Can we predict the spatial-temporal dynamics of NDVI and the responses to climate change in ecosystems around the world? Can we predict the insights that will be needed for sustainable management and climate resilience?

Understanding the intricate relationship between climate variables and the Normalized Difference Vegetation Index (NDVI) is essential for effective ecosystem management. This study focuses on the spatiotemporal dynamics of NDVI and its interaction with climate variables in the ecologically diverse Khyber Pakhtunkhwa (KPK) Province, Pakistan, from 2000 to 2022. The research methodology involves analyzing satellite images and meteorological datasets to examine NDVI and surface latent heat flux (SHF), total precipitation (TPP), temperature (T), soil temperature (ST), and total pressure (TP). KPK Province's ecological significance and complex climate-vegetation interactions drive the selection of this study area. The study uses multiple linear regression analysis to investigate how T, TPP, SHF, and TP influence NDVI. The Mann-Kendall test detects trends, with Sen's slope estimator quantifying trend magnitudes. Additionally, correlation coefficients provide insights into longterm changes and association strengths. The findings highlight a consistent upward trend in mean NDVI over the 23 years, revealing an overall increase in NDVI, particularly in vegetation-dense areas where it rose from 0.27 to 0.32. The research showed an annual growth rate of 0.84% in the entire area, with specific vegetated zones exhibiting a slightly lower rate of 0.80%. However, the average yearly increase in NDVI is higher in vegetationspecific zones (0.00237) compared to the whole area (0.00151). This increase in NDVI occurs alongside a statistically significant decrease in SHF and PPT, suggesting a complex adaptation of vegetation to changing climate conditions in the KPK Province. In contrast, SHF exhibits a statistically significant negative slope of − 5.952e-06 (p < 0.05), indicating a pronounced downward trend. Similarly, Sen's slope estimate for precipitation demonstrates a significant negative trend of − 0.0001 (p < 0.05), showing diminishing precipitation. The study uncovers intricate linkages between climate variables and vegetation dynamics within KPK Province. These insights have far-reaching implications, guiding decision-making in land management, conservation efforts, and global climate resilience strategies. Ultimately, the research underscores the critical role of data-driven approaches in shaping a greener and more sustainable future. Keywords: Remote sensing Vegetation cover dynamics Climatic factors NDVI Land management.1. Introduction: Understanding ecosystem functioning and evaluating the impact of climate change on terrestrial ecosystems depends significantly on vegetation dynamics (Coppin et al., 2004; Usoltsev et al., 2020). Due to the geographical and temporal heterogeneity caused by regional changes in vegetation cover, vegetation dynamics reveal inherent complexity. A large-scale, real-time monitoring system for vegetation is difficult to create using conventional field-based techniques (Satsuma, 2009). In recent years, remote sensing techniques have gained prominence for their capability to monitor vegetation changes at a large scale, offering continuous, high-resolution spatial-temporal dataset on vegetation growth dynamics (Banerjee et al., 2023; Duarte et al., 2014; Frutuoso et al., 2021; Majeed et al., 2022). Studying vegetation dynamics and climate is essential because many ecological zones in Khyber Pakhtunkhwa (KPK) Province include mountains, forests, and agricultural plains (Khan et al., 2021a, 2021b). Significant climate fluctuations characterized by distinct seasons and irregular precipitation patterns occur throughout the region (Hui and Jackson, 2006). Effective land management and climate change adaptation strategies depend on understanding the relationships between climatic variables and vegetation dynamics in the region (Olmos-Trujillo et al., 2020; Zhao et al., 2020). Vegetation dynamics are frequently recognized using satellite-derived vegetation indices such as the Normalized Difference Vegetation Index (NDVI) (Usman et al., 2013), Enhanced Vegetation Index (EVI) (Matsushita et al., 2007), and Leaf Area Index (LAI) (Miura et al., 2015). The increase in the average global temperature highlights the urgency of studying vegetation changes and their connection to climate change (Pande et al., 2023). Vegetation is frequently a biological indicator of climate change due to its sensitivity to climatic changes (Ahmad et al., 2022; Lentile et al., 2009). Therefore, investigating how vegetation changes react to climatic factors is crucial for preserving local ecological systems, sustainable development, and better comprehending evolutionary processes in terrestrial ecosystems (Anees et al., 2022b). Climate change has been implicated in the recent lengthening of the growing season and the rise in vegetation cover in the middle and high latitudes of the northern hemisphere (Chen et al., 2018; Kong et al., 2017). The long-term dynamics of vegetation growth are significantly influenced by climatic factors, which also determine the distribution of terrestrial flora in space and the characteristics of local ecosystem services (Khan et al., 2021a, 2021b; Muhammad et