (Climate Action), Goal 15 (Life on Land), and Goal 12 (Responsible Consumption and Production). Additionally, the study supports the objectives of the UN Decade on Ecosystem Restoration (2021–2030), which seeks to prevent, halt, and reverse ecosystem degradation worldwide. By examining the ecological impacts of gold mining in forest reserves, this research provides critical insights to guide restoration efforts and promote sustainable development in Ghana and beyond. 2.0 Materials and Methods 2.1 Description of Study Area The study was conducted in the Oda River Forest Reserve, located in the Ashanti Region of Ghana. This forest reserve spans an area of 164.43 square kilometers and straddles two political districts: Amansie South and Amansie Central (GSS, 2021) (Fig. 1 ). Approximately 80% of the reserve lies within the Amansie Central District, whose capital is Jacobu, while the remaining 20% falls within the Amansie South District, with Manso Adubia as its capital (GSS, 2021). The Amansie Central District is located at latitude 6° 06' 01.0"N and longitude 1° 53' 13.4"W, while the Amansie South District lies at latitude 6° 13' 58.9"N and longitude 1° 57' 02.4"W. Together, these districts cover a total surface area of approximately 2,074 square kilometers (800.78 square miles), representing about 5.9% of the total land area of the Ashanti Region (GSS, 2021). The Amansie Central and Amansie South Districts benefit from a strategic location within Ghana's forest zone, where the forest plays a crucial role in enhancing the local climate, thereby supporting agricultural production. These districts are predominantly agrarian, providing employment opportunities for a majority of the population both within and beyond their boundaries. In addition to agriculture, they hold significant potential for gold deposits and are home to extensive natural forest vegetation, further contributing to their economic and ecological importance (Donkor et al., 2023 ). The predominant vegetation in the Amansie Central and South Districts is semi-deciduous forest, with notable tree species including Odum, Wawa, Edinam, Mahogany, and Sapele. The districts are home to two major forest reserves: Oda and Subin. However, the vegetation has been significantly degraded due to human activities such as bushfires, illegal logging, and unauthorized gold mining. Consequently, primary forest exists only in scattered patches, while secondary forest has become more widespread (Owusu et al., 2020 ). The Oda, Offin, and Fena are the three major rivers in the district, complemented by several seasonal and perennial water bodies. The Offin River flows along the southeastern border, serving as the boundary between the Ashanti and Central Regions. However, anthropogenic activities such as gold dredging and illegal logging along these water bodies have significantly reduced the size of the rivers. These activities not only threaten the ecological health of the rivers but also pose serious environmental and livelihood challenges for the surrounding communities. 2.2 Geographic Information System Data Collection and Processing The study utilized Sentinel-2A images as the primary remote sensing data, chosen for their high-resolution imagery and extended temporal availability (Table 1 ; Zhao, 2019). Images from 2018, 2020, 2022, and 2023 were obtained from the Copernicus Data Space Ecosystem and covered the study area within Landsat path 193 and row 056. Pre-processing included atmospheric and geometric corrections, following standard protocols (Pradhan et al., 2005; Güneralp et al., 2013). Terrain-corrected (L1T) images were converted from digital numbers (DN) to radiance using a radiometric calibration model. The geometric correction was done by projecting all the images onto the UTM zone 30 North projection system. The composite images were clipped to a defined area of interest in ArcMap 10.5. Sentinel-2A’s reflective bands, covering visible, near-infrared, and short-wavelength infrared wavelengths, were resampled to a consistent 10 m resolution to ensure uniformity in analysis and enhance classification accuracy. A supervised Maximum Likelihood Classification (MLC) technique, widely used for land cover studies (Pushpendra et al., 2014), was employed to classify land cover into two signature classes: forest and mining (Table 1 ). Training areas were developed by selecting polygons for each class, and training pixels were derived for classification. Band combination (RGB = SWIR-NIR-Green or 12-8-3 for Sentinel-2A) was used for enhanced visualization. Classification accuracy was tested using a confusion matrix based on ground truth Regions of Interest (ROIs). Post-processing involved applying a filtering technique to generalize classified images by replacing isolated pixels with the most common neighbouring class (Ahmed et al., 2012). The Kappa coefficient (K) was calculated for each year to assess classification accuracy. This statistic evaluates the agreement between classified and reference data while accounting for random chance (Congalton, 2001). Kappa values range from 0 (chance agreement) to 1 (perfect agreement). Values below 0.4 indicate poor agreement, 0.4–0.55 indicate fair agreement, 0.55–0.7 represent good agreement, 0.7–0.85 indicate very good agreement, and values above 0.85 signify excellent agreement (Bokaie et al., 2016; Pal & Ziaul, 2017). Figure 2 summarises the workflow. Table 1 Sentinel 2A and classes of land cover types Number Satellite Acquisition date Tile Identifier 1 Sentinel 2A 2018-01-10 N0510_R065_T30NXN 2 Sentinel 2A 2020-12-26 N0510_R065_T30NXN 3 Sentinel 2A 2022-01-04 N0510_R065_T30NXN 4 Sentinel 2A 2023-08-13 N0510_R065_T30NXN Classes of land cover types 1. Forest : Lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use. 2. Mined site : A land cover category that includes areas where there are mining activities 2.3 Change Detection Change detection was conducted to identify, visualize, and analyze land cover changes within the study area. Pixel-based post-classification techniques were used, as they are widely recognized for their speed, convenience, descriptive nature, and ease of interpretation (Tewkesbury et al., 2015). The analysis covered four time periods: two years from 2018 to 2020, two years from 2020 to 2022, one year from 2022 to 2023, and five years from 2018 to 2023. These periods were chosen because mining activities started in the forest reserve in 2018. Training data were extracted and used to train Maximum Likelihood Classification (MLC) models for each of the four time periods. Feature selection was applied to identify the best-performing models. For consistency, the same training and test samples were used across the different time periods, as the intervals between them were relatively short and closely aligned with the reference data. The trained models were then applied to classify the images, producing land cover maps for each time period. Post-classification filtering was performed to refine the maps while maintaining the original precision. To ensure unique change values for each direction of land cover transition, the classification output of the initial time period was reclassified using the formula: 𝑟 = 𝑐 ∙ 5 + 1, where ‘c’ represents the initial class, and ‘r’ is the new class. The newer image was subsequently subtracted from the older image, generating a transition matrix table with unique values for each direction of change. This method provided a detailed and systematic analysis of land cover dynamics, enabling the identification of specific patterns and directions of change over the study period. 2.4 Inventory Data Collection A systematic approach was employed to assess the diversity of tree species across different mining intensities within compartments 13, 14, and 17. Mining sites in each compartment were categorized into three intensity levels: heavy (where almost all vegetation was removed), moderate (40%-80% vegetation removal), and low (less than 40% vegetation removal). Non-mined areas, where no mining activity had occurred, were also included. In each compartment, one plot measuring 25 m x 25 m was established in each of the mined and non-mined areas, resulting in a total of 12 plots. Nested within these main plots were smaller plots measuring 5 m x 5 m and 1 m x 1 m. The main plots (25 m x 25 m) were used to inventory all standing tree species with a diameter at breast height (dbh) greater than 10 cm, as well as lianas and climbers. In the 5 m x 5 m nested plots, saplings with a girth between 2 cm and less than 10 cm, as well as shrubs, were inventoried. Seedlings with a girth of at least 2 cm were recorded within the smallest 1 m x 1 m nested plots. The data collection was conducted with the assistance of two experienced botanists from the Forestry Commission, an expert field technician from the Council for Scientific Research, and two local informants, following the guidelines provided by Hawthorne and Jongkind ( 2006 ). The diameter at breast height (1.30 m above the ground) for all tree species was measured using a diameter tape and recorded. Heights of trees and saplings were measured using a hypsometer, while the heights of seedlings were measured with a linear tape. 2.5 Inventory Data processing and Analysis Species richness was estimated as the number of species per plot while plant diversity was assessed via Shannon-Wiener diversity index (Eq. 1). \(\:{\text{H}}^{{\prime\:}}=-\sum\:_{i=1}^{\text{S}}{P}_{i}\:\text{l}\text{n}\:\left({P}_{i}\:\right)\) ……………………………………………………………Eq. 1 Where H’ = Shannon Weiner diversity index, S = number of species, In = natural log, P i is the proportion of individuals in the sample belonging to the i th species i.e. (n i /N); n i = number of the individual of species, and N = importance value index or total number of the individuals of all species. Aboveground biomass carbon stocks in trees were estimated using Eq. 2 (Brown, 1997) while belowground carbon stocks were estimated after using Eq. 3 (Cairns et al ., 1997). The estimated biomass was converted to carbon stock by multiplying by 0.5. To estimate carbon loss due to mining, the mean values of mined areas were subtracted from the mean value of non-mined sites and then multiplied by emission factor (3.66) to convert to potential carbon dioxide emission due to illegal mining (IPCC 2007 ; IPCC 2014 ). ln (Wt) = 2.530 × ln (DBH) − 2.134 ……………… Eq. 2 Where Wt is biomass (kg) and DBH is diameter at breast height (cm). BGB = exp (-1.085 + 0.926 * ln (AGB)) ………………Eq. 3 To assess the impact of mining on plant diversity carbon stocks and potential carbon emission, a one-way ANOVA was conducted followed by a Tukey post hoc test. Mean values were considered significant at α = 0.05. 