Assessment of Land Use/land Cover Dynamics and Deforestation Trends in Southwestern Nigeria Using Multi-temporal Landsat Imagery (1986–2026)

Assessment of Land Use/land Cover Dynamics and Deforestation Trends in Southwestern Nigeria Using Multi-temporal Landsat Imagery (1986–2026) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Land Use/land Cover Dynamics and Deforestation Trends in Southwestern Nigeria Using Multi-temporal Landsat Imagery (1986–2026) Samuel K. Udofia, Ajibade Ariori, Emmanuel Wunude, Chinwe Ugwuzor This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9449847/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Land use and land cover (LULC) changes have a significant role in regulating micro-climate systems through the carbon cycle and ecosystem services. However, increasing anthropogenic activities, particularly deforestation and forest degradation, have significantly altered the landscape structure of southwestern Nigeria. This study examines spatio-temporal changes in land use and land cover between 1986 and 2026. Multi-temporal dataset from the United States Geological Survey (USGS) (Landsat) satellite images for the years 1986, 2016, and 2026 were used. Band stacking, mosaicking, and a supervised/unsupervised classification method guided by Intergovernmental Panel on Climate Change (IPCC) standards. Maximum Likelihood (MAXLIKE) was used within ArcGIS 10.8 to carry out classification. Land use/land cover classes were adapted from the USGS classification schema. Post-classification comparison was conducted to show Change detection analysis through multi-date imagery. Results revealed substantial landscape transformation over the 40 years. Savannah woodland declined by 17,743.97 km², forest cover decreased by 2,316.99 km² at an annual loss rate of 35.66 km², while agricultural land expanded by 3,407.95 km². These findings indicate significant anthropogenic pressure on natural vegetation, particularly through deforestation and land conversion for agriculture. The study underscores the urgent need for sustainable land management policies and forest conservation strategies to enhance carbon sequestration capacity and mitigate the impacts of climate change in Southwestern Nigeria. Climate Change Remote Sensing Landuse Landsat deforestation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background to Study The diversity of plant species within an ecosystem is intricately linked to environmental factors such as climate, soil, and geology. These variables collectively shape the floristic composition of plants, influencing the biomass and carbon makeup of natural forests and savannah woodlands. Vegetation encompasses all plant species and ground cover, forming distinct clusters that characterize the spatiotemporal attributes of a location and the growth forms of dominant plants. Forest loss can arise from both human activities and natural phenomena, although human-induced causes are more prevalent. Agricultural and urban expansion stand out as major contributors to the diminished carbon sequestration capacity of vegetation and the subsequent reduction in carbon stocks. Fasona, Adeonipekun, Agboola, Akintuyi, Bello, Ogundipe, Omojola (2020) have pinpointed key drivers of deforestation and forest degradation in Southwest Nigeria, including activities like lumbering, pole extraction, fuelwood and charcoal production, crop cultivation, urban growth, and animal grazing, all of which result in significant carbon emissions. While human activities predominantly drive forest loss, natural causes such as climate change, wildfires, and flooding played a role in tropical deforestation during the 1980s and 1990s. In the case of Nigeria, the primary forests have dwindled to 13,944 km 2 , with a staggering 95% loss due to deforestation at an annual rate of 5% between 2010 and 2015. Presently, only 10% of the original forest in Nigeria remains (Oyebo, Bisong, & Morakinyo, 2010 ). focused on estimating aboveground biomass or carbon stocks across diverse land uses (Sun & Liu, 2020 ). In recent decades, deforestation and forest degradation have emerged as pressing environmental concerns, profoundly affecting global carbon cycles. These processes involve the clearing of forests, either for agricultural expansion, urban development, or unsustainable logging, leading to significant global warming. The released carbon compounds, primarily in the form of carbon dioxide (CO 2 ), contribute to the greenhouse effect, exacerbating climate change and its associated consequences in southwestern Nigeria. Therefore, the major issues faced are the ability to quantify the amount of forest loss over a very large area, like southwest Nigeria and quantifying the forest of past years. Plate 1: Uncoordinated logging in the study area (source: Author 2017) 2. Study Area 2.1 Geographical Location The study area encompasses the southwestern states of Nigeria, including Ogun, Osun, Oyo, Ondo, and parts of Ekiti. As shown in Fig. 1 , the study area is delineated by longitude 3° 05' to 5° 25' East and Latitude 6° 05' North to 9° 10' North, covering an expansive area of approximately 6,364,030 hectares (63,641 km 2 ). Spanning from the northern savannah grasslands and woodlands to the lush southern forested regions, the study area boasts a diverse range of ecosystems. Bordered by the Republic of Benin to the west, Edo State to the east, Kwara State to the north, and Lagos State to the south, it occupies a strategic position within Nigeria's geographical landscape. The choice of this study area is guided by several factors, chief among them being its substantial forest reserves, which represent some of the largest in Nigeria. This makes it an ideal location for investigating forest dynamics, carbon fluxes, and Land use changes. Moreover, the region is experiencing alarming rates of deforestation, making it a focal point for research aimed at understanding and mitigating this phenomenon. The urgency of addressing deforestation in the study area is underscored by reports from the Food and Agriculture Organization (FAO, 2017) highlighting the rapid pace of forest loss in the region. Geologically, the landscape of the study area is characterized by a dissected plain developed on tertiary sediments. This geological setting influences soil composition, topography, and hydrology, shaping the distribution and characteristics of vegetation across the region. Understanding the geological context is essential for interpreting Land use patterns, ecosystem dynamics, and carbon sequestration potential within the study area. Overall, the study area's unique blend of ecological diversity, extensive forest reserves, and pressing environmental challenges makes it a compelling subject for scientific inquiry and conservation efforts. By conducting research in this region, scholars and policymakers aim to generate insights that can inform sustainable land management strategies, mitigate deforestation, and safeguard the region's invaluable natural heritage for future generations. 3. Methodology 3.1 Data types and sources Data used for this study were obtained from various sources, as shown in Table 1 . The data collected from the primary sources included field measurements, plant sampling, and enumeration. The other data collected from secondary sources included satellite images. The satellite images were obtained for different years from the United States Geological Survey (USGS). In contrast, the Radiation data and the fraction of the absorbed photosynthetically active radiation (FAPAR) were obtained from the cds.climate.Copernicus. eu. Table 1 Characteristics and Sources of Data Sensor Identification Spatial Resolution Date Source Application Landsat 5 Tm Path 191 Row 055, Path 191 Row 054, Path 190, Row 055 Spatial resolution – Pan – 15m B-IR 30mx30m TIR – 60m Spectral resolution – 8 bands 1986 US Geological Surveys, glovis.usgs.gov Land use change Analysis, LST, VI, NDWI Landsat 7 ETM+ Path 191 Row 055, Path 191 Row 054, Path 190, Row 055 Spatial resolution – Pan – 15m B-IR 30mx30m TIR – 60m Spectral resolution – 8 bands 2006 US Geological Surveys, glovis.usgs.gov Land use change Analysis, LST, VI, NDWI Landsat 8 OLI Path 191 Row 055, Path 191 Row 054, Path 190, Row 055 Spatial resolution – Pan – 15m B-IR 30mx30m TIR – 60m Spectral resolution – 8 bands 2016 US Geological Surveys, glovis.usgs.gov Land use change Analysis, LST, VI, NDWI Administrative map Southwest 1:250,000 GIS & Remote Sensing Lab, Department of Geography, Unilag State Boundary Delineation 3.2 Land use/Land cover classification The classification was done in line with the IPCC's good practice guidance. Stacking and mosaicking of the image bands to generate a composite image was carried out within the ArcMap GIS environment. For the Landsat TM imagery of 1986, ETM of 2001, the band combinations 2, 4, and 7 (blue, near-infrared, and far-infrared) gave the best band combination for the extraction of vegetation and Land use classes, while the OLI of 2016 was 3, 5, and 8 (blue, near-infrared and far-infrared). To carry out a detailed ecosystem classification that will reveal the various subclasses of the land cover, the third level of Anderson et al. ( 1976 ) schema was employed. A supervised/unsupervised classification method was carried out; training sites established according to spectral reflectance were developed into signature files. The signature classes were subjected to a soft classifier algorithm in the Image Classification plugin on ArcGIS 10.3 Software. The Hybrid classification was used for this study due to the large extent of the study area, as the entire locations could not be visited. Thus, Maximum likelihood (MAXLIKE) was employed. MAXLIKE is a powerful classification technique that acts on the differences between the classes of the spectral radiance and the variability and degree and type of correlation between bands (covariance matrices) (Eastman 2001 ). This method entailed using the Linear Pixel Unmixing. This method was employed when pixels integrate with discrete areas and conceptually fuzzy classes that arise from variability in the underlying classes (Borsoi et al., 2021 ). Table 2 Classification schema S/N Level I Level II 1 Urban or Built-up Land Residential Commercial and Services Industrial Transportation, Communications, etc 2 Agricultural Land Cropland Plantation Scattered Cultivation 4 Forest Land Heavy Forest Light Forest Disturbed Forest 5 Water Streams and River Lakes Reservoirs 6 Wetland Mangrove Marsh 7 Open Surface Sandy Areas Bare Exposed Rock Quarries Recreational Construction area The land use/land cover of the study area was classified using the schema presented in Table 2 . This was adopted and modified from the USGS classification schema of 1969. The classes presented in the table were used, and it is reflected in the land uses of this project. 3.3 Land Use and Landcover Change Analysis A post-classification approach utilized in this research involves the interpretation and classification of vector land use/land cover data derived from satellite imagery, as outlined by Fichera et al. ( 2012 ). This methodology is essential for understanding changes in land use and land cover over time, providing valuable insights into landscape dynamics and environmental trends. Change detection analysis plays a pivotal role in this process, allowing researchers to identify and quantify alterations in land cover types between two or more time periods. In the context of this study, change detection for land use and land cover entails comparing pixels from multi-date satellite images of the same location to detect changes, as elucidated by Vivekananda et al. ( 2021 ). This involves aligning and overlaying images acquired at different time points, enabling the detection of differences in pixel values indicative of land cover changes. The application of change detection algorithms within Geographic Information Systems (GIS) software, such as ArcGIS 10.8, facilitates the automated identification and analysis of land cover changes across the study area. The process begins by preprocessing the satellite images to ensure consistency and accuracy in the data. This may involve geometric correction, radiometric calibration, and atmospheric correction to remove distortions and artefacts introduced during image acquisition. Once pre-processed, the images are classified into discrete land cover classes using supervised or unsupervised classification algorithms. Each pixel in the image is assigned to a specific land cover category based on its spectral characteristics, as captured by the satellite sensors. After classification, change detection is performed by comparing the classified images from different time periods. This comparison involves identifying pixels that have undergone changes in land cover type between the two dates. Change detection algorithms analyze the spectral signatures of corresponding pixels in the two images, flagging areas where significant differences occur. These differences may include land cover conversions, such as the transition from forest to agricultural land, urban expansion, or deforestation. ArcGIS 10.8 provides a robust platform for conducting change detection analysis, offering a range of tools and functionalities specifically designed for this purpose. The DN (digital number) values of corresponding pixels in the images acquired at time t 1 and t 2 are compared using these tools, allowing for the identification of areas where land cover changes have occurred. The output of the change detection process is typically a thematic map highlighting the locations and extent of land cover changes across the study area. To further analyze and interpret the detected changes, researchers generate a land change matrix table. This table provides a systematic overview of the different types of land cover changes observed, categorizing areas that have remained stable and those that have undergone conversion to a different land cover class. By quantifying the extent and nature of land cover changes, researchers can assess the drivers and impacts of land use change, informing land management strategies and environmental policy decisions. 