Is it possible to investigate spatiotemporal dynamics of NDVI and climate-induced responses in ecosystems: insights 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.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.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.