Forest Cover Change Assessment in Benue State, Nigeria (2005–2020) Using Remote Sensing and Geographic Information System

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This study assesses the spatio-temporal dynamics of forest cover in selected communal and reserved forests of Benue State, Nigeria, between 2005 and 2020 using high- resolution satellite imagery (QuickBird and GeoEye; 0.5–0.65 m) integrated with Geographic Information Systems (GIS) and Remote Sensing techniques. Supervised image classification using the Maximum Likelihood Classifier was applied, followed by post-classification comparison to quantify forest loss, farmland expansion, and changes in built-up areas. Classification reliability was evaluated using overall accuracy and Kappa statistics. Results indicate substantial forest depletion across most study sites, with forest cover declining by approximately 20–60%, while farmland and built-up areas expanded correspondingly. A few forest patches exhibited relative stability or slight regeneration, suggesting localized management practices or site-specific resilience. Analysis of change patterns indicates that agricultural expansion, population growth, and proximity to urban centers are major drivers of forest conversion. Future projections based on Markov Chain modeling suggest continued forest decline if current land-use trends persist. The findings highlight the suitability of high-resolution imagery for monitoring fragmented forest landscapes and underscore the need for targeted forest management, sustainable land-use planning, and community-based conservation strategies to support climate action and biodiversity conservation in Benue State. Geographic Information Systems Remote sensing GIS Forest cover change Land use dynamics Change detection Landscape sustainability Benue State Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Forest ecosystems constitute a critical component of the global environment, providing essential ecosystem services such as carbon sequestration, biodiversity conservation, hydrological regulation, and livelihood support for millions of people. Despite their importance, forests across the tropics have experienced widespread degradation and conversion over recent decades, largely driven by agricultural expansion, urban growth, logging, and energy-related biomass extraction (Assede et al., 2023 ; Sahoo et al., 2021 ). Globally, deforestation and forest degradation contribute significantly to greenhouse gas emissions and biodiversity loss, reinforcing concerns about climate change and ecosystem resilience (FAO, 2010; Turner et al., 2007 ). Sub-Saharan Africa exhibits distinct deforestation dynamics compared with other tropical regions, as forest conversion is predominantly associated with smallholder agriculture, fuelwood extraction, and informal land-use practices rather than large-scale industrial agriculture (Assede et al., 2023 ; Singh, 2022 ). In West Africa, forest landscapes are increasingly fragmented due to population growth, rural livelihood dependence on land resources, and limited enforcement of forest management policies (Rachid et al., 2023 ). These processes have intensified pressure on forest–savanna transition zones, where land-use competition between agriculture and forest conservation is particularly acute. Nigeria ranks among the countries with the highest rates of forest loss in Africa, with deforestation driven by subsistence farming, settlement expansion, logging, and charcoal production (Fasona et al., 2022 ; Mohiuddin, 2026 ). Within the Middle Belt region, including Benue State, forest cover decline has been repeatedly linked to agricultural expansion and fuelwood dependence associated with rural livelihoods (Bombom & Yemisi, 2024 ; Mbasaanga et al., 2025 ). Although forests in this region play an important role in supporting food systems and household energy needs, continued degradation threatens biodiversity, soil fertility, and climate regulation functions. Benue State represents a critical case for examining forest cover dynamics due to its ecological position within a forest–savanna mosaic and its prominence as an agricultural hub in Nigeria. Previous studies in and around Makurdi have documented vegetation degradation and land-cover transitions using remotely sensed data (Anule & Ujoh, 2017 ; Jande & Nsofor, 2002 ). However, many existing assessments are either spatially limited or temporally constrained, providing insufficient evidence on long-term forest cover trajectories across the broader state landscape. Consequently, there remains a need for spatially explicit, multi-temporal analyses that quantify forest loss patterns and identify dominant land-cover transitions over extended periods. Remote Sensing (RS) and Geographic Information Systems (GIS) provide reliable and cost-effective tools for monitoring forest cover change, particularly in data-scarce environments. Multi-temporal satellite imagery enables consistent observation of land-cover dynamics over decadal timescales, while supervised classification and post-classification change detection techniques facilitate the identification of conversion pathways between forest and non-forest land uses (Kussul et al., 2017 ; Liu et al., 2002 ). These approaches are widely applied in forest monitoring studies to support environmental assessment, land-use planning, and sustainability-oriented decision-making (Assede et al., 2023 ; Pazúr et al., 2021 ). In this context, the present study employs multi-temporal satellite imagery and GIS techniques to assess forest cover change in Benue State, Nigeria, between 2005 and 2020. Using supervised Maximum Likelihood Classification, accuracy assessment, and post-classification change detection, the study maps major land-use/land-cover categories, quantify changes in forest extent, examines spatial patterns of forest loss, and identifies dominant transition pathways. By providing spatially explicit evidence of forest dynamics, the study contributes to environmental monitoring efforts and supports sustainable land-use planning and forest management initiatives aligned with national and global sustainability objectives. Unlike many forest cover studies that rely on medium-resolution imagery, this study adopts high-resolution satellite data to capture the fine-scale structure of fragmented forest patches typical of savanna–woodland transition zones. This approach reduces mixed-pixel effects and improves boundary delineation in heterogeneous agricultural landscapes. 2. Materials and Methods 2.1 Study Area and Forest Selection The study was conducted in Benue State, located in the Middle Belt region of Nigeria and covering approximately 34,059 km² across 23 Local Government Areas (Fig. 1 ). The State lies within a forest–savanna transition zone characterized by mosaic vegetation patterns shaped by agricultural expansion, settlement growth, and population pressure. Forest resources in Benue State consist mainly of gazetted forest reserves and communal (village) forests, which support rural livelihoods through fuelwood collection, non-timber forest products, and smallholder agriculture. Source: GIS Laboratory, MOAUM. The study population comprised all gazetted communal and reserved forests in Benue State, totaling 129 forest units (52 forest reserves and 77 communal forests). Given the fragmented nature of forest cover and the need for spatial representativeness, a two-stage cluster sampling strategy was adopted. First, the State was stratified into three clusters based on the senatorial districts (North-East, North-West, and South). Within each senatorial district, four Local Government Areas were purposively selected. From each selected Local Government Area, one communal forest or one forest reserve was purposively selected, yielding a total of twelve forest sites. Selection was based on technical criteria, including clear visibility on high-resolution satellite imagery across all study epochs (2005, 2010, 2015, and 2020), sufficient spatial extent for image classification, and absence of persistent cloud cover or data gaps. This approach ensured that the selected forests were both spatially representative and methodologically suitable for multi-temporal analysis. 2.2 Research Design and Data Sources An ex-post facto research design with a multi-temporal analytical approach was adopted to assess historical forest cover changes without experimental manipulation. Forest cover dynamics were examined for four epochs (2005, 2010, 2015, and 2020) to capture long-term trends and land-cover transition pathways. The study relied primarily on high-resolution satellite imagery. Multi-temporal images consisted of QuickBird data for 2005 and 2010 and GeoEye data for 2015 and 2020, all sourced from Google Earth Pro. These datasets provide sub-meter spatial resolution ranging from 0.5 to 0.65 m, which is suitable for detecting small and fragmented forest patches typical of savanna–woodland transition zones. To ensure inter-temporal consistency and minimize atmospheric interference, all images were selected from the dry season period between January and March. Ancillary data included a topographic map of Benue State for spatial reference and boundary delineation, as well as ground reference data collected using a handheld Global Positioning System (GPS) during field visits. In addition, Google Earth imagery was used as ancillary visual reference data to support training sample selection and to complement field-based reference points during accuracy assessment. 2.3 Image Processing and Classification Land-use and land-cover classification was guided by the FAO forest definition framework and the Anderson et al. ( 2001 ) classification system. Given the savanna context of the study area, forest and other wooded land categories were aggregated into a single Forest class to enhance interpretability and reduce artificial fragmentation. Non-forest land was subdivided into Farmland and Built-up/Bare surface classes. Water bodies were excluded from the classification scheme, as none of the selected forest areas contained permanent surface water features. All satellite images underwent standard preprocessing procedures, including geometric correction and visual inspection to ensure spatial alignment. Supervised image classification was carried out using the Maximum Likelihood Classifier, selected for its robustness in handling normally distributed spectral signatures. Training samples were generated using a combination of field knowledge, GPS reference points, and ancillary visual interpretation. Approximately 30 training samples per class were digitized for each epoch to ensure classification consistency. Given the fragmented nature of the forest landscape, specific measures were taken to address spatial resolution constraints. The use of sub-meter QuickBird and GeoEye imagery enabled accurate delineation of narrow forest strips and isolated forest remnants that would otherwise be affected by mixed-pixel effects in medium-resolution datasets. Post-classification comparison was adopted for change detection to minimize cumulative spatial error across temporal epochs, while a minimum mapping unit was applied to suppress classification noise from isolated pixels. 