Climate Change Affected Vegetation Dynamics in the Northern Xinjiang of China: Evaluation by SPEI and NDVI

Investigation of 200 anthropogenic activities in a representative alpine peatland in the Altay Mountains, northwestern China

Peatlands records can be used to reconstruct and understand the history of environmental evolution, as well as a more accurate reflection of human activities. The black carbon (BC) and polycyclic aromatic hydrocarbons (PAHs) are ideal natural archives of anthropogenic activities. To identify the information of anthropogenic activities recorded by peatlands in the middle and high latitudes of the alpine mountains in the arid and semi-arid regions of China. this study analyzed the concentrations of BC, δ13C ratios of BC, PAHs, and molecular diagnostic ratios of PHAs (including Benzo(a) anthracene (BaA), Chrysene (Chr), fluoranthene (Flt), anthracene (Ant), phenanthrene (Phe), Benzo(a) pyrene (BaP), and pyrene (Pyr) in a 30-cm peat profile from the Altay Mountain, northwestern China. Our results revealed concentrations of BC from 11.71 to 67.5 mg·g-1, and PAHs from 168.09 to 263.53 ng·g-1. The δ13CBC value ranged from - 31.37 to - 26.27‰, with an average of - 29.54‰, indicating that the BC mainly comes from biomass combustion. The ratios of BaA/(BaA + Chr), Flt/(Flt + Pyr), and Ant/(Ant + Phe) exceeded 0.35, 0.5, and 0.1, respectively, revealing that the PAHs pollutants mainly originated from the combustion of biomass and fossil fuel burning. Furthermore, based on these findings and our knowledge of social development in Altay, industrial transport and tourism have influenced the emission, transport, and deposition of BC and PAH in peatlands in the Altay mountains since the 1980s. After 1980, pollutant concentrations decreased with the implementation of environmental policies. The results not only reveal the influence of anthropogenic activities on the sedimentary characteristics of peatlands in the Altay Mountains, but also provide an important theoretical basis for the conservation of fragile mountain peatlands.

An evaluation framework for quantifying vegetation loss and recovery in response to meteorological drought based on SPEI and NDVI

Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear. Using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI) data, this study developed an evaluation framework for exploring the responses of vegetation loss and recovery to meteorological drought, and applied it to the humid subtropical Pearl River basin (PRB) in southern China for estimating the loss and recovery of three vegetation types (forest, grassland, cropland) during drought using the observed NDVI changes. Results indicate that vegetation is more sensitive to drought in high-elevation areas (lag time < 3 months) than that in low-elevation areas (lag time > 8 months). Vegetation loss (especially in cropland) is found to be more sensitive to drought duration than drought severity and peak. No obvious linear relationship between drought intensity and the extent of vegetation loss is found. Regardless of the intensity, drought can cause the largest probability of mild loss of vegetation, followed by moderate loss, and the least probability of severe loss. Large spatial variability in the probability of vegetation loss and recovery time is found over the study domain, with a higher probability (up to 50 %) of drought-induced vegetation loss and a longer recovery time (>7 months) mostly in the high-elevation areas. Further analysis suggests that forest shows higher but cropland shows lower drought resistance than other vegetation types, and grassland requires a shorter recovery time (4.2-month) after loss than forest (5.1-month) and cropland (4.8-month).

Vegetation cover change and its response to climate extremes in the Yellow River Basin

Extreme climate events have increased in frequency and severity under the background of climate change, with vegetation growth exhibiting a sensitive response to them. By assimilating GIMMS NDVI and MODIS NDVI using the Residual Network, we obtained a long time series and high resolution NDVI dataset of the Yellow River Basin (YRB). The dataset was utilized for examining the spatiotemporal variability of NDVI and analyzing the response of vegetation cover to climate extremes with meteorological data. Our findings reveal the following: (1) A significant rise in NDVI was seen in the YRB, displaying a mean growth rate of 0.019/10a (p < 0.001). However, seasonal differences exist. The mean NDVI of multi-year declines from southeast to northwest, while the overall trend of vegetation cover improves. (2) The NDVI response to extreme temperature exhibits noticeable spatiotemporal differences. Daytime extreme high temperature in the northern YRB is negatively correlated with NDVI, while they are positively correlated in the lower YRB and the southern part of the middle YRB. Nighttime extreme high temperature exhibits a positive correlation with NDVI. Overall, NDVI displays a stronger response to extreme precipitation than to extreme temperature, with a negative correlation with CWD and a positive correlation with PRCPTOT. (3) The NDVI demonstrates a lagged response to climate extremes in the YRB, with a greater lag in response to extreme temperature than extreme precipitation. The research findings can provide scientific support for the future management and planning of vegetation in the YRB, as well as contribute to the promotion of ecological environment regulation and sustainable development.

