Spatial relationship between climatologies and changes in global vegetation activity

The impact of climate change and human activities to vegetation carbon sequestration variation in Sichuan and Chongqing

Exploring the vegetation carbon cycle and the factors influencing vegetation carbon sequestration in areas with complex plateau-basin topography and fragile ecosystems is crucial. In this study, spatial and temporal characteristics of carbon sequestration by vegetation in Sichuan and Chongqing from 2010 to 2020 and the influencing factors were investigated through simulations of net primary productivity (NPP) using the modified Carnegie-Ames-Stanford approach (CASA) and the Thornthwaite Memorial (TM) model and using chemical equations of photochemical reactions. The results indicated that: The spatial distribution of carbon sequestration capacity (CSC) trends showed an increase in the east (the most prominent increased trend along the mountainous areas of the basin) and a decrease in the west (western Sichuan plateau). Differences exist in the impact factors of CSC in different regions. In the basin margins and mountainous areas, where the proportion of forests was high, a combination of climate change and human activities contributed to the increase in CSC. The relatively warm and humid meteorological conditions in the hinterland of the basin were more conducive to the increase in CSC, and climate change also affected the region more significantly. In contrast, in the relatively high altitude of western Sichuan, controlled human activities were the key to improving CSC. The results of the study contribute to the understanding of the basic theory of vegetation carbon cycle in areas with complex plateau-basin topography and fragile ecosystems, as well as to provide suggestions for ecological shelter construction and ecological restoration in the upper Yangtze River.

Compound droughts slow down the greening of the Earth

Vegetation response to soil and atmospheric drought has raised extensively controversy, however, the relative contributions of soil drought, atmospheric drought, and their compound droughts on global vegetation growth remain unclear. Combining the changes in soil moisture (SM), vapor pressure deficit (VPD), and vegetation growth (normalized difference vegetation index [NDVI]) during 1982-2015, here we evaluated the trends of these three drought types and quantified their impacts on global NDVI. We found that global VPD has increased 0.22 ± 0.05 kPa·decade^-1 during 1982-2015, and this trend was doubled after 1996 (0.32 ± 0.16 kPa·decade^-1 ) than before 1996 (0.16 ± 0.15 kPa·decade^-1 ). Regions with large increase in VPD trend generally accompanied with decreasing trend in SM, leading to a widespread increasing trend in compound droughts across 37.62% land areas. We further found compound droughts dominated the vegetation browning since late 1990s, contributing to a declined NDVI of 64.56%. Earth system models agree with the dominant role of compound droughts on vegetation growth, but their negative magnitudes are considerably underestimated, with half of the observed results (34.48%). Our results provided the evidence of compound droughts-induced global vegetation browning, highlighting the importance of correctly simulating the ecosystem-scale response to the under-appreciated exposure to compound droughts as it will increase with climate change.

Global analysis of the correlation and propagation among meteorological, agricultural, surface water, and groundwater droughts

Groundwater drought monitoring relies on ground observation data, which cannot be used to reflect large-scale droughts in groundwater resources. The Gravity Recovery and Climate Experiment Satellite (GRACE) improved the situation and provided a new solution for groundwater drought research. However, the propagation relationship among global different drought types has not been fully explored. We employed CRU precipitation data, MERR2 reanalysis soil moisture data, GLDAS and GRACE data to calculate SPI (Standardized precipitation index), SSI (Standardized soil moisture index), SRI (Standardized runoff index), and GDI (Groundwater drought index), characterizing meteorological, agricultural, surface water and groundwater droughts, respectively. The Pearson correlation coefficient was adopted to study the propagation time of these four types of droughts. The results showed that the average propagation times for the different drought types are meteorological drought to surface water drought (3.5 months), meteorological drought to agricultural drought (5.7 months), agricultural drought to groundwater drought (12.97 months), surface water drought to groundwater drought (13.78 months), and meteorological drought to groundwater drought (14.47 months) from longest to shortest. (2) Climate conditions had a significant impact on the propagation time of different drought types. Low temperatures in cold climates resulted in the longest drought propagation time, while dry summer climates in temperate climates reduced drought propagation time. There were weaker propagation relationships in arid climates. In tropical climates, precipitation may not be the main driving factor for drought propagation. (3) Different land cover types show significant differences in the propagation of groundwater droughts, with forests having a longer propagation time from meteorological drought to agricultural drought or surface water drought than grassland and cropland, and forests having the shortest propagation time when meteorological drought, agricultural drought, and surface water drought is propagated to groundwater drought. Woody plants have deeper root systems than herbaceous plants and can draw up deeper groundwater. Forests have greater water storage capacity and weaker groundwater recharge than grasslands and croplands, resulting in forests being more resistant to agricultural and surface water droughts and less resistant to groundwater droughts during meteorological droughts. This study can help to clarify the propagation laws among different drought types and understand the internal mechanisms that affect the development of drought during the water cycle.

Impacts of Climate Change and Land Use/Cover Change on the Net Primary Productivity of Vegetation in the Qinghai Lake Basin

The Qinghai Lake Basin acts as a natural barrier, preventing the western desert from spreading eastward. This is an important link in preserving the ecological stability of the northeastern region of the Qinghai-Tibet Plateau (QTP). Therefore, quantitative research into the net primary productivity (NPP) of vegetation and its driving force in the Qinghai Lake Basin is required. The effects of land use/cover change (LUCC) and climate change on NPP in the Qinghai Lake Basin were studied using R-contribution ratio and partial correlation analysis methods using MOD17A3H products, Land Use/Land Cover (LULC) data, and meteorological data. (1) The LULC of the Qinghai Lake Basin showed a trend that "the area of grassland, cultivated land, and unused land continued to decrease, while the area of other LULC types increased" from 2000 to 2020, according to the study's findings. Grassland, water bodies, construction land, and unused land dominated the mutual transformation of LULC types. (2) The NPP of the basin showed a growing trend, with a growth rate of 3.93 gC·m^-2·a^-1 before 2010 and 0.88 gC·m^-2·a^-1 after 2010. Significant regional heterogeneity was found in NPP, with gradients decreasing from southeast to northwest. (3) The impact of LUCC on overall NPP changes had gradually increased. Climate change has been the primary driver of NPP changes in the Qinghai Lake Basin over the last 20 years.

Spatiotemporal Variation of Urban Plant Diversity and above Ground Biomass in Haikou, China

Understanding the drivers of urban plant diversity (UPD) and above ground biomass (AGB) in urbanized areas is critical for urban ecosystem services and biodiversity protection. The relationships between UPD and AGB have been investigated simultaneously. However, the drivers of UPD and AGB have been explored independently in tropical coastal areas at different time points. To fill this gap, we conducted a remote sensing interpretation, field plant plot surveys, and compiled socioeconomic and urban greening management survey data. We conducted spatial analyses to investigate the relationships among UPD and socioeconomic variables across different primary and secondary urban functional units (UFUs) in the tropical urban ecosystems of the coastal city of Haikou, China. The primary UFUs with the highest AGB were the recreation and leisure districts in 2015 and 2021. In 2015, AGB was mainly correlated with the number of herb species in undeveloped land and the districts of industry, business, recreation, and leisure. In 2021, AGB was affected primarily by the frequency of fertilizing, maintenance, and watering. Our study found that the relationship between UPD and AGB varied across time and space in Haikou. The plant diversity and AGB's response to human activities and socioeconomics appear to have a time-lag effect. These results provide new insights in understanding how management decisions affect urban vegetation and could be used to guide future urban green space planning in Haikou.

