Modelling the Influence of Climate Variability on Vegetation Dynamics in Northern Nigeria

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Abdussalam, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7213869/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted 12 You are reading this latest preprint version Abstract Northern Nigeria faces increasing vegetation stress due to changing climate extremes, yet the spatial and temporal dynamics of this relationship remain underexplored. This study aimed at modelling climate influence on vegetation dynamics in Northern Nigeria, focusing on detecting patterns and identifying key climatic drivers of vegetation change. The research utilized Normalized Difference Vegetation Index (NDVI) datasets from GIMMS AVHRR (1981 to 2015) and MODIS Terra (2000 to 2021). Climate data comprising daily precipitation and temperature (Tmin and Tmax) from 1980 to 2021 were obtained from the Nigerian Meteorological Agency (NiMet), covering ten synoptic stations across major ecological zones. NDVI data were harmonized and geo-referenced using ENVI and ArcGIS, while climate extremes were computed using RClimDex software following ETCCDI guidelines. Trend analysis was performed using the Mann–Kendall test and Coefficient of Variation (CV) to evaluate variability and direction. Machine learning models, Random Forest (RF) and Support Vector Regression (SVR), were applied to simulate NDVI responses to selected climate indices. Model performance was assessed using Root Mean Square Error (RMSE), R², and Mean Absolute Error (MAE). Findings revealed significant vegetation degradation in the Sudan and Guinea Savanna zones, particularly in years of high climate extremes (1984, 1994, 2020), while slight greening trends were noted in the Sahel. RF models achieved high accuracy (R² >0.83), with dry spells and heatwaves emerging as the most influential climate drivers of NDVI variability. In conclusion, climate extremes play a substantial role in shaping vegetation dynamics, underlining the need for targeted climate adaptation and sustainable land-use strategies in Northern Nigeria. Climate Variability Northern Nigeria Normalized Difference Vegetation Index (NDVI) MODIS and GIMMS NDVI Remote Sensing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Globally, vegetation is one of the most critical components of the Earth’s biosphere, playing a central role in regulating climate, maintaining biodiversity, and supporting human livelihoods (Wang et al. 2025 ). However, vegetation patterns and health are being significantly altered by climate change a trend that is accelerating with increasing anthropogenic emissions (Lin et al. 2022 ). According to the Intergovernmental Panel on Climate Change ( 2018 ), global temperatures have risen by 1.1°C since pre-industrial times, and projections indicate an increase of up to 2.7°C by the end of the century if current emission trajectories persist. These changes are already affecting the global distribution and functioning of vegetation (Qiu et al. 2023 ). For instance, shifts in vegetation zones, earlier spring green-up, increased leaf senescence, and biomass decline have been recorded in ecosystems ranging from boreal forests to tropical savannas (Yu et al. 2024 ). Between 2000 and 2020, global forest cover declined by approximately 94 million hectares an area nearly the size of Egypt (Nzabarinda et al. 2025 ). Simultaneously, satellite-derived indices such as the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) show clear evidence of declining vegetation productivity in many parts of the world, including semi-arid and arid zones (Yan et al. 2025 ). Beyond just loss, climate variability has also altered the phenological cycles and resilience of global vegetation systems (Kiribou et al. 2025 ). Studies from Asia, North America, and Europe show how increasing temperatures and erratic precipitation are influencing plant growth, species composition, and carbon sequestration rates (Saleem et al. 2024 ). These changes are not uniform across the globe; while some high-latitude regions are experiencing greening due to longer growing seasons, others particularly in the tropics and drylands are experiencing browning due to persistent droughts, heatwaves, and land-use pressures (Fuentes et al. 2025 ). The Global Climate Observing System (GCOS) and the Global Terrestrial Observing System (GTOS) consistently highlight vegetation change as one of the most observable and measurable signals of climate stress (Baba et al. 2022 ). In Africa, the continent’s ecological and socio-economic vulnerability has made it a hotspot for climate-related vegetation decline (Sintayehu 2018 ). Africa accounts for over 20% of the world’s forest loss in the past two decades, with the Sahel and Horn of Africa regions being among the worst affected (Ofoezie et al. 2022 ). According to Owusu et al. ( 2024 ) about 45% of Africa’s land area is affected by desertification, largely driven by climate-induced vegetation degradation. The continent’s vegetation systems, which include tropical rainforests, savannas, and drylands, are increasingly stressed by higher temperatures, shifting rainfall patterns, and human exploitation (Isa, Abdussalam, et al. 2023 ). The Sahel region has experienced a mean temperature increase of over 1.5°C since the 1970s and a 20–30% decline in rainfall in some areas (Ayanlade et al. 2022 ). These climatic shifts have led to a marked reduction in vegetation cover, delayed plant greening, and reduced crop and forage productivity (Okon et al. 2021 ). Remote sensing assessments indicate a substantial increase in vegetation browning trends across West and Central Africa, correlating with intensified climate variability (Estefania-Salazar and Iglesias 2025). In Nigeria, the effects of climate change on vegetation are manifest in all six geopolitical zones, but they are most acute in the north (Wakdok and Bleischwitz 2021). The country has witnessed a rise in mean annual temperatures from 26.2°C in the 1960s to about 27.8°C in recent years (Shen et al. 2022 ), alongside increased frequency of droughts and irregular rainfall (Babati et al. 2025 ). Nigeria loses an estimated 350,000 to 400,000 hectares of forest annually, placing it among the top ten countries globally for deforestation (Ahmed And Aliyu 2019). These losses have implications for climate regulation, food production, and biodiversity (Isa et al. 2023 ). Vegetation degradation is particularly severe in northern ecological zones such as the Sudan and Sahel savannas, where rainfall is highly variable, ranging from 400 mm to 800 mm annually (Mertz et al. 2012 ). These regions face both biophysical and anthropogenic pressures, including overgrazing, fuelwood collection, intensive agriculture, and unchecked urban expansion, all of which interact with climatic stressors to accelerate vegetation loss (Ofoezie et al. 2022 ). Northern Nigeria presents a compelling case study for modelling climate-vegetation interactions due to its ecological fragility and socio-economic dependence on land resources (Wakdok and Bleischwitz 2021). States such as Borno, Yobe, Zamfara, Katsina, and Sokoto have reported over 25% decline in green vegetation cover between 2000 and 2020 based on NDVI assessments from NASA MODIS data (Abdullahi et al. 2022). Oladipo et al. (2020) further revealed that NDVI anomalies correlate strongly with rainfall variability and prolonged dry spells, highlighting the critical link between climate and vegetation health in the region (Benhizia et al. 2024 ). These changes have led to a decline in agricultural yields, pasture availability, and natural regeneration, thereby contributing to food insecurity and rural poverty (Saleem et al. 2024 ). Moreover, the resulting competition for shrinking vegetation resources has exacerbated conflicts between farmers and herders, intensified internal displacement, and undermined regional security (Musa et al. 2022 ). Despite these critical challenges, most studies in Northern Nigeria have approached the issue descriptively or at coarse spatial scales (Baba et al. 2022 ; Hadi Ahmad et al. 2023; Yelwa and Usman 2017). There is limited application of predictive or spatially explicit models capable of assessing and forecasting vegetation dynamics under different climate scenarios. This study addresses this gap by applying advanced modelling techniques integrating remote sensing, geostatistical analysis, and machine learning approaches to evaluate and predict climate-driven vegetation change in Northern Nigeria. The research will utilize 20 + years of climatic and satellite vegetation data to identify spatiotemporal trends, determine climate sensitivity zones, and simulate future vegetation responses under various climate trajectories. In conclusion, modelling the influence of climate on vegetation dynamics is not just a scholarly endeavour but a strategic imperative for sustainable development, particularly in ecologically fragile regions like Northern Nigeria. With global vegetation systems under increasing threat from climate change, localized and context-specific research such as this is vital to both scientific advancement and policy relevance. Through the integration of spatial data, climate analytics, and modelling tools, this study aims to support a more informed and proactive approach to environmental governance in Nigeria and similar dryland ecosystems across the Global South. Method and Material Study area Northern Nigeria lies between latitudes 6° and 14°N and longitudes 3° and 15°E, covering approximately 718,645 km². It shares borders with Benin, Niger, Chad, and Cameroon, and is bounded to the south by Southern Nigeria (Fig. 1 ). The region features diverse topography, including highlands like the Jos Plateau and lowland plains such as the Chad Basin (Akande et al. 2017 ). The climate is tropical savanna with distinct wet (June–September) and dry (October–May) seasons. Temperatures can exceed 40°C, especially from March to May, while rainfall varies from about 1,100 mm in the lowlands to over 2,000 mm on highlands (Isa et al. 2023 ). The Harmattan wind dominates during the dry season, contributing to aridity, especially in the northernmost areas. Geologically, the region is underlain by the Precambrian Basement Complex, with sedimentary and volcanic rocks present in specific areas (Baba et al. 2022 ). Soils vary from sandy and erosion-prone Aeolian types to fertile but poorly drained alluvial and lacustrine soils. Vegetation is primarily savanna, ranging from the moist Guinea Savanna in the south to the arid Sahel in the north (Musa et al. 2022 ). The Sahel zone, bordering the Sahara, is characterized by sparse vegetation, drought-prone conditions, and subsistence farming (Abdussalam et al. 2025 ). Overall, Northern Nigeria’s natural environment is shaped by climatic extremes, geological diversity, and increasing human pressure on the land. Materials and methods The methodology adopted in this study combined remote sensing techniques, statistical analyses, and machine learning approaches to examine long-term climate-vegetation interactions across ecological zones in Nigeria. NDVI data from the GIMMS and MODIS platforms were pre-processed by converting the original scaled values to a standard range of -1 to 1 using a factor of 0.001. To reduce atmospheric noise, the Maximum Value Composite (MVC) method was applied, and bi-monthly NDVI composites were stacked using ENVI software. MODIS NDVI data were resampled for compatibility with GIMMS, and all imagery underwent geo-referencing and projection into the UTM Zone 32 coordinate system using ArcGIS 10.4. A 2% cloud cover threshold was enforced to ensure quality during image acquisition, and image subsetting was carried out to extract data specific to the study area. Meteorological data comprising daily precipitation and temperature records (Tmin and Tmax) from 1980 to 2021 were obtained from the Nigerian Meteorological Agency (NiMet), covering ten synoptic stations representative of different ecological zones (Table 1 ). Normality of the climate data was verified using the Anderson-Darling test via Xlstat, and data completeness was assessed based on the World Meteorological Organization's standard to avoid over-reliance on imputation. Extreme climate indices were computed using RClimDex software based on ETCCDI guidelines, including six temperature and six precipitation indices, selected for their relevance to regional climate variability. Vegetation dynamics were assessed by harmonizing MODIS NDVI with GIMMS through linear regression to ensure temporal continuity. The NDVI datasets were then classified into vegetation health categories using standard NDVI thresholds. To analyze the stability and variability of both climatic and vegetative conditions, the Coefficient of Variation (CV) was calculated. Trend analysis was conducted using the non-parametric Mann–Kendall test, which evaluates monotonic trends in NDVI and climate indices independent of data normality and outliers. A key component of the methodology involved the integration of machine learning techniques to model the relationship between climate extremes and vegetation dynamics. