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Summary: A long‐standing debate looks at whether air or soil dryness is more limiting to vegetation water use and productivity. The answer has large implications for future ecosystem functioning, as atmospheric dryness is predicted to increase globally while changes in soil moisture are predicted to be far more variable. Here, I review the complexities that contribute to this debate, including the strong coupling between atmospheric and soil dryness, and the widespread heterogeneity in vegetation hydraulic traits, acclimations, and adaptations to water stress. I discuss solutions to improve understanding and modeling of vegetation sensitivity to dryness, including how different types of observational data can be used together to gain insight into vegetation response to water stress across spatial and temporal scales. [ABSTRACT FROM AUTHOR]

Armed conflicts are acknowledged for their effects on the environment, including ecosystem degradation and biodiversity loss, but also ecological restoration. Both processes impact post-conflict opportunities for peace and development. The armed conflict in Northern Uganda (1986–2008) profoundly affected local communities and their natural environment. While some areas suffered environmental degradation others underwent ecological restoration. Understanding this historical pattern is crucial for post-conflict natural resource management and peace and development prospects. This article aims to analyse vegetation changes in Northern Uganda during and after the conflict, exploring potential drivers of such changes and their implications for sustainable peace and development. Utilising remote sensing analysis and literature review, we observe a post-conflict 'greening' trend alongside a more equal vegetation deterioration and restoration spatial distribution. We propose that if these trends are driven by agriculture expansion, the way this is organised and articulated with natural vegetation will be central for peace and development. [ABSTRACT FROM AUTHOR]

Understanding the influence of climatic factors on vegetation dynamics and cumulative effects is critical for global sustainable development. However, the response of vegetation to climate and the underlying mechanisms in different climatic zones remains unclear. In this study, we analyzed the response of vegetation gross primary productivity (GPP) to climatic factors and the cumulative effects across various vegetation types and climatic zones, utilizing data on precipitation (Pr), temperature (Ta), and the standardized precipitation evapotranspiration index (SPEI). The results showed that: (1) GPP showed significant differences among the seven climatic zones, with the highest value observed in zone VII, reaching 1860.07 gC·m− 2, and the lowest in zone I, at 126.03 gC·m− 2. (2) GPP was significantly and positively correlated with temperature in climatic zones I, IV, V, and VI and with precipitation in climatic zones I, II, and IV. Additionally, a significant positive correlated was found between SPEI and GPP in climatic zones I, II, and IV. (3) Drought exerted a cumulative effect on GPP in 45.10% of the regions within China, with an average cumulative duration of 5 months. These effects persisted for 6–8 months in zones I, II, and VII, and for 2–4 months in zones III, IV and VI. Among different vegetation types, forests experienced longest cumulative effect time of 6 months, followed by grasslands (5 months), croplands (4 months), and shrublands (4 months). The cumulative time scale decreased with increasing annual SPEI. The varying responses and accumulation of GPP to drought among different vegetation types in various climatic zones underscore the complexity of vegetation-climate interactions the response and accumulation of GPP to drought. [ABSTRACT FROM AUTHOR]

Wood density (WD) is an important quality and functional trait of wood. However, despite the relationships between WD and abiotic factors being important to model or predict spatial distributions of functional traits, as well as responses of vegetation to climate changes, in current Earth system models or dynamic global vegetation models (ESMs/DGVMs), WD is often oversimplified, being defined as a globally uniform constant either for all plant functional types (PFTs) or for each individual PFT. Such oversimplifications may lead to simulation biases in the morphology of woody PFTs, as well as ecosystem transition and vegetation–atmosphere interactions. Moreover, existing conclusions about the relationships between WD and abiotic factors drawn from field observations remain mixed, making model parameterization improvements difficult. This study systematically investigated the influences of climate and soil factors on WD across various PFTs. Optimal fitting models for predicting WD within each PFT were then constructed by utilizing our collated global database of 138 604 observations. For WDs of tree PFTs, climate emerges as a more influential factor than soil characteristics, whereas for shrub PFTs the effects of climate and soil are of equivalent significance. Across all six PFTs, correlation coefficients between predictions by fitting models and observed WD range from 0.49 to 0.93. The predicted and observed WD exhibit good agreement across climate space. It is expected that the incorporation of our research findings into DGVMs will improve the simulation of tree height and forest fractional coverage, particularly in the central forest areas and forest transition zones. [ABSTRACT FROM AUTHOR]