Varying Responses of Vegetation Greenness to the Diurnal Warming across the Global

The distribution of global warming has been varying both diurnally and seasonally. Little is known about the spatiotemporal variations in the relationships between vegetation greenness and day- and night-time warming during the last decades. We investigated the global inter- and intra-annual responses of vegetation greenness to the diurnal asymmetric warming during the period of 1982-2015, using the normalized different vegetation index (NDVI, a robust proxy for vegetation greenness) obtained from the NOAA/AVHRR NDVI GIMMS3g dataset and the monthly average daily maximum (T_max) and minimum temperature (T_min) obtained from the gridded Climate Research Unit, University of East Anglia. Several findings were obtained: (1) The strength of the relationship between vegetation greenness and the diurnal temperature varied on inter-annual and seasonal timescales, indicating generally weakening warming effects on the vegetation activity across the global. (2) The decline in vegetation response to T_max occurred mainly in the mid-latitudes of the world and in the high latitudes of the northern hemisphere, whereas the decline in the vegetation response to T_min primarily concentrated in low latitudes. The percentage of areas with a significantly negative trend in the partial correlation coefficient between vegetation greenness and diurnal temperature was greater than that of the areas showing the significant positive trend. (3) The trends in the correlation between vegetation greenness and diurnal warming showed a complex spatial pattern: the majority of the study areas had undergone a significant declining strength in the vegetation greenness response to T_max in all seasons and to T_min in seasons except autumn. These findings are expected to have important implications for studying the diurnal asymmetry warming and its effect on the terrestrial ecosystem.

Regional and Phased Vegetation Responses to Climate Change Are Different in Southwest China

Southwestern China (SW) is simultaneously affected by the East Asian monsoon, South Asian monsoon and westerly winds, forming a complex and diverse distribution pattern of climate types, resulting in a low interpretation rate of vegetation changes by climate factors in the region. This study explored the response characteristics of vegetation to climatic factors in the whole SW and the core area of typical climate type and the phased changes in response, adopting the form of “top-down”, using linear trend method, moving average method and correlation coefficient, and based on the climate data of CRU TS v. 4.02 for the period 1982–2017 and the annual maximum, 3/4 quantile, median, 1/4 quantile, minimum and average (abbreviated as P100, P75, P50, P25, P5 and Mean) of GIMMS NDVI, which were to characterize vegetation growth conditions. Coupling with the trend and variability of climate change, we identified four major types of climate change in the SW, including the significant increase in both temperature and precipitation (T+*-P+*), the only significant increase in temperature and decrease (T+*-P−) or increase (T+*-P+) of precipitation and no significant change (NSC). We then screened out nine typical areas of climate change types (i.e., core areas (CAs)), followed by one T+*-P+* area, which was located in the center of the lake basin of the Qiangtang Plateau. The response of vegetation to climatic factors in T+*-P+* area/T+*-P+ areas and T+*-P− areas/NSC areas were mainly manifested in an increase and a significant decrease, which makes the response characteristics of vegetation to climatic factors in the whole SW have different directionality at different growth stages. Our results may provide new ideas for clearly showing the complexity and heterogeneity of the vegetation response to climate change in the region under the background of global warming.

Widespread decline in winds promoted the growth of vegetation

Vegetation dynamics are sensitive to climate change. Wind is an important climate factor that can affect carbon fluxes by altering carbon uptake and emission rates; however, the impact of wind has not been fully considered in previous studies; therefore, exploring the characteristics of vegetation responses to wind speed is crucial to sustainable natural resource utilization and ecological restoration. In this study, the global leaf area index (LAI) from 1984 to 2013 was used to investigate the vegetation spatial heterogeneities, change processes, and relative contributions of climate change. The differences in vegetation responses to climate factors, such as precipitation (PRE), temperature (TEM), and wind speed (WD), were compared by considering the effects of wind. The results revealed that (1) the global vegetation (86.24%) exhibited a greening trend, among which evergreen broad-leaved forests (0.0052 a^-1) changed the most. (2) The wind speed explained 31.54% of the vegetation variations, which is higher than the contribution of other factors. (3) Reduction of wind speed had a positive impact on vegetation changes. The contribution of climate to vegetation growth increased by 8.14% when considering the effects wind speed, particularly in India and South America. Wind speed effects were essential for enhancing the vegetation dynamics assessment and improving the prediction accuracy of the model.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.

Time-Lag Effect of Vegetation Response to Volumetric Soil Water Content: A Case Study of Guangdong Province, Southern China

The content of soil water affects the physiological activities of vegetation, and the type of vegetation also affects the soil water balance. It is of great significance to study the response of vegetation to soil moisture change, which is helpful for understanding the vulnerability of vegetation for regional and environmental protections. The response of vegetation to soil moisture in Guangdong Province from mid-October 2015 to the end of March 2017 was studied by using cloudy region drought index (CRDI) as the drought index and volumetric soil water content (VSWC) as the soil moisture index to measure the level of water stress on vegetation. Taking the peak and valley positions of CRDI and VSWC as characteristic points, the lag time of vegetation to volumetric soil water content was obtained by judging the difference between the peak and valley positions of the two indexes. The results indicate that the response of vegetation to volumetric soil water content in Guangdong lagged 3.33 periods (9–35 days) on average. When VSWC is sufficient, there is no obvious difference in time-lag between different types of vegetation. However, when VSWC is relatively insufficient, grass shows the fastest response to the change of volumetric soil water content. Both longitude and soil moisture affect the lag time of vegetation. Under the same conditions, the higher the soil humidity is, the longer the lag time is, and the longer the delay time is with the greater longitude. CRDI can reflect the time-lag effect between vegetation and VSWC in Guangdong, indicating it is a sensitive and applicable index for characterizing the time-lag phenomena of vegetation to soil moisture.

Where Anthropogenic Activity Occurs, Anthropogenic Activity Dominates Vegetation Net Primary Productivity Change

Anthropogenic activities and climate change affect the type, structure and function of ecosystems, resulting in important changes in vegetation net primary productivity (NPP). Therefore, in this study we used the vegetation photosynthesis model (VPM) to reveal the spatiotemporal variations in NPP in Xinjiang from 2000 to 2019. The impacts of climate change and anthropogenic activities on NPP changes were quantified and separated by the residual analysis-control variables (RES-CON) method. The results showed that the average NPP in Xinjiang increased by 17.77% from 2000 to 2019. Anthropogenic activities and climate change generally had a positive impact on NPP from 2000 to 2019. The most important anthropogenic activity was land use and land cover (LULC) transformation from grass to arable land, which significantly increased vegetation productivity. Regarding climate change, precipitation has played a significant role in promoting the productivity of vegetation. Overall, the average contribution of climate change (temperature and precipitation) to NPP variation (21.44%) is much greater than the contribution of anthropogenic activities (3.46%), but in areas where anthropogenic activities occur, the average contribution of anthropogenic activities to NPP variation (75.01%) is much greater than the average contribution of climate change (15.53%). Where there are no anthropogenic activities, the average contribution of climate change to NPP variation is 21.72%. In summary, anthropogenic activities are the main driver of NPP variation in areas where anthropogenic activities occur, while the total area in Xinjiang where climate change is the most important driver is larger than the total area where anthropogenic activities are the dominant driver.