3.0 Results 3.1 Impact of mining on forest cover dynamics between 2018 and 2023 The classification results demonstrate a high level of reliability, with overall accuracies consistently exceeding 86%, signifying strong agreement between classified and reference data (Table 2 ). Both the producer’s and user’s accuracy for all land-use classes across all years exceeded 0.60 while the Kappa coefficient, which quantifies classification agreement beyond chance, ranged from 0.81 to 0.88, indicating a strong level of agreement. The overall classification accuracy was recorded for the 2018 period was 91%, followed by 2020 (90%), 2022 (89%), and 2023 (86%), reflecting a consistently high classification performance across the years. The results of this study highlight the significant impact of gold mining on land use and land cover (LULC) dynamics in the Oda River Forest Reserve between 2018 and 2023 (Fig. 3 ). Over the five-year period, forest cover experienced a steady decline, reducing from 16,959.89 ha in 2018 to 15,952.82 ha in 2023, representing a total loss of 5.9% (Tables 3 and 4 ). In contrast, mining areas expanded dramatically from 52.78 ha in 2018 to 1,059.85 ha in 2023, reflecting an alarming 1,917.6% increase. The most rapid expansion of mining activities occurred between 2022 and 2023, emphasizing the intensifying pressure on forest resources. These findings underscore the dual threats of deforestation and biodiversity loss posed by mining activities, which continue to drive significant land cover changes in the reserve. Table 2 Accuracy assessment for the classified image 2018 2020 2022 2023 LULC Categories PA UA PA UA PA UA PA UA Forest 0.91 0.84 0.97 0.76 1 0.72 1 0.6 Mining 0.93 0.8 0.91 0.84 0.91 0.82 0.89 0.82 Overall Accuracy 0.91 0.9 0.89 0.86 Kappa 0.88 0.87 0.85 0.81 NB: PA = Producer accuracy, UA = User accuracy Table 3 Distribution of LULC categories (in Ha) and the annual rate of change in 2018, 2020, 2022, and 2023. LULC Mining (ha) Forest (ha) 2018 52.78 16959.89 2020 348.76 16663.91 2022 689.23 16323.44 2023 1059.85 15952.82 2018–2020 (%) 1.7 -1.7 2020–2022 (%) 2.0 -2.0 2022–2023 (%) 2.2 -2.2 2018–2023 (%) 5.9 -5.9 Table 4 Change Detection (ha) matrix showing LULC changed patterns from 2018 – 2023 2023 Illegal mining Forest Initial total Gross loss Illegal mining 32.82 19.96 52.78 19.96 2018 Forest 1027.03 15932.86 16959.89 1027.03 Final total 1059.85 15952.82 17012.67 Gross gain 1027.03 19.96 Net Change 1007.07 -1007.07 2018 - 2023 3.2 Impact of mining on plant community structure and diversity The results demonstrate that illegal mining activities drastically reduced plant species richness across various plant categories, with heavily mined areas exhibiting a complete absence of plant species (Fig. 4 ). This was particularly evident for trees, where non-mined areas had the highest species richness (mean = 13.33, SEM = 0.96) compared to low and moderate mining areas, which showed intermediate richness values (mean = 8.00 and 2.67, respectively). For shrubs, non-mined areas exhibited the highest species richness (mean = 5.33, SEM = 0.16), while low mining areas had lower richness (mean = 4.00, SEM = 0.27), and heavily and moderately mined areas recorded no shrub species. A similar trend was observed for liners and climbers, where non-mined areas had a mean species richness of 6.33 (SEM = 0.68), while low mining areas exhibited reduced richness (mean = 1.67, SEM = 0.79). Heavily and moderately mined areas consistently recorded no species for all plant categories. These findings highlight the severe ecological disruption caused by illegal mining, as mining activities result in the destruction of habitats necessary for supporting plant communities. The Shannon diversity index, a measure of species diversity that considers both abundance and evenness, was significantly lower in mined areas across all plant categories (Fig. 4 ). For trees, non-mined areas recorded the highest Shannon index (mean = 2.41, SEM = 0.07), while heavily mined areas had zero, indicating a complete loss of plant species diversity. Similarly, for shrubs, non-mined areas exhibited a Shannon index of 1.65 (SEM = 0.03), while low mining areas had a reduced index of 1.37 (SEM = 0.07). Heavily and moderately mined areas recorded no diversity for shrubs. Liners and climbers also showed significant reductions in diversity, with non-mined areas achieving the highest Shannon index (mean = 1.78, SEM = 0.11). Low mining areas exhibited reduced diversity (mean = 0.46, SEM = 0.22), while heavily and moderately mined areas had no diversity. These results indicate that even low-intensity mining activities reduce species diversity, while heavy and moderate mining activities lead to complete ecosystem homogenization. This loss of diversity disrupts ecological balance, reducing the resilience and functionality of the forest ecosystem. Gold mining significantly impacted the structural attributes of plants, including height and diameter, across all categories (Fig. 5 ). For trees, non-mined areas exhibited the tallest trees (mean height = 19.88 m, SEM = 0.44) and the largest DBH (mean = 45.32 cm, SEM = 2.08). Moderate and low mining areas showed reduced height and DBH values, with moderate mining areas having the highest DBH (mean = 50.40 cm) and low mining areas showing a mean DBH of 26.05 cm. Heavily mined areas recorded no trees, highlighting the severe disruption caused by mining activities. Similarly, for shrubs, non-mined areas had the tallest shrubs (mean height = 3.00 m, SEM = 0.33) and the largest DBH (mean = 2.84 cm, SEM = 0.30). Low mining areas exhibited reduced height and DBH values (mean height = 2.02 m, SEM = 0.15; mean DBH = 2.00 cm, SEM = 0.09), while heavily and moderately mined areas had no shrubs. Liners and climbers followed a similar pattern, with non-mined areas having the tallest individuals (mean height = 42.60 m, SEM = 6.12) and the largest DBH (mean = 17.43 cm, SEM = 1.26). Low mining areas exhibited intermediate values (mean height = 12.30 m, SEM = 5.81; mean DBH = 3.67 cm, SEM = 1.73), while heavily and moderately mined areas recorded no individuals. 3.3 Potential CO 2 emission across mined and non-mined forest landscape The study presents a clear correlation between the intensity of illegal mining and the associated carbon emissions. The results indicated that non-mined areas had the highest mean total carbon (C) stock at 689.11 Mg C ha, with no carbon loss or emissions (Table 5 ). Conversely, heavily mined areas showed a complete loss of carbon stock, resulting in the emission of 2522.15 tCO2e. Moderate and low mining intensities also resulted in substantial carbon losses and emissions, albeit to a lesser extent than heavy mining. Specifically, moderate mining resulted in a carbon loss of 332.42 Mg C ha and emissions of 1216.66 tCO2e, while low mining causes a carbon loss of 466.88 Mg C ha and emissions of 1708.76 tCO2e. Table 5 Potential CO 2 emission across mined sites compared to non-mined sites Mining intensity Biomass C stocks (Mg C ha − 1 ) Biomass C loss (Mg C ha − 1 ) Emission (tCO 2 e) Low 222.24 466.88 1708.76 Moderate 356.69 332.42 1216.66 Non-mined area 689.11 0.00 0.00 Heavily mined 0.00 689.11 2522.15 4.0 Discussion 4.1 Impact of gold mining on forest cover dynamics (2018–2023) The classification technique was robust with overall accuracy values ranging from 0.86 to 0.91 and Kappa coefficients ranging from 0.81 to 0.88, reinforcing the high level of agreement between the classified images and the ground truth data (Table 2 ; Congalton & Green, 2009 ; Foody, 2009 ; Lu et al., 2004 ). The producer's and user's accuracies for all LULC classes consistently exceeded 0.6 across the years, suggesting the classification model effectively distinguished between different site-use types with a high degree of reliability (Olofsson et al., 2014 ; Roy & Roy, 2010 ). The observed trends align with previous studies that have documented the environmental degradation caused by mining in Ghana (e.g. Agyemang et al. 2018; Kumi et al., 2021 ; Amponsah et al., 2022 ). For instance, Agyemang et al. (2018) reported extensive deforestation and habitat loss in mining-affected areas, with forest reserves becoming focal points for gold mining activities due to their rich mineral deposits. Similarly, Asner et al. ( 2013 ) highlighted that gold mining in tropical forest regions, including Ghana, has resulted in widespread deforestation, habitat fragmentation, and soil degradation. Furthermore, the progressive deforestation observed aligns with global patterns where illegal mining has been identified as a primary driver of forest loss and biodiversity depletion, particularly in tropical regions (Asner et al., 2013 ; Nascimento et al., 2020). It further mirrors findings in the Amazon rainforest, where mining activities have similarly resulted in extensive deforestation and habitat fragmentation (Sonter et al., 2017 ). The economic profitability of artisanal and small-scale gold mining, coupled with high global demand for gold, is a significant motivating factor for these activities (Agyemang et al., 2018; Dirnbock et al., 2011). Advances in mining technology have further enabled deeper and more extensive exploitation of forested areas, exacerbating the problem (Telmer and Veiga, 2009 ; Takyi et al., 2021 ). The observed trends of deforestation between 2018 and 2023 may also point to a spillover effect whereby displaced farmers clear additional forested areas for agricultural expansion, compounding the deforestation problem. Studies by Amponsah et al. ( 2022 ) in the Atewa Range Forest Reserve reported that mining activities displaced farming communities, triggering agricultural intensification and further forest loss. This underscores the interconnectedness of land use practices and the cascading effects of mining on agricultural systems and forest ecosystems. The significant loss of forest cover has profound ecological implications such as carbon sequestration, water regulation, soil fertility, and habitat for diverse flora and fauna (Chazdon, 2014 ). The reduction in forest area by 5.9% over the study period potentially compromises these essential services and exacerbates climate change impacts. Furthermore, the fragmentation of wildlife habitats and the disruption of ecological corridors significantly affect biodiversity (Ninan & Perrings, 2012 ). Studies have shown that habitat fragmentation limits species movement, reduces genetic diversity, and increases vulnerability to extinction (Laurance et al., 2014 ). In the Oda River Reserve, such fragmentation likely hampers wildlife migration and ecosystem resilience. Besides, the removal of vegetation and topsoil for mining exacerbates land degradation through soil erosion, compaction, and the formation of gullies, which collectively diminish land productivity (Owolabi, 2020 ). Interestingly, Ghana's recent decision to legalize mining in forest reserves adds a complex dimension to these findings. While legalization aims to regulate mining activities and ensure environmental accountability, it also raises concerns about the potential intensification of forest exploitation (Amponsah et al. 2022 ). Miners may interpret the policy as tacit approval of forest exploitation and without stringent environmental safeguards, such mining activities may accelerate forest loss, biodiversity decline, and disruption of ecosystem services, such as carbon sequestration and water regulation (Laurance et al. 2014 ; Aboagye, 2016). Studies in similar contexts have shown that weak enforcement of regulations can lead to both legal and illegal mining coexisting, with overlapping negative impacts on ecosystems (Asner et al., 2013 ; Santana & Berkes, 2020). If mining in forest reserves is to be sustainable, it must be accompanied by effective land management strategies, strict compliance monitoring, and significant investments in reforestation and habitat restoration. Community participation in decision-making processes is crucial to ensuring that mining activities do not undermine conservation efforts or disrupt the livelihoods of local populations (Agyemang et al., 2018). 