Climate change has emerged as a major concern at both regional and global scales in recent decades. Northwestern Algeria is particularly vulnerable as a semi-arid zone, where changes in climate and land cover (LC) will have a significant impact on vegetation in the long term. This study analyses, through a multidimensional approach, the influence of climate change and LC on vegetation dynamics. Hierarchical partitioning (HP) analysis was conducted to determine the most influential climatic variables (precipitation, temperature) on the dynamics of NDVI. The results show that the annual NDVI shows a fluctuating spatial trend between decrease and increase in different regions. Trends in seasonal NDVI are spatially varied and less uniform. Variations in precipitation are stable, while temperatures show clear and consistent significant increases across the region, with a general tendency to increase (p<0.01) in spring and summer. In mountainous areas, NDVI shows an increasing trend both annually and seasonally. The correlation (r²) between NDVI, temperature and precipitation (0.75–1.0) over the different seasons reveals significant seasonal and regional variability. LC transition patterns also influence spatio-temporal trends in vegetation cover. They reveal that the rate of change of NDVI varies between LC types and regions, with resilience in forests and grasslands. These variations have significant implications for vegetation dynamics, as observed by NDVI. [ABSTRACT FROM AUTHOR]

Introduction: Vegetation plays a crucial role in terrestrial ecosystems, acting as a vital link connecting the lithosphere, hydrosphere, and atmosphere in terms of energy flow and material cycling. Changes in surface vegetation significantly regulate the water cycle, energy flow within terrestrial surfaces, and global carbon balance. Methods: This study focuses on nine major river basins in China to quantitatively investigate the impacts of climate factors, vegetation dynamics, and land use changes on carbon use efficiency (CUE) and water use efficiency (WUE). Results: The primary controlling factors of WUE trends are NDVI (average contribution: 33.75% ± 6.90%) and VPD (average contribution: 28.04% ± 3.98%). NDVI predominates in the Haihe, Yellow River, Yangtze River, Pearl River, and Songliao River basins, while shortwave radiation (Srad) dominates in the southeastern rivers and inland river areas, and humidity (Shum) in the southwestern river basins. For CUE trends, the main controlling factors are Srad (average contribution: 36.46% ± 3.40%) and precipitation (Pre) (average contribution: 26.72% ± 5.20%). NDVI negatively influences the Huaihe River and southeastern river basins, while Pre negatively influences the Songliao River and Yellow River basins, and Srad negatively influences the Huaihe and southwestern river basins. Pre predominates in the Huaihe, Songliao, Haihe, southwestern river basins, and inland river areas, while Srad predominates in the Pearl River, Yangtze River, and Yellow River basins. Discussion: Climate factors and vegetation dynamics have significant regional impacts on WUE and CUE across different river basins, especially the roles of NDVI and VPD on WUE, and Srad and precipitation on CUE. These differences underscore the importance of developing region-specific management strategies to optimize ecosystem services in each basin. [ABSTRACT FROM AUTHOR]

Shifting cultivation, an age-old agricultural practice, is a major factor in forest cover change across Southeast Asia, where repeated cycles of vegetation disturbance and regrowth lead to far-reaching environmental and socio-economic impacts. The present study aims to assess the spatio-temporal patterns of vegetation disturbance and regrowth caused by shifting cultivation in Tripura state of India, over the past three decades, utilizing temporal segmentation of time-series Landsat data. The study analyzed vegetation disturbance and regrowth patterns in a shifting cultivation landscape from 1991 to 2020 using normalized burn ratio trends through LandTrendr, validated by the TimeSync tool. Six land use and land cover classes, viz., forests and trees outside forests, rubber plantation, shifting cultivation, water bodies, agriculture, and settlements, were mapped with an accuracy of 83.08% using a random forest classifier. This classification enabled the identification of the effective study area, which included forest areas with shifting cultivation patches. The analysis revealed that 2533.96 km2 of the study area remained undisturbed, 568.43 km2 experienced low-magnitude disturbance, 1501.11 km2 were moderately disturbed, and 184.82 km2 were highly disturbed. The shifting cultivation cycles in the study area show considerable variation. Low-magnitude disturbance indicates a single slash-and-burn event in the past three decades, moderate-magnitude disturbance involves fallow periods of over 10 years, and high-magnitude disturbance occurs with fallow periods of less than 8 years. The study revealed a shift from traditional shifting cultivation to more permanent agricultural practices in parts of Tripura. In response to incentives for commercial crops, cultivators are increasingly adopting long-term cultivation, including the growing of pineapple, areca nut, and rubber, as well as intercropping with papaya, banana, lemon, tapioca, pepper, and ginger. These plots are, however, abandoned if the yields become economically unviable. The expansion of monoculture cultivation significantly reduces the available area for shifting cultivation, thereby compelling jhum cultivators to revisit their jhum patches more frequently. This study is a first-of-its-kind attempt in the Indian context and sheds light on changing patterns in an age-old agricultural practice. [ABSTRACT FROM AUTHOR]

