Spatiotemporal variation of vegetation and its responses to climate change in Pakistan from 2001 to 2021

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However, the impact of temperature and precipitation fluctuations on vegetation dynamics in these regions remains uncertain. Pakistan's unique ecology and complex climate-vegetation relationships make it an ideal location for studying vegetation changes. This study examines changes in vegetation coverage and its response to climatic factors (temperature and precipitation) from 2001 to 2021. This study utilized satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250m spatial resolution and statistical analyses, including correlation calculations and multiple linear regression. We aimed to practically investigate whether and why vegetation distributes imbalanced along the entire country,is essential for adaptation to global climate change. The findings highlight (1) a notable upward trend in mean Normalized Different Vegetation Index (NDVI) over the past 21 years, with a significant increase from 0.19 to 0.33, indicating an overall enhancement; (2) the NDVI analysis reveals that about 32% of area exhibits an increasing trend, with high vegetation health, while other areas show a declining trend; (3) the results indicate that NDVI increased across Pakistan's. Khyber Pakhtunkhwa, Punjab, and Kashmir showed increasing NDVI, with 27% slight development and 9% dramatic development. In contrast, Baluchistan, Gilgit-Baltistan, and Sindh experienced NDVI degradation, with 63% slight degradation and 1% dramatic degradation; (4) the results show that precipitation is the main driver of vegetation growth in Pakistan, accounting for 70% of variability, while temperature contributes around 30%. Overall, this study improves our understanding of Pakistan's changing vegetation, identifying key factors and informing strategies for sustainable ecosystem management and climate change adaptation. Vegetation dynamic Temperature Precipitation Correlation analysis Multiple linear regression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Terrestrial vegetation plays a crucial role in regulating climate, carbon cycles, and water conservation (Guo et al. 2020 ). However, global climate change has led to varying vegetation change patterns worldwide. While some regions like Europe, the US, China, and Australia have experienced "greening" trends, others like southern Africa, northern America, and Southeast Asia have undergone "browning" or vegetation decline (Zhao et al. 2018 ). Climate change has significantly impacted vegetation growth, the mechanisms of climate change on vegetation are complex since vegetation can be influenced by the changing climate while simultaneously adapting to various environments (Challinor et al. 2014 ). Global warming has altered vegetation patterns, causing shifts in seasonal events such as earlier greening, changes in flowering patterns, and altered leaf fall timing (Vitasse et al. 2018 ). As global warming is expected to continue, monitoring vegetation changes is essential. Vegetation coverage is closely linked to ecosystem health, productivity, and ecological cycles, making it crucial for maintaining ecosystem balance and resilience (Zhang et al. 2014 ). Climate change has led to a increasing attention in understanding changes in vegetation cover and the underlying climate factors. Precipitation affects vegetation in in arid and semiarid regions, while temperature promotes growth in humid regions (Nemani et al. 2002 ). Climatic change and human activity are mostly responsible for the vegetation greening. More than 55% of the northern high latitudes and the Tibetan Plateau have experiencing a greening trend, positively impacting vegetation variability (Ma et al. 2024 ). The Intergovernmental Panel on Climate Change's Sixth Assessment Report highlighted the impact of recent climate change on terrestrial vegetation as a major topic of discussion (Masson-Delmotte et al. 2021 ). Plant physiological responses are closely linked to climate and vegetation changes serve as a broad indicator of environmental changes at regional and global levels (Huang et al. 2020 ; Sun et al. 2015 ). Therefore, vegetation dynamics become a research hotpot in the global change fields, exploring the spatiotemporal variations of vegetation and the response mechanisms as it provides valuable insights into preserving the global ecosystem. In the arid and semi-arid regions, vegetation dynamics are especially dramatic. Around 15% of Earth's surface is composed up of semi-arid zones, which have highly varied spatial and temporal patterns of temperature and rainfall pattern, which leads to dramatic variations in the spatiotemporal distribution, growth, and production of vegetation. (Hu et al. 2021 ). This is because the great sensitivity of vegetation in these regions to climate change. Climate change have a major impact on the general health of vegetation as well as the amount of vegetation cover (Ayyogari et al. 2014 ; Terzioğlu et al. 2015 ). Precipitation are the most important climate factors affect the spatial and temporal distribution of plants (Ren et al. 2023 ; Qaisrani et al. 2021 ). Changes in temperature can impact the rates of photosynthesis and respiration in vegetation and the precipitation is the main limited factor influencing vegetation growth in arid and semi-arid area. For instance, Hawinke et al. (Hawinkel et al. 2016 ) discovered that the interannual fluctuation of precipitation could contribute to the bulk vegetation variability. According to Mo et al. (Mo et al. 2019 ), the spatial pattern of precipitation and temperature both have effects on vegetation growth in arid mountain-oasis river basin in northwest China. Therefore, understanding vegetation dynamics and climate interactions is vital, as vegetation changes are occurring across worldwide, driven by climatic factors. Remote sensing methods are widely used to vegetation dynamic research, due to the large spatial extents and long time series of satellite imagery. The Normalized Different Vegetation Index (NDVI) is a satellite-based indicator of plant health and vitality that assesses vegetation greenness quantitatively and provides a broad picture of vegetation change (Banerjee et al. 2023 ). The NDVI provides highly accurate assessment of both sparsely and extensively vegetated regions. According to Huenneke et al. (Huenneke et al. 2001 ), it is a useful tool for analyzing the characteristics and spatial distribution of local vegetation. Many studies have examined the dynamics of vegetation in different regions using satellite-based remotely sensed data sets, primarily emphasizing the relationship with climatic conditions (Pelletier et al. 2016 ). Ahmad et al. (Ahmad et al. 2023 ) studied vegetation trends in Pakistan and used MODIS NDVI through Hurst exponent analysis to examine the constancy of vegetation dynamics in the future and found increasing vegetation trend. Xue et al. (Xue et al. 2021 ) used the MOD13A2 NDVI data to assess the vegetation dynamics using the linear regression method and Pearson correlation analysis. They found through correlation analysis that precipitation contributed more to NDVI than temperature. Wang et al. (Wang et al. 2023 ) proposed geographical detector (GeoDetector) method to determine the contributions of independent variables to dependent variables. To get deeper understandings into spatiotemporal variation, it is essential to take the contributions and interactions of different driving factors into account. The Multiple Linear Regression (MLR) analysis, as proposed by Wang et al. (Wang et al. 2015 ), can be used to quantify the contributions of independent variables to the dependent variables. This approach considering good agreement between predicted and observed anomalies, this technique was found to be effective for arid and semi-arid environments (Wang et al. 2015 ). On the other hand, without a solid scientific foundation for identifying the mechanisms driving vegetation change and an all-encompassing method for monitoring natural resources. It is also necessary to take the relationships between vegetation and different climate variables into consideration, because various climate variables might affect vegetation at the same time (Gloor et al. 2015 ). Research has also demonstrated that vegetation responds differently to various climate variables (Caocao et al. 2008 ; Herrmann et al. 2005 ; Rousvel et al. 2013 ; Srivastava et al. 1997 ). Therefore, further research is needed to study the spatiotemporal variation of vegetation and its responses to climate, and environmental factors, and to address the flexibility and transferability of existing methods across worldwide ecosystems. Pakistan a typical arid and semi-arid region located in the south Asia, providing an ideal location for investigating the complex interactions between climate and vegetation. While previous studies in Pakistan have examined vegetation patterns and climate relationships at a regional scale, some research has also focused on the impacts of drought on vegetation and the effects of land use/land cover changes and their effects on vegetation (Khan et al. 2013 ; Atif et al. 2018 ). Despite these efforts, the profound impact of climate change on Pakistan's ecological systems over the past few years remains a pressing concern. Notably, a comprehensive, nationwide analysis of spatiotemporal vegetation dynamics is still lacking, highlighting the need for systematic, long-term studies to investigate the intricate relationships between climate and vegetation across various spatial and temporal scales. Therefore, given the discussion above, the spatiotemporal variation in NDVI were analyzed in Pakistan from 2001 to 2021, as well as the driving forces of climate change on the NDVI spatial heterogeneity. The specific objectives of this study were to: (1) examine the spatiotemporal changes of vegetation (NDVI) and climate factors (precipitation and temperature); (2) assess the correlations between the NDVI and temperature and precipitation; and (3) identify the dominant climatic factors that affect the dynamic changes of vegetation at pixel scale in Pakistan. These results can offer scientific direction for improved adaptation to climate change. 