Global Vegetation Change Research Articles

Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020

Abstract. Leaf area index (LAI) with an explicit biophysical meaning is a critical variable to characterize terrestrial ecosystems. Long-term global datasets of LAI have served as fundamental data support for monitoring vegetation dynamics and exploring its interactions with other Earth components. However, current LAI products face several limitations associated with spatiotemporal consistency. In this study, we employed the back propagation neural network (BPNN) and a data consolidation method to generate a new version of the half-month 1/12∘ Global Inventory Modeling and Mapping Studies (GIMMS) LAI product, i.e., GIMMS LAI4g, for the period 1982–2020. The significance of the GIMMS LAI4g was the use of the latest PKU GIMMS normalized difference vegetation index (NDVI) product and 3.6 million high-quality global Landsat LAI samples to remove the effects of satellite orbital drift and sensor degradation and to develop spatiotemporally consistent BPNN models. The results showed that the GIMMS LAI4g exhibited overall higher accuracy and lower underestimation than its predecessor (GIMMS LAI3g) and two mainstream LAI products (Global LAnd Surface Satellite (GLASS) LAI and Long-term Global Mapping (GLOBMAP) LAI) using field LAI measurements and Landsat LAI samples. Its validation against Landsat LAI samples revealed an R2 of 0.96, root mean square error of 0.32 m2 m−2, mean absolute error of 0.16 m2 m−2, and mean absolute percentage error of 13.6 % which meets the accuracy target proposed by the Global Climate Observation System. It outperformed other LAI products for most vegetation biomes in a majority area of the land. It efficiently eliminated the effects of satellite orbital drift and sensor degradation and presented a better temporal consistency before and after the year 2000. The consolidation with the reprocessed MODIS LAI allows the GIMMS LAI4g to extend the temporal coverage from 2015 to a recent period (2020), producing the LAI trend that maintains high consistency before and after 2000 and aligns with the reprocessed MODIS LAI trend during the MODIS era. The GIMMS LAI4g product could potentially facilitate mitigating the disagreements between studies of the long-term global vegetation changes and could also benefit the model development in earth and environmental sciences. The GIMMS LAI4g product is open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.7649107 (Cao et al., 2023).

Remote Sensing Monitoring and Assessment of Global Vegetation Status and Changes during 2016-2020.

Vegetation plays a fundamental role within terrestrial ecosystems, serving as a cornerstone of their functionality. Presently, these crucial ecosystems face a myriad of threats, including deforestation, overgrazing, wildfires, and the impact of climate change. The implementation of remote sensing for monitoring the status and dynamics of vegetation ecosystems has emerged as an indispensable tool for advancing ecological research and effective resource management. This study takes a comprehensive approach by integrating ecosystem monitoring indicators and aligning them with the objectives of SDG15. We conducted a thorough analysis by leveraging global 500 m resolution products for vegetation Leaf Area Index (LAI) and land cover classification spanning the period from 2016 to 2020. This encompassed the calculation of annual average LAI, identification of anomalies, and evaluation of change rates, thereby enabling a comprehensive assessment of the global status and transformations occurring within major vegetation ecosystems. In 2020, a discernible rise in the annual Average LAI of major vegetation ecosystems on a global scale became evident when compared to data from 2016. Notably, the ecosystems demonstrating a slight increase in area constituted the largest proportion (34.23%), while those exhibiting a significant decrease were the least prevalent (6.09%). Within various regions, such as Eastern Europe, Central Africa, and South Asia, substantial increases in both forest ecosystem area and annual Average LAI were observed. Furthermore, Eastern Europe and Central America recorded significant expansions in both grassland ecosystem area and annual average LAI. Similarly, regions experiencing notable growth in both cropland ecosystem areas and annual average LAI encompassed Southern Africa, Northern Europe, and Eastern Africa.

Modeling the influence of daily temperature and precipitation extreme indices on vegetation dynamics in Katsina State using statistical downscaling model (SDM)