4. Findings 4.1 Static Land use and Land cover 1984 to 2017 The study area covers a land area of about 67,255.83 km 2 . The distribution of the static Land use/Land cover for the area in 1986 is shown in Table 3 and Fig. 2 . Savannah grassland and woodland cover 12,089.99 km 2 and 10,355.38 km 2, respectively. In the natural forest class, highly disturbed forest covered an area of 12,611.37 km 2 , while the intensive arable cultivation in the Agriculture primary class covered 8,537.40 km 2 , thereby accounting for 17.98% and 15.4%, 18.75% and 12.69% of the study area, Other ecosystem classes in increasing order included fallow (Scattered cultivation) (8.72%), Tree crop/secondary forest (8.03%) undisturbed forest (6.1%), Rock/Montane forest (3.57%), minimally disturbed forest (3.17%), swamp (1.49%), urban (1.32%), sandbar (0.35%), Oil palm (0.68%) River/Lake (0.1%) and Coastal grassland (0.07%) respectively. The Land use statistics for 2016 show that savannah woodland had an area of about 17,743.97 km 2 (26.38%). It is closely followed by a highly disturbed forest with a total area of about 12,390.57 km 2 (18.42%), and intensive arable cultivation follows the highly disturbed forest with about 11,330.40 km 2 (16.85%). The table reveals that the land use classes with the lowest land area are rubber 9.34 km 2 (0.01%), quarry 4.37 km 2 (0.01%), coastal grassland 39.32 km 2 (0.06%), and other ones, as shown in Table 3 above. The table reveals that some other land uses, such as oil palm, marshland, Banana was no longer available in 2026. The Land use statistics for 2026, as shown in the table, reveal that savannah woodland had the highest land area of about 20,577 km 2 (30%), intensive arable cultivation had a land area of 14,064 km 2 (20.9%), and Teak/Gmelina plantation had a land area of 10,625.85 km 2 (15.8%). On the other hand, the table shows that banana plantations had the lowest land area, of 0.47 km 2 (0%). The statistics show that Oil palm land use had no record area. The analysis of Land use data spanning from 1986 to 2026 reveals dynamic changes in land cover within the study area. These changes have significant implications and reflect the complex interplay between environmental and anthropogenic factors. Initially, savannah grassland and woodland dominated the land cover in 1986, but subsequent years witnessed substantial shifts in their areas, indicating the sensitivity of these ecosystems to various influences. These findings align with research by Hirota et al. ( 2011 ), which emphasizes the susceptibility of savannah ecosystems to environmental and human-induced changes. Moreover, the emergence of new land cover classes such as Teak/Gmelina plantation and bare surfaces underscores the impact of human activities, including afforestation initiatives and land degradation processes. These changes reflect broader trends in land use and highlight the need for sustainable land management practices, as discussed by Chazdon et al. ( 2009 ) and Veldkamp et al . (2001). Agricultural transitions and land use intensification are evident from the significant increase in intensive arable cultivation areas over the years. This suggests shifts in agricultural practices characterized by intensification or expansion, as noted by Verburg et al. ( 2019 ). However, the loss of certain land cover types like oil palm and banana plantations raises concerns about the underlying factors driving these changes, such as market dynamics and policy shifts impacting agricultural practices, as highlighted by Gibbs et al. ( 2010 ) and Carlson et al . (2012). Forest dynamics and conservation challenges are also prominent themes in the Land use data analysis. The observed changes in forest cover, including declines in highly disturbed forests and increases in plantation forests, underscore the ongoing challenges of deforestation, forest degradation, and afforestation efforts. Addressing these challenges requires effective conservation strategies that promote sustainable land management practices, as advocated by Gaveau et al. ( 2014 ). Urbanization and infrastructure development have contributed to the expansion of urban areas, leading to habitat fragmentation and loss of natural ecosystems. These trends underscore the need for integrated Land use planning approaches that balance economic development with environmental sustainability, as emphasized by Seto et al. ( 2012 ) and McDonald et al . (2008). The dynamic changes in land use and land cover observed in the study area highlight the complexity of interactions between human activities, environmental processes, and policy interventions. Addressing these challenges requires interdisciplinary approaches informed by ecological, social, and economic considerations as portrayed in the conceptual framework of this study. Foley et al. ( 2005 ) and Lambin et al . (2003) emphasize the importance of integrated Land use planning and stakeholder engagement in promoting sustainable land management practices. Table 3 LULC Characteristics of 1986, 2006 and 2016 S/N Primary Class Secondary Class 1986 (km 2 ) % 2006 (km 2 ) % 2016 (km 2 ) % 1 Agriculture Banana 0.47 0 2 Agriculture Fallow (Scattered Cultivation) 5,864.70 8.72 6,022.36 8.95 1,626.17 2.42 3 Agriculture Intensive Arable Cultivation 8,537.40 12.69 11,330.40 16.85 14,064.09 20.91 4 Agriculture Tree Crop/Secondary Forest 5,583.14 8.3 4,993.49 7.42 8,367.32 12.44 5 Bare Surface Quarry 4.37 0.01 2.58 0 6 Bare Surface Rock/Montane Forest 2,400.93 3.57 3,692.08 5.49 191.92 0.29 7 Built-up Area Urban 890.44 1.32 1,944.15 2.89 4,877.45 7.25 8 Natural Forest Highly Disturbed Forest 12,611.37 18.75 12,390.57 18.42 3,817.42 5.68 9 Natural Forest Minimally Disturbed Forest 2,130.66 3.17 2,433.65 3.62 1,770.13 2.63 10 Natural Forest Undisturbed Forest 4,104.14 6.1 1,272.74 1.89 7.13 0.01 11 Plantation Oil Palm 456.62 0.68 12 Plantation Rubber 9.34 0.01 30.37 0.05 13 Plantation Teak/Gmelina 874.49 1.3 4,961.81 7.38 10,625.84 15.8 14 Savanna Savannah Grassland 12,089.99 17.98 127.16 0.19 798.26 1.19 15 Savanna Savannah Woodland 10,355.38 15.4 17,743.97 26.38 20,577.33 30.6 16 Water River/Lake 68.09 0.1 129.56 0.19 93.99 0.14 17 Wetland Coastal Grassland 47.98 0.07 39.32 0.06 6.15 0.01 18 Wetland Marshland 1.25 0 171.61 0.26 19 Wetland Sand bar 234.01 0.35 105.65 0.16 0.02 0 20 Wetland Swamp 1,005.24 1.49 55.21 0.08 227.57 0.34 Total 67,255.83 100 67,255.83 100 67,255.83 100 Table 3 shows the natural forest ecosystem class facing competition from agriculture and savannah ecosystems. New landcover classes, such as bare surface, also emerged. Savannah woodland, highly disturbed forest, and intensive arable cultivation were the most important classes in 2006 with 17743.97 km 2 , 12390.57, and 11330.40 km 2 (26.38%, 18.42%, and 16.85 km 2 ) of the area, respectively. Fallow, tree crop and Teak/ Gmelina account for 6022.36 km 2 (8.95%), 4993.49 km 2 (7.42%) and 4961.81 km 2 (7.38%) respectively. Other ecosystem classes mapped for the area in 2006 in order of increase include Quarry 4.37 km 2 (0.01%), rubber plantation 9.34 km 2 (0.01%), coastal grassland 39.32 km 2 (0.06) swamp 55.21 km 2 (0.08%) sandbar 105.65 km 2 (0.16%), savanna grassland 127.16 km 2 (0.19%) undisturbed forest 1272.74 km 2 (1.89%), urban 1944.15 km 2 (2.89%), Minimally disturbed forest 2433.65 km 2 (3.62%) and rock/montane forest 3692.08 km 2 (5.49%). Rubber plantation land use and cover classes emerged in 2006, while oil palm was lost in an area. The distribution of land use and cover classes in the study area in 2006 provides valuable insights into the patterns of human-environment interactions and land management practices. The dominance of savannah woodland, highly disturbed forest, and intensive arable cultivation underscores the extensive conversion of natural ecosystems for agricultural purposes. These land cover classes are indicative of deforestation, land clearance, and agricultural expansion, driven by socio-economic factors such as population growth, urbanization, and agricultural intensification (Lambin & Geist, 2006). Fallow land, tree crops, and Teak/Gmelina plantations represent areas undergoing various stages of land use transition. Fallow land may result from shifting cultivation practices or temporary abandonment of agricultural fields, while tree crops and plantations reflect efforts to diversify agricultural production or promote agroforestry systems (Turner II et al ., 2007). The emergence of rubber plantations highlights shifts in land use priorities and economic activities. Rubber cultivation may be driven by market demand, government policies, or incentives for cash crop production, leading to changes in land cover and land use intensity (Mertens et al ., 2002). The decline in oil palm plantation area suggests changes in the dynamics of palm oil production, influenced by factors such as market trends, land availability, and environmental regulations. This trend underscores the need for monitoring and sustainable management of oil palm plantations to mitigate environmental impacts and ensure the long-term sustainability of palm oil production (Gibbs et al., 2010 ). Overall, the distribution of land use and cover classes reflects the complex interactions between human activities and environmental processes shaping the landscape of the study area. Understanding these dynamics is essential for sustainable land use planning, conservation efforts, and natural resource management strategies. Table 3 shows the emergence of a Banana Plantation of 0.47 km 2 in the ecosystem of the study area in 2016. the savannah woodland and intensive arable cultivation submerged other landcover ecosystems and claimed an area of 20577.33 km 2 (30.6%) and 14064.09 km 2 (20.91%) of the total area respectively, Another land use and landcover ecosystem that also had a relatively high increase rate in 2016 were Teak/Gmelina and Tree crop/ secondary forest with an area of 10625.84(15.8%) km 2 and 8367.32 km 2 (12.44%) respectively. The expansion of agricultural activities, particularly intensive arable cultivation and plantation crops such as bananas, reflects shifts in land use priorities, market demands, and agricultural practices. The substantial increase in savannah woodland and intensive arable cultivation areas, accounting for 30.6% and 20.91% of the total area, respectively, underscores the extensive conversion of natural ecosystems for agricultural purposes. This expansion may be driven by factors such as population growth, urbanization, agricultural intensification, and government policies promoting agricultural development (Lambin & Geist, 2006). Additionally, the notable increase in Teak/Gmelina plantations and Tree crop/secondary forest areas further illustrates the diversification of agricultural production and land use intensification strategies. Plantation forestry, including Teak and Gmelina cultivation, may be promoted for timber production, agroforestry systems, or reforestation efforts, contributing to landscape transformation and ecosystem services provisioning (Mertens et al ., 2002). These changes in land use and land cover patterns have significant implications for biodiversity conservation, ecosystem services provision, and socio-economic development. Understanding the drivers and impacts of these changes is essential for informing land use planning, environmental management strategies, and sustainable development initiatives aimed at balancing agricultural production, conservation objectives, and socio-economic priorities. 4.2 Trend of Landcover Change between 1986 and 2016 The analysis of land cover changes presented in Table 3 provides valuable insights into the dynamics of land use transformations in the study area over two distinct time periods: 1986 to 2006 and 2016 to 2026. These findings highlight both positive and negative changes in various land cover types, reflecting the complex interplay of natural and anthropogenic factors shaping the landscape (Lambin et al ., 2003). During the period from 1986 to 2026, the study reveals substantial changes in land cover, with savannah grassland experiencing the most significant decrease in area, amounting to 11291.73 km² or a reduction of 17.79%. This decline may be attributed to factors such as agricultural expansion, urbanization, and deforestation, which often result in the conversion of natural habitats to croplands or settlements (Foley et al., 2005 ). Conversely, savannah woodland exhibited a positive change, with an increase in area by 7388.59 km² or 10.00%. This could be indicative of afforestation efforts or natural regeneration processes occurring within the study area (Hirota et al., 2011 ). However, not all land cover types experienced significant changes during this period. Coastal grassland, for instance, showed a minimal loss of only 8.66 km² or 0.01%. Similarly, quarry and rubber plantation areas recorded negligible gains, indicating relatively stable land use patterns in these categories (Gaveau et al., 2014 ). These findings underscore the importance of considering local context and specific land cover dynamics when assessing landscape changes and their drivers. The analysis further extends to the period between 2016 and 2026, revealing continued shifts in land cover composition. Highly disturbed forests emerged as the most affected category, experiencing a substantial loss of -8573.14 km² or 12.75%. This trend is concerning as it suggests ongoing deforestation and land degradation processes, likely driven by factors such as logging, agricultural expansion, and infrastructure development (Sodhi et al., 2010 ). Similarly, fallow and shifting cultivation land cover recorded significant losses, highlighting the vulnerability of these areas to Land use changes and degradation (Gibbs et al., 2010 ). In contrast, certain land cover types demonstrated notable gains during the same period. Teak and Gmelina plantation, for example, exhibited the highest increase in area, with a gain of 5664.03 km² or 8.42%. This expansion could be attributed to reforestation initiatives, agroforestry practices, or commercial plantation establishment aimed at meeting timber demand (Chazdon et al., 2009 ). Additionally, urban areas and intensive agriculture witnessed considerable growth, reflecting urbanization trends and agricultural intensification efforts in response to population growth and economic development (Lambin & Meyfroidt, 2011 ). Overall, the findings underscore the dynamic nature of land cover changes in the study area and highlight the complex interactions between human activities and environmental processes. Conservation efforts, sustainable land management practices, and effective monitoring mechanisms are essential for mitigating the adverse impacts of land cover changes, preserving critical habitats, and promoting ecosystem resilience in the face of ongoing global environmental changes. 