2. Materials and Methods 2.1. Study area Pakistan is located in South Asia (23 ◦ 35′-37 ◦ 05′N and 60 ◦ 50′-77 ◦ 50′E), which is bordered by India, Afghanistan, Iran, China, the Himalayas and Arabian Sea (Fig. 1 ). The country have six administrative entities, that are KPK (Khyber Pakhtunkhwa), Gilgat Baltistan, Baluchistan, Punjab and Islamabad, Sindh and Azad Kashmir. The total geographical area is 881,912 km 2 (Hussain et al. 2022 ). The climate types of Pakistan range from tropical to temperate ones. The annual mean temperature ranges from 23 to 26°C, with T min and T max values of 16–19 and 29–33°C, respectively. The mean annual precipitation in the northern region is > 800 mm and in the southern region is ~ 50 mm (Ali et al. 2021 ). Arid to semi-arid climates with low precipitation and high temperatures are found over 80% of the country (Khatoon and Ali 2004 ). This country's landscape varies greatly, with high mountain ranges in the north and wide plains in the Indus Basin. The natural vegetation is comprised of grasses, bushes, and forests. Pakistan's unique environmental conditions and climate lead to four distinct seasons here: a hot and dry spring from March to May, the rainy summer season (June to September), often referred to as the southwest monsoon period, autumn season (October and November), a decreasing monsoon season and dry and cold winter from December to February. The diverse climate and ecology of Pakistan make it an ideal research site for vegetation dynamics and its responses of climate change in arid and semi-arid region. 2.2. Data source In this study, the NDVI (Normalized Different Vegetation Index) data were used to illustrate the changing characteristics of the vegetation in Pakistan. NDVI can be involved not exclusively to concentrate on spatiotemporal varieties in vegetation, yet in addition to mirror the vegetation input and reaction on environment (Martínez and Gilabert 2009 ). The Moderate Resolution Imaging Spectroradiometer (MODIS) product, known as MOD13Q1, provided the NDVI dataset, which were obtained from USGS Earth Explore ( http://earthexplorer.usgs.gov ). MODQ13Q1 data with 250-m spatial resolution and of 16 days composite from 2001 to 2021 and the NDVI for twelve months (January to December) were analyzed to get the annual NDVI value. The maximum monthly NDVI values were used from January to December to obtain the annual NDVI dataset by maximum value composite (MVC) method (Tao et al. 2022 ). The annual NDVI data from 2001 to 2021 have been generated using ArcGIS 10.8. Temperature and precipitation were employed to represent climate factors. The annual temperature and annual precipitation data from 2001 to 2021 were downloaded from Worldclim ( https://www.worldclim.org/data/index.html ) (from 30 second, 0.93 × 0.93 = 0.86 km 2 ). Worldclim data are downscaled from CRU-TS-4.06 by using Worldclim 2.1 for the bias correction (Climate Research Unit, University of East Anglia). The gridded monthly time series data of Worldclim are reanalysis for average temperature and mean precipitation from 2001 to 2021 and were used to analyze the inter relationship between NDVI. For each year the average temperature and total precipitation were calculated. 2.3. Methods 2.3.1. Trend analysis The change trend of NDVI and climatic variables for each pixel between 2001 to 2021 are fitted using the ordinary least squares method (Liu et al. 2021 ).The following formula is used to calculate the slope parameter. $$\:Slope=\frac{\varvec{n}{\sum\:}_{\varvec{i}=1}^{\varvec{n}}{\varvec{i}\:\varvec{Y}}_{\varvec{i}}\:-\:{\sum\:}_{\varvec{i}=1}^{\varvec{n}}\varvec{i}{\sum\:}_{\varvec{i}=1}^{\varvec{n}}{\varvec{Y}}_{\varvec{i}}\:}{\varvec{n}{\sum\:}_{\varvec{i}=1}^{\varvec{n}}{\varvec{i}}^{2}\:-({\sum\:}_{\varvec{i}=1}^{\varvec{n}}\varvec{i}{)}^{2}\:}$$ 1 where Y i is the variable connected with the i- th observation and slope represents the linear trend, and n is the cumulative number of year in this study 2001 to 2021 (21 year). The Slope > 0 and Slope < 0 represent trends that are increasing and declining, respectively. The vegetation change types are classified into four categories based on varying slope values: dramatic degradation (Slope ≤ -0.01), slight degradation (-0.01 < Slope < 0), slight development (0 < Slope < 0.01) and dramatic development (Slope ≥ 0.01) (Bashir et al. 2020 ). 2.3.2. Correlation analysis The correlation coefficient was calculated as in Zhong et al. (Zhong et al. 2021 ). The correlation coefficient between NDVI and temperature, and between NDVI and precipitation are calculated by the following equation: $$\:{r}_{\varvec{x}\varvec{y}}=\:\frac{{\sum\:}_{\varvec{i}=1}^{\varvec{n}}[\:\left({\varvec{x}}_{\varvec{i}}-\stackrel{̄}{\varvec{x}}\right)({\varvec{y}}_{\varvec{i}}-\varvec{ȳ})]}{\sqrt{{{\sum\:}_{\varvec{i}=1}^{\varvec{n}}\left({\varvec{x}}_{\varvec{i}}-\varvec{x}̄\right)}^{2}{{\sum\:}_{\varvec{i}=1}^{\varvec{n}}\left({\varvec{y}}_{\varvec{i}}-\varvec{ȳ}\right)}^{2}\:}}$$ 2 where r symbolize the correlation between x and y variables, x i is the NDVI, y i indicates the temperature or precipitation in the consistent time period in the study area, x̄ is the average NDVI in the study area from 2001 to 2021 and ȳ represents the average temperature or precipitation. 2.3.3. Respective contributions of precipitation and temperature variability to the NDVI To discover the dominant climate factors that effects the vegetation dynamics in the study area from 2001 to 2021, we conducted Multiple Linear Regression (MLR) analysis for the NDVI variability and climate factors. Before entering into the MLR model the data were normalized to make them comparable (Sun et al. 2013 ). The dataset of NDVI, temperature and precipitation were normalized by min-max normalization method by the following formula: $$\:{y}_{i}=\:\frac{{x}_{i}\:-{\:min}_{1}\le\:j\le\:n\:\left\{{x}_{j}\right\}}{{max}_{1}\le\:j\le\:n\:\left\{{x}_{j}\right\}\:-{\:min}_{1}\le\:j\le\:n\:\left\{{x}_{j}\right\}}$$ 3 where max 1 ≤ j ≤ n {x j } is the maximum value of the sample data and min 1 ≤ j ≤ n {x j } is the minimum value of the sample data. The new data set y i Ɛ [0, 1] is dimensionless. We used standardized data to perform MLR analysis at pixel level (Hua et al. 2017 ). The equation are as follows: $$\:y={\beta\:}_{0}+\sum\:_{i=1}^{n}{\beta\:}_{i}{x}_{i}+\in\:$$ 4 where x i is used for temperature and precipitation (independent variable), y for NDVI (dependent variable), i is the number of independent variables, β i is the regression coefficient and Ɛ for random error. By this equation we got the result of the binary regression of the NDVI, temperature and precipitation. After comparing the absolute value of standard regression coefficient of precipitation (R E − P ) and temperature (R E − T ) it can be easily determined whether temperature or precipitation has a greater influence on the NDVI variation at the pixel level. If the result (⎪R E − P ⎪ –⎪ R E − T ⎪) ≤ 0, it showed that NDVI is dominated by temperature, if the (R E−P –R E−T ) > 0, it showed that NDVI is dominated by precipitation. 3. Result 3.1. Temporal and spatial variations of NDVI in Pakistan 3.1.1. The temporal change of NDVI The linear regression model was used for the study of vegetation change trend in Pakistan from 2001 to 2021. The average NDVI in Pakistan was 0.29. The lowest NDVI value was observed in 2001 and the highest NDVI value was noted in 2020, with annual mean NDVI of 0.19 and 0.33, respectively. In the past 21 year, fluctuation was observed in the vegetation growth trend, with an increase of 0.003/year for annual NDVI (Fig. 2 ). The positive slope showed significant improvement in vegetation growth in the study area from 2001 to 2021. 3.1.2. The spatial distribution of NDVI The spatial distribution of average annual NDVI in Pakistan from 2001 to 2021 showed obvious spatial heterogeneity (Fig. 3 a). The area of annual mean NDVI higher than 0.24 was 32% of the total study area, mostly scattered in the Punjab, KPK, Kashmir and Sindh regions. However, the annual mean NDVI was mostly low in the Baluchistan, Gilgat Baltistan and some portion of Sindh and Punjab province of the country. 3.1.3. The change trend of NDVI There are clear spatial differences in the vegetation changes based on the slope of NDVI for each pixel in the study area from 2001 to 2021 (Fig. 4 ). We found obvious spatial differences in vegetation changes (Fig. 4 a). Overall result of the study area showed that, the regions with significantly improved vegetation cover were mainly distributed in Punjab and Islamabad, Azad Kashmir, KPK, and certain areas of Sind province. The area with insignificantly decreased NDVI was mainly found in Baluchistan Gilgat Baltistan and some parts of Sindh province. Moreover, there was a dramatic degradation of 1%, slight degradation of 63%, whereas slight development was of 27% and 9% of the annual vegetation had dramatic development during the study period (Fig. 4 b). The area wherever NDVI changed dramatic development was 72721.23 km 2 of the total area, slight development was 209064.92 km 2 , however the slight degradation 493562.2 km 2 and dramatic degradation was 11883.01 km 2 (Fig. 4 b). 3.2. Temporal and spatial variations of climate factors in Pakistan 3.2.1. The temporal and spatial distributions of temperature and precipitation The temporal and spatial distributions of temperature is shown in Fig. 5 a and Fig. 6 a. The annual mean temperature was found in the range of -13.01 to 37.52°C, Spatially, temperature was higher in Sindh, Punjab province, some parts of Baluchistan, KPK and Kashmir regions and lower in Gilgat regions and some area of KPK and Kashmir, where temperature is affected by the Mountains, which have lower average annual temperature. The temperature showed upward trend from southwest to northeast in the study area, commonly because of warm climate in the plane regions of Baluchistan Sindh and Punjab (South Punjab). The highest average temperature in Sindh, Punjab and Baluchistan (maximum area) varied from 31.25 to 37.51°C from 2001 to 2021, whereas Gilgat had the lowest average temperature, ranging from − 13.01 to 0.50 °C. Temporally, the average annual temperature in the study area is 26.68°C and the highest average annual temperature was 28.20°C recorded in 2001, while the lowest average annual temperature was 23.16°C in 2002 (Fig. 6 a). There is an overall increase in temperature from 2001 to 2021 in the study area, with an increase rate of 0.048 °C/year. Similarly, the spatial variation of precipitation over the study area is shown in Fig. 5 b. The annual mean precipitation was found in the range of 1.56 to 125.00 mm, the annual precipitation was higher in Kashmir and KPK regions while lower in Sindh, Baluchistan, Gilgat Baltistan and Punjab province. From 2001 to 2021, the research area's precipitation increased at a slow rate. The mean annual precipitation was 22.99 mm and the rate of increase is 0. 053 mm/year as shown in Fig. 6 b. The highest annual mean precipitation was 30.41 mm in 2020 and the lowest precipitation was 15.92 mm in 2002 (Fig. 6 b). 