Vegetation serves as a crucial indicator of ecological environment and plays a vital role in preserving ecosystem stability. However, as urbanization escalates rapidly, natural vegetation landscapes are undergoing continuous transformation. Paradoxically, vegetation is pivotal in mitigating the ecological and environmental challenges posed by urban sprawl. The middle and lower Yangtze River Basin (MLYRB) in China, particularly its economically thriving lower reaches, has witnessed a surge in urbanization. Consequently, this study explored the spatiotemporal variations of normalized difference vegetation index (NDVI) in the MLYRB, with an emphasis on elucidating the impact of climate change and urbanization on vegetation dynamics. The results indicate that a significant increasing trend in NDVI across the MLYRB from 2000 to 2020, a pattern that is expected to persist. An improvement in vegetation was observed in 94.12% of the prefecture-level cities in the study area, predominantly in the western and southern regions. Temperature and wind speed stand out as dominant contributors to this improvement. Nevertheless, significant vegetation degradation was detected in some highly urbanized cities in the central and eastern parts of the study area, mainly attributed to the negative effects of escalating urbanization. Interestingly, a positive correlation between NDVI and the urbanization rate was observed, which may be largely related to proactive ecological preservation policies. Additionally, global climatic oscillations were identified as a key force driving periodic NDVI variations. These findings hold significant importance in promoting harmonious urbanization and ecological preservation, thereby providing invaluable insights for future urban ecological planning efforts. [ABSTRACT FROM AUTHOR]

It is anticipated that global warming will modify precipitation and evapotranspiration patterns, consequently affecting water availability. Changes in water availability pose challenges to freshwater supply, food security, and ecosystem sustainability. However, the variations and driving mechanisms of water availability in the arid and semiarid regions of Northern China remain unclear. This study evaluates the accuracy of three evapotranspiration products and analyzes the changes in water availability in the arid and semiarid regions of Northern China over the past 39 years (1982–2020) along with their driving factors. The results indicate that during this period, precipitation increased at a rate of 7.5 mm/decade, while evapotranspiration rose at a higher rate of 13 mm/decade, resulting in a decline in water availability of 5.5 mm/decade. Spatially, approximately 30.17% of the area exhibited a significant downward trend in water availability, while 65.65% remained relatively stable. Evapotranspiration is the dominant factor leading to the decrease in water availability, with a contribution rate of 63.41%. The increase in evapotranspiration was primarily driven by temperature (32.53% contribution) and the saturation vapor pressure deficit (24.72% contribution). The decline in water availability may further exacerbate drought risks in arid and semiarid regions. The research results can provide a scientific basis for developing water resource management strategies and ecological restoration strategies under environmental change. [ABSTRACT FROM AUTHOR]

The ecosystem's gross primary productivity (GPP) and greenness, as indicated by the normalized difference vegetation index (NDVI), are both essential ecological indicators used to evaluate how ecosystems responded to climate variability. However, the relationships between NDVI and GPP under the influence of drying and wetting and its characteristics along aridity (AI) gradients were not yet fully understood. In this study, we investigated the relationships of the NDVI-GPP (i.e., the strength of the coupling and the sensitivity, as quantified by the coefficient of determination (R2) and slope of the linear regression, respectively) along the aridity gradients during the growing season from 1982 to 2018 in China. The results show that the coupling between NDVI and GPP was stronger (i.e., high R2) in semi-arid regions (0.24) compared to humid and hyper-humid regions (R2 values were 0.11). For different plant functional types (PFTs), decoupling occurred in ENF with a determination coefficient value (R2) of 0.04, whereas GRA shows a higher coupling with an R2 of 0.20. The coupling trend experienced a shift in semi-arid regions, characterized by an aridity index (AI) ranging from 0.20 to 0.50. Additionally, the sensitivity of GPP to NDVI also decreased with increasing aridity. The slope values were 0.19, 0.21, 0.24, 0.20, 0.11, and 0.11 in hyper-arid, arid, semi-arid, dry sub-humid, humid, and hyper-humid, respectively. What is more, asynchronous changes in vegetation productivity and greenness can be detected by capturing the inter-annual variability (IAV) of NDVI and GPP. The IAV of GPP steadily decreased with the aridity gradients, while the IAV of NDVI present fluctuated, suggesting that NDVI was more variable than GPP under the influence of drying and wetting conditions. Our study suggests that there may be a stronger trade-off between ecosystem greenness and photosynthesis in more humid areas. [ABSTRACT FROM AUTHOR]

