Disentangling Climate and Disturbance Effects on Regional Vegetation Greening Trends

Methodological challenges and new perspectives of shifting vegetation phenology in eddy covariance data

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake.

Reversal of soil moisture constraint on vegetation growth in North China

The response of vegetation growth to soil moisture varies greatly from space and time under climate change and anthropogenic activities. As an important grain producer in China, the vegetation growth and grain production of North China are constrained by the region's water resources. With the significant increase in vegetation greenness in North China over the last 40 years, it is essential to explore the changes in soil moisture constraints on vegetation growth to water management. However, to what degree vegetation growth responds to soil moisture and how the response varies spatiotemporally in North China remain unclear. In this study, the response patterns of vegetation growth to soil moisture at different depths and the spatiotemporal trend patterns of their relationships were explored thoroughly based on long time series remote sensing data in North China over the past 40 years. The results showed that compared to forests, the growth of grasslands and crops with one maturity per year and two maturity per year in North China was more constrained by soil moisture. Due to the combined effects of climatic conditions and human activities, vegetation growth in North China has been significantly less constrained by soil moisture over the last 40 years. This was especially seen in one maturity per year crop and natural vegetation in Shanxi and central Shandong. However, with the significant increase in temperature, potential evapotranspiration and water demand of the crop, the moisture constraints on vegetation growth in North China have begun to show an increasing trend since the early 2000s, especially for irrigated crop in central and southern North China. These findings highlight a comprehensive understanding of the vegetation response to soil moisture from the time-varying perspective and provide a theoretical basis for water management and appropriate planning of agricultural water use in North China.

Biogeographic variability in wildfire severity and post-fire vegetation recovery across the European forests via remote sensing-derived spectral metrics

Wildfires have large-scale and profound effects on forest ecosystems, and they force burned forest areas toward a wide range of post-fire successional trajectories from simple reduction of ecosystem functions to transitions to other stable non-forest states. Fire disturbances represent a key driver of changes in forest structure and composition due to post-fire succession processes, thus contributing to modify ecosystem resilience to subsequent disturbances. Here, we aimed to provide useful insights into wildfire severity and post-fire recovery processes at the European continental scale, contributing to improved description and interpretation of large-scale wildfire spatial patterns and their effects on forest ecosystems in the context of climate change. We analyzed fire severity and short-term post-fire vegetation recovery patterns across the European forests between 2004 and 2015 using Corine Land Cover Forest classes and bioregions, based on MODIS-derived spectral metrics of the relativized burn ratio (RBR), normalized difference vegetation index (NDVI) and relative recovery indicator (RRI). The RBR-based fire severity showed geographic differences and interannual variability in the Boreal bioregion compared to that in other biogeographic regions. The NBR-based RRI showed a slower post-fire vegetation recovery rate with respect to the NDVI, highlighting the differential sensitivities of the analyzed remote sensing-spectral metrics. Moreover, the RRI showed a significant decreasing trend during the observation period, suggesting a growing lag in post-fire vegetation recovery across European forests.

Unraveling the Multiple Drivers of Greening-Browning and Leaf Area Variability in a Socioeconomically Sensitive Drought-Prone Region

The complex attribution of climatic, hydrologic, and anthropogenic drivers to vegetation and agricultural production and their consequential societal impacts are not well understood, especially in socioeconomically sensitive states like Maharashtra, India. Here, we analyzed trends and variability in the MODIS leaf area index (LAI) time series, along with spatiotemporal patterns in precipitation, groundwater storage, agriculture statistics, and irrigation infrastructure, to identify their influences on the vegetation response and discuss their implications for farmers. The state showed greening in all biomes except forests, with a net gain of 17.478 × 103 km2 of leaf area during 2003–2019, where more than 70% of the trend in LAI is represented in croplands. Maximum greening was observed in irrigated croplands, attributable to increased crop productivity, whereas inadequate irrigation facilities with erratic rainfall patterns and droughts were primarily responsible for cropland browning. We discerned the dynamics and variability of vegetation response by incorporating a spectrum of synergistic feedbacks from multiple confounding drivers and found that uneven distribution of water availability across the administrative divisions governed the quantitative distinction in leaf area change. Despite the observed greening trends, the state witnessed a high number of farmer suicides related to droughts and agriculture failures hampering their socioeconomic security; therefore, improved irrigation infrastructure and comprehensive policy interventions are crucial for abatement of farmer distress.

The greening of the Northern Great Plains and its biogeochemical precursors

Vegetation greenness has increased across much of the global land surface over recent decades. This trend is projected to continue-particularly in northern latitudes-but future greening may be constrained by nutrient availability needed for plant carbon (C) assimilation in response to CO₂ enrichment (eCO₂ ). eCO₂ impacts foliar chemistry and function, yet the relative strengths of these effects versus climate in driving patterns of vegetative greening remain uncertain. Here we combine satellite measurements of greening with a 135 year record of plant C and nitrogen (N) concentrations and stable isotope ratios (δ¹³ C and δ¹⁵ N) in the Northern Great Plains (NGP) of North America to examine N constraints on greening. We document significant greening over the past two decades with the highest proportional increases in net greening occurring in the dries and warmest areas. In contrast to the climate dependency of greening, we find spatially uniform increases in leaf-level intercellular CO₂ and intrinsic water use efficiency that track rising atmospheric CO₂ . Despite large spatial variation in greening, we find sustained and climate-independent declines in foliar N over the last century. Parallel declines in foliar δ¹⁵ N and increases in C:N ratios point to diminished N availability as the likely cause. The simultaneous increase in greening and decline in foliar N across our study area points to increased N use efficiency (NUE) over the last two decades. However, our results suggest that plant NUE responses are likely insufficient to sustain observed greening trends in NGP grasslands in the future.