3.2.2. The spatiotemporal variations of temperature and precipitation The variations of temperature and precipitation from 2001 to 2021 in pixel scale are shown in Fig. 7 a and Fig. 7 b. The result showed that 35% of the total area comprised up of regions where temperature were increased (Fig. 7 a). Approximately, the Baluchistan and Sindh province regions of the study area, demonstrated increased in temperature. The warm and humid air mass from the southeast, results in increased temperature in the southeast regions. There was a temperature increase, with the southeast low altitudes showing a rising trend. Similarly, the spatial variation of precipitation is shown in Fig. 7 b. The area having increased precipitation were 49% during 2001 to 2021, were mostly distributed in the Punjab, KPK, Kashmir and some area of Sindh and Baluchistan province of the study area. 3.3.The correlation of NDVI and climate factors The results of pixel-wise correlation coefficients between NDVI and climatic factors (temperature and precipitation) are shown in Fig. 8 . The result showed that the NDVI and temperature exhibit a positive correlation (R > 0; P < 0.01) in 46% of the total area, whereas a negative correlation was observed in 56% of the pixels. Notably, positive correlations were predominantly found in Sindh province. Conversely, negative correlations between NDVI and temperature were mainly concentrated in Baluchistan, Punjab, and Khyber Pakhtunkhwa (KPK), (Fig. 8 a). Similarly, the results of correlation between NDVI and precipitation shown that the positive correlation (R > 0; P < 0.01) was accounted 66% of the study area while 34% showed a negative correlation. Positive correlations between NDVI and precipitation were predominantly observed in in the area of Baluchistan, KPK, Kashmir and some parts of Sindh, Punjab province. Similarly the percentage of the study area with negative correlations between NDVI and precipitation was, mainly observed in Sindh, Gilgat Baltistan and some parts of Punjab province (Fig. 8 b). Temperature and precipitation had partial correlations values of 0.74 (p < 0.01) and 0.86 (p < 0.01), respectively. This results revealed that there was a positive partial correlation between the NDVI and these two climatic factors. 3.4. Main dominant climate factors A multiple linear regression model was employed to investigate the comparative contributions of climatic factors to NDVI (Fig. 9 ). The results reveal that temperature dominates NDVI variation in approximately 70% of the total area, while the area where precipitation dominates NDVI variation accounts for 30%. Both temperature and precipitation significantly impact vegetation growth, exhibiting distinct spatial patterns. Notably, positive relationships between NDVI and temperature are observed in northern, southern, and western Pakistan (Baluchistan, Gilgat Baltistan and parts of Sindh, Punjab and Khyber Pakhtunkhwa province), except in central regions, where precipitation emerges as the most dominant climatic factor driving vegetation growth. These findings highlight the heterogeneous relationships between vegetation and climatic factors across the study area, with varying dominant climatic factors influencing NDVI changes. Interestingly, the central region, characterized by significant vegetation trends (Fig. 9 ), shows precipitation as the dominant climatic factor. The areas where precipitation dominates NDVI variation are primarily located in Kashmir, most of Punjab, Khyber Pakhtunkhwa, and parts of Sindh provinces. 4. Discussion 4.1. Vegetation variation features in Pakistan Previous research has used plant cover change to investigate land degradation as vegetation cover is a dynamic component of the ecosystem (Bashir et al. 2020 ; Tian et al. 2015 ; Tong et al. 2016 ). Furthermore, the study of vegetation cover changes and how they interact with climate change has gained significant attention (Hall-Beyer 2003 ; Park and Sohn 2010 ). Zhou et al. (Zhou et al. 2001 ), claimed that a major factor influencing vegetation patterns is climate change. The findings of the research show that vegetation's reaction to climate change is not consistent. Regarding local climate, responses differ from place to place. Ali et al. (Ali et al. 2019 ), observed that there is a strong correlation between vegetation elements and water accessibility in the South Asian region. According to Haroon et al. (Haroon et al. 2016 ), decreased precipitation areas experienced submerged pressure, which prevented plants from growing there. This suggests that the interaction between vegetation cover and climate change is complex and spatially variable, with water accessibility playing a critical role in determining vegetation dynamics. The NDVI values showed an increasing trend in Pakistan from 2001 to 2021, with an annual increase of 0.003/year. Over the 21-year period, vegetation growth fluctuated, but overall, vegetation change in Pakistan showed a significant increase, consistent with previous studies in Pakistan and globally (Ahmad et al. 2023 ; Zhang et al. 2016 ; Guo et al. 2017 ; WANG et al. 2016). The annual mean NDVI values and trends in Pakistan showed spatial variations, likely due to climatic factors. High NDVI values were found in Punjab, Kashmir, KPK, and parts of Sindh, while low values were observed in Gilgit, Baluchistan, and northern areas. The results suggest that vegetation changes can indicate climate change, leading to severe climatic consequences in the region (Fig. 3 a and 4 a). The result showing similarity to the finding (Liu et al. 2020 ). Vegetation dynamics have increased globally and regionally in response to climate change. Studies, such as one in the central Himalaya, have shown significant increases in vegetation growth from 1982 to 2011 (Zhang et al. 2013 ; Baniya et al. 2018 ). Similarly, Pakistan has experienced an increasing trend in annual mean NDVI 3–55]. This is consistent with global temperature trends, which have shown a steady increase, with Pakistan's temperature rising by + 0.048°C/year, (Fig. 8 a) was consistent with findings from other studies conducted in the world (Shrestha et al. 1999 ), for example + 0.006°C/year during 1997 to 1994 and + 0.04°C/year from 1975 to 2007 (Sharma 2009 ). The increasing trend in Pakistan's annual mean NDVI closely matches the rising temperature trend. Although precipitation changes were relatively small, a significant increase was observed. A linear regression model revealed a significant trend towards increasing vegetation over the past 21 years, despite some limitations in accounting for non-linear changes (Emamian et al. 2021 ). This study found increasing trends in vegetation across the study area, with Punjab, KPK, Kashmir, and Sindh provinces showing the highest percentage of significant vegetation trends (24.83%). The findings emphasize the profound impact of climate change on vegetation dynamics, highlighting the need for region-specific strategies to mitigate vegetation degradation and promote sustainable ecosystem management (Emamian et al. 2021 ). 4.2. The responses of vegetation dynamics to climate change Annual NDVI increased during 2001 to 2021, consistent with results from several other parts of world, especially in the northern hemisphere, which have shown increasing trends in NDVI. The spatial distribution of precipitation over Pakistan is dominated by the monsoon; however, the northern regions observed comparatively higher amounts of precipitation than the other regions of the country. This study finds a significant correlation in the limited region between NDVI, temperature and precipitation. Topographical effects may affect the correlation coefficient between NDVI and climatic parameters; in addition, precipitation falling in the form of snow can reduce the surface reflectance (Achard and Estreguil 1995 ). Therefore, caution is required when assessing correlation with NDVI in such specific locations. This inconsistency is as Pearson correlation coefficient analysis was accompanied in this study, however some other researcher did not find the correlation vegetation greenness and precipitation, apart from the influence of temperature. These findings may have been caused by the extensive agricultural activities that are taking place in these regions. Similar findings were reported by (Chen et al. 2024 ) who linked India's widespread greening to agricultural practices. Despite no change in vegetation trends, the south-east and western sections of Sindh Province and the south-eastern part of Baluchistan displayed the maximum number of pixels. These areas consist mainly of desert and barren territory. The pixel-wise correlation indicated relatively stronger and more significant positive correlation of NDVI with precipitation than with temperature. Precipitation is important in dry regions region of Pakistan, where the average annual precipitation is very low. However, plants show quick responses to temperature but delayed responses to precipitation, indicating that the vegetation growth is most strongly influenced by increasing temperature (Yang et al. 2019 ). The results showed that the climatic factors of vegetation changes existing significant regional differences in the study area. The mean annual temperature was recognized as dominate manipulating factor on the spatial distribution of NDVI. For vegetation, precipitation has a more significant impact on growth in arid regions than temperature. These regions frequently have limited precipitation with substantial evaporation, which results in limited water resources (Xue et al. 2021 ).Moreover in arid and semi-arid regions, the availability of water is also a major limiting factor for the growth of vegetation, elevation and soil types also affected the spatial distribution of vegetation, and moreover elevation is the key factor persuading vegetation change, particularly in the northern. Vegetation depends on water accessibility, which is main issue in South Asia that showed that vegetation exploitation could be enhanced by the increased aridity coming about form of low precipitation rate and higher evaporative interest of climate. The areas with lower precipitation, vegetation did not properly flourish (Haroon et al. 2016 ; Ali et al. 2019 ). Normally our results indicated that NDVI shows significant positive correlation with temperature and precipitation, which means that increase in temperature and precipitation has a stimulating effect on NDVI. Though some studies shown that vegetation greenness and precipitation are negative correlated (Gloor et al. 2015 ; Zhao et al. 2017 ). The impact of temperature on vegetation greenness was greater than the impact of precipitation. This results showing similarities. 