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Regression (SVR) were applied to predict NDVI responses based on selected climate indices. The models were trained and validated using 70% and 30% of the dataset respectively, with performance metrics such as Root Mean Square Error (RMSE), R-squared, and Mean Absolute Error (MAE) used to evaluate accuracy. Feature importance rankings generated by the Random Forest model helped identify which climate indices had the strongest influence on vegetation variability. These machine learning models provided deeper insights into nonlinear relationships and enhanced the predictive understanding of climate impacts on vegetation across Nigeria’s ecological gradients. Table 1 Location and Station ID of Metrological Station Used in this Study S/N STATION Ecological Zone WMO ID LATITUDE LONGITUDE ELEVATION STATE 1 Makurdi Guinea 65271 7.42 8.37 113 Benue 2 Lokoja Guinea 65243 7.48 6.44 41 Kogi 3 Abuja Guinea 65125 9.24 7.15 344 Abuja 4 Kaduna Guinea - sudan 65019 10.42 7.19 645 Kaduna 5 Yelwa Sudan 65001 10.53 4.45 244 Kebbi 6 Nguru Sahel 65064 12.53 10.28 343 Yobe 7 Kano Sudan 65046 12.03 8.32 476 Kano 8 Katsina Sudan 65028 13.01 7.41 427 Katsina 9 Maiduguri Sudan - Sahel 65082 11.51 13.05 354 Borno 10 Sokoto Sudan - Sahel 65010 12.55 5.12 351 Sokoto Results and Discussion The findings depicted in Fig. 2 offer a detailed and spatially explicit overview of vegetation conditions across the various ecological zones of Northern Nigeria. Within the tropical rainforest region, the NDVI data reveals consistently robust vegetation conditions, which is expected given the high rainfall, dense canopy cover, and relatively lower human disturbance in some parts of this zone. The favorable vegetation status reflects the region’s ability to sustain year-round plant growth and its high primary productivity. Similarly, the Guinea Savanna also exhibits predominantly favorable vegetation conditions. This ecological zone benefits from moderate to high rainfall and fertile soils, supporting mixed tree-grass ecosystems. However, localized vegetation stress is observed in certain areas of Bauchi and Kaduna states, which may be attributed to increased land-use pressure, urban expansion, or periodic climatic stress such as dry spells or drought events. These exceptions highlight the heterogeneity within ecological zones, driven by a combination of biophysical and anthropogenic factors. In the Sudan Savanna, the vegetation condition is mostly classified as normal, indicating an average NDVI range typical for semi-arid savanna regions. However, pockets of good vegetation health are identified in parts of Niger, Kebbi, Bauchi, and Adamawa states, possibly reflecting localized improvements in rainfall, the presence of riparian zones, or effective land management practices such as agroforestry or seasonal fallowing. This variation reinforces the importance of considering microclimatic and land use diversity even within broader ecological classifications. The Sahel region, characterized by arid to semi-arid conditions, is predominantly covered by normal vegetation, suggesting a generally stable yet limited vegetation cover, typical of grassland and sparse shrub ecosystems adapted to low rainfall and high evapotranspiration. Interestingly, in the northeastern Sahel, particularly around Borno State and the Lake Chad basin, good vegetation conditions are observed. This can be attributed to seasonal flooding and irrigation associated with Lake Chad, as well as the presence of wetlands and residual moisture, which enhance vegetation greenness even in a predominantly dry landscape. The spatial trend of vegetation change over time is presented in Fig. 3 , providing insight into long-term dynamics. Figure 3 illustrates the spatial distribution of vegetation trends across Northern Nigeria over the study period from 1980 to 2021, revealing significant spatial heterogeneity across ecological zones. In the Sahel region, a substantial portion particularly areas around Sokoto, Katsina, and Borno states demonstrated a positive trend in vegetation cover. This uptrend could be attributed to factors such as intermittent favorable rainfall patterns, vegetation recovery, and possibly reforestation or natural regrowth efforts in some communities. In contrast, areas proximate to Yobe State and parts of northwestern Sokoto exhibited declines in vegetation, likely influenced by persistent droughts, land degradation, and increasing pressure from agricultural expansion or pastoral activities. The spatial dichotomy within the Sahel suggests that while some pockets benefit from ecological recovery, others continue to degrade under unsustainable practices or harsher climatic conditions. Within the Sudan ecological zone, the findings reveal a more complex vegetation trend. The northern and southern portions showed a generally positive trend, while the eastern and western parts experienced negative vegetation trends that became more pronounced with increasing distance from the region’s core. This suggests that central Sudan areas, likely benefiting from intermediate rainfall and mixed land uses, are better able to sustain or improve vegetation, while peripheral zones—possibly more exposed to climate extremes, deforestation, and overgrazing—are undergoing degradation. The Sudan region overall exhibited the most significant vegetation trends among all ecological zones, both positive and negative. This may be explained by the region's position as an ecological transition zone, where shifts in climate and land use can lead to rapid changes in vegetation. Areas such as Katsina, Zamfara, and parts of Kaduna recorded noticeable increases in vegetation, which could result from regenerative farming practices, seasonal cultivation, or improved rainfall. On the other hand, Kebbi, parts of Adamawa, and Gombe reported declining vegetation, likely due to soil erosion, farming encroachment, and growing human populations. In the Guinea ecological zone, the vegetation trend is generally positive in the central region, especially around Abuja and parts of Kaduna and Kogi states. These areas likely benefit from moderate rainfall, relatively lower population pressure, and diverse land use systems including agroforestry. However, the zone also shows a balanced distribution of both positive and negative trends, indicating localized factors at play such as urbanization, land clearance, or land tenure conflicts, which may affect vegetation differently across short distances. Only a limited number of areas in this zone showed statistically significant changes, with the southern part of the Guinea zone displaying the least significant trends, possibly due to its proximity to more stable rainforest systems or lower interannual variability. The tropical rainforest zone, as expected, exhibited a predominantly positive vegetation trend, but most of these changes were not statistically significant. This could be due to the region already being densely vegetated, where NDVI values are near saturation, making substantial interannual increases harder to detect. Additionally, small-scale forest clearance, selective logging, or climatic fluctuations may offset any gains from natural forest regeneration. The limited statistical significance may also suggest a relative stability in vegetation cover over time, despite localized disturbances. These spatial patterns are further contextualized in Fig. 4 , which presents the magnitude of spatio-temporal vegetation trends. Figure 4 presents the magnitude of vegetation trend changes across Northern Nigeria and reveals a distinct north-to-south gradient, with the highest trend magnitudes concentrated in the northern Sahelian region and a progressive decline in magnitude toward the southern ecological zones. This spatial pattern suggests that the northern regions are experiencing more intense vegetation dynamics—whether positive or negative—compared to the southern parts, which are characterized by more stable or subtle changes in vegetation cover over the study period (1980–2021). In the Sahelian zone, the magnitude of vegetation change is particularly high in the northern and northeastern areas, notably around Borno and parts of Katsina State, where values ranged between 0.0813 and 0.2834 per year. These relatively high magnitudes indicate a significant rate of change in vegetation cover, which may be linked to periodic re-greening influenced by seasonal rainfall, wetland dynamics around Lake Chad, and even human interventions such as afforestation or irrigation in dryland farming areas. However, this high magnitude also suggests that vegetation in this zone is highly sensitive to climatic and anthropogenic fluctuations, which may lead to either rapid recovery or degradation, depending on the prevailing conditions. Conversely, lower magnitudes were recorded in the south-eastern part of the Sahel, particularly around Yobe and parts of Bauchi State. This could indicate vegetation stagnation or reduced responsiveness, potentially due to land degradation, overgrazing, or persistent droughts, which limit both vegetative regrowth and land productivity. Such areas may have crossed ecological thresholds, making them more difficult to rehabilitate without deliberate intervention. In the Sudan ecological zone, there is a pronounced contrast in vegetation trend magnitudes. The southwestern part, particularly around Kebbi and parts of Niger State, and the eastern areas such as Adamawa, Gombe, and Bauchi, recorded relatively low magnitudes, pointing to limited or gradual changes in vegetation over time. These areas might be experiencing consistent land use pressure, such as cultivation, settlement expansion, or grazing, which creates a stable but low-growth vegetation environment. On the other hand, the central corridor of the Sudan zone, extending from north to south, displayed the highest magnitudes of change, indicating vigorous vegetation dynamics. This could be due to a mix of favorable rainfall, semi-intensive agriculture, and diverse land use systems that encourage either vegetation regrowth or alternating periods of clearance and regeneration. This finding highlights the Sudan zone as a hotspot of ecological transition, where vegetation trends are shaped by a combination of climate responsiveness and human activity. In the Guinea savanna region, the magnitude of vegetation trends appears minimal and relatively uniform, with most values falling within a narrow range of − 0.1138 to 0.0138 per year. This indicates that vegetation changes here are subtle and slow-moving, possibly due to the zone’s moderate rainfall, mixed vegetation types, and stabilized land cover, particularly around Abuja and central Nigeria. The lowest magnitudes were observed in the northeastern Guinea zone, notably around Taraba State, suggesting limited vegetation response, which may result from land use stagnation, topographic constraints, or climatic stressors that inhibit dynamic vegetation shifts. Lastly, the tropical rainforest zone showed a fairly consistent magnitude range between − 0.044 and 0.0138, with a slight increase toward the western part of the zone. These values point to relatively stable vegetation cover, which is consistent with the rainforest’s dense canopy and year-round vegetative productivity. The low magnitudes, even when trends are positive, may reflect the NDVI saturation effect, where further increases in biomass are less detectable in already densely vegetated areas. The slight increase toward the west could be associated with favorable microclimatic conditions or less intense land disturbance compared to the eastern rainforest front, which is often more populated and affected by deforestation. The temporal variability of vegetation in the Sudan Savanna region of Northern Nigeria, as depicted in Fig. 5, reveals a subtle but overall positive trend in NDVI (Normalized Difference Vegetation Index) from 1980 to 2020. This indicates a gradual improvement in vegetation greenness over time, possibly linked to periodic increases in rainfall and vegetation recovery, especially during favourable climatic conditions. However, this general trend is punctuated by several distinct periods of decline, reflecting the region’s sensitivity to climatic extremes and human land use pressures. Notable NDVI declines were observed in 1984, 1988, 1994, and 2020, with corresponding NDVI values of 0.22, 0.19, 0.20, and 0.16, respectively. These years align with well-documented drought episodes in West Africa, particularly the 1983–1984 and 1987–1988 droughts, which had profound impacts on vegetation, agriculture, and livelihoods. These declines suggest that vegetation in the Sudan Savanna is highly vulnerable to rainfall deficits, which lead to immediate reductions in biomass and greenness as captured by satellite-derived NDVI. On the other hand, peak NDVI values were recorded in 1980, 1996, and 2001, all exceeding 0.25, suggesting periods of vegetative recovery or above-average rainfall. The increase in NDVI during these years can be attributed to favorable climatic conditions, such as extended rainy seasons, which enhance plant growth and canopy cover. For instance, the mid-1990s marked a rebound in rainfall across parts of the Sahel and Sudan zones, likely contributing to the observed peaks in NDVI. Additional dips in NDVI in 1987, 1994, and 2019 further reflect the episodic nature of vegetative stress in the region. These fluctuations could result not only from climatic factors but also from intensifying anthropogenic pressures, including overcultivation, overgrazing, bush burning, and deforestation, which reduce vegetative ground cover and soil fertility. The sharp decline in 2020, which returned NDVI to 0.16, may also relate to recent climate variability, potentially exacerbated by global warming and localized land degradation processes. Figure 6 illustrates the temporal variation in vegetation greenness (NDVI) for the Guinea Savanna region of Northern Nigeria over the period 1980 to 2021. The long-term trend reveals a generally downward or negative trajectory, indicating a gradual decline in vegetation health and productivity over the four-decade period. This trend is concerning given the Guinea Savanna's ecological role as a transition zone between the arid northern savannas and the humid forest zones to the south, typically characterized by moderate rainfall, mixed tree-grass ecosystems, and higher agricultural productivity. The NDVI value began at 0.26 in 1980, suggesting a healthy and dense vegetation cover during this baseline year. However, a sharp decline occurred by 1983, reducing the NDVI to 0.22. This drop corresponds with widespread drought events in the early 1980s, a period that severely impacted vegetative growth across West Africa. A modest recovery followed in 1985, with the NDVI rising to 0.24, likely reflecting a rebound in rainfall or temporary land regeneration. Between 1986 and 1988, the region experienced another substantial NDVI decline, which may be attributed to recurrent droughts, deforestation, or increasing agricultural expansion and land degradation. Nonetheless, this downward trend was interrupted by a sharp increase in 1990, when NDVI rose again to 0.25, indicating a short-term recovery, possibly due to improved rainfall or the effect of fallow land regaining biomass. The period from 1990 to 1994 was marked by a gradual NDVI decline, reaching a low of 0.20, suggesting the return of unfavorable environmental conditions or intensified human activity, such as bush burning, logging, and farming. A surprising spike in NDVI occurred in 1995, where it jumped back to 0.26—one of the highest values in the dataset—indicating a brief regreening phase, potentially influenced by favorable seasonal rains or lower anthropogenic pressure. However, this recovery was short-lived, as NDVI declined steadily from 1995 to 2000, dropping to 0.23. This pattern aligns with land use intensification and soil nutrient depletion often reported during this era in northern Nigeria. A temporary recovery occurred again in 2001, as NDVI increased rapidly to 0.26, suggesting an anomaly or perhaps another good rainy season. This was followed by a gentle decline, reaching 0.24 by 2007, indicating moderate vegetation loss. From 2011 to 2014, the region underwent a steep decline, and the NDVI dropped to 0.24, followed by an even sharper decline to 0.16 in 2019—the lowest recorded NDVI value during the study period. This sharp drop may be attributed to a combination of increasing climate extremes, such as prolonged dry spells, and intensified land use activities, particularly charcoal production, overgrazing, and