Characterisation of Vegetation Response to Climate Change: A Review

Climate change extreme events have consequential impacts that influence the responses of vegetation dynamics as well as ecosystem functioning and sustainable human well-being. Therefore, vegetation response to climate change (VRCC) needs to be explored to foster specific-organised management programmes towards ecological conservation and targeted restoration policy to various climate extreme threats. This review aimed to explore the existing literature to characterise VRCC and to identify solutions and techniques fundamental in designing strategies for targeted effective adaptation and mitigation to achieve sustainable planning outcomes. Accordingly, this review emphasised recent theoretical and practical research on the vegetation-climate responses and their related impacts in the wake of climate change and its debilitating impacts on vegetation. Consequently, this study proposes the Information-based model (IBM), needed to examine Factors–forms of Impacts–Solutions (Techniques)–Risks assessment to identify and provide insights about VRCC in a given region. In conclusion, two enablers of adaptive indicators and the novel systems-based serve as a key policy formulation for sustainability in strengthening the goals of global involvement of local and sub-national governments and institutions in the effective management of vegetation and ecosystem protection.

Nonlinear relationship of greening and shifts from greening to browning in vegetation with nature and human factors along the Silk Road Economic Belt

Understanding the trends in vegetation evolution and their driving factors is critical to revealing changes in ecosystem's structure and function. However, less is known about the nonlinear relationship between greening trends and greening to browning shifts in vegetation and its driving factors. Based on ensemble empirical mode decomposition (EEMD) and Boosted Regression Tree (BRT) methods, we investigated the nonlinear relationships of the greening trends and greening to browning shifts in vegetation to climate change, elevation, and human activities along the Silk Road Economic Belt (SREB). Results indicated that: (1) For vegetation with greening trends, although the total importance of climate-related variables was greater than human activities (59.57% and 35.33%, respectively), Land use changes (14.91%) and CO₂ (9.41%) were more important than the other nature-related variables. In addition, volumetric soil moisture (8.72%), precipitation of warmest quarter (6.50%), and daily snow water equivalent (5.75%) were also very important. For vegetation with greening to browning shifts, the total contributions of climate-related variables were also larger than human activities (69.10% and 19.36%, respectively). Moreover, elevation (11.54%), vapor pressure deficit (9.85%), annual mean temperature (6.84%), min temperature of the coldest month (5.98%), soil moisture (5.16%), and annual precipitation (5.11%) might be the main reasons. (2) Increased cropland and grassland, along with the land conversions from shrub land/sparse vegetated to forest could promote vegetation greening. Increased rates of CO₂, volumetric soil moisture, warmest quarter precipitation, and daily snow water equivalent could also promote it. For vegetation with greening to browning shifts, low-altitude areas, increased vapor pressure deficit, decreased cold temperature, and drought could promote vegetation shift greening to browning. The nonlinear analysis can correctly reveal the actual trends in vegetation and its responses to driving factors, which will further provide adaptation strategies to protect ecology.

Estimating Ecological Responses to Climatic Variability on Reclaimed and Unmined Lands Using Enhanced Vegetation Index

Climatic impact on re-established ecosystems at reclaimed mined lands may have changed. However, little knowledge is available about the difference in vegetation–climate relationships between reclaimed and unmined lands. In this study, ecological responses to climatic variability on reclaimed and neighbouring unmined lands were estimated using remote-sensing data at the Pingshuo Mega coal mine, one of the largest coal mines with long-term reclamation history in China. Time-series MODIS enhanced vegetation index (EVI) data and meteorological data from 1997 to 2017 were collected. Results show significantly different vegetation–climate relationships between reclaimed and unmined lands. First, the accumulation periods of all climatic variables were much longer on reclaimed mining lands. Second, vegetation on reclaimed lands responded to variabilities in temperature, rainfall, air humidity, and wind speed, while undisturbed vegetation only responded to variabilities of temperature and air humidity. Third, climatic variability made a much higher contribution to EVI variation on reclaimed land (20.0–46.5%) than on unmined land (0.7–1.7%). These differences were primarily caused by limited ecosystem resilience, and changed site hydrology and microclimate on reclaimed land. Thus, this study demonstrates that the legacy effects of surface mining can critically change on-site vegetation–climate relationships, which impacts the structure, functions, and stability of reclaimed ecosystems. Vegetation–climate relationships of reclaimed ecosystems deserve further research, and remote-sensing vegetation data are an effective source for relevant studies.

A Spatial and Temporal Assessment of Vegetation Greening and Precipitation Changes for Monitoring Vegetation Dynamics in Climate Zones over Africa

Vegetation is vital, and its greening depends on access to water. Thus, precipitation has a considerable influence on the health and condition of vegetation and its amount and timing depend on the climatic zone. Therefore, it is extremely important to monitor the state of vegetation according to the movements of precipitation in climatic zones. Although a lot of research has been conducted, most of it has not paid much attention to climatic zones in the study of plant health and precipitation. Thus, this paper aims to study the plant health in five African climatic zones. The linear regression model, the persistence index (PI), and the Pearson correlation coefficients were applied for the third generation Normalized Difference Vegetation Index (NDVI3g), with Climate Hazard Group infrared precipitation and Climate Change Initiative Land Cover for 34 years (1982 to 2015). This involves identifying plants in danger of extinction or in dramatic decline and the relationship between vegetation and rainfall by climate zone. The forest type classified as tree cover, broadleaved, deciduous, closed to open (>15%) has been degraded to 74% of its initial total area. The results also revealed that, during the study period, the vegetation of the tropical, polar, and warm temperate zones showed a higher rate of strong improvement. Although arid and boreal zones show a low rate of strong improvement, they are those that experience a low percentage of strong degradation. The continental vegetation is drastically decreasing, especially forests, and in areas with low vegetation, compared to more vegetated areas, there is more emphasis on the conservation of existing plants. The variability in precipitation is excessively hard to tolerate for more types of vegetation.

Trends in vegetation productivity related to climate change in China's Pearl River Delta

Climate change will be a powerful stressor on ecosystems and biodiversity in the second half of the 21^st century. In this study, we used the satellite-derived Normalized Difference Vegetation Index (NDVI) to examine a 34-year trend along with the response of vegetation to climate indicators surrounding the world’s largest megacity: the Pearl River Delta (PRD) of China. An overall increasing trend is observed in vegetation productivity metrics over the study period 1982 to 2015. Increase in winter productivity in both natural ecosystems and croplands is more related to increasing temperatures (r = 0.5–0.78), than to changes in rainfall. For growing season productivity, negative correlations with temperature were observed in cropland regions, and some forests in the northern part of PRD region, suggesting high-temperature stress on crop production and forest vegetation. However, increased winter and spring temperatures provide higher opportunities for cropping in winter. During the decade 1995–2004, vegetation productivity metrics showed a reversal in the upward trend. The geographical and biological complexity of the region under significant climatic and development impacts suggests causative factors would be synergistic. These include our observed decrease in sunshine hours, increasing cloud cover associated with atmospheric aerosols from industrial and urban development, direct pollution effects on plant growth, and exceedance of high temperature growth thresholds.