4.2 Impact of gold mining on plant community structure and diversity The findings demonstrate the severe ecological impacts of illegal mining on plant diversity and forest structure, highlighting significant declines across all plant categories, including trees, saplings, seedlings, shrubs, and climbers (Figs. 4 and 5 ). The absence of species richness and the complete lack of plant density in heavily mined areas exemplify the destructive nature of mining activities on forest ecosystems (Espejo et al., 20218). This aligns with previous studies that have documented similar outcomes, where mining operations result in deforestation, habitat destruction, and biodiversity loss (Hilson, 2002 ; Agyemang et al., 2010). The stark contrast between heavily mined and non-mined areas underscores the disruptive extent of mining on forest ecosystems, with non-mined areas consistently exhibiting higher species richness, density, and diversity indices (Nero, 2021 ). The Shannon index, a key measure of species diversity, highlighted the significant ecological imbalance caused by illegal mining. Non-mined areas recorded significantly higher Shannon index values across all plant categories, indicating greater species heterogeneity and ecological stability (Figs. 4 and 5 ). In heavily mined areas, the Shannon index dropped to zero for trees, saplings, and climbers, reflecting a complete loss of biodiversity. This pattern is consistent with findings from other studies that emphasize the homogenization of plant communities and the loss of ecological balance in mining-affected regions (Attuquayefio et al., 2005; Anane et al., 2013). The significant reduction in tree density from 304.30 trees/ha in non-mined areas to zero in heavily mined areas further illustrates the magnitude of habitat destruction. These results corroborate earlier studies that have reported similar trends in mining-impacted areas, where vegetation loss is a direct consequence of large-scale deforestation and soil degradation (Mensah et al., 2015 ; Ramirez et al., 2018 ). Gold mining activities also severely impacted the regenerative capacity of the forest, as evidenced by the absence of saplings and seedlings in heavily mined areas (Fig. 4 ). This finding is alarming, as it indicates a long-term disruption of natural regeneration processes. Saplings and seedlings are critical to the recovery and sustainability of forest ecosystems, and their absence in heavily mined areas suggests a collapse in ecological succession. Studies by Attuquayefio and Fobil ( 2005 ) and Anane and Cobbinah (2013) have similarly shown that mining activities disrupt soil structure and nutrient availability, inhibiting plant growth and regeneration. The reduced DBH and height of saplings and shrubs in mined areas further emphasize the detrimental impact of mining on plant growth and maturity. Soil compaction, a common consequence of mining activities, likely contributed to these reductions by limiting root growth, water infiltration, and nutrient uptake (Hilson, 2002 ; McShea & Rappole, 2000 ). Moreover, the physical processes involved in mining, such as heavy machinery use and soil excavation, lead to the destruction of topsoil and the loss of essential soil microorganisms. These changes impair the forest's ability to support diverse plant communities and regenerate naturally (Adjei et al., 2021; Agyemang et al., 2010). The negative effects of mining extended to shrubs and climbers (Figs. 4 and 5 ), which play vital roles in maintaining forest structure and biodiversity. The significant reduction in shrub density and height in mined areas highlights the extent to which mining disrupts forest ecosystems. Shrubs are essential for soil stabilization and habitat provision, and their decline can lead to increased soil erosion and loss of ecosystem services (Gillison & Brewer, 2005; Dirzo & Raven, 2003 ). Similarly, the complete absence of climbers in heavily mined areas underscores the severe ecological disruption caused by mining. Climbers rely on biotic interactions, such as seed dispersal and pollination, which are often disrupted by the removal of key plant and animal species in mining-affected areas (Asase et al., 2013). This disruption of ecological networks further compounds the loss of biodiversity and ecological functionality. 4.3 Potential impact of illegal mining on CO 2 emission The results (Table 5 ) clearly illustrate the destructive impact of mining in forest reserves on carbon sequestration. Forests in non-mined areas maintain high carbon stocks, which are crucial for mitigating climate change. When these forests are disturbed by mining activities, the carbon stored in biomass is released back into the atmosphere, contributing significantly to greenhouse gas (GHG) emissions. This phenomenon is supported by numerous studies that highlight deforestation and land-use change as major sources of CO 2 emissions (Baccini et al., 2012 ). The carbon emissions resulting from illegal mining in the Oda River Forest Reserve align with findings from other regions experiencing similar activities. For instance, Rudel et al. ( 2009 ) discuss how deforestation from mining and agriculture in the Amazon Basin contributes to regional and global CO 2 emissions. Similarly, Asner et al. ( 2013 ) document the high carbon losses associated with gold mining in the Peruvian Amazon, noting significant reductions in forest carbon stocks and increased CO 2 emissions. When trees are felled, the carbon stored in their trunks, branches, and leaves is oxidized and released as CO 2 . Additionally, soil disturbance from mining activities can release soil organic carbon (SOC), further exacerbating emissions (Don et al., 2011 ). This dual release from both biomass and soil underscores the severe impact of illegal mining on carbon dynamics. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the importance of maintaining forest cover to mitigate climate change (IPCC, 2014 ). The significant carbon losses documented in this study highlight the need for urgent measures to curb illegal mining and protect forest ecosystems. 5.0 Conclusions The findings of this study demonstrate the ecological consequences of gold mining on forest cover, biodiversity, and carbon dynamics. Over the five-year period from 2018 to 2023, forest cover declined significantly, with a total loss of 5.9%, while mining areas expanded by 1,917.6%. These changes illustrate the dual threats of deforestation and environmental degradation posed by mining activities. Heavily mined areas were devoid of tree, shrub, and climber species, while non-mined areas maintained significantly higher species richness and diversity. Even in areas with low or moderate mining activity, significant reductions in plant abundance and diversity were observed, pointing to the pervasive impact of mining on forest ecosystems. Such biodiversity loss has far-reaching implications for ecosystem functionality, resilience, and the provision of critical services, including habitat provision and soil stabilization. The structural integrity of forest vegetation was also significantly affected by mining activities. Non-mined areas demonstrated significantly higher tree height and diameter at breast height (DBH), reflecting healthier and more mature forests capable of supporting diverse flora and fauna. In contrast, mined areas exhibited reduced structural attributes, with heavily mined areas entirely devoid of vegetation. This loss of structural complexity compromises the forest’s ability to sequester carbon, regulate water cycles, and sustain wildlife populations, further exacerbating the environmental consequences of mining. Furthermore, mined areas showed substantial to complete carbon loss and significant potential CO 2 emissions which emphasize the need for urgent action to address mining challenges and restore degraded landscapes. We concluded that mining in forest reserves threatens biodiversity and climate change mitigation via carbon sequestration. Restoration efforts, including reforestation with native species and soil rehabilitation, are critical to restoring biodiversity and ecosystem functionality. Declarations Conflict of Interest Statement : The Authors declare no conflict of interest. Funding statement: No funding or grant was received for this study. Data availability: All relevant data are included within the main manuscript and the accompanying supplementary information files. Ethics, Consent to Participate, and Consent to Publish declarations : not applicable Acknowledgement: We extend our gratitude to Forestry Commission for their technical advice and support during data collection. We are thankful to staff of Oda River Forest Reserve for their assistance during data collection. Authors’ contribution statements: MA; conceptualization, project administration, investigation, data curation, formal analysis, methodology, software, visualization, writing- original draft, supervision, writing–review and editing, and validation. SA: conceptualization, methodology, writing - review and editing, supervision, and project administration. SK: writing - review and editing, validation, visualization, and project administration. GN: resources, conceptualization, methodology, investigation, and writing- original draft. AA: methodology, formal analysis, writing - review and editing, and visualization. References Acheampong E.O., Colin Macgregor C., Sean Sloan S. & Jeffrey Sayer J. (2016). Deforestation is driven by agricultural expansion in Ghana's Forest Reserves. DOI:10.1016/j.sciaf.2019.e00146 Alvarez-Berríos NL & Aide MT. (2015). 