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There is a strong signal showing that the climate in Xinjiang, China has changed from warm-dry to warm-wet since the early 1980s, leading to an increase in vegetation cover. Based on a regression analysis and Hurst index method, this study investigated the spatial-temporal characteristics and interrelationships of the vegetation dynamics and climat...
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The Tibetan Plateau has been recognized as one of the most sensitive areas responding to climate change, and has becomes a hotspot for coupled studies on terrestrial ecosystem variation and climate change. Leaf area index (LAI) is a key indicator that reflects vegetation dynamics and has been widely used to analyze the responses of vegetation to cl...
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... TEM, with a 30.10% influence, also plays a crucial role in this ecological process. Notably, PRE consistently stands out as the foremost natural driver of forest and grassland vegetation succession in Xinjiang, a conclusion supported by previous studies (Cao et al., 2011;Liang et al., 2015). ...
For the past several decades, climate change has been driving vegetation dynamics in arid regions worldwide. This study investigates vegetation dynamics and their links to climate from 1990 to 2020 in Xinjiang, China, using data from 30-m resolution land use and cover change, remote sensing, and climate reanalysis. Our approach encompasses a range of analytical techniques, including transfer matrix analysis, modeling, correlation, regression, and trend analysis. During the study period, there were major vegetation conversions from grassland to forestland in the mountains, and from cropland to grassland in the plains. Climate change emerged as an important trigger of these changes, as evidenced by the increase in net primary productivity in most vegetation types, except for cropland-grassland and grassland-cropland conversions. Precipitation and soil moisture were the most influential climatic factors, contributing 15.1% and 15.2%, respectively, to natural vegetation changes. The study also found that evapotranspiration serves as a key mechanism for moisture dissipation in the hydrological cycle of vegetation dynamics. The interplay between precipitation, soil moisture, and evapotranspiration is a critical pattern of climatic influence that shapes vegetation dynamics across zones of intersection, increase, decrease, and change. These insights are invaluable for informing vegetation conservation and development strategies in Xinjiang and other similar environments facing climate-driven ecological transitions.
... Statistical data indicates that landslides in the Ili Valley are primarily concentrated on slopes facing southeast (45-225°) (Fig. 13c), likely due to the slower evaporation, higher humidity and cooler temperatures in these areas. Conversely, active slopes facing northwest tend to be drier and warmer and have sparse vegetation (Liang et al. 2015), which results in better coherence and more MPs being detected. Combining the ascending and descending orbit data provides a more comprehensive detection of the study area, with the ascending orbit data more applicable to this particular study. ...
Loess landslides in mountainous regions of the Ili Valley have resulted in numerous casualties as well as huge economic losses. However, the characteristics and driving mechanisms of surface deformation related to loess landslides in mountainous areas remain unclear, thus limiting our ability to identify, monitor, and warn populations of potential catastrophic events. This study was conducted in a typical mountainous area of the Ili Valley, where landslides have been documented by field investigations, unmanned aerial vehicle images, and light detection and ranging data. With ascending and descending Sentinel‑1 time series synthetic aperture radar images, acquired using the small baselines subset method, surface deformation was observed for the period from October 2014 to October 2021, and loess landslides were concurrently mapped to delineate hazardous areas. Using the methods of this study, we were able to identify 74.4% of previously documented landslides. Additionally, we observed a seasonal time-series of deformation that had a time delay of less than one month and was responsive to rainfall. Our analysis of the characteristics and driving mechanisms of creeping landslides in the Ili Valley led to the compilation of a new inventory of active slopes that will offer valuable guidance for land managers tasked with implementing disaster prevention measures.
... Vegetation influences the energy balance, carbon balance, hydrological cycle, greenhouse gas fixation, and climate stabilization (Zhang et al., 2013). A number of studies have shown that vegetation dynamics in recent decades have been closely related to climate change (Jiapaer et al., 2015;Ren et al., 2020). In recent decades, rapid urbanization (Tan et al., 2016), forest degradation and desertification (Sun et al., 2015;Hassan et al., 2018) have led to significant changes in the types of vegetation systems in China. ...
Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman–Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5–6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them.
... Xinjiang Province is situated in northwestern China, spanning from 73°20'E to 96°25'E and 34°15'N to 49°10'N (Jiapaer et al., 2015). With a total land area of 1.66 million km 2 , it accounts for approximately one-sixth of China's land area (Yu et al., 2020). ...