Vegetation Cover Change and Its Attribution in China from 2001 to 2018

It is confirmed that China has been greening over the last two decades. Such greening and its driving factors are therefore significant for understanding the relationship between vegetation and environments. However, studies on vegetation changes and attribution analyses at the national scale are limited in China after 2000. In this study, fractional vegetation cover (FVC) data from Global Land Surface Satellite (GLASS) was used to detect vegetation change trends from 2001 to 2018, and the effects of CO2, temperature, shortwave radiation, precipitation, and land cover change (LCC) on FVC changes were quantified using generalized linear models (GLM). The results showed that (1) FVC in China increased by 14% from 2001 to 2018 with a greening rate of approximately 0.0019/year (p < 0.01), which showed an apparent greening trend. (2) On the whole, CO2, climate-related factors, and LCC accounted for 88% of FVC changes in China, and the drivers explained 82%, 89%, 90%, and 89% of the FVC changes in the Qinghai–Tibet region, northwest region, northern region, and southern region, respectively. CO2 was the major driving factor for FVC changes, accounting for 31% of FVC changes in China, indicating that CO2 was an essential factor in vegetation growth research. (3) The statistical results of pixels with land cover changes showed that LCC explained 12% of FVC changes, LCC has played a relatively important role and this phenomenon may be related to the ecological restoration projects. This study enriches the study of vegetation changes and its driving factors, and quantitatively describes the response relationship between vegetation and its driving factors. The results have an important significance for adjusting terrestrial ecosystem services.

Elevational Movement of Vegetation Greenness on the Tibetan Plateau: Evidence from the Landsat Satellite Observations during the Last Three Decades

The Tibetan Plateau, the highest plateau in the world, has experienced strong climate warming during the last few decades. The greater increase of temperature at higher elevations may have strong impacts on the vertical movement of vegetation activities on the plateau. Although satellite-based observations have explored this issue, these observations were normally provided by the coarse satellite data with a spatial resolution of more than hundreds of meters (e.g., GIMMS and MODIS), which could lead to serious mixed-pixel effects in the analyses. In this study, we employed the medium-spatial-resolution Landsat NDVI data (30 m) during 1990–2019 and investigated the relationship between temperature and the elevation-dependent vegetation changes in six mountainous regions on the Tibetan Plateau. Particularly, we focused on the elevational movement of the vegetation greenness isoline to clarify whether the vegetation greenness isoline moves upward during the past three decades because of climate warming. Results show that vegetation greening occurred in all six mountainous regions during the last three decades. Increasing temperatures caused the upward movement of greenness isoline at the middle and high elevations (>4000 m) but led to the downward movement at lower elevations for the six mountainous regions except for Nyainqentanglha. Furthermore, the temperature sensitivity of greenness isoline movement changes from the positive value to negative value by decreasing elevations, suggesting that vegetation growth on the plateau is strongly regulated by other factors such as water availability. As a result, the greenness isoline showed upward movement with the increase of temperature for about 59% pixels. Moreover, the greenness isoline movement increased with the slope angles over the six mountainous regions, suggesting the influence of terrain effects on the vegetation activities. Our analyses improve understandings of the diverse response of elevation-dependent vegetation activities on the Tibetan Plateau.

Time lag of vegetation growth on the Loess Plateau in response to climate factors: Estimation, distribution, and influence

Vegetation growth is importantly affected by seasonal weather patterns. The time lag between changes in these patterns and the vegetative response is an important factor in vegetation-climate interactions and can vary considerably with the spatial heterogeneity of an ecosystem. In this study of the Loess Plateau, China, highly accurate time lags were determined at the daily scale using linear regression based on a multiyear normalized difference vegetation index (NDVI) dataset (1982-2015) and the corresponding reconstructed monthly climate series. We found that lag length varied among catchments, vegetation zones, and land-cover types. The mean time lag between precipitation and the NDVI response varied from 7.9 days to 17.7 days across the catchments; for temperature and NDVI, the lag in response ranged from 6.2 days to 25.3 days. Across the six vegetation zones that range from the southeast to the northwest of the Loess Plateau, both the precipitation-NDVI and temperature-NDVI lag lengths were largest in the central zones and lower in the zones closer to the edges of the plateau. Among the different land-cover types, grassland had the largest lags between precipitation or temperature change and the NDVI response, followed by shrubland, arable land, and then forests. The possible impact of vegetative time lags on sediment yield was also investigated for the first time, and this information has great relevance for soil conservation on the Loess Plateau and sediment reduction in the lower Yellow River.

Controls of Climate and Land-Use Change on Terrestrial Net Primary Productivity Variation in a Subtropical Humid Basin

Knowledge of vegetation dynamics in relation to climatic changes and human activities is essential for addressing the terrestrial carbon cycle in the context of global warming. Scientific detection and quantitative attribution of vegetation dynamic changes in different climatic zones and human activities are the focus and challenge of the relevant research. Taking the Poyang Lake basin as the research area, this study aimed to reveal how climate and land use drive changes in net primary productivity (NPP) in the subtropical humid basin. Change patterns of vegetation NPP and their relationships with meteorological factors across the basin were first investigated based on the estimation of 18 year (2000–2017 year) NPP by using a typical light energy utilization model, the Carnegie-Ames-Stanford Approach (CASA) model. Quantitative analysis was then conducted to explicitly distinguish the driving effects of climate change and land-use change on NPP dynamics in two different periods. Results show that annual NPP and total production (TP) of the Poyang Lake basin increased significantly from 2000 to 2017. During this period, land-use change in the basin was driven by the process of urbanization expansion and the efforts of ecological protection. Climatically, the temperature is the major influencing climatic factor in determining vegetation productivity in the subtropical humid basin, followed by precipitation and solar radiation. In addition, our investigation also revealed that with comparison to the period of 2000s, the increased TP of the Poyang Lake basin due to climate change in 2010s was much bigger than the decreased TP due to land-use change. However, in the areas where the land-use change occurred, the decreased TP was mainly attributed to the impact of land-use change, even though climate change showed a positive effect of increasing productivity.

Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data

Monitoring of abnormal changes on the earth’s surface (e.g., forest disturbance) has improved greatly in recent years because of satellite remote sensing. However, high computational costs inherently associated with processing and analysis of satellite data often inhibit large-area and sub-annual monitoring. Normal seasonal variations also complicate the detection of abnormal changes at sub-annual scale in the time series of satellite data. Recently, however, computationally powerful platforms, such as the Google Earth Engine (GEE), have been launched to support large-area analysis of satellite data. Change detection methods with the capability to detect abnormal changes in time series data while accounting for normal seasonal variations have also been developed but are computationally intensive. Here, we report an implementation of BFASTmonitor (Breaks For Additive Season and Trend monitor) on GEE to support large-area and sub-annual change monitoring using satellite data available in GEE. BFASTmonitor is a data-driven unsupervised change monitoring approach that detects abnormal changes in time series data, with near real-time monitoring capabilities. Although BFASTmonitor has been widely used in forest cover loss monitoring, it is a generic change monitoring approach that can be used to monitor changes in a various time series data. Using Landsat time series for normalised difference moisture index (NDMI), we evaluated the performance of our GEE BFASTmonitor implementation (GEE BFASTmonitor) by detecting forest disturbance at three forest areas (humid tropical forest, dry tropical forest, and miombo woodland) while comparing it to the original R-based BFASTmonitor implementation (original BFASTmonitor). A map-to-map comparison showed that the spatial and temporal agreements on forest disturbance between the original and our GEE BFASTmonitor implementations were high. At each site, the spatial agreement was more than 97%, whereas the temporal agreement was over 94%. The high spatial and temporal agreement show that we have properly translated and implemented the BFASTmonitor algorithm on GEE. Naturally, due to different numerical solvers being used for regression model fitting in R and GEE, small differences could be observed in the outputs. These differences were most noticeable at the dry tropical forest and miombo woodland sites, where the forest exhibits strong seasonality. To make GEE BFASTmonitor accessible to non-technical users, we developed a web application with simplified user interface. We also created a JavaScript-based GEE BFASTmonitor package that can be imported as a module. Overall, our GEE BFASTmonitor implementation fills an important gap in large-area environmental change monitoring using earth observation data.