4.3. Limitations and implications This study highlights the significant impact of temperature and precipitation on NDVI, providing valuable insights into vegetation dynamics, but also underscores the complexity of environmental interactions and the need for further research to account for additional factors influencing vegetation growth. Despite these findings, several limitations remain, including the exclusion of environmental factors such as sunlight, soil fertility, and CO 2 concentration, as well as human-induced land cover changes, highlighting the need for future studies to integrate these variables for a more comprehensive understanding of vegetation dynamics (Wang et al. 2020 ; Ochoa-Hueso et al. 2020 ). In real terms, temperature and precipitation have no independent impact on NDVI. According to Moleses et al. (Moles et al. 2014 ), increased temperatures have the potential to increase evaporation and have an impact on precipitation through evapotranspiration. Instead of focusing on determinants, the goal of this study was to identify the main factors that affect NDVI. It is challenging to pinpoint the key variables that affect NDVI, however after looking at precipitation and temperature, it is an ideal method for exploring the impact on NDVI as they are the most prominent variable in plant growth. Furthermore, this study did not account for vegetation changes brought on by human activity-induced changes in land cover, such as deforestation and agriculture. However, this research contributes significantly to the existing body of knowledge by quantifying the relationships between temperature, precipitation, and NDVI, informing climate change mitigation and adaptation strategies for sustainable ecosystem management. 5. Conclusion This study investigated the spatiotemporal variation in NDVI across Pakistan from 2001 to 2021, examining the influence of temperature and precipitation on NDVI spatial heterogeneity. Overall, over the previous 21 years, there has been a notable rise in the annual mean NDVI, implying the improvements of vegetation in Pakistan. Precipitation and temperature positively impact NDVI, with precipitation having a greater effect. Throughout the study period, the country noticed an increase in temperature and precipitation, which was accompanied by greener vegetation. The correlation between precipitation and NDVI was significantly greater in terms of regional dispersion than the temperature correlation. The regions with annual low precipitation exhibit minimum NDVI response to precipitation. Additionally, there is a distinct regional distribution for the contributions of temperature variability and precipitation to NDVI dynamics. Furthermore, precipitation and temperature variations affect NDVI dynamics in a visible spatial distribution. Significant positive relationships are found between NDVI and temperature over the South and East Pakistan (Sindh and Punjab province), excepting for the northwestern regions, representing that temperature is the extreme significant climatic factor contributing to the vegetation growth. Climate change has had a major impact on Pakistan's ecological systems, especially in the past few years. Our results improved our awareness of how Pakistan's vegetation is changing and highlighted the relative significance of the factors influencing NDVI variations. These results could also provide theoretical references and suggestions for vegetation protection in the country. Declarations Conflicts of interest: The authors declare no conflict of interest. Ethics approval: The authors confirm that their research was conducted in accordance with established ethical guidelines and standards. Competing intrests The authors report no conflicts of interest Funding This work was supported by The Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0308). Author Contribution KU: Writing – original draft, performed experiments and analysis, Conceptualization and designed experiments. HW: Writing – review & editing, Investigation. WL: Writing – review & editing. H: Writing – review & editing. UH: Writing – review & editing. ZZ: Writing – review & editing, Supervision, Resources, Funding acquisition. All authors read and approved the final manuscript Acknowledgement We would like to acknowledge Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China and School of Ecology and Environmental Sciences, Yunnan University, Kunming, China Data Availability The Moderate Resolution Imaging Spectroradiometer (MODIS) product, known as MOD13Q1, provided the NDVI dataset, which were obtained from USGS Earth Explore (http://earthexplorer.usgs.gov).The annual temperature and annual precipitation data from 2001 to 2021 were downloaded from Worldclim (https://www.worldclim.org/data/index.html) References Achard F, Estreguil C (1995) Forest classification of Southeast Asia using NOAA AVHRR data. 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18:01:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42580,"visible":true,"origin":"","legend":"\u003cp\u003eThe trend of annual mean Normalized Different Vegetation Index in Pakistan from 2001 to 2021\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/f49bc1d6886f19a5f3242b60.png"},{"id":75188603,"identity":"3dc5c0a4-5066-449c-80c7-53feb663f739","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":587060,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of average annual Normalized Different Vegetation Index (\u003cstrong\u003ea\u003c/strong\u003e) and area ratios of each Normalized Different Vegetation Index class (\u003cstrong\u003eb\u003c/strong\u003e) during 2001-2021 in Pakistan\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/232aa1352e15f9575ecf4ba0.png"},{"id":75188598,"identity":"38bbcc08-f052-4b09-9ac4-926541e0d10f","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":586588,"visible":true,"origin":"","legend":"\u003cp\u003eThe change trend of annual Normalized Different Vegetation Index (\u003cstrong\u003ea\u003c/strong\u003e) and area ratio of different change classes of Normalized Different Vegetation Index (\u003cstrong\u003eb\u003c/strong\u003e) during 2001 to 2021 in Pakistan\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/9cb982e5b48aa5285f95fa74.png"},{"id":75188588,"identity":"6a3a6939-26d8-4eca-8cbc-6dde43af6ae5","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":727586,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of average (\u003cstrong\u003ea\u003c/strong\u003e) temperature and (\u003cstrong\u003eb\u003c/strong\u003e) precipitation during 2001-2021 in Pakistan\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/ecbcd648e70d2fd59d7dc237.png"},{"id":75188597,"identity":"d7ae1341-61ed-4c1b-9b0b-ce14e2746f55","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":363889,"visible":true,"origin":"","legend":"\u003cp\u003eThe temporal change trends of (\u003cstrong\u003ea\u003c/strong\u003e) temperature and (\u003cstrong\u003eb\u003c/strong\u003e) precipitation during 2001-2021 in Pakistan\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/3abcc1471be063adb9a91a96.png"},{"id":75188589,"identity":"4f6d663e-7649-4825-ae78-025ae7e32bab","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":710179,"visible":true,"origin":"","legend":"\u003cp\u003eThe spatial change trends of (\u003cstrong\u003ea\u003c/strong\u003e) temperature and (\u003cstrong\u003eb\u003c/strong\u003e) precipitation during 2001-2021 in Pakistan\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/90c87810452c0404e563f0ed.png"},{"id":75188593,"identity":"cccadaf5-9f6a-4c57-be16-79557e1b76fa","added_by":"auto","created_at":"2025-01-31 18:01:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1121434,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial patterns of correlations between (\u003cstrong\u003ea\u003c/strong\u003e) Normalized Different Vegetation Index and temperature and (\u003cstrong\u003eb\u003c/strong\u003e) Normalized Different Vegetation Index and precipitation during 2001-2021 in Pakistan\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/8383dc3110253c42fd0dd4e8.png"},{"id":75190667,"identity":"65a72dfc-4e69-45c1-b265-9f9c9c86cc62","added_by":"auto","created_at":"2025-01-31 18:09:46","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":522408,"visible":true,"origin":"","legend":"\u003cp\u003eMain dominant climate factors related to Normalized Different Vegetation Index change in Pakistan\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/0e15fbb4f2436d7161dd883a.png"},{"id":75191718,"identity":"489ac334-98e1-4568-9a43-2145b1b0b237","added_by":"auto","created_at":"2025-01-31 18:33:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6217314,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5801698/v1/3499e7a9-8795-49a2-8964-3eb26243c899.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatiotemporal variation of vegetation and its responses to climate change in Pakistan from 2001 to 2021","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eTerrestrial vegetation plays a crucial role in regulating climate, carbon cycles, and water conservation (Guo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, global climate change has led to varying vegetation change patterns worldwide. While some regions like Europe, the US, China, and Australia have experienced \"greening\" trends, others like southern Africa, northern America, and Southeast Asia have undergone \"browning\" or vegetation decline (Zhao et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Climate change has significantly impacted vegetation growth, the mechanisms of climate change on vegetation are complex since vegetation can be influenced by the changing climate while simultaneously adapting to various environments (Challinor et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Global warming has altered vegetation patterns, causing shifts in seasonal events such as earlier greening, changes in flowering patterns, and altered leaf fall timing (Vitasse et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). As global warming is expected to continue, monitoring vegetation changes is essential. Vegetation coverage is closely linked to ecosystem health, productivity, and ecological cycles, making it crucial for maintaining ecosystem balance and resilience (Zhang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Climate change has led to a increasing attention in understanding changes in vegetation cover and the underlying climate factors. Precipitation affects vegetation in in arid and semiarid regions, while temperature promotes growth in humid regions (Nemani et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Climatic change and human activity are mostly responsible for the vegetation greening. More than 55% of the northern high latitudes and the Tibetan Plateau have experiencing a greening trend, positively impacting vegetation variability (Ma et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The Intergovernmental Panel on Climate Change's Sixth Assessment Report highlighted the impact of recent climate change on terrestrial vegetation as a major topic of discussion (Masson-Delmotte et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Plant physiological responses are closely linked to climate and vegetation changes serve as a broad indicator of environmental changes at regional and global levels (Huang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sun et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Therefore, vegetation dynamics become a research hotpot in the global change fields, exploring the spatiotemporal variations of vegetation and the response mechanisms as it provides valuable insights into preserving the global ecosystem.