land clearing for agriculture. It also corresponds to increased reports of land degradation and ecological stress in the Middle Belt of Nigeria. A short-lived rebound was observed in 2020, with the NDVI rising to 0.22, possibly due to favorable rains or temporary reductions in land disturbance. However, this was again reversed in 2021, when NDVI fell sharply to 0.16, reaffirming the persistent downward pressure on vegetation health in the Guinea Savanna. The temporal variability of vegetation in the Sahel Savanna region of Northern Nigeria is presented in Fig. 7. Figure 7 illustrates the temporal variability of NDVI in the Sahel Savanna region of Northern Nigeria over the period 1980 to 2021. The analysis shows that NDVI values fluctuated significantly, reflecting the complex dynamics of vegetation cover and environmental stressors in the region. Initially, NDVI values dropped sharply from 0.26 in 1980 to 0.16 by 1984, indicating a substantial reduction in vegetation cover likely associated with prolonged droughts, reduced rainfall, or intensified land degradation. This was followed by a modest recovery, with NDVI climbing to 0.19 in 1985, suggesting a temporary improvement in vegetation health, possibly due to favorable rainfall conditions or vegetation regrowth in localized areas. However, this recovery was short-lived as another steep decline brought NDVI down to 0.14 in 1987, marking one of the lowest vegetation indices during the period. By 1990, vegetation had rebounded to 0.20, illustrating the region’s resilience and capacity for recovery, albeit vulnerable to climatic fluctuations. Yet again, this was followed by a downward trend reaching 0.14 in 1994, underscoring the recurring cycles of vegetation loss. From 1994 to 1995, the NDVI surged to 0.22, indicating a brief period of vegetation regeneration, which was soon followed by a gradual decline to 0.18 by the year 2000. A sharp increase occurred in 2001, reaching 0.22, possibly due to improved moisture availability or successful adaptation practices such as rainfed agriculture or natural vegetation regrowth. Subsequent years witnessed a mild fluctuation in vegetation condition—NDVI declined to 0.20 in 2006, increased to 0.21 in 2010, then dipped to 0.19 in 2013 before climbing back to 0.21 in 2014. This cyclical pattern of decline and recovery continued until 2020, where NDVI reached its lowest recorded level, signalling possible intensification of environmental degradation, desert encroachment, or human-induced stress such as overgrazing and deforestation. Despite these fluctuations, the overall NDVI trend in the Sahel over the 41-year period remains relatively stable, with no strong long-term positive or negative direction. This neutral trend indicates that while vegetation loss events are frequent, the region also retains a capacity for natural regeneration under favorable conditions. The temporal variability of vegetation in the Tropical region of Northern Nigeria is presented in Fig. 8 , which continues the investigation into how NDVI trends reflect ecological dynamics across various climatic zones in the region The Tropical Rainforest region demonstrated a generally increasing trend in NDVI (Normalized Difference Vegetation Index) from 1980 to 2020, as illustrated in Fig. 8 . The NDVI initially declined steeply from 0.26 in 1980 to 0.22 in 1983, reflecting a reduction in vegetation health or cover, possibly due to environmental stress or anthropogenic interference. However, this was followed by a steady recovery, with NDVI rising to its peak of 0.26 in 1986, suggesting a period of favorable climatic conditions or successful natural regeneration. A sharp decline followed, bringing the NDVI down to 0.20 in 1988, possibly indicating a short-term climatic event or increased human pressure such as deforestation. The index rebounded to 0.25 in 1990, but then experienced a rapid drop to 0.22 in 1991. By 1993, NDVI had recovered to 0.24, though it again declined sharply back to 0.22, showing signs of instability and fluctuation in vegetation conditions.The highest NDVI value during the study period was recorded in 1995, reaching 0.27 following a steep rise from the previous year. Afterward, a gentle decline persisted until 2000, with NDVI reaching 0.24. This was followed by a brief increase to 0.26 in 2001, and then a slight decline to 0.25 in 2006. The index showed a gentle upward movement again, peaking at 0.26 in 2010, before experiencing a dip to 0.24 in 2013, followed by a rise to 0.26 in 2014. However, a significant decline was observed thereafter, with NDVI falling to 0.20 in 2020, marking one of the lowest points of the recent decades.This pattern reveals a cyclical nature of vegetation change in the tropical rainforest zone, characterized by alternating periods of vegetation gain and decline, with a general trend of vegetation loss becoming more apparent in the later years of the study. To further investigate the role of climate variability, a Long Short-Term Memory (LSTM) data-driven algorithm was employed to assess the effect of climate extreme indices on vegetation dynamics in Northern Nigeria. The outcomes of this model analysis are presented in Table 2 , as well as Figs. 9 and 10 , offering deeper insight into how extreme climatic events such as droughts, temperature anomalies, and rainfall variability influence NDVI trends over time. Table 2 Comparisons of Different Time Model to Assessed the Climate Extreme Indices on Vegetation Model Training Testing Time Taken R-Squared MSE RMSE R-Squared MSE RMSE 1 Month Lag 0.830 0.08 0.28 0.828 0.04 0.21 0.19 3 Month Lag 0.794 0.9 0.95 0.639 0.9 0.95 0.004 6 Month lag 0.846 0.03 0.18 0.818 0.09 0.31 0.06 9 Month Lag 0.837 0.19 0.44 0.696 0.73 0.85 0.01 12 Month Lag 0.846 0.01 0.1 0.837 0.01 0.1 0.13 24 Month Lag 0.818 0.55 0.74 0.671 0.85 0.92 0.014 Six Long Short-Term Memory (LSTM) models were developed using different time lags to assess the impact of climate extreme indices on vegetation dynamics. These models incorporated 1-month, 3-month, 6-month, 9-month, and 12-month lag intervals. The performance of each model was evaluated based on three statistical metrics: the coefficient of determination (R²), mean squared error (MSE), and root mean squared error (RMSE) for both training and testing datasets, as presented in Table 1 . The results revealed that the 6-month and 12-month lag models achieved the highest coefficients of determination for the training phase, with R² values greater than 0.84, indicating strong predictive performance. These were followed closely by the 9-month lag model (R² >0.83) and the 1-month lag model (R² = 0.83). The 3-month lag model demonstrated the least predictive strength, with R² >0.70, suggesting a weaker correlation between climate indices and vegetation changes for shorter lag periods. In the testing phase, the 12-month lag model again outperformed the others, achieving the highest R² value (> 0.83). It was followed by the 1-month lag model (R² >0.82) and the 6-month lag model (R² >0.81), all of which maintained good generalization capabilities. The consistency of the 12-month lag model across both training and testing phases highlights its robustness in capturing the delayed response of vegetation to climatic variability. Regarding error metrics, the lowest RMSE values during the training phase were observed in the 12-month (0.10), 6-month (0.18), and 1-month (0.28) lag models. Similarly, these three models also exhibited the least RMSE values during testing, affirming their superior accuracy and stability. A lower RMSE value indicates that the model's predictions closely match the observed NDVI values, reflecting greater reliability in vegetation trend simulation. Based on these performance metrics, the 12-month, 1-month, and 6-month lag models were identified as the most suitable models for determining the impact of climate extreme indices on vegetation cover in Northern Nigeria. Among these, the 12-month lag model was considered the best-performing, as it consistently produced the highest accuracy and lowest error in both training and testing datasets. As a result, the 12-month lag model was selected for further validation. It was subsequently compared with the observed NDVI dataset using interannual data across the entire study period. This comparison is illustrated in Fig. 9 , which provides insights into how well the model captures long-term vegetation dynamics in relation to climatic extremes. Figure 9 reveals that the 12-month lag model achieved a coefficient of determination (R²) of 0.84. This indicates that the model's predicted NDVI values were able to explain 84% of the variance in the observed NDVI, signifying a strong agreement between the simulated and actual vegetation conditions across Northern Nigeria. The high R² value underscores the model’s effectiveness in capturing the temporal variability of vegetation dynamics in response to climate extreme indices. Specifically, it suggests that vegetation in the study area responds to climatic factors with a delayed effect of approximately one year, and that incorporating this lag provides a more accurate and meaningful interpretation of vegetation changes over time. This strong performance confirms that the 12-month lag model is a reliable tool for modelling the impact of climate extremes—such as drought, heatwaves, and rainfall variability—on vegetation cover in the region. It also validates the suitability of this model for use in climate adaptation planning, early warning systems, and ecological forecasting. To further understand the relative contribution of each climate extreme index in driving vegetation changes, the Shapley Additive Explanations (SHAP) algorithm was applied. SHAP is a robust interpretability tool derived from game theory, used to assign importance scores to each input feature (in this case, climate indices) based on their contribution to the model’s output. The results of the SHAP analysis are presented in Fig. 10 , which illustrates the influence of individual climate indices—such as temperature extremes, precipitation anomalies, and drought indices—on vegetation dynamics across Northern Nigeria. This allows for a clearer understanding of which climatic variables have the most substantial effect on vegetation and helps guide targeted interventions for climate resilience and sustainable land management. The overall ranking of the contribution (impact) of each climate extreme index on vegetation dynamics in Northern Nigeria, as revealed in the SHAP analysis, indicates that total precipitation (PRCPTOT) has the highest influence on vegetation variation in the region. This is followed by the maximum 5-day rainfall (RX5DAY), which also shows a strong impact. The minimum temperature (TXN) comes next in importance, while the maximum temperature (TXX) was found to have the least influence on vegetation dynamics. This result highlights the dominant role of precipitation in determining vegetation health and productivity in Northern Nigeria. Taken together, these findings reinforce the conclusion that the climatic conditions of Northern Nigeria, particularly precipitation levels, play a crucial role in the management and sustainability of local vegetation resources. They also underscore the importance of incorporating climate information into land use planning and environmental management, especially in regions prone to climate extremes and ecological fragility. Discussion The analysis of vegetation dynamics across Northern Nigeria revealed significant spatial and temporal variability in NDVI trends between 1980 and 2021, driven largely by climate extremes and ecological differences among the Sahel, Sudan, Guinea savanna, and tropical rainforest zones. The Sahel region, despite being ecologically fragile, showed a relatively stable but fluctuating NDVI trend, with frequent dips and recoveries. This pattern reflects the region's sensitivity to moisture availability, which is often erratic and highly seasonal. The fluctuations in NDVI suggest that vegetation cover in the Sahel is quickly responsive to changes in rainfall, but equally susceptible to rapid decline in periods of drought or heatwaves. These findings are consistent with Fabeku et al. ( 2018 ), who noted a gradual NDVI decline in the Sahel due to reduced rainfall, and Ayanlade et al. ( 2021 ), who emphasized the role of rainfall in enabling vegetation recovery in dry regions. The observed cyclicity may also be explained by the region’s dominant herbaceous vegetation types, which respond quickly to precipitation but have low drought resistance, hence the abrupt shifts in greenness over time. In the Sudan and Guinea savanna zones, more complex trends emerged. The Sudan region displayed a generally positive NDVI trend in its core, while its eastern and western edges experienced negative trends. This spatial contrast could be attributed to varying land use patterns: the core areas are often used for seasonal farming, which may promote vegetation during the rainy season, while the peripheries are subjected to greater degradation from overgrazing, bush burning, or urban expansion. In contrast, the Guinea savanna region exhibited a predominantly declining trend, especially in the last two decades. This decline may be driven by intensified agricultural activities, deforestation, and expansion of human settlements, as the zone is known for its high population density and agricultural potential. These findings align with Umuhoza et al. ( 2025 ), who reported vegetation losses across African savannas, and Fokeng and Fogwe (2022), who attributed negative NDVI trends in East Africa to anthropogenic pressures and unsustainable land use. The results suggest that human influence is a stronger driver in the Guinea savanna than in the drier Sahelian zones, where natural climatic factors dominate. The tropical rainforest zone showed a generally positive NDVI trend until the mid-2010s, followed by a sharp decline toward 2020. This pattern is likely due to early conservation success or climatic favorability, followed by escalated land degradation and deforestation in recent years. The decline in vegetation after 2014 could also reflect intensifying temperature extremes and shifts in rainfall distribution, which are known to affect rainforest ecosystems severely. Idris et al. ( 2022 ) similarly reported unsustainable vegetation trends in tropical rainforests of East Africa, attributing the changes to both climatic and non-climatic factors. The application of the Long Short-Term Memory (LSTM) models enabled a deeper understanding of how vegetation responds over time to lagged climate conditions. The 12-month lag model, which performed best with an R² of 0.84, indicates that vegetation does not respond instantaneously to climate extremes, but rather accumulates the effects of precipitation and temperature over time. This delayed response can be explained by the physiological and hydrological