Search

Vegetation degeneration has become a serious ecological problem for karst regions in the Anthropocene. According to the deficiency of long serial and high-resolution analysis of karst vegetation, this paper reconstructed the variation of vegetation landscape changes from 1987 to 2020 in a typical karst region of China. Using Landsat time series data, the dynamic changes and driving factors of natural karst vegetation were identified at the landscape scale. On the premise of considering the time-lag effect, the main climatic factors that influence vegetation growth were presented at the interannual timescale. Then, the approach of residual analysis was adopted to distinguish the dominant factors affecting vegetation growth. Results of trend analysis revealed that 21.5% of the forestland showed an overall significant decline in vegetation growth, while only 1.5% showed an increase in vegetation growth during the study period. Precipitation and radiation were the dominant meteorological factors influencing vegetation at the interannual timescale, as opposed to temperature. More than 70% of the natural vegetation growth was dominated by climatic factors. The area percentage of negative human impact has increased gradually since 2009 and reached 18.5% in 2020, indicating the currently serious situation of vegetation protection; fortunately, in recent years, human disturbances on vegetation have been mitigated in karst areas with the promotion of ecological conservation and restoration projects. [ABSTRACT FROM AUTHOR]

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. [ABSTRACT FROM AUTHOR]

Rapid global changes are altering regional hydrothermal conditions, especially in ecologically vulnerable areas such as coastal regions, subsequently influencing the dynamics of vegetation growth. However, there is limited research investigating the response of vegetation in these regions to extreme climates and the associated time lag-accumulation relationships. This study utilized a combined approach of gradual and abrupt analysis to examine the spatiotemporal patterns of vegetation dynamics in the coastal provinces of China from 2000 to 2019. Additionally, we evaluated the time lag-accumulation response of vegetation to extreme climate events. The results showed that (1) extreme high temperatures and extreme precipitation had increased over the past two decades, with greater warming observed in high latitudes and concentrated precipitation increases in water-rich southern regions; (2) both gradual and abrupt analyses indicate significant vegetation improvement in coastal provinces; (3) significant lag-accumulation relationships were observed between vegetation and extreme climate in the coastal regions of China, and the time-accumulation effects were stronger than the time lag effects. The accumulation time of extreme temperatures was typically less than one month, and the accumulation time of extreme precipitation was 2–3 months. These findings are important for predicting the growth trend of coastal vegetation, understanding environmental changes, and anticipating ecosystem evolution. [ABSTRACT FROM AUTHOR]

How to distinguish the relative role of climate change and human activities in vegetation dynamics has attracted increasing attention. However, most of the current studies concentrate on arid and semiarid regions, while the relative contributions of climate change and human activities to vegetation changes remain unclear in warm-humid regions. Based on the normalized difference vegetation index (NDVI) and climatic variables (temperature, precipitation, radiation) during 2001–2020, this study used the Theil–Sen median trend analysis, partial correlation analysis, and residual trend analysis to analyze the spatiotemporal pattern of vegetation trends, the response of vegetation to climate variations, and the climatic and anthropogenic contributions to vegetation dynamics in the warm and humid Guangdong Province of south China. Results showed that the NDVI in most areas exhibited an increasing trend. Changes in climatic variables displayed different spatial variations which, however, were not significant in most areas. Vegetation responded diversely to climate change with temperature as the most important climatic factor for vegetation improvement in most areas, while precipitation was the dominant climatic factor in the southern edge region and radiation was the dominant climatic factor in the central and western regions. Vegetation in most areas was influenced by both climate change and human activities, but the contribution rate of human activities was commonly much higher than climate change. The findings of this study are expected to enhance our understanding of the relative climatic and anthropogenic contributions to vegetation changes in warm-humid regions and provide a scientific basis for future ecological policies and ecosystem management in highly urbanized regions. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R2 = 0.26, RMSE = 0.41 m2/m2) and typical steppe (R2 = 0.32, RMSE = 0.38 m2/m2); the GEOV2 LAI performed better in desert steppe (R2 = 0.39, RMSE = 0.30 m2/m2). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R2 = 0.94), MODIS LAI (R2 = 0.92), and GLOBMAP LAI (R2 = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. [ABSTRACT FROM AUTHOR]