2.4 Change Detection and Rate of Change Analysis Forest cover change was quantified using post-classification comparison to identify transitions between land-cover classes across successive epochs. Change matrices were generated to capture forest loss, regeneration, and persistence between 2005–2010, 2010–2015, 2015–2020, and across the full study period (2005–2020). The annual rate of forest cover change was calculated using the expression: $$\:R=\frac{{A}_{2}-{A}_{1}}{{A}_{1}\times\:t}$$ where \(\:R\) represents the annual rate of change, \(\:{A}_{1}\) and \(\:{A}_{2}\) denote forest area at the initial and final time periods, respectively, and \(\:t\) is the time interval in years. This approach enabled comparison of forest loss and gain rates across different forest sites and time intervals. 2.5 Accuracy Assessment Classification accuracy was evaluated using an error matrix approach. Overall accuracy and the Kappa Index of Agreement were computed for each epoch to assess classification reliability. Reference data for accuracy assessment were derived from field-collected GPS points and ancillary visual interpretation. The results indicate good to very good agreement across all study sites, supporting the reliability of the classified outputs used for subsequent forest cover change analysis. 3. Results 3.1 Land Use/Land Cover Distribution (2005–2020) The land use/land cover (LULC) classifications for 2005, 2010, 2015 and 2020 indicate substantial changes in forest and non-forest classes across the selected forest landscapes of Benue State (Figs. 2–13). Across all study sites, the dominant mapped classes were forest, farmland, and built-up/bare surface, reflecting the agricultural and settlement-driven land conversion processes prevalent in the state. In Agila Forest (Ado LGA), forest cover declined from 97.44 ha (44.56%) in 2005 to 52.63 ha (24.07%) in 2020, while farmland increased from 111.78 ha (51.12%) to 156.40 ha (71.52%) over the same period (Table 1 ). Built-up/bare surface remained relatively small but fluctuated across the epochs. A similar but more severe conversion pattern occurred in Agan Forest (Makurdi LGA), where forest cover declined from 32.13 ha (63.20%) in 2005 to 2.21 ha (4.35%) in 2020, while farmland expanded from 22.28% to 79.61% (Table 2 ). Table 1 Land use and Land cover distribution of Agila Forest Cover change in Ado LGA, Benue State from 2005–2020 LULC Type Area in Hectares 2005 % 2010 % 2015 % 2020 % Forest Cover 97.44 44.56 79.20 36.22 76.21 34.85 52.63 24.07 Farmland 111.78 51.12 119.78 54.78 139.03 63.57 156.40 71.52 Built up Area/Bare surface 9.46 4.32 19.68 9.00 3.46 1.58 9.63 4.41 Total 219 100.00 219 100.00 219 100.00 219 100.00 Source: Author’s GIS Analysis 2025 Table 2 Land use and Land Cover distribution of Agan Forest in Makurdi LGA, Benue State from 2005–2020 Year LULC Type Area in Hectares 2005 % 2010 % 2015 % 2020 % Forest Cover 32.13 63.20 31.29 61.54 23.66 46.62 2.21 4.35 Farmland 11.32 22.28 13.65 26.85 24.20 47.69 40.49 79.61 Built up Area/Bare surface 7.38 14.52 5.90 11.61 2.89 5.69 8.16 16.04 Total 51 51 51 51 Source: Author’s GIS Analysis 2025 Comparable forest depletion patterns were observed in other sites including Gboko Forest Reserve, Mbapa, Mbamo, Okokolo, and Tse-Mker, where farmland and, to a lesser extent, built-up/bare surface expanded at the expense of forest cover (Appendix Tables a, c, d, i, j). In contrast, Mbasaan (Guma LGA) and Mbaikuna (Kwande LGA) showed relative stability and slight forest gains over the 15-year period, suggesting localized resilience or lower conversion pressure (Appendix Tables b and f). 3.2 Temporal Trends in Forest Cover Change (2005–2020) Forest cover changes substantially across the twelve sites. Table 3 summarizes forest cover percentages for 2005 and 2020 and highlights the magnitude of change over the study period. Table 3 Summarized percentage forest cover across all sampled sites Forest Cover S/No Study Site Local Government 2005 (%) 2020 (%) Change (%) 1 Agan Makurdi 63.20 4.35 −58.85 2 Mbamo Katsina-Ala 63.49 5.69 −57.80 3 Gboko Gboko 74.28 21.26 −53.02 4 Mbapa Gwer West 82.59 31.87 −50.72 5 Okokolo Otukpo 85.20 56.59 −28.61 6 Tse-Mker Vandeikya 62.77 36.16 −26.61 7 Agila Ado 44.56 24.07 −20.49 8 Mbanor Konshisha 55.17 40.17 −15.00 9 Ipunu Oju 58.49 45.33 −13.16 10 Idekpa Idekpe 49.55 43.26 −6.29 11 Mbaikuna Kwande 80.64 81.20 + 0.56 12 Mbasaan Guma 75.07 78.15 + 3.08 Source: Author’s GIS Analysis 2025 The greatest decline occurred in Agan (− 58.85%), Mbamo (− 57.80%), Gboko (− 53.02%), and Mbapa (− 50.72%), indicating intense conversion pressure. Moderate declines were recorded in Okokolo (− 28.61%), Tse-Mker (− 26.61%), and Agila (− 20.49%), while smaller declines occurred in Idekpa (− 6.29%), Ipunu (− 13.16%), and Mbanor (− 15.00%). By contrast, Mbaikuna (+ 0.56%) and Mbasaan (+ 3.08%) recorded slight net increases in forest cover between 2005 and 2020 (Table 3 ). These results demonstrate that while forest loss is widespread, the rate and intensity of change are highly site-specific, suggesting spatially differentiated drivers and pressures across Benue State. 3.3 Spatial Patterns of Forest Loss The classified maps show distinct spatial patterns of forest conversion and fragmentation across the study sites (Figs. 2–13). Forests located near major urban centers and areas of intensified land use showed pronounced fragmentation, characterized by reduced contiguous forest blocks and the emergence of dispersed forest patches embedded in farmland and built-up/bare surfaces. For example, Agan (Makurdi LGA) and Gboko Forest Reserve exhibited strong fragmentation patterns and visible conversion to farmland and settlement-related land uses (Figs. 3 and 4). Conversely, relatively stable forest patches such as Mbasaan (Guma LGA) and Mbaikuna (Kwande LGA) retained high forest cover through the epochs, suggesting lower conversion intensity, possible community protection, or local site conditions that constrain agricultural expansion (Figs. 5 and 9). Overall, spatial evidence indicates that forest loss and fragmentation are concentrated in sites experiencing stronger land-use competition and landscape accessibility. 3.4 Forest Cover Transition Pathways Post-classification comparison indicates that forest-to-farmland conversion constitutes the dominant land-cover transition across most sites, consistent with the observed increases in farmland area over time (Tables 1 and 2 ). In several forests, forest loss is accompanied by increases in built-up/bare surface, particularly in sites influenced by settlement expansion and infrastructure development (e.g., Makurdi and Gboko environments). In the few sites where forest cover increased slightly (Mbasaan and Mbaikuna), the mapped changes suggest limited non-forest-to-forest transitions, which may reflect fallow-driven regeneration, localized protection, or reduced conversion pressure. Overall, the transition patterns confirm that agricultural expansion remains the principal competing land use driving forest decline across the selected forest landscapes. 3.5 Classification Accuracy Assessment Classification accuracy was assessed for each forest and each epoch (2005, 2010, 2015 and 2020) using overall accuracy (OA) and the Kappa Index (κ). Accuracy results show generally strong performance across sites, with most OA values ≥ 90% and κ values indicating good to very good agreement (Stehman & Czaplewski, 1998 ), supporting the reliability of the classified maps for change detection. Table 4 Overall Accuracy (%) and Kappa Index (κ) by Forest and Year (2005–2020) 2005 2010 2015 2020 Forest LGA OA κ OA Κ OA κ OA κ Agila Ado 96 0.96 90 0.89 94 0.93 88 0.87 Agan Makurdi 98 0.98 94 0.93 98 0.98 98 0.98 Gboko Gboko 90 0.89 96 0.96 94 0.93 94 0.93 Mbasaan Guma 84 0.83 86 0.88 82 0.80 74 0.72 Mbapa Gwer West 98 0.96 94 0.93 98 0.98 94 0.93 Mbamo Katsina-Ala 96 0.96 96 0.96 94 0.93 98 0.98 Mbanor Konshisha 92 0.91 92 0.91 92 0.91 96 0.96 Mbaikuna Kwande 80 0.78 84 0.83 94 0.93 88 0.87 Idekpa Obi 92 0.91 88 0.87 98 0.98 98 0.98 Ipunu Oju 86 0.85 78 0.76 76 0.74 88 0.97 Okokolo Otukpo 98 0.98 96 0.96 88 0.87 96 0.96 Tse-Mker Vandeikya 92 0.91 94 0.93 92 0.91 84 0.83 Source: Author’s GIS analysis (2022) While most classifications show high agreement, lower accuracy were recorded in some cases (e.g., Mbasaan 2020; Ipunu 2015), indicating comparatively higher uncertainty for those specific classifications. Nevertheless, the overall accuracy profile provides adequate confidence for interpreting the mapped LULC patterns and changing trajectories. 4.6 Annual Rate of Forest Cover Change Annual rates of change were computed for forest, farmland and built-up/bare surface for each site over three intervals (2005–2010, 2010–2015, 2015–2020) and for the full period (2005–2020) as derived from annual rate of change calculations. The annual rate results highlight marked variation in forest loss intensity across sites, including periods of accelerated decline and relative stability. Forests experiencing the most rapid annual decline over the full period include Agan (− 1.99 ha; −3.92% annually), Gboko (− 10.99 ha; −3.53%), Mbapa (− 8.12 ha; −3.38%), and Mbamo/Katsina-Ala (− 2.80 ha; −3.85%), indicating persistent conversion pressure. Agan also recorded pronounced acceleration in 2015–2020 (− 4.29 ha; −8.45% annually), reflecting severe depletion during the final interval. Moderate annual forest losses were observed in Agila (− 2.99 ha; −1.37%), Mbanor (− 2.02 ha; −1.00%), Ipunu (− 1.46 ha; −0.88%), Okokolo (− 0.55 ha; −1.91%), and Tse-Mker (− 0.49 ha; −1.77%). By contrast, Mbasaan recorded a slight net annual gain in forest cover (+ 0.30 ha; +0.21%), and Mbaikuna remained largely stable with a marginal annual gain (+ 0.01 ha; +0.04%), reflecting localized resilience or reduced conversion intensity. Across most sites, annual forest decline coincided with annual increases in farmland area, confirming that agricultural expansion remains the predominant land-use transition associated with forest cover loss. 4. Discussion This study provides geospatial evidence of substantial forest cover change across selected forest landscapes in Benue State between 2005 and 2020. The observed decline in forest cover across most sites, coupled with expansion of farmland and built-up/bare surfaces, reflects broader land-use transition dynamics reported across Nigeria and the West African sub-region (Assede et al., 2023 ; Fasona et al., 2022 ; Mbasaanga et al., 2025 ). 