Compensatory response of ecosystem carbon-water cycling following severe drought in Southwestern China

Climate change has increased the frequency and length of droughts, but many uncertainties remain regarding the impacts of this aridification on terrestrial ecosystem function. Vegetation water use efficiency and carbon sequestration capacity are crucial determinants that both respond to and mediate the effects of drought. However, it is important to note that the consequences of drought on these processes can persist for years. A deeper exploration of these "drought legacy effects" will help improve our understanding of how climate change alter ecosystem carbon-water cycling. Here, we investigate the spatial patterns of drought legacy effects on remotely-sensed vegetation greenness (NDVI), net primary productivity (NPP) and water use efficiency (WUE) in southwestern China, a biodiversity hotspot that was impacted by an extreme drought in 2009-2010, with a particular focus on the tradeoff between WUE and NPP. Despite widespread negative drought legacy effects in NDVI (impacting 61.26 % of the study region), there was a general increase in NPP (58.68 %) and a decrease in WUE (67.53 %) in the year following drought (2011). This drought legacy effect was most evident in forests, while drought legacies in grasslands were less common. Drought legacies were also most apparent in relatively warm and humid areas. During the study period (2002 to 2018), we found that drought impacts on WUE also lagged behind changes in NPP by 1-2 years in forests, which highlights how drought legacies may manifest differently across ecosystem processes. Our study demonstrated that severe drought conditions may significantly affect the carbon sequestration capacity and water use efficiency of vegetation in southwestern China, and that forests may compensate for the detrimental effects of water stress by increasing water use and biomass growth after drought episodes.

Vegetation Dynamics and Food Security against the Background of Ecological Restoration in Hubei Province, China

A series of ecological restoration projects have been proposed to solve ecological problems resulting from human activities. The project of returning farmlands to forests, initiated in 1999, was the most widely implemented ecological restoration project in China. Large amounts of cropland with steep slopes have been converted to forests or grasslands to promote vegetation restoration, reduce soil erosion, and control nonpoint source pollution. Therefore, identifying the dynamics of vegetation and food security is crucial for further decision making. Based on the mean normalized difference vegetation index (NDVI) and grain yield data, this study explored the vegetation dynamics and food security of Hubei Province against the background of ecological restoration. The results show that, on a whole, the NDVI significantly increased from 2000 to 2018. The spatial agglomeration of the NDVI decreased between 2000 and 2008 and then increased from 2009 onwards. High-high NDVI agglomerations were more concentrated in mountainous areas. Food security was not threatened, and the grain yield in Hubei Province and most of the cities exhibited significant upward trends, as a whole. The change trend of the grain yield was not stable during the period from 2000 to 2018. The grain yield for Hubei Province and almost all of the cities decreased during the first 5 to 11 years, probably due to the sharp decrease in the sloping cropland areas against the background of ecological restoration. Grain yield was more sensitive and had a longer downward trend in regions with steeper slopes. Increasing trends in grain yield were detected during the last 6 to 10 years for most of the cities, and this can mainly be attributed to the newly added croplands that were created from land with other kinds of land uses, the increase in grain productivity, and strict cropland protection policies. The project of returning farmlands to forests is suggested as a long-term policy from the perspective of ecological restoration, and effective measures should also be continuously taken to maintain grain production and food security.