Vegetation Changes and Their Response to Global Change Based on NDVI in the Koshi River Basin of Central Himalayas Since 2000

Vegetation forms a main component of the terrestrial biosphere owing to its crucial role in land cover and climate change, which has been of wide concern for experts and scholars. In this study, we used MODIS (moderate-resolution imaging spectroradiometer) NDVI (Normalized Difference Vegetation Index) data, land cover data, meteorological data, and DEM (Digital Elevation Model) data to do vegetation change and its relationship with climate change. First, we investigated the spatio-temporal patterns and variations of vegetation activity in the Koshi River Basin (KRB) in the central Himalayas from 2000 to 2018. Then, we combined NDVI change with climate factors using the linear method to examine their relationship, after that we used the literature review method to explore the influence of human activities to vegetation change. At the regional scale, the NDVIGS (Growth season NDVI) significantly increased in the KRB in 2000–2018, with significant greening over croplands in KRB in India. Further, the croplands and forest in the KRB in Nepal were mainly influenced by human interference. For example, improvements in agricultural fertilization and irrigation facilities as well as the success of the community forestry program in the KRB in Nepal increased the NDVIGS of the local forest. Climate also had a certain impact on the increase in NDVIGS. A significant negative correlation was observed between NDVIGS trend and the annual minimum temperature trend (TMN) in the KRB in India, but an insignificant positive correlation was noted between it and the total annual precipitation trend (PRE). NDVIGS significantly decreased over a small area, mainly around Kathmandu, due to urbanization. Increases in NDVIGS in the KRB have thus been mainly affected by human activities, and climate change has helped increase it to a certain extent.

Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.

Desertification–Scientific Versus Political Realities

Desertification is defined as land degradation occurring in the global drylands. It is one of the global problems targeted under the Sustainable Development Goals (SDG 15). The aim of this article is to review the history of desertification and to evaluate the scientific evidence for desertification spread and severity. First quantitative estimates of the global extent and severity of desertification were dramatic and resulted in the establishment of the UN Convention to Combat Desertification (UNCCD) in 1994. UNCCD’s task is to mitigate the negative impacts of desertification in drylands. Since the late 1990s, science has become increasingly critical towards the role of desertification in sustainable land use and food production. Many of the dramatic global assessments of desertification in the 1970s and 1980s were heavily criticized by scientists working in drylands. The used methodologies and the lack of ground-based evidence gave rise to critical reflections on desertification. Some even called desertification a myth. Later desertification assessments relied on remote sensing imagery and mapped vegetation changes in drylands. No examples of large areas completely degraded were found in the scientific literature. In science, desertification is now perceived as a local feature that certainly exists but is not as devastating as was earlier believed. However, the policy arena continues to stress the severity of the problem. Claims that millions of hectares of once productive land are annually lost due to desertification are regularly made. This highlights the disconnection between science and policy, and there is an urgent need for better dialogue in order to achieve SDG 15.

Assessing Terrestrial Ecosystem Resilience using Satellite Leaf Area Index

Quantitative approaches to measuring and assessing terrestrial ecosystem resilience, which expresses the ability of an ecosystem to recover from disturbances without shifting to an alternative state or losing function and services, is critical and essential to forecasting how terrestrial ecosystems will respond to global change. However, global and continuous terrestrial resilience measurement is fraught with difficulty, and the corresponding attribution of resilience dynamics is lacking in the literature. In this study, we assessed global terrestrial ecosystem resilience based on the long time-series GLASS LAI product and GIMMS AVHRR LAI 3g product, and validated the results using drought and fire events as the main disturbance indicators. We also analyzed the spatial and temporal variations of global terrestrial ecosystem resilience and attributed their dynamics to climate change and environmental factors. The results showed that arid and semiarid areas exhibited low resilience. We found that evergreen broadleaf forest exhibited the highest resilience (mean resilience value (from GLASS LAI): 0.6). On a global scale, the increase of mean annual precipitation had a positive impact on terrestrial resilience enhancement, while we found no consistent relationships between mean annual temperature and terrestrial resilience. For terrestrial resilience dynamics, we observed three dramatic raises of disturbance frequency in 1989, 1995, and 2001, respectively, along with three significant drops in resilience correspondingly. Our study mapped continuous spatiotemporal variation and captured interannual variations in terrestrial ecosystem resilience. This study demonstrates that remote sensing data are effective for monitoring terrestrial resilience for global ecosystem assessment.

Quantifying Drought Sensitivity of Mediterranean Climate Vegetation to Recent Warming: A Case Study in Southern California

A combination of drought and high temperatures (“global-change-type drought”) is projected to become increasingly common in Mediterranean climate regions. Recently, Southern California has experienced record-breaking high temperatures coupled with significant precipitation deficits, which provides opportunities to investigate the impacts of high temperatures on the drought sensitivity of Mediterranean climate vegetation. Responses of different vegetation types to drought are quantified using the Moderate Resolution Imaging Spectroradiometer (MODIS) data for the period 2000–2017. The contrasting responses of the vegetation types to drought are captured by the correlation and regression coefficients between Normalized Difference Vegetation Index (NDVI) anomalies and the Palmer Drought Severity Index (PDSI). A novel bootstrapping regression approach is used to decompose the relationships between the vegetation sensitivity (NDVI–PDSI regression slopes) and the principle climate factors (temperature and precipitation) associated with the drought. Significantly increased sensitivity to drought in warmer locations indicates the important role of temperature in exacerbating vulnerability; however, spatial precipitation variations do not demonstrate significant effects in modulating drought sensitivity. Based on annual NDVI response, chaparral is the most vulnerable community to warming, which will probably be severely affected by hotter droughts in the future. Drought sensitivity of coastal sage scrub (CSS) is also shown to be very responsive to warming in fall and winter. Grassland and developed land will likely be less affected by this warming. The sensitivity of the overall vegetation to temperature increases is particularly concerning, as it is the variable that has had the strongest secular trend in recent decades, which is expected to continue or strengthen in the future. Increased temperatures will probably alter vegetation distribution, as well as possibly increase annual grassland cover, and decrease the extent and ecological services provided by perennial woody Mediterranean climate ecosystems as well.

Effects of Climate Factors and Human Activities on the Ecosystem Water Use Efficiency throughout Northern China

Global climate changes have increased the imbalance of water resources, especially in northern China, which comprises typical arid and semiarid regions. Large-scale afforestation has been implemented over the past three decades in northern China. The ecosystem water use efficiency (WUE) connects the carbon cycle and water cycle of the terrestrial ecosystems and is defined as the ratio of the gross primary productivity (GPP) to the evapotranspiration. However, there are still an insufficient number of studies on the impact of the afforestation on the WUE. In this study, we applied the random forest (RF) model to explore the impacts of climate and nonclimate factors on the WUE in northern China. The results showed that in areas with high precipitation, the forests had the highest WUE, while in the arid areas, the croplands had the highest WUE. Of the total area, 44.34% showed a significant increase, and 5.89% showed a significant decrease in the WUE from 1982–2015 in northern China. The main driving factors for the changes in the WUE were climate factors, including the precipitation, temperature and solar radiation, which contributed to approximately 84% of the WUE trends, while human activities, such as afforestation, contributed to approximately 16% of the WUE trends. Overall, although the climate had a larger impact on the WUE dynamics than the human activities, our results suggested that the impacts of the afforestation programs on forest carbon and water cycles should be considered in the context of climate change.