al., 2023). On the other hand, anthropogenic activities have significantly impacted regional ecosystems' functionality and services by rapidly altering vegetation's spatial extent and health within shorter periods (Banerjee et al., 2021; Roberts et al., 2015). Consequently, studying vegetation changes and their relationship to climate change is a crucial area of research in global environmental change (Fay et al., 2011; Mal et al., 2021). Temperature and precipitation influence vegetation dynamics, impacting plant physiology (Anees et al., 2024; Pan et al., 2023), phenology, growth rates (Shobairi et al., 2022), and water availability. In order to evaluate primary productivity, species diversity, and ecological health, it is crucial to understand the direct and indirect effects of these climatic conditions on vegetation (Andreevich et al., 2020; Hüttich et al., 2009). Observing the changes in vegetation across large areas and extended times requires the use of remote sensing techniques, most notably satellite images. As a satellite-based indicator of plant health and vitality, the NDVI quantitatively measures vegetation greenness and broadly shows aspects of vegetation change (Banerjee et al., 2020; Wiesmeier et al., 2011). Both densely and sparsely vegetated regions can be detected with high precision using the NDVI. Therefore, it is a valuable tool for examining local vegetation's spatial distribution and characteristics (Huenneke et al., 2001). Moreover, NDVI is essential for supporting local and global environmental studies contexts and provides insightful data for ecological research (Kremer and Running, 1993). Several investigations have used satellite-based remotely sensed datasets to analyze vegetation dynamics in many regions, mainly highlighting the correlation with climatic factors (Pelletier et al., 2016). Higher NDVI and rainfall variability were found during the growth season in an analysis of climate variability and vegetation dynamics (Fokeng and Fogwe, 2022; Gao et al., 2022; Prav˘ alie ˘ et al., 2022; Wahla et al., 2023). Fragile ecosystems are highly susceptible to climatic changes and human activities (Sohail et al., 2023), especially regarding vegetation dynamics and the linkages between climate change (Bellanthudawa and Chang, 2022). The connection between climate parameters like temperature and total precipitation, and the growth of vegetation is marked by complex interactions, as evidenced by the correlations observed with two key indicators: NDVI and GPP (Gross Primary Productivity) (Le et al., 2010a).Ghaderpour et al. (2023) reported a positive trend in the NDVI, a slight temperature rise, and an overall slight reduction in precipitation across different ecoregions have been observed. Additionally, there is a more substantial yearly consistency between NDVI and Land Surface Temperature (LST) compared to the relationship between NDVI and precipitation, with regional differences in these connections being particularly noticeable within Italy. Assessment of vegetation dynamics revealed an overall improvement in vegetation, especially in the centralwestern regions in the Qilian Mountains. The key determinants of these variations were sunshine duration, wind speed, and T, with TPP affecting vegetation differently at various altitudes (Zhang et al., 2021). Similarly, the LST in South Asia showed a notable increase in daytime cooling in the Indus Valley desert. At the same time, nighttime warming was observed in regions such as the Gissaro-Alai open woodlands. Furthermore, it established a strong connection between land and sea surface temperatures at night, which has implications for regional sustainability (Shawky et al., 2023). The influence of climate change on agricultural production in 12 Asian nations demonstrates that increased T typically promote production, except in South-Eastern Asia. Meanwhile, fluctuating rainfall patterns have diverse effects, highlighting the need for increased agricultural investment to ensure food security (Pickson et al., 2023). Ecological afforestation initiatives have substantially impacted vegetation restoration in these areas, highlighting the vital importance of environmental engineering in improving forest growth and productivity (He et al., 2022). By analyzing long-term NDVI data in conjunction with climate variables, valuable insights can be gained into spatiotemporal vegetation patterns and their relationships to climate factors A thorough understanding of the spatiotemporal dynamics of vegetation and its interactions with climate factors in the research area still needs to be improved (Usoltsev et al., 2022). Although research on vegetation dynamics and climate change has been emphasized in the past, it needs to address these specific climatic variables, which are investigated in our research. The objectives of this study are twofold: (1) to evaluate the spatiotemporal dynamics of vegetation in KPK Province over 23 years and (2) to investigate how climate factors, such as T, SHF, and TPP, influence these dynamics. We utilize the ERA5-Land dataset from the Copernicus Climate Change Service to achieve these objectives, which provide a gridded, high-resolution climate dataset with global coverage and long-term records. Additionally, NDVI data from Landsat satellite images are employed. By quantifying the influence of climate factors on vegetation dynamics and identifying future trends and interannual changes, this study aims to enhance our understanding of the intricate relationships between local climate and vegetation dynamics. 