NDVI-Based Greening of Alpine Steppe and Its Relationships with Climatic Change and Grazing Intensity in the Southwestern Tibetan Plateau

Alpine vegetation on the Southwestern Tibetan Plateau (SWTP) is sensitive and vulnerable to climate change and human activities. Climate warming and human actions (mainly ecological restoration, social-economic development, and grazing) have already caused the degradation of alpine grasslands on the Tibetan Plateau (TP) to some extent. However, it remains unclear how human activities (mainly grazing) have regulated vegetation variation under climate change and ecological restoration since 2000. This study used the normalized difference vegetation index (NDVI) and social statistic data to explore the spatiotemporal changes and the relationship between the NDVI and climatic change, human activities, and grazing intensity. The results revealed that the NDVI increased by 0.006/10a from 2000 to 2020. Significant greening, mainly distributed in Rikaze, with partial browning, has been found in the SWTP. The correlation analysis results showed that precipitation is the most critical factor affecting the spatial distribution of NDVI, and the NDVI is correlated positively with temperature and precipitation in most parts of the SWTP. We found that climate change and human activities co-affected the vegetation change in the SWTP, and human activities leading to vegetation greening since 2000. The NDVI and grazing intensity were mainly negatively correlated, and the grazing caused vegetation degradation to some extent. This study provides practical support for grassland use, grazing management, ecological restoration, and regional sustainable development for the TP and similar alpine areas.

Is There Spatial Dependence or Spatial Heterogeneity in the Distribution of Vegetation Greening and Browning in Southeastern China?

Vegetation is an indispensable component of terrestrial ecosystems and plays an irreplaceable role in mitigation of climate change. Global vegetation changes (i.e., greening and browning) still occur frequently, however, little is known about the spatial relationships between these two processes. Based on the normalized difference vegetation index (NDVI) dataset from 1998 to 2018 in Fujian Province, China. The Theil-Sen and Mann-Kendall tests were used to explore temporal changes in vegetation growing, then the spatial relationships of greening and browning was distinguished with bivariate spatial autocorrelation analysis, and the spatial variation in the relationship between vegetation changes and driving factors was explored by the geographical detector. The results showed that from 1998 to 2018, the average NDVI value increased from 0.75 to 0.83; 89.61% of the study area experienced vegetation greening, while 5.7% experienced significant browning, with active vegetation changes occurred along roads and nearby cities. The spatial autocorrelation results showed that the spatial relationships between vegetation greening and browning were dominated by spatial heterogeneity (i.e., high greening and low browning, H-L clusters accounting for 60% and low greening and high browning, L-H clusters accounting for 14%), but we also revealed that there were still quite a few places (4%) with spatial dependence (i.e., high greening and browning, H-H clusters), occurring around urban areas and along roads. The factor detector indicated that the nighttime light intensity was among the most dominant factor of vegetation changes, followed by elevation and slope. Although the individual effect of the distance to roads was relatively weak on the vegetation changes, its indirect effect was found to be the strongest by the interaction detector, which was obtained from the interactions much larger than its independent impact. Simultaneously, the risk detector revealed that the greening preferred occurring in places with lower nighttime light intensity (<1.1 nW cm−2sr−1), higher elevation (>43.4 m) and slope (>6.3°). Moreover, we found that the vegetation changes primarily occurred within a distance of 1685.4 m from roads. Our findings could deepen the understanding of vegetation change patterns and provide advice for mitigating the impact on the vegetation changes.

Toward Sustainability: Dynamics of Total Carbon Dioxide Emissions, Aggregate Income, Non-Renewable Energy, and Renewable Power

The purpose of energy sustainability policy is to support both economic growth and environmental quality. With climate change accelerating, economies must reduce carbon emissions. Low-carbon economics can balance the oft-contradictory policy aims of income growth and carbon reduction. Carbon pricing and renewable substitutes can pave the way. This analysis probes the dynamics of the adjustments toward the ideals of low-carbon economics through Granger causality testing of total carbon emissions, income, nonrenewable energy consumption, and renewable power. Cointegration regressions and a panel data vector error correction model are used to demonstrate the aforementioned variables’ long-term balance and short-term adjustment, respectively. Two panels of countries, namely 18 European Union and 32 Organization of Economic Co-operation and Development countries, are investigated with 1990–2021 data. Determinants for the success of low-carbon development and the implications of border regulations and taxation of carbon footprint are also discussed. Economic competitiveness, as well as increases in commodity prices, would initially emerge as interferences and then induce carbon reduction and accelerate the adoption and development of green technology.