Responses of vegetation activity to the daytime and nighttime warming in Northwest China

Though temperature over the past three decades has shown an asynchronous warming trend between daytime and nighttime, the response of vegetation activity to such non-uniform warming is still not very clear. In this study, the least squares linear trend analysis and geographic information system spatial analysis were conducted to analyze the spatiotemporal patterns of the daytime and nighttime warming based on the daily temperature data from 1982 to 2015 in Northwest China. The normalized difference vegetation index (NDVI) from Global Inventory Monitoring and Modeling System and vegetation type data were used to investigate the responses of vegetation activity to the daytime and nighttime warming using the partial correlation analysis. Our results suggested that (1) there was a very significant increasing trend in both daytime and nighttime temperatures in Northwest China from 1982 to 2015; night temperatures increased about 1.2 times faster than daytime temperatures, showing diurnal asymmetric warming; (2) the responses of vegetation activity to daytime and nighttime warming in Northwest China showed a distinct spatial pattern; the change in night temperatures had a more significant (positive in most regions) effect on vegetation; (3) various types of vegetation responded differently to asymmetric daytime and nighttime warming. Grassland NDVI, broad-leaved, and coniferous forest NDVI significantly responded to daytime warming. Shrub NDVI and desert NDVI significantly responded to night warming. These findings can deepen the understanding of the effects of the daytime and nighttime warming on vegetation activities in arid regions in the context of the current asymmetric warming.

Impact of Urbanization and Climate on Vegetation Coverage in the Beijing–Tianjin–Hebei Region of China

Worldwide urbanization leads to ecological changes around urban areas. However, few studies have quantitatively investigated the impacts of urbanization on vegetation coverage so far. As an important indicator measuring regional environment change, fractional vegetation cover (FVC) is widely used to analyze changes in vegetation in urban areas. In this study, on the basis of a partial derivative model, we quantified the effect of temperature, precipitation, radiation, and urbanization represented as nighttime light on vegetation coverage changes in the Beijing–Tianjin–Hebei (BTH) region during its period of rapid resident population growth from 2001 to 2011. The results showed that (1) the FVC of the BTH region varied from 0.20 to 0.26, with significant spatial heterogeneity. The FVC increased in small cities such as Cangzhou and in the Taihang Mountains, while it decreased in megacities with populations greater than 1 million, such as Beijing and Zhangjiakou Bashang. (2) The BTH region experienced rapid urbanization, with the area of artificial surface increasing by 18.42%. From the urban core area to the fringe area, the urbanization intensity decreased, but the urbanization rate increased. (3) Urbanization and precipitation had the greatest effect on FVC changes. Urbanization dominated the FVC changes in the expanded area, while precipitation had the greatest impacts on the FVC changes in the core area. For future studies on the major influencing factors of FVC changes, quantitative analysis of the contribution of urbanization to FVC changes in urban regions is crucial and will provide scientific perspectives for sustainable urban planning.

Greening Implication Inferred from Vegetation Dynamics Interacted with Climate Change and Human Activities over the Southeast Qinghai–Tibet Plateau

Vegetation dynamics are sensitive to climate change and human activities, as vegetation interacts with the hydrosphere, atmosphere, and biosphere. The Yarlung Zangbo River (YZR) basin, with the vulnerable ecological environment, has experienced a series of natural disasters since the new millennium. Therefore, in this study, the vegetation dynamic variations and their associated responses to environmental changes in the YZR basin were investigated based on Normalized Difference Vegetation Index (NDVI) and Global Land Data Assimilation System (GLDAS) data from 2000 to 2016. Results showed that (1) the YZR basin showed an obvious vegetation greening process with a significant increase of the growing season NDVI (Zc = 2.31, p < 0.05), which was mainly attributed to the wide greening tendency of the downstream region that accounted for over 50% area of the YZR basin. (2) Regions with significant greening accounted for 25.4% of the basin and were mainly concentrated in the Nyang River and Parlung Tsangpo River sub-basins. On the contrary, the browning regions accounted for <25% of the basin and were mostly distributed in the urbanized cities of the midstream, implying a significant influence of human activities on vegetation greening. (3) The elevation dependency of the vegetation in the YZR basin was significant, showing that the vegetation of the low-altitude regions was better than that of the high-altitude regions. The greening rate exhibited a significantly more complicated relationship with the elevation, which increased with elevated altitude (above 3500 m) and decreased with elevated altitude (below 3500 m). (4) Significantly positive correlations between the growing season NDVI and surface air temperature were detected, which were mainly distributed in the snow-dominated sub-basins, indicating that glaciers and snow melting processes induced by global warming play an important role in vegetation growth. Although basin-wide non-significant negative correlations were found between precipitation and growing season NDVI, positive influences of precipitation on vegetation greening occurred in the arid and semi-arid upstream region. These findings could provide important information for ecological environment protection in the YZR basin and other high mountain regions.

Human disturbance caused stronger influences on global vegetation change than climate change

Global vegetation distribution has been influenced by human disturbance and climate change. The past vegetation changes were studied in numerous studies while few studies had addressed the relative contributions of human disturbance and climate change on vegetation change. To separate the influences of human disturbance and climate change on the vegetation changes, we compared the existing vegetation which indicates the vegetation distribution under human influences with the potential vegetation which reflects the vegetation distribution without human influences. The results showed that climate-induced vegetation changes only occurred in a few grid cells from the period 1982–1996 to the period 1997–2013. Human-induced vegetation changes occurred worldwide, except in the polar and desert regions. About 3% of total vegetation distribution was transformed by human activities from the period 1982–1996 to the period 1997–2013. Human disturbances caused stronger damage to global vegetation change than climate change. Our results indicated that the regions where vegetation experienced both human disturbance and climate change are eco-fragile regions.

Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016

Since the implementation of China’s afforestation and conservation projects during recent decades, an increasing number of studies have reported greening trends in the karst regions of southwest China using coarse-resolution satellite imagery, but small-scale changes in the heterogenous landscapes remain largely unknown. Focusing on two typical karst regions in the Nandong and Xiaojiang watersheds in Yunnan province, we processed 2,497 Landsat scenes from 1988 to 2016 using the Google Earth Engine cloud platform and analyzed vegetation trends and associated drivers. We found that both watersheds experienced significant increasing trends in annual fractional vegetation cover, at a rate of 0.0027 year−1 and 0.0020 year−1, respectively. Notably, the greening trends have been intensifying during the conservation period (2001–2016) even under unfavorable climate conditions. Human-induced ecological engineering was the primary factor for the increased greenness. Moreover, vegetation change responded differently to variations in topographic gradients and lithological types. Relatively more vegetation recovery was found in regions with moderate slopes and elevation, and pure limestone, limestone and dolomite interbedded layer as well as impure carbonate rocks than non-karst rocks. Partial correlation analysis of vegetation trends and temperature and precipitation trends suggested that climate change played a minor role in vegetation recovery. Our findings contribute to an improved understanding of the mechanisms behind vegetation changes in karst areas and may provide scientific supports for local afforestation and conservation policies.

