Changes in Growing Season Vegetation and Their Associated Driving Forces in China during 2001–2012

Dynamics of soil organic carbon and nitrogen and their relations to hydrothermal variability in dryland

Carbon (C) and nitrogen (N) cycles of terrestrial ecosystems play key roles in global climate change and ecosystem sustainability. In recent decades, climate change has threatened the nutrient balance of dryland ecosystems. However, its impact on soil organic carbon (SOC) and soil total nitrogen (STN) in drylands of China are still unclear. In this study, the structural equation model (SEM) was used to explain the relationship between environmental variables used by the best model and SOC or STN. Then Adaptive Boosting Regressor (AdaBoost), Gradient Boosting Regression (GBRT), Extreme gradient boosting Regression (XGBoost) and Random Forest Regression (RF) were used to establish the prediction model of SOC and STN based on soil samples along with environmental variables. The performance of these models was assessed based on a 10-fold cross-validation method using three statistical indicators. Finally, we predicted the SOC and STN of soil samples from 2000 to 2019 based on the best model. Overall, the RF model performed better at predicting SOC and STN in drylands than the other three prediction models (AdaBoost, GBRT, XGBoost). Climate factors were the main factors affecting SOC and STN in the study area. In the Alashan, a dryland in northern China, the precipitation in the growing season increased from 2000 to 2019, at a rate of 12.9 mm/decade. During the same period, the annual sunshine duration significantly decreased by 66 h/decade. Along with interannual hydrothermal variability, SOC showed a fluctuating upward trend at a rate of 0.04 g/kg/decade, while STN exhibited a fluctuating downward trend at 0.003 g/kg/decade from 2000 to 2019. Due to the effects of climate change, dryland were considered as potential sites for carbon sequestration. However, due to the annual hydrothermal variance causing dynamic annual changes, it was deemed unstable. Moreover, it would cause STN loss, which might reduce soil fertility. More attention should be paid to STN monitoring in dryland in the future.

Land Use and Climate Change Altered the Ecological Quality in the Luanhe River Basin

Monitoring and assessing ecological quality (EQ) can help to understand the status and dynamics of the local ecosystem. Moreover, land use and climate change increase uncertainty in the ecosystem. The Luanhe River Basin (LHRB) is critical to the ecological security of the Beijing–Tianjin–Hebei region. To support ecosystem protection in the LHRB, we evaluated the EQ from 2001 to 2020 based on the Remote Sensing Ecological Index (RSEI) with the Google Earth Engine (GEE). Then, we introduced the coefficient of variation, Theil–Sen analysis, and Mann–Kendall test to quantify the variation and trend of the EQ. The results showed that the EQ in LHRB was relatively good, with 61.08% of the basin rated as ‘good’ or ‘excellent’. The spatial distribution of EQ was low in the north and high in the middle, with strong improvement in the north and serious degradation in the south. The average EQ ranged from 0.58 to 0.64, showing a significant increasing trend. Furthermore, we found that the expansion of construction land has caused degradation of the EQ, whereas climate change likely improved the EQ in the upper and middle reaches of the LHRB. The results could help in understanding the state and trend of the eco-environment in the LHRB and support decision-making in land-use management and climate change.

Land-Greening Hotspot Changes in the Yangtze River Economic Belt during the Last Four Decades and Their Connections to Human Activities

The spatial patterns of the normalized difference vegetation index (NDVI) changes in the Yangtze River Economic Belt (YREB) and their potential causes during the last four decades remain unclear. To clarify this issue, this study firstly depicts the spatial patterns of the NDVI changes using global inventory modelling and mapping studies (GIMMS) NDVI data and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data. Secondly, the Mann–Kendall test, regression residual analysis and cluster analysis are used to diagnose the potential causes of the NDVI changes. The results show that the regional mean NDVI exhibited an uptrend from 1982 to 2019, which consists of two prominent uptrend periods, i.e., 1982–2003 and 2003–2019. There has been a shift of greening hotspots. The first prominent greening trend from 1982 to 2003 mainly occurred in the eastern agricultural area, while the second prominent greening uptrend from 2003 to 2019 mainly occurred at the junction of Chongqing, Guizhou and Yunnan. The greening trend and shift of greening hotspots were slightly caused by climate change, but mainly caused by human activities. The first greening trend was closely related to the agricultural progress, and the second greening trend was associated with the rapid economic development and implementation of ecology restoration policies.