2. Methods and material 2.1. Study area KPK, formerly known as Northwest Frontier Province, is situated in northwest Pakistan between 31◦ 4′ and 36◦ 57′ N latitude and 69◦ 16′ and 74◦ 7′ E longitude. This province is situated where the slopes of the Hindu Kush Mountains meet the Iranian plateau and the Eurasian land plate. In contrast, peripheral eastern regions are located near the Indian subcontinent, which gives way to the Indus-watered hills approaching South Asia. KPK encompasses a significant population and vast land area, making it essential for ecological studies and conservation efforts (Rahman et al., 2022). It shares borders with Gilgit-Baltistan, Islamabad Capital Territory, Baluchistan, Punjab, Azad Kashmir, and Afghanistan. KPK exhibits diverse topography, including rugged mountain ranges, valleys, plains surrounded by hills, undulating submontane regions, and productive agricultural farms (Tariq et al., 2023) (Fig. 1). With a population estimated at 35.5 million in the 2017 Census, KPK stands out for its predominantly rural population, accounting for over 83% of the total inhabitants (Authority et al., 2021; Ul-Haq et al., 2019; Wazir and Goujon, 2019). The province showcases remarkable climatic diversity, encompassing various climate types in Pakistan. While the northern mountainous regions experience milder summers and harsh winters, the prevailing air in the province tends to be dry, resulting in significant daily and annual T fluctuations. Furthermore, precipitation patterns exhibit variations across the region, with the eastern border receiving the highest rainfall, especially during the monsoon season from midJune to mid-September (Islam et al., 2023). Researchers and academics have paid considerable attention to the unique biodiversity and socio-cultural dynamics of KPK Province (Bacha et al., 2021; Khan et al., 2019). In addition to its diverse biosphere, unique habitats, and rare and endangered species, the region offers a wealth of opportunities for ecological studies. In addition, the province's diverse environmental conditions, from semi-arid areas in the south to alpine regions in the north, provide an ideal setting for studying climate change, agricultural practices, and resource management. 2.2. Data collection Two steps are involved in data collection: (1) Gathering predictors and response variables such as NDVI is the first phase (Table 1). (2) Validating the suitability of the predictor variables used in the model. The Copernicus Climate Change Service (C3S)’s ERA5-Land monthly averaged dataset (https://cds.climate.copernicus.eu/) was used as the explanatory variables. The ERA5-Land Reanalysis dataset has a high geographical resolution of roughly 9 km and spans the entire world (Tariq et al., 2023). The model contains many meteorological variables, including TPP, T, SHF, soil temperature Level 1 (ST1), and TP (Buehler,2009). These variables were chosen because they are essential determinants of climate dynamics and offer insightful information about the investigated land-based climate processes (Pandya et al., 2013). The ERA5-Land dataset uses cutting-edge data assimilation methods to incorporate observations from various sources, improving the data's accuracy and dependability (Yulianto et al., 2019). The dataset's openaccess nature, made possible by the Copernicus Climate Data Store (CDS), encourages transparency and makes it easier for scientists to work together (Wang et al., 2019). The United States Geological Survey's (USGS) Landsat dataset (https://earthexplorer.usgs.gov/) served as the source for the response variable in this study (Table 2). The preprocessing was performed on the Landsat dataset, including Landsat 4–5 thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat Operational Land Imager (OLI) to improve its quality and applicability for vegetation research. The Environment for Visualizing Images (ENVI) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) algorithm, a popular and effective method for minimizing interferences in satellite data, was utilized for radiometric calibration and atmospheric correction (Duane and Brotons, 2018). Other preprocessing methods, including Image Registration and Mosaic, were used to guarantee accurate data alignment and easy integration. These procedures were pivotal in rectifying potential discrepancies in alignment among different Landsat sensors such as TM, ETM+, and OLI for the periods spanning 2000 to 2022. This alignment was crucial for facilitating a comprehensive and consistent analysis of vegetative dynamics over the specified time frames (Amici et al., 2017). The scan lines in Landsat 7 (ETM+) were removed using an image-enhancing technique such as Linear Transformation (Markham et al., 2004; Pringle et al., 2009; Scaramuzza et al., 2004) and Local Histogram Matching (Hossain et al., 2015; Yin et al., 2017). This technique involved the utilization of the Landsat_gapfills.sav plugin/extension in the ENVI (Liu et al., 2013). The Landsat dataset provided a series of multispectral imagery covering the entire study area. We analyzed vegetation dynamics and interannual fluctuations in NDVI using the Landsat dataset since it provides a continuous record of multispectral datasets from 2000 to 2022. Given that NDVI is recognized for its capability to represent changes in vegetation cover and productivity as well as its sensitivity to changes in vegetation health and diversity (Huang et al., 2021; Anees et al., 2022b). NDVI facilitates the comparison of datasets from different locations and times for our spatiotemporal study. Additionally, its 30-m spatial resolution enables the assessment of local vegetation patterns and fine-scale variations (Tariq et al., 2021), consistent with the study's objectives of analyzing the correlation between climate changes and variations in NDVI in Pakistan's KPK province. 