Asymmetric Effects of Daytime and Nighttime Warming on Boreal Forest Spring Phenology

Vegetation phenology is the most intuitive and sensitive biological indicator of environmental conditions, and the start of the season (SOS) can reflect the rapid response of terrestrial ecosystems to climate change. At present, the model based on mean temperature neglects the role of the daytime maximum temperature (TMAX) and the nighttime minimum temperature (TMIN) in providing temperature accumulation and cold conditions at leaf onset. This study analyzed the spatiotemporal variations of spring phenology for the boreal forest from 2001 to 2017 based on the moderate-resolution imaging spectro-radiometer (MODIS) enhanced vegetation index (EVI) data (MOD13A2) and investigated the asymmetric effects of daytime and nighttime warming on the boreal forest spring phenology during TMAX and TMIN preseason by partial correlation analysis. The results showed that the spring phenology was delayed with increasing latitude of the boreal forest. Approximately 91.37% of the region showed an advancing trend during the study period, with an average advancement rate of 3.38 ± 0.08 days/decade, and the change rates of different land cover types differed, especially in open shrubland. The length of the TMIN preseason was longer than that of the TMAX preseason and diurnal temperatures showed an asymmetrical increase during different preseasons. The daytime and nighttime warming effects on the boreal forest are asymmetrical. The TMAX has a greater impact on the vegetation spring phenology than TMIN as a whole and the effect also has seasonal differences; the TMAX mainly affects the SOS in spring, while TMIN has a greater impact in winter. The asymmetric effects of daytime and nighttime warming on the SOS in the boreal forest were highlighted in this study, and the results suggest that diurnal temperatures should be added to the forest terrestrial ecosystem model.

Ecosystem service change caused by climatological and non-climatological drivers: a Swiss case study

Understanding the drivers of ecosystem change and their effects on ecosystem services are essential for management decisions and verification of progress towards national and international sustainability policies (e.g., Aichi Biodiversity Targets, Sustainable Development Goals). We aim to disentangle spatially the effect of climatological and non-climatological drivers on ecosystem service supply and trends. Therefore, we explored time series of three ecosystem services in Switzerland between 2004 and 2014: carbon dioxide regulation, soil erosion prevention, and air quality regulation. We applied additive models to describe the spatial variation attributed to climatological (i.e., temperature, precipitation and relative sunshine duration) and non-climatological drivers (i.e., random effects representing other spatially structured processes) that may affect ecosystem service change. Obtained results indicated strong influences of climatological drivers on ecosystem service trends in Switzerland. We identified equal contributions of all three climatological drivers on trends of carbon dioxide regulation and soil erosion prevention, while air quality regulation was more strongly influenced by temperature. Additionally, our results showed that climatological and non-climatological drivers affected ecosystem services both negatively and positively, depending on the regions (in particular lower and higher altitudinal areas), drivers, and services assessed. Our findings highlight stronger effects of climatological compared to non-climatological drivers on ecosystem service change in Switzerland. Furthermore, drivers of ecosystem change display a spatial heterogeneity in their influence on ecosystem service trends. We propose an approach building on an additive model to disentangle the effect of climatological and non-climatological drivers on ecosystem service trends. Such analyses should be extended in the future to ecosystem service flow and demand to complete ecosystem service assessments and to demonstrate and communicate more clearly the benefits of ecosystem services for human well-being.

Spatial variation of human influences on grassland biomass on the Qinghai-Tibetan plateau

An improved understanding of increased human influence on ecosystems is needed for predicting ecosystem processes and sustainable ecosystem management. We studied spatial variation of human influence on grassland ecosystems at two scales across the Qinghai-Tibetan Plateau (QTP), where increased human activities may have led to ecosystem degradation. At the 10 km scale, we mapped human-influenced spatial patterns based on a hypothesis that spatial patterns of biomass that could not be attributed to environmental variables were likely correlated to human activities. In part this hypothesis could be supported via a positive correlation between biomass unexplained by environmental variables and livestock density. At the 500 m scale, using distance to settlements within a radius of 8 km as a proxy of human-influence intensity, we found both negatively human-influenced areas where biomass decreased closer to settlements (regions with higher livestock density) and positively human-influenced areas where biomass increased closer to settlements (regions with lower livestock density). These results suggest complex relationships between livestock grazing and biomass, varying between spatial scales and regions. Grazing may boost biomass production across the whole QTP at the 10 km scale. However, overgrazing may reduce it near settlements at the 500 m scale. Our approach of mapping and understanding human influence on ecosystems at different scales could guide pasture management to protect grassland in vulnerable regions on the QTP and beyond.

Global vegetation productivity responses to the West Pacific Warm Pool

Sea surface temperatures (SSTs) strongly influence atmospheric circulation and the Earth's climate, which in turn significantly affects vegetation productivity. Most of the previous studies on the subject have focused on links between the El Niño-Southern Oscillation (ENSO) and vegetation productivity, but few studies have addressed the effects of West Pacific Warm Pool (WPWP) on that although the early stages of the ENSO phenomenon may first develop there. In this paper, we use the mean SST values in the WPWP to construct a climate index, known as the WPWP index (WPI), and study the impacts of the WPWP on global vegetation productivity. We provide evidence for a robust link among the alternating warm and cool WPI pattern, terrestrial vegetation productivity and carbon balance. The analysis is based on both satellite observations and model simulations. The results of this study show that the warm and cool WPWP phases have inverse effects on land surface temperature and precipitation. A warm (cool) WPWP is associated with a warmer (cooler) climate on global land surfaces as well as a drier (wetter) climate in southern hemisphere, and hence enhances (suppresses) vegetation productivity in the latitudes of approximately 10-70°N and suppresses (enhances) vegetation growth in the latitudes of approximately 10-30°S. The underlying mechanism is also discussed. The WPI serves as a meaningful climate index for studying the ocean-vegetation teleconnections.

Global patterns of extreme drought-induced loss in land primary production: Identifying ecological extremes from rain-use efficiency

Quantifying the ecological patterns of loss of ecosystem function in extreme drought is important to understand the carbon exchange between the land and atmosphere. Rain-use efficiency [RUE; gross primary production (GPP)/precipitation] acts as a typical indicator of ecosystem function. In this study, a novel method based on maximum rain-use efficiency (RUE_max) was developed to detect losses of ecosystem function globally. Three global GPP datasets from the MODIS remote sensing data (MOD17), ground upscaling FLUXNET observations (MPI-BGC), and process-based model simulations (BESS), and a global gridded precipitation product (CRU) were used to develop annual global RUE datasets for 2001-2011. Large, well-known extreme drought events were detected, e.g. 2003 drought in Europe, 2002 and 2011 drought in the U.S., and 2010 drought in Russia. Our results show that extreme drought-induced loss of ecosystem function could impact 0.9% ± 0.1% of earth's vegetated land per year and was mainly distributed in semi-arid regions. The reduced carbon uptake caused by functional loss (0.14 ± 0.03 PgC/yr) could explain >70% of the interannual variation in GPP in drought-affected areas (p ≤ 0.001). Our results highlight the impact of ecosystem function loss in semi-arid regions with increasing precipitation variability and dry land expansion expected in the future.