Recent research has demonstrated that global warming significantly enhances peak vegetation growth on the Tibetan Plateau (TP), underscoring the influence of climatic factors on vegetation dynamics. Nevertheless, the effects of different drought types on peak vegetation growth remain underexplored. This study utilized satellite-derived gross primary productivity (GPP) and the normalized difference vegetation index (NDVI) to assess the impacts of soil moisture (SM) and vapor pressure deficit (VPD) on peak vegetation growth (GPPmax and NDVImax) across the TP from 2001 to 2022. Our findings indicate that NDVImax and GPPmax exhibited increasing trends in most regions, displaying similar spatial patterns, with 65.28% of pixels showing an increase in NDVImax and 72.98% in GPPmax. In contrast, the trend for SM primarily showed a decrease (80.86%), while VPD showed an increasing trend (74.75%). Through partial correlation analysis and ridge regression, we found that peak vegetation growth was significantly affected by SM or VPD in nearly 20% of the study areas, although the magnitude of these effects varied considerably. Furthermore, we revealed that hydrothermal conditions modulated the responses of peak vegetation growth to SM and VPD. In regions with annual precipitation less than 650 mm and an annual mean temperature below 10 °C, decreased SM and increased VPD generally inhibited peak vegetation growth. Conversely, in warm and humid areas, lower SM and higher VPD promoted peak vegetation growth. These findings are crucial for deepening our understanding of vegetation phenology and its future responses to climate change. [ABSTRACT FROM AUTHOR]

Summary Direct human activity and global climatic changes are threatening the existence of many vegetated habitats. Seedling establishment, one of the riskiest plant life stages, must be successful for such habitats to persist. The establishment of seedlings is known to be enhanced by nurse effects, but most studies to date have looked at the nursing effects of plants while sidelining inanimate objects. Nevertheless, nurse objects can support seedling establishment via diverse mechanisms such as moderating abiotic stresses like extreme temperatures and drought, reducing negative biological interactions such as herbivory while enhancing positive processes like seed dispersal, and providing protection from physical disturbances such as trampling and fire. The robust nature of nurse objects highlights their potential in habitat restoration. The addition of nurse objects allows a simple, single‐effort rehabilitation strategy that can later draw on natural seed dispersal and establishment. By achieving a better understanding of the processes in which nurse objects are involved we should be able to better predict vegetation dynamics and manipulate them to minimize adverse processes and support regeneration in natural habitats. [ABSTRACT FROM AUTHOR]

Limestone pavements contain a unique flora, dominated by ferns with a mix of herbaceous species more often found in woodlands, heathlands and grasslands. The crevices between the rocks are known as grikes, they provide a shaded habitat which, depending on pavement structure, can protect plants from grazing. Despite being of conservation importance and supporting a number of rare and scarce plant species there has been very little research into vegetation change in limestone pavements. This study repeated a survey originally conducted between 1972 and 1975. The Ward and Evans survey visited 535 limestone pavement units spread across England, Scotland and Wales and recorded grike vegetation. This study was able to resurvey 516 of those pavements and repeat the survey using the original methods. Expansion of tree and shrub cover has been observed in some pavements but not previously quantified so tree cover was assessed using historic aerial photographs and satellite imagery. On average species richness per pavement increased but there was a very wide range of change in species richness across individual pavement units ranging from a loss of 56 species to an increase of 38. Dissimilarity averaged 0.41. The area of pavement covered by trees or large shrubs increased by 62% between the two surveys but the number of pavements with zero tree cover also increased. Breaking down pavement units into those with low (<5%), medium (5%–30%) and high (>30%) current tree cover shows that species richness increased in open pavements but was reduced in pavements with high tree cover. Pavement units with high tree cover also showed higher dissimilarity and lower levels of indicators of disturbance. In pavements with high tree cover Ellenberg light (L) values were significantly lower than in open pavements. Open pavements showed higher levels of competitive species (based on Grime CSR values) but no change difference in Ellenberg nutrient (N) values. Vegetation in limestone pavements has changed considerably in the period between the two surveys with negative effects of high tree and shrub cover particularly problematic. There is an urgent need for investigation to support management decisions in this habitat. Read the free Plain Language Summary for this article on the Journal blog. [ABSTRACT FROM AUTHOR]