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08:38:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6259522/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6259522/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79916440,"identity":"71012bcd-63f7-4440-a28f-c2c0b9759b69","added_by":"auto","created_at":"2025-04-04 12:44:51","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":230633,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of study area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/dca518b2bc725fa62dd9c42d.jpeg"},{"id":79915785,"identity":"938a53f2-304a-41eb-87d9-28e536adb478","added_by":"auto","created_at":"2025-04-04 12:36:51","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84561,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMethodological flow chart\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/1d209fa3443d58a8b8b56f3c.png"},{"id":79915790,"identity":"a008f5b5-d983-4604-b82e-30d540bd3d8d","added_by":"auto","created_at":"2025-04-04 12:36:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":251516,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLULC Classes maps for the periods\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/73c9da1c69435d379d69385f.png"},{"id":79915788,"identity":"182ca055-afa6-4e8f-b0d3-3692f05b2884","added_by":"auto","created_at":"2025-04-04 12:36:51","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":197447,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpecies richness and diversity of plant categories across the different mining intensities\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/f3795ed350a5d4a4bada1565.png"},{"id":79916444,"identity":"969d214c-26b9-4466-89b0-2005320aad16","added_by":"auto","created_at":"2025-04-04 12:44:51","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":286559,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural characteristics of different plant categories across different mining intensities\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/d649d0e66ce81c0c5b128be9.png"},{"id":79916780,"identity":"c320f117-44d1-46c8-a1a1-49c9942b6ffa","added_by":"auto","created_at":"2025-04-04 12:52:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2256743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6259522/v1/b918ce1b-e84f-4d0f-acf2-4a08077e5a5e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mining in Ghanaian Forest Reserves: Impacts on Forest Cover, Biodiversity and Carbon Stocks","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eForests are among the most valuable ecosystems globally, providing a wide range of ecosystem services essential for human survival and biodiversity conservation (Ankomah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; FAO, 2020; Hendriks et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amponsah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Globally, forests cover about 31% of the Earth's land area, accounting for approximately 4.06\u0026nbsp;billion hectares (FAO, 2020). These ecosystems serve as critical carbon sinks, sequestering around 7.6\u0026nbsp;billion metric tons of carbon annually (Pan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), and house over 80% of terrestrial species of animals, plants, and fungi (Malhi et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beyond their role in mitigating climate change, forests support the livelihoods of over 1.6\u0026nbsp;billion people worldwide and contribute significantly to global food security and water regulation (Ankomah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Amponsah-Tawiah et al. 2016; Chazdon et al., 2016). Locally, forest reserves in Ghana are pivotal for sustaining biodiversity, regulating microclimates, and providing essential resources for rural communities. Ghana\u0026rsquo;s forests account for approximately 9.2\u0026nbsp;million hectares, comprising both on-reserve and off-reserve areas, and are home to several endemic species of flora and fauna (Ankomah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Forestry Commission of Ghana, 2016).\u003c/p\u003e \u003cp\u003eHowever, despite their ecological and socio-economic importance, forests are increasingly under threat from anthropogenic activities, with gold mining emerging as one of the most significant drivers of deforestation and forest degradation (Angelsen and Kaimowitz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Espejo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Amponsah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The threats to forests globally are multifaceted, including agricultural expansion, infrastructure development, logging, and mining (Angelsen and Kaimowitz, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Ankomah, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Bennett-Lartey \u0026amp; Adu-Dapaah, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Among these, mining poses a particularly acute threat due to its capacity to cause irreversible ecological damage (Amankwah, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Espejo et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Mining activities often lead to deforestation, habitat fragmentation, and soil erosion, thereby compromising the structural and functional integrity of forest ecosystems (Sonter et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). On a global scale, mining accounts for 7% of annual forest loss in tropical regions (Alvarez-Berr\u0026iacute;os \u0026amp; Aide, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In Ghana, the situation is no different, with gold mining emerging as a primary driver of forest loss and ecosystem degradation. Gold mining, particularly in forest reserves, has escalated in recent years, exacerbated by the government\u0026rsquo;s legalization of mining in these protected areas (Yoda, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This policy shift has intensified forest encroachment, with approximately 1.5% of Ghana's forest cover lost annually, significantly impacting biodiversity and ecosystem services (Obenga et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Acheampong et al., 2019; Ankomah et el., 2020). The environmental impacts of mining extend beyond deforestation. The removal of forest cover leads to the destruction of habitats, resulting in the loss of flora and fauna diversity (Asiedu, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Attuquayefio et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Obenga et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Forest reserves in Ghana, which serve as biodiversity hotspots, are particularly vulnerable to mining activities. These reserves host a rich array of plant species, including economically valuable timber and non-timber species. However, mining activities disrupt seed dispersal, regeneration processes, and soil nutrient cycles, leading to a decline in plant diversity and the degradation of ecosystem health (Asiedu, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bach, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mensah et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Attuquayefio et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Boamah, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). At the regional scale, the loss of biodiversity diminishes the resilience of ecosystems to climate change and other environmental stressors, while at the global scale, it contributes to the ongoing biodiversity crisis, with over 1\u0026nbsp;million species at risk of extinction (IPBES, 2019; Kuffour et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother critical consequence of mining in forest reserves is its impact on carbon stocks and greenhouse gas emissions. Forests act as major carbon reservoirs, storing approximately 662 gigatons of carbon in their biomass and soil (Pan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Houghton, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Global Forest Watch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The destruction of forest cover through mining releases significant amounts of carbon dioxide into the atmosphere, contributing to global warming. In Ghana, deforestation and land-use changes, primarily driven by mining, account for 24% of the country\u0026rsquo;s total greenhouse gas emissions (Forestry Commission of Ghana, 2016; Global Forest Watch, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The loss of carbon stocks not only undermines Ghana\u0026rsquo;s commitments to the Paris Agreement but also threatens the global goal of limiting temperature rise to 1.5\u0026deg;C above pre-industrial levels (IPCC, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, the legalization of mining in forest reserves in Ghana (Environmental Protection (Mining in Forest Reserves) Regulation 2022 (L.I. 2462) highlights critical gaps in policy and research; it is unclear how this policy shift impacts on biodiversity, carbon stocks and emissions. Moreover, while the economic contributions of gold mining are undeniable, the ecological costs have received limited attention (Sobeng et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Yoda, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Existing studies have largely focused on deforestation trends, with minimal emphasis on the impacts of mining on biodiversity and carbon stocks (Pan et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Sobeng et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Therefore, the study sought to address these gaps by providing a comprehensive assessment of the impacts of gold mining on forest cover dynamics, plant diversity, and carbon stocks in Ghana. By integrating ecological, spatial, and carbon accounting approaches, the study aims to generate robust evidence to inform sustainable land-use policies and practices.\u003c/p\u003e \u003cp\u003eThe findings of this research have broader implications for global environmental goals and development agenda. The study aligns with the African Union\u0026rsquo;s Agenda 2063, which emphasizes sustainable resource management and biodiversity conservation as pillars for achieving a prosperous and environmentally sustainable Africa. It also contributes to the United Nations Sustainable Development Goals (SDGs), particularly Goal 13 (Climate Action), Goal 15 (Life on Land), and Goal 12 (Responsible Consumption and Production). Additionally, the study supports the objectives of the UN Decade on Ecosystem Restoration (2021\u0026ndash;2030), which seeks to prevent, halt, and reverse ecosystem degradation worldwide. By examining the ecological impacts of gold mining in forest reserves, this research provides critical insights to guide restoration efforts and promote sustainable development in Ghana and beyond.