4.1 Forest Cover Dynamics in Relation to Previous Studies The magnitude and spatial variability of forest loss observed in this study are consistent with earlier remote sensing–based assessments in Nigeria’s Middle Belt, which identify agricultural expansion and settlement growth as dominant drivers of deforestation (Bombom & Yemisi, 2024 ; Jande & Nsofor, 2002 ). Forests such as Agan, Gboko, Mbamo, and Mbapa exhibited rapid depletion, aligning with findings that forests located near urban centers and agriculturally productive zones experience accelerated conversion due to competing land-use demands (Fasona et al., 2022 ). Conversely, the relative stability observed in Mbasaan and Mbaikuna forests corroborates studies suggesting that localized management practices, reduced accessibility, or terrain-related constraints can moderate deforestation rates in otherwise pressure-prone regions (Assede et al., 2023 ). Similar patterns of heterogeneous forest change have been documented in other savanna–forest transition zones across sub-Saharan Africa, where land-use outcomes are shaped by both biophysical conditions and localized socio-economic dynamics (Singh, 2022 ). 4.2 Agricultural Expansion as the Dominant Conversion Pathway Results from land-cover transitions and annual rate analysis confirm that forest-to-farmland conversion constitutes the principal pathway of forest loss across the study area. This finding aligns with regional evidence that smallholder agriculture remains the most significant proximate cause of deforestation in Nigeria and much of West Africa (Mensah et al., 2025 ; Mbasaanga et al., 2025 ). The strong correspondence between declining forest cover and increasing farmland area supports the Land Change Science perspective, which emphasizes the role of livelihood-driven land-use decisions in shaping landscape transformations (Turner et al., 2007 ). In this context, forest loss is not merely an environmental phenomenon but is closely embedded within local food security strategies and rural survival systems. 4.3 Spatial Heterogeneity and Urban Influence The spatial patterns revealed by the classified maps indicate pronounced fragmentation in forests located near major urban centers such as Makurdi and Gboko. These findings are consistent with studies demonstrating that urban expansion and infrastructure development intensify forest conversion through increased demand for land, fuelwood, and construction materials (Ahmed et al., 2024 ; Bombom & Yemisi, 2024 ). Fragmentation not only reduces total forest area but also compromises ecological integrity by isolating forest patches and diminishing habitat connectivity. In contrast, forests located in relatively remote areas or under stronger communal stewardship exhibited lower rates of conversion and, in some cases, slight regeneration. This spatial heterogeneity underscores the importance of place-specific interventions rather than uniform forest management policies. 4.4 Implications for Environmental Sustainability and Climate Policy The observed decline in forest cover has significant implications for biodiversity conservation, carbon sequestration, and climate resilience in Benue State. Forest degradation reduces carbon storage capacity, thereby contributing indirectly to greenhouse gas emissions and undermining climate mitigation efforts (Sahoo et al., 2021 ). At the local scale, continued forest loss threatens ecosystem services critical to rural livelihoods, including fuelwood supply, soil protection, and non-timber forest products (Rachid et al., 2023 ). From a policy perspective, these findings highlight challenges to achieving Sustainable Development Goal (SDG) 15 (Life on Land) and SDG 13 (Climate Action), particularly in sub-national contexts where land-use pressures are intense and enforcement capacity is limited (Bhale, 2024 ). Integrating spatial forest monitoring into land-use planning and development decision-making is therefore essential. 4.5 Methodological Implications for Forest Monitoring The study demonstrates the effectiveness of multi-temporal satellite imagery and GIS-based change detection for monitoring forest dynamics in fragmented savanna–forest landscapes. The generally high classification accuracy achieved across sites validates the use of supervised classification approaches for forest change analysis at local and regional scales (Kussul et al., 2017 ; Liu et al., 2002 ). Nevertheless, variations in accuracy across sites and years also highlight the influence of landscape heterogeneity and underscore the importance of accuracy assessment in interpreting change detection results (Stehman & Czaplewski, 1998 ). 5. Conclusion and Recommendations 5.1 Conclusion This study examined spatio-temporal forest cover dynamics in selected communal and reserved forest landscapes of Benue State, Nigeria, using high-resolution satellite imagery integrated with Geographic Information Systems and Remote Sensing techniques. Analysis of multi-temporal QuickBird and GeoEye imagery for the period 2005–2020 revealed substantial forest cover decline across most of the sampled sites, accompanied by corresponding expansion of farmland and built-up areas. The magnitude and rate of forest loss varied spatially, reflecting differences in land-use pressure, proximity to settlements, and forest management regimes. While several forest patches experienced severe fragmentation and decline, a few sites exhibited relative stability, suggesting the influence of localized management practices or site-specific ecological conditions. The findings demonstrate the suitability of high-resolution imagery for monitoring forest cover change in fragmented savanna–woodland transition zones, where medium-resolution datasets may underestimate forest remnants due to mixed-pixel effects. The integration of supervised classification, post-classification change detection, and accuracy assessment provided reliable estimates of forest dynamics and enabled comparison across multiple forest types. Overall, the results highlight agricultural expansion, settlement growth, and associated livelihood pressures as dominant drivers of forest conversion in the study area, with important implications for biodiversity conservation, carbon sequestration, and sustainable land management. From a policy perspective, continued forest degradation in Benue State poses a challenge to achieving national and global sustainability targets, particularly those related to climate action and the conservation of terrestrial ecosystems. Targeted forest management interventions, strengthened regulation of land-use change, and increased community participation in forest stewardship are essential to slow forest loss and enhance ecosystem resilience. Future research could build on this work by extending the temporal coverage of analysis, incorporating object-based or machine-learning classification approaches, and integrating socio-economic and institutional variables to better explain observed spatial patterns of forest change. Such efforts would deepen understanding of forest–livelihood interactions in savanna–woodland landscapes and further support evidence-based forest management and land-use planning in Nigeria. 5.2 Recommendations Based on the study’s findings, the following recommendations are proposed: Routine integration of satellite-based forest monitoring into state-level environmental management systems is recommended to enable timely detection of forest loss and support evidence-based decision-making. Forests identified as deforestation hotspots, such as those near urban centers and agriculturally productive zones, should be prioritized for targeted conservation, restoration, or controlled land-use interventions. Adoption of land-use strategies such as agroforestry, improved fallow systems, and land-use zoning can help reduce pressure on remaining forest patches while supporting agricultural productivity. The relative stability observed in some communal forests suggests that local stewardship can contribute to forest conservation. Strengthening community participation and awareness may enhance forest resilience in similar contexts. Spatial data on forest cover change should inform sub-national implementation of sustainability and climate-related policies, including efforts aligned with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land). 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I., & Soulé, M. (2023). Non-timber forest products (NTFPs) as climate actions in West Africa Sahel: a review. Journal of Business and Environmental Management , 2 (1), 1-23. Ramachandran, P., Roy, P. S., Chakravarthi, S., Sanjay, K., & Joshi, P. K. (2018). Long-term land use and land cover changes (1920–2015) in Eastern Ghats, India: Pattern of dynamics and challenges in plant species conservation. Environmental Monitoring and Assessment, 190 (3), 157. https://doi.org/10.1007/s10661-018-6553-x Sahoo, G., Wani, A., Rout, S., Sharma, A., & Prusty, A. K. (2021). Impact and contribution of forest in mitigating global climate change. Des. Eng , 4 , 667-682. Singh, S. (2022). Forest fire emissions: A contribution to global climate change. Frontiers in Forests and Global Change , 5 , 925480. Stehman, S. V., & Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. Remote Sensing of Environment , 64(3), 331–344. Thyer, B. A. (2012). The handbook of social work research methods (2nd ed.). Sage Publications. Turner, B. L., Lambin, E. F., & Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. Proceedings of the National Academy of Sciences, 104 (52), 20666–20671. https://doi.org/10.1073/pnas.0704119104 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8824690","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587881939,"identity":"a7fb91ad-8668-4ad0-95a3-a8ccca2f256a","order_by":0,"name":"Peter Terngu Anule","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYLCCBww2CRAWG7FaEhjSSNdymAQt5uxnj0kkVJzPk5+RncDwoewwgzn/AvxaLHvy0iQSztwuNriRu4FxxrnDDJYzHuDXYnAgx0wise124gaJ3A3MvG2HGQxuHCCg5fwboJZ/5xLnzwBq+UuUlhsgWxoOJDYAHcbMCNJyvoGAX2a8MbZIOJZcbHDm7YaDPefSeQxu4NcBDJ8cwxsfauzy5NtzNz74UWYtZ3CekMOQOSC1PAwSCSRogQB+AraMglEwCkbBiAMAH5JKuRz+NrgAAAAASUVORK5CYII=","orcid":"","institution":"Rev. Fr. Moses Orshio Adasu University, Makurdi, Nigeria","correspondingAuthor":true,"prefix":"","firstName":"Peter","middleName":"Terngu","lastName":"Anule","suffix":""},{"id":587881940,"identity":"fc5353c2-c8f8-4d2f-9d87-e596bce9b36e","order_by":1,"name":"Christian Yakubu Oche","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Christian","middleName":"Yakubu","lastName":"Oche","suffix":""},{"id":587881941,"identity":"00e02e01-117b-4fa6-8e90-7130b43147ec","order_by":2,"name":"Daniel Serki Ortserga","email":"","orcid":"","institution":"Rev. Fr. Moses Orshio Adasu University, Makurdi, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"Serki","lastName":"Ortserga","suffix":""},{"id":587881942,"identity":"3fad132b-1272-469f-828a-e2c7c7907728","order_by":3,"name":"Keneth Abaagu Uchua","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Keneth","middleName":"Abaagu","lastName":"Uchua","suffix":""},{"id":587881943,"identity":"52550030-b069-417f-b64d-9a3459b4a5c7","order_by":4,"name":"William Terseer Hundui","email":"","orcid":"","institution":"Rev. Fr. Moses Orshio Adasu University, Makurdi, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"William","middleName":"Terseer","lastName":"Hundui","suffix":""},{"id":587881944,"identity":"07cf3c10-3c9f-4aec-b9df-136ea5feb2e3","order_by":5,"name":"Terwase Shabu","email":"","orcid":"","institution":"Rev. Fr. Moses Orshio Adasu University, Makurdi, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Terwase","middleName":"","lastName":"Shabu","suffix":""},{"id":587881945,"identity":"a5f03a9f-438d-4f34-aecc-ed2bd25e1c5c","order_by":6,"name":"Monday Akpegi Onah","email":"","orcid":"","institution":"Rev. Fr. 