Land use and land cover (LULC) are critical components of terrestrial ecosystems, influencing biodiversity, climate, and human livelihoods. Over the past century, anthropogenic pressures have dramatically altered these landscapes, leading to unprecedented rates of change that threaten ecological stability and human well-being ( Millennium Ecosystem Assessment, 2005 ; Ojima, Galvin, & Turner, 1994 ). In Zambia, particularly in Kafue National Park, these dynamics have significant implications for biodiversity conservation and resource management. The park is not only a vital ecological zone but also an area experiencing pressures from agricultural expansion, urbanization, and other human activities that pose risks to its ecological integrity. The transformation of LULC is a double-edged sword; while it can contribute to economic development and human well-being, it often results in biodiversity loss and ecosystem degradation ( Foley et al., 2005 ; Lambin & Geist, 2008 ).

Accurate LULC mapping is essential for addressing challenges such as biodiversity loss and urban sprawl. Recent studies emphasize the need for advanced techniques, like machine learning and cellular automata models, to predict and manage LULC changes effectively ( Mutale et al., 2024 ; Mutale & Qiang, 2024 ). Consequently, it is crucial to assess LULC changes and their trajectories to inform sustainable land management practices. Such assessments are essential for monitoring environmental changes and developing policies that can mitigate the impacts of climate change and promote biodiversity conservation ( Singh, 2023 ). The integration of remote sensing and Geographic Information Systems (GIS) has revolutionized the way LULC changes are studied. These technologies allow researchers to analyze landscape dynamics over extensive temporal and spatial scales ( Lambin, Geist, & Lepers, 2003 ; Li et al., 2015 ; Ojima, Galvin, & Turner, 1994 ).

The availability of high-resolution satellite imagery, such as Landsat, has enabled the creation of comprehensive databases that facilitate the monitoring of LULC changes and their implications for ecosystems ( Boyle et al., 2014 ; Majerovičová et al., 2022 ). Furthermore, methodologies, like intensity analysis and land cover change trajectory analysis, have emerged as effective tools for evaluating the complexities of LULC dynamics ( Baral, Wen, & Urriola, 2018 ; Badjana et al., 2015 ). Intensity analysis quantifies the magnitude of changes, while trajectory analysis traces historical patterns at the pixel level, offering insights into the drivers and consequences of LULC changes ( Li et al., 2015 ). In Kafue National Park, understanding LULC dynamics is critical for addressing the challenges posed by human activities such as poaching, land clearing for agriculture, and infrastructure development. These activities not only threaten the park’s biodiversity but also undermine its role in providing essential ecosystem services. As in other regions of Africa, Kafue has seen significant alterations in its landscape, driven by socio-economic factors and demographic pressures ( Awoke & Bewket, 2014 ; Lambin & Geist, 2006 ). The conversion of natural habitats into agricultural land and settlements has far-reaching consequences for both ecological stability and community livelihoods. Notably, previous studies have highlighted the dire consequences of LULC changes across Africa. Between 1975 and 2000, approximately 5% of woodlands and grasslands and 16% of natural forest cover disappeared, equating to a loss of over 50,000 km² of natural vegetation each year ( Eva et al., 2006 ). These trends are not isolated to specific regions but represent a broader pattern of land degradation that is evident in many parts of the continent. In Zambia, agricultural expansion and encroachment into protected areas exacerbate the decline of natural habitats, leading to biodiversity loss and increasing the vulnerability of ecosystems to climate change ( Ahialey et al., 2024 ). Therefore, this study aims to investigate the historical dynamics of land use and land cover in Kafue National Park from 2014 to 2024. The study adopts a synergistic approach that combines remote sensing and GIS to comprehensively analyze LULC changes and their spatial-temporal dynamics. The specific objectives include: 1) identifying and mapping the major LULC categories for the years 2014 and 2024, 2) determining the types and processes of LULC dynamics and their rates of occurrence, and 3) analyzing land change trajectories. Through these objectives, this research seeks to contribute to the existing body of knowledge on LULC dynamics and their implications for biodiversity conservation and sustainable land management. The findings will not only enhance our understanding of land use changes in Kafue National Park but will also provide insights for policymakers and land managers seeking to balance conservation efforts with the needs of local communities.