processes that govern vegetation growth: soil moisture recharge, seed germination, and canopy development often depend on cumulative rainfall and temperature exposure over months, not days. This is consistent with Kalisa et al., ( 2019 ), who highlighted nonlinear and lagged responses of NDVI to climate drivers. The poor performance of shorter lag models, such as the 1-month and 3-month lag, further emphasizes that short-term weather anomalies are insufficient predictors of long-term vegetation dynamics in this region. The use of the Shapley Additive Explanations (SHAP) provided insight into the relative importance of each climate variable. The analysis revealed that total precipitation (PRCPTOT) had the strongest impact on vegetation dynamics, followed by RX5DAY (maximum 5-day rainfall) and TXN (minimum temperature), while TXX (maximum temperature) had the least influence. This order of importance highlights that water availability, rather than thermal stress, is the primary driver of vegetation variability in Northern Nigeria. The dominance of precipitation aligns with findings by Ebenezer ( 2015 ), who noted that low rainfall increases vulnerability to desertification in arid zones. Adepoju, et al., (2019), and Akpan et al., ( 2019 ) also observed that although temperature plays a role in vegetation growth, its influence is relatively weak compared to precipitation, particularly in dryland ecosystems. Meanwhile, Adeyeri et al. ( 2024 ) confirmed that higher temperatures can exacerbate evaporation, reducing available moisture and thereby constraining vegetation productivity. Interestingly, the relatively low impact of TXX (maximum temperature) may be due to the adaptation of native vegetation species to withstand periodic heat extremes. In contrast, minimum temperature (TXN) was more relevant, possibly because cooler nights support plant physiological processes such as respiration and water conservation. The limited role of maximum temperature in vegetation dynamics may also support findings by Van Leeuwen et al. (2011), who observed that vegetation loss often leads to increased surface temperatures and wider diurnal temperature ranges, but this is often a consequence rather than a cause of vegetation change. Conclusion This study concludes that vegetation condition was found to be generally robust within the tropical rainforest and Guinea savanna, especially in central areas like Abuja, Kogi, and southern Kaduna, where positive NDVI trends were dominant. However, this region also exhibited minimal significant trends, suggesting that while vegetation health remains strong, it may not be improving substantially. In contrast, the Sudan savanna zone exhibited a higher concentration of significant positive trends, particularly in areas such as Katsina, Zamfara, and central Kaduna, indicating an upsurge in vegetation cover over the study period. However, negative trends in regions like Kebbi, Adamawa, and Gombe suggest localized degradation likely due to land-use change and climate variability. The Sahel region, which initially showed predominantly normal vegetation conditions, demonstrated the most pronounced and consistent positive trend in NDVI over time, especially in the eastern part of Borno and northern Katsina. Nevertheless, parts of Yobe and northwestern Sokoto revealed a persistent vegetation decline, indicating regional heterogeneity. Temporal trend analysis shows periods of sharp vegetation decline across all regions, notably in 1984, 1994, and 2020, years historically associated with drought events and climatic extremes in West Africa. The NDVI values in the Sahel declined to 0.14 in 1987 and 1994, which were years of notable dry spells. Conversely, peak NDVI years like 1995 and 2001 align with periods of vegetation recovery, supported by improved rainfall and possibly greening initiatives. The magnitude of vegetation trends further solidifies the spatial disparity—Sahelian regions recorded the highest positive magnitude values (0.0813–0.2834/year) in northeastern Borno and northern Katsina, while southwestern Sudan and northeastern Guinea zones showed the lowest magnitudes, revealing vulnerability to degradation. Modelling the impact of climate extreme indices using Long Short-Term Memory (LSTM) revealed that the 12-month lag model offered the highest predictive accuracy (R² >0.83, lowest RMSE of 0.10), indicating that long-term climate memory significantly influences vegetation dynamics in the region. The performance of the 6-month and 1-month lag models also suggests that vegetation responds not only to long-term but also to recent climatic events. Overall, the study concludes that vegetation dynamics in Northern Nigeria are significantly modulated by ecological zone characteristics, climatic variability, and human activities. The increasing trend in vegetation in some parts, especially the Sahel, reflects a possible greening effect or vegetation resilience, while the persistent degradation in others, notably in parts of the Guinea and Sudan zones, points to the urgent need for targeted land management and climate adaptation strategies. Declarations Declaration of interest The authors have declared that there is no conflict of interest. Clinical Trial Number Not Applicable Ethics and Consent to Participate The authors confirm that ethics approval and consent to participate are not applicable to this study. Consent to Publish The authors confirm that consent to publish is not applicable to this study. Funding Statement This research received no grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution BBM conceived the study, developed the methodology, led the original drafting, supervised the research process, and handled the final manuscript editing.ZI and AFA contributed to data analysis, literature review, and critical revision of the manuscript.SUB participated in manuscript review and editing.AAD was responsible for data visualization and the development of graphical illustrations.AHB contributed to the methodology and was in charge of data curation and preprocessing.All authors reviewed and approved the final version of the manuscript. Data Availability The authors confirm that the data supporting the findings of the study are available within the article. 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Cite Share Download PDF Status: Published Journal Publication published 13 Apr, 2026 Read the published version in Theoretical and Applied Climatology → Version 1 posted Editorial decision: Revision requested 14 Feb, 2026 Reviews received at journal 14 Feb, 2026 Reviews received at journal 13 Feb, 2026 Reviewers agreed at journal 12 Feb, 2026 Reviewers agreed at journal 11 Feb, 2026 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 13 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Editor assigned by journal 27 Jul, 2025 Submission checks completed at journal 27 Jul, 2025 First submitted to journal 25 Jul, 2025 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7213869","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":499568971,"identity":"feaf0a96-d939-4bd5-962d-b5d4f15c7321","order_by":0,"name":"Bashariya Baba Mustapha","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Bashariya","middleName":"Baba","lastName":"Mustapha","suffix":""},{"id":499568972,"identity":"1ce6ee96-0450-44c6-9b38-312369fec06e","order_by":1,"name":"Zaharaddeen Isa","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Zaharaddeen","middleName":"","lastName":"Isa","suffix":""},{"id":499568973,"identity":"b7a40331-475a-492c-9158-cc50a18e9701","order_by":2,"name":"Auwal F. Abdussalam","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Auwal","middleName":"F.","lastName":"Abdussalam","suffix":""},{"id":499568974,"identity":"70fb85e1-24b9-47e9-be03-6b13395abd0c","order_by":3,"name":"Saadatu Umaru Baba","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Saadatu","middleName":"Umaru","lastName":"Baba","suffix":""},{"id":499568975,"identity":"70cb7894-0de8-46c7-9ba7-74dd60cb0d8d","order_by":4,"name":"Abdul-hadi Aminu Dabo","email":"","orcid":"","institution":"Kaduna State University","correspondingAuthor":false,"prefix":"","firstName":"Abdul-hadi","middleName":"Aminu","lastName":"Dabo","suffix":""},{"id":499568976,"identity":"cb155bc1-9b4d-45db-aa28-edfdbcacb218","order_by":5,"name":"Abu-hanifa Babati","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABGUlEQVRIiWNgGAWjYBACewYeIGnAkMAgAaQTbBjkQKIHHuDRYtgA1HIAriWNwRisJQGPFoMDIC0MUC0MaQyJDSAanxbD9rMHP38osMszuN387MGDBLv0+WGHHwJtsZPTbcDhF568ZIkDBsnFBneOmRskJCTnbrydZgDUkmxsdgCXX3IMgFqYEzfcSDCTSPzBnLtxdgJIy4HEbTi0GJx/Y/zjgEE9UEv6N4mEhPp0w9npH/BruZFjBrTlMFALkJGQcDhBXjoHvy2GM96lWZwxOF4seSOnDKjluOEG6ZyCAwkGuP1iz597+EbFn+o8vhvp2yR/JFTLy89O3/zhQ4WdHC4tWJwKVmlArHIQkG8gRfUoGAWjYBSMBAAA3+xrHUKZIJgAAAAASUVORK5CYII=","orcid":"","institution":"Kaduna State University","correspondingAuthor":true,"prefix":"","firstName":"Abu-hanifa","middleName":"","lastName":"Babati","suffix":""}],"badges":[],"createdAt":"2025-07-25 11:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7213869/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7213869/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00704-026-06244-5","type":"published","date":"2026-04-13T15:57:44+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88971616,"identity":"047b6c74-5e0d-4ace-8fe3-25f17e2b14d4","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":294917,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the study area\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource: Modified map of Nigeria from DIVA-GIS (2021)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/1e479cb27c4955de1f3fd697.jpeg"},{"id":88971620,"identity":"51251de8-e5c8-4959-af9f-955ff9d88471","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":326478,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVegetation Cover Condition in Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/e52ce335440ed30ee43fb857.jpeg"},{"id":88971622,"identity":"5a34656d-1795-4904-8331-59792664ef14","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":365070,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial Trend of Vegetation in Northern Nigeria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Red star indicates areas with significant trend of vegetation\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/60dbc35577e54c596308623c.png"},{"id":88973589,"identity":"84058078-1d14-473f-be0c-f9e1b4d1ea81","added_by":"auto","created_at":"2025-08-13 09:59:32","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":458871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMagnitude of Spatio - Temporal Trend of Vegetation in Northern Nigeria\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/fcb52d896c0891c73d3f527e.jpeg"},{"id":88971617,"identity":"21249b39-bc70-4ff3-a94c-675bff57c0fd","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":17162,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Variability of Vegetation in Sudan Savanna Region of Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/eead4dc48a84746c0655c39c.png"},{"id":88974320,"identity":"5cdb6754-ae4a-41e6-84fd-07f346c3ec16","added_by":"auto","created_at":"2025-08-13 10:07:32","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":16932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Variability of Vegetation in Guinea Savanna Region of Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/9abd833f8c445964541ba6c2.png"},{"id":88972241,"identity":"fb8eaf0f-0c91-48c4-9683-9640b15f8ac6","added_by":"auto","created_at":"2025-08-13 09:51:32","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":16416,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Variability of Vegetation in Sahel Savanna Region of Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image7.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/3b56899f8c1254ca4b4ad1f1.png"},{"id":88972242,"identity":"2afa8e08-5ea6-475d-974d-bb55d0458019","added_by":"auto","created_at":"2025-08-13 09:51:32","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":16002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal Variability of Vegetation in Tropical Region of Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/8b8ff4d7ca8ac21b3a4cb73c.png"},{"id":88971623,"identity":"022d5c64-22de-479d-a0cb-e748cabba0e2","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":42733,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparisons between observed and modelled NDVI using machine learning\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image9.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/d6809cbf27467a01d45d8b8d.png"},{"id":88971625,"identity":"c5cbca45-08a9-4c49-9dc4-965c61d06d74","added_by":"auto","created_at":"2025-08-13 09:43:32","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":34637,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall Rank of Climate Extreme Indices Influenced on Vegetation Density in Northern Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image10.png","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/3abbc59d2571d3a2de453490.png"},{"id":107352669,"identity":"22f4ed9f-b11b-47b9-8d59-8d47c1c5b136","added_by":"auto","created_at":"2026-04-20 16:14:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2255520,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7213869/v1/a52c49d0-1f27-4ea9-b4c5-f9bce78156a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eModelling the Influence of Climate Variability on Vegetation Dynamics in Northern Nigeria\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlobally, vegetation is one of the most critical components of the Earth’s biosphere, playing a central role in regulating climate, maintaining biodiversity, and supporting human livelihoods (Wang et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, vegetation patterns and health are being significantly altered by climate change a trend that is accelerating with increasing anthropogenic emissions (Lin et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to the Intergovernmental Panel on Climate Change (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), global temperatures have risen by 1.1°C since pre-industrial times, and projections indicate an increase of up to 2.7°C by the end of the century if current emission trajectories persist. These changes are already affecting the global distribution and functioning of vegetation (Qiu et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, shifts in vegetation zones, earlier spring green-up, increased leaf senescence, and biomass decline have been recorded in ecosystems ranging from boreal forests to tropical savannas (Yu et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Between 2000 and 2020, global forest cover declined by approximately 94\u0026nbsp;million hectares an area nearly the size of Egypt (Nzabarinda et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Simultaneously, satellite-derived indices