Analysis of Effects of Recent Changes in Hydrothermal Conditions on Vegetation in Central Asia

Understanding the relationship of hydrothermal conditions to vegetation changes is conducive to revealing the feedback mechanism connecting climate variations and vegetation. Based on the methods of Theil–Sen median analysis, and the Mann–Kendall trend test, this research investigated the spatiotemporal vegetation dynamics in Central Asia using the Normalized Difference Vegetation Index (NDVI) and grid climate data from 1982 to 2015. Further, the contributions of hydrothermal conditions to vegetation changes were quantified using a boosted regression tree model (BRT). The results demonstrated that the spatiotemporal characteristics of vegetation dynamics exhibited significant differences in different seasons, and most pixels showed increasing trends in the growing season and spring. Boosted regression tree analysis indicated that the contributions of hydrothermal conditions to vegetation dynamics exhibited temporal and spatial heterogeneity. During the annual, growing season, and summer examination periods, the contribution value of the increase in warming conditions (temperature or potential evapotranspiration) to vegetation degradation in the region due to the hydrothermal tradeoff effect (water) was 49.92%, 44.10%, and 44.95%, respectively. Moreover, the increase in warming conditions promoted vegetation growth, with a contribution value of 59.73% in spring. The contribution value of the increase in wetting conditions (precipitation or soil moisture) to vegetation growth was 48.46% in northern Central Asia, but the contribution value of the increase in warming conditions to vegetation degradation was 59.49% in Ustyurt Upland and the Aral Sea basin in autumn. However, the increase in warming conditions facilitated irrigation vegetation growth, with a contribution value of 59.86% in winter. The increasing potential evapotranspiration was the main factor affecting vegetation degradation in the Kyzylkum Desert and Karakum Desert during the annual, growing season, and autumn examination periods. Precipitation and soil moisture played decisive roles in vegetation dynamics in northern Central Asia during the growing season, summer, and autumn. This research provides reference information for ecological restoration in Central Asia.

Incorporating Vegetation Type Transformation with NDVI Time-Series to Study the Vegetation Dynamics in Xinjiang

Time-series normalized difference vegetation index (NDVI) is commonly used to conduct vegetation dynamics, which is an important research topic. However, few studies have focused on the relationship between vegetation type and NDVI changes. We investigated changes in vegetation in Xinjiang using linear regression of time-series MOD13Q1 NDVI data from 2001 to 2020. MCD12Q1 vegetation type data from 2001 to 2019 were used to analyze transformations among different vegetation types, and the relationship between the transformation of vegetation type and NDVI was analyzed. Approximately 63.29% of the vegetation showed no significant changes. In the vegetation-changed area, approximately 93.88% and 6.12% of the vegetation showed a significant increase and decrease in NDVI, respectively. Approximately 43,382.82 km2 of sparse vegetation and 25,915.44 km2 of grassland were transformed into grassland and cropland, respectively. Moreover, 17.4% of the area with transformed vegetation showed a significant increase in NDVI, whereas 14.61% showed a decrease in NDVI. Furthermore, in areas with NDVI increased, the mean NDVI slopes of pixels in which sparse vegetation transferred to cropland, sparse vegetation transferred to grassland, and grassland transferred to cropland were 9.8 and 3.2 times that of sparse vegetation, and 1.97 times that of grassland, respectively. In areas with decreased NDVI, the mean NDVI slopes of pixels in which cropland transferred to sparse vegetation, grassland transferred to sparse vegetation were 1.75 and 1.36 times that of sparse vegetation, respectively. The combination of vegetation type transformation NDVI time-series can assist in comprehensively understanding the vegetation change characteristics.