Land Use and Land Cover (LULC) dynamics are integral to understanding human–environment interactions, particularly in ecologically sensitive areas like Kafue National Park, Zambia. Over the decade between 2014 and 2024, the park’s landscape has undergone significant transformations due to a combination of human activities and natural factors. These changes have profound implications for biodiversity conservation, ecosystem stability, and sustainable land management. This discussion evaluates the study’s findings on LULC changes, focusing on classification accuracy and observed trends, while contextualizing the results with existing literature.

The LULC classification achieved an overall accuracy of 84.1%, reflecting substantial agreement between classified data and reference points, thus underscoring the reliability of the process. Despite achieving an overall classification accuracy of 83.1%, the Sparse Forest class exhibited a notably low Producer’s Accuracy (29%), indicating substantial omission errors. This suggests that a significant portion of actual sparse forest areas were misclassified, primarily as dense forest or bare land. Such confusion is common in remote sensing, especially in landscapes with gradual transitions between closed and open canopy or in areas affected by selective logging, regrowth, or fire, where spectral overlap and pixel heterogeneity often lead to misclassification ( Foody, 2002 ; Lu & Weng, 2007 ). The moderate spatial resolution (30 m) of Landsat imagery further compounds this challenge, as it may not adequately capture fine-scale heterogeneity within transitional forest zones. Although efforts were made to refine training samples, the spectral similarity among vegetation classes likely contributed to classification uncertainty. Future research could benefit from integrating higher-resolution imagery (e.g., Sentinel-2), texture analysis, or object-based classification approaches to improve the discrimination of sparse vegetation types. For this study, the classification still captures the overall trend of degradation affecting sparse forest areas, though results should be interpreted with caution in this specific class. In contrast, Wetland and Water Bodies achieved 100% User’s Accuracy, attributed to their distinct spectral signatures that enhance classification precision. However, the small sample size for Wetland (three reference points) likely inflated the accuracy metric, a limitation discussed by Pontius and Millones (2011) . Bare Land performed well with a User’s Accuracy of 96%, consistent with Fritz et al. (2011) , who emphasized the high reliability of classifying open, non-vegetated areas due to their unique spectral properties.

Change detection analysis revealed significant LULC transformations between 2014 and 2024, as depicted in maps and trend analysis. Sparse Forest and Wetland experienced drastic declines of 72.88% and 73.49%, respectively, while Bare Land expanded by 55.26%. Dense Forests demonstrated relative stability, with a slight increase of 0.23%, and Water Bodies decreased by 45.56%. Although settlements and agriculture are prohibited in national parks like Kafue due to strict preservation policies, their impacts are indirectly evident in the increased Bare Land and decreased Sparse Forest. These changes likely result from illegal activities or external pressures near park boundaries. Factors such as agricultural expansion and charcoal production by nearby communities are plausible drivers, aligning with Chidumayo (2013) , who documented similar drivers of forest loss in Zambia. Geist and Lambin (2002) similarly highlighted the role of population pressures in exacerbating forest degradation. These findings are consistent with Eva et al. (2006) , who reported widespread vegetation loss across Africa due to agricultural expansion and urbanization. To investigate the drivers of land cover change further, we conducted a proximity analysis (see Figure) using the Near Analysis tool in ArcGIS. Summary statistics showed that all land cover types lie within a narrow range, between 896.85 m and 1285.75 m from major roads. Water bodies were the closest (896.85 m), followed by dense forest (1054.80 m), bare land (1062.62 m), wetlands (1166.46 m), and sparse forest (1285.75 m). Although the sparse forest had the greatest mean distance, the margin is relatively small (less than 400 m), suggesting that all LULC classes are situated within zones of potential human influence. The presence of degraded classes such as bare land and reduced sparse forest close to roads supports the assertion that infrastructure increases exposure to logging, burning, and informal agricultural expansion. Moreover, sparse forests, being more open and easier to clear, may be particularly targeted in these fringe zones. These findings underscore the importance of accessibility in driving LULC changes within protected areas and align with global observations that road networks, including unofficial routes, act as catalysts for deforestation and land degradation in protected and indigenous areas ( da Silva et al., 2023 ). The decline in Wetlands is particularly alarming, with their area decreasing from 48747.44 hectares in 2014 to 12923.54 hectares in 2024, representing a 73.49% reduction. Wetlands are critical for biodiversity, water filtration, and carbon storage. The change detection analysis indicates that Wetlands were predominantly replaced by Bare Land and Sparse Forest. This reduction aligns with Davidson’s estimate that over 64% of global wetlands have been lost since 1900 ( Davidson, 2014 ). Factors such as agricultural drainage, sedimentation, and water abstraction likely contributed to wetland loss in Kafue. Wulder et al. (2008) and Gao (1996) emphasized that wetlands are especially vulnerable during dry seasons when lower water levels heighten their susceptibility to misclassification and degradation. Additionally, climate change-induced alterations in rainfall patterns and evaporation rates, as discussed by the Intergovernmental Panel on Climate Change (IPCC) (2014) , may have compounded these losses.