4.3 Pattern Savannah and Forest Conversion The analysis of land cover changes from 1986 to 2026 reveals significant transformations in various land cover categories, reflecting the complex interactions between human activities and environmental processes (Gaveau et al., 2014 ). Highly disturbed forest areas witnessed a substantial decline from 12,611.37 km² in 1986 to 3,817.42 km² in 2026, indicative of intensive Land use changes likely driven by urbanization, industrialization, or logging activities (Gaveau et al., 2014 ; Sodhi et al., 2010 ). Conversely, undisturbed forest areas experienced a drastic reduction from 4,104.14 km² to 7.13 km² over the same period, highlighting severe environmental degradation and the encroachment of human activities into previously pristine forest landscapes (Gaveau et al., 2014 ). This decline in undisturbed forest areas underscores the urgent need for conservation efforts to protect remaining forest habitats and preserve biodiversity (Sodhi et al., 2010 ). In contrast, minimally disturbed forest areas exhibited moderate stability, suggesting relatively lower levels of human disturbance compared to highly disturbed and undisturbed forest ecosystems (Gaveau et al., 2014 ). The stability in minimally disturbed forest areas may indicate the effectiveness of conservation measures or land management practices aimed at mitigating environmental impacts (Gaveau et al., 2014 ; Sodhi et al., 2010 ). Furthermore, savannah grassland areas remained relatively stable with a slight increase by 2016, indicating resilience to significant land cover changes observed in other ecosystems (Gaveau et al., 2014 ). This stability in savannah grassland areas may be attributed to natural ecological processes or sustainable land management practices that maintain ecosystem integrity (Gaveau et al., 2014 ). Overall, the dynamic patterns in land cover changes highlight the complex dynamics of human-environment interactions and the need for integrated approaches to land management and conservation (Sodhi et al., 2010 ). Addressing the drivers of land cover changes, such as urbanization, deforestation, and agricultural expansion, requires coordinated efforts involving policymakers, stakeholders, and local communities to promote sustainable development and biodiversity conservation (Gaveau et al., 2014 ). Table 4 Land cover statistics of Forest and Savannah Landcover 1986 (km 2 ) 2016 (km 2 ) 2026 (km 2 ) Highly Disturbed Forest 12611.37 12390.57 3817.42 Minimally Disturbed Forest 2130.66 2433.65 1770.13 Undisturbed Forest 4104.14 1272.74 7.13 Savannah Grassland 12089.99 127.16 798.26 Savannah Woodland 10355.38 17743.97 20577.33 The savannah woodland areas within the study region showed notable fluctuations over the three-decade period, with significant implications for land cover dynamics and environmental sustainability. The area covered by savannah woodland expanded considerably from 10,355.38 km² in 1986 to 20,577.33 km² in 2026. This substantial increase suggests potential afforestation efforts or changes in land management practices during the study period. These fluctuations underscore the dynamic nature of land cover changes within the study region and the complex interactions between human activities and natural ecosystems. The expansion of savannah woodland areas may be influenced by various factors, including reforestation initiatives, natural regeneration processes, or shifts in land use practices such as agricultural abandonment or land restoration efforts. However, these trends also highlight the urgent need for conservation efforts, sustainable land management practices, and effective monitoring mechanisms to safeguard critical habitats, preserve biodiversity, and mitigate the adverse effects of human activities on the environment. While the expansion of savannah woodland areas may indicate positive changes in land cover, it is essential to ensure that these changes are sustainable and do not lead to unintended consequences such as habitat fragmentation, loss of biodiversity, or degradation of ecosystem services. Furthermore, ongoing monitoring and assessment of land cover dynamics are essential to track changes over time, identify emerging threats, and inform adaptive management strategies. By implementing proactive conservation measures, promoting sustainable land use practices, and engaging stakeholders in participatory decision-making processes, it is possible to achieve a balance between human development and environmental conservation objectives. Overall, the fluctuations observed in savannah woodland areas underscore the dynamic nature of land cover changes and the importance of proactive conservation efforts to maintain ecological integrity, promote biodiversity conservation, and enhance the resilience of ecosystems in the face of environmental change. Table 5 presents the statistical overview of forest and savannah areas within the study region. In 1986, the highly disturbed forest covered an extensive area of 12,611.37 km 2 . However, by 2026, this area had undergone significant reduction, shrinking to 3,817.42 km 2 . This substantial decrease indicates a profound conversion of highly disturbed forest within the study area over the three-decade period. Figure 6 provides a visual representation of land cover conversions within the study area. It reveals that the savannah grassland experienced the most substantial conversion, with an area reduction of approximately 13,000 km 2 . Following closely behind, the highly disturbed forest underwent a conversion of about 9,000 km 2 . Interestingly, the figure illustrates that the lowest conversion rate for forest occurred between 1986 and 2026, suggesting a period of relatively slower forest loss during this timeframe compared to other periods. These findings underscore the significant changes in land cover dynamics within the study region over the past three decades. The considerable reduction in highly disturbed forest area highlights the extensive conversion of forested land for various purposes such as agriculture, urbanization, and infrastructure development. Similarly, the substantial conversion of savannah grassland underscores the pressures faced by natural ecosystems due to human activities and land use changes. Understanding these land cover conversions is crucial for assessing the impacts on biodiversity, ecosystem services, and carbon stocks within the study area. Furthermore, these findings can inform land management strategies, conservation efforts, and policy interventions aimed at mitigating further land degradation and promoting sustainable land use practices. By monitoring land cover changes and their associated impacts, stakeholders can work towards preserving valuable ecosystems, protecting biodiversity, and mitigating the adverse effects of land cover change on the environment and society. 4.4 LULC Projection model from 2026 to 2066 The land use and land cover projection of the study area for the primary classes is shown in Table 5 , and the result reveals that the projection statistics for the urban area have a progressive expansion from 3209.75 km 2 in 2026 to 4042.35 in 2066. On the other hand, the forest area had a sharp reduction in 2020 and might become 0 from 2022 to 2066. This result is evident since the population of the study area will continue to increase, while this increasing population depends on the forest resources for livelihood, the forest will continue to be reduced due to overexploitation, which is enhanced by weak polices. The analysis of land cover changes over the period from 1986 to 2026 reveals significant trends with profound implications for environmental health and biodiversity conservation. Firstly, there has been a notable decline in highly disturbed forest areas, amounting to 8573.14 km² during this period. This decline underscores the severity of environmental degradation and Land use changes within the study area, likely influenced by factors such as urbanization, logging activities, and agricultural expansion. These findings align with research by Gaveau et al. ( 2014 ) and Sodhi et al. ( 2010 ), highlighting the urgent need for conservation efforts and sustainable land management practices to mitigate further degradation. Table 6 Simulated land use land cover from 2026 to 2066 for STsim Primary Land cover classes (sqkm) Time step 2026 2036 2046 2056 2066 Agricultural 5117.27 1444.43 410.71 115.90 29.14 Bare Surface 293.24 96.85 26.90 6.95 3.81 Builtup Area 33930.34 41711.84 43909.54 44523.58 44699.79 Grassland 7403.07 2025.97 562.26 158.05 43.04 Natural Forest 0.45 0.45 0.45 0.45 0.45 Plantation 1652.70 510.70 137.43 36.99 8.74 Woodland 18858.76 21465.59 22208.55 22413.90 22470.85 Source: Author, 2020 Similarly concerning is the drastic decline in undisturbed forest areas, shrinking from 4104.14 km² to 7.13 km² over the same period. This loss of pristine forest habitats poses significant threats to biodiversity and ecosystem services, emphasizing the importance of conservation strategies aimed at protecting remaining forested areas and restoring degraded landscapes. This aligns with the findings of Gibbs et al. ( 2010 ) and Chazdon et al. ( 2009 ), highlighting the critical need for biodiversity conservation and habitat restoration efforts. On a contrasting note, the analysis reveals substantial fluctuations in savannah woodland areas, with an increase from 10355.38 km² in 1986 to 20577.33 km² in 2026. These dynamic changes suggest the influence of afforestation efforts or alterations in land management practices, highlighting the potential for ecosystem restoration and reforestation initiatives to mitigate deforestation and land degradation. This resonates with the research of Hirota et al. ( 2011 ) and Gibbs et al. ( 2010 ), emphasizing the role of afforestation in promoting landscape resilience and biodiversity conservation. The observed land cover changes underscore the importance of conservation efforts, sustainable land management practices, and effective monitoring mechanisms to protect critical habitats and preserve biodiversity. Integrated approaches that consider ecological, social, and economic dimensions are essential for addressing the drivers of land cover change and promoting sustainable development. These findings align with the research of Foley et al. ( 2005 ) and Lambin et al . (2011), emphasizing the need for comprehensive strategies to mitigate the adverse impacts of human activities on the environment and promote the long-term health and resilience of ecosystems. Conclusion This study examined the land use and land cover dynamics in Southwestern Nigeria from 1986 to 2026, employing multi-temporal Landsat imagery downloaded from the United States Geological Survey. Remote sensing and GIS techniques in ArcGIS 10.8 were used to carry out hybrid (supervised/unsupervised) classification using Maximum Likelihood and post-classification change detection. The research revealed significant landscape transformations across the region. Results show a substantial reduction in savannah woodland and forest, and an expansion of agricultural land. The consistency of forest loss highlights increasing pressure from anthropogenic activities, particularly from deforestation and land use conversion. The study reveals the efficacy of geospatial technologies for spatio-temporal environmental monitoring and provides empirical evidence to support sustainable land management planning. To mitigate further environmental degradation and climate-related impacts, there is an urgent need for strengthened forest conservation policies, improved land use planning frameworks, afforestation and reforestation programs, and enforcement of environmental protection regulations. Sustained monitoring using remote sensing and GIS tools should remain central to policy implementation and environmental governance in Southwestern Nigeria. Declarations Funding: No Funding Clinical Trial Number : Not Applicable Consent from Authors to Publish : Samuel Kevin Udofia Ajibade Ariori Emmanuel Wunude Chinwe Ugwuzor Reviewed_Samuel_Udofia_Landuse Ethics approval and consent to participate This study is based on remote sensing analysis using satellite imagery and does not involve human participants, plant specimen collection, or field sampling. Therefore, ethical approval, consent to participate, plant collection guidelines, and herbarium documentation are not applicable. Consent for publication Not applicable. This study does not involve human participants, personal data, or identifiable images. Data availability The datasets analysed during the current study are publicly available from the United States Geological Survey EarthExplorer repository and grid3 repository (https://earthexplorer.usgs.gov/) and (https://grid3.org). Processed data and analysis outputs are available from the corresponding author on reasonable request. References Anderson R, Hardy EE, Roach JT, Witmer RE. (1976). A land use and land cover classification system for use with remote sensor data. USGS Professional Paper 964 , Sioux Falls, SD, USA. 191, 82–101. Borsoi RA, Imbiriba T, Bermudez JCM, Richard C, Chanussot J, Drumetz L, Tourneret JY, Zare A, Jutten C. Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review. IEEE Geoscience Remote Sens Magazine. 2021;9(4):223–70. 10.1109/MGRS.2021.3071158 . Chazdon RL, Brancalion PHS, Laestadius L, Bennett-Curry A, Buckingham K, Kumar C, Polasky S. (2009). When is a forest a forest? Forest concepts and definitions in the era of forest and landscape restoration. 