\u003c/p\u003e \u003cp\u003eIn the arid and semi-arid regions, vegetation dynamics are especially dramatic. Around 15% of Earth's surface is composed up of semi-arid zones, which have highly varied spatial and temporal patterns of temperature and rainfall pattern, which leads to dramatic variations in the spatiotemporal distribution, growth, and production of vegetation. (Hu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This is because the great sensitivity of vegetation in these regions to climate change. Climate change have a major impact on the general health of vegetation as well as the amount of vegetation cover (Ayyogari et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Terzioğlu et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Precipitation are the most important climate factors affect the spatial and temporal distribution of plants (Ren et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Qaisrani et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Changes in temperature can impact the rates of photosynthesis and respiration in vegetation and the precipitation is the main limited factor influencing vegetation growth in arid and semi-arid area. For instance, Hawinke et al. (Hawinkel et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) discovered that the interannual fluctuation of precipitation could contribute to the bulk vegetation variability. According to Mo et al. (Mo et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), the spatial pattern of precipitation and temperature both have effects on vegetation growth in arid mountain-oasis river basin in northwest China. Therefore, understanding vegetation dynamics and climate interactions is vital, as vegetation changes are occurring across worldwide, driven by climatic factors.\u003c/p\u003e \u003cp\u003eRemote sensing methods are widely used to vegetation dynamic research, due to the large spatial extents and long time series of satellite imagery. The Normalized Different Vegetation Index (NDVI) is a satellite-based indicator of plant health and vitality that assesses vegetation greenness quantitatively and provides a broad picture of vegetation change (Banerjee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The NDVI provides highly accurate assessment of both sparsely and extensively vegetated regions. According to Huenneke et al. (Huenneke et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), it is a useful tool for analyzing the characteristics and spatial distribution of local vegetation. Many studies have examined the dynamics of vegetation in different regions using satellite-based remotely sensed data sets, primarily emphasizing the relationship with climatic conditions (Pelletier et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Ahmad et al. (Ahmad et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studied vegetation trends in Pakistan and used MODIS NDVI through Hurst exponent analysis to examine the constancy of vegetation dynamics in the future and found increasing vegetation trend. Xue et al. (Xue et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) used the MOD13A2 NDVI data to assess the vegetation dynamics using the linear regression method and Pearson correlation analysis. They found through correlation analysis that precipitation contributed more to NDVI than temperature. Wang et al. (Wang et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) proposed geographical detector (GeoDetector) method to determine the contributions of independent variables to dependent variables. To get deeper understandings into spatiotemporal variation, it is essential to take the contributions and interactions of different driving factors into account. The Multiple Linear Regression (MLR) analysis, as proposed by Wang et al. (Wang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), can be used to quantify the contributions of independent variables to the dependent variables. This approach considering good agreement between predicted and observed anomalies, this technique was found to be effective for arid and semi-arid environments (Wang et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). On the other hand, without a solid scientific foundation for identifying the mechanisms driving vegetation change and an all-encompassing method for monitoring natural resources. It is also necessary to take the relationships between vegetation and different climate variables into consideration, because various climate variables might affect vegetation at the same time (Gloor et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Research has also demonstrated that vegetation responds differently to various climate variables (Caocao et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Herrmann et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rousvel et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Srivastava et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Therefore, further research is needed to study the spatiotemporal variation of vegetation and its responses to climate, and environmental factors, and to address the flexibility and transferability of existing methods across worldwide ecosystems.\u003c/p\u003e \u003cp\u003ePakistan a typical arid and semi-arid region located in the south Asia, providing an ideal location for investigating the complex interactions between climate and vegetation. While previous studies in Pakistan have examined vegetation patterns and climate relationships at a regional scale, some research has also focused on the impacts of drought on vegetation and the effects of land use/land cover changes and their effects on vegetation (Khan et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Atif et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Despite these efforts, the profound impact of climate change on Pakistan's ecological systems over the past few years remains a pressing concern. Notably, a comprehensive, nationwide analysis of spatiotemporal vegetation dynamics is still lacking, highlighting the need for systematic, long-term studies to investigate the intricate relationships between climate and vegetation across various spatial and temporal scales.\u003c/p\u003e \u003cp\u003eTherefore, given the discussion above, the spatiotemporal variation in NDVI were analyzed in Pakistan from 2001 to 2021, as well as the driving forces of climate change on the NDVI spatial heterogeneity. The specific objectives of this study were to: (1) examine the spatiotemporal changes of vegetation (NDVI) and climate factors (precipitation and temperature); (2) assess the correlations between the NDVI and temperature and precipitation; and (3) identify the dominant climatic factors that affect the dynamic changes of vegetation at pixel scale in Pakistan. These results can offer scientific direction for improved adaptation to climate change.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study area\u003c/h2\u003e \u003cp\u003ePakistan is located in South Asia (23\u003csup\u003e◦\u003c/sup\u003e35\u0026prime;-37\u003csup\u003e◦\u003c/sup\u003e05\u0026prime;N and 60\u003csup\u003e◦\u003c/sup\u003e50\u0026prime;-77\u003csup\u003e◦\u003c/sup\u003e50\u0026prime;E), which is bordered by India, Afghanistan, Iran, China, the Himalayas and Arabian Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The country have six administrative entities, that are KPK (Khyber Pakhtunkhwa), Gilgat Baltistan, Baluchistan, Punjab and Islamabad, Sindh and Azad Kashmir. The total geographical area is 881,912 km \u003csup\u003e2\u003c/sup\u003e (Hussain et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The climate types of Pakistan range from tropical to temperate ones. The annual mean temperature ranges from 23 to 26\u0026deg;C, with T\u003csub\u003emin\u003c/sub\u003e and T\u003csub\u003emax\u003c/sub\u003e values of 16\u0026ndash;19 and 29\u0026ndash;33\u0026deg;C, respectively. The mean annual precipitation in the northern region is \u0026gt;\u0026thinsp;800 mm and in the southern region is ~\u0026thinsp;50 mm (Ali et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Arid to semi-arid climates with low precipitation and high temperatures are found over 80% of the country (Khatoon and Ali \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This country's landscape varies greatly, with high mountain ranges in the north and wide plains in the Indus Basin. The natural vegetation is comprised of grasses, bushes, and forests. Pakistan's unique environmental conditions and climate lead to four distinct seasons here: a hot and dry spring from March to May, the rainy summer season (June to September), often referred to as the southwest monsoon period, autumn season (October and November), a decreasing monsoon season and dry and cold winter from December to February. The diverse climate and ecology of Pakistan make it an ideal research site for vegetation dynamics and its responses of climate change in arid and semi-arid region.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data source\u003c/h2\u003e \u003cp\u003eIn this study, the NDVI (Normalized Different Vegetation Index) data were used to illustrate the changing characteristics of the vegetation in Pakistan. NDVI can be involved not exclusively to concentrate on spatiotemporal varieties in vegetation, yet in addition to mirror the vegetation input and reaction on environment (Mart\u0026iacute;nez and Gilabert \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The Moderate Resolution Imaging Spectroradiometer (MODIS) product, known as MOD13Q1, provided the NDVI dataset, which were obtained from USGS Earth Explore (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://earthexplorer.usgs.gov\u003c/span\u003e\u003cspan address=\"http://earthexplorer.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). MODQ13Q1 data with 250-m spatial resolution and of 16 days composite from 2001 to 2021 and the NDVI for twelve months (January to December) were analyzed to get the annual NDVI value. The maximum monthly NDVI values were used from January to December to obtain the annual NDVI dataset by maximum value composite (MVC) method (Tao et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The annual NDVI data from 2001 to 2021 have been generated using ArcGIS 10.8.