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sampled Forests\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSource: GIS Laboratory, MOAUM.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/6363a6e0c8b5f9f8fa3a1f16.jpeg"},{"id":102311961,"identity":"351fe80b-6a79-43f4-8352-cf1106a9c624","added_by":"auto","created_at":"2026-02-10 11:59:30","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":91470,"visible":true,"origin":"","legend":"\u003cp\u003eLand cover of Agila Forest in Ado Local Government Area\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/bc24d1c21ca7df4f33462951.jpeg"},{"id":102312031,"identity":"be26d78a-5a7c-4680-950d-737ae479b255","added_by":"auto","created_at":"2026-02-10 11:59:59","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81056,"visible":true,"origin":"","legend":"\u003cp\u003eAgan Forest in Makurdi Local Government\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/029bb921a105b2f7f2e18523.jpeg"},{"id":102312085,"identity":"8ba68164-5ee4-4404-9af7-f1b53c0db017","added_by":"auto","created_at":"2026-02-10 12:00:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136022,"visible":true,"origin":"","legend":"\u003cp\u003eGboko Forest Reserve cover change in Gboko LGA\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/29b88352227b3ccc84b44208.jpeg"},{"id":102312011,"identity":"05a20d3d-f0bb-453c-8f15-21e2a130a1d7","added_by":"auto","created_at":"2026-02-10 11:59:48","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":68650,"visible":true,"origin":"","legend":"\u003cp\u003eMbasaan Communal Forest in Guma Local Government\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/89c66293c4eae7b8b10cd3b4.jpeg"},{"id":102312128,"identity":"215f1d38-9023-423f-bfd5-08f4f8278992","added_by":"auto","created_at":"2026-02-10 12:00:15","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":116412,"visible":true,"origin":"","legend":"\u003cp\u003eMbapa Communal Forest at Gwer West Local Government\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/ddd7516d6f277799eaa303b5.jpeg"},{"id":102312026,"identity":"83cc5e38-304e-4fa3-9c3d-045a6ffc1d15","added_by":"auto","created_at":"2026-02-10 11:59:57","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":145615,"visible":true,"origin":"","legend":"\u003cp\u003eMbamo Forest at Katsina-Ala Local Government\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/ea1a303abd01ebbad7e8a7a8.jpeg"},{"id":102311972,"identity":"d844f574-1810-4c4c-b2c7-79631b6a566c","added_by":"auto","created_at":"2026-02-10 11:59:34","extension":"jpeg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":179797,"visible":true,"origin":"","legend":"\u003cp\u003eMbanor Forest Land cover change in Konshisha LGA\u003c/p\u003e","description":"","filename":"floatimage8.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/30bcca4b7d2532f4d08a2680.jpeg"},{"id":102311998,"identity":"5bde5a98-f638-4f48-995b-2953885781a5","added_by":"auto","created_at":"2026-02-10 11:59:45","extension":"jpeg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":96293,"visible":true,"origin":"","legend":"\u003cp\u003eMbaikuna Forest at Kwande Local Government\u003c/p\u003e","description":"","filename":"floatimage9.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/3a9ea8ff036ee03b949fb6c5.jpeg"},{"id":102311832,"identity":"a6526cbc-5b52-4408-8b56-898429ad2edb","added_by":"auto","created_at":"2026-02-10 11:59:10","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":161067,"visible":true,"origin":"","legend":"\u003cp\u003eIdekpa Forest at Obi Local government\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/6ce1af341e564577b5c50247.jpeg"},{"id":102312192,"identity":"924ef530-3ae3-4db5-9cff-47257cd7bf3d","added_by":"auto","created_at":"2026-02-10 12:00:42","extension":"jpeg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":146772,"visible":true,"origin":"","legend":"\u003cp\u003eIpunu communal Forest at Oju Local Government\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/bdc7099faa46206431a67947.jpeg"},{"id":102312020,"identity":"70679f96-15e8-4e67-94d1-275bd6525daf","added_by":"auto","created_at":"2026-02-10 11:59:51","extension":"jpeg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":140496,"visible":true,"origin":"","legend":"\u003cp\u003eOkokolo Forest at Otukpo LGA\u003c/p\u003e","description":"","filename":"floatimage12.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/73a6396faf62904e05276921.jpeg"},{"id":102312404,"identity":"47ed722f-7a03-4c4d-894a-2dfaaaf2c54b","added_by":"auto","created_at":"2026-02-10 12:01:40","extension":"jpeg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":166247,"visible":true,"origin":"","legend":"\u003cp\u003eTse-Mker Forest in Vandeikya LGA\u003c/p\u003e","description":"","filename":"floatimage13.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/4931169e6dacb9ea05244dad.jpeg"},{"id":102312476,"identity":"0cf0abc1-6889-404c-ab9c-8292cdeec324","added_by":"auto","created_at":"2026-02-10 12:02:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2970892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/49f380f1-072b-4e83-a960-5d094e02540e.pdf"},{"id":102312155,"identity":"b74c7912-1c7b-4d39-bbda-f4626a09b556","added_by":"auto","created_at":"2026-02-10 12:00:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1628389,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-8824690/v1/b806c40c487e4bed89a2bab5.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eForest Cover Change Assessment in Benue State, Nigeria (2005–2020) Using Remote Sensing and Geographic Information System\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eForest ecosystems constitute a critical component of the global environment, providing essential ecosystem services such as carbon sequestration, biodiversity conservation, hydrological regulation, and livelihood support for millions of people. Despite their importance, forests across the tropics have experienced widespread degradation and conversion over recent decades, largely driven by agricultural expansion, urban growth, logging, and energy-related biomass extraction (Assede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sahoo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Globally, deforestation and forest degradation contribute significantly to greenhouse gas emissions and biodiversity loss, reinforcing concerns about climate change and ecosystem resilience (FAO, 2010; Turner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSub-Saharan Africa exhibits distinct deforestation dynamics compared with other tropical regions, as forest conversion is predominantly associated with smallholder agriculture, fuelwood extraction, and informal land-use practices rather than large-scale industrial agriculture (Assede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Singh, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In West Africa, forest landscapes are increasingly fragmented due to population growth, rural livelihood dependence on land resources, and limited enforcement of forest management policies (Rachid et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). These processes have intensified pressure on forest\u0026ndash;savanna transition zones, where land-use competition between agriculture and forest conservation is particularly acute.\u003c/p\u003e \u003cp\u003eNigeria ranks among the countries with the highest rates of forest loss in Africa, with deforestation driven by subsistence farming, settlement expansion, logging, and charcoal production (Fasona et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mohiuddin, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Within the Middle Belt region, including Benue State, forest cover decline has been repeatedly linked to agricultural expansion and fuelwood dependence associated with rural livelihoods (Bombom \u0026amp; Yemisi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Mbasaanga et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Although forests in this region play an important role in supporting food systems and household energy needs, continued degradation threatens biodiversity, soil fertility, and climate regulation functions.\u003c/p\u003e \u003cp\u003eBenue State represents a critical case for examining forest cover dynamics due to its ecological position within a forest\u0026ndash;savanna mosaic and its prominence as an agricultural hub in Nigeria. Previous studies in and around Makurdi have documented vegetation degradation and land-cover transitions using remotely sensed data (Anule \u0026amp; Ujoh, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Jande \u0026amp; Nsofor, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, many existing assessments are either spatially limited or temporally constrained, providing insufficient evidence on long-term forest cover trajectories across the broader state landscape. Consequently, there remains a need for spatially explicit, multi-temporal analyses that quantify forest loss patterns and identify dominant land-cover transitions over extended periods.