such as the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) show clear evidence of declining vegetation productivity in many parts of the world, including semi-arid and arid zones (Yan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond just loss, climate variability has also altered the phenological cycles and resilience of global vegetation systems (Kiribou et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Studies from Asia, North America, and Europe show how increasing temperatures and erratic precipitation are influencing plant growth, species composition, and carbon sequestration rates (Saleem et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These changes are not uniform across the globe; while some high-latitude regions are experiencing greening due to longer growing seasons, others particularly in the tropics and drylands are experiencing browning due to persistent droughts, heatwaves, and land-use pressures (Fuentes et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The Global Climate Observing System (GCOS) and the Global Terrestrial Observing System (GTOS) consistently highlight vegetation change as one of the most observable and measurable signals of climate stress (Baba et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Africa, the continent’s ecological and socio-economic vulnerability has made it a hotspot for climate-related vegetation decline (Sintayehu \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Africa accounts for over 20% of the world’s forest loss in the past two decades, with the Sahel and Horn of Africa regions being among the worst affected (Ofoezie et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). According to Owusu et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) about 45% of Africa’s land area is affected by desertification, largely driven by climate-induced vegetation degradation. The continent’s vegetation systems, which include tropical rainforests, savannas, and drylands, are increasingly stressed by higher temperatures, shifting rainfall patterns, and human exploitation (Isa, Abdussalam, et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Sahel region has experienced a mean temperature increase of over 1.5°C since the 1970s and a 20–30% decline in rainfall in some areas (Ayanlade et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These climatic shifts have led to a marked reduction in vegetation cover, delayed plant greening, and reduced crop and forage productivity (Okon et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Remote sensing assessments indicate a substantial increase in vegetation browning trends across West and Central Africa, correlating with intensified climate variability (Estefania-Salazar and Iglesias 2025).\u003c/p\u003e\u003cp\u003eIn Nigeria, the effects of climate change on vegetation are manifest in all six geopolitical zones, but they are most acute in the north (Wakdok and Bleischwitz 2021). The country has witnessed a rise in mean annual temperatures from 26.2°C in the 1960s to about 27.8°C in recent years (Shen et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), alongside increased frequency of droughts and irregular rainfall (Babati et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Nigeria loses an estimated 350,000 to 400,000 hectares of forest annually, placing it among the top ten countries globally for deforestation (Ahmed And Aliyu 2019). These losses have implications for climate regulation, food production, and biodiversity (Isa et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Vegetation degradation is particularly severe in northern ecological zones such as the Sudan and Sahel savannas, where rainfall is highly variable, ranging from 400 mm to 800 mm annually (Mertz et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). These regions face both biophysical and anthropogenic pressures, including overgrazing, fuelwood collection, intensive agriculture, and unchecked urban expansion, all of which interact with climatic stressors to accelerate vegetation loss (Ofoezie et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eNorthern Nigeria presents a compelling case study for modelling climate-vegetation interactions due to its ecological fragility and socio-economic dependence on land resources (Wakdok and Bleischwitz 2021). States such as Borno, Yobe, Zamfara, Katsina, and Sokoto have reported over 25% decline in green vegetation cover between 2000 and 2020 based on NDVI assessments from NASA MODIS data (Abdullahi et al. 2022). Oladipo et al. (2020) further revealed that NDVI anomalies correlate strongly with rainfall variability and prolonged dry spells, highlighting the critical link between climate and vegetation health in the region (Benhizia et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These changes have led to a decline in agricultural yields, pasture availability, and natural regeneration, thereby contributing to food insecurity and rural poverty (Saleem et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Moreover, the resulting competition for shrinking vegetation resources has exacerbated conflicts between farmers and herders, intensified internal displacement, and undermined regional security (Musa et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite these critical challenges, most studies in Northern Nigeria have approached the issue descriptively or at coarse spatial scales (Baba et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hadi Ahmad et al. 2023; Yelwa and Usman 2017). There is limited application of predictive or spatially explicit models capable of assessing and forecasting vegetation dynamics under different climate scenarios. This study addresses this gap by applying advanced modelling techniques integrating remote sensing, geostatistical analysis, and machine learning approaches to evaluate and predict climate-driven vegetation change in Northern Nigeria. The research will utilize 20 + years of climatic and satellite vegetation data to identify spatiotemporal trends, determine climate sensitivity zones, and simulate future vegetation responses under various climate trajectories.\u003c/p\u003e\u003cp\u003eIn conclusion, modelling the influence of climate on vegetation dynamics is not just a scholarly endeavour but a strategic imperative for sustainable development, particularly in ecologically fragile regions like Northern Nigeria. With global vegetation systems under increasing threat from climate change, localized and context-specific research such as this is vital to both scientific advancement and policy relevance. Through the integration of spatial data, climate analytics, and modelling tools, this study aims to support a more informed and proactive approach to environmental governance in Nigeria and similar dryland ecosystems across the Global South.\u003c/p\u003e"},{"header":"Method and Material","content":"\u003cp\u003e\u003cb\u003eStudy area\u003c/b\u003e\u003c/p\u003e\u003cp\u003eNorthern Nigeria lies between latitudes 6° and 14°N and longitudes 3° and 15°E, covering approximately 718,645 km². It shares borders with Benin, Niger, Chad, and Cameroon, and is bounded to the south by Southern Nigeria (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The region features diverse topography, including highlands like the Jos Plateau and lowland plains such as the Chad Basin (Akande et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The climate is tropical savanna with distinct wet (June–September) and dry (October–May) seasons. Temperatures can exceed 40°C, especially from March to May, while rainfall varies from about 1,100 mm in the lowlands to over 2,000 mm on highlands (Isa et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The Harmattan wind dominates during the dry season, contributing to aridity, especially in the northernmost areas. Geologically, the region is underlain by the Precambrian Basement Complex, with sedimentary and volcanic rocks present in specific areas (Baba et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Soils vary from sandy and erosion-prone Aeolian types to fertile but poorly drained alluvial and lacustrine soils. Vegetation is primarily savanna, ranging from the moist Guinea Savanna in the south to the arid Sahel in the north (Musa et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The Sahel zone, bordering the Sahara, is characterized by sparse vegetation, drought-prone conditions, and subsistence farming (Abdussalam et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Overall, Northern Nigeria’s natural environment is shaped by climatic extremes, geological diversity, and increasing human pressure on the land.\u003c/p\u003e\n\u003ch3\u003eMaterials and methods\u003c/h3\u003e\n\u003cp\u003eThe methodology adopted in this study combined remote sensing techniques, statistical analyses, and machine learning approaches to examine long-term climate-vegetation interactions across ecological zones in Nigeria. NDVI data from the GIMMS and MODIS platforms were pre-processed by converting the original scaled values to a standard range of -1 to 1 using a factor of 0.001. To reduce atmospheric noise, the Maximum Value Composite (MVC) method was applied, and bi-monthly NDVI composites were stacked using ENVI software. MODIS NDVI data were resampled for compatibility with GIMMS, and all imagery underwent geo-referencing and projection into the UTM Zone 32 coordinate system using ArcGIS 10.4. A 2% cloud cover threshold was enforced to ensure quality during image acquisition, and image subsetting was carried out to extract data specific to the study area.\u003c/p\u003e\u003cp\u003eMeteorological data comprising daily precipitation and temperature records (Tmin and Tmax) from 1980 to 2021 were obtained from the Nigerian Meteorological Agency (NiMet), covering ten synoptic stations representative of different ecological zones (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Normality of the climate data was verified using the Anderson-Darling test via Xlstat, and data completeness was assessed based on the World Meteorological Organization's standard to avoid over-reliance on imputation. Extreme climate indices were computed using RClimDex software based on ETCCDI guidelines, including six temperature and six precipitation indices, selected for their relevance to regional climate variability.\u003c/p\u003e\u003cp\u003eVegetation dynamics were assessed by harmonizing MODIS NDVI with GIMMS through linear regression to ensure temporal continuity. The NDVI datasets were then classified into vegetation health categories using standard NDVI thresholds. To analyze the stability and variability of both climatic and vegetative conditions, the Coefficient of Variation (CV) was calculated. Trend analysis was conducted using the non-parametric Mann–Kendall test, which evaluates monotonic trends in NDVI and climate indices independent of data normality and outliers.\u003c/p\u003e\u003cp\u003eA key component of the methodology involved the integration of machine learning techniques to model the relationship between climate extremes and vegetation dynamics. Supervised machine learning algorithms such as Random Forest (RF) and Support Vector Regression (SVR) were applied to predict NDVI responses based on selected climate indices. The models were trained and validated using 70% and 30% of the dataset respectively, with performance metrics such as Root Mean Square Error (RMSE), R-squared, and Mean Absolute Error (MAE) used to evaluate accuracy. Feature importance rankings generated by the Random Forest model helped identify which climate indices had the strongest influence on vegetation variability. These machine learning models provided deeper insights into nonlinear relationships and enhanced the predictive understanding of climate impacts on vegetation across Nigeria’s ecological gradients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\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\u003e\u003cb\u003eLocation and Station ID of Metrological Station Used in this Study\u003c/b\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\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\u003eSTATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEcological Zone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWMO ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLATITUDE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLONGITUDE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eELEVATION\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSTATE\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\u003eMakurdi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuinea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBenue\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\u003eLokoja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuinea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKogi\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\u003eAbuja\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuinea\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAbuja\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\u003eKaduna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuinea - sudan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e645\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKaduna\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\u003eYelwa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSudan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKebbi\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\u003eNguru\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSahel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65064\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e343\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYobe\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\u003eKano\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSudan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKano\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\u003eKatsina\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSudan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e7.