\u003c/p\u003e \u003cp\u003eTemperature and precipitation were employed to represent climate factors. The annual temperature and annual precipitation data from 2001 to 2021 were downloaded from Worldclim (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldclim.org/data/index.html\u003c/span\u003e\u003cspan address=\"https://www.worldclim.org/data/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (from 30 second, 0.93 \u0026times; 0.93\u0026thinsp;=\u0026thinsp;0.86 km\u003csup\u003e2\u003c/sup\u003e). Worldclim data are downscaled from CRU-TS-4.06 by using Worldclim 2.1 for the bias correction (Climate Research Unit, University of East Anglia). The gridded monthly time series data of Worldclim are reanalysis for average temperature and mean precipitation from 2001 to 2021 and were used to analyze the inter relationship between NDVI. For each year the average temperature and total precipitation were calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Methods\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Trend analysis\u003c/h2\u003e \u003cp\u003eThe change trend of NDVI and climatic variables for each pixel between 2001 to 2021 are fitted using the ordinary least squares method (Liu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).The following formula is used to calculate the slope parameter.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Slope=\\frac{\\varvec{n}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}{\\varvec{i}\\:\\varvec{Y}}_{\\varvec{i}}\\:-\\:{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\varvec{i}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}{\\varvec{Y}}_{\\varvec{i}}\\:}{\\varvec{n}{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}{\\varvec{i}}^{2}\\:-({\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\varvec{i}{)}^{2}\\:}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the variable connected with the \u003cem\u003ei-\u003c/em\u003eth observation and slope represents the linear trend, and \u003cem\u003en\u003c/em\u003e is the cumulative number of year in this study 2001 to 2021 (21 year). The Slope\u0026thinsp;\u0026gt;\u0026thinsp;0 and Slope\u0026thinsp;\u0026lt;\u0026thinsp;0 represent trends that are increasing and declining, respectively. The vegetation change types are classified into four categories based on varying slope values: dramatic degradation (Slope \u0026le; -0.01), slight degradation (-0.01\u0026thinsp;\u0026lt;\u0026thinsp;Slope\u0026thinsp;\u0026lt;\u0026thinsp;0), slight development (0\u0026thinsp;\u0026lt;\u0026thinsp;Slope\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and dramatic development (Slope\u0026thinsp;\u0026ge;\u0026thinsp;0.01) (Bashir et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Correlation analysis\u003c/h2\u003e \u003cp\u003eThe correlation coefficient was calculated as in Zhong et al. (Zhong et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The correlation coefficient between NDVI and temperature, and between NDVI and precipitation are calculated by the following equation:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{r}_{\\varvec{x}\\varvec{y}}=\\:\\frac{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}[\\:\\left({\\varvec{x}}_{\\varvec{i}}-\\stackrel{̄}{\\varvec{x}}\\right)({\\varvec{y}}_{\\varvec{i}}-\\varvec{ȳ})]}{\\sqrt{{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\left({\\varvec{x}}_{\\varvec{i}}-\\varvec{x}̄\\right)}^{2}{{\\sum\\:}_{\\varvec{i}=1}^{\\varvec{n}}\\left({\\varvec{y}}_{\\varvec{i}}-\\varvec{ȳ}\\right)}^{2}\\:}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003er\u003c/em\u003e symbolize the correlation between \u003cem\u003ex\u003c/em\u003e and \u003cem\u003ey\u003c/em\u003e variables, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the NDVI, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e indicates the temperature or precipitation in the consistent time period in the study area, x̄ is the average NDVI in the study area from 2001 to 2021 and \u003cem\u003eȳ\u003c/em\u003e represents the average temperature or precipitation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Respective contributions of precipitation and temperature variability to the NDVI\u003c/h2\u003e \u003cp\u003eTo discover the dominant climate factors that effects the vegetation dynamics in the study area from 2001 to 2021, we conducted Multiple Linear Regression (MLR) analysis for the NDVI variability and climate factors. Before entering into the MLR model the data were normalized to make them comparable (Sun et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The dataset of NDVI, temperature and precipitation were normalized by min-max normalization method by the following formula:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{y}_{i}=\\:\\frac{{x}_{i}\\:-{\\:min}_{1}\\le\\:j\\le\\:n\\:\\left\\{{x}_{j}\\right\\}}{{max}_{1}\\le\\:j\\le\\:n\\:\\left\\{{x}_{j}\\right\\}\\:-{\\:min}_{1}\\le\\:j\\le\\:n\\:\\left\\{{x}_{j}\\right\\}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003emax\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026le;\u0026thinsp;j\u0026thinsp;\u0026le;\u0026thinsp;n {x\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e}\u003c/em\u003e is the maximum value of the sample data and \u003cem\u003emin\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e\u0026thinsp;\u003cem\u003e\u0026le;\u0026thinsp;j\u0026thinsp;\u0026le;\u0026thinsp;n {x\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e\u003cem\u003e}\u003c/em\u003e is the minimum value of the sample data. The new data set \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e \u003cem\u003eƐ\u003c/em\u003e [0, 1] is dimensionless. We used standardized data to perform MLR analysis at pixel level (Hua et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The equation are as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:y={\\beta\\:}_{0}+\\sum\\:_{i=1}^{n}{\\beta\\:}_{i}{x}_{i}+\\in\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is used for temperature and precipitation (independent variable), \u003cem\u003ey\u003c/em\u003e for NDVI (dependent variable), \u003cem\u003ei\u003c/em\u003e is the number of independent variables, \u003cem\u003eβ\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the regression coefficient and \u003cb\u003eƐ\u003c/b\u003e for random error. By this equation we got the result of the binary regression of the NDVI, temperature and precipitation. After comparing the absolute value of standard regression coefficient of precipitation (R\u003csub\u003eE \u0026minus; P\u003c/sub\u003e) and temperature (R\u003csub\u003eE \u0026minus; T\u003c/sub\u003e) it can be easily determined whether temperature or precipitation has a greater influence on the NDVI variation at the pixel level. If the result (⎪R\u003csub\u003eE \u0026minus; P\u003c/sub\u003e ⎪ \u0026ndash;⎪ R\u003csub\u003eE \u0026minus; T\u003c/sub\u003e⎪) \u0026le; 0, it showed that NDVI is dominated by temperature, if the (R\u003csub\u003eE\u0026minus;P\u003c/sub\u003e \u0026ndash;R\u003csub\u003eE\u0026minus;T\u003c/sub\u003e)\u0026thinsp;\u0026gt;\u0026thinsp;0, it showed that NDVI is dominated by precipitation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Temporal and spatial variations of NDVI in Pakistan\u003c/h2\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1. The temporal change of NDVI\u003c/h2\u003e \u003cp\u003eThe linear regression model was used for the study of vegetation change trend in Pakistan from 2001 to 2021. The average NDVI in Pakistan was 0.29. The lowest NDVI value was observed in 2001 and the highest NDVI value was noted in 2020, with annual mean NDVI of 0.19 and 0.33, respectively. In the past 21 year, fluctuation was observed in the vegetation growth trend, with an increase of 0.003/year for annual NDVI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The positive slope showed significant improvement in vegetation growth in the study area from 2001 to 2021.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2. The spatial distribution of NDVI\u003c/h2\u003e \u003cp\u003eThe spatial distribution of average annual NDVI in Pakistan from 2001 to 2021 showed obvious spatial heterogeneity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The area of annual mean NDVI higher than 0.24 was 32% of the total study area, mostly scattered in the Punjab, KPK, Kashmir and Sindh regions. However, the annual mean NDVI was mostly low in the Baluchistan, Gilgat Baltistan and some portion of Sindh and Punjab province of the country.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3. The change trend of NDVI\u003c/h2\u003e \u003cp\u003eThere are clear spatial differences in the vegetation changes based on the slope of NDVI for each pixel in the study area from 2001 to 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We found obvious spatial differences in vegetation changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Overall result of the study area showed that, the regions with significantly improved vegetation cover were mainly distributed in Punjab and Islamabad, Azad Kashmir, KPK, and certain areas of Sind province. The area with insignificantly decreased NDVI was mainly found in Baluchistan Gilgat Baltistan and some parts of Sindh province. Moreover, there was a dramatic degradation of 1%, slight degradation of 63%, whereas slight development was of 27% and 9% of the annual vegetation had dramatic development during the study period (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The area wherever NDVI changed dramatic development was 72721.23 km\u003csup\u003e2\u003c/sup\u003e of the total area, slight development was 209064.92 km\u003csup\u003e2\u003c/sup\u003e, however the slight degradation 493562.2 km\u003csup\u003e2\u003c/sup\u003e and dramatic degradation was 11883.01 km\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Temporal and spatial variations of climate factors in Pakistan\u003c/h2\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. The temporal and spatial distributions of temperature and precipitation\u003c/h2\u003e \u003cp\u003eThe temporal and spatial distributions of temperature is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea. The annual mean temperature was found in the range of -13.01 to 37.52\u0026deg;C, Spatially, temperature was higher in Sindh, Punjab province, some parts of Baluchistan, KPK and Kashmir regions and lower in Gilgat regions and some area of KPK and Kashmir, where temperature is affected by the Mountains, which have lower average annual temperature. The temperature showed upward trend from southwest to northeast in the study area, commonly because of warm climate in the plane regions of Baluchistan Sindh and Punjab (South Punjab). The highest average temperature in Sindh, Punjab and Baluchistan (maximum area) varied from 31.25 to 37.51\u0026deg;C from 2001 to 2021, whereas Gilgat had the lowest average temperature, ranging from \u0026minus;\u0026thinsp;13.01 to 0.50 \u0026deg;C. Temporally, the average annual temperature in the study area is 26.68\u0026deg;C and the highest average annual temperature was 28.20\u0026deg;C recorded in 2001, while the lowest average annual temperature was 23.16\u0026deg;C in 2002 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). There is an overall increase in temperature from 2001 to 2021 in the study area, with an increase rate of 0.048 \u0026deg;C/year.