38(8), 531–545. DeFries RS, Foley JA, Asner GP. Land use choices: balancing human needs and ecosystem function. Front Ecol Environ. 2004;2(5):249–57. Eastman JR. (2001) IDRISI Selva Tutorial, Manual Version 17.0, Clark University, January Available: http://uhulag.mendelu.cz/files/pagesdata/eng/gis/idrisi_selva_tutorial.pdf Fasona M, Oloukoi G, Olorunfemi F, Elias P, Adedayo V. (2014) Natural resource management and livelihoods in the Nigerian savanna. Penthouse Publication , Ibadan, 237pages, ISBN: 978-978-52153-2-8. Fichera C, Modica G, Pollino M. (2012). Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. Eur J Remote Sens. Foley JA, DeFries R, Asner GP, Barford C, Bonan G, Carpenter SR, Helkowski JH. Global consequences of land use. Science. 2005;309(5734):570–4. Gaveau DL, Sloan S, Molidena E, Yaen H, Sheil D, Abram NK, Meijaard E. (2014). Four decades of forest persistence, clearance and logging on Borneo. PLoS ONE, 9(7), e101654. Gibbs HK, Ruesch AS, Achard F, Clayton MMK, Holmgren P, Ramankutty N. FoleyJ.A. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s Proc. Natl. Acad. Sci ., 107 (2010), pp. 16732–16737. Gibbs HK, Brown S, Niles JO, Foley JA. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2(4):045023. Hirota M, Holmgren M, Van Nes EH, Scheffer M. Global resilience of tropical forest and savanna to critical transitions. Science. 2011;334(6053):232–5. Houghton RA, House JI, Pongratz J, van der Werf GR, DeFries RS, Hansen MC, Le Quer´ e´ C, Ramankutty N. (2012) Carbon emissions from land use and Land cover change Biogeosciences , 9, 5125–5142, 2012 www. biogeosciences.net/9/5125/2012/ doi:10.5194/bg-9-5125-2012. Lambin EF, Meyfroidt P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465–3472. Lambin EF, Meyfroidt P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465–3472. Li X, Zhou W, Ouyang Z, Xu W. Land use and Land cover change and their effects on the landscape of the Zhangye oasis, 1948–1998. Landsc Urban Plann. 2003;64(3):107–22. National Forest Policy. (2006). Federal Ministry of Environment, Abuja. Nigeria REDD+ Programme. Nigeria's Readiness Preparation Proposal for REDD+. Abuja, Nigeria: Federal Ministry of Environment; 2016. Oyebo M, Bisong F, Morakinyo T. (2010). A Preliminary Assessment of the Context for REDD in Nigeria. An informative document of the Federal Ministry of Environment, the Cross River State's Forestry Commission, and UNDP, p. 367. Seto KC, Güneralp B, Hutyra LR. (2012). Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. Proceedings of the National Academy of Sciences, 109(40), 16083–16088. Sodhi NS, Koh LP, Brook BW, Ng PK. Southeast Asian biodiversity: an impending disaster. Trends Ecol Evol. 2010;25(10):509–14. Sun W, Liu X. Review on carbon storage estimation of forest ecosystem and applications in China. Ecosyst. 2020;7(4). https://doi.org/10.1186/s40663-019-0210-2 . Turner BL, Lambin EF, 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. U.S. Department of Agriculture (USDA). Watershed Condition Classification Technical Guide. Washington, DC, USA: USDA; 2011. Udo RK. Geographical regions of Nigeria. University of California Press Berkeley and Los Angeles California; 1970. UNDRIP. (2008). Retrieved from http://www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf Verburg PH, Crossman N, Ellis EC, Heinimann A, Hostert P, Mertz O, Zhen L. Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene. 2019;26:100213. Vivekananda GN, Swathi R, Sujith AVLN. Multi-temporal image analysis for LULC classification and change detection. Eur J remote Sens. 2021;54(sup2):189–99. Williams CA, Hanan NP, Neff JC, et al. Africa and the global carbon cycle. Carbon Balance Manage. 2007;2:3. https://doi.org/10.1186/1750-0680-2-3 . World Commission on Environment and Development. (WCED, 1987). Our Common Future. Retrieved from http://www.un-documents.net/ourcommon-future.pdf Table Table 5 is available in the Supplementary Files section. Plate Plate 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Plate1.docx Table5.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 May, 2026 Reviewers agreed at journal 13 May, 2026 Reviews received at journal 13 May, 2026 Reviews received at journal 12 May, 2026 Reviewers agreed at journal 12 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 11 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers invited by journal 05 May, 2026 Editor assigned by journal 24 Apr, 2026 Submission checks completed at journal 22 Apr, 2026 First submitted to journal 22 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9449847","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":639785846,"identity":"cb10aa97-26f9-4579-bcfd-87f29d85ee21","order_by":0,"name":"Samuel K. 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5","display":"","copyAsset":false,"role":"figure","size":159038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLand use/land cover change between 1986 and 2026\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/936b3e2ea2f9a1df05036e11.png"},{"id":109263933,"identity":"6798ba8b-ca66-45d0-9728-25d3d1d29d70","added_by":"auto","created_at":"2026-05-14 12:08:21","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":153129,"visible":true,"origin":"","legend":"\u003cp\u003eForest and savannah conversion pattern\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/8f90e160be0a72077379ee63.jpeg"},{"id":109263930,"identity":"86d5ba8a-69a6-472d-a426-fbd33852b059","added_by":"auto","created_at":"2026-05-14 12:08:21","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":49790,"visible":true,"origin":"","legend":"\u003cp\u003eFuture land use projection for the study area from 2026 to 2066\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/2311e175260f30f25fe9f8c5.png"},{"id":109296232,"identity":"1c3b4e67-7e78-4e93-95a0-a8f47457f743","added_by":"auto","created_at":"2026-05-15 08:46:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5282321,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/021d5e2e-c52c-4723-a56e-0554a77e3d04.pdf"},{"id":109263905,"identity":"5c7d64af-59d1-4092-ba3b-a2666624c46c","added_by":"auto","created_at":"2026-05-14 12:08:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":258919,"visible":true,"origin":"","legend":"","description":"","filename":"Plate1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/840b793cd6f514893df32225.docx"},{"id":109263967,"identity":"a38af8fc-3353-46a5-95b2-7ffaa4c98a22","added_by":"auto","created_at":"2026-05-14 12:08:27","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":29051,"visible":true,"origin":"","legend":"","description":"","filename":"Table5.docx","url":"https://assets-eu.researchsquare.com/files/rs-9449847/v1/350c6bcb3ff25eb48e25fa66.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssessment of Land Use/land Cover Dynamics and Deforestation Trends in Southwestern Nigeria Using Multi-temporal Landsat Imagery (1986–2026)\u003c/p\u003e","fulltext":[{"header":"1. Background to Study","content":"\u003cp\u003eThe diversity of plant species within an ecosystem is intricately linked to environmental factors such as climate, soil, and geology. These variables collectively shape the floristic composition of plants, influencing the biomass and carbon makeup of natural forests and savannah woodlands. Vegetation encompasses all plant species and ground cover, forming distinct clusters that characterize the spatiotemporal attributes of a location and the growth forms of dominant plants.\u003c/p\u003e \u003cp\u003eForest loss can arise from both human activities and natural phenomena, although human-induced causes are more prevalent. Agricultural and urban expansion stand out as major contributors to the diminished carbon sequestration capacity of vegetation and the subsequent reduction in carbon stocks. Fasona, Adeonipekun, Agboola, Akintuyi, Bello, Ogundipe, Omojola (2020) have pinpointed key drivers of deforestation and forest degradation in Southwest Nigeria, including activities like lumbering, pole extraction, fuelwood and charcoal production, crop cultivation, urban growth, and animal grazing, all of which result in significant carbon emissions. While human activities predominantly drive forest loss, natural causes such as climate change, wildfires, and flooding played a role in tropical deforestation during the 1980s and 1990s. In the case of Nigeria, the primary forests have dwindled to 13,944 km\u003csup\u003e2\u003c/sup\u003e, with a staggering 95% loss due to deforestation at an annual rate of 5% between 2010 and 2015. Presently, only 10% of the original forest in Nigeria remains (Oyebo, Bisong, \u0026amp; Morakinyo, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). focused on estimating aboveground biomass or carbon stocks across diverse land uses (Sun \u0026amp; Liu, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent decades, deforestation and forest degradation have emerged as pressing environmental concerns, profoundly affecting global carbon cycles. These processes involve the clearing of forests, either for agricultural expansion, urban development, or unsustainable logging, leading to significant global warming. The released carbon compounds, primarily in the form of carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e), contribute to the greenhouse effect, exacerbating climate change and its associated consequences in southwestern Nigeria. Therefore, the major issues faced are the ability to quantify the amount of forest loss over a very large area, like southwest Nigeria and quantifying the forest of past years.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePlate 1: Uncoordinated logging in the study area (source: Author 2017)\u003c/b\u003e \u003c/p\u003e"},{"header":"2. Study Area","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Geographical Location\u003c/h2\u003e \u003cp\u003eThe study area encompasses the southwestern states of Nigeria, including Ogun, Osun, Oyo, Ondo, and parts of Ekiti. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the study area is delineated by longitude 3\u0026deg; 05' to 5\u0026deg; 25' East and Latitude 6\u0026deg; 05' North to 9\u0026deg; 10' North, covering an expansive area of approximately 6,364,030 hectares (63,641 km\u003csup\u003e2\u003c/sup\u003e). Spanning from the northern savannah grasslands and woodlands to the lush southern forested regions, the study area boasts a diverse range of ecosystems. Bordered by the Republic of Benin to the west, Edo State to the east, Kwara State to the north, and Lagos State to the south, it occupies a strategic position within Nigeria's geographical landscape.\u003c/p\u003e \u003cp\u003eThe choice of this study area is guided by several factors, chief among them being its substantial forest reserves, which represent some of the largest in Nigeria. This makes it an ideal location for investigating forest dynamics, carbon fluxes, and Land use changes. Moreover, the region is experiencing alarming rates of deforestation, making it a focal point for research aimed at understanding and mitigating this phenomenon. The urgency of addressing deforestation in the study area is underscored by reports from the Food and Agriculture Organization (FAO, 2017) highlighting the rapid pace of forest loss in the region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGeologically, the landscape of the study area is characterized by a dissected plain developed on tertiary sediments. This geological setting influences soil composition, topography, and hydrology, shaping the distribution and characteristics of vegetation across the region. Understanding the geological context is essential for interpreting Land use patterns, ecosystem dynamics, and carbon sequestration potential within the study area. Overall, the study area's unique blend of ecological diversity, extensive forest reserves, and pressing environmental challenges makes it a compelling subject for scientific inquiry and conservation efforts. By conducting research in this region, scholars and policymakers aim to generate insights that can inform sustainable land management strategies, mitigate deforestation, and safeguard the region's invaluable natural heritage for future generations.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data types and sources\u003c/h2\u003e \u003cp\u003eData used for this study were obtained from various sources, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The data collected from the primary sources included field measurements, plant sampling, and enumeration. The other data collected from secondary sources included satellite images. The satellite images were obtained for different years from the United States Geological Survey (USGS). In contrast, the Radiation data and the fraction of the absorbed photosynthetically active radiation (FAPAR) were obtained from the cds.climate.Copernicus. eu.