Climate change significantly impacts local, regional, and global vegetation changes. These changes have continued to threaten ecosystems, especially in dryland areas where moisture is scarce, and the livelihoods of rural communities are at risk because of such changes, as well as their capacity to provide outputs and sustain the livelihoods of rural communities. This study aims to derive a simulation model of vegetation conditions concerning daily temperature and extreme precipitation indices in Katsina State, Nigeria. This study uses remote sensing and Geographic Information System (GIS)-based analysis, time series analysis of precipitation, maximum and minimum temperature, and twenty-four indices defined by the Expert Team on Climate Change Detection to evaluate the influence of Temp and precipitation extreme indices on vegetation dynamics/NDVI. The results indicate that the statistical downscaling model (SDSM) is performing satisfactorily in predicting maximum and minimum temperatures, along with precipitation, for the time horizon of the simulation. Mean precipitation of 1.98 mm (RCP2.6), 2.03 mm (RCP4.5), and 2.07 mm (RCP8.5) was revealed in the study area. Tmax and Tmin were projected under low emissions at 20.1 0C and 14.25 0C (RCP2.6), 20.13 °C and 14.26 0C (RCP4.5), and 20.15 0C and 14.27 0C (RCP8.5) respectively. All the scenarios present increasing minimum and maximum temperatures and decreasing precipitation, except for RCP8.5, which predicted a more adverse trend. However, in Katsina State, the Normalized Difference Vegetation Index (NDVI), precipitation, Tmax, and Tmin, extreme temperature and precipitation indices, and drought indices successfully demonstrated spatial and temporal variability.

Structural complexity biases vegetation greenness measures.

Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes.

Identifying the Main Factors Influencing Significant Global Vegetation Changes

Understanding the dynamics of vegetation change is crucial for comprehending ecosystem functioning and its response to anthropogenic activities and climate change. This study investigates significant vegetation changes worldwide and aims to identify the dominant factors responsible for these changes. By analyzing long-term data on vegetation dynamics and climatic factors, this research identifies regions with significant global vegetation changes and determines the main factors leading to such changes at the grid scale. The results reveal important insights into the drivers of vegetation change. Firstly, the study finds that the area experiencing significant browning from April to July is larger than the area exhibiting significant greening. Secondly, on an annual scale, anthropogenic activity emerges as the main factor driving significant vegetation greening, while climate change becomes the primary factor causing vegetation browning from July to September. Thirdly, in regions dominated by climate change, temperature is identified as the primary climatic factor contributing to significant vegetation greening. Additionally, the study reveals that the primary climatic factors causing significant vegetation browning are temperature followed by soil moisture, with temperature being the main factor in most months. These findings contribute to a deeper understanding of the mechanisms driving global vegetation changes and have implications for sustainable development and climate action.

An innovative index for separating the effects of temperature and precipitation on global vegetation change

Temperature and precipitation changes are among the vital climatic driving forces of global vegetation change. However, the strategy to separate the relative contributions of these two critical climatic factors is still lacking. Here, we propose an index CRTP (contribution ratio of temperature and precipitation) to quantify their impacts on vegetation and then construct the CRTP classification prediction models based on climatic, geographic, and environmental factors using the Random Forest classifier. We find that precipitation predominates more than 70% of the significant vegetation change, mainly located in the low and middle latitudes during 2000-2021. Precipitation will remain the dominant climatic factor affecting global vegetation change in the coming six decades, whereas areas with temperature-dominated vegetation change will expand under higher radiative forcings. Hopefully, the promising index CRTP will be applied in the research about climatic attribution for regional vegetation degradation, monitoring drought-type conversion, and alarming the potential ecological risk.

Competition between biogeochemical drivers and land-cover changes determines urban greening or browning

Urban vegetation, a harbinger of future global vegetation change, is controlled by complex urban environments. The urban-rural gradient in vegetation greenness trends and their responses to biogeochemical drivers (e.g. elevated atmospheric CO2 concentration and climate warming) and land-cover changes, however, remain unclear. Here we used satellite-derived enhanced vegetation index to examine the greenness trends for 1500-plus cities in China for 2000–2019. We developed a conceptual framework to differentiate between the contributions of four key drivers to the greenness trends: two biogeochemical drivers, a background biogeochemical driver (BBD) and an urban biogeochemical driver (UBD), and two drivers of land-cover changes, urban expansion or densification (UED) and urban green recovery (UGR). We find that the greening trends gradually decreased from urban cores to urban new towns and then to browning trends in urban fringes. The significant greening in urban cores was mainly contributed by BBD (25.6%) and UBD (52.3%). While the minor greening in urban new towns was contributed by both BBD (33.1%) and UBD (24.1%) and weakened by UED (−39.7%). The UED (−64.4%) dominated the browning in urban fringes. These results suggest that biogeochemical drivers and land-cover changes jointly regulated the urban-rural gradient in greenness trends, which contributes to the assessment of future global vegetation change driven by complex environmental changes.