Identification of Regional Drought Processes in North China Using MCI Analysis

Comprehensive identification of drought events is of great significance for monitoring and evaluating drought processes. Based on the date of daily precipitation, temperature and drought-affected area of 403 meteorological stations in North China from 1960 to 2019, the Comprehensive Drought Process Intensity Index (CDPII) has been developed by using the Meteorological-drought Composite Index (MCI) and regional drought process identification method, as well as the EIDR theory method. The regional drought processes in the past 60 years in North China, including Beijing, Tianjin, Hebei, Shanxi and Middle Inner Mongolia, were analyzed and identified. The result shows that the distribution characteristic of droughts with different intensities is as follows: The number of days of all annual-average mild droughts, moderate droughts and severe droughts was highest in Tianjin and that of extreme droughts was highest in Shanxi. The number of days of mild droughts was highest in May and lowest in January. The number of days of moderate droughts was highest in June. The number of days with mild and moderate drought showed an overall increasing trend, while the number of days with severe drought and above showed an overall decreasing trend (through a 95% significance test). The number of drought days was the highest in the 1990s. The annual frequency of drought is between 66.7% and 86.7%; the drought frequency in Hebei is the highest at 86.7%, followed by Beijing at 80%. There were 75 regional drought processes in North China from 1960 to 2019, and the correlation coefficient between process intensity and the drought-affected area was 0.55, which passed the 99% significance test. The comprehensive intensity of drought process from 27 April to 1 September 1972 was the strongest. From 18 May to 31 October 1965, the drought lasted 167 days. The overall drought intensity had a slight weakening trend in the past 60 years. A total of 75 regional drought processes occurred in North China, and the process intensity showed a trend of wavy decline with a determination coefficient (R2) of 0.079 (95% significance test). Overall, the regional drought process identification method and strength assessment result tally with the drought disaster, which can better identify the regional drought process. Furthermore, including the last days, the average intensity, average scope comprehensive strength, there are many angles to monitor and evaluate the drought and drought process. These provide a reference for drought control and decision-making.

Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades

Climate change has proven to have a profound impact on the growth of vegetation from various points of view. Understanding how vegetation changes and its response to climatic shift is of vital importance for describing their mutual relationships and projecting future land–climate interactions. Arid areas are considered to be regions that respond most strongly to climate change. Xinjiang, as a typical dryland in China, has received great attention lately for its unique ecological environment. However, comprehensive studies examining vegetation change and its driving factors across Xinjiang are rare. Here, we used the remote sensing datasets (MOD13A2 and TerraClimate) and data of meteorological stations to investigate the trends in the dynamic change in the Normalized Difference Vegetation Index (NDVI) and its response to climate change from 2000 to 2019 across Xinjiang based on the Google Earth platform. We found that the increment rates of growth-season mean and maximum NDVI were 0.0011 per year and 0.0013 per year, respectively, by averaging all of the pixels from the region. The results also showed that, compared with other land use types, cropland had the fastest greening rate, which was mainly distributed among the northern Tianshan Mountains and Southern Junggar Basin and the northern margin of the Tarim Basin. The vegetation browning areas primarily spread over the Ili River Valley where most grasslands were distributed. Moreover, there was a trend of warming and wetting across Xinjiang over the past 20 years; this was determined by analyzing the climate data. Through correlation analysis, we found that the contribution of precipitation to NDVI (R2 = 0.48) was greater than that of temperature to NDVI (R2 = 0.42) throughout Xinjiang. The Standardized Precipitation and Evapotranspiration Index (SPEI) was also computed to better investigate the correlation between climate change and vegetation growth in arid areas. Our results could improve the local management of dryland ecosystems and provide insights into the complex interaction between vegetation and climate change.