Analyzing vegetation coverage and land-use and land cover (LULC) characteristics helps to understand the interaction between human activities and the natural environment. The coastal regions of the Guangdong Province are economically active areas with frequent human activities, located in the advantageous natural environment of South China. This study analyzed the spatiotemporal characteristics of the normalized difference vegetation index (NDVI) and LULC from 2000 to 2020, to explore the response of NDVI changes to LULC changes. The results show that (1) the overall NDVI is relatively high, with a proportion of 85.37% to 89.48% of areas with higher coverage and above categories, mainly distributed in the east and west. Vegetation coverage showed an increasing trend. (2) The LULC in this area is mainly composed of forest land (46.5% to 47.5%) and cultivated land (30.7% to 33.4%), with forest land mainly distributed in relatively high-altitude regions and cultivated land mainly distributed in the plains. The changes in LULC from 2015 to 2020 were relatively significant, mainly due to the mutual transfer of cultivated land and forest land. In addition, built-up land continued to expand from 2000 to 2020, mainly in the Pearl River Delta. (3) The NDVI decreases come from the transfer of various types of land to built-up land, mainly in the Pearl River Delta region, while the NDVI increase comes from the stability and mutual transfer of cultivated land. The net contribution rate of forest land change to vegetation cover change is the most significant (−38.903% to 23.144%). This study has reference significance for the spatiotemporal characteristics of vegetation cover changes in coastal areas and their response to land-use changes, as well as coastal management and sustainable development. [ABSTRACT FROM AUTHOR]

Many hydrological models incorporate vegetation-related parameters to describe hydrological processes more precisely. These parameters should adjust dynamically in response to seasonal changes in vegetation. However, due to limited information or methodological constraints, vegetation-related parameters in hydrological models are often treated as fixed values, which restricts model performance and hinders the accurate representation of hydrological responses to vegetation changes. To address this issue, a vegetation-related dynamic-parameter framework is applied on the Xinanjiang (XAJ) model, which is noted as Eco-XAJ. The dynamic-parameter framework establishes the regression between the Normalized Difference Vegetation Index (NDVI) and the evapotranspiration parameter K. Two routing methods are used in the models, i.e., the unit hydrograph (XAJ-UH and Eco-XAJ-UH) and the Linear Reservoir (XAJ-LR and Eco-XAJ-LR). The original XAJ model and the modified Eco-XAJ model are applied to the Ou River Basin, with detailed comparisons and analyses conducted under various scenarios. The results indicate that the Eco-XAJ model outperforms the original model in long-term discharge simulations, with the NSE increasing from 0.635 of XAJ-UH to 0.647 of Eco-XAJ-UH. The Eco-XAJ model also reduces overestimation and incorrect peak flow simulations during dry seasons, especially in the year 1991. In drought events, the modified model significantly enhances water balance performance. The Eco-XAJ-UH outperforms the XAJ-UH in 9 out of 16 drought events, while the Eco-XAJ-LR outperforms the XAJ-LR in 14 out of 16 drought events. The results demonstrate that the dynamic-parameter model, in regard to vegetation changes, offers more accurate simulations of hydrological processes across different scenarios, and its parameters have reasonable physical interpretations. [ABSTRACT FROM AUTHOR]