Spatial and Temporal Analyses of Vegetation Changes at Multiple Time Scales in the Qilian Mountains

The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10−3, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.

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.

Spatiotemporal Pattern of Vegetation Ecology Quality and Its Response to Climate Change between 2000–2017 in China

Vegetation ecology quality (VEQ) is an important indicator for evaluating environmental quality and ecosystem balance. The VEQ in China has changed significantly with global warming and gradual intensification of human activities. It is crucial to research the spatiotemporal characteristics of VEQ and its response to climate change in China. However, most previous studies used a single indicator to reflect VEQ in China, which needs to combine the effects of multiple indicators to reveal its variation characteristics. Based on the six remote sensing indicators, fractional vegetation cover, leaf area index, net primary productivity, vegetation wetness, land surface temperature, and water use efficiency of vegetation, the vegetation ecology quality index (VEQI) was constructed by principal component analysis in this paper. The spatio-temporal distribution and trend characteristic of VEQ within disparate ecosystems in China from 2000 to 2017 were studied. How continuous climate change affected VEQ over time was also analyzed. The results showed that the differences in spatial distribution between the excellent and poor VEQ regions were significant, with the proportion of excellent regions being much larger than that of poor regions. The VEQ has been ameliorated continuously during the past 18 years. Simultaneously, the VEQ would be ameliorated persistently in the future. Differences in the distribution and variation trend of VEQ occurred in disparate ecosystems. The VEQ of broadleaved forest was the best, while that of shrubs and arctic grassland ecosystem was the worst. The VEQ characteristics were different in disparate climate zones, with the best VEQ in the tropical monsoon climate zone and the worst in the plateau mountain climate zone. Except for desert vegetation and paddy field-dominated vegetation, VEQ of other ecosystems were significantly negatively correlated with altitude. Generally, moderate precipitation and temperature were favorable to improve VEQ in China. VEQ during the peak growing season was negatively correlated with temperature and positively correlated with precipitation, and the influence of precipitation on VEQ was stronger than that of temperature. Our results can be used to enact relevant management measures and policies.

NDVI Dynamics and Its Response to Climate Change and Reforestation in Northern China

Vegetation is an important component of the terrestrial ecosystem that plays an essential role in the exchange of water and energy in climate and biogeochemical cycles. This study investigated the spatiotemporal variation of normalized difference vegetation index (NDVI) in northern China using the GIMMS-MODIS NDVI during 1982–2018. We explored the dominant drivers of NDVI change using regression analyses. Results show that the regional average NDVI for northern China increased at a rate of 0.001 year−1. NDVI improved and degraded area corresponded to 36.1% and 9.7% of the total investigated area, respectively. Climate drivers were responsible for NDVI change in 46.2% of the study area, and the regional average NDVI trend in the region where the dominant drivers were temperature (T), precipitation (P), and the combination of precipitation and temperature (P&T), increased at a rate of 0.0028, 0.0027, and 0.0056 year−1, respectively. We conclude that P has positive dominant effects on NDVI in the subregion VIAiia, VIAiic, VIAiib, VIAib of temperate grassland region, and VIIBiia of temperate desert region in northern China. T has positive dominant effects on NDVI in the alpine vegetation region of Qinghai Tibet Plateau. NDVI is negatively dominated by T in the subregion VIIBiib, VIIBib, VIIAi, and VIIBi of temperate desert regions. Human activities affect NDVI directly by reforestation, especially in Shaanxi, Shanxi, and Hebei provinces.

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

Drought and vegetation dynamics in the northern Xinjiang Uygur Autonomous Region of China (NXC), the centre of Asia with arid climate, were assessed using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). Analyses were performed through the use of Sen’s method and Spearman’s correlation to investigate variations in the NDVI and the impacts of drought on vegetation from 1998 to 2015. The severity of droughts in the NXC was assessed by the SPEI, which was revealed to increase over the last 60 years at a rate of 0.017 per decade. This indicates that an alleviating tendency of drought intensity occurred in the NXC. Specifically, the spatial pattern of drought intensity increased gradually from the north-western to south-eastern regions. The average yearly NDVI was 0.28 and increased slightly by 0.001 yr−1 (r = 0.94, p = 3.64) between 1998 and 2015. Additionally, the NDVI showed an obviously spatial heterogeneity, with greater values in the west and small values in the east. Significantly, positive correlations between SPEI and NDVI were observed, while drought exerted a five-year lag effect on vegetation.