... With a total land area of 1.66 million km 2 , it accounts for approximately one-sixth of China's land area (Yu et al., 2020). The province encompasses a delicate ecological zone characterized by a complex arid environment, with mountainous areas comprising 51.4% and plain areas comprising 48.6% of the total land area (Luo et al., 2019;Jiapaer et al., 2015). Xinjiang exhibits diverse landforms, including the Altai Mountains in the north, the Kunlun Mountains and A-erh-chin Mountains in the south, and the Tian Shan Mountains spanning the central part of the province. ...
Findings reveal that the majority of studied areas are classified as bare lands, while the lowest amount is covered by lichens and mosses. Grassland and cropland occupy major areas of the region, with highest normalized difference vegetation index (NDVI) value saw in 2020, showing dense vegetation in the western, northwestern and northern regions. Afforestation efforts shown positive results, with a 4% increase in forested area between 2000 and 2022.
RÉSUMÉ: Processus de modélisation des changements spatio-temporels de la couverture végétale et de sa relation avec les facteurs de changement dans les zones arides et humides du Xianjiang (Chine). Les résultats révèlent que la majorité des zones étudiées sont classées comme des terres nues, tandis que la plus petite partie est couverte de lichens et de mousses. Les prairies et les terres cultivées occupent une grande partie de la région, la valeur la plus élevée de l'indice de végétation par différence normalisée (NDVI) étant observée en 2020, ce qui indique une végétation dense dans les régions de l'ouest, du nord-ouest et du nord. Les efforts de reboisement ont donné des résultats positifs, avec une augmentation de 4% de la superficie forestière entre 2000 et 2022.
REZUMAT: Procesul de modelare a schimbărilor spațio-temporale ale acoperirii vegetale și relația acesteia cu factori ai zonelor uscate și zonelor umede din Xianjiang (China). Rezultatele arată că majoritatea ariilor studiate sunt clasificate ca terenuri goale, cea mai mică proporție este acoperită de licheni și mușchi. Pajiștile și terenurile cultivate ocupă arii mari ale regiunii, cu o valoare mare a indicelui de vegetație (NDVI) în 2020, indicând vegetație densă în regiunile de vest, nord-vest și nord. Eforturile de împădurire au arătat rezultate positive, cu o creștere cu 4% a suprafeței împădurite între 2000 și 2022. Shobairi S. O. R. et al.-Modeling process of the changes vegetation cover in in Xianjiang (China) (27~74) 28
... Xinjiang Province is situated in northwestern China, spanning from 73°20'E to 96°25'E and 34°15'N to 49°10'N (Jiapaer et al., 2015). With a total land area of 1.66 million km 2 , it accounts for approximately one-sixth of China's land area (Yu et al., 2020). ...
... With a total land area of 1.66 million km 2 , it accounts for approximately one-sixth of China's land area (Yu et al., 2020). The province encompasses a delicate ecological zone characterized by a complex arid environment, with mountainous areas comprising 51.4% and plain areas comprising 48.6% of the total land area (Luo et al., 2019;Jiapaer et al., 2015). Xinjiang exhibits diverse landforms, including the Altai Mountains in the north, the Kunlun Mountains and A-erh-chin Mountains in the south, and the Tian Shan Mountains spanning the central part of the province. ...
... Our results also indicate that temperature and precipitation continued to rise in mountainous areas, thus promoting forest growth (figure 4). The warm and humid climate of the Tarim Basin has improved vegetation growth, vitality, and coverage over the past few decades (Jiapaer et al 2015). In situ observations and satellite-based normalized difference vegetation index data indicate that a favourable climate can lead to an extended vegetation growing season (Hu et al 2016). ...
Climate warming has pronounced impacts on high-elevation regions, including arid Central Asia, and has multiple impacts on the environment. Forests in these mountainous areas provide essential services by regulating regional climate, sequestering carbon, and supporting soil and water conservation. However, trends in forest productivity and their response to climate change remain unclear. To address this knowledge gap, we collected tree cores from five sample plots in the western Tianshan region. We used tree-ring widths to reconstruct net primary productivity (NPP) and investigated the sensitivity of forest NPP to climate change by analysing weather station data and employing LASSO regression to identify climatic factors influencing forest growth. Our results demonstrate that the reconstructed forest NPP effectively captured significant carbon shifts and revealed a non-significant increase in forest productivity associated with climate warming and higher precipitation between 1970 and 2020 at low and middle elevations in the Tianshan mountains. Humidity is the primary limiting factor affecting forest growth in this region. Conversely, the relationship between temperature and forest growth is not consistent as precipitation increases. Our findings suggest that continued warming will exacerbate water stress in forests.