The expansion of Bare Land reflects intensified human activity, including land clearing for cultivation and infrastructure development. Similar trends were observed by Awoke and Bewket (2014) in Ethiopia, where population growth and agricultural expansion drove land degradation. In Kafue, bushfires, often set for agricultural purposes or poaching, may further contribute to the expansion of degraded landscapes. Chidumayo and Gumbo (2010) noted that fire plays a significant role in transforming forested areas into Bare Land, a dynamic likely at play in this region.

The slight increase in Dense Forests contrasts with regional deforestation trends reported by Hansen et al. (2013) , who documented extensive forest loss in tropical regions. This anomaly may reflect localized factors such as effective park management, natural regeneration, or reduced human activity in certain forested areas. Rozario et al. (2017) observed similar patterns of forest stability in protected areas, attributing these trends to conservation efforts that mitigate large-scale deforestation. However, localized degradation within Dense Forests may not have been fully captured due to the spatial resolution of Landsat imagery, a limitation highlighted by Zhang and Lu (2008) . The reduction in Water Bodies, by 45.56%, from 26,868.81 hectares in 2014 to 14,626.74 hectares in 2024, underscores the pressures of human activities and environmental factors on aquatic ecosystems. Upstream deforestation and land clearing likely increased sediment loads in rivers and lakes, reducing their capacity to retain water, as discussed by Kondolf et al. (2014) . Damming, water abstraction, and sedimentation may have further contributed to these losses. Pekel et al. (2016) reported similar global trends in surface water reduction, linking them to a combination of human activities and climate variability. When compared with regional and global studies, the findings reveal both consistencies and anomalies. The decline in Sparse Forests and wetlands aligns with broader patterns of vegetation loss across Africa, as reported by Lambin and Geist (2006) . However, the slight increase in Dense Forests deviates from widespread deforestation trends observed in Zambia and other tropical regions. This discrepancy underscores the importance of localized analyses to capture unique dynamics in protected areas. Similarly, the expansion of Bare Land aligns with Lambin et al. (2003) , who identified land degradation as a consequence of weak governance and socio-economic pressures. This finding contrasts with Turner et al. (2007) , who observed reforestation trends in areas experiencing rural-to-urban migration and land

The LULC dynamics in Kafue National Park between 2014 and 2024 highlight the complex interplay of human activities, environmental factors, and conservation efforts. The dramatic declines in Sparse Forests and wetlands emphasize the urgent need for targeted interventions to mitigate land degradation and biodiversity loss. The slight stability of Dense Forest and the expansion of Bare Land reflect the contrasting impacts of conservation and socio-economic pressures. These findings provide critical insights for policymakers and land managers aiming to balance biodiversity conservation with sustainable development objectives, contributing to broader efforts to address land use challenges in Zambia and beyond. Furthermore, similar studies should be carried out in other National Parks in order to identify the key drivers of forest degradation and encroachment patterns.

The authors would like to extend their gratitude to the anonymous reviewers and editors for their invaluable feedback, which will significantly improve the quality of the manuscript. Additionally, we express our appreciation to the U.S. Geological Survey (USGS), NASA, and Google Earth for providing open-source Landsat datasets and imagery crucial for the study.