As a vital ecological barrier in China, Yellow River Basin (YRB) is strategically significant for China's national development and modernization. However, YRB has fragile ecosystems, and is sensitive to climatic change. Extreme climate events (e.g., heavy precipitation, heatwaves, and extreme hot and cold) occur frequently in this basin, but the implications (positive and negative effects) of these events on vegetation dynamics remains insufficiently understood. Combing with net primary productivity (NPP), the normalized difference vegetation index (NDVI) and extreme climate indexes, we explored the spatio–temporal characteristics of plants' growth and extreme climate, together with the reaction of plants' growth to extreme climate in the Yellow River Basin. This study demonstrated that annual NPP and NDVI of cropland, forest, and grassland in the study region all revealed a climbing tendency. The multi-year monthly averaged NPP and NDVI were characterized by a typical unimodal distribution, with the maximum values of NPP (66.18 gC·m−2) and NDVI (0.54) occurring in July and August, respectively. Spatially, multi–year averaged of vegetation indicators decreased from southeast to northwest. During the study period, carbon flux (NPP) and vegetation index (NDVI) both exhibited improvement in most of the YRB. The extreme precipitation indexes and extreme high temperature indexes indicated an increasing tendency; however, the extreme low temperature indexes reduced over time. NPP and NDVI were negatively associated with extreme low temperature indexes and positively correlated with extreme high temperature indexes, and extreme precipitation indicators other than consecutive dry days. Time lag cross–correlation analysis displayed that the influences of extreme temperature indexes on vegetation indexes (NPP and NDVI) were delayed by approximately six months, while the effects of extreme precipitation indexes were immediate. The study outcomes contribute to our comprehension of plants' growth, and also their reaction to extreme climates, and offer essential support for evidence–based ecological management practices in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Water resources management and planning traditionally focus on visible liquid or blue water. However, green water also maintains social development and ecosystem services. Therefore, blue and green water should be incorporated into the watershed management system for evaluating water resources. To analyze the water resources of the Hanjiang River Basin, the SWAT model was set up using long-term and high-precision geographic data. The methods of wavelet analysis and Pearson's correlation analysis were used to explore the influence mechanism of climate and vegetation changes on the blue and green water flow (BWF and GWF) of the main ecosystems in the basin. The results showed that: (1) The spatial–temporal distribution of the BWF and GWF in the main ecosystems of the basin over the past 50 years was uneven. Forest ecosystems and farmland ecosystems have a greater concentration of water resources in the south, while grassland ecosystems have a greater concentration of water resources in the east. (2) Climate dominates the BWF and GWF changes in the main ecosystems of the basin. The BWF and the precipitation change cycle are synergistic, and the GWF and the temperature change cycle are synergistic. (3) The correlation between vegetation and BWF and GWF in the farmland ecosystem is significant. Vegetation affects the hydrological change process of the BWF and GWF at the microscale. This study can provide data support and scientific rules for ecosystem water resource management in the basin. [ABSTRACT FROM AUTHOR]

The fraction of absorbed photosynthetically active radiation (FPAR) is a key biophysiological parameter of terrestrial ecosystems. However, due to a lack of data with adequate spatial resolution and in long enough time series, there have been limitations in exploring the spatiotemporal changes of vegetation and response to climate change. In this study, a 1 km spatial resolution and 8-day period length dataset (FPARANN) was developed covering the years 1980 to 2018 and evaluated on spatiotemporal change consistency by validating with Gross Primary Production (GPP) observations from the Chinese Flux Observation and Research Network (ChinaFLUX), and comparison with other FPAR products. FPARANN provided a comparable performance in capturing seasonal change observed through GPP, according to the coefficient of determination (R2): 0.50, 0.51, 0.70 and 0.74 averaged for all sites, forest sites, grassland sites and cropland flux sites, respectively. The new data had more spatial similarity to the MODIS FPAR product (FPARMCD15A2) with a greater R2 (0.77) and a lower RMSE (0.12) than other products. With a newly developed dataset, combined with FPARANN (1980–2003) and FPARMCD15A2 (2004–2018), an overall increasing trend in FPAR was found for over 81% of the vegetated area of China from 1980 to 2018. FPAR increased more rapidly for over 83.7% of the area in the 2010s, and at a slower pace for over 62.1% of the area in the early 2000s, which was attributed to a decadal shifting of climate change. This study implies the new dataset is useful in quantifying vegetation changes and would be an important data source for future study of the carbon cycle, soil erosion, or evapotranspiration, with great application potential. [ABSTRACT FROM AUTHOR]