\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\u003eCharacteristics and Sources of Data\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSensor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIdentification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial Resolution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eApplication\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 5 Tm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath 191 Row 055, Path 191 Row 054, Path 190, Row 055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial resolution \u0026ndash; Pan \u0026ndash; 15m B-IR 30mx30m TIR \u0026ndash; 60m Spectral resolution \u0026ndash; 8 bands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUS Geological Surveys, glovis.usgs.gov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand use change Analysis, LST, VI, NDWI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 7 ETM+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath 191 Row 055, Path 191 Row 054, Path 190, Row 055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial resolution \u0026ndash; Pan \u0026ndash; 15m B-IR 30mx30m TIR \u0026ndash; 60m Spectral resolution \u0026ndash; 8 bands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUS Geological Surveys, glovis.usgs.gov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand use change Analysis, LST, VI, NDWI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandsat 8 OLI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath 191 Row 055, Path 191 Row 054, Path 190, Row 055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpatial resolution \u0026ndash; Pan \u0026ndash; 15m B-IR 30mx30m TIR \u0026ndash; 60m Spectral resolution \u0026ndash; 8 bands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUS Geological Surveys, glovis.usgs.gov\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLand use change Analysis, LST, VI, NDWI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdministrative map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouthwest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:250,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGIS \u0026amp; Remote Sensing Lab, Department of Geography, Unilag\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eState Boundary Delineation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Land use/Land cover classification\u003c/h2\u003e \u003cp\u003eThe classification was done in line with the IPCC's good practice guidance. Stacking and mosaicking of the image bands to generate a composite image was carried out within the ArcMap GIS environment. For the Landsat TM imagery of 1986, ETM of 2001, the band combinations 2, 4, and 7 (blue, near-infrared, and far-infrared) gave the best band combination for the extraction of vegetation and Land use classes, while the OLI of 2016 was 3, 5, and 8 (blue, near-infrared and far-infrared). To carry out a detailed ecosystem classification that will reveal the various subclasses of the land cover, the third level of Anderson et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1976\u003c/span\u003e) schema was employed. A supervised/unsupervised classification method was carried out; training sites established according to spectral reflectance were developed into signature files. The signature classes were subjected to a soft classifier algorithm in the Image Classification plugin on ArcGIS 10.3 Software. The Hybrid classification was used for this study due to the large extent of the study area, as the entire locations could not be visited. Thus, Maximum likelihood (MAXLIKE) was employed. MAXLIKE is a powerful classification technique that acts on the differences between the classes of the spectral radiance and the variability and degree and type of correlation between bands (covariance matrices) (Eastman \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). This method entailed using the Linear Pixel Unmixing. This method was employed when pixels integrate with discrete areas and conceptually fuzzy classes that arise from variability in the underlying classes (Borsoi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\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\u003eClassification schema\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLevel II\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban or Built-up Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidential\u003c/p\u003e \u003cp\u003eCommercial and Services\u003c/p\u003e \u003cp\u003eIndustrial\u003c/p\u003e \u003cp\u003eTransportation, Communications, etc\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\u003eAgricultural Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCropland\u003c/p\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003cp\u003eScattered Cultivation\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\u003eForest Land\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeavy Forest\u003c/p\u003e \u003cp\u003eLight Forest\u003c/p\u003e \u003cp\u003eDisturbed Forest\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\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStreams and River\u003c/p\u003e \u003cp\u003eLakes\u003c/p\u003e \u003cp\u003eReservoirs\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\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMangrove\u003c/p\u003e \u003cp\u003eMarsh\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\u003eOpen Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandy Areas\u003c/p\u003e \u003cp\u003eBare Exposed Rock\u003c/p\u003e \u003cp\u003eQuarries\u003c/p\u003e \u003cp\u003eRecreational\u003c/p\u003e \u003cp\u003eConstruction area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe land use/land cover of the study area was classified using the schema presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. This was adopted and modified from the USGS classification schema of 1969. The classes presented in the table were used, and it is reflected in the land uses of this project.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Land Use and Landcover Change Analysis\u003c/h2\u003e \u003cp\u003eA post-classification approach utilized in this research involves the interpretation and classification of vector land use/land cover data derived from satellite imagery, as outlined by Fichera et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This methodology is essential for understanding changes in land use and land cover over time, providing valuable insights into landscape dynamics and environmental trends. Change detection analysis plays a pivotal role in this process, allowing researchers to identify and quantify alterations in land cover types between two or more time periods.\u003c/p\u003e \u003cp\u003eIn the context of this study, change detection for land use and land cover entails comparing pixels from multi-date satellite images of the same location to detect changes, as elucidated by Vivekananda et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This involves aligning and overlaying images acquired at different time points, enabling the detection of differences in pixel values indicative of land cover changes. The application of change detection algorithms within Geographic Information Systems (GIS) software, such as ArcGIS 10.8, facilitates the automated identification and analysis of land cover changes across the study area.\u003c/p\u003e \u003cp\u003eThe process begins by preprocessing the satellite images to ensure consistency and accuracy in the data. This may involve geometric correction, radiometric calibration, and atmospheric correction to remove distortions and artefacts introduced during image acquisition. Once pre-processed, the images are classified into discrete land cover classes using supervised or unsupervised classification algorithms. Each pixel in the image is assigned to a specific land cover category based on its spectral characteristics, as captured by the satellite sensors.\u003c/p\u003e \u003cp\u003eAfter classification, change detection is performed by comparing the classified images from different time periods. This comparison involves identifying pixels that have undergone changes in land cover type between the two dates. Change detection algorithms analyze the spectral signatures of corresponding pixels in the two images, flagging areas where significant differences occur. These differences may include land cover conversions, such as the transition from forest to agricultural land, urban expansion, or deforestation.\u003c/p\u003e \u003cp\u003eArcGIS 10.8 provides a robust platform for conducting change detection analysis, offering a range of tools and functionalities specifically designed for this purpose. The DN (digital number) values of corresponding pixels in the images acquired at time t\u003csub\u003e1\u003c/sub\u003e and t\u003csub\u003e2\u003c/sub\u003e are compared using these tools, allowing for the identification of areas where land cover changes have occurred. The output of the change detection process is typically a thematic map highlighting the locations and extent of land cover changes across the study area.\u003c/p\u003e \u003cp\u003eTo further analyze and interpret the detected changes, researchers generate a land change matrix table. This table provides a systematic overview of the different types of land cover changes observed, categorizing areas that have remained stable and those that have undergone conversion to a different land cover class. By quantifying the extent and nature of land cover changes, researchers can assess the drivers and impacts of land use change, informing land management strategies and environmental policy decisions.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Findings","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Static Land use and Land cover 1984 to 2017\u003c/h2\u003e \u003cp\u003eThe study area covers a land area of about 67,255.83 km\u003csup\u003e2\u003c/sup\u003e. The distribution of the static Land use/Land cover for the area in 1986 is shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Savannah grassland and woodland cover 12,089.99 km\u003csup\u003e2\u003c/sup\u003e and 10,355.38 km\u003csup\u003e2,\u003c/sup\u003e respectively. In the natural forest class, highly disturbed forest covered an area of 12,611.37 km\u003csup\u003e2\u003c/sup\u003e, while the intensive arable cultivation in the Agriculture primary class covered 8,537.40 km\u003csup\u003e2\u003c/sup\u003e, thereby accounting for 17.98% and 15.4%, 18.75% and 12.69% of the study area, Other ecosystem classes in increasing order included fallow (Scattered cultivation) (8.72%), Tree crop/secondary forest (8.03%) undisturbed forest (6.1%), Rock/Montane forest (3.57%), minimally disturbed forest (3.17%), swamp (1.49%), urban (1.32%), sandbar (0.35%), Oil palm (0.68%) River/Lake (0.1%) and Coastal grassland (0.07%) respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Land use statistics for 2016 show that savannah woodland had an area of about 17,743.97 km\u003csup\u003e2\u003c/sup\u003e (26.38%). It is closely followed by a highly disturbed forest with a total area of about 12,390.57 km\u003csup\u003e2\u003c/sup\u003e (18.42%), and intensive arable cultivation follows the highly disturbed forest with about 11,330.40 km\u003csup\u003e2\u003c/sup\u003e (16.85%). The table reveals that the land use classes with the lowest land area are rubber 9.34 km\u003csup\u003e2\u003c/sup\u003e (0.01%), quarry 4.37 km\u003csup\u003e2\u003c/sup\u003e(0.01%), coastal grassland 39.32 km\u003csup\u003e2\u003c/sup\u003e(0.06%), and other ones, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e above. The table reveals that some other land uses, such as oil palm, marshland, Banana was no longer available in 2026.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe Land use statistics for 2026, as shown in the table, reveal that savannah woodland had the highest land area of about 20,577 km\u003csup\u003e2\u003c/sup\u003e (30%), intensive arable cultivation had a land area of 14,064 km\u003csup\u003e2\u003c/sup\u003e (20.9%), and Teak/Gmelina plantation had a land area of 10,625.85 km\u003csup\u003e2\u003c/sup\u003e (15.8%).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOn the other hand, the table shows that banana plantations had the lowest land area, of 0.47 km\u003csup\u003e2\u003c/sup\u003e(0%). The statistics show that Oil palm land use had no record area.\u003c/p\u003e \u003cp\u003eThe analysis of Land use data spanning from 1986 to 2026 reveals dynamic changes in land cover within the study area. These changes have significant implications and reflect the complex interplay between environmental and anthropogenic factors. Initially, savannah grassland and woodland dominated the land cover in 1986, but subsequent years witnessed substantial shifts in their areas, indicating the sensitivity of these ecosystems to various influences. These findings align with research by Hirota et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which emphasizes the susceptibility of savannah ecosystems to environmental and human-induced changes. Moreover, the emergence of new land cover classes such as Teak/Gmelina plantation and bare surfaces underscores the impact of human activities, including afforestation initiatives and land degradation processes. These changes reflect broader trends in land use and highlight the need for sustainable land management practices, as discussed by Chazdon et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and Veldkamp \u003cem\u003eet al\u003c/em\u003e. (2001).\u003c/p\u003e \u003cp\u003eAgricultural transitions and land use intensification are evident from the significant increase in intensive arable cultivation areas over the years. This suggests shifts in agricultural practices characterized by intensification or expansion, as noted by Verburg et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the loss of certain land cover types like oil palm and banana plantations raises concerns about the underlying factors driving these changes, such as market dynamics and policy shifts impacting agricultural practices, as highlighted by Gibbs et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Carlson \u003cem\u003eet al\u003c/em\u003e. (2012).\u003c/p\u003e \u003cp\u003eForest dynamics and conservation challenges are also prominent themes in the Land use data analysis. The observed changes in forest cover, including declines in highly disturbed forests and increases in plantation forests, underscore the ongoing challenges of deforestation, forest degradation, and afforestation efforts. Addressing these challenges requires effective conservation strategies that promote sustainable land management practices, as advocated by Gaveau et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Urbanization and infrastructure development have contributed to the expansion of urban areas, leading to habitat fragmentation and loss of natural ecosystems. These trends underscore the need for integrated Land use planning approaches that balance economic development with environmental sustainability, as emphasized by Seto et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) and McDonald \u003cem\u003eet al\u003c/em\u003e. (2008).