\u003c/p\u003e"},{"header":"2.0 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Description of Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Oda River Forest Reserve, located in the Ashanti Region of Ghana. This forest reserve spans an area of 164.43 square kilometers and straddles two political districts: Amansie South and Amansie Central (GSS, 2021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Approximately 80% of the reserve lies within the Amansie Central District, whose capital is Jacobu, while the remaining 20% falls within the Amansie South District, with Manso Adubia as its capital (GSS, 2021). The Amansie Central District is located at latitude 6\u0026deg; 06' 01.0\"N and longitude 1\u0026deg; 53' 13.4\"W, while the Amansie South District lies at latitude 6\u0026deg; 13' 58.9\"N and longitude 1\u0026deg; 57' 02.4\"W. Together, these districts cover a total surface area of approximately 2,074 square kilometers (800.78 square miles), representing about 5.9% of the total land area of the Ashanti Region (GSS, 2021). The Amansie Central and Amansie South Districts benefit from a strategic location within Ghana's forest zone, where the forest plays a crucial role in enhancing the local climate, thereby supporting agricultural production. These districts are predominantly agrarian, providing employment opportunities for a majority of the population both within and beyond their boundaries. In addition to agriculture, they hold significant potential for gold deposits and are home to extensive natural forest vegetation, further contributing to their economic and ecological importance (Donkor et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe predominant vegetation in the Amansie Central and South Districts is semi-deciduous forest, with notable tree species including Odum, Wawa, Edinam, Mahogany, and Sapele. The districts are home to two major forest reserves: Oda and Subin. However, the vegetation has been significantly degraded due to human activities such as bushfires, illegal logging, and unauthorized gold mining. Consequently, primary forest exists only in scattered patches, while secondary forest has become more widespread (Owusu et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The Oda, Offin, and Fena are the three major rivers in the district, complemented by several seasonal and perennial water bodies. The Offin River flows along the southeastern border, serving as the boundary between the Ashanti and Central Regions. However, anthropogenic activities such as gold dredging and illegal logging along these water bodies have significantly reduced the size of the rivers. These activities not only threaten the ecological health of the rivers but also pose serious environmental and livelihood challenges for the surrounding communities.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Geographic Information System Data Collection and Processing\u003c/h2\u003e \u003cp\u003eThe study utilized Sentinel-2A images as the primary remote sensing data, chosen for their high-resolution imagery and extended temporal availability (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Zhao, 2019). Images from 2018, 2020, 2022, and 2023 were obtained from the Copernicus Data Space Ecosystem and covered the study area within Landsat path 193 and row 056. Pre-processing included atmospheric and geometric corrections, following standard protocols (Pradhan et al., 2005; G\u0026uuml;neralp et al., 2013). Terrain-corrected (L1T) images were converted from digital numbers (DN) to radiance using a radiometric calibration model. The geometric correction was done by projecting all the images onto the\u003c/p\u003e \u003cp\u003eUTM zone 30 North projection system. The composite images were clipped to a defined area of interest in ArcMap 10.5. Sentinel-2A\u0026rsquo;s reflective bands, covering visible, near-infrared, and short-wavelength infrared wavelengths, were resampled to a consistent 10 m resolution to ensure uniformity in analysis and enhance classification accuracy.\u003c/p\u003e \u003cp\u003eA supervised Maximum Likelihood Classification (MLC) technique, widely used for land cover studies (Pushpendra et al., 2014), was employed to classify land cover into two signature classes: forest and mining (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Training areas were developed by selecting polygons for each class, and training pixels were derived for classification. Band combination (RGB\u0026thinsp;=\u0026thinsp;SWIR-NIR-Green or 12-8-3 for Sentinel-2A) was used for enhanced visualization. Classification accuracy was tested using a confusion matrix based on ground truth Regions of Interest (ROIs). Post-processing involved applying a filtering technique to generalize classified images by replacing isolated pixels with the most common neighbouring class (Ahmed et al., 2012). The Kappa coefficient (K) was calculated for each year to assess classification accuracy. This statistic evaluates the agreement between classified and reference data while accounting for random chance (Congalton, 2001). Kappa values range from 0 (chance agreement) to 1 (perfect agreement). Values below 0.4 indicate poor agreement, 0.4\u0026ndash;0.55 indicate fair agreement, 0.55\u0026ndash;0.7 represent good agreement, 0.7\u0026ndash;0.85 indicate very good agreement, and values above 0.85 signify excellent agreement (Bokaie et al., 2016; Pal \u0026amp; Ziaul, 2017). Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises the workflow.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSentinel 2A and classes of land cover types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSatellite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcquisition date\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTile Identifier\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel 2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2018-01-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0510_R065_T30NXN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel 2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2020-12-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0510_R065_T30NXN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel 2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2022-01-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0510_R065_T30NXN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSentinel 2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2023-08-13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN0510_R065_T30NXN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClasses of land cover types\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eForest\u003c/b\u003e: Lands of more than 0.5 hectares, with a tree canopy cover of more than 10 percent, which are not primarily under agricultural or urban land use.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMined site\u003c/b\u003e: A land cover category that includes areas where there are mining activities\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Change Detection\u003c/h2\u003e \u003cp\u003eChange detection was conducted to identify, visualize, and analyze land cover changes within the study area. Pixel-based post-classification techniques were used, as they are widely recognized for their speed, convenience, descriptive nature, and ease of interpretation (Tewkesbury et al., 2015). The analysis covered four time periods: two years from 2018 to 2020, two years from 2020 to 2022, one year from 2022 to 2023, and five years from 2018 to 2023. These periods were chosen because mining activities started in the forest reserve in 2018. Training data were extracted and used to train Maximum Likelihood Classification (MLC) models for each of the four time periods. Feature selection was applied to identify the best-performing models. For consistency, the same training and test samples were used across the different time periods, as the intervals between them were relatively short and closely aligned with the reference data. The trained models were then applied to classify the images, producing land cover maps for each time period. Post-classification filtering was performed to refine the maps while maintaining the original precision. To ensure unique change values for each direction of land cover transition, the classification output of the initial time period was reclassified using the formula: \u0026#119903; = \u0026#119888; ∙ 5\u0026thinsp;+\u0026thinsp;1, where \u0026lsquo;c\u0026rsquo; represents the initial class, and \u0026lsquo;r\u0026rsquo; is the new class. The newer image was subsequently subtracted from the older image, generating a transition matrix table with unique values for each direction of change. This method provided a detailed and systematic analysis of land cover dynamics, enabling the identification of specific patterns and directions of change over the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Inventory Data Collection\u003c/h2\u003e \u003cp\u003eA systematic approach was employed to assess the diversity of tree species across different mining intensities within compartments 13, 14, and 17. Mining sites in each compartment were categorized into three intensity levels: heavy (where almost all vegetation was removed), moderate (40%-80% vegetation removal), and low (less than 40% vegetation removal). Non-mined areas, where no mining activity had occurred, were also included. In each compartment, one plot measuring 25 m x 25 m was established in each of the mined and non-mined areas, resulting in a total of 12 plots. Nested within these main plots were smaller plots measuring 5 m x 5 m and 1 m x 1 m. The main plots (25 m x 25 m) were used to inventory all standing tree species with a diameter at breast height (dbh) greater than 10 cm, as well as lianas and climbers. In the 5 m x 5 m nested plots, saplings with a girth between 2 cm and less than 10 cm, as well as shrubs, were inventoried. Seedlings with a girth of at least 2 cm were recorded within the smallest 1 m x 1 m nested plots. The data collection was conducted with the assistance of two experienced botanists from the Forestry Commission, an expert field technician from the Council for Scientific Research, and two local informants, following the guidelines provided by Hawthorne and Jongkind (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The diameter at breast height (1.30 m above the ground) for all tree species was measured using a diameter tape and recorded. Heights of trees and saplings were measured using a hypsometer, while the heights of seedlings were measured with a linear tape.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Inventory Data processing and Analysis\u003c/h2\u003e \u003cp\u003eSpecies richness was estimated as the number of species \u003cem\u003eper\u003c/em\u003e plot while plant diversity was assessed via Shannon-Wiener diversity index (Eq.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{H}}^{{\\prime\\:}}=-\\sum\\:_{i=1}^{\\text{S}}{P}_{i}\\:\\text{l}\\text{n}\\:\\left({P}_{i}\\:\\right)\\)\u003c/span\u003e \u003c/span\u003e \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere H\u0026rsquo; = Shannon Weiner diversity index, S\u0026thinsp;=\u0026thinsp;number of species, In =\u0026thinsp;natural log, P\u003csub\u003ei\u003c/sub\u003e is the proportion of individuals in the sample belonging to the i\u003csup\u003eth\u003c/sup\u003e species i.e. (n\u003csub\u003ei\u003c/sub\u003e/N); n\u003csub\u003ei\u003c/sub\u003e = number of the individual of species, and N\u0026thinsp;=\u0026thinsp;importance value index or total number of the individuals of all species.\u003c/p\u003e \u003cp\u003eAboveground biomass carbon stocks in trees were estimated using Eq.\u0026nbsp;2 (Brown, 1997) while belowground carbon stocks were estimated after using Eq.