Ecological protection and high-quality development of the Yellow River Basin (YRB), China, aroused remarkable concerns from China's Central Government, and has been a major national strategy. The Inner Mongolia reach of the Yellow River Basin (IM-YRB) is a typical dryland with pervasive vegetation restoration through the actions of the ecological projects that have been conducted in recent years. However, how climate changes and human activities, such as land use and land cover (LULC) changes, jointly impact vegetation variations in this region remains poorly understood. Here, using an explainable machine learning technique, we evaluated linkages between the kernel normalized difference vegetation index (kNDVI) and air temperature, precipitation, soil moisture, and LULC changes, and relevant marginal contributions of these four drivers to the observed vegetation changes. The grassland fraction on a pixel level was selected as the quantitative LULC variable due to its key role in regional LULC. We found that interannual kNDVI changes in most areas of this study region were negatively sensitive to temperature, but positively sensitive to precipitation and soil moisture, with area fractions of 71.74%, 96.93%, and 89.33%, respectively. The area fraction of negative kNDVI sensitivity to LULC was roughly equivalent to that of positive kNDVI sensitivity. The contributions of air temperature, precipitation, soil moisture, and LULC to overall kNDVI changes were 21.54%, 33.32%, 32.19%, and 12.95%, respectively. Moisture conditions also play a critical role in vegetation changes, which was reflected by the fluctuating growth of kNDVI as interannual changes in precipitation. Nonetheless, kNDVI changes are also affected by LULC, and LULC became the dominant factor behind the kNDVI anomalies over the grassland restoration regions from barren over the IM-YRB. This research provides theoretical support for dryland vegetation restoration under the influence of climate change. [ABSTRACT FROM AUTHOR]

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change's negative effects and maintain water resources' sustainability in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

In the context of global climate change, many studies have focused on the interannual vegetation variation trends and their response to precipitation and temperature, but ignored the effects of seasonal variability. This study explored the relationship between normalized difference vegetation index (NDVI) and seasonal climate elements in the Wuliangsu Lake Basin area from 1990 to 2020, and quantified the impacts of human activities on vegetation dynamics. We used Landsat series data to analyze the spatial and temporal variation of the NDVI using the trend analysis method, the Theil–Sen median, the Mann–Kendall test, and the Hurst index. Then, we used meteorological data and land use data to quantify the effects of human activities using residual analysis, and correlation methods to determine the driving forces of NDVI variations. The results showed that the NDVI changes presented obvious regional characteristics, with a decreasing trend from southeast to northwest in Wuliangsu Lake Basin. Due to global warming, the start of the growing season (SOS) is 4.3 days (2001 to 2010) and 6.8 days (2011 to 2020) earlier compared with 1990 to 2000. The end of the season (EOS) is advanced by 3.6 days (2001 to 2010), and delayed by 8.9 days (2011 to 2020). Seasonal (spring, summer, autumn, and winter) NDVIs with precipitation and temperature show spatial heterogeneity. Further, changes in grasslands and woodlands were vulnerable to climate change and human activities. Since the beginning of the 21st century, human activity was the driving force for vegetation improvement in the Dengkou, west-central, north and southwest regions, where ecological instability is weak. This finding can provide a theoretical basis for the implementation of the same type of ecological restoration projects and the construction of ecological civilization, and contribute to the regional green and sustainable development. [ABSTRACT FROM AUTHOR]

Gross primary productivity (GPP) represents total vegetation productivity and is crucial in regional or global carbon balance. The Northeast China (NEC), abundant in vegetation resources, has a relatively large vegetation productivity; however, under obvious climate change (especially warming), whether and how will the vegetation productivity and ecosystem function of this region changed in a long time period needs to be revealed. With the help of GPP products provided by the Global LAnd Surface Satellite (GLASS) program, this paper gives an overview of the regional feedback of vegetation productivity to the changing climate (including temperature, precipitation, and solar radiation) across the NEC from 1982 to 2015. Analyzing results show a slight positive response of vegetation productivities to warming across the NEC with an overall increasing trend of GPPGS (accumulated GPP within the growing season of each year) at 4.95 g C/m2. yr−2 over the last three decades. More specifically, the growth of crops, rather than forests, contributes more to the total increasing productivity, which is mainly induced by the agricultural technological progress as well as warming. As for GPP in forested area in the NEC, the slight increment of GPPGS in northern, high-latitude forested region of the NEC was caused by warming, while non-significant variation of GPPGS was found in southern, low-latitude forested region. In addition, an obvious greening trend, as reported in other regions, was also found in the NEC, but GPPGS of forests in southern NEC did not have significant variations, which indicated that vegetation productivity is not bound to increase simultaneously with greening, except for these high-latitude forested areas in the NEC. The regional feedback of vegetation productivity to climate change in the NEC can be an indicator for vegetations growing in higher latitudes in the future under continued climate change. [ABSTRACT FROM AUTHOR]