\u003c/p\u003e \u003cp\u003eRemote Sensing (RS) and Geographic Information Systems (GIS) provide reliable and cost-effective tools for monitoring forest cover change, particularly in data-scarce environments. Multi-temporal satellite imagery enables consistent observation of land-cover dynamics over decadal timescales, while supervised classification and post-classification change detection techniques facilitate the identification of conversion pathways between forest and non-forest land uses (Kussul et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). These approaches are widely applied in forest monitoring studies to support environmental assessment, land-use planning, and sustainability-oriented decision-making (Assede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Paz\u0026uacute;r et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this context, the present study employs multi-temporal satellite imagery and GIS techniques to assess forest cover change in Benue State, Nigeria, between 2005 and 2020. Using supervised Maximum Likelihood Classification, accuracy assessment, and post-classification change detection, the study maps major land-use/land-cover categories, quantify changes in forest extent, examines spatial patterns of forest loss, and identifies dominant transition pathways. By providing spatially explicit evidence of forest dynamics, the study contributes to environmental monitoring efforts and supports sustainable land-use planning and forest management initiatives aligned with national and global sustainability objectives.\u003c/p\u003e \u003cp\u003eUnlike many forest cover studies that rely on medium-resolution imagery, this study adopts high-resolution satellite data to capture the fine-scale structure of fragmented forest patches typical of savanna\u0026ndash;woodland transition zones. This approach reduces mixed-pixel effects and improves boundary delineation in heterogeneous agricultural landscapes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area and Forest Selection\u003c/h2\u003e \u003cp\u003eThe study was conducted in Benue State, located in the Middle Belt region of Nigeria and covering approximately 34,059 km\u0026sup2; across 23 Local Government Areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The State lies within a forest\u0026ndash;savanna transition zone characterized by mosaic vegetation patterns shaped by agricultural expansion, settlement growth, and population pressure. Forest resources in Benue State consist mainly of gazetted forest reserves and communal (village) forests, which support rural livelihoods through fuelwood collection, non-timber forest products, and smallholder agriculture.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eSource: GIS Laboratory, MOAUM.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe study population comprised all gazetted communal and reserved forests in Benue State, totaling 129 forest units (52 forest reserves and 77 communal forests). Given the fragmented nature of forest cover and the need for spatial representativeness, a two-stage cluster sampling strategy was adopted. First, the State was stratified into three clusters based on the senatorial districts (North-East, North-West, and South). Within each senatorial district, four Local Government Areas were purposively selected. From each selected Local Government Area, one communal forest or one forest reserve was purposively selected, yielding a total of twelve forest sites. Selection was based on technical criteria, including clear visibility on high-resolution satellite imagery across all study epochs (2005, 2010, 2015, and 2020), sufficient spatial extent for image classification, and absence of persistent cloud cover or data gaps. This approach ensured that the selected forests were both spatially representative and methodologically suitable for multi-temporal analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Design and Data Sources\u003c/h2\u003e \u003cp\u003eAn ex-post facto research design with a multi-temporal analytical approach was adopted to assess historical forest cover changes without experimental manipulation. Forest cover dynamics were examined for four epochs (2005, 2010, 2015, and 2020) to capture long-term trends and land-cover transition pathways.\u003c/p\u003e \u003cp\u003eThe study relied primarily on high-resolution satellite imagery. Multi-temporal images consisted of QuickBird data for 2005 and 2010 and GeoEye data for 2015 and 2020, all sourced from Google Earth Pro. These datasets provide sub-meter spatial resolution ranging from 0.5 to 0.65 m, which is suitable for detecting small and fragmented forest patches typical of savanna\u0026ndash;woodland transition zones. To ensure inter-temporal consistency and minimize atmospheric interference, all images were selected from the dry season period between January and March.\u003c/p\u003e \u003cp\u003eAncillary data included a topographic map of Benue State for spatial reference and boundary delineation, as well as ground reference data collected using a handheld Global Positioning System (GPS) during field visits. In addition, Google Earth imagery was used as ancillary visual reference data to support training sample selection and to complement field-based reference points during accuracy assessment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Image Processing and Classification\u003c/h2\u003e \u003cp\u003eLand-use and land-cover classification was guided by the FAO forest definition framework and the Anderson et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) classification system. Given the savanna context of the study area, forest and other wooded land categories were aggregated into a single Forest class to enhance interpretability and reduce artificial fragmentation. Non-forest land was subdivided into Farmland and Built-up/Bare surface classes. Water bodies were excluded from the classification scheme, as none of the selected forest areas contained permanent surface water features.\u003c/p\u003e \u003cp\u003eAll satellite images underwent standard preprocessing procedures, including geometric correction and visual inspection to ensure spatial alignment. Supervised image classification was carried out using the Maximum Likelihood Classifier, selected for its robustness in handling normally distributed spectral signatures. Training samples were generated using a combination of field knowledge, GPS reference points, and ancillary visual interpretation. Approximately 30 training samples per class were digitized for each epoch to ensure classification consistency.\u003c/p\u003e \u003cp\u003eGiven the fragmented nature of the forest landscape, specific measures were taken to address spatial resolution constraints. The use of sub-meter QuickBird and GeoEye imagery enabled accurate delineation of narrow forest strips and isolated forest remnants that would otherwise be affected by mixed-pixel effects in medium-resolution datasets. Post-classification comparison was adopted for change detection to minimize cumulative spatial error across temporal epochs, while a minimum mapping unit was applied to suppress classification noise from isolated pixels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Change Detection and Rate of Change Analysis\u003c/h2\u003e \u003cp\u003eForest cover change was quantified using post-classification comparison to identify transitions between land-cover classes across successive epochs. Change matrices were generated to capture forest loss, regeneration, and persistence between 2005\u0026ndash;2010, 2010\u0026ndash;2015, 2015\u0026ndash;2020, and across the full study period (2005\u0026ndash;2020).\u003c/p\u003e \u003cp\u003eThe annual rate of forest cover change was calculated using the expression:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:R=\\frac{{A}_{2}-{A}_{1}}{{A}_{1}\\times\\:t}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:R\\)\u003c/span\u003e\u003c/span\u003erepresents the annual rate of change, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{1}\\)\u003c/span\u003e\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{2}\\)\u003c/span\u003e\u003c/span\u003edenote forest area at the initial and final time periods, respectively, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003eis the time interval in years. This approach enabled comparison of forest loss and gain rates across different forest sites and time intervals.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Accuracy Assessment\u003c/h2\u003e \u003cp\u003eClassification accuracy was evaluated using an error matrix approach. Overall accuracy and the Kappa Index of Agreement were computed for each epoch to assess classification reliability. Reference data for accuracy assessment were derived from field-collected GPS points and ancillary visual interpretation. The results indicate good to very good agreement across all study sites, supporting the reliability of the classified outputs used for subsequent forest cover change analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Land Use/Land Cover Distribution (2005\u0026ndash;2020)\u003c/h2\u003e \u003cp\u003eThe land use/land cover (LULC) classifications for 2005, 2010, 2015 and 2020 indicate substantial changes in forest and non-forest classes across the selected forest landscapes of Benue State (Figs.\u0026nbsp;2\u0026ndash;13). Across all study sites, the dominant mapped classes were forest, farmland, and built-up/bare surface, reflecting the agricultural and settlement-driven land conversion processes prevalent in the state.\u003c/p\u003e \u003cp\u003eIn Agila Forest (Ado LGA), forest cover declined from 97.44 ha (44.56%) in 2005 to 52.63 ha (24.07%) in 2020, while farmland increased from 111.78 ha (51.12%) to 156.40 ha (71.52%) over the same period (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Built-up/bare surface remained relatively small but fluctuated across the epochs. A similar but more severe conversion pattern occurred in Agan Forest (Makurdi LGA), where forest cover declined from 32.13 ha (63.20%) in 2005 to 2.21 ha (4.35%) in 2020, while farmland expanded from 22.28% to 79.61% (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eLand use and Land cover distribution of Agila Forest Cover change in Ado LGA, Benue State from 2005\u0026ndash;2020\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 \u003cp\u003eLULC Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eArea in Hectares\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e97.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e119.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e139.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e156.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up Area/Bare surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e100.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eSource: Author\u0026rsquo;s GIS Analysis 2025\u003c/em\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=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLand use and Land Cover distribution of Agan Forest in Makurdi LGA, Benue State from 2005\u0026ndash;2020\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\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eYear\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 Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e \u003cp\u003eArea in Hectares\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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest Cover\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e46.