\u003c/p\u003e \u003cp\u003eThe dynamic changes in land use and land cover observed in the study area highlight the complexity of interactions between human activities, environmental processes, and policy interventions. Addressing these challenges requires interdisciplinary approaches informed by ecological, social, and economic considerations as portrayed in the conceptual framework of this study. Foley et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Lambin \u003cem\u003eet al\u003c/em\u003e. (2003) emphasize the importance of integrated Land use planning and stakeholder engagement in promoting sustainable land management practices.\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\u003eLULC Characteristics of 1986, 2006 and 2016\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS/N\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary Class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003cp\u003e(km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2006 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2016 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBanana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\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\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFallow (Scattered Cultivation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,864.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,022.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,626.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.42\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\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntensive Arable Cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,537.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11,330.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14,064.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.91\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\u003eAgriculture\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTree Crop/Secondary Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,583.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,993.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,367.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.44\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\u003eBare Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuarry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\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\u003eBare Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRock/Montane Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,400.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,692.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e191.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.29\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\u003eBuilt-up Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e890.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,944.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,877.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.25\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\u003eNatural Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHighly Disturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,611.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12,390.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,817.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.68\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\u003eNatural Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimally Disturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,130.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2,433.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1,770.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.63\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\u003eNatural Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUndisturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,104.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,272.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\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\u003ePlantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOil Palm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e456.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRubber\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTeak/Gmelina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e874.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,961.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10,625.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSavanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSavannah Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12,089.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e127.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e798.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSavanna\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSavannah Woodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10,355.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17,743.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20,577.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRiver/Lake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e129.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e93.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoastal Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e39.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarshland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e171.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSand bar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e234.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e105.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWetland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSwamp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,005.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e55.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e227.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \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\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e67,255.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e67,255.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e67,255.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the natural forest ecosystem class facing competition from agriculture and savannah ecosystems. New landcover classes, such as bare surface, also emerged. Savannah woodland, highly disturbed forest, and intensive arable cultivation were the most important classes in 2006 with 17743.97 km\u003csup\u003e2\u003c/sup\u003e, 12390.57, and 11330.40 km\u003csup\u003e2\u003c/sup\u003e (26.38%, 18.42%, and 16.85 km\u003csup\u003e2\u003c/sup\u003e) of the area, respectively. Fallow, tree crop and Teak/ Gmelina account for 6022.36 km\u003csup\u003e2\u003c/sup\u003e (8.95%), 4993.49 km\u003csup\u003e2\u003c/sup\u003e (7.42%) and 4961.81 km\u003csup\u003e2\u003c/sup\u003e (7.38%) respectively. Other ecosystem classes mapped for the area in 2006 in order of increase include Quarry 4.37 km\u003csup\u003e2\u003c/sup\u003e (0.01%), rubber plantation 9.34 km\u003csup\u003e2\u003c/sup\u003e (0.01%), coastal grassland 39.32 km\u003csup\u003e2\u003c/sup\u003e (0.06) swamp 55.21 km\u003csup\u003e2\u003c/sup\u003e (0.08%) sandbar 105.65 km\u003csup\u003e2\u003c/sup\u003e (0.16%), savanna grassland 127.16 km\u003csup\u003e2\u003c/sup\u003e (0.19%) undisturbed forest 1272.74 km\u003csup\u003e2\u003c/sup\u003e (1.89%), urban 1944.15 km\u003csup\u003e2\u003c/sup\u003e (2.89%), Minimally disturbed forest 2433.65 km\u003csup\u003e2\u003c/sup\u003e (3.62%) and rock/montane forest 3692.08 km\u003csup\u003e2\u003c/sup\u003e (5.49%). Rubber plantation land use and cover classes emerged in 2006, while oil palm was lost in an area.\u003c/p\u003e \u003cp\u003eThe distribution of land use and cover classes in the study area in 2006 provides valuable insights into the patterns of human-environment interactions and land management practices.\u003c/p\u003e \u003cp\u003eThe dominance of savannah woodland, highly disturbed forest, and intensive arable cultivation underscores the extensive conversion of natural ecosystems for agricultural purposes. These land cover classes are indicative of deforestation, land clearance, and agricultural expansion, driven by socio-economic factors such as population growth, urbanization, and agricultural intensification (Lambin \u0026amp; Geist, 2006). Fallow land, tree crops, and Teak/Gmelina plantations represent areas undergoing various stages of land use transition. Fallow land may result from shifting cultivation practices or temporary abandonment of agricultural fields, while tree crops and plantations reflect efforts to diversify agricultural production or promote agroforestry systems (Turner II \u003cem\u003eet al\u003c/em\u003e., 2007). The emergence of rubber plantations highlights shifts in land use priorities and economic activities. Rubber cultivation may be driven by market demand, government policies, or incentives for cash crop production, leading to changes in land cover and land use intensity (Mertens \u003cem\u003eet al\u003c/em\u003e., 2002). The decline in oil palm plantation area suggests changes in the dynamics of palm oil production, influenced by factors such as market trends, land availability, and environmental regulations. This trend underscores the need for monitoring and sustainable management of oil palm plantations to mitigate environmental impacts and ensure the long-term sustainability of palm oil production (Gibbs et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Overall, the distribution of land use and cover classes reflects the complex interactions between human activities and environmental processes shaping the landscape of the study area. Understanding these dynamics is essential for sustainable land use planning, conservation efforts, and natural resource management strategies.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the emergence of a Banana Plantation of 0.47 km\u003csup\u003e2\u003c/sup\u003e in the ecosystem of the study area in 2016. the savannah woodland and intensive arable cultivation submerged other landcover ecosystems and claimed an area of 20577.33 km\u003csup\u003e2\u003c/sup\u003e (30.6%) and 14064.09 km\u003csup\u003e2\u003c/sup\u003e (20.91%) of the total area respectively, Another land use and landcover ecosystem that also had a relatively high increase rate in 2016 were Teak/Gmelina and Tree crop/ secondary forest with an area of 10625.84(15.8%) km\u003csup\u003e2\u003c/sup\u003eand 8367.32 km\u003csup\u003e2\u003c/sup\u003e (12.44%) respectively.\u003c/p\u003e \u003cp\u003eThe expansion of agricultural activities, particularly intensive arable cultivation and plantation crops such as bananas, reflects shifts in land use priorities, market demands, and agricultural practices. The substantial increase in savannah woodland and intensive arable cultivation areas, accounting for 30.6% and 20.91% of the total area, respectively, underscores the extensive conversion of natural ecosystems for agricultural purposes. This expansion may be driven by factors such as population growth, urbanization, agricultural intensification, and government policies promoting agricultural development (Lambin \u0026amp; Geist, 2006). Additionally, the notable increase in Teak/Gmelina plantations and Tree crop/secondary forest areas further illustrates the diversification of agricultural production and land use intensification strategies. Plantation forestry, including Teak and Gmelina cultivation, may be promoted for timber production, agroforestry systems, or reforestation efforts, contributing to landscape transformation and ecosystem services provisioning (Mertens \u003cem\u003eet al\u003c/em\u003e., 2002). These changes in land use and land cover patterns have significant implications for biodiversity conservation, ecosystem services provision, and socio-economic development. Understanding the drivers and impacts of these changes is essential for informing land use planning, environmental management strategies, and sustainable development initiatives aimed at balancing agricultural production, conservation objectives, and socio-economic priorities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Trend of Landcover Change between 1986 and 2016\u003c/h2\u003e \u003cp\u003eThe analysis of land cover changes presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provides valuable insights into the dynamics of land use transformations in the study area over two distinct time periods: 1986 to 2006 and 2016 to 2026. These findings highlight both positive and negative changes in various land cover types, reflecting the complex interplay of natural and anthropogenic factors shaping the landscape (Lambin \u003cem\u003eet al\u003c/em\u003e., 2003).