Climate change and human activities significantly affected environmental changes in drylands. However, the relative roles remain unclear regarding these factors' effects on environment changes in drylands. Herein, we analyzed vegetation change trends using remote-sensing datasets to determine the interactions of vegetation, climate, and anthropogenic activities in an arid region of China, Kubuqi Desert. Our study showed that 67.64% of the pixels of fractional vegetation coverage (FVC) increased in 2020 in comparison with those of 1986. The FVC exhibited a significant greening trend (0.0011/yr, p < 0.05) in 1986–2020 as a whole. This greening trend revealed two distinct periods separated by a turning point in 2001. There was no clear trend of FVC before 2001, and then there was a dramatically greening trend since 2001 in most regions of the study area. The increasing rate (0.0036/yr) in the later period was three times higher than the entire period. The accelerated increasing trend was due to the variable compound effects of climate and human activities. The correlation between FVC and precipitation was mainly positive, which outweighs the significantly negative correlation between vegetation and temperature. However, both climatic factors cannot well explain the trends of vegetation dynamics, implying a possible role for human activities. Generally, climate change and anthropogenic activities contributed 42.15% and 57.85% to the overall vegetation variations in 1986–2020. Specifically, the relative role of the two factors was vastly different in two distinct periods. Climate change led the dominant roles (58.68%) in the vegetation variations in 1986–2001, while anthropogenic activities dominated (86.79%) in driving vegetation recovery in the period after 2001. Due to the massive ecological conservation programs such as the Grain for Green Project launched in 2001, substantial deserts have been transformed into grasslands and forests. This analysis highlights the ecological policies largely responsible for vegetation restoration and provides references for ecological protection and sustainable development in eco-fragile ecosystems. [ABSTRACT FROM AUTHOR]

Global climate change profoundly influences the patterns of vegetation growth. However, the disparities in vegetation responses induced by regional climate characteristics are generally weakened in large-scale studies. Meanwhile, distinct climatic drivers of vegetation growth result in the different reactions of different vegetation types to climate variability. Hence, it is an extraordinary challenge to detect and attribute vegetation growth changes. In this study, the spatiotemporal distribution and dynamic characteristics of climate change effects on vegetation growth from 2000 to 2020 were investigated by the normalized difference vegetation index (NDVI) dataset during the growing season (April–October). Meanwhile, we further detected the climate-dominated factor between different vegetation types (i.e., forest, shrub, and grass) within the Chaohe watershed located in temperate northern China. The results revealed a continuous greening trend over the entire study period, despite slowing down since 2007 (p < 0.05). Growing-season precipitation (P) was identified as the dominant climatic factor of the greening trend (p < 0.05), and approximately 34.83% of the vegetated area exhibited a significant response to increasing P. However, continued warming-induced intensive evaporation demand caused the vegetation growth to slow down. Hereinto, the areas with a significantly positive response of forest growth to temperature decreased from 24.38% to 18.06% (p < 0.05). In addition, solar radiation (SW) corresponds to the vegetation trend in the watershed (p < 0.05), and the significantly positive SW-influenced areas increased from 9.24% and 2.64% to 11.78% and 3.37% in forests and shrubland, respectively (p < 0.05). Our findings highlight the nonlinearity of long-term vegetation growth trends with climate variation and the cause of this divergence, which provide vital insights into forecasting vegetation responses to future climate change. [ABSTRACT FROM AUTHOR]