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFarmland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e40.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e79.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuilt up Area/Bare surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51\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\u003cem\u003eSource: Author\u0026rsquo;s GIS Analysis 2025\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eComparable forest depletion patterns were observed in other sites including Gboko Forest Reserve, Mbapa, Mbamo, Okokolo, and Tse-Mker, where farmland and, to a lesser extent, built-up/bare surface expanded at the expense of forest cover (Appendix Tables a, c, d, i, j). In contrast, Mbasaan (Guma LGA) and Mbaikuna (Kwande LGA) showed relative stability and slight forest gains over the 15-year period, suggesting localized resilience or lower conversion pressure (Appendix Tables b and f).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal Trends in Forest Cover Change (2005\u0026ndash;2020)\u003c/h2\u003e \u003cp\u003eForest cover changes substantially across the twelve sites. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes forest cover percentages for 2005 and 2020 and highlights the magnitude of change over the study period.\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\u003eSummarized percentage forest cover across all sampled sites\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eForest Cover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/No\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStudy Site\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLocal Government\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChange\u003c/p\u003e \u003cp\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\" colname=\"c2\"\u003e \u003cp\u003eAgan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMakurdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;58.85\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\u003eMbamo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKatsina-Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;57.80\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\u003eGboko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGboko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;53.02\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\u003eMbapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGwer West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e31.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;50.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOkokolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOtukpo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;28.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTse-Mker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVandeikya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e36.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;26.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;20.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMbanor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKonshisha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;15.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIpunu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOju\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;13.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdekpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdekpe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;6.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMbaikuna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKwande\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e81.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMbasaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGuma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e+\u0026thinsp;3.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eSource: Author\u0026rsquo;s GIS Analysis 2025\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe greatest decline occurred in Agan (\u0026minus;\u0026thinsp;58.85%), Mbamo (\u0026minus;\u0026thinsp;57.80%), Gboko (\u0026minus;\u0026thinsp;53.02%), and Mbapa (\u0026minus;\u0026thinsp;50.72%), indicating intense conversion pressure. Moderate declines were recorded in Okokolo (\u0026minus;\u0026thinsp;28.61%), Tse-Mker (\u0026minus;\u0026thinsp;26.61%), and Agila (\u0026minus;\u0026thinsp;20.49%), while smaller declines occurred in Idekpa (\u0026minus;\u0026thinsp;6.29%), Ipunu (\u0026minus;\u0026thinsp;13.16%), and Mbanor (\u0026minus;\u0026thinsp;15.00%). By contrast, Mbaikuna (+\u0026thinsp;0.56%) and Mbasaan (+\u0026thinsp;3.08%) recorded slight net increases in forest cover between 2005 and 2020 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results demonstrate that while forest loss is widespread, the rate and intensity of change are highly site-specific, suggesting spatially differentiated drivers and pressures across Benue State.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Spatial Patterns of Forest Loss\u003c/h2\u003e \u003cp\u003eThe classified maps show distinct spatial patterns of forest conversion and fragmentation across the study sites (Figs.\u0026nbsp;2\u0026ndash;13). Forests located near major urban centers and areas of intensified land use showed pronounced fragmentation, characterized by reduced contiguous forest blocks and the emergence of dispersed forest patches embedded in farmland and built-up/bare surfaces. For example, Agan (Makurdi LGA) and Gboko Forest Reserve exhibited strong fragmentation patterns and visible conversion to farmland and settlement-related land uses (Figs.\u0026nbsp;3 and 4).\u003c/p\u003e \u003cp\u003eConversely, relatively stable forest patches such as Mbasaan (Guma LGA) and Mbaikuna (Kwande LGA) retained high forest cover through the epochs, suggesting lower conversion intensity, possible community protection, or local site conditions that constrain agricultural expansion (Figs.\u0026nbsp;5 and 9). Overall, spatial evidence indicates that forest loss and fragmentation are concentrated in sites experiencing stronger land-use competition and landscape accessibility.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Forest Cover Transition Pathways\u003c/h2\u003e \u003cp\u003ePost-classification comparison indicates that forest-to-farmland conversion constitutes the dominant land-cover transition across most sites, consistent with the observed increases in farmland area over time (Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In several forests, forest loss is accompanied by increases in built-up/bare surface, particularly in sites influenced by settlement expansion and infrastructure development (e.g., Makurdi and Gboko environments).\u003c/p\u003e \u003cp\u003eIn the few sites where forest cover increased slightly (Mbasaan and Mbaikuna), the mapped changes suggest limited non-forest-to-forest transitions, which may reflect fallow-driven regeneration, localized protection, or reduced conversion pressure. Overall, the transition patterns confirm that agricultural expansion remains the principal competing land use driving forest decline across the selected forest landscapes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Classification Accuracy Assessment\u003c/h2\u003e \u003cp\u003eClassification accuracy was assessed for each forest and each epoch (2005, 2010, 2015 and 2020) using overall accuracy (OA) and the Kappa Index (κ). Accuracy results show generally strong performance across sites, with most OA values\u0026thinsp;\u0026ge;\u0026thinsp;90% and κ values indicating good to very good agreement (Stehman \u0026amp; Czaplewski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), supporting the reliability of the classified maps for change detection.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall Accuracy (%) and Kappa Index (κ) by Forest and Year (2005\u0026ndash;2020)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eκ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΚ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eκ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eκ\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgila\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMakurdi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGboko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGboko\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbasaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGuma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbapa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGwer West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbamo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKatsina-Ala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbanor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKonshisha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMbaikuna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKwande\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIdekpa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIpunu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOju\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOkokolo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOtukpo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTse-Mker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVandeikya\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eSource: Author\u0026rsquo;s GIS analysis (2022)\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhile most classifications show high agreement, lower accuracy were recorded in some cases (e.g., Mbasaan 2020; Ipunu 2015), indicating comparatively higher uncertainty for those specific classifications. Nevertheless, the overall accuracy profile provides adequate confidence for interpreting the mapped LULC patterns and changing trajectories.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.6 Annual Rate of Forest Cover Change\u003c/h2\u003e \u003cp\u003eAnnual rates of change were computed for forest, farmland and built-up/bare surface for each site over three intervals (2005\u0026ndash;2010, 2010\u0026ndash;2015, 2015\u0026ndash;2020) and for the full period (2005\u0026ndash;2020) as derived from annual rate of change calculations. The annual rate results highlight marked variation in forest loss intensity across sites, including periods of accelerated decline and relative stability.