\u003c/p\u003e \u003cp\u003eSimilarly, the spatial variation of precipitation over the study area is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb. The annual mean precipitation was found in the range of 1.56 to 125.00 mm, the annual precipitation was higher in Kashmir and KPK regions while lower in Sindh, Baluchistan, Gilgat Baltistan and Punjab province. From 2001 to 2021, the research area's precipitation increased at a slow rate. The mean annual precipitation was 22.99 mm and the rate of increase is 0. 053 mm/year as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb. The highest annual mean precipitation was 30.41 mm in 2020 and the lowest precipitation was 15.92 mm in 2002 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. The spatiotemporal variations of temperature and precipitation\u003c/h2\u003e \u003cp\u003eThe variations of temperature and precipitation from 2001 to 2021 in pixel scale are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb. The result showed that 35% of the total area comprised up of regions where temperature were increased (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Approximately, the Baluchistan and Sindh province regions of the study area, demonstrated increased in temperature. The warm and humid air mass from the southeast, results in increased temperature in the southeast regions. There was a temperature increase, with the southeast low altitudes showing a rising trend. Similarly, the spatial variation of precipitation is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb. The area having increased precipitation were 49% during 2001 to 2021, were mostly distributed in the Punjab, KPK, Kashmir and some area of Sindh and Baluchistan province of the study area.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3.The correlation of NDVI and climate factors\u003c/h2\u003e \u003cp\u003eThe results of pixel-wise correlation coefficients between NDVI and climatic factors (temperature and precipitation) are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. The result showed that the NDVI and temperature exhibit a positive correlation (R\u0026thinsp;\u0026gt;\u0026thinsp;0; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in 46% of the total area, whereas a negative correlation was observed in 56% of the pixels. Notably, positive correlations were predominantly found in Sindh province. Conversely, negative correlations between NDVI and temperature were mainly concentrated in Baluchistan, Punjab, and Khyber Pakhtunkhwa (KPK), (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eSimilarly, the results of correlation between NDVI and precipitation shown that the positive correlation (R\u0026thinsp;\u0026gt;\u0026thinsp;0; P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) was accounted 66% of the study area while 34% showed a negative correlation. Positive correlations between NDVI and precipitation were predominantly observed in in the area of Baluchistan, KPK, Kashmir and some parts of Sindh, Punjab province. Similarly the percentage of the study area with negative correlations between NDVI and precipitation was, mainly observed in Sindh, Gilgat Baltistan and some parts of Punjab province (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Temperature and precipitation had partial correlations values of 0.74 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 0.86 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively. This results revealed that there was a positive partial correlation between the NDVI and these two climatic factors.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Main dominant climate factors\u003c/h2\u003e \u003cp\u003eA multiple linear regression model was employed to investigate the comparative contributions of climatic factors to NDVI (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The results reveal that temperature dominates NDVI variation in approximately 70% of the total area, while the area where precipitation dominates NDVI variation accounts for 30%. Both temperature and precipitation significantly impact vegetation growth, exhibiting distinct spatial patterns. Notably, positive relationships between NDVI and temperature are observed in northern, southern, and western Pakistan (Baluchistan, Gilgat Baltistan and parts of Sindh, Punjab and Khyber Pakhtunkhwa province), except in central regions, where precipitation emerges as the most dominant climatic factor driving vegetation growth. These findings highlight the heterogeneous relationships between vegetation and climatic factors across the study area, with varying dominant climatic factors influencing NDVI changes. Interestingly, the central region, characterized by significant vegetation trends (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e), shows precipitation as the dominant climatic factor. The areas where precipitation dominates NDVI variation are primarily located in Kashmir, most of Punjab, Khyber Pakhtunkhwa, and parts of Sindh provinces.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Vegetation variation features in Pakistan\u003c/h2\u003e \u003cp\u003ePrevious research has used plant cover change to investigate land degradation as vegetation cover is a dynamic component of the ecosystem (Bashir et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tian et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tong et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Furthermore, the study of vegetation cover changes and how they interact with climate change has gained significant attention (Hall-Beyer \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Park and Sohn \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Zhou et al. (Zhou et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), claimed that a major factor influencing vegetation patterns is climate change. The findings of the research show that vegetation's reaction to climate change is not consistent. Regarding local climate, responses differ from place to place. Ali et al. (Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), observed that there is a strong correlation between vegetation elements and water accessibility in the South Asian region. According to Haroon et al. (Haroon et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), decreased precipitation areas experienced submerged pressure, which prevented plants from growing there. This suggests that the interaction between vegetation cover and climate change is complex and spatially variable, with water accessibility playing a critical role in determining vegetation dynamics.\u003c/p\u003e \u003cp\u003eThe NDVI values showed an increasing trend in Pakistan from 2001 to 2021, with an annual increase of 0.003/year. Over the 21-year period, vegetation growth fluctuated, but overall, vegetation change in Pakistan showed a significant increase, consistent with previous studies in Pakistan and globally (Ahmad et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zhang et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Guo et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; WANG et al. 2016). The annual mean NDVI values and trends in Pakistan showed spatial variations, likely due to climatic factors. High NDVI values were found in Punjab, Kashmir, KPK, and parts of Sindh, while low values were observed in Gilgit, Baluchistan, and northern areas. The results suggest that vegetation changes can indicate climate change, leading to severe climatic consequences in the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The result showing similarity to the finding (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Vegetation dynamics have increased globally and regionally in response to climate change. Studies, such as one in the central Himalaya, have shown significant increases in vegetation growth from 1982 to 2011 (Zhang et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Baniya et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Similarly, Pakistan has experienced an increasing trend in annual mean NDVI 3\u0026ndash;55]. This is consistent with global temperature trends, which have shown a steady increase, with Pakistan's temperature rising by +\u0026thinsp;0.048\u0026deg;C/year, (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea) was consistent with findings from other studies conducted in the world (Shrestha et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), for example\u0026thinsp;+\u0026thinsp;0.006\u0026deg;C/year during 1997 to 1994 and +\u0026thinsp;0.04\u0026deg;C/year from 1975 to 2007 (Sharma \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The increasing trend in Pakistan's annual mean NDVI closely matches the rising temperature trend. Although precipitation changes were relatively small, a significant increase was observed. A linear regression model revealed a significant trend towards increasing vegetation over the past 21 years, despite some limitations in accounting for non-linear changes (Emamian et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study found increasing trends in vegetation across the study area, with Punjab, KPK, Kashmir, and Sindh provinces showing the highest percentage of significant vegetation trends (24.83%). The findings emphasize the profound impact of climate change on vegetation dynamics, highlighting the need for region-specific strategies to mitigate vegetation degradation and promote sustainable ecosystem management (Emamian et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e4.2. The responses of vegetation dynamics to climate change\u003c/h2\u003e \u003cp\u003eAnnual NDVI increased during 2001 to 2021, consistent with results from several other parts of world, especially in the northern hemisphere, which have shown increasing trends in NDVI. The spatial distribution of precipitation over Pakistan is dominated by the monsoon; however, the northern regions observed comparatively higher amounts of precipitation than the other regions of the country. This study finds a significant correlation in the limited region between NDVI, temperature and precipitation. Topographical effects may affect the correlation coefficient between NDVI and climatic parameters; in addition, precipitation falling in the form of snow can reduce the surface reflectance (Achard and Estreguil \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Therefore, caution is required when assessing correlation with NDVI in such specific locations. This inconsistency is as Pearson correlation coefficient analysis was accompanied in this study, however some other researcher did not find the correlation vegetation greenness and precipitation, apart from the influence of temperature. These findings may have been caused by the extensive agricultural activities that are taking place in these regions. Similar findings were reported by (Chen et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) who linked India's widespread greening to agricultural practices. Despite no change in vegetation trends, the south-east and western sections of Sindh Province and the south-eastern part of Baluchistan displayed the maximum number of pixels. These