Water memory effects and their impacts on global vegetation productivity and resilience

Memory effects refer to the impacts of antecedent climate conditions on current vegetation productivity. This temporal linkage has been found to be strong in arid and semi-arid regions. However, the dominant climatic factors that determine such patterns are still unclear. Here, we defined'water-memory effects' as the persistent effects of antecedent precipitation on the vegetation productivity for a given memory length (from 1 to up to 12 months). Based on satellite observations and climate data, we quantified the length of water-memory effects and evaluated the contributions of antecedent precipitation on current vegetation. Our results showed that vegetation productivity was highly dependent on antecedent precipitation in arid and semi-arid regions. The average length of water memory was approximately 5.6 months. Globally, water-memory effects could explain the geographical pattern and strength of memory effects, indicating that precipitation might be the dominant climatic factor determining memory effects because of its impact on water availability. Moreover, our results showed vegetation in regions with low mean annual precipitation or a longer water memory has lower engineering resilience (i.e. slower recovery rate) to disturbances. These findings will enable better assessment of memory effects and improve our understanding of the vulnerability of vegetation to climate change.

Forest dynamics to precipitation and temperature in the Gulf of Mexico coastal region

The forest is one of the most significant components of the Gulf of Mexico (GOM) coast. It provides livelihood to inhabitant and is known to be sensitive to climatic fluctuations. This study focuses on examining the impacts of temperature and precipitation variations on coastal forest. Two different regression methods, ordinary least squares (OLS) and geographically weighted regression (GWR), were employed to reveal the relationship between meteorological variables and forest dynamics. OLS regression analysis shows that changes in precipitation and temperature, over a span of 12 months, are responsible for 56% of NDVI variation. The forest, which is not particularly affected by the average monthly precipitation in most months, is observed to be affected by cumulative seasonal and annual precipitation explicitly. Temperature and precipitation almost equally impact on NDVI changes; about 50% of the NDVI variations is explained in OLS modeling, and about 74% of the NDVI variations is explained in GWR modeling. GWR analysis indicated that both precipitation and temperature characterize the spatial heterogeneity patterns of forest dynamics.

Climatic factors driving vegetation declines in the 2005 and 2010 Amazon droughts

Along with global climate change, the occurrence of extreme droughts in recent years has had a serious impact on the Amazon region. Current studies on the driving factors of the 2005 and 2010 Amazon droughts has focused on the influence of precipitation, whereas the impacts of temperature and radiation have received less attention. This study aims to explore the climate-driven factors of Amazonian vegetation decline during the extreme droughts using vegetation index, precipitation, temperature and radiation datasets. First, time-lag effects of Amazonian vegetation responses to precipitation, radiation and temperature were analyzed. Then, a multiple linear regression model was established to estimate the contributions of climatic factors to vegetation greenness, from which the dominant climate-driving factors were determined. Finally, the climate-driven factors of Amazonian vegetation greenness decline during the 2005 and 2010 extreme droughts were explored. The results showed that (i) in the Amazon vegetation greenness responded to precipitation, radiation and temperature, with apparent time lags for most averaging interval periods associated with vegetation index responses of 0–4, 0–9 and 0–6 months, respectively; (ii) on average, the three climatic factors without time lags explained 27.28±21.73% (mean±1 SD) of vegetation index variation in the Amazon basin, and this value increased by 12.22% and reached 39.50±27.85% when time lags were considered; (iii) vegetation greenness in this region in non-drought years was primarily affected by precipitation and shortwave radiation, and these two factors altogether accounted for 93.47% of the total explanation; and (iv) in the common epicenter of the two droughts, pixels with a significant variation in precipitation, radiation and temperature accounted for 36.68%, 40.07% and 10.40%, respectively, of all pixels showing a significant decrease in vegetation index in 2005, and 15.69%, 2.01% and 45.25% in 2010, respectively. Overall, vegetation greenness declines during the 2005 and 2010 extreme droughts were adversely influenced by precipitation, radiation and temperature; this study provides evidence of the influence of multiple climatic factors on vegetation during the 2005 and 2010 Amazon droughts.

ENSO elicits opposing responses of semi-arid vegetation between Hemispheres

Semi-arid ecosystems are key contributors to the global carbon cycle and may even dominate the inter-annual variability (IAV) and trends of the land carbon sink, driven largely by the El Niño–Southern Oscillation (ENSO). The linkages between dynamics of semi-arid ecosystems and climate at the hemispheric scale however are not well known. Here, we use satellite data and climate observations from 2000 to 2014 to explore the impacts of ENSO on variability of semi-arid ecosystems, using the Ensemble Empirical Mode Decomposition method. We show that the responses of semi-arid vegetation to ENSO occur in opposite directions, resulting from opposing controls of ENSO on precipitation between the Northern Hemisphere (positively correlated to ENSO) and the Southern Hemisphere (negatively correlated to ENSO). Also, the Southern Hemisphere, with a robust negative coupling of temperature and precipitation anomalies, exhibits stronger and faster responses of semi-arid ecosystems to ENSO than the Northern Hemisphere. Our findings suggest that natural coherent variability in semi-arid ecosystem productivity responded to ENSO in opposite ways between two hemispheres, which may imply potential prediction of global semi-arid ecosystem variability, particularly based on variability in tropical Pacific Sea Surface Temperatures.

Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China

Variability in satellite measurements of terrestrial greenness in drylands is widely observed in land surface processes and global change studies. Yet the underlying causes differ and are not fully understood. Here, we used the GeogDetector model, a new spatial statistical approach, to examine the individual and combined influences of physiographic factors on dryland vegetation greenness changes, and to identify the most suitable characteristics of each principal factor for stimulating vegetation growth. Our results indicated that dryland greenness was predominantly affected by precipitation, soil type, vegetation type, and temperature, either separately or in concert. The interaction between pairs of physiographic factors enhanced the influence of any single factor and displayed significantly non-linear influences on vegetation greenness. Our results also implied that vegetation greenness could be promoted by adopting favorable ranges or types of major physiographical factors, thus beneficial for ecological conservation and restoration that aimed at mitigating environmental degradation.

Revealing turning points in ecosystem functioning over the Northern Eurasian agricultural frontier

The collapse of the Soviet Union in 1991 has been a turning point in the World history that left a unique footprint on the Northern Eurasian ecosystems. Conducting large scale mapping of environmental change and separating between naturogenic and anthropogenic drivers is a difficult endeavor in such highly complex systems. In this research a piece-wise linear regression method was used for breakpoint detection in Rain-Use Efficiency (RUE) time series and a classification of ecosystem response types was produced. Supported by earth observation data, field data, and expert knowledge, this study provides empirical evidence regarding the occurrence of drastic changes in RUE (assessment of the timing, the direction and the significance of these changes) in Northern Eurasian ecosystems between 1982 and 2011. About 36% of the study area (3.4 million km(2) ) showed significant (P < 0.05) trends and/or turning points in RUE during the observation period. A large proportion of detected turning points in RUE occurred around the fall of the Soviet Union in 1991 and in the following years which were attributed to widespread agricultural land abandonment. Our study also showed that recurrent droughts deeply affected vegetation productivity throughout the observation period, with a general worsening of the drought conditions in recent years. Moreover, recent human-induced turning points in ecosystem functioning were detected and attributed to ongoing recultivation and change in irrigation practices in the Volgograd region, and to increased salinization and increased grazing intensity around Lake Balkhash. The ecosystem-state assessment method introduced here proved to be a valuable support that highlighted hotspots of potentially altered ecosystems and allowed for disentangling human from climatic disturbances.