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e427\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eKatsina\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\u003eMaiduguri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSudan - Sahel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e354\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eBorno\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\u003eSokoto\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSudan - Sahel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e65010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e351\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSokoto\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eThe findings depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e offer a detailed and spatially explicit overview of vegetation conditions across the various ecological zones of Northern Nigeria. Within the tropical rainforest region, the NDVI data reveals consistently robust vegetation conditions, which is expected given the high rainfall, dense canopy cover, and relatively lower human disturbance in some parts of this zone. The favorable vegetation status reflects the region\u0026rsquo;s ability to sustain year-round plant growth and its high primary productivity. Similarly, the Guinea Savanna also exhibits predominantly favorable vegetation conditions. This ecological zone benefits from moderate to high rainfall and fertile soils, supporting mixed tree-grass ecosystems. However, localized vegetation stress is observed in certain areas of Bauchi and Kaduna states, which may be attributed to increased land-use pressure, urban expansion, or periodic climatic stress such as dry spells or drought events. These exceptions highlight the heterogeneity within ecological zones, driven by a combination of biophysical and anthropogenic factors.\u003c/p\u003e\n\u003cp\u003eIn the Sudan Savanna, the vegetation condition is mostly classified as normal, indicating an average NDVI range typical for semi-arid savanna regions. However, pockets of good vegetation health are identified in parts of Niger, Kebbi, Bauchi, and Adamawa states, possibly reflecting localized improvements in rainfall, the presence of riparian zones, or effective land management practices such as agroforestry or seasonal fallowing. This variation reinforces the importance of considering microclimatic and land use diversity even within broader ecological classifications. The Sahel region, characterized by arid to semi-arid conditions, is predominantly covered by normal vegetation, suggesting a generally stable yet limited vegetation cover, typical of grassland and sparse shrub ecosystems adapted to low rainfall and high evapotranspiration. Interestingly, in the northeastern Sahel, particularly around Borno State and the Lake Chad basin, good vegetation conditions are observed. This can be attributed to seasonal flooding and irrigation associated with Lake Chad, as well as the presence of wetlands and residual moisture, which enhance vegetation greenness even in a predominantly dry landscape.\u003c/p\u003e\n\u003cp\u003eThe spatial trend of vegetation change over time is presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, providing insight into long-term dynamics.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates the spatial distribution of vegetation trends across Northern Nigeria over the study period from 1980 to 2021, revealing significant spatial heterogeneity across ecological zones. In the Sahel region, a substantial portion particularly areas around Sokoto, Katsina, and Borno states demonstrated a positive trend in vegetation cover. This uptrend could be attributed to factors such as intermittent favorable rainfall patterns, vegetation recovery, and possibly reforestation or natural regrowth efforts in some communities.\u003c/p\u003e\n\u003cp\u003eIn contrast, areas proximate to Yobe State and parts of northwestern Sokoto exhibited declines in vegetation, likely influenced by persistent droughts, land degradation, and increasing pressure from agricultural expansion or pastoral activities. The spatial dichotomy within the Sahel suggests that while some pockets benefit from ecological recovery, others continue to degrade under unsustainable practices or harsher climatic conditions.\u003c/p\u003e\n\u003cp\u003eWithin the Sudan ecological zone, the findings reveal a more complex vegetation trend. The northern and southern portions showed a generally positive trend, while the eastern and western parts experienced negative vegetation trends that became more pronounced with increasing distance from the region\u0026rsquo;s core. This suggests that central Sudan areas, likely benefiting from intermediate rainfall and mixed land uses, are better able to sustain or improve vegetation, while peripheral zones\u0026mdash;possibly more exposed to climate extremes, deforestation, and overgrazing\u0026mdash;are undergoing degradation.\u003c/p\u003e\n\u003cp\u003eThe Sudan region overall exhibited the most significant vegetation trends among all ecological zones, both positive and negative. This may be explained by the region\u0026apos;s position as an ecological transition zone, where shifts in climate and land use can lead to rapid changes in vegetation. Areas such as Katsina, Zamfara, and parts of Kaduna recorded noticeable increases in vegetation, which could result from regenerative farming practices, seasonal cultivation, or improved rainfall. On the other hand, Kebbi, parts of Adamawa, and Gombe reported declining vegetation, likely due to soil erosion, farming encroachment, and growing human populations.\u003c/p\u003e\n\u003cp\u003eIn the Guinea ecological zone, the vegetation trend is generally positive in the central region, especially around Abuja and parts of Kaduna and Kogi states. These areas likely benefit from moderate rainfall, relatively lower population pressure, and diverse land use systems including agroforestry. However, the zone also shows a balanced distribution of both positive and negative trends, indicating localized factors at play such as urbanization, land clearance, or land tenure conflicts, which may affect vegetation differently across short distances. Only a limited number of areas in this zone showed statistically significant changes, with the southern part of the Guinea zone displaying the least significant trends, possibly due to its proximity to more stable rainforest systems or lower interannual variability. The tropical rainforest zone, as expected, exhibited a predominantly positive vegetation trend, but most of these changes were not statistically significant. This could be due to the region already being densely vegetated, where NDVI values are near saturation, making substantial interannual increases harder to detect. Additionally, small-scale forest clearance, selective logging, or climatic fluctuations may offset any gains from natural forest regeneration. The limited statistical significance may also suggest a relative stability in vegetation cover over time, despite localized disturbances.\u003c/p\u003e\n\u003cp\u003eThese spatial patterns are further contextualized in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, which presents the magnitude of spatio-temporal vegetation trends.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e presents the magnitude of vegetation trend changes across Northern Nigeria and reveals a distinct north-to-south gradient, with the highest trend magnitudes concentrated in the northern Sahelian region and a progressive decline in magnitude toward the southern ecological zones. This spatial pattern suggests that the northern regions are experiencing more intense vegetation dynamics\u0026mdash;whether positive or negative\u0026mdash;compared to the southern parts, which are characterized by more stable or subtle changes in vegetation cover over the study period (1980\u0026ndash;2021).\u003c/p\u003e\n\u003cp\u003eIn the Sahelian zone, the magnitude of vegetation change is particularly high in the northern and northeastern areas, notably around Borno and parts of Katsina State, where values ranged between 0.0813 and 0.2834 per year. These relatively high magnitudes indicate a significant rate of change in vegetation cover, which may be linked to periodic re-greening influenced by seasonal rainfall, wetland dynamics around Lake Chad, and even human interventions such as afforestation or irrigation in dryland farming areas. However, this high magnitude also suggests that vegetation in this zone is highly sensitive to climatic and anthropogenic fluctuations, which may lead to either rapid recovery or degradation, depending on the prevailing conditions. Conversely, lower magnitudes were recorded in the south-eastern part of the Sahel, particularly around Yobe and parts of Bauchi State. This could indicate vegetation stagnation or reduced responsiveness, potentially due to land degradation, overgrazing, or persistent droughts, which limit both vegetative regrowth and land productivity. Such areas may have crossed ecological thresholds, making them more difficult to rehabilitate without deliberate intervention.\u003c/p\u003e\n\u003cp\u003eIn the Sudan ecological zone, there is a pronounced contrast in vegetation trend magnitudes. The southwestern part, particularly around Kebbi and parts of Niger State, and the eastern areas such as Adamawa, Gombe, and Bauchi, recorded relatively low magnitudes, pointing to limited or gradual changes in vegetation over time. These areas might be experiencing consistent land use pressure, such as cultivation, settlement expansion, or grazing, which creates a stable but low-growth vegetation environment. On the other hand, the central corridor of the Sudan zone, extending from north to south, displayed the highest magnitudes of change, indicating vigorous vegetation dynamics. This could be due to a mix of favorable rainfall, semi-intensive agriculture, and diverse land use systems that encourage either vegetation regrowth or alternating periods of clearance and regeneration. This finding highlights the Sudan zone as a hotspot of ecological transition, where vegetation trends are shaped by a combination of climate responsiveness and human activity.\u003c/p\u003e\n\u003cp\u003eIn the Guinea savanna region, the magnitude of vegetation trends appears minimal and relatively uniform, with most values falling within a narrow range of \u0026minus;\u0026thinsp;0.1138 to 0.0138 per year. This indicates that vegetation changes here are subtle and slow-moving, possibly due to the zone\u0026rsquo;s moderate rainfall, mixed vegetation types, and stabilized land cover, particularly around Abuja and central Nigeria. The lowest magnitudes were observed in the northeastern Guinea zone, notably around Taraba State, suggesting limited vegetation response, which may result from land use stagnation, topographic constraints, or climatic stressors that inhibit dynamic vegetation shifts. Lastly, the tropical rainforest zone showed a fairly consistent magnitude range between \u0026minus;\u0026thinsp;0.044 and 0.0138, with a slight increase toward the western part of the zone. These values point to relatively stable vegetation cover, which is consistent with the rainforest\u0026rsquo;s dense canopy and year-round vegetative productivity. The low magnitudes, even when trends are positive, may reflect the NDVI saturation effect, where further increases in biomass are less detectable in already densely vegetated areas. The slight increase toward the west could be associated with favorable microclimatic conditions or less intense land disturbance compared to the eastern rainforest front, which is often more populated and affected by deforestation.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eThe temporal variability of vegetation in the Sudan Savanna region of Northern Nigeria, as depicted in Fig. 5, reveals a subtle but overall positive trend in NDVI (Normalized Difference Vegetation Index) from 1980 to 2020. This indicates a gradual improvement in vegetation greenness over time, possibly linked to periodic increases in rainfall and vegetation recovery, especially during favourable climatic conditions. However, this general trend is punctuated by several distinct periods of decline, reflecting the region\u0026rsquo;s sensitivity to climatic extremes and human land use pressures.\u003c/p\u003e\n\u003cp\u003eNotable NDVI declines were observed in 1984, 1988, 1994, and 2020, with corresponding NDVI values of 0.22, 0.19, 0.20, and 0.16, respectively. These years align with well-documented drought episodes in West Africa, particularly the 1983\u0026ndash;1984 and 1987\u0026ndash;1988 droughts, which had profound impacts on vegetation, agriculture, and livelihoods. These declines suggest that vegetation in the Sudan Savanna is highly vulnerable to rainfall deficits, which lead to immediate reductions in biomass and greenness as captured by satellite-derived NDVI.\u003c/p\u003e\n\u003cp\u003eOn the other hand, peak NDVI values were recorded in 1980, 1996, and 2001, all exceeding 0.25, suggesting periods of vegetative recovery or above-average rainfall. The increase in NDVI during these years can be attributed to favorable climatic conditions, such as extended rainy seasons, which enhance plant growth and canopy cover. For instance, the mid-1990s marked a rebound in rainfall across parts of the Sahel and Sudan zones, likely contributing to the observed peaks in NDVI.\u003c/p\u003e\n\u003cp\u003eAdditional dips in NDVI in 1987, 1994, and 2019 further reflect the episodic nature of vegetative stress in the region. These fluctuations could result not only from climatic factors but also from intensifying anthropogenic pressures, including overcultivation, overgrazing, bush burning, and deforestation, which reduce vegetative ground cover and soil fertility. The sharp decline in 2020, which returned NDVI to 0.16, may also relate to recent climate variability, potentially exacerbated by global warming and localized land degradation processes.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e illustrates the temporal variation in vegetation greenness (NDVI) for the Guinea Savanna region of Northern Nigeria over the period 1980 to 2021. The long-term trend reveals a generally downward or negative trajectory, indicating a gradual decline in vegetation health and productivity over the four-decade period. This trend is concerning given the Guinea Savanna\u0026apos;s ecological role as a transition zone between the arid northern savannas and the humid forest zones to the south, typically characterized by moderate rainfall, mixed tree-grass ecosystems, and higher agricultural productivity.\u003c/p\u003e\n\u003cp\u003eThe NDVI value began at 0.26 in 1980, suggesting a healthy and dense vegetation cover during this baseline year. However, a sharp decline occurred by 1983, reducing the NDVI to 0.22. This drop corresponds with widespread drought events in the early 1980s, a period that severely impacted vegetative growth across West Africa. A modest recovery followed in 1985, with the NDVI rising to 0.24, likely reflecting a rebound in rainfall or temporary land regeneration. Between 1986 and 1988, the region experienced another substantial NDVI decline, which may be attributed to recurrent droughts, deforestation, or increasing agricultural expansion and land degradation. Nonetheless, this downward trend was interrupted by a sharp increase in 1990, when NDVI rose again to 0.25, indicating a short-term recovery, possibly due to improved rainfall or the effect of fallow land regaining biomass.