\u0026nbsp;3 (Cairns \u003cem\u003eet al\u003c/em\u003e., 1997). The estimated biomass was converted to carbon stock by multiplying by 0.5. To estimate carbon loss due to mining, the mean values of mined areas were subtracted from the mean value of non-mined sites and then multiplied by emission factor (3.66) to convert to potential carbon dioxide emission due to illegal mining (IPCC \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; IPCC \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eln (Wt)\u0026thinsp;=\u0026thinsp;2.530 \u0026times; ln (DBH)\u0026thinsp;\u0026minus;\u0026thinsp;2.134 \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip; Eq.\u0026nbsp;2\u003c/p\u003e \u003cp\u003eWhere Wt is biomass (kg) and DBH is diameter at breast height (cm).\u003c/p\u003e \u003cp\u003eBGB\u0026thinsp;=\u0026thinsp;exp (-1.085\u0026thinsp;+\u0026thinsp;0.926 * ln (AGB)) \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;Eq.\u0026nbsp;3\u003c/p\u003e \u003cp\u003eTo assess the impact of mining on plant diversity carbon stocks and potential carbon emission, a one-way ANOVA was conducted followed by a Tukey post hoc test. Mean values were considered significant at α\u0026thinsp;=\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Impact of mining on forest cover dynamics between 2018 and 2023\u003c/h2\u003e \u003cp\u003eThe classification results demonstrate a high level of reliability, with overall accuracies consistently exceeding 86%, signifying strong agreement between classified and reference data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Both the producer\u0026rsquo;s and user\u0026rsquo;s accuracy for all land-use classes across all years exceeded 0.60 while the Kappa coefficient, which quantifies classification agreement beyond chance, ranged from 0.81 to 0.88, indicating a strong level of agreement. The overall classification accuracy was recorded for the 2018 period was 91%, followed by 2020 (90%), 2022 (89%), and 2023 (86%), reflecting a consistently high classification performance across the years.\u003c/p\u003e \u003cp\u003eThe results of this study highlight the significant impact of gold mining on land use and land cover (LULC) dynamics in the Oda River Forest Reserve between 2018 and 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Over the five-year period, forest cover experienced a steady decline, reducing from 16,959.89 ha in 2018 to 15,952.82 ha in 2023, representing a total loss of 5.9% (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In contrast, mining areas expanded dramatically from 52.78 ha in 2018 to 1,059.85 ha in 2023, reflecting an alarming 1,917.6% increase. The most rapid expansion of mining activities occurred between 2022 and 2023, emphasizing the intensifying pressure on forest resources. These findings underscore the dual threats of deforestation and biodiversity loss posed by mining activities, which continue to drive significant land cover changes in the reserve.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy assessment for the classified image\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC Categories\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eUA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMining\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall Accuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eNB: PA\u0026thinsp;=\u0026thinsp;Producer accuracy, UA\u0026thinsp;=\u0026thinsp;User accuracy\u003c/b\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of LULC categories (in Ha) and the annual rate of change in 2018, 2020, 2022, and 2023.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLULC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMining (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eForest (ha)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16959.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e348.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16663.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16323.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1059.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15952.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2020 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u0026ndash;2022 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u0026ndash;2023 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u0026ndash;2023 (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 4 Change Detection (ha) matrix showing LULC changed patterns from 2018 \u0026ndash; 2023\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllegal mining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInitial total\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGross loss\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIllegal mining\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32.82\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e19.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e52.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1027.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e15932.86\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e16959.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1027.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eFinal total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1059.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e15952.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e17012.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGross gain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1027.03\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e19.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 64px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003eNet Change\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e1007.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e-1007.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 129px;\"\u003e\u003cbr\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 64px;\" colspan=\"7\"\u003e\u003cstrong\u003e2018 - 2023\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003c/br\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Impact of mining on plant community structure and diversity\u003c/h2\u003e \u003cp\u003eThe results demonstrate that illegal mining activities drastically reduced plant species richness across various plant categories, with heavily mined areas exhibiting a complete absence of plant species (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This was particularly evident for trees, where non-mined areas had the highest species richness (mean\u0026thinsp;=\u0026thinsp;13.33, SEM\u0026thinsp;=\u0026thinsp;0.96) compared to low and moderate mining areas, which showed intermediate richness values (mean\u0026thinsp;=\u0026thinsp;8.00 and 2.67, respectively). For shrubs, non-mined areas exhibited the highest species richness (mean\u0026thinsp;=\u0026thinsp;5.33, SEM\u0026thinsp;=\u0026thinsp;0.16), while low mining areas had lower richness (mean\u0026thinsp;=\u0026thinsp;4.00, SEM\u0026thinsp;=\u0026thinsp;0.27), and heavily and moderately mined areas recorded no shrub species. A similar trend was observed for liners and climbers, where non-mined areas had a mean species richness of 6.33 (SEM\u0026thinsp;=\u0026thinsp;0.68), while low mining areas exhibited reduced richness (mean\u0026thinsp;=\u0026thinsp;1.67, SEM\u0026thinsp;=\u0026thinsp;0.79). Heavily and moderately mined areas consistently recorded no species for all plant categories. These findings highlight the severe ecological disruption caused by illegal mining, as mining activities result in the destruction of habitats necessary for supporting plant communities.\u003c/p\u003e \u003cp\u003eThe Shannon diversity index, a measure of species diversity that considers both abundance and evenness, was significantly lower in mined areas across all plant categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For trees, non-mined areas recorded the highest Shannon index (mean\u0026thinsp;=\u0026thinsp;2.41, SEM\u0026thinsp;=\u0026thinsp;0.07), while heavily mined areas had zero, indicating a complete loss of plant species diversity. Similarly, for shrubs, non-mined areas exhibited a Shannon index of 1.65 (SEM\u0026thinsp;=\u0026thinsp;0.03), while low mining areas had a reduced index of 1.37 (SEM\u0026thinsp;=\u0026thinsp;0.07). Heavily and moderately mined areas recorded no diversity for shrubs. Liners and climbers also showed significant reductions in diversity, with non-mined areas achieving the highest Shannon index (mean\u0026thinsp;=\u0026thinsp;1.78, SEM\u0026thinsp;=\u0026thinsp;0.11). Low mining areas exhibited reduced diversity (mean\u0026thinsp;=\u0026thinsp;0.46, SEM\u0026thinsp;=\u0026thinsp;0.22), while heavily and moderately mined areas had no diversity. These results indicate that even low-intensity mining activities reduce species diversity, while heavy and moderate mining activities lead to complete ecosystem homogenization. This loss of diversity disrupts ecological balance, reducing the resilience and functionality of the forest ecosystem.\u003c/p\u003e \u003cp\u003eGold mining significantly impacted the structural attributes of plants, including height and diameter, across all categories (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). For trees, non-mined areas exhibited the tallest trees (mean height\u0026thinsp;=\u0026thinsp;19.88 m, SEM\u0026thinsp;=\u0026thinsp;0.44) and the largest DBH (mean\u0026thinsp;=\u0026thinsp;45.32 cm, SEM\u0026thinsp;=\u0026thinsp;2.08). Moderate and low mining areas showed reduced height and DBH values, with moderate mining areas having the highest DBH (mean\u0026thinsp;=\u0026thinsp;50.40 cm) and low mining areas showing a mean DBH of 26.05 cm. Heavily mined areas recorded no trees, highlighting the severe disruption caused by mining activities. Similarly, for shrubs, non-mined areas had the tallest shrubs (mean height\u0026thinsp;=\u0026thinsp;3.00 m, SEM\u0026thinsp;=\u0026thinsp;0.33) and the largest DBH (mean\u0026thinsp;=\u0026thinsp;2.84 cm, SEM\u0026thinsp;=\u0026thinsp;0.30). Low mining areas exhibited reduced height and DBH values (mean height\u0026thinsp;=\u0026thinsp;2.02 m, SEM\u0026thinsp;=\u0026thinsp;0.15; mean DBH\u0026thinsp;=\u0026thinsp;2.00 cm, SEM\u0026thinsp;=\u0026thinsp;0.09), while heavily and moderately mined areas had no shrubs. Liners and climbers followed a similar pattern, with non-mined areas having the tallest individuals (mean height\u0026thinsp;=\u0026thinsp;42.60 m, SEM\u0026thinsp;=\u0026thinsp;6.12) and the largest DBH (mean\u0026thinsp;=\u0026thinsp;17.43 cm, SEM\u0026thinsp;=\u0026thinsp;1.26). Low mining areas exhibited intermediate values (mean height\u0026thinsp;=\u0026thinsp;12.30 m, SEM\u0026thinsp;=\u0026thinsp;5.81; mean DBH\u0026thinsp;=\u0026thinsp;3.67 cm, SEM\u0026thinsp;=\u0026thinsp;1.73), while heavily and moderately mined areas recorded no individuals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Potential CO\u003csub\u003e2\u003c/sub\u003e emission across mined and non-mined forest landscape\u003c/h2\u003e \u003cp\u003eThe study presents a clear correlation between the intensity of illegal mining and the associated carbon emissions. The results indicated that non-mined areas had the highest mean total carbon (C) stock at 689.11 Mg C ha, with no carbon loss or emissions (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Conversely, heavily mined areas showed a complete loss of carbon stock, resulting in the emission of 2522.15 tCO2e. Moderate and low mining intensities also resulted in substantial carbon losses and emissions, albeit to a lesser extent than heavy mining. Specifically, moderate mining resulted in a carbon loss of 332.42 Mg C ha and emissions of 1216.66 tCO2e, while low mining causes a carbon loss of 466.88 Mg C ha and emissions of 1708.76 tCO2e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePotential CO\u003csub\u003e2\u003c/sub\u003e emission across mined sites compared to non-mined sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMining intensity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiomass C stocks (Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiomass C loss (Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmission (tCO\u003csub\u003e2\u003c/sub\u003ee)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e466.