2.3. Methodology 2.3.1. Quantitative assessment of vegetation cover patterns The evaluation of vegetation cover patterns in our study was mainly conducted by calculating the NDVI, a reliable measure of vegetation health and density (Anees et al., 2022a). The estimation was carried out following a comprehensive processing and analysis of the satellite images, illustrated in Fig. 2. The computation of NDVI entails the examination of the reflectance values in the near-infrared and red spectral bands (Eq. (1)). This was accomplished by utilizing a methodology that aligns with Raza and Ahmad (2017), who emphasized the efficacy of NDVI in depicting changes in vegetation. The Landsat dataset, with a spatial resolution of 30 m, facilitated a comprehensive assessment of local vegetation patterns, as proposed by Chuvieco et al. (2016). NDVI = (NIR− Red)/(NIR + Red) (1) where NIR refers to the reflectance of near-infrared light, and Red represents the reflectance of red light. The formula, corroborated by the research of Aldersley et al. (2011), enabled us to evaluate the health and density of vegetation in our designated research region precisely. The associations between the predictor variables collected from the ERA5-Land dataset and the response variable derived from the Landsat NDVI dataset were analyzed for each year ranging from 2000 to 2022. The methodology used in this study applied multiple linear regression (MLR) analysis. Compared to other methods, MLR ‘multiple predictors’ simultaneous effects on the response variable can be examined (Eq. (2)) (Song et al., 2018). The NDVI was the dependent variable in constructing the MLR for each year range. In contrast, the independent variables were TPP, T, SHF, TP, and ST1.showed a modest decrease while being the highest for the entire period. On the other hand, during the same year, a substantial amount of heat was radiated from the surface to the atmosphere. These data show that NDVI is especially sensitive to negative surface heat flux due to the strong heat radiation to the atmosphere. Additionally, the present study shows that the SHF predictor strongly affects the variability in NDVI values (R = 0.013, P 0.05). The substantial rise in SHF, which affects the NDVI, shows that the situation is exacerbating in 2021. Even though the T significantly positively influences NDVI (R = 0.09, P 0.05), 2006 showed an increasing peak in NDVI despite decreasing T. The inter-annual variation analyses in our study allowed us to explore the relationship between the mean NDVI and climatic conditions. By examining changes in NDVI over consecutive years, we could identify patterns and trends in vegetation dynamics. We then compared these variations with corresponding climatic variables, such as TPP, T, and SHF, to assess their influence on the mean NDVI. We used statistical methods such as correlation analysis, trend analysis, and significance testing to establish a link between the mean NDVI and the climate and to quantify its intensity and direction. This method shows how vegetation's overall productivity and density respond to climatic variations throughout time. 3.3. Understanding the dynamics of environmental variables: trend analysis Trend analysis is crucial for evaluating the dynamics of environmental factors (Qamer et al., 2015). The Mann-Kendall trend test was used in our study to determine whether there were any patterns, and the results gave us important new information on the trends (Fig. 8). The response and the predictors show a statistically significant (p < 0.05) trend. Time series data benefit significantly from using Sen's slope, a statistical tool for determining the slope of a trend line (Muoghalu and Okeesan, 2005). The response and the predictor variables were subjected to Sen's slope. For the distribution of NDVI values with z-scores of 4.33, the Sen's slope was determined to be 0.0044, indicating a significant increase (p-value 0.05). The 95% confidence interval (0.00292 to 0.00614) is thought to contain a genuine positive slope (Fig. 9). Substantial evidence is shown against the null hypothesis by the estimated slope for the SHF, which was − 5.952e-06 with a negative z-value of − 9.2552 and an incredibly low p-value of only 2.2e-16. The SHF measurements appear to be trending downward, as indicated by the negative slope of the 95% confidence interval (− 1.869e-05 to − 2.207e-07). This study's trend analysis offers crucial new understandings of the temporal evolution of environmental factors. The results demonstrate a statistically significant downward trend in T with a slope of − 0.00011 (p < 0.05). The 95% confidence