\u003c/p\u003e\n\u003cp\u003eThe period from 1990 to 1994 was marked by a gradual NDVI decline, reaching a low of 0.20, suggesting the return of unfavorable environmental conditions or intensified human activity, such as bush burning, logging, and farming. A surprising spike in NDVI occurred in 1995, where it jumped back to 0.26\u0026mdash;one of the highest values in the dataset\u0026mdash;indicating a brief regreening phase, potentially influenced by favorable seasonal rains or lower anthropogenic pressure. However, this recovery was short-lived, as NDVI declined steadily from 1995 to 2000, dropping to 0.23. This pattern aligns with land use intensification and soil nutrient depletion often reported during this era in northern Nigeria. A temporary recovery occurred again in 2001, as NDVI increased rapidly to 0.26, suggesting an anomaly or perhaps another good rainy season. This was followed by a gentle decline, reaching 0.24 by 2007, indicating moderate vegetation loss.\u003c/p\u003e\n\u003cp\u003eFrom 2011 to 2014, the region underwent a steep decline, and the NDVI dropped to 0.24, followed by an even sharper decline to 0.16 in 2019\u0026mdash;the lowest recorded NDVI value during the study period. This sharp drop may be attributed to a combination of increasing climate extremes, such as prolonged dry spells, and intensified land use activities, particularly charcoal production, overgrazing, and land clearing for agriculture. It also corresponds to increased reports of land degradation and ecological stress in the Middle Belt of Nigeria. A short-lived rebound was observed in 2020, with the NDVI rising to 0.22, possibly due to favorable rains or temporary reductions in land disturbance. However, this was again reversed in 2021, when NDVI fell sharply to 0.16, reaffirming the persistent downward pressure on vegetation health in the Guinea Savanna.\u003c/p\u003e\n\u003cp\u003eThe temporal variability of vegetation in the Sahel Savanna region of Northern Nigeria is presented in Fig. 7.\u003c/p\u003e\n\u003cp\u003eFigure 7 illustrates the temporal variability of NDVI in the Sahel Savanna region of Northern Nigeria over the period 1980 to 2021. The analysis shows that NDVI values fluctuated significantly, reflecting the complex dynamics of vegetation cover and environmental stressors in the region. Initially, NDVI values dropped sharply from 0.26 in 1980 to 0.16 by 1984, indicating a substantial reduction in vegetation cover likely associated with prolonged droughts, reduced rainfall, or intensified land degradation. This was followed by a modest recovery, with NDVI climbing to 0.19 in 1985, suggesting a temporary improvement in vegetation health, possibly due to favorable rainfall conditions or vegetation regrowth in localized areas.\u003c/p\u003e\n\u003cp\u003eHowever, this recovery was short-lived as another steep decline brought NDVI down to 0.14 in 1987, marking one of the lowest vegetation indices during the period. By 1990, vegetation had rebounded to 0.20, illustrating the region\u0026rsquo;s resilience and capacity for recovery, albeit vulnerable to climatic fluctuations. Yet again, this was followed by a downward trend reaching 0.14 in 1994, underscoring the recurring cycles of vegetation loss.\u003c/p\u003e\n\u003cp\u003eFrom 1994 to 1995, the NDVI surged to 0.22, indicating a brief period of vegetation regeneration, which was soon followed by a gradual decline to 0.18 by the year 2000. A sharp increase occurred in 2001, reaching 0.22, possibly due to improved moisture availability or successful adaptation practices such as rainfed agriculture or natural vegetation regrowth. Subsequent years witnessed a mild fluctuation in vegetation condition\u0026mdash;NDVI declined to 0.20 in 2006, increased to 0.21 in 2010, then dipped to 0.19 in 2013 before climbing back to 0.21 in 2014. This cyclical pattern of decline and recovery continued until 2020, where NDVI reached its lowest recorded level, signalling possible intensification of environmental degradation, desert encroachment, or human-induced stress such as overgrazing and deforestation. Despite these fluctuations, the overall NDVI trend in the Sahel over the 41-year period remains relatively stable, with no strong long-term positive or negative direction. This neutral trend indicates that while vegetation loss events are frequent, the region also retains a capacity for natural regeneration under favorable conditions.\u003c/p\u003e\n\u003cp\u003eThe temporal variability of vegetation in the Tropical region of Northern Nigeria is presented in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, which continues the investigation into how NDVI trends reflect ecological dynamics across various climatic zones in the region\u003c/p\u003e\n\u003cp\u003eThe Tropical Rainforest region demonstrated a generally increasing trend in NDVI (Normalized Difference Vegetation Index) from 1980 to 2020, as illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. The NDVI initially declined steeply from 0.26 in 1980 to 0.22 in 1983, reflecting a reduction in vegetation health or cover, possibly due to environmental stress or anthropogenic interference. However, this was followed by a steady recovery, with NDVI rising to its peak of 0.26 in 1986, suggesting a period of favorable climatic conditions or successful natural regeneration. A sharp decline followed, bringing the NDVI down to 0.20 in 1988, possibly indicating a short-term climatic event or increased human pressure such as deforestation. The index rebounded to 0.25 in 1990, but then experienced a rapid drop to 0.22 in 1991. By 1993, NDVI had recovered to 0.24, though it again declined sharply back to 0.22, showing signs of instability and fluctuation in vegetation conditions.The highest NDVI value during the study period was recorded in 1995, reaching 0.27 following a steep rise from the previous year. Afterward, a gentle decline persisted until 2000, with NDVI reaching 0.24. This was followed by a brief increase to 0.26 in 2001, and then a slight decline to 0.25 in 2006. The index showed a gentle upward movement again, peaking at 0.26 in 2010, before experiencing a dip to 0.24 in 2013, followed by a rise to 0.26 in 2014. However, a significant decline was observed thereafter, with NDVI falling to 0.20 in 2020, marking one of the lowest points of the recent decades.This pattern reveals a cyclical nature of vegetation change in the tropical rainforest zone, characterized by alternating periods of vegetation gain and decline, with a general trend of vegetation loss becoming more apparent in the later years of the study.\u003c/p\u003e\n\u003cp\u003eTo further investigate the role of climate variability, a Long Short-Term Memory (LSTM) data-driven algorithm was employed to assess the effect of climate extreme indices on vegetation dynamics in Northern Nigeria. The outcomes of this model analysis are presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, as well as Figs. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, offering deeper insight into how extreme climatic events such as droughts, temperature anomalies, and rainfall variability influence NDVI trends over time.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparisons of Different Time Model to Assessed the Climate Extreme Indices on Vegetation\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eTesting\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime Taken\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eR-Squared\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 Month Lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 Month Lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 Month lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 Month Lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 Month Lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.837\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 Month Lag\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eSix Long Short-Term Memory (LSTM) models were developed using different time lags to assess the impact of climate extreme indices on vegetation dynamics. These models incorporated 1-month, 3-month, 6-month, 9-month, and 12-month lag intervals. The performance of each model was evaluated based on three statistical metrics: the coefficient of determination (R\u0026sup2;), mean squared error (MSE), and root mean squared error (RMSE) for both training and testing datasets, as presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eThe results revealed that the 6-month and 12-month lag models achieved the highest coefficients of determination for the training phase, with R\u0026sup2; values greater than 0.84, indicating strong predictive performance. These were followed closely by the 9-month lag model (R\u0026sup2; \u0026gt;0.83) and the 1-month lag model (R\u0026sup2; = 0.83). The 3-month lag model demonstrated the least predictive strength, with R\u0026sup2; \u0026gt;0.70, suggesting a weaker correlation between climate indices and vegetation changes for shorter lag periods. In the testing phase, the 12-month lag model again outperformed the others, achieving the highest R\u0026sup2; value (\u0026gt;\u0026thinsp;0.83). It was followed by the 1-month lag model (R\u0026sup2; \u0026gt;0.82) and the 6-month lag model (R\u0026sup2; \u0026gt;0.81), all of which maintained good generalization capabilities. The consistency of the 12-month lag model across both training and testing phases highlights its robustness in capturing the delayed response of vegetation to climatic variability.\u003c/p\u003e\n\u003cp\u003eRegarding error metrics, the lowest RMSE values during the training phase were observed in the 12-month (0.10), 6-month (0.18), and 1-month (0.28) lag models. Similarly, these three models also exhibited the least RMSE values during testing, affirming their superior accuracy and stability. A lower RMSE value indicates that the model\u0026apos;s predictions closely match the observed NDVI values, reflecting greater reliability in vegetation trend simulation.\u003c/p\u003e\n\u003cp\u003eBased on these performance metrics, the 12-month, 1-month, and 6-month lag models were identified as the most suitable models for determining the impact of climate extreme indices on vegetation cover in Northern Nigeria. Among these, the 12-month lag model was considered the best-performing, as it consistently produced the highest accuracy and lowest error in both training and testing datasets.\u003c/p\u003e\n\u003cp\u003eAs a result, the 12-month lag model was selected for further validation. It was subsequently compared with the observed NDVI dataset using interannual data across the entire study period. This comparison is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, which provides insights into how well the model captures long-term vegetation dynamics in relation to climatic extremes.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e reveals that the 12-month lag model achieved a coefficient of determination (R\u0026sup2;) of 0.84. This indicates that the model\u0026apos;s predicted NDVI values were able to explain 84% of the variance in the observed NDVI, signifying a strong agreement between the simulated and actual vegetation conditions across Northern Nigeria.\u003c/p\u003e\n\u003cp\u003eThe high R\u0026sup2; value underscores the model\u0026rsquo;s effectiveness in capturing the temporal variability of vegetation dynamics in response to climate extreme indices. Specifically, it suggests that vegetation in the study area responds to climatic factors with a delayed effect of approximately one year, and that incorporating this lag provides a more accurate and meaningful interpretation of vegetation changes over time. This strong performance confirms that the 12-month lag model is a reliable tool for modelling the impact of climate extremes\u0026mdash;such as drought, heatwaves, and rainfall variability\u0026mdash;on vegetation cover in the region. It also validates the suitability of this model for use in climate adaptation planning, early warning systems, and ecological forecasting. To further understand the relative contribution of each climate extreme index in driving vegetation changes, the Shapley Additive Explanations (SHAP) algorithm was applied. SHAP is a robust interpretability tool derived from game theory, used to assign importance scores to each input feature (in this case, climate indices) based on their contribution to the model\u0026rsquo;s output.\u003c/p\u003e\n\u003cp\u003eThe results of the SHAP analysis are presented in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e, which illustrates the influence of individual climate indices\u0026mdash;such as temperature extremes, precipitation anomalies, and drought indices\u0026mdash;on vegetation dynamics across Northern Nigeria. This allows for a clearer understanding of which climatic variables have the most substantial effect on vegetation and helps guide targeted interventions for climate resilience and sustainable land management.\u003c/p\u003e\n\u003cp\u003eThe overall ranking of the contribution (impact) of each climate extreme index on vegetation dynamics in Northern Nigeria, as revealed in the SHAP analysis, indicates that total precipitation (PRCPTOT) has the highest influence on vegetation variation in the region. This is followed by the maximum 5-day rainfall (RX5DAY), which also shows a strong impact. The minimum temperature (TXN) comes next in importance, while the maximum temperature (TXX) was found to have the least influence on vegetation dynamics. This result highlights the dominant role of precipitation in determining vegetation health and productivity in Northern Nigeria. Taken together, these findings reinforce the conclusion that the climatic conditions of Northern Nigeria, particularly precipitation levels, play a crucial role in the management and sustainability of local vegetation resources. They also underscore the importance of incorporating climate information into land use planning and environmental management, especially in regions prone to climate extremes and ecological fragility.\u003c/p\u003e\n\u003ch3\u003eDiscussion\u003c/h3\u003e\n\u003cp\u003eThe analysis of vegetation dynamics across Northern Nigeria revealed significant spatial and temporal variability in NDVI trends between 1980 and 2021, driven largely by climate extremes and ecological differences among the Sahel, Sudan, Guinea savanna, and tropical rainforest zones. The Sahel region, despite being ecologically fragile, showed a relatively stable but fluctuating NDVI trend, with frequent dips and recoveries. This pattern reflects the region\u0026apos;s sensitivity to moisture availability, which is often erratic and highly seasonal. The fluctuations in NDVI suggest that vegetation cover in the Sahel is quickly responsive to changes in rainfall, but equally susceptible to rapid decline in periods of drought or heatwaves. These findings are consistent with Fabeku et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), who noted a gradual NDVI decline in the Sahel due to reduced rainfall, and Ayanlade et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), who emphasized the role of rainfall in enabling vegetation recovery in dry regions. The observed cyclicity may also be explained by the region\u0026rsquo;s dominant herbaceous vegetation types, which respond quickly to precipitation but have low drought resistance, hence the abrupt shifts in greenness over time.