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1708.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e356.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e332.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1216.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-mined area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e689.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavily mined\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e689.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2522.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Impact of gold mining on forest cover dynamics (2018\u0026ndash;2023)\u003c/h2\u003e \u003cp\u003eThe classification technique was robust with overall accuracy values ranging from 0.86 to 0.91 and Kappa coefficients ranging from 0.81 to 0.88, reinforcing the high level of agreement between the classified images and the ground truth data (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Congalton \u0026amp; Green, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Foody, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Lu et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The producer's and user's accuracies for all LULC classes consistently exceeded 0.6 across the years, suggesting the classification model effectively distinguished between different site-use types with a high degree of reliability (Olofsson et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Roy \u0026amp; Roy, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed trends align with previous studies that have documented the environmental degradation caused by mining in Ghana (e.g. Agyemang et al. 2018; Kumi et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Amponsah et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For instance, Agyemang et al. (2018) reported extensive deforestation and habitat loss in mining-affected areas, with forest reserves becoming focal points for gold mining activities due to their rich mineral deposits. Similarly, Asner et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) highlighted that gold mining in tropical forest regions, including Ghana, has resulted in widespread deforestation, habitat fragmentation, and soil degradation. Furthermore, the progressive deforestation observed aligns with global patterns where illegal mining has been identified as a primary driver of forest loss and biodiversity depletion, particularly in tropical regions (Asner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Nascimento et al., 2020). It further mirrors findings in the Amazon rainforest, where mining activities have similarly resulted in extensive deforestation and habitat fragmentation (Sonter et al., \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The economic profitability of artisanal and small-scale gold mining, coupled with high global demand for gold, is a significant motivating factor for these activities (Agyemang et al., 2018; Dirnbock et al., 2011). Advances in mining technology have further enabled deeper and more extensive exploitation of forested areas, exacerbating the problem (Telmer and Veiga, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Takyi et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The observed trends of deforestation between 2018 and 2023 may also point to a spillover effect whereby displaced farmers clear additional forested areas for agricultural expansion, compounding the deforestation problem. Studies by Amponsah et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) in the Atewa Range Forest Reserve reported that mining activities displaced farming communities, triggering agricultural intensification and further forest loss. This underscores the interconnectedness of land use practices and the cascading effects of mining on agricultural systems and forest ecosystems.\u003c/p\u003e \u003cp\u003eThe significant loss of forest cover has profound ecological implications such as carbon sequestration, water regulation, soil fertility, and habitat for diverse flora and fauna (Chazdon, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The reduction in forest area by 5.9% over the study period potentially compromises these essential services and exacerbates climate change impacts. Furthermore, the fragmentation of wildlife habitats and the disruption of ecological corridors significantly affect biodiversity (Ninan \u0026amp; Perrings, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies have shown that habitat fragmentation limits species movement, reduces genetic diversity, and increases vulnerability to extinction (Laurance et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the Oda River Reserve, such fragmentation likely hampers wildlife migration and ecosystem resilience. Besides, the removal of vegetation and topsoil for mining exacerbates land degradation through soil erosion, compaction, and the formation of gullies, which collectively diminish land productivity (Owolabi, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, Ghana's recent decision to legalize mining in forest reserves adds a complex dimension to these findings. While legalization aims to regulate mining activities and ensure environmental accountability, it also raises concerns about the potential intensification of forest exploitation (Amponsah et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Miners may interpret the policy as tacit approval of forest exploitation and without stringent environmental safeguards, such mining activities may accelerate forest loss, biodiversity decline, and disruption of ecosystem services, such as carbon sequestration and water regulation (Laurance et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Aboagye, 2016). Studies in similar contexts have shown that weak enforcement of regulations can lead to both legal and illegal mining coexisting, with overlapping negative impacts on ecosystems (Asner et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Santana \u0026amp; Berkes, 2020). If mining in forest reserves is to be sustainable, it must be accompanied by effective land management strategies, strict compliance monitoring, and significant investments in reforestation and habitat restoration. Community participation in decision-making processes is crucial to ensuring that mining activities do not undermine conservation efforts or disrupt the livelihoods of local populations (Agyemang et al., 2018).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Impact of gold mining on plant community structure and diversity\u003c/h2\u003e \u003cp\u003eThe findings demonstrate the severe ecological impacts of illegal mining on plant diversity and forest structure, highlighting significant declines across all plant categories, including trees, saplings, seedlings, shrubs, and climbers (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The absence of species richness and the complete lack of plant density in heavily mined areas exemplify the destructive nature of mining activities on forest ecosystems (Espejo et al., 20218). This aligns with previous studies that have documented similar outcomes, where mining operations result in deforestation, habitat destruction, and biodiversity loss (Hilson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Agyemang et al., 2010). The stark contrast between heavily mined and non-mined areas underscores the disruptive extent of mining on forest ecosystems, with non-mined areas consistently exhibiting higher species richness, density, and diversity indices (Nero, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Shannon index, a key measure of species diversity, highlighted the significant ecological imbalance caused by illegal mining. Non-mined areas recorded significantly higher Shannon index values across all plant categories, indicating greater species heterogeneity and ecological stability (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In heavily mined areas, the Shannon index dropped to zero for trees, saplings, and climbers, reflecting a complete loss of biodiversity. This pattern is consistent with findings from other studies that emphasize the homogenization of plant communities and the loss of ecological balance in mining-affected regions (Attuquayefio et al., 2005; Anane et al., 2013). The significant reduction in tree density from 304.30 trees/ha in non-mined areas to zero in heavily mined areas further illustrates the magnitude of habitat destruction. These results corroborate earlier studies that have reported similar trends in mining-impacted areas, where vegetation loss is a direct consequence of large-scale deforestation and soil degradation (Mensah et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ramirez et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGold mining activities also severely impacted the regenerative capacity of the forest, as evidenced by the absence of saplings and seedlings in heavily mined areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This finding is alarming, as it indicates a long-term disruption of natural regeneration processes. Saplings and seedlings are critical to the recovery and sustainability of forest ecosystems, and their absence in heavily mined areas suggests a collapse in ecological succession. Studies by Attuquayefio and Fobil (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Anane and Cobbinah (2013) have similarly shown that mining activities disrupt soil structure and nutrient availability, inhibiting plant growth and regeneration. The reduced DBH and height of saplings and shrubs in mined areas further emphasize the detrimental impact of mining on plant growth and maturity. Soil compaction, a common consequence of mining activities, likely contributed to these reductions by limiting root growth, water infiltration, and nutrient uptake (Hilson, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; McShea \u0026amp; Rappole, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Moreover, the physical processes involved in mining, such as heavy machinery use and soil excavation, lead to the destruction of topsoil and the loss of essential soil microorganisms. These changes impair the forest's ability to support diverse plant communities and regenerate naturally (Adjei et al., 2021; Agyemang et al., 2010).