Inhibitive Effects of Recent Exceeding Air Temperature Optima of Vegetation Productivity and Increasing Water Limitation on Photosynthesis Reversed Global Greening

AbstractGlobal terrestrial vegetation dynamics have been rapidly altered by climate change. A widespread vegetation greenness over a large part of the planet from the 1980s to early this century has been reported, whereas weakening of CO2 fertilization effects and increasing climate extremes and the adverse impact of increasing rate of warming and severity of drought on vegetation growth were also reported. Earth system models project that the land carbon sink will decrease in size in response to an increase in warming during this century. How global vegetation is changing during this century in response to global warming and water availability across spatial and temporal scales remains uncertain. Our understanding of the widespread vegetation greening or browning processes and identifying the biogeochemical mechanisms remain incomplete. Here we use multiple long‐term satellite leaf area index (LAI) records to investigate vegetation growth trends from 1982 to 2018. We find that the widespread increase of growing‐season integrated LAI (greening) since 1980s was reversed (p‐value < 0.05) around the year 2000 over 90% of the global vegetated area, and continued in only 10% of the global vegetated area. The reversal of greening trend was largely explained by the inhibitive effects of excessive optimal temperature on photosynthesis in most of the tropics and low latitudes, and by increasing water limitation (increasing in atmospheric vapor pressure deficit and decreasing in soil water availability) in the northern high latitudes (>45°N). Overall, the reversal of greening trend since 2000 weakened the negative feedback of carbon sequestration on the climatic system and should be considered in the strategies for climate warming mitigation and adaptation. Our findings of the diversity of processes that drive browning across bioclimatic‐zones and ecosystems and of how those driving processes are changing would enhance our ability to project global future vegetation change and its climatic and abiotic consequences.

Assessing the synergistic modulation of evapotranspiration by global impervious surface and vegetation changes

The different characteristics of impervious urban surfaces and natural underlying surfaces affect water and heat balances, with consequent impacts on evapotranspiration (ET). The global impervious surface area in 1982 was only 34% of that in 2015, motivating an assessment of the net effect of impervious surface changes on ET under rapid impervious surface expansion. In this study, we incorporated the van Dijk model's interception evaporation (Ei) algorithm into the PT-JPL (Priestley-Taylor Jet Propulsion Laboratory) model, and added an impervious surface evaporation (Eu) module, to enable urban ecosystem simulations. This improved model (named the PT-JPLim model) was validated against annual mean ET calculated by P-Q in 8,205 catchments, and against ET measured at 203 flux towers in natural ecosystems and 11 flux towers in urban ecosystems. Calibration showed that the PT-JPLim model performed well (catchments: R = 0.36; natural ecosystems: R = 0.78, RMSE = 13.17 mm; urban ecosystems: R = 0.79, RMSE = 11.74 mm). Additionally, we examined the reliability of modelled ET and its partitioning, by comparison with observed soil evaporation (Eb), Ei, and satellite-based products. By controlling for the impervious surface fraction in the model, it was concluded that the net increase in Eu due to increased impervious surface changes was 0.23 mm/yr, with larger increases in eastern China, India, Europe and southeastern North America than in other regions. The effects of impervious surface and vegetation changes on ET were then analyzed for global impervious surface, with a ratio of 1/4.1 for the effects of impervious surfaces to vegetated surfaces on ET. The contributions of transpiration (Et), Eu, and Eb changes to ET changes on impervious surfaces were 65%, 27% and 8% respectively. These results indicate that synergistic modulation of ET by impervious surface changes and vegetation changes should not be ignored when studying urban eco-hydrological processes.

Attribution of NDVI Dynamics over the Globe from 1982 to 2015

Satellite remote sensing has witnessed a global widespread vegetation greening since the 1980s. However, reliable observation-based quantitative knowledge on global greening remains obscure due to uncertainties in model simulations and the contribution of natural variability is largely unknown. Here, we revisit the attribution of global vegetation changes using the Time Series Segment and Residual Trend (TSS-RESTREND) method. Results showed global vegetation significantly greening over 40.6% of the vegetated grids, whereas vegetation significantly browning over 11.6% of the vegetated grids. The attribution results based on the TSS-RESTREND method show that CO2 fertilization (CO2) plays an influential role in vegetation changes over 61.4% of the global vegetated areas, followed by land use (LU, 23.5%), climate change (CC, 7.3%), and climate variability (CV, 1.5%). The vegetation greening can be largely attributed to CO2 fertilization while the vegetation browning is mainly caused by LU. Meanwhile, we also identify positive impacts of LU and CC on vegetation change in arid regions but negative impacts in humid regions. Our findings indicate spatial heterogeneity in causes behind global vegetation changes, providing more detailed references for global vegetation modeling.