Understanding the impacts and extent of both climate change and human activities on ecosystems is crucial to sustainable development. With low anti-interference ability, arid and semi-arid ecosystems are particularly sensitive to disturbances from both climate change and human activities. We investigated how and to what extent climate variation and human activities influenced major indicators that are related to ecosystem functions and conditions in the past decades in Xinjiang, a typical arid and semi-arid region in China. We analyzed the changing trends of evapotranspiration (ET), gross primary productivity (GPP) and leaf area index (LAI) derived from the Moderate-Resolution Imaging Spectroradiometer (MODIS) satellite product and the Breathing Earth System Simulator (BESS) model in Xinjiang for different climate zones. We separated and quantified the contributions of climate forcing and human activities on the trends of the studied ecosystem indicators using the residual analysis method for different climate zones in Xinjiang. The results show that GPP and LAI increased and ET decreased from 2001 to 2015 in Xinjiang. Factors that dominate the changes in ecosystem indicators vary considerably across different climate zones. Precipitation plays a positive role in impacting vegetation indicators in arid and hyper-arid zones and temperature has a negative correlation with both GPP and LAI in hyper-arid zones in Xinjiang. Results based on residual analysis indicate that human activities could account for over 72% of variation in the changes in each ecosystem indicator. Human activities have large impacts on each vegetation indicator change in hyper-arid and arid zones and their relative contribution has a mean value of 79%. This study quantifies the roles of climate forcing and human activities in the changes in ecosystem indicators across different climate zones, suggesting that human activities largely influence ecosystem processes in the arid and semi-arid regions of Xinjiang in China. [ABSTRACT FROM AUTHOR]

Vegetation dynamics and their responses to climate change are of significant spatial and temporal heterogeneity. The Sichuan–Tibet Railway (STR) is a major construction project of the 14th Five-Year Plan for Economic and Social Development of the People's Republic of China that is of great significance to promoting the social and economic development of Sichuan–Tibet areas. The planned railway line crosses areas with a complex geological condition and fragile ecological environment, where the regional vegetation dynamics are sensitive to climate change, topographic conditions and human activities. So, analyzing the vegetation variations in the complex vertical ecosystem and exploring their responses to hydrothermal factors are critical for providing technical support for the ecological program's implementation along the route of the planned railway line. Based on MOD13Q1 Normalized Difference Vegetation Index (NDVI) data for the growing season (May to October) during 2001–2020, a Theil-Sen trend analysis, Mann–Kendall test, Hurst exponent analysis and partial correlation analysis were used to detect the vegetation dynamics, predict the vegetation sustainability, examine the relationship between vegetation change and hydrothermal factors, regionalize the driving forces for vegetation growth and explore the interannual variation pattern of driving factors. The growing season NDVI along the Ya'an–Linzhi section of the STR showed a marked rate of increase (0.0009/year) during the past 20 years, and the vegetation's slight improvement areas accounted for the largest proportion (47.53%). Among the three hydrothermal parameters (temperature, precipitation and radiation), the correlation between vegetation growth and the temperature was the most significant, and the vegetation response to precipitation was the most immediate. The vegetation changes were affected by the combined impact of climatic and non-climatic factors, and the proportion of hydrothermal factors' combined driving force slightly increased during the study period. Based on the Hurst exponent, the future vegetation sustainability of the area along the Ya'an–Linzhi section of the STR faces a risk of degradation, and more effective conservations should be implemented during the railway construction period to protect the regional ecological environment. [ABSTRACT FROM AUTHOR]

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS. [ABSTRACT FROM AUTHOR]

Vegetation in the terrestrial ecosystem, sensitive to climate change and human activities, exerts a crucial influence on the carbon cycles in land, ocean, and atmosphere. Discrimination between climate and human-induced vegetation dynamics is advocated but still limited, especially in coastal China, which is characterized by a developed economy, a large population, and high food production, but also by unprecedented climate change and warming. Taking coastal China as the research area, our study used the normalized difference vegetation index (NDVI) in growing seasons, as well as precipitation, temperature, and sunlight hours datasets, adopted residual trend analysis at pixel and regional scales in coastal China from 2000–2019 and aims to (1) delineate the patterns and processes of vegetation changes, and (2) separate the relative contributions of climate and human activities by adopting residual trend analysis. The results indicated that (1) coastal China experienced the most vegetation greening (83.04% of the whole region) and partial degradation (16.86% of the whole region) with significant spatial heterogeneity; (2) compared with climate change, human activities have a greater positive impact on NDVI, and the regions were mainly located in the north of the North China Plain and the south of southern China; (3) the relative contribution rates of climate change and human activities were detected to be 0–60% and 60–100%, respectively; (4) in the northern coastal areas, the improvement of cultivated land management greatly promoted the greening of vegetation and thus the increase of grain yield, while in southern coastal areas, afforestation and the restoration of degraded forest were responsible for vegetation restoration; and (5) similar results obtained by partial correlation between nighttime lights and NDVI indicated the reliability of the residual trend analysis. The linear relationships of precipitation, temperature, and radiation on NDVI may limit the accurate estimation of climate drivers on vegetation, and further ecosystem process-modeling approaches can be used to estimate the relative contribution of climate change and human activities. The findings in our research emphasized that the attribution for vegetation dynamics with heterogeneity can provide evidence for the designation of rational ecological conservation policies. [ABSTRACT FROM AUTHOR]