\u003c/p\u003e \u003cp\u003eThe negative effects of mining extended to shrubs and climbers (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), which play vital roles in maintaining forest structure and biodiversity. The significant reduction in shrub density and height in mined areas highlights the extent to which mining disrupts forest ecosystems. Shrubs are essential for soil stabilization and habitat provision, and their decline can lead to increased soil erosion and loss of ecosystem services (Gillison \u0026amp; Brewer, 2005; Dirzo \u0026amp; Raven, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Similarly, the complete absence of climbers in heavily mined areas underscores the severe ecological disruption caused by mining. Climbers rely on biotic interactions, such as seed dispersal and pollination, which are often disrupted by the removal of key plant and animal species in mining-affected areas (Asase et al., 2013). This disruption of ecological networks further compounds the loss of biodiversity and ecological functionality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Potential impact of illegal mining on CO\u003csub\u003e2\u003c/sub\u003e emission\u003c/h2\u003e \u003cp\u003eThe results (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) clearly illustrate the destructive impact of mining in forest reserves on carbon sequestration. Forests in non-mined areas maintain high carbon stocks, which are crucial for mitigating climate change. When these forests are disturbed by mining activities, the carbon stored in biomass is released back into the atmosphere, contributing significantly to greenhouse gas (GHG) emissions. This phenomenon is supported by numerous studies that highlight deforestation and land-use change as major sources of CO\u003csub\u003e2\u003c/sub\u003e emissions (Baccini et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The carbon emissions resulting from illegal mining in the Oda River Forest Reserve align with findings from other regions experiencing similar activities. For instance, Rudel et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) discuss how deforestation from mining and agriculture in the Amazon Basin contributes to regional and global CO\u003csub\u003e2\u003c/sub\u003e emissions. Similarly, Asner et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) document the high carbon losses associated with gold mining in the Peruvian Amazon, noting significant reductions in forest carbon stocks and increased CO\u003csub\u003e2\u003c/sub\u003e emissions. When trees are felled, the carbon stored in their trunks, branches, and leaves is oxidized and released as CO\u003csub\u003e2\u003c/sub\u003e. Additionally, soil disturbance from mining activities can release soil organic carbon (SOC), further exacerbating emissions (Don et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This dual release from both biomass and soil underscores the severe impact of illegal mining on carbon dynamics. The Intergovernmental Panel on Climate Change (IPCC) emphasizes the importance of maintaining forest cover to mitigate climate change (IPCC, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The significant carbon losses documented in this study highlight the need for urgent measures to curb illegal mining and protect forest ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusions","content":"\u003cp\u003eThe findings of this study demonstrate the ecological consequences of gold mining on forest cover, biodiversity, and carbon dynamics. Over the five-year period from 2018 to 2023, forest cover declined significantly, with a total loss of 5.9%, while mining areas expanded by 1,917.6%. These changes illustrate the dual threats of deforestation and environmental degradation posed by mining activities. Heavily mined areas were devoid of tree, shrub, and climber species, while non-mined areas maintained significantly higher species richness and diversity. Even in areas with low or moderate mining activity, significant reductions in plant abundance and diversity were observed, pointing to the pervasive impact of mining on forest ecosystems. Such biodiversity loss has far-reaching implications for ecosystem functionality, resilience, and the provision of critical services, including habitat provision and soil stabilization. The structural integrity of forest vegetation was also significantly affected by mining activities. Non-mined areas demonstrated significantly higher tree height and diameter at breast height (DBH), reflecting healthier and more mature forests capable of supporting diverse flora and fauna. In contrast, mined areas exhibited reduced structural attributes, with heavily mined areas entirely devoid of vegetation. This loss of structural complexity compromises the forest\u0026rsquo;s ability to sequester carbon, regulate water cycles, and sustain wildlife populations, further exacerbating the environmental consequences of mining. Furthermore, mined areas showed substantial to complete carbon loss and significant potential CO\u003csub\u003e2\u003c/sub\u003e emissions which emphasize the need for urgent action to address mining challenges and restore degraded landscapes. We concluded that mining in forest reserves threatens biodiversity and climate change mitigation via carbon sequestration. Restoration efforts, including reforestation with native species and soil rehabilitation, are critical to restoring biodiversity and ecosystem functionality.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of Interest Statement\u003c/strong\u003e: The Authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement:\u0026nbsp;\u003c/strong\u003eNo funding or grant was received for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eAll relevant data are included within the main manuscript and the accompanying supplementary information files.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations\u003c/strong\u003e: not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003eWe extend our gratitude to Forestry Commission for their technical advice and support during data collection. We are thankful to staff of Oda River Forest Reserve for their assistance during data collection. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution statements:\u0026nbsp;\u003c/strong\u003eMA; conceptualization, project administration, investigation, data curation, formal analysis, methodology, software, visualization, writing- original draft, supervision, writing\u0026ndash;review and editing, and validation. SA: conceptualization, methodology, writing - review and editing, supervision, and project administration. SK: writing - review and editing, validation, visualization, and project administration. GN: resources, conceptualization, methodology, investigation, and writing- original draft. AA: methodology, formal analysis, writing - review and editing, and visualization.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcheampong E.O., Colin Macgregor C., Sean Sloan S. \u0026amp; Jeffrey Sayer J. 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Accessed on:30-01-2025\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"mining, forest reserves, biodiversity, carbon emission, land use and land cover changes, gold","lastPublishedDoi":"10.21203/rs.3.rs-6259522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6259522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eGhana recently legalized mining in forest reserves but the impacts of this policy shift on forest cover, biodiversity and carbon stocks are not well documented. We analysed forest cover dynamics between 2018 and 2023 in the Oda River Forest Reserve and inventoried data from 12 plots in non-mined and mined (low, moderate and heavily) sites for its consequences on biodiversity and carbon stocks. Forest cover declined by 5.9%, shrinking from 16,959.89 ha in 2018 to 15,952.82 ha in 2023, while illegal mining expanded astronomically by 1,917.6%, increasing from 52.78 ha to 1,059.85 ha, with the most rapid expansion occurring between 2022 and 2023. The study revealed significant reductions in plant species richness and diversity across trees, shrubs, and climbers in mined areas, with heavily mined zones exhibiting a complete absence of vegetation. The Shannon diversity index and structural attributes such as tree height and diameter also significantly declined, reflecting the widespread ecological disruption caused by mining activities. Non-mined areas demonstrated higher biodiversity (S\u0026thinsp;=\u0026thinsp;13.33, H\u0026thinsp;=\u0026thinsp;2.41), greater structural complexity, and maintained the highest carbon stocks (689.11 Mg C ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), emphasizing their role in mitigating climate change. In contrast, heavily mined areas exhibited complete carbon loss, resulting in substantial potential CO\u003csub\u003e2\u003c/sub\u003e emissions (2,522.15 tCO\u003csub\u003e2\u003c/sub\u003ee). Our results demonstrate the urgent need for effective land management policies, enforcement of mining regulations, and restoration efforts, including reforestation with native species. Addressing mining in forest reserves is critical to preserving biodiversity, mitigating climate change, and ensuring the resilience of forest ecosystems.\u003c/p\u003e","manuscriptTitle":"Mining in Ghanaian Forest Reserves: Impacts on Forest Cover, Biodiversity and Carbon Stocks","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-04 12:36:46","doi":"10.21203/rs.3.rs-6259522/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-22T07:23:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T04:11:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-13T15:17:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-08T20:07:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9324665657582174420970833650176144156","date":"2025-04-07T01:49:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63553207417139218888852387370468787933","date":"2025-04-03T17:19:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"303549496404810998513105064704310083537","date":"2025-04-03T13:24:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-03T07:36:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-01T06:31:25+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T06:28:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Forests","date":"2025-03-19T08:28:11+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4c8152e1-bf82-49b5-abac-d52c379c1c90","owner":[],"postedDate":"April 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-19T14:08:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-04 12:36:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6259522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6259522","identity":"rs-6259522","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}