Response of NDVI of Natural Vegetation to Climate Changes and Drought in China

Temporal and spatial changes in vegetation and their influencing factors are of great significance for the assessment of climate change and sustainable development of ecosystems. This study applied the Asymmetric Gaussians (AG) fitting method, Mann-Kendall test, and correlation analysis to the Global Inventory Monitoring and Modeling System (GIMMS) third-generation Normalized Difference Vegetation Index and gridded climate and drought data for 1982–2015. The temporal and spatial changes to NDVI for natural grassland and forest during the growing season were analyzed. Relationships among NDVI, climate change, and droughts were also analyzed to reveal the influence of vegetation change. The results showed that: (1) Land use/cover change (LUCC) in China was mainly represented by increases in agricultural land (Agrl) and urban and rural land (Uril), and decreases in unutilized land (Bald), grassland, forest, and permanent glacier and snow (Snga). The increase in agricultural land was mainly distributed in the western northwest arid area (WNW) and northern North China (NNC), whereas regions with severe human activities such as southern South China (SNC), western South China (WSC), and eastern South China (ESC) showed significant decreases in agricultural land due to conversion to urban and rural land. (2) The start of the growing season (SOS) was advanced in WNW, SNC, WSC, and ESC, and the end of growing season (EOS) was delayed in WNW, NNC, and SNC. The growing season length (GSL) of natural vegetation in China has been extended by eight days over the last 34 years. However, the phenology of the Qinghai-Tibet Plateau (TP) was opposite to that of the other regions and the GSL showed an insignificant decreasing trend. (3) The NDVI increased significantly, particularly in the SNC, WSC, ESC, and the grassland of the WNW. Precipitation was found to mainly control the growth of vegetation in the arid and semi-arid regions of northwest China (WNW and ENW), and precipitation had a much greater impact on grassland than on forests. Temperature had an impact on the growth of vegetation throughout China, particularly in SNC, ESC, and WSC. (4) The Standardized Precipitation Evapotranspiration Index (SPEI) showed a downward trend, indicating an aridification trend in China, particularly in ENW, NNC, and WNW. Similar to precipitation, the main areas affected by drought were WNW and ENW and grassland was found to be more sensitive to drought than forest. The results of this study are of great significance for predicting the response of ecosystem productivity to climate change under future climate change scenarios.

Evaluation on the Change Characteristics of Ecosystem Service Function in the Northern Xinjiang Based on Land Use Change

Monitoring the interannual changes in land use and the temporal and spatial characteristics of the ecosystem services value (ESV) can help to comprehensively and objectively understand the distribution of regional ecological patterns. The mountain–oasis–desert transition zone in the northern Tianshan Mountain region of Xinjiang, China, is a geographically unique area with a highly sensitive ecosystem. As a data source, the study uses Landsat TM images from 1990, 2000, 2010, and 2018 along with GIS-extracted data to calculate the dynamic degree of land use. As well, the spatial and temporal patterns of land use change and ESV are quantitatively analyzed by using the equivalent factor method, sensitivity index, and spatial correlation studies. The results reveal the following: (1) From 1990 to 2018, the land use changes in the northern Tianshans are relatively drastic, mainly due to the increase in cultivated land, grassland and construction land, and the decrease in forest land, water, and unused land. (2) The ESV increases and then decreases, for a total loss of about 271.63 × 108 yuan. The largest decrease is in forest value, and the largest increase (around 129.94%) is in construction land. (3) The spatial distribution pattern of ESV in the northern Tianshans is apparent, showing high in the north and southwest, and low in the central and southeast portions of the study area. Additionally, there is a visible spatial correlation and aggregation in ESV. The present research can provide theoretical support for the environmental protection of the ecologically vulnerable area of the northern Tianshans as well as for further construction across the region.

Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.

Study on the Relationship between Snowmelt Runoff for Different Latitudes and Vegetation Growth Based on an Improved SWAT Model in Xinjiang, China

Rivers located in high altitude mountainous areas provide a large number of water resources and are also high-risk areas for seasonal snow melt floods. The accurate calculation and simulation of snow melting processes can provide reliable data for flood disaster prediction. In order to make the Soil and Water Assessment Tool (SWAT) model more suitable for high altitude mountainous areas, the effect of the daily accumulated temperature on the precipitation pattern and snow melting is fully considered. Applying the modified model to three mountain systems with different latitudes in Xinjiang can not only improve our understanding of the characteristics of snowmelt flooding but can also be used to test the applicability of the modified model. Through comparison, it was found that the simulation accuracy of the modified model of the flood peak value was improved by 56.19%. The correlation coefficient between the Normalized Difference Vegetation Index (NDVI) and snowmelt increased from 0.27 to 0.68. This study provides a new method for accurately understanding the process of snowmelt runoff in the mountainous area and provides new insights into the effects of snowmelt runoff on vegetation growth at different latitudes.