\u003c/p\u003e \u003cp\u003eDuring the period from 1986 to 2026, the study reveals substantial changes in land cover, with savannah grassland experiencing the most significant decrease in area, amounting to 11291.73 km\u0026sup2; or a reduction of 17.79%. This decline may be attributed to factors such as agricultural expansion, urbanization, and deforestation, which often result in the conversion of natural habitats to croplands or settlements (Foley et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Conversely, savannah woodland exhibited a positive change, with an increase in area by 7388.59 km\u0026sup2; or 10.00%. This could be indicative of afforestation efforts or natural regeneration processes occurring within the study area (Hirota et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHowever, not all land cover types experienced significant changes during this period. Coastal grassland, for instance, showed a minimal loss of only 8.66 km\u0026sup2; or 0.01%. Similarly, quarry and rubber plantation areas recorded negligible gains, indicating relatively stable land use patterns in these categories (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). These findings underscore the importance of considering local context and specific land cover dynamics when assessing landscape changes and their drivers. The analysis further extends to the period between 2016 and 2026, revealing continued shifts in land cover composition. Highly disturbed forests emerged as the most affected category, experiencing a substantial loss of -8573.14 km\u0026sup2; or 12.75%. This trend is concerning as it suggests ongoing deforestation and land degradation processes, likely driven by factors such as logging, agricultural expansion, and infrastructure development (Sodhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Similarly, fallow and shifting cultivation land cover recorded significant losses, highlighting the vulnerability of these areas to Land use changes and degradation (Gibbs et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, certain land cover types demonstrated notable gains during the same period. Teak and Gmelina plantation, for example, exhibited the highest increase in area, with a gain of 5664.03 km\u0026sup2; or 8.42%. This expansion could be attributed to reforestation initiatives, agroforestry practices, or commercial plantation establishment aimed at meeting timber demand (Chazdon et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Additionally, urban areas and intensive agriculture witnessed considerable growth, reflecting urbanization trends and agricultural intensification efforts in response to population growth and economic development (Lambin \u0026amp; Meyfroidt, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the findings underscore the dynamic nature of land cover changes in the study area and highlight the complex interactions between human activities and environmental processes. Conservation efforts, sustainable land management practices, and effective monitoring mechanisms are essential for mitigating the adverse impacts of land cover changes, preserving critical habitats, and promoting ecosystem resilience in the face of ongoing global environmental changes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Pattern Savannah and Forest Conversion\u003c/h2\u003e \u003cp\u003eThe analysis of land cover changes from 1986 to 2026 reveals significant transformations in various land cover categories, reflecting the complex interactions between human activities and environmental processes (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Highly disturbed forest areas witnessed a substantial decline from 12,611.37 km\u0026sup2; in 1986 to 3,817.42 km\u0026sup2; in 2026, indicative of intensive Land use changes likely driven by urbanization, industrialization, or logging activities (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sodhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConversely, undisturbed forest areas experienced a drastic reduction from 4,104.14 km\u0026sup2; to 7.13 km\u0026sup2; over the same period, highlighting severe environmental degradation and the encroachment of human activities into previously pristine forest landscapes (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This decline in undisturbed forest areas underscores the urgent need for conservation efforts to protect remaining forest habitats and preserve biodiversity (Sodhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, minimally disturbed forest areas exhibited moderate stability, suggesting relatively lower levels of human disturbance compared to highly disturbed and undisturbed forest ecosystems (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The stability in minimally disturbed forest areas may indicate the effectiveness of conservation measures or land management practices aimed at mitigating environmental impacts (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Sodhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Furthermore, savannah grassland areas remained relatively stable with a slight increase by 2016, indicating resilience to significant land cover changes observed in other ecosystems (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). This stability in savannah grassland areas may be attributed to natural ecological processes or sustainable land management practices that maintain ecosystem integrity (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOverall, the dynamic patterns in land cover changes highlight the complex dynamics of human-environment interactions and the need for integrated approaches to land management and conservation (Sodhi et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Addressing the drivers of land cover changes, such as urbanization, deforestation, and agricultural expansion, requires coordinated efforts involving policymakers, stakeholders, and local communities to promote sustainable development and biodiversity conservation (Gaveau et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\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\u003eLand cover statistics of Forest and Savannah\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLandcover\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1986 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2016 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2026 (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly Disturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12611.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12390.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3817.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimally Disturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2130.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2433.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1770.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUndisturbed Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4104.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1272.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSavannah Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12089.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e798.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSavannah Woodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10355.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17743.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20577.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe savannah woodland areas within the study region showed notable fluctuations over the three-decade period, with significant implications for land cover dynamics and environmental sustainability. The area covered by savannah woodland expanded considerably from 10,355.38 km\u0026sup2; in 1986 to 20,577.33 km\u0026sup2; in 2026. This substantial increase suggests potential afforestation efforts or changes in land management practices during the study period. These fluctuations underscore the dynamic nature of land cover changes within the study region and the complex interactions between human activities and natural ecosystems. The expansion of savannah woodland areas may be influenced by various factors, including reforestation initiatives, natural regeneration processes, or shifts in land use practices such as agricultural abandonment or land restoration efforts.\u003c/p\u003e \u003cp\u003eHowever, these trends also highlight the urgent need for conservation efforts, sustainable land management practices, and effective monitoring mechanisms to safeguard critical habitats, preserve biodiversity, and mitigate the adverse effects of human activities on the environment. While the expansion of savannah woodland areas may indicate positive changes in land cover, it is essential to ensure that these changes are sustainable and do not lead to unintended consequences such as habitat fragmentation, loss of biodiversity, or degradation of ecosystem services.\u003c/p\u003e \u003cp\u003eFurthermore, ongoing monitoring and assessment of land cover dynamics are essential to track changes over time, identify emerging threats, and inform adaptive management strategies. By implementing proactive conservation measures, promoting sustainable land use practices, and engaging stakeholders in participatory decision-making processes, it is possible to achieve a balance between human development and environmental conservation objectives.\u003c/p\u003e \u003cp\u003eOverall, the fluctuations observed in savannah woodland areas underscore the dynamic nature of land cover changes and the importance of proactive conservation efforts to maintain ecological integrity, promote biodiversity conservation, and enhance the resilience of ecosystems in the face of environmental change.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the statistical overview of forest and savannah areas within the study region. In 1986, the highly disturbed forest covered an extensive area of 12,611.37 km\u003csup\u003e2\u003c/sup\u003e. However, by 2026, this area had undergone significant reduction, shrinking to 3,817.42 km\u003csup\u003e2\u003c/sup\u003e. This substantial decrease indicates a profound conversion of highly disturbed forest within the study area over the three-decade period.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides a visual representation of land cover conversions within the study area. It reveals that the savannah grassland experienced the most substantial conversion, with an area reduction of approximately 13,000 km\u003csup\u003e2\u003c/sup\u003e. Following closely behind, the highly disturbed forest underwent a conversion of about 9,000 km\u003csup\u003e2\u003c/sup\u003e. Interestingly, the figure illustrates that the lowest conversion rate for forest occurred between 1986 and 2026, suggesting a period of relatively slower forest loss during this timeframe compared to other periods.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThese findings underscore the significant changes in land cover dynamics within the study region over the past three decades. The considerable reduction in highly disturbed forest area highlights the extensive conversion of forested land for various purposes such as agriculture, urbanization, and infrastructure development. Similarly, the substantial conversion of savannah grassland underscores the pressures faced by natural ecosystems due to human activities and land use changes.\u003c/p\u003e \u003cp\u003eUnderstanding these land cover conversions is crucial for assessing the impacts on biodiversity, ecosystem services, and carbon stocks within the study area. Furthermore, these findings can inform land management strategies, conservation efforts, and policy interventions aimed at mitigating further land degradation and promoting sustainable land use practices. By monitoring land cover changes and their associated impacts, stakeholders can work towards preserving valuable ecosystems, protecting biodiversity, and mitigating the adverse effects of land cover change on the environment and society.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.4 LULC Projection model from 2026 to 2066\u003c/h2\u003e \u003cp\u003eThe land use and land cover projection of the study area for the primary classes is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and the result reveals that the projection statistics for the urban area have a progressive expansion from 3209.75 km\u003csup\u003e2\u003c/sup\u003e in 2026 to 4042.35 in 2066. On the other hand, the forest area had a sharp reduction in 2020 and might become 0 from 2022 to 2066. This result is evident since the population of the study area will continue to increase, while this increasing population depends on the forest resources for livelihood, the forest will continue to be reduced due to overexploitation, which is enhanced by weak polices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe analysis of land cover changes over the period from 1986 to 2026 reveals significant trends with profound implications for environmental health and biodiversity conservation. Firstly, there has been a notable decline in highly disturbed forest areas, amounting to 8573.14 km\u0026sup2; during this period. This decline underscores the severity of environmental degradation and Land use changes within the study area, likely influenced by factors such as urbanization, logging activities, and agricultural expansion. These findings align with research by Gaveau et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Sodhi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), highlighting the urgent need for conservation efforts and sustainable land management practices to mitigate further degradation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSimulated land use land cover from 2026 to 2066 for STsim\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrimary Land cover classes (sqkm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eTime step\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2026\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2036\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2046\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2056\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2066\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgricultural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5117.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1444.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e410.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e115.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e29.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBare Surface\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e293.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e96.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBuiltup Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33930.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41711.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43909.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44523.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e44699.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7403.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2025.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e562.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e158.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlantation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1652.