... As a dynamic element of the terrestrial ecosystem, vegetation is generally exploited to assess the significant impact on the water cycle and terrestrial climate conditions on a local, regional, and global scale (Liu and Lei 2015;Xu et al. 2015;Zhang and Wu 2020). Climate change and anthropogenic activities are two primary forces that play a major part in vegetation dynamics and there has been an intensification focused on identifying their influence on the development of vegetation (Chen et al. 2015;Jiapaer et al. 2015;Kong et al. 2020). Generally, remote sensing data e.g., normalized differential vegetation index (NDVI) with the appropriate spatial and temporal resolution is used as a powerful proxy for monitoring vegetation dynamics (Fathi-Taperasht et al. 2023), in response to environmental factors like climate change and human activities. ...
Attributing vegetation changes and assessment of its temporal response patterns can provide valuable information for natural resource management, especially in fragile ecosystems. Hence, this study investigates the dynamics and temporal response patterns of different land use classes based on the Normalized Difference Vegetation Index (NDVI) in Pakistan's Haro River Basin (HRB) as a test study. So, for this purpose, advanced analytical tools such as Breaks for Additive Season and Trend (BFAST), Ensemble Empirical Mode Decomposition (EEMD), Residual Trend Analysis, and correlation coefficients were employed. Based on overall analysis, a significant increasing trend in monthly NDVI changes was observed between 2002 and 2021 and showcasing a positive correlation with climatic factors in diverse land use classes. Spatial analysis revealed distinct variations in the time lag response between climatic parameters and NDVI, with approximately 36.38% and 11.38% of the area exhibiting statistically significant lag time effects of 0-month and 1-month, respectively. The analysis revealed varying rates of relative contributions of climatic change (ranging from 71.88 to 78.26%) and anthropogenic activities (i.e., 21.74–31.25%). Notably, based on individual land use classes, climate change also emerged as the dominant driving factor, accounting for more than 55% of the observed changes to different classes as well. This study breaks new ground by using advanced methods to understand how climate and human activities shape vegetation in different River basins, offering crucial insights for global ecological research and restoration efforts.
... Initially, these studies focused on the influence of specific climate factors on vegetation growth. For instance, Zhou et al. demonstrated that rising temperatures can enhance photosynthesis in vegetation [24]; Jiapaer et al. indicated that rainfall can alter soil aeration conditions, increase soil moisture, thereby affecting plant growth [25]; Jiang et al. pointed out that solar radiation is the primary climate factor impacting NDVI changes in South China [26]. However, there is inconsistency regarding the correlation between hydrothermal factors and vegetative growth across different regions. ...
Climate conditions have a significant impact on the growth of vegetation in terrestrial ecosystems, and the response of vegetation to climate shows different lag effects with the change in spatial pattern and category of the ecosystem. Exploring the interaction mechanism between climate and vegetation growth is helpful to promote the sustainable development of the regional ecological environment. Using normalized vegetation index (NDVI) and meteorological data, based on univariate linear regression and partial correlation analysis, this study explores the temporal and spatial pattern and change trend of vegetation cover in regions and node cities along the “six economic corridors”, and analyzes the environmental stress of vegetation growth and the lag effect of climate response. This study shows that there are great differences in the overall vegetation coverage along the “six economic corridors”. The vegetation coverage in Southeast Asia is the best and that in central and West Asia is the worst. The vegetation coverage in the study area shows an improvement trend, accounting for 39.6% of the total area. There are significant differences in the lag effect of vegetation response and the main climate factors affecting vegetation growth, which is related to the diversity of vegetation and climate characteristics. In this study, we selected regions along the “six economic corridors” that exhibit large latitude and altitude gradients, diverse climate types, and significant seasonal changes and spatial differences in climate conditions as our research areas. Additionally, we considered the impact of different regions and various types of vegetation on their response to climate change. This is of great significance for gaining a deeper understanding of the response mechanism of global climate change and vegetation ecology. Furthermore, our research can provide valuable information to support the ecological environment protection of different typical vegetation against extreme climates, ultimately contributing to the sustainable development of “the Belt and Road”.
... The temporal-spatial development of drought events characteristics are a primarily operative (Tucker et al. 2005;Zhou et al. 2023). In addition, drought-monitoring methods include the vegetation index (VI), such as NDVI (Jiapaer et al. 2015), and VCI are used global (Kogan et al. 2005;Zheng et al. 2023). A previous study showed that vegetation trends are closely related to environment change under different ecological units (Dhorde and Patel 2016;Wu et al. 2017). ...