interval provides additional evidence for this negative slope, which shows that the T decreased significantly and consistently during the study period. TPP exhibits a similar strong downward trend, with a Sen's slope estimate of − 0.0001 and a z-value that is 14.699 times more negative than normal. This negative slope implies that precipitation decreased consistently and significantly during the study period. The significance of these trends is further reinforced by the Mann-Kendall test, which presents strong evidence of substantial trends in T and TPP and the NDVI and SHF. SHF's negative and significant impact on NDVI suggests that higher SHF levels negatively affect vegetation dynamics. On the other hand, the positive effects of T and TPP on NDVI indicate that higher T and TPP levels benefit vegetation growth. Overall, the trend analysis deepened our understanding of the dynamics of these variables and their implications for ecosystem functioning and climate change impacts. A spatial analysis of vegetation cover and overall trends in KPK province showed distinct patterns (Fig. 10). We observed varied trends in vegetation cover and broader regional trends. Our study revealed that a relatively small fraction of the vegetative area, amounting to 240.31 km2 (0.34%), showed a marked decrease in vegetation, statistically significant at (p < 0.05). On the other hand, a majority of the area, approximately 27,681.65 km2 (58%) of the total vegetative cover, remained largely unchanged. However, the most prominent observation was the significant increase in vegetative cover across an extensive area of 42,015.51 km2 , accounting for 60.08% of the total vegetative landscape. Extending our analysis to broader NDVI change patterns across the KPK province, we discovered that a small part of the region, approximately 563.1409 km2 (0.56%) of the entire province, experienced a notable decrease in the NDVI. The majority of the area, encompassing around 45,421.5625 km2 or roughly 45.02% of the province, showed no significant alteration. However, similar to changes in vegetation cover, a noteworthy shift was observed with a considerable increase over an extensive area spanning about 54,905.7802 km2 (54.42%) of KPK's total area. The findings demonstrate a range of variations in vegetation dynamics and overall environmental patterns across KPK. The research highlights regions experiencing environmental degradation and areas showing stability and substantial improvement. 4. Discussion The current study evaluated the spatiotemporal vegetation dynamics in KPK Province and examined how climate variables affected these dynamics. We gained insights into the relationships between climate variables and vegetation cover over 23 years. We found a statistically significant correlation between the NDVI and the predictor variables. The vegetation dynamics of the study area were greatly influenced by these variables, as shown by the adjusted R-squared value of 0.604 (p < 0.5). Specifically, T and TPP positively impacted NDVI, whereas SHF exhibited a significant adverse effect. These results are consistent with earlier research that emphasized the influence of T and TPP on vegetation patterns (Ali et al., 2021; Fatima et al., 2022; Muchoney and Haack, 1994; Siyal et al., 2017). For instance, (Ferchichi et al., 2022; Le et al., 2010b) found that T and TPP patterns significantly influence annual vegetation, with warmer T conducive to plant growth and consistent TPP contributing to better vegetation growth. Similarly, (da Silva et al., 2023; Teng et al., 2023; Zhang et al., 2022) reported that T and TPP influence vegetation patterns, with the T being more influential at the start of the growing season and precipitation having more significant impacts on the end of the growing season in an arid environment. Due to the Government of Pakistan's Billion Tree Tsunami afforestation programs, which have significantly increased the extent of plant cover in the study area, the apparent increasing trajectory in vegetation density within the northern regions is readily observable (Haq et al., 2018). SHF has significantly impacted the NDVI's fluctuation in 2016 and 2021, depicting significant negative associations. This phenomenon reduces the amount of moisture available in the terrestrial ecosystem, changing the environmental factors favouring vegetation development (Akram et al., 2022; Aslam et al., 2022; Oliveira et al., 2012). We also performed trend analysis and correlation coefficient estimates to fully comprehend the long-term variations in climate variables and their relationship to NDVI. The Mann-Kendall test is inappropriate for data containing periodicities (such as seasonal effects). Before calculating the Mann-Kendall test, our efforts were intriguing to eliminate known periodic effects from the data in a preprocessing phase to ensure the test's efficacy. The second parameter of the Mann-Kendall test tends to produce more negative results for smaller datasets, i.e., the computation for detecting trends is more accurate in the more significant time series (Lü et al., 2009). The Mann-Kendall test for NDVI showed a significant upward trend across the research period. The NDVI consistently increased, indicating a favorable change in vegetation cover, as evidenced by the positive Sen's slope (S) value. This discovery is