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

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

Interannual variability in summer climate change controls GPP long-term changes

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with implications for the functioning of the Earth system and the provision of ecosystem services. How vegetation responds to a changing environment is an important scientific issue, but there is a lack of coverage of the relative contributions that long-term variation and interannual variability in vegetation across seasons play in ecosystem response to global change. Here, we used four terrestrial ecosystem models provided by MsTMIP to examine four key environmental drivers of gross primary productivity (GPP) change over the period 1901–2010. Our findings showed that (1) for all seasons, interannual variability in climate change are the main environmental factor controlling seasonal GPP variability. (2) Summer is the key season controlling the variation of annual GPP, and its long-term trend and interannual variability can explain 61.50% of the variation of grassland GPP in China. (3) Interannual variability in summer climate change exceeded the CO2 fertilization effect and nitrogen deposition as the controlling component (more than 40%) of long-term variation in Chinese grassland GPP. These studies highlight the important role of interannual variability in climate in reshaping the seasonality of vegetation growth, and will provide a precursor to future environmental drivers that can be precisely attributed to global vegetation change.

An Innovative Index for Separating the Effects of Temperature and Precipitation on Global Vegetation Changean Innovative Index for Separating the Effects of Temperature and Precipitation on Global Vegetation Change

An Innovative Index for Separating the Effects of Temperature and Precipitation on Global Vegetation Changean Innovative Index for Separating the Effects of Temperature and Precipitation on Global Vegetation Change

1.36 million years of Mediterranean forest refugium dynamics in response to glacial–interglacial cycle strength

The sediment record from Lake Ohrid (Southwestern Balkans) represents the longest continuous lake archive in Europe, extending back to 1.36 Ma. We reconstruct the vegetation history based on pollen analysis of the DEEP core to reveal changes in vegetation cover and forest diversity during glacial-interglacial (G-IG) cycles and early basin development. The earliest lake phase saw a significantly different composition rich in relict tree taxa and few herbs. Subsequent establishment of a permanent steppic herb association around 1.2 Ma implies a threshold response to changes in moisture availability and temperature and gradual adjustment of the basin morphology. A change in the character of G-IG cycles during the Early-Middle Pleistocene Transition is reflected in the record by reorganization of the vegetation from obliquity- to eccentricity-paced cycles. Based on a quantitative analysis of tree taxa richness, the first large-scale decline in tree diversity occurred around 0.94 Ma. Subsequent variations in tree richness were largely driven by the amplitude and duration of G-IG cycles. Significant tree richness declines occurred in periods with abundant dry herb associations, pointing to aridity affecting tree population survival. Assessment of long-term legacy effects between global climate and regional vegetation change reveals a significant influence of cool interglacial conditions on subsequent glacial vegetation composition and diversity. This effect is contrary to observations at high latitudes, where glacial intensity is known to control subsequent interglacial vegetation, and the evidence demonstrates that the Lake Ohrid catchment functioned as a refugium for both thermophilous and temperate tree species.

Global changes explain the long-term demographic trend of the Eurasian common lizard (Squamata: Lacertidae).

The demographic trend of a species depends on the dynamics of its local populations, which can be compromised by local or by global phenomena. However, the relevance of local and global phenomena has rarely been investigated simultaneously. Here, we tested whether local phenomena compromised a species’ demographic trend using the Eurasian common lizard Zootoca vivipara, the terrestrial reptile exhibiting the widest geographic distribution, as a model species. We analyzed the species’ ancient demographic trend using genetic data from its 6 allopatric genetic clades and tested whether its demographic trend mainly depended on single clades or on global phenomena. Zootoca vivipara’s effective population size increased since 2.3 million years ago and started to increase steeply and continuously from 0.531 million years ago. Population growth rate exhibited 2 maxima, both occurring during global climatic changes and important vegetation changes on the northern hemisphere. Effective population size and growth rate were negatively correlated with global surface temperatures, in line with global parameters driving long-term demographic trends. Zootoca vivipara’s ancient demography was neither driven by a single clade, nor by the 2 clades that colonized huge geographic areas after the last glaciation. The low importance of local phenomena, suggests that the experimentally demonstrated high sensitivity of this species to short-term ecological changes is a response in order to cope with short-term and local changes. This suggests that what affected its long-term demographic trend the most, were not these local changes/responses, but rather the important and prolonged global climatic changes and important vegetation changes on the northern hemisphere, including the opening up of the forest by humans.