This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression models to quantify the effects of climatic drivers on vegetation growth and compare their relative contributions. Results show that on average, the growing season NDVI significantly increased by 0.0007 year-1, with 76.5% of the research area showed increasing NDVI from 1982 to 2011. Seasonally, NDVI increased at high rates during the spring and autumn while changed slightly during the summer. At a national scale, the growing season NDVI was significantly and positively correlated to temperature and precipitation, with temperature being the dominant factor. At regional scales, the growing season NDVI was dominated by increasing temperature in most forest-covered areas in southern China and dominated by precipitation in most grassland in northern China. Within the past three decades, the increasing trend of national mean NDVI abruptly changed in 1994, slowing down from 0.0008 year-1 to 0.0003 year-1. To be regional specific, the growing season NDVI in forest covered southern China has accelerated together with temperature since mid 1990s, while parts of the grassland in northern China have undergone stalled NDVI trends corresponding to slowed temperature increment and dropped precipitation since around 2000. Typical region analysis suggested that apart from long term trends and abrupt change points of climatic drivers, the processes of NDVI variation were also affected by other external factors such as drought and afforestation. Further studies are needed to investigate the nonlinear responses of vegetation growth to climatic drivers and effects of non-climate factors on vegetation growth. [ABSTRACT FROM AUTHOR]

After 2000, China's vegetation underwent great changes associated with climate change and urbanization. Although many studies have been conducted to quantify the contributions of climate and human activities to vegetation, few studies have quantitatively examined the comprehensive contributions of climate, urbanization, and CO2 to vegetation in China's 32 major cities. In this study, using Global Land Surface Satellite (GLASS) fractional vegetation cover (FVC) between 2001 and 2018, we investigated the trend of FVC in China's 32 major cities and quantified the effects of CO2, urbanization, and climate by using generalized linear models (GLMs). We found the following: (1) From 2001 to 2018, the FVC in China generally illustrated an increasing trend, although it decreased in 23 and 21 cities in the core area and expansion area, respectively. (2) Night light data showed that the urban expansion increased to varying degrees, with an average increasing ratio of approximately 168%. The artificial surface area increased significantly, mainly from cropland, forest, grassland, and tundra. (3) Climate factors and CO2 were the major factors that affected FVC change. The average contributions of climate factors, CO2, and urbanization were 40.6%, 39.2%, and 10.6%, respectively. This study enriched the understanding of vegetation cover change and its influencing factors, helped to explain the complex biophysical mechanism between vegetation and environment, and guided sustainable urban development. [ABSTRACT FROM AUTHOR]