Persistent Vegetation Greening and Browning Trends Related to Natural and Human Activities in the Mount Elgon Ecosystem

Many developing nations are facing severe food insecurity partly because of their dependence on rainfed agriculture. Climate variability threatens agriculture-based community livelihoods. With booming population growth, agricultural land expands, and natural resource extraction increases, leading to changes in land use and land cover characterized by persistent vegetation greening and browning. This can modify local climate variability due to changing land–atmosphere interactions. Yet, for landscapes with significant interannual variability, such as the Mount Elgon ecosystem in Kenya and Uganda, characterizing these changes is a difficult task and more robust methods have been recommended. The current study combined trend (Mann–Kendall and Sen’s slope) and breakpoint (bfast) analysis methods to comprehensively examine recent vegetation greening and browning in Mount Elgon at multiple time scales. The study used both Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Climate Hazards group Infrared Precipitation with Stations (CHIRPS) data and attempted to disentangle nature- versus human-driven vegetation greening and browning. Inferences from a 2019 field study were valuable in explaining some of the observed patterns. The results indicate that Mount Elgon vegetation is highly variable with both greening and browning observable at all time scales. Mann–Kendall and Sen’s slope revealed major changes (including deforestation and reforestation), while bfast detected most of the subtle vegetation changes (such as vegetation degradation), especially in the savanna and grasslands in the northeastern parts of Mount Elgon. Precipitation in the area had significantly changed (increased) in the post-2000 era than before, particularly in 2006–2010, thus influencing greening and browning during this period. The greenness–precipitation relationship was weak in other periods. The integration of Mann–Kendall and bfast proved useful in comprehensively characterizing vegetation greenness. Such a comprehensive description of Mount Elgon vegetation dynamics is an important first step to instigate policy changes for simultaneously conserving the environment and improving livelihoods that are dependent on it.

Interactive Effects of Climatic Factors on Seasonal Vegetation Dynamics in the Central Loess Plateau, China

The interactive effects of climatic factors (precipitation and temperature) on vegetation growth can be characterized by their effect on vegetation seasonal dynamics. The interactive effects, seasonal trend of vegetation growth, and its future consistency (potential for future trend) have not been adequately studied in the literature. In this work, using the Enhanced Vegetation Index (EVI) and gridded climate data at a resolution of 250 m in the central Loess Plateau region, we examined seasonal vegetation dynamics with climate changes and the interactive effects of climatic factors on vegetation growth at the pixel and regional scales from the period 2000 to 2015. Vegetation cover in the Central Loess Plateau in China has dramatically changed due to the Grain-for-Green (GFG) ecological restoration program, which was designed to convert cropland to forestland or grassland since 1999. Our results show that the EVI increased significantly during the 16 year period and is likely to continue to increase in the near future. Relatively small Hurst exponents for forestland suggests that the potential for a future increased trend will be weak for the forest. Large Hurst exponents for grassland indicate its strong potential of further increase. Significant increases in spring precipitation have promoted vegetation growth, while significant decreases in summer temperature have had negative effects on vegetation growth. For temperatures between 10 to 20 °C, the impact of temperature on vegetation growth has a clear positive relationship with the moderator variable precipitation. For precipitation < 200 mm in the growing season, the impact of precipitation on vegetation growth has a clearly positive relationship with the moderator variable temperature. Results of this study will provide useful and important guidelines for designing forestland and grassland restoration plans in arid, semiarid and sub-humid regions.

Climatic Drivers of Greening Trends in the Alps

Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000–2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000–2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

Spatio-Temporal Variability in Remotely Sensed Vegetation Greenness Across Yellowstone National Park

The study’s objective was to quantify the responses of vegetation greenness and productivity to climate variability and change across complex topographic, climatic, and ecological gradients in Yellowstone National Park through the use of remotely sensed data. The climate change signal in Yellowstone was pronounced, including substantial warming, an abrupt decline in snowpack, and more frequent droughts. While phenological studies are increasing in Yellowstone, the near absence of long-term and continuous ground-based phenological measurements motivated the study’s application of remotely sensed data to aid in identifying ecological vulnerabilities and guide resource management in light of on ongoing environmental change. Correlation, time-series, and empirical orthogonal function analyses for 1982–2015 focused on Daymet data and vegetation indices (VIs) from the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS). The study’s key questions address unique time scales. First, what are the dominant meteorological drivers of variability in vegetation greenness on seasonal to interannual time scales? Key results include: (1) Green-up is the most elevation- and climate-sensitive phenological stage, with La Niña-induced cool, wet conditions or an anomalously deep snowpack delaying the green-up wave. (2) Drought measures were the dominant contributors towards phenological variability, as winter–spring drought corresponded to enhanced April–June greening and spring–summer drought corresponded to reduced August–September greening. Second, how have patterns of productivity changed in response to climate change and disturbances? Key results include: (1) The park predominantly exhibited positive productivity trends, associated with lodgepole pine re-establishment and growth following the 1988 fires. (2) Landscapes which were undisturbed by the 1988 fires showed no apparent sign of warming-induced greening. This study motivates a systematic investigation of remote-sensing data across western parks to identify ecological vulnerabilities and support the development of climate change vulnerability assessments and adaptation strategies.