Changes in global terrestrial ecosystem water use efficiency are closely related to soil moisture

Ecosystem water use efficiency (WUE), defined as the ratio between gross primary productivity (GPP) and evapotranspiration (ET), is an indicator of the tradeoff between carbon assimilation and water loss that is controlled by climate and ecosystem structure. However, how GPP and ET impact WUE remains poorly understood. In this study, we provide a global analysis of WUE trends from 1982 to 2011 using multi-model ensemble mean WUE values derived from seven process-based carbon cycle models and investigate the relative effects of leaf area index (LAI), soil moisture (SM), and vapor pressure deficit (VPD) on GPP and ET. Increasing WUE trend was derived for all models, with an average rate of 0.0057 ± 0.0018 g C·kg^-1 H₂O·yr^-1 (p = 0.00), with a spatially increasing WUE across ~84% of the global land area, and increasing trends which are statistically significant over ~72% (p < 0.05). Spatially, GPP primarily dominated WUE variability in humid regions, i.e., boreal Eurasia, eastern America, and the tropics, whereas ET dominated WUE variability in dryland regions, i.e., northeast China, the Middle East, southern South America, and South Australia. Soil moisture is likely the most influential factor on GPP and ET variations, with ~63% and ~61% of the global land area dominated by SM, and therefore WUE, for GPP and ET respectively from 1982 to 2011. Our findings enrich the understanding of WUE trends and provide direct evidence for SM-induced variability in WUE.

Greening and Browning of the Hexi Corridor in Northwest China: Spatial Patterns and Responses to Climatic Variability and Anthropogenic Drivers

The arid region of northwest China provides a unique terrestrial ecosystem to identify the response of vegetation activities to natural and anthropogenic changes. To reveal the influences of climate and anthropogenic factors on vegetation, the Normalized Difference Vegetation Index (NDVI), climate data, and land use and land cover change (LUCC) maps were used for this study. We analyzed the spatiotemporal change of NDVI during 2000–2015. A partial correlation analysis suggested that the contribution of precipitation (PRE) and temperature (TEM) on 95.43% of observed greening trends was 47% and 20%, respectively. The response of NDVI in the eastern section of the Qilian Mountains (ESQM) and the western section of the Qilian Mountains (WSQM) to PRE and TEM showed opposite trends. The multiple linear regressions used to quantify the contribution of anthropogenic activity on the NDVI trend indicated that the ESQM and oasis areas were mainly affected by anthropogenic activities (26%). The observed browning trend in the ESQM was attributed to excessive consumption of natural resources. A buffer analysis and piecewise regression methods were further applied to explore the influence of urbanization on NDVI and its change rate. The study demonstrated that urbanization destroys the vegetation cover within the developed city areas and extends about 4 km beyond the perimeter of urban areas and the NDVI of buffer cities (counties) in the range of 0–4 km (0–3 km) increased significantly. In the range of 5–15 (4–10) km (except for Jiayuguan), climate factors were the major drivers of a slight downtrend in the NDVI. The relationship of land use change and NDVI trends showed that construction land, urban settlement, and farmland expanded sharply by 171.43%, 60%, and 10.41%, respectively. It indicated that the rapid process of urbanization and coordinated urban-rural development shrunk ecosystem services.

Association analysis between spatiotemporal variation of vegetation greenness and precipitation/temperature in the Yangtze River Basin (China)

The variation in vegetation greenness provides good understanding of the sustainable management and monitoring of land surface ecosystems. The present paper discusses the spatial-temporal changes in vegetation and controlling factors in the Yangtze River Basin (YRB) using Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) for the period 2001-2013. Theil-Sen Median trend analysis, Pearson correlation coefficients, and residual analysis have been used, which shows decreasing trend of the annual mean NDVI over the whole YRB. Spatially, the regions with significant decreasing trends were mainly located in parts of central YRB, and pronounced increasing trends were observed in parts of the eastern and western YRB. The mean NDVI during spring and summer seasons increased, while it decreased during autumn and winter seasons. The seasonal mean NDVI shows spatial heterogeneity due to the vegetation types. The correlation analysis shows a positive relation between NDVI and temperature over most of the YRB, whereas NDVI and precipitation show a negative correlation. The residual analysis shows an increase in NDVI in parts of eastern and western YRB and the decrease in NDVI in the small part of Yangtze River Delta (YRD) and the mid-western YRB due to human activities. In general, climate factors were the principal drivers of NDVI variation in YRB in recent years.