Vegetation regulates the exchange of terrestrial carbon and water fluxes and connects the biosphere, hydrosphere, and atmosphere. Over the last four decades, vegetation greening has been observed worldwide using satellite technology. China has also experienced a notably widespread greening trend. However, the responsiveness of vegetation dynamics to elevated CO2 concentration, climate change, and human activities remains unclear. In this study, we attempted to explore the impact of natural (precipitation, air temperature), biogeochemical (CO2), and anthropogenic drivers (nighttime light, afforestation area) on changes in vegetation greenness in the Haihe River Basin (HRB) during 2002–2018 at the county-level. We further determined the major factors affecting the variation in satellite-derived normalized difference vegetation index (NDVI) from moderate resolution imaging spectroradiometer (MODIS) for each county. The results indicated that over 85% of the counties had a significantly increased NDVI trend, and the average linear trend of annual NDVI across the study region was 0.0037 per year. The largest contributor to the NDVI trend was CO2 (mean contribution 45%), followed by human activities (mean contribution of 27%). Additionally, afforestation was a pronounced driving force for NDVI changes in mountainous areas, resulting from ecosystem restoration efforts. Our findings emphasize the crucial role of CO2 fertilization in vegetation cover change, while considering CO2 concentration, climate change, and human activities, and shed light on the significant influences of afforestation programs on water resources, especially in mountainous areas. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic activities are widely considered the main factors affecting vegetation growth. However, their relative contributions are under debate. Within the non-climatic impact, detailed human activities, particularly government policy adjustments, are less investigated. In this study, we develop a fractional vegetation coverage (FVC) extraction method based on MODIS-EVI satellite data to analyze the spatiotemporal variation of vegetation and its attributions in the China–Mongolia–Russia Economic Corridor (CMREC). The average FVC has improved, with a general increase of 0.02/10a from 2000 to 2020. We construct a driving factor identification system for FVC change, based on partial and multiple correlation coefficients, and we divide the driving forces of FVC changes into seven climate-driven types and one non-climate-driven type. The results reveal that FVC changes caused by climatic factors account for 28.2% of CMREC. The most prominent greening (19.5%) is precipitation-driven, and is extensively distributed in Khentii Aimag, Mongolia; southeast Inner Mongolia; west Jilin Province; and southwest Heilongjiang Province, China. Moreover, we quantify the relative contribution of climatic and non-climatic factors to significant FVC change using the first-difference multivariate regression method. The results indicate that the effects of non-climatic factors on vegetation change outweigh those of climatic factors in most areas. According to the land cover change and regional policy adjustment, anthropogenic activities such as afforestation, reclamation, and planting structure adjustment explain most vegetation improvement in the Northeast Plain; eastern Inner Mongolia; and the Hetao Irrigation District, China. Meanwhile, both vegetation improvement and degradation disperse concurrently in the Mongolian and Russian parts of CMREC, where climate change and anthropogenic activities positively and negatively affect vegetation change, respectively. Despite the greening in most CMREC, it must be noted that human-induced greening is unsustainable to some degree. The overdevelopment of black soil area and sandy land, adverse effects of afforestation projects, and natural hazards related to weather and climate extremes altogether threaten the local ecological security in the long run. Therefore, governments should develop new desertification countermeasures in accordance with the laws of nature, and enhance international cooperation to guarantee the ecological safety of CMREC. [ABSTRACT FROM AUTHOR]

Understanding the driving mechanism of vegetation changes is essential for vegetation restoration and management. Vegetation coverage in the Poyang Lake basin (PYLB) has changed dramatically under the context of climate change and human activities in recent decades. It remains challenging to quantify the relative contribution of natural and anthropogenic factors to vegetation change due to their complicated interaction effects. In this study, we selected the Normalized Difference Vegetation Index (NDVI) as an indicator of vegetation growth and used trend analysis and the Mann-Kendall test to analyze its spatiotemporal change in the PYLB from 2000 to 2020. Then we applied the Geodetector model, a novel spatial analysis method, to quantify the effects of natural and anthropogenic factors on vegetation change. The results showed that most regions of the basin were experiencing vegetation restoration and the overall average NDVI value in the basin increased from 0.756 to 0.809 with an upward yearly trend of +0.0026. Land-use type exerted the greatest influence on vegetation change, followed by slope, elevation, and soil types. Except for conversions to construction land, most types of land use conversion induced an increase in NDVI in the basin. The influence of one factor on vegetation NDVI was always enhanced when interacting with another. The interaction effect of land use types and population density was the largest, which could explain 45.6% of the vegetation change, indicating that human activities dominated vegetation change in the PYLB. Moreover, we determined the ranges or types of factors most suitable for vegetation growth, which can be helpful for decision-makers to optimize the implementation of ecological projects in the PYLB in the future. The results of this study could improve the understanding of the driving mechanisms of vegetation change and provide a valuable reference for ecological restoration in subtropical humid regions. [ABSTRACT FROM AUTHOR]