\u003c/p\u003e \u003cp\u003eForests experiencing the most rapid annual decline over the full period include Agan (\u0026minus;\u0026thinsp;1.99 ha; \u0026minus;3.92% annually), Gboko (\u0026minus;\u0026thinsp;10.99 ha; \u0026minus;3.53%), Mbapa (\u0026minus;\u0026thinsp;8.12 ha; \u0026minus;3.38%), and Mbamo/Katsina-Ala (\u0026minus;\u0026thinsp;2.80 ha; \u0026minus;3.85%), indicating persistent conversion pressure. Agan also recorded pronounced acceleration in 2015\u0026ndash;2020 (\u0026minus;\u0026thinsp;4.29 ha; \u0026minus;8.45% annually), reflecting severe depletion during the final interval.\u003c/p\u003e \u003cp\u003eModerate annual forest losses were observed in Agila (\u0026minus;\u0026thinsp;2.99 ha; \u0026minus;1.37%), Mbanor (\u0026minus;\u0026thinsp;2.02 ha; \u0026minus;1.00%), Ipunu (\u0026minus;\u0026thinsp;1.46 ha; \u0026minus;0.88%), Okokolo (\u0026minus;\u0026thinsp;0.55 ha; \u0026minus;1.91%), and Tse-Mker (\u0026minus;\u0026thinsp;0.49 ha; \u0026minus;1.77%). By contrast, Mbasaan recorded a slight net annual gain in forest cover (+\u0026thinsp;0.30 ha; +0.21%), and Mbaikuna remained largely stable with a marginal annual gain (+\u0026thinsp;0.01 ha; +0.04%), reflecting localized resilience or reduced conversion intensity.\u003c/p\u003e \u003cp\u003eAcross most sites, annual forest decline coincided with annual increases in farmland area, confirming that agricultural expansion remains the predominant land-use transition associated with forest cover loss.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study provides geospatial evidence of substantial forest cover change across selected forest landscapes in Benue State between 2005 and 2020. The observed decline in forest cover across most sites, coupled with expansion of farmland and built-up/bare surfaces, reflects broader land-use transition dynamics reported across Nigeria and the West African sub-region (Assede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fasona et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mbasaanga et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Forest Cover Dynamics in Relation to Previous Studies\u003c/h2\u003e \u003cp\u003eThe magnitude and spatial variability of forest loss observed in this study are consistent with earlier remote sensing\u0026ndash;based assessments in Nigeria\u0026rsquo;s Middle Belt, which identify agricultural expansion and settlement growth as dominant drivers of deforestation (Bombom \u0026amp; Yemisi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Jande \u0026amp; Nsofor, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Forests such as Agan, Gboko, Mbamo, and Mbapa exhibited rapid depletion, aligning with findings that forests located near urban centers and agriculturally productive zones experience accelerated conversion due to competing land-use demands (Fasona et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, the relative stability observed in Mbasaan and Mbaikuna forests corroborates studies suggesting that localized management practices, reduced accessibility, or terrain-related constraints can moderate deforestation rates in otherwise pressure-prone regions (Assede et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Similar patterns of heterogeneous forest change have been documented in other savanna\u0026ndash;forest transition zones across sub-Saharan Africa, where land-use outcomes are shaped by both biophysical conditions and localized socio-economic dynamics (Singh, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Agricultural Expansion as the Dominant Conversion Pathway\u003c/h2\u003e \u003cp\u003eResults from land-cover transitions and annual rate analysis confirm that forest-to-farmland conversion constitutes the principal pathway of forest loss across the study area. This finding aligns with regional evidence that smallholder agriculture remains the most significant proximate cause of deforestation in Nigeria and much of West Africa (Mensah et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mbasaanga et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The strong correspondence between declining forest cover and increasing farmland area supports the Land Change Science perspective, which emphasizes the role of livelihood-driven land-use decisions in shaping landscape transformations (Turner et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In this context, forest loss is not merely an environmental phenomenon but is closely embedded within local food security strategies and rural survival systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Spatial Heterogeneity and Urban Influence\u003c/h2\u003e \u003cp\u003eThe spatial patterns revealed by the classified maps indicate pronounced fragmentation in forests located near major urban centers such as Makurdi and Gboko. These findings are consistent with studies demonstrating that urban expansion and infrastructure development intensify forest conversion through increased demand for land, fuelwood, and construction materials (Ahmed et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Bombom \u0026amp; Yemisi, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Fragmentation not only reduces total forest area but also compromises ecological integrity by isolating forest patches and diminishing habitat connectivity.\u003c/p\u003e \u003cp\u003eIn contrast, forests located in relatively remote areas or under stronger communal stewardship exhibited lower rates of conversion and, in some cases, slight regeneration. This spatial heterogeneity underscores the importance of place-specific interventions rather than uniform forest management policies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Implications for Environmental Sustainability and Climate Policy\u003c/h2\u003e \u003cp\u003eThe observed decline in forest cover has significant implications for biodiversity conservation, carbon sequestration, and climate resilience in Benue State. Forest degradation reduces carbon storage capacity, thereby contributing indirectly to greenhouse gas emissions and undermining climate mitigation efforts (Sahoo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). At the local scale, continued forest loss threatens ecosystem services critical to rural livelihoods, including fuelwood supply, soil protection, and non-timber forest products (Rachid et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a policy perspective, these findings highlight challenges to achieving Sustainable Development Goal (SDG) 15 (Life on Land) and SDG 13 (Climate Action), particularly in sub-national contexts where land-use pressures are intense and enforcement capacity is limited (Bhale, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Integrating spatial forest monitoring into land-use planning and development decision-making is therefore essential.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Methodological Implications for Forest Monitoring\u003c/h2\u003e \u003cp\u003eThe study demonstrates the effectiveness of multi-temporal satellite imagery and GIS-based change detection for monitoring forest dynamics in fragmented savanna\u0026ndash;forest landscapes. The generally high classification accuracy achieved across sites validates the use of supervised classification approaches for forest change analysis at local and regional scales (Kussul et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Nevertheless, variations in accuracy across sites and years also highlight the influence of landscape heterogeneity and underscore the importance of accuracy assessment in interpreting change detection results (Stehman \u0026amp; Czaplewski, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Recommendations","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study examined spatio-temporal forest cover dynamics in selected communal and reserved forest landscapes of Benue State, Nigeria, using high-resolution satellite imagery integrated with Geographic Information Systems and Remote Sensing techniques. Analysis of multi-temporal QuickBird and GeoEye imagery for the period 2005\u0026ndash;2020 revealed substantial forest cover decline across most of the sampled sites, accompanied by corresponding expansion of farmland and built-up areas. The magnitude and rate of forest loss varied spatially, reflecting differences in land-use pressure, proximity to settlements, and forest management regimes. While several forest patches experienced severe fragmentation and decline, a few sites exhibited relative stability, suggesting the influence of localized management practices or site-specific ecological conditions.\u003c/p\u003e \u003cp\u003eThe findings demonstrate the suitability of high-resolution imagery for monitoring forest cover change in fragmented savanna\u0026ndash;woodland transition zones, where medium-resolution datasets may underestimate forest remnants due to mixed-pixel effects. The integration of supervised classification, post-classification change detection, and accuracy assessment provided reliable estimates of forest dynamics and enabled comparison across multiple forest types. Overall, the results highlight agricultural expansion, settlement growth, and associated livelihood pressures as dominant drivers of forest conversion in the study area, with important implications for biodiversity conservation, carbon sequestration, and sustainable land management.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, continued forest degradation in Benue State poses a challenge to achieving national and global sustainability targets, particularly those related to climate action and the conservation of terrestrial ecosystems. Targeted forest management interventions, strengthened regulation of land-use change, and increased community participation in forest stewardship are essential to slow forest loss and enhance ecosystem resilience.\u003c/p\u003e \u003cp\u003eFuture research could build on this work by extending the temporal coverage of analysis, incorporating object-based or machine-learning classification approaches, and integrating socio-economic and institutional variables to better explain observed spatial patterns of forest change. Such efforts would deepen understanding of forest\u0026ndash;livelihood interactions in savanna\u0026ndash;woodland landscapes and further support evidence-based forest management and land-use planning in Nigeria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Recommendations\u003c/h2\u003e \u003cp\u003eBased on the study\u0026rsquo;s findings, the following recommendations are proposed:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eRoutine integration of satellite-based forest monitoring into state-level environmental management systems is recommended to enable timely detection of forest loss and support evidence-based decision-making.