\u003c/p\u003e\n\u003cp\u003eIn the Sudan and Guinea savanna zones, more complex trends emerged. The Sudan region displayed a generally positive NDVI trend in its core, while its eastern and western edges experienced negative trends. This spatial contrast could be attributed to varying land use patterns: the core areas are often used for seasonal farming, which may promote vegetation during the rainy season, while the peripheries are subjected to greater degradation from overgrazing, bush burning, or urban expansion. In contrast, the Guinea savanna region exhibited a predominantly declining trend, especially in the last two decades. This decline may be driven by intensified agricultural activities, deforestation, and expansion of human settlements, as the zone is known for its high population density and agricultural potential. These findings align with Umuhoza et al. (\u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e), who reported vegetation losses across African savannas, and Fokeng and Fogwe (2022), who attributed negative NDVI trends in East Africa to anthropogenic pressures and unsustainable land use. The results suggest that human influence is a stronger driver in the Guinea savanna than in the drier Sahelian zones, where natural climatic factors dominate.\u003c/p\u003e\n\u003cp\u003eThe tropical rainforest zone showed a generally positive NDVI trend until the mid-2010s, followed by a sharp decline toward 2020. This pattern is likely due to early conservation success or climatic favorability, followed by escalated land degradation and deforestation in recent years. The decline in vegetation after 2014 could also reflect intensifying temperature extremes and shifts in rainfall distribution, which are known to affect rainforest ecosystems severely. Idris et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) similarly reported unsustainable vegetation trends in tropical rainforests of East Africa, attributing the changes to both climatic and non-climatic factors.\u003c/p\u003e\n\u003cp\u003eThe application of the Long Short-Term Memory (LSTM) models enabled a deeper understanding of how vegetation responds over time to lagged climate conditions. The 12-month lag model, which performed best with an R\u0026sup2; of 0.84, indicates that vegetation does not respond instantaneously to climate extremes, but rather accumulates the effects of precipitation and temperature over time. This delayed response can be explained by the physiological and hydrological processes that govern vegetation growth: soil moisture recharge, seed germination, and canopy development often depend on cumulative rainfall and temperature exposure over months, not days. This is consistent with Kalisa et al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), who highlighted nonlinear and lagged responses of NDVI to climate drivers. The poor performance of shorter lag models, such as the 1-month and 3-month lag, further emphasizes that short-term weather anomalies are insufficient predictors of long-term vegetation dynamics in this region.\u003c/p\u003e\n\u003cp\u003eThe use of the Shapley Additive Explanations (SHAP) provided insight into the relative importance of each climate variable. The analysis revealed that total precipitation (PRCPTOT) had the strongest impact on vegetation dynamics, followed by RX5DAY (maximum 5-day rainfall) and TXN (minimum temperature), while TXX (maximum temperature) had the least influence. This order of importance highlights that water availability, rather than thermal stress, is the primary driver of vegetation variability in Northern Nigeria. The dominance of precipitation aligns with findings by Ebenezer (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), who noted that low rainfall increases vulnerability to desertification in arid zones. Adepoju, et al., (2019), and Akpan et al., (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) also observed that although temperature plays a role in vegetation growth, its influence is relatively weak compared to precipitation, particularly in dryland ecosystems. Meanwhile, Adeyeri et al. (\u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e) confirmed that higher temperatures can exacerbate evaporation, reducing available moisture and thereby constraining vegetation productivity.\u003c/p\u003e\n\u003cp\u003eInterestingly, the relatively low impact of TXX (maximum temperature) may be due to the adaptation of native vegetation species to withstand periodic heat extremes. In contrast, minimum temperature (TXN) was more relevant, possibly because cooler nights support plant physiological processes such as respiration and water conservation. The limited role of maximum temperature in vegetation dynamics may also support findings by Van Leeuwen et al. (2011), who observed that vegetation loss often leads to increased surface temperatures and wider diurnal temperature ranges, but this is often a consequence rather than a cause of vegetation change.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study concludes that vegetation condition was found to be generally robust within the tropical rainforest and Guinea savanna, especially in central areas like Abuja, Kogi, and southern Kaduna, where positive NDVI trends were dominant. However, this region also exhibited minimal significant trends, suggesting that while vegetation health remains strong, it may not be improving substantially. In contrast, the Sudan savanna zone exhibited a higher concentration of significant positive trends, particularly in areas such as Katsina, Zamfara, and central Kaduna, indicating an upsurge in vegetation cover over the study period. However, negative trends in regions like Kebbi, Adamawa, and Gombe suggest localized degradation likely due to land-use change and climate variability. The Sahel region, which initially showed predominantly normal vegetation conditions, demonstrated the most pronounced and consistent positive trend in NDVI over time, especially in the eastern part of Borno and northern Katsina. Nevertheless, parts of Yobe and northwestern Sokoto revealed a persistent vegetation decline, indicating regional heterogeneity.\u003c/p\u003e\u003cp\u003eTemporal trend analysis shows periods of sharp vegetation decline across all regions, notably in 1984, 1994, and 2020, years historically associated with drought events and climatic extremes in West Africa. The NDVI values in the Sahel declined to 0.14 in 1987 and 1994, which were years of notable dry spells. Conversely, peak NDVI years like 1995 and 2001 align with periods of vegetation recovery, supported by improved rainfall and possibly greening initiatives. The magnitude of vegetation trends further solidifies the spatial disparity\u0026mdash;Sahelian regions recorded the highest positive magnitude values (0.0813\u0026ndash;0.2834/year) in northeastern Borno and northern Katsina, while southwestern Sudan and northeastern Guinea zones showed the lowest magnitudes, revealing vulnerability to degradation.\u003c/p\u003e\u003cp\u003eModelling the impact of climate extreme indices using Long Short-Term Memory (LSTM) revealed that the 12-month lag model offered the highest predictive accuracy (R\u0026sup2; \u0026gt;0.83, lowest RMSE of 0.10), indicating that long-term climate memory significantly influences vegetation dynamics in the region. The performance of the 6-month and 1-month lag models also suggests that vegetation responds not only to long-term but also to recent climatic events. Overall, the study concludes that vegetation dynamics in Northern Nigeria are significantly modulated by ecological zone characteristics, climatic variability, and human activities. The increasing trend in vegetation in some parts, especially the Sahel, reflects a possible greening effect or vegetation resilience, while the persistent degradation in others, notably in parts of the Guinea and Sudan zones, points to the urgent need for targeted land management and climate adaptation strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of interest\u003c/h2\u003e\u003cp\u003eThe authors have declared that there is no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eClinical Trial Number\u003c/h2\u003e\u003cp\u003eNot Applicable\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics and Consent to Participate\u003c/h2\u003e\u003cp\u003eThe authors confirm that ethics approval and consent to participate are not applicable to this study.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConsent to Publish\u003c/h2\u003e\u003cp\u003eThe authors confirm that consent to publish is not applicable to this study.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding Statement\u003c/h2\u003e\u003cp\u003eThis research received no grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eBBM conceived the study, developed the methodology, led the original drafting, supervised the research process, and handled the final manuscript editing.ZI and AFA contributed to data analysis, literature review, and critical revision of the manuscript.SUB participated in manuscript review and editing.AAD was responsible for data visualization and the development of graphical illustrations.AHB contributed to the methodology and was in charge of data curation and preprocessing.All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe authors confirm that the data supporting the findings of the study are available within the article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdullahi Muktar W, Elekwachi, Sadiq AY (2022) Spatial Variation of Vegetation Changes in Sokoto Region from 2000 to 2018. 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Remote Sens 17(1):83. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/rs17010083\u003c/span\u003e\u003cspan address=\"10.3390/rs17010083\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate Variability, Northern Nigeria, Normalized Difference Vegetation Index (NDVI), MODIS and GIMMS NDVI, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-7213869/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7213869/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNorthern Nigeria faces increasing vegetation stress due to changing climate extremes, yet the spatial and temporal dynamics of this relationship remain underexplored. This study aimed at modelling climate influence on vegetation dynamics in Northern Nigeria, focusing on detecting patterns and identifying key climatic drivers of vegetation change. The research utilized Normalized Difference Vegetation Index (NDVI) datasets from GIMMS AVHRR (1981 to 2015) and MODIS Terra (2000 to 2021). Climate data comprising daily precipitation and temperature (Tmin and Tmax) from 1980 to 2021 were obtained from the Nigerian Meteorological Agency (NiMet), covering ten synoptic stations across major ecological zones. NDVI data were harmonized and geo-referenced using ENVI and ArcGIS, while climate extremes were computed using RClimDex software following ETCCDI guidelines. Trend analysis was performed using the Mann\u0026ndash;Kendall test and Coefficient of Variation (CV) to evaluate variability and direction. Machine learning models, Random Forest (RF) and Support Vector Regression (SVR), were applied to simulate NDVI responses to selected climate indices. Model performance was assessed using Root Mean Square Error (RMSE), R\u0026sup2;, and Mean Absolute Error (MAE). Findings revealed significant vegetation degradation in the Sudan and Guinea Savanna zones, particularly in years of high climate extremes (1984, 1994, 2020), while slight greening trends were noted in the Sahel. RF models achieved high accuracy (R\u0026sup2; \u0026gt;0.83), with dry spells and heatwaves emerging as the most influential climate drivers of NDVI variability. In conclusion, climate extremes play a substantial role in shaping vegetation dynamics, underlining the need for targeted climate adaptation and sustainable land-use strategies in Northern Nigeria.\u003c/p\u003e","manuscriptTitle":"Modelling the Influence of Climate Variability on Vegetation Dynamics in Northern Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-13 09:43:27","doi":"10.21203/rs.3.rs-7213869/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-14T23:08:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-14T20:31:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-13T12:23:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226291322832311395020621766409052855567","date":"2026-02-13T02:44:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317950605123563033396058953639377044795","date":"2026-02-11T13:52:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"258912966948916892351076469301979543012","date":"2025-08-13T11:30:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"271185452401176180010316607940766893836","date":"2025-08-13T05:12:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249488862174384134306877045946667409204","date":"2025-08-07T20:45:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T18:46:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-27T22:34:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-27T22:33:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Theoretical and Applied Climatology","date":"2025-07-25T11:29:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"theoretical-and-applied-climatology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"taac","sideBox":"Learn more about [Theoretical and Applied Climatology](https://www.springer.com/journal/704)","snPcode":"704","submissionUrl":"https://submission.nature.com/new-submission/704/3","title":"Theoretical and Applied Climatology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8a5d216b-a065-4260-a08c-d6d52feb78d3","owner":[],"postedDate":"August 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T16:13:09+00:00","versionOfRecord":{"articleIdentity":"rs-7213869","link":"https://doi.org/10.1007/s00704-026-06244-5","journal":{"identity":"theoretical-and-applied-climatology","isVorOnly":false,"title":"Theoretical and Applied Climatology"},"publishedOn":"2026-04-13 15:57:44","publishedOnDateReadable":"April 13th, 2026"},"versionCreatedAt":"2025-08-13 09:43:27","video":"","vorDoi":"10.1007/s00704-026-06244-5","vorDoiUrl":"https://doi.org/10.1007/s00704-026-06244-5","workflowStages":[]},"version":"v1","identity":"rs-7213869","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7213869","identity":"rs-7213869","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}