The Jinhua–Quzhou basin in China is one of the most susceptible areas to drought. Due to the loss of vegetation and great fluctuations in rainfall and surface temperature, global warming occurs. Timely, accurate, and effective drought monitoring is crucial for protecting local vegetation and determining which vegetation is most vulnerable to increased LST during the period 1982–2019. It assumes a strong correlation between loss of vegetation cover, changes in monsoon climate, drought, and increases in land surface temperature (LST). Due to significantly increased in LST, low precipitation and vegetation cover, NDVI, TVDI, VCI, and NAP are useful in characterizing drought mitigation strategies. The temperature vegetation drought index (TVDI), normalized difference vegetation index (NDVI), vegetation condition index (VCI), and monthly precipitation anomaly percentage (NAP) can be helped to characterize drought reduction strategies. Monthly NDVI, NAP, VCI, TVDI, normalized vegetation supply water index (NVSWI), temperature condition index (TCI), vegetation health index (VHI), and heat map analysis indicate that the Jinhua–Quzhou basin experienced drought during 1984, 1993, 2000, and 2011. Seasonal SR, WVP, WS, NDVI, VCI, and NAP charts confirm that the Jinhua–Quzhou basin was affected by severe drought in 1984, which continued and led to severe droughts in 1993, 2000, and 2011. Regression analysis showed a significant positive correlation between NDVI, TVDI, VCI, and NAP values, while NVSWI, TVDI, and VHI showed positive signs of good drought monitoring strategies. The research results confirm the correlation between loss of vegetation cover and LST, which is one of the causes of global warming. The distribution of drought changed a trend indicating that compared with the Jinhua region; the Quzhou region has more droughts. The changing trend of drought has characteristics from 1982 to 2019, and there are significant differences in drought changing trends between different Jinhua–Quzhou basin areas. Overall, from 1982 to 2019, the frequency of drought showed a downward trend. We believe that these results will provide useful tools for drought management plans and play a relevant role in mitigating the effects of drought and protecting humanity from climate hazards.
... The climate in the northwestern region of China has undergone a warm-wet tendency since the 1980 s Shi et al., 2002), and it has shown an eastward expansion trend . As the largest area within the arid region of northwest China, Xinjiang has also experienced a warm-wet trend, mainly concentrated in its northwest region (Jiapaer et al., 2015). ...
... For instance, Zhang et al. (2021) only considered the response of vegetation to precipitation and temperature, while factors such as evapotranspiration, soil moisture, and vapor pressure are also important for vegetation growth (Gao & Zhao, 2022;Zhu et al., 2016). Furthermore, the impact of warm-wet conditions on vegetation and the changing patterns of vegetation over long time series and seasonal scales are not yet clear (Jiapaer et al., 2015;Xue et al., 2021;Yao et al., 2019). ...
... The greening areas are primarily located in the Altai Mountains, Tianshan Mountains, Junggar Basin in northern Xinjiang, and the western region of the Kunlun Mountains in southern Xinjiang. This finding aligns with current research on vegetation in Xinjiang conducted by Jiapaer et al. (2015) and Xue et al. (2021). ...
A R T I C L E I N F O Keywords: Normalized difference vegetation index (NDVI) leaf area index (LAI) Warm-wet tendency Temporal and spatial variations Xinjiang A B S T R A C T Recently, the warm-wet tendency in northwestern China has become a hot research topic. How does vegetation change under this tendency, and what are the impacts of climate change on vegetation? To address these questions, the dynamic variations in vegetation and their relationships with five climate factors (i.e., Pre: precipitation , Tmp: temperature, SM: root zone soil moisture, Vap: vapor pressure, and Pet: potential evapotrans-piration) across Xinjiang are comprehensively analyzed during the period of 1982-2021. The spatiotemporal variations in vegetation are analyzed using the normalized difference vegetation index (NDVI) and leaf area index (LAI), employing the Mann-Kendall (M− K) and empirical orthogonal function (EOF) approaches. The key findings indicate that a significant greening trend is observed, with a value of 0.00226 m 2 m-2 year − 1 according to the annual LAI. For the seasonal variations, the vegetation had the largest increasing trend in summer (JJA: June, July, August) compared with the other seasons, with significant values of 0.000876 year − 1 and 0.00382 m 2 m-2 year − 1 for the NDVI and LAI, respectively (p < 0.05). The spring (MAM: March, April, May) and the growing season (GS) also have significant increasing trends based on the LAI. Spatially, approximately 40 % of the areas have an increasing trend, indicating greening variations, which are mainly distributed in the mountainous area of northwestern Xinjiang. The EOF results also suggest that the vegetation in the mountainous area of northwestern Xinjiang has a greening trend. The vegetation is significantly positively correlated with the five climate factors, which illustrates their positive influence on the vegetation. Our study helps to better understand the long-term vegetation variations under the warm-wet tendency, which provides an important scientific basis for net primary production (NPP) variations and the carbon cycle in Xinjiang.