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e510.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e137.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e36.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.74\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18858.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21465.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22208.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22413.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22470.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author, 2020\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilarly concerning is the drastic decline in undisturbed forest areas, shrinking from 4104.14 km\u0026sup2; to 7.13 km\u0026sup2; over the same period. This loss of pristine forest habitats poses significant threats to biodiversity and ecosystem services, emphasizing the importance of conservation strategies aimed at protecting remaining forested areas and restoring degraded landscapes. This aligns with the findings of Gibbs et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Chazdon et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), highlighting the critical need for biodiversity conservation and habitat restoration efforts.\u003c/p\u003e \u003cp\u003eOn a contrasting note, the analysis reveals substantial fluctuations in savannah woodland areas, with an increase from 10355.38 km\u0026sup2; in 1986 to 20577.33 km\u0026sup2; in 2026. These dynamic changes suggest the influence of afforestation efforts or alterations in land management practices, highlighting the potential for ecosystem restoration and reforestation initiatives to mitigate deforestation and land degradation. This resonates with the research of Hirota et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Gibbs et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), emphasizing the role of afforestation in promoting landscape resilience and biodiversity conservation.\u003c/p\u003e \u003cp\u003eThe observed land cover changes underscore the importance of conservation efforts, sustainable land management practices, and effective monitoring mechanisms to protect critical habitats and preserve biodiversity. Integrated approaches that consider ecological, social, and economic dimensions are essential for addressing the drivers of land cover change and promoting sustainable development. These findings align with the research of Foley et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Lambin \u003cem\u003eet al\u003c/em\u003e. (2011), emphasizing the need for comprehensive strategies to mitigate the adverse impacts of human activities on the environment and promote the long-term health and resilience of ecosystems.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study examined the land use and land cover dynamics in Southwestern Nigeria from 1986 to 2026, employing multi-temporal Landsat imagery downloaded from the United States Geological Survey. Remote sensing and GIS techniques in ArcGIS 10.8 were used to carry out hybrid (supervised/unsupervised) classification using Maximum Likelihood and post-classification change detection. The research revealed significant landscape transformations across the region. Results show a substantial reduction in savannah woodland and forest, and an expansion of agricultural land. The consistency of forest loss highlights increasing pressure from anthropogenic activities, particularly from deforestation and land use conversion. The study reveals the efficacy of geospatial technologies for spatio-temporal environmental monitoring and provides empirical evidence to support sustainable land management planning. To mitigate further environmental degradation and climate-related impacts, there is an urgent need for strengthened forest conservation policies, improved land use planning frameworks, afforestation and reforestation programs, and enforcement of environmental protection regulations. Sustained monitoring using remote sensing and GIS tools should remain central to policy implementation and environmental governance in Southwestern Nigeria.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e No Funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number\u003c/strong\u003e: Not Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent from Authors to Publish\u003c/strong\u003e: Samuel Kevin Udofia\u003c/p\u003e\n\u003cp\u003eAjibade Ariori\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEmmanuel Wunude\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChinwe Ugwuzor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eReviewed_Samuel_Udofia_Landuse\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is based on remote sensing analysis using satellite imagery and does not involve human participants, plant specimen collection, or field sampling. Therefore, ethical approval, consent to participate, plant collection guidelines, and herbarium documentation are not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study does not involve human participants, personal data, or identifiable images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are publicly available from the United States Geological Survey EarthExplorer repository and grid3 repository (https://earthexplorer.usgs.gov/) and (https://grid3.org). Processed data and analysis outputs are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson R, Hardy EE, Roach JT, Witmer RE. (1976). 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FoleyJ.A. (2010). Tropical forests were the primary sources of new agricultural land in the 1980s and 1990s \u003cem\u003eProc. Natl. Acad. Sci\u003c/em\u003e., 107 (2010), pp. 16732\u0026ndash;16737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGibbs HK, Brown S, Niles JO, Foley JA. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environ Res Lett. 2007;2(4):045023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHirota M, Holmgren M, Van Nes EH, Scheffer M. Global resilience of tropical forest and savanna to critical transitions. Science. 2011;334(6053):232\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoughton RA, House JI, Pongratz J, van der Werf GR, DeFries RS, Hansen MC, Le Quer\u0026acute; e\u0026acute; C, Ramankutty N. (2012) Carbon emissions from land use and Land cover change \u003cem\u003eBiogeosciences\u003c/em\u003e, 9, 5125\u0026ndash;5142, 2012 www.\u003cdiv class=\"ExternalRefDOI\"\u003ebiogeosciences.net/9/5125/2012/\u003c/div\u003e doi:10.5194/bg-9-5125-2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambin EF, Meyfroidt P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465\u0026ndash;3472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambin EF, Meyfroidt P. (2011). Global land use change, economic globalization, and the looming land scarcity. Proceedings of the National Academy of Sciences, 108(9), 3465\u0026ndash;3472.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi X, Zhou W, Ouyang Z, Xu W. Land use and Land cover change and their effects on the landscape of the Zhangye oasis, 1948\u0026ndash;1998. 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Proceedings of the National Academy of Sciences, 104(52), 20666\u0026ndash;20671.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eU.S. Department of Agriculture (USDA). Watershed Condition Classification Technical Guide. Washington, DC, USA: USDA; 2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUdo RK. Geographical regions of Nigeria. University of California Press Berkeley and Los Angeles California; 1970.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUNDRIP. (2008). Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf\u003c/span\u003e\u003cspan address=\"http://www.un.org/esa/socdev/unpfii/documents/DRIPS_en.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerburg PH, Crossman N, Ellis EC, Heinimann A, Hostert P, Mertz O, Zhen L. Land system science and sustainable development of the earth system: A global land project perspective. Anthropocene. 2019;26:100213.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVivekananda GN, Swathi R, Sujith AVLN. Multi-temporal image analysis for LULC classification and change detection. Eur J remote Sens. 2021;54(sup2):189\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliams CA, Hanan NP, Neff JC, et al. Africa and the global carbon cycle. Carbon Balance Manage. 2007;2:3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/1750-0680-2-3\u003c/span\u003e\u003cspan address=\"10.1186/1750-0680-2-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Commission on Environment and Development. (WCED, 1987). Our Common Future. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.un-documents.net/ourcommon-future.pdf\u003c/span\u003e\u003cspan address=\"http://www.un-documents.net/ourcommon-future.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 5 is available in the Supplementary Files section.\u003c/p\u003e"},{"header":"Plate","content":"\u003cp\u003ePlate 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Climate Change, Remote Sensing, Landuse, Landsat, deforestation","lastPublishedDoi":"10.21203/rs.3.rs-9449847/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9449847/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLand use and land cover (LULC) changes have a significant role in regulating micro-climate systems through the carbon cycle and ecosystem services. However, increasing anthropogenic activities, particularly deforestation and forest degradation, have significantly altered the landscape structure of southwestern Nigeria. This study examines spatio-temporal changes in land use and land cover between 1986 and 2026.\u003c/p\u003e \u003cp\u003eMulti-temporal dataset from the United States Geological Survey (USGS) (Landsat) satellite images for the years 1986, 2016, and 2026 were used. Band stacking, mosaicking, and a supervised/unsupervised classification method guided by Intergovernmental Panel on Climate Change (IPCC) standards. Maximum Likelihood (MAXLIKE) was used within ArcGIS 10.8 to carry out classification. Land use/land cover classes were adapted from the USGS classification schema. Post-classification comparison was conducted to show Change detection analysis through multi-date imagery.\u003c/p\u003e \u003cp\u003eResults revealed substantial landscape transformation over the 40 years. Savannah woodland declined by 17,743.97 km\u0026sup2;, forest cover decreased by 2,316.99 km\u0026sup2; at an annual loss rate of 35.66 km\u0026sup2;, while agricultural land expanded by 3,407.95 km\u0026sup2;. These findings indicate significant anthropogenic pressure on natural vegetation, particularly through deforestation and land conversion for agriculture. The study underscores the urgent need for sustainable land management policies and forest conservation strategies to enhance carbon sequestration capacity and mitigate the impacts of climate change in Southwestern Nigeria.\u003c/p\u003e","manuscriptTitle":"Assessment of Land Use/land Cover Dynamics and Deforestation Trends in Southwestern Nigeria Using Multi-temporal Landsat Imagery (1986–2026)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-14 12:08:07","doi":"10.21203/rs.3.rs-9449847/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"114252392208174511255867377123128125737","date":"2026-05-17T11:56:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50792264866122892351849365987893434736","date":"2026-05-13T13:51:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T13:31:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:15:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"335371879842903853629387122951791321573","date":"2026-05-12T08:13:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169440617172882733590513939203407124513","date":"2026-05-11T19:46:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"259535497456734970812893711735802119040","date":"2026-05-11T15:43:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"20460116673078444386053363483103247334","date":"2026-05-06T02:48:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-05-05T19:55:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-24T10:56:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-23T03:53:47+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Forests","date":"2026-04-23T03:49:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-forests","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Forests](https://link.springer.com/journal/44415)","snPcode":"44415","submissionUrl":"https://submission.nature.com/new-submission/44415/3","title":"Discover Forests","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40067178-31cf-4455-882a-0410d1d951f8","owner":[],"postedDate":"May 14th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"114252392208174511255867377123128125737","date":"2026-05-17T11:56:32+00:00","index":58,"fulltext":""},{"type":"reviewerAgreed","content":"50792264866122892351849365987893434736","date":"2026-05-13T13:51:02+00:00","index":57,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-13T13:31:44+00:00","index":56,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-12T10:15:24+00:00","index":54,"fulltext":""},{"type":"reviewerAgreed","content":"335371879842903853629387122951791321573","date":"2026-05-12T08:13:12+00:00","index":53,"fulltext":""},{"type":"reviewerAgreed","content":"169440617172882733590513939203407124513","date":"2026-05-11T19:46:05+00:00","index":52,"fulltext":""},{"type":"reviewerAgreed","content":"259535497456734970812893711735802119040","date":"2026-05-11T15:43:14+00:00","index":51,"fulltext":""},{"type":"reviewerAgreed","content":"20460116673078444386053363483103247334","date":"2026-05-06T02:48:26+00:00","index":32,"fulltext":""},{"type":"reviewersInvited","content":"30","date":"2026-05-05T19:55:39+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-14T12:08:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-14 12:08:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9449847","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9449847","identity":"rs-9449847","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}