areas consist mainly of desert and barren territory. The pixel-wise correlation indicated relatively stronger and more significant positive correlation of NDVI with precipitation than with temperature. Precipitation is important in dry regions region of Pakistan, where the average annual precipitation is very low. However, plants show quick responses to temperature but delayed responses to precipitation, indicating that the vegetation growth is most strongly influenced by increasing temperature (Yang et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe results showed that the climatic factors of vegetation changes existing significant regional differences in the study area. The mean annual temperature was recognized as dominate manipulating factor on the spatial distribution of NDVI. For vegetation, precipitation has a more significant impact on growth in arid regions than temperature. These regions frequently have limited precipitation with substantial evaporation, which results in limited water resources (Xue et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).Moreover in arid and semi-arid regions, the availability of water is also a major limiting factor for the growth of vegetation, elevation and soil types also affected the spatial distribution of vegetation, and moreover elevation is the key factor persuading vegetation change, particularly in the northern. Vegetation depends on water accessibility, which is main issue in South Asia that showed that vegetation exploitation could be enhanced by the increased aridity coming about form of low precipitation rate and higher evaporative interest of climate. The areas with lower precipitation, vegetation did not properly flourish (Haroon et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ali et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Normally our results indicated that NDVI shows significant positive correlation with temperature and precipitation, which means that increase in temperature and precipitation has a stimulating effect on NDVI. Though some studies shown that vegetation greenness and precipitation are negative correlated (Gloor et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The impact of temperature on vegetation greenness was greater than the impact of precipitation. This results showing similarities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Limitations and implications\u003c/h2\u003e \u003cp\u003eThis study highlights the significant impact of temperature and precipitation on NDVI, providing valuable insights into vegetation dynamics, but also underscores the complexity of environmental interactions and the need for further research to account for additional factors influencing vegetation growth. Despite these findings, several limitations remain, including the exclusion of environmental factors such as sunlight, soil fertility, and CO\u003csub\u003e2\u003c/sub\u003e concentration, as well as human-induced land cover changes, highlighting the need for future studies to integrate these variables for a more comprehensive understanding of vegetation dynamics (Wang et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ochoa-Hueso et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In real terms, temperature and precipitation have no independent impact on NDVI. According to Moleses et al. (Moles et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), increased temperatures have the potential to increase evaporation and have an impact on precipitation through evapotranspiration. Instead of focusing on determinants, the goal of this study was to identify the main factors that affect NDVI. It is challenging to pinpoint the key variables that affect NDVI, however after looking at precipitation and temperature, it is an ideal method for exploring the impact on NDVI as they are the most prominent variable in plant growth. Furthermore, this study did not account for vegetation changes brought on by human activity-induced changes in land cover, such as deforestation and agriculture. However, this research contributes significantly to the existing body of knowledge by quantifying the relationships between temperature, precipitation, and NDVI, informing climate change mitigation and adaptation strategies for sustainable ecosystem management.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study investigated the spatiotemporal variation in NDVI across Pakistan from 2001 to 2021, examining the influence of temperature and precipitation on NDVI spatial heterogeneity. Overall, over the previous 21 years, there has been a notable rise in the annual mean NDVI, implying the improvements of vegetation in Pakistan. Precipitation and temperature positively impact NDVI, with precipitation having a greater effect. Throughout the study period, the country noticed an increase in temperature and precipitation, which was accompanied by greener vegetation. The correlation between precipitation and NDVI was significantly greater in terms of regional dispersion than the temperature correlation. The regions with annual low precipitation exhibit minimum NDVI response to precipitation. Additionally, there is a distinct regional distribution for the contributions of temperature variability and precipitation to NDVI dynamics. Furthermore, precipitation and temperature variations affect NDVI dynamics in a visible spatial distribution. Significant positive relationships are found between NDVI and temperature over the South and East Pakistan (Sindh and Punjab province), excepting for the northwestern regions, representing that temperature is the extreme significant climatic factor contributing to the vegetation growth. Climate change has had a major impact on Pakistan's ecological systems, especially in the past few years. Our results improved our awareness of how Pakistan's vegetation is changing and highlighted the relative significance of the factors influencing NDVI variations. These results could also provide theoretical references and suggestions for vegetation protection in the country.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003ch2\u003eEthics approval:\u003c/h2\u003e\n\u003cp\u003eThe authors confirm that their research was conducted in accordance with established ethical guidelines and standards.\u003c/p\u003e\n\u003ch2\u003eCompeting intrests\u003c/h2\u003e\n\u003cp\u003eThe authors report no conflicts of interest\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by The Second Tibetan Plateau Scientific Expedition and Research (STEP) program (Grant No. 2019QZKK0308).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eKU: Writing \u0026ndash; original draft, performed experiments and analysis, Conceptualization and designed experiments. HW: Writing \u0026ndash; review \u0026amp; editing, Investigation. WL: Writing \u0026ndash; review \u0026amp; editing. H: Writing \u0026ndash; review \u0026amp; editing. UH: Writing \u0026ndash; review \u0026amp; editing. ZZ: Writing \u0026ndash; review \u0026amp; editing, Supervision, Resources, Funding acquisition. All authors read and approved the final manuscript\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would like to acknowledge Ministry of Education Key Laboratory for Transboundary Ecosecurity of Southwest China and School of Ecology and Environmental Sciences, Yunnan University, Kunming, China\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe Moderate Resolution Imaging Spectroradiometer (MODIS) product, known as MOD13Q1, provided the NDVI dataset, which were obtained from USGS Earth Explore (http://earthexplorer.usgs.gov).The annual temperature and annual precipitation data from 2001 to 2021 were downloaded from Worldclim (https://www.worldclim.org/data/index.html)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAchard F, Estreguil C (1995) Forest classification of Southeast Asia using NOAA AVHRR data. 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Journal of Geophysical Research: Atmospheres 106 (D17):20069-20083\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vegetation dynamic, Temperature, Precipitation, Correlation analysis, Multiple linear regression","lastPublishedDoi":"10.21203/rs.3.rs-5801698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5801698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eChanges in vegetation cover and its relationship with climate factors are crucial for ecosystem stability, especially in arid and semiarid regions like Pakistan. However, the impact of temperature and precipitation fluctuations on vegetation dynamics in these regions remains uncertain. Pakistan's unique ecology and complex climate-vegetation relationships make it an ideal location for studying vegetation changes. This study examines changes in vegetation coverage and its response to climatic factors (temperature and precipitation) from 2001 to 2021. This study utilized satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) at a 250m spatial resolution and statistical analyses, including correlation calculations and multiple linear regression. We aimed to practically investigate whether and why vegetation distributes imbalanced along the entire country,is essential for adaptation to global climate change. The findings highlight (1) a notable upward trend in mean Normalized Different Vegetation Index (NDVI) over the past 21 years, with a significant increase from 0.19 to 0.33, indicating an overall enhancement; (2) the NDVI analysis reveals that about 32% of area exhibits an increasing trend, with high vegetation health, while other areas show a declining trend; (3) the results indicate that NDVI increased across Pakistan's. Khyber Pakhtunkhwa, Punjab, and Kashmir showed increasing NDVI, with 27% slight development and 9% dramatic development. In contrast, Baluchistan, Gilgit-Baltistan, and Sindh experienced NDVI degradation, with 63% slight degradation and 1% dramatic degradation; (4) the results show that precipitation is the main driver of vegetation growth in Pakistan, accounting for 70% of variability, while temperature contributes around 30%. Overall, this study improves our understanding of Pakistan's changing vegetation, identifying key factors and informing strategies for sustainable ecosystem management and climate change adaptation.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal variation of vegetation and its responses to climate change in Pakistan from 2001 to 2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-31 18:01:39","doi":"10.21203/rs.3.rs-5801698/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c23443a3-8f07-4b1b-bc49-793149bad752","owner":[],"postedDate":"January 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-31T18:01:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-31 18:01:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5801698","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5801698","identity":"rs-5801698","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}