The spatiotemporal evolution of vegetation and its influencing factors can be used to explore the relationships among vegetation, climate change, and human activities, which are of great importance for guiding scientific management of regional ecological environments. In recent years, remote sensing technology has been widely used in dynamic monitoring of vegetation. In this study, the normalized difference vegetation index (NDVI) and standardized precipitation–evapotranspiration index (SPEI) from 1998 to 2017 were used to study the spatiotemporal variation of NDVI in China. The influences of climate change and human activities on NDVI variation were investigated based on the Mann–Kendall test, correlation analysis, and other methods. The results show that the growth rate of NDVI in China was 0.003 year−1. Regions with improved and degraded vegetation accounted for 71.02% and 22.97% of the national territorial area, respectively. The SPEI decreased in 60.08% of the area and exhibited an insignificant drought trend overall. Human activities affected the vegetation cover in the directions of both destruction and restoration. As the elevation and slope increased, the correlation between NDVI and SPEI gradually increased, whereas the impact of human activities on vegetation decreased. Further studies should focus on vegetation changes in the Continental Basin, Southwest Rivers, and Liaohe River Basin. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Vegetation fluctuation is sensitive to climate change, and this response exhibits a time lag. Traditionally, scholars estimated this lag effect by considering the immediate prior lag (e.g., where vegetation in the current month is impacted by the climate in a certain prior month) or the lag accumulation (e.g., where vegetation in the current month is impacted by the last several months). The essence of these two methods is that vegetation growth is impacted by climate conditions in the prior period or several consecutive previous periods, which fails to consider the different impacts coming from each of those prior periods. Therefore, this study proposed a new approach, the weighted time-lag method, in detecting the lag effect of climate conditions coming from different prior periods. Essentially, the new method is a generalized extension of the lag-accumulation method. However, the new method detects how many prior periods need to be considered and, most importantly, the differentiated climate impact on vegetation growth in each of the determined prior periods. We tested the performance of the new method in the Loess Plateau by comparing various lag detection methods by using the linear model between the climate factors and the normalized difference vegetation index (NDVI). The case study confirmed four main findings: (1) the response of vegetation growth exhibits time lag to both precipitation and temperature; (2) there are apparent differences in the time lag effect detected by various methods, but the weighted time-lag method produced the highest determination coefficient (R2) in the linear model and provided the most specific lag pattern over the determined prior periods; (3) the vegetation growth is most sensitive to climate factors in the current month and the last month in the Loess Plateau but reflects a varied of responses to other prior months; and (4) the impact of temperature on vegetation growth is higher than that of precipitation. The new method provides a much more precise detection of the lag effect of climate change on vegetation growth and makes a smart decision about soil conservation and ecological restoration after severe climate events, such as long-lasting drought or flooding. [ABSTRACT FROM AUTHOR]

Vegetation is known to be sensitive to both climate change and anthropogenic disturbance in the karst region. However, the relationship between an abrupt change in vegetation and its driving factors is unclear at multiple timescales. Based on the non-parametric Mann-Kendall test and the ensemble empirical mode decomposition (EEMD) method, the abrupt changes in vegetation and its possible relationships with the driving factors in the karst region of southwest China during 1982–2015 are revealed at multiple timescales. The results showed that: (1) the Normalized Difference Vegetation Index (NDVI) showed an overall increasing trend and had an abrupt change in 2001. After the abrupt change, the greening trend of the NDVI in the east and the browning trend in the west, both changed from insignificant to significant. (2) After the abrupt change, at the 2.5-year time scale, the correlation between the NDVI and temperature changed from insignificantly negative to significantly negative in the west. Over the long-term trend, it changed from significantly negative to significantly positive in the east, but changed from significantly positive to significantly negative in the west. The abrupt change primarily occurred on the long-term trend. (3) After the abrupt change, 1143.32 km2 farmland was converted to forests in the east, and the forest area had significantly increased. (4) At the 2.5-year time scale, the abrupt change in the relationships between the NDVI and climate factors was primarily driven by climate change in the west, especially rising temperatures. Over the long-term trend, it was caused by ecological protection projects in the east, but by rising temperatures in the west. The integration of the abrupt change analysis and multiple timescale analysis help assess the relationship of vegetation changes with climate changes and human activities accurately and comprehensively, and deepen our understanding of the driving mechanism of vegetation changes, which will further provide scientific references for the protection of fragile ecosystems in the karst region. [ABSTRACT FROM AUTHOR]

Based on 541 Landsat images between 1988 and 2016, the normalized difference vegetation indices (NDVIs) of the wetland vegetation at Xitugou (XTG) and Wowachi (WWC) inside the Dunhuang Yangguan National Nature Reserve (YNNR) in northwest China were calculated for assessing the impacts of climate change on wetland vegetation in the YNNR. It was found that the wetland vegetation at the XTG and WWC had both shown a significant increasing trend in the past 20–30 years and the increase in both the annual mean temperature and annual peak snow depth over the Altun Mountains led to the increase of the wetland vegetation. The influence of the local precipitation on the XTG wetland vegetation was greater than on the WWC wetland vegetation, which demonstrates that in extremely arid regions, the major constraint to the wetland vegetation is the availability of water in soils, which is greatly related to the surface water detention and discharge of groundwater. At both XTG and WWC, the snowmelt from the Altun Mountains is the main contributor to the groundwater discharge, while the local precipitation plays a lesser role in influencing the wetland vegetation at the WWC than at the XTG, because the wetland vegetation grows on a relatively flat terrain at the WWC, while it grows on a stream channel at the XTG. [ABSTRACT FROM AUTHOR]