Long-Term Variation of Global GEOV2 and MODIS Leaf Area Index (LAI) and Their Uncertainties: An Insight into the Product Stabilities

Leaf area index (LAI) is an essential climate variable that is crucial to understand the global vegetation change. Long-term satellite LAI products have been applied in many global vegetation change studies. However, these LAI products contain various uncertainties that are not been fully considered in current studies. The objective of this study is to explore the uncertainties in the global LAI products and the uncertainty variations. Two global LAI datasets—the European Geoland2 Version 2 (GEOV2) and Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2019)—were investigated. The qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) embedded in the product quality layers were analyzed to identify the temporal anomalies in the quality profile. The results show that the global GEOV2 (0.042/10a) and MODIS (0.034/10a) LAI values have steadly increased from 2003 to 2019. The global LAI uncertainty (0.016/10a) and relative uncertainty (0.3%/10a) from GEOV2 have also increased gradually, especially during the growing season from April to October. The uncertainty increase is larger for woody biomes than for herbaceous types. Contrastingly, the MODIS LAI product uncertainty remained stable over the study period. The uncertainty increase indicated by GEOV2 is partly attributed to the sensor shift in the product series. Further algorithm enhancement is necessary to improve the cross-sensor performance. This study highlights the importance of studying the LAI uncertainty and the uncertainty variation. Temporal variations in the LAI products and the product quality revealed herein have significant implications on global vegetation change studies.

Nearly Half of Global Vegetated Area Experienced Inconsistent Vegetation Growth in Terms of Greenness, Cover, and Productivity

AbstractThe considerable interest in detecting global vegetation changes based on satellite observations is increasing. However, studies rely on single indices to explore the driving mechanisms of the greening trend might exacerbate uncertainties of global ecosystem change. Thus, vegetation growth dynamics from various biophysical properties required to be monitored comprehensively. In this study, a consistent framework for evaluating vegetation growth trends was developed based on five widely used satellite‐derived products of MODIS Collection 6; the consistency in vegetation growth was mapped; and the factors that affected the consistency of vegetation growth were explored. The results showed that, during 2000‐2015, 45.6% of global vegetated area experienced inconsistent trends in vegetation greenness, cover and productivity, especially in evergreen broadleaf forests, grasslands, open shrublands, woody savannas and croplands. Only 5.4% of global vegetated area exhibited simultaneous trends in greenness, cover and productivity, and the inconsistent areas were expanding in the study period. Contradictory vegetation changes were mainly reflected in the opposite trends of vegetation greenness and productivity in evergreen broadleaf forests. Moreover, the inconsistency change was mainly manifested in the greenness‐dominated vegetation enhancement, without enhanced productivity. The increment difference between NPP and GPP also showed respiration losses greatly offset the effect of vegetation greenness or cover on productivity. This study provides integrated insights for understanding the inconsistency of vegetation structural and functional changes in the context of global greening.

Future greening of the Earth may not be as large as previously predicted

Changes in global vegetation growth and its drivers during recent decades have been well studied with satellite data, ecosystem models and field experiments. However, a systematic understanding of how global vegetation will respond to projected changes in climate and atmospheric composition is still lacking. Here, we analyze changes in projected global leaf area index (LAI) from 16 Coupled Model Intercomparison Project phase 5 (CMIP5) and 17 phase 6 (CMIP6) Earth System Models (ESMs) during the 21st century under future scenarios RCP2.6, RCP4.5 and RCP8.5 and future scenarios SSP1–2.6, SSP2–4.5, SSP3–7.0 and SSP5–8.5, respectively. In addition to the widely-used multi-model ensemble mean (MME) method, we employed the Reliability Ensemble Averaging (REA) strategy to integrate the modeled results. The REA integration weights were determined by combining the performance of the ESMs in simulating present-day LAI changes, which was based on a comparison with three long-term remote sensing LAI data sets, with their convergence of future predictions. The results suggest that global LAI will increase under all seven future scenarios with both integration methods. The magnitude of LAI growth is expected to increase with the forcing levels of the scenarios. The ESMs integrated with the REA weights predicted significantly smaller magnitudes and lower uncertainties in global LAI growth by the end of the 21st century than those integrated with the MME method. Both REA and MME results suggest that the growth in atmospheric CO2 concentration is the main positive driver of the projected increment of global LAI, which is partly offset by the negative effects of global warming. Our improved comprehensive prediction based on CMIP5 and CMIP6 ESMs using the REA integration strategy provides a more robust estimation of global vegetation change during the 21st century, and is expected to help better understand the state of the planet in the coming decades.

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.

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

AbstractLeaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.