Climate Dynamics of the Spatiotemporal Changes of Vegetation NDVI in Northern China from 1982 to 2015

As an important part of a terrestrial ecosystem, vegetation plays an important role in the global carbon-water cycle and energy flow. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological station data, climate reanalysis data, and land cover data, this study analyzed the climate dynamics of the spatiotemporal variations of vegetation NDVI in northern China from 1982 to 2015. The results showed that growth season NDVI (NDVIgs) increased significantly at 0.006/10a (p < 0.01) in 1982–2015 on the regional scale. The period from 1982 to 2015 was divided into three periods: the NDVIgs increased by 0.026/10a (p < 0.01) in 1982–1990, decreased by −0.002/10a (p > 0.1) in 1990–2006, and then increased by 0.021/10a (p < 0.01) during 2006–2015. On the pixel scale, the increases in NDVIgs during 1982–2015, 1982–1990, 1990–2006, and 2006–2015 accounted for 74.64%, 85.34%, 48.14%, and 68.78% of the total area, respectively. In general, the dominant climate drivers of vegetation growth had gradually switched from solar radiation, temperature, and precipitation (1982–1990) to precipitation and temperature (1990–2015). For woodland, high coverage grassland, medium coverage grassland, low coverage grassland, the dominant climate drivers had changed from temperature and solar radiation, solar radiation and precipitation, precipitation and solar radiation, solar radiation to precipitation and solar radiation, precipitation, precipitation and temperature, temperature and precipitation. The areas controlled by precipitation increased significantly, mainly distributed in arid, sub-arid, and sub-humid areas. The dominant climate drivers for vegetation growth in the plateau climate zone or high-altitude area changed from solar radiation to temperature and precipitation, and then to temperature, while in cold temperate zone, changed from temperature to solar radiation. These results are helpful to understand the climate dynamics of vegetation growth, and have important guiding significance for vegetation protection and restoration in the context of global climate change.

Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China

Since the Silk-road Economic belt initiatives were proposed, Xinjiang has provided a vital strategic link between China and Central Asia and even Eurasia. However, owing to the weak and vulnerable ecosystem in this arid region, even a slight climate change would probably disrupt vegetation dynamics and land cover change. Thus, there is an urgent need to determine the Normalized Difference Vegetation Index (NDVI) and Land-use/Land-cover (LULC) responses to climate change. Here, the extreme-point symmetric mode decomposition (ESMD) method and linear regression method (LRM) were applied to recognize the variation trends of the NDVI, temperature, and precipitation between the growing season and other seasons. Combining the transfer matrix of LULC, the Pearson correlation analysis was utilized to reveal the response of NDVI to climate change and climate extremes. The results showed that: (1) Extreme temperature showed greater variation than extreme precipitation. Both the ESMD and the LRM exhibited an increased volatility trend for the NDVI, with the significant improvement regions mainly located in the margin of basins. (2) Since climate change had a warming trend, the permanent snow has been reduced by 20,436 km². The NDVI has a higher correlation to precipitation than temperature. Furthermore, the humid trend could provide more suitable conditions for vegetation growth, but the warm trend might prevent vegetation growth. Spatially, the response of the NDVI in North Xinjiang (NXC) was more sensitive to precipitation than that in South Xinjiang (SXC). Seasonally, the NDVI has a greater correlation to precipitation in spring and summer, but the opposite occurs in autumn. (3) The response of the NDVI to extreme precipitation was stronger than the response to extreme temperature. The reduction in diurnal temperature variation was beneficial to vegetation growth. Therefore, continuous concentrated precipitation and higher night-time-temperatures could enhance vegetation growth in Xinjiang. This study could enrich the understanding of the response of land cover change and vegetation dynamics to climate extremes and provide scientific support for eco-environment sustainable management in the arid regions.