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eForests identified as deforestation hotspots, such as those near urban centers and agriculturally productive zones, should be prioritized for targeted conservation, restoration, or controlled land-use interventions.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAdoption of land-use strategies such as agroforestry, improved fallow systems, and land-use zoning can help reduce pressure on remaining forest patches while supporting agricultural productivity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe relative stability observed in some communal forests suggests that local stewardship can contribute to forest conservation. Strengthening community participation and awareness may enhance forest resilience in similar contexts.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSpatial data on forest cover change should inform sub-national implementation of sustainability and climate-related policies, including efforts aligned with Sustainable Development Goals 13 (Climate Action) and 15 (Life on Land).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on \u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgber, P. M., T. Shabu, D. P. Dam, M. A. Onah \u0026amp; J.O. Aile, (2025). Assessment of the Effect of Anthropogenic Activities on Forest Resources in Kwande Local Government Area, Benue State, Nigeria.\u003c/li\u003e\n\u003cli\u003eAhmed, Z., Gui, D., Abd‐Elmabod, S. K., Murtaza, G., \u0026amp; Ali, S. (2024). An overview of global desertification control efforts: Key challenges and overarching solutions. \u003cem\u003eSoil Use and Management\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(4), e13154.\u003c/li\u003e\n\u003cli\u003eAnderson, J. R., Hardy, E. E., Roach, J. T., \u0026amp; Witmer, R. E. (2001). \u003cem\u003eA land use and land cover classification system for use with remote sensor data\u003c/em\u003e. U.S. Geological Survey Professional Paper 964.\u003c/li\u003e\n\u003cli\u003eAnule, P., \u0026amp; Ujoh, F. (2017). Geospatial analysis of wetlands degradation in Makurdi, Nigeria. \u003cem\u003eThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 1075-1081.\u003c/li\u003e\n\u003cli\u003eAssede, E. S., Orou, H., Biaou, S. S., Geldenhuys, C. J., Ahononga, F. C., \u0026amp; Chirwa, P. W. (2023). Understanding drivers of land use and land cover change in Africa: A review. \u003cem\u003eCurrent Landscape Ecology Reports\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(2), 62-72.\u003c/li\u003e\n\u003cli\u003eBhale, A. (2024). The Forest and Human Symbiotic Relationship in Achieving Sustainable Development Goals (SDGs).\u003c/li\u003e\n\u003cli\u003eBombom, L. S., \u0026amp; Yemisi, R. (2024). Herdsmen-Farmers Conflict in Nigeria's Middle Belt Region. \u003cem\u003eEnvironmental Security in Africa: Conflicts, Politics, and Development\u003c/em\u003e, 195-209.\u003c/li\u003e\n\u003cli\u003eCarr, E. R., Wingard, P. M., Yorty, S. C., Thompson, M. C., Jensen, N. K., \u0026amp; Roberson, J. (2007). Applying DPSIR to sustainable development. \u003cem\u003eInternational journal of sustainable development \u0026amp; world ecology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(6), 543-555.\u003c/li\u003e\n\u003cli\u003eFasona, M. J., Akintuyi, A. O., Adeonipekun, P. A., Akoso, T. M., Udofia, S. K., Agboola, O. O., ... \u0026amp; Ogundipe, O. T. (2022). Recent trends in land-use and cover change and deforestation in south–west Nigeria. \u003cem\u003eGeoJournal\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e(3), 1411-1437.\u003c/li\u003e\n\u003cli\u003eFood and Agriculture Organization (FAO). (2010). \u003cem\u003eGlobal forest resources assessment 2010: Main report\u003c/em\u003e. FAO Forestry Paper 163.\u003c/li\u003e\n\u003cli\u003eJande, J. A., \u0026amp; Nsofor, G. N. (2002). Assessment of vegetation degradation in Makurdi Area and its Environs using Remotely Sensed data. In \u003cem\u003eProc. 4th AARSE conf: Geoinformation for sustainable development in Africa, Abuja, Nigeria\u003c/em\u003e (pp. 14-18).\u003c/li\u003e\n\u003cli\u003eKiage, L. M., \u0026amp; Liu, K. B. (2007). Sedimentological and palaeoecological evidence of environmental change in East Africa. \u003cem\u003eQuaternary International, 162–163\u003c/em\u003e, 61–75. https://doi.org/10.1016/j.quaint.2006.10.016.\u003c/li\u003e\n\u003cli\u003eKussul, N., Lavreniuk, M., Shelestov, A., \u0026amp; Skakun, S. (2017). Remote sensing data analysis for crop monitoring: Methods and applications. \u003cem\u003eComputers and Electronics in Agriculture, 140\u003c/em\u003e, 123–134. https://doi.org/10.1016/j.compag.2017.05.024\u003c/li\u003e\n\u003cli\u003eLiu, D., Mason, D. C., \u0026amp; Brown, C. (2002). Land cover classification using remote sensing: Maximum likelihood and Bayesian approaches. \u003cem\u003eInternational Journal of Remote Sensing, 23\u003c/em\u003e(2), 439–447. https://doi.org/10.1080/01431160110054893.\u003c/li\u003e\n\u003cli\u003eMbasaanga, S. S., Aondoakaa, M. A., Origbo, B. U., Sambe, L. N., \u0026amp; Nyiekpoughul, T. (2025). Determination of drivers of deforestation and forest degradation in north-western part of Benue state, Nigeria. \u003cem\u003eFUDMA Journal of Animal Production and Environmental Science\u003c/em\u003e, \u003cem\u003e1\u003c/em\u003e(1), 117-132.\u003c/li\u003e\n\u003cli\u003eMensah, J. K., Birch, E. L., \u0026amp; Langnel, Z. (2025). Exploring clean energy transitions in informal settlements: Sustainable energy toward achieving SDG 7. \u003cem\u003eJournal of Urban Affairs\u003c/em\u003e, 1-15.\u003c/li\u003e\n\u003cli\u003eMohiuddin, A. K. (2026). Global Deforestation in Focus: Uncovering the Scale and Forces Behind Deforestation.\u003c/li\u003e\n\u003cli\u003ePazúr, R., Price, B., \u0026amp; Atkinson, P. M. (2021). Fine temporal resolution satellite sensors with global coverage: an opportunity for landscape ecologists. Landscape Ecology, 36(8), 2199-2213.\u003c/li\u003e\n\u003cli\u003eRachid, M. S. A., Hannatou, S. I., \u0026amp; Soulé, M. (2023). Non-timber forest products (NTFPs) as climate actions in West Africa Sahel: a review. \u003cem\u003eJournal of Business and Environmental Management\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 1-23.\u003c/li\u003e\n\u003cli\u003eRamachandran, P., Roy, P. S., Chakravarthi, S., Sanjay, K., \u0026amp; Joshi, P. K. (2018). Long-term land use and land cover changes (1920–2015) in Eastern Ghats, India: Pattern of dynamics and challenges in plant species conservation. \u003cem\u003eEnvironmental Monitoring and Assessment, 190\u003c/em\u003e(3), 157. https://doi.org/10.1007/s10661-018-6553-x\u003c/li\u003e\n\u003cli\u003eSahoo, G., Wani, A., Rout, S., Sharma, A., \u0026amp; Prusty, A. K. (2021). Impact and contribution of forest in mitigating global climate change. \u003cem\u003eDes. Eng\u003c/em\u003e, \u003cem\u003e4\u003c/em\u003e, 667-682.\u003c/li\u003e\n\u003cli\u003eSingh, S. (2022). Forest fire emissions: A contribution to global climate change. \u003cem\u003eFrontiers in Forests and Global Change\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, 925480.\u003c/li\u003e\n\u003cli\u003eStehman, S. V., \u0026amp; Czaplewski, R. L. (1998). Design and analysis for thematic map accuracy assessment: Fundamental principles. \u003cem\u003eRemote Sensing of Environment\u003c/em\u003e, 64(3), 331–344.\u003c/li\u003e\n\u003cli\u003eThyer, B. A. (2012). \u003cem\u003eThe handbook of social work research methods\u003c/em\u003e (2nd ed.). Sage Publications.\u003c/li\u003e\n\u003cli\u003eTurner, B. L., Lambin, E. F., \u0026amp; Reenberg, A. (2007). The emergence of land change science for global environmental change and sustainability. \u003cem\u003eProceedings of the National Academy of Sciences, 104\u003c/em\u003e(52), 20666–20671. https://doi.org/10.1073/pnas.0704119104\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Rev. Fr. Moses Orshio Adasu University, Makurdi","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Remote sensing, GIS, Forest cover change, Land use dynamics, Change detection, Landscape sustainability, Benue State","lastPublishedDoi":"10.21203/rs.3.rs-8824690/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8824690/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eForest resources in Nigeria are under increasing pressure from agricultural expansion, urbanization, and other anthropogenic activities, particularly in fragmented savanna\u0026ndash;woodland landscapes. This study assesses the spatio-temporal dynamics of forest cover in selected communal and reserved forests of Benue State, Nigeria, between 2005 and 2020 using \u003cb\u003ehigh-\u003c/b\u003eresolution satellite imagery (QuickBird and GeoEye; 0.5\u0026ndash;0.65 m) integrated with Geographic Information Systems (GIS) and Remote Sensing techniques. Supervised image classification using the Maximum Likelihood Classifier was applied, followed by post-classification comparison to quantify forest loss, farmland expansion, and changes in built-up areas. Classification reliability was evaluated using overall accuracy and Kappa statistics. Results indicate substantial forest depletion across most study sites, with forest cover declining by approximately 20\u0026ndash;60%, while farmland and built-up areas expanded correspondingly. A few forest patches exhibited relative stability or slight regeneration, suggesting localized management practices or site-specific resilience. Analysis of change patterns indicates that agricultural expansion, population growth, and proximity to urban centers are major drivers of forest conversion. Future projections based on Markov Chain modeling suggest continued forest decline if current land-use trends persist. The findings highlight the suitability of high-resolution imagery for monitoring fragmented forest landscapes and underscore the need for targeted forest management, sustainable land-use planning, and community-based conservation strategies to support climate action and biodiversity conservation in Benue State.\u003c/p\u003e","manuscriptTitle":"Forest Cover Change Assessment in Benue State, Nigeria (2005–2020) Using Remote Sensing and Geographic Information System","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 11:50:54","doi":"10.21203/rs.3.rs-8824690/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ad813380-f131-4a3a-a8d0-f4ed77bd927d","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62645793,"name":"Geographic Information Systems"}],"tags":[],"updatedAt":"2026-02-10T11:50:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 11:50:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8824690","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8824690","identity":"rs-8824690","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}