significant because it implies good vegetation development conditions during the studied period. The Mann-Kendall test revealed a strong negative trend for SHF. This suggests a substantial change in surface heat flux patterns, which may impact how energy is exchanged between the atmosphere and the land surface. The observed negative trend in SHF emphasizes how crucial it is to consider these modifications when determining the study area's overall energy balance. Moreover, the study conducted by Ferchichi et al. (2022) and Tao et al. (2022) provides additional evidence for the significant adverse effect of SHF on vegetation. The researchers observed that forest vegetation patterns strongly influence T and TPP, affecting climate by influencing the atmosphere's moisture, energy, and momentum exchanges. According to inter-annual variation analyses, the mean NDVI and its relationship to climatic conditions showed noticeable changes. Indicating the significant effects of rainfall on vegetation growth, the considerable increases in NDVI found in 2006 and 2019 correlated with greater precipitation levels during these years. In contrast, a dramatic reduction in NDVI was seen in 2016, ascribed to drought conditions brought on by little rainfall and high T. These results underline the NDVI's vulnerability to climate changes and the significance of sufficient moisture availability for vegetation health. The analysis of year-to-year variations shows that rainfall significantly impacts vegetation growth, which aligns with the findings of Qi et al. (2022) and Ranjan et al. (2022). They observed that precipitation affects the timing of vegetation growth in arid/semiarid regions, with greater sensitivity to rainfall before the growing season in drier areas and less sensitivity to T in wetter regions. The trend analysis and coefficient analysis results also showed that the response variables had abrupt changes in specific years, such as 2008, 2009, 2016, and 2021, which coincided with significant climatic events and their effects on the research area. These results highlight the necessity for effective climate change adaptation methods in the area and the susceptibility of vegetation dynamics to climatic changes. It is significant to mention that there are some constraints to our study. Recognizing potential limitations affecting how our results should be interpreted and how broadly they can be applied is critical. First, the spatial and temporal coverage of the datasets we used for our analysis may constrain how far we may extend our results from the given study area and time frame. Additionally, the use of remote sensing data has inherent drawbacks in terms of geographic resolution, accuracy, and possible atmospheric impacts. These drawbacks could potentially impact the reliability of the vegetation indices derived from this data. For better accuracy of results and to overcome these limitations, we have used medium-resolution data (Landsat) compared to coarser resolution data such as MODIS. Finally, even though they are acceptable for our investigation, specific statistical approaches have inherent assumptions and constraints It is crucial to understand that every model and analysis method has limitations. In addition to suggesting opportunities for future development and improvement in comparable studies, we have provided a fair and thorough understanding of our research findings by addressing these potential limitations. It is imperative to prioritize these efforts to preserve the long-term ecological health and sustainability of the KPK Province. To better understand the intricate connections between climate and vegetation dynamics, future studies should concentrate on incorporating extra factors, such as soil moisture and land cover. By showcasing the practical relevance of our research, we can bridge the gap between scientific knowledge and real-world applications. We can help the area adopt more efficient conservation and management techniques by implementing these suggestions and expanding our expertise. 5. Conclusion In conclusion, this study assessed the spatiotemporal dynamics of vegetation in the Khyber Pakhtunkhwa Province of Pakistan and investigated the influence of climate variables on these dynamics. The findings demonstrated a significant relationship between the NDVI and T, SHF, and TPP, underscoring the importance of these factors in shaping vegetation patterns. The observed trends and year-to-year variations emphasized the vulnerability of vegetation dynamics to climatic fluctuations, highlighting the urgent need for sustainable management strategies to mitigate the potential adverse impacts of climate change on local ecosystems. These findings indicate the need for tailored responses to the anticipated adverse effects of climate change on regional ecosystems. According to the results, we recommend taking the following particular measures: (1) Using climate-resilient land management techniques, such as conservation measures and afforestation, to increase vegetation resilience to climate change; (2) Improving monitoring technologies to track vegetation dynamics and climate variables to enable prompt adaptive management actions; (3) Supporting community involvement and education initiatives to encourage local involvement in sustainable land management and conservation initiatives.