The fossil record of the U.S. Pacific Northwest preserves many Middle Miocene floras with potential for revealing long-term climate-vegetation dynamics during the Miocene Climatic Optimum. However, the possibility of strong, eccentricity-paced climate oscillations and concurrent, intense volcanism may obscure the signature of prevailing, long-term Miocene climate change. To test the hypothesis that volcanic disturbance drove Middle Miocene vegetation dynamics, high-resolution, stratigraphic pollen records and other paleobotanical data from nine localities of the Sucker Creek Formation were combined with sedimentological and geochemical evidence of disturbance within an updated chronostratigraphic framework based on new U-Pb zircon ages from tuffs. The new ages establish a refined, minimum temporal extent of the Sucker Creek Formation, ~15.8 to ~14.8 Ma, and greatly revise the local and regional chronostratigraphic correlations of its dispersed outcrop belt. Our paleoecological analysis at one ~15.52 Ma locality reveals two abrupt shifts in pollen spectra coinciding with the deposition of thick ash-flow tuffs, wherein vegetation dominated by Cupressaceae/Taxaceae, probably representing a Glyptostrobus oregonensis swamp, and upland conifers was supplanted by early-successional forests with abundant Alnus and Betula. Another ephemeral shift from Cupressaceae/Taxaceae swamp taxa in favor of upland conifers Pinus and Tsuga correlates with a shift from low-Ti shale to high-Ti claystone, suggesting a link between altered surface hydrology and vegetation. In total, three rapid vegetation shifts coincide with ash-flow tuffs and are attributed to volcanic disturbance. Longer-term variability between localities, spanning ~1 Myr of the Miocene Climatic Optimum, is chiefly attributed to eccentricity-paced climate change. Overall, Succor Creek plant associations changed frequently over ≤105 years timespans, reminiscent of Quaternary vegetation records. Succor Creek stratigraphic palynology suggests that numerous and extensive collection of stratigraphically controlled samples is necessary to understand broader vegetation trends through time. [ABSTRACT FROM AUTHOR]

As the main component of terrestrial ecosystem, vegetation plays a very important role in regional ecosystem environmental change, global carbon cycle and climate regulation. The Lower Mekong region (LMR) is at the core of Southeast Asia, its vegetation changes will affect the regional ecosystem and climate. Five countries of LMR were selected as the study area, based on MODIS (Moderate-Resolution Imaging Spectroradiometer) NDVI(Normal Difference Vegetation Index) data from 2000 to 2022, using the Sen's slope estimator, Mann–Kendall trend test and geographic detector to study the spatial and temporal variation trends and driving forces of vegetation coverage. The results showed that:(1) From 2000 to 2022,the vegetation coverage in the LMR showed an overall fluctuating upward trend, the annual average Fractional Vegetation Cover(FVC) value was 0.70, mainly with high vegetation coverage and relatively high vegetation coverage. Vegetation distribution had obviously spatial heterogeneity, and the vegetation of Myanmar, Laos and Vietnam was significantly larger than Thailand and Cambodia.(2) The variation trend analysis of Sen_MK showed that the proportion of improved and degraded vegetation coverage areas in the LMR were 56.33% and 37.55% respectively. The vegetation improvement area was much larger than the vegetation degradation area during 2000–2022. According to the variation trend analysis of different countries, the vegetation coverage improvement area in Vietnam, Myanmar and Thailand were larger than the degraded , the overall vegetation coverage variation trend were good. However, in Laos and Cambodia, the degraded areas were larger than the improved, the overall variation trends of coverage were not good.(3) The results of geographic detector showed that the Land Use and Land Cover(LULC) had the greatest influence on vegetation coverage in the study area.The influencing factors of vegetation coverage were different in the LMR. For Vietnam, Thailand and Laos,elevation and slope factors were second only to LULC, for Myanmar and Cambodia, the influence of precipitation factor was second only to LULC. The results provide scientific data support for understanding the ecological environment status and future changes in the research area. [ABSTRACT FROM AUTHOR]

Grazing is a major ecological driver that influences vegetation dynamics globally. We investigated the long‐term effects of different grazing regimes on the vegetation structure of the Central Anatolian steppes, a region characterized by its unique convergence of biogeographical influences and historical land use. We employed the spatially explicit FATELAND model to simulate vegetation dynamics over a 50‐year period under three distinct grazing scenarios: no grazing, moderate grazing, and overgrazing. Our simulations incorporated a range of plant functional traits to predict changes across five different vegetation types in Central Anatolia, including woodland steppes and treeless steppes. The simulations revealed that moderate grazing supports the diversity and abundance of various plant functional groups, excluding resprouter trees, which flourish under no grazing conditions. In contrast, overgrazing leads to significant reductions in the abundance of perennial forbs and both spiny and non‐spiny subshrubs, often resulting in a shift toward grassland dominated by resprouter gramineae or an annual herb‐dominated grassland, depending on the initial abundance of gramineae. Our findings highlight the critical role of grazing management in maintaining biodiversity and ecological stability in steppe ecosystems. While moderate grazing can enhance plant functional group diversity, overgrazing significantly threatens the ecological integrity of the Central Anatolian steppes. In conclusion, our modeling approach reveals that the grazing regime is a major driver in shaping the vegetation structure of Central Anatolian steppes. Grazing management strategies that are adjusted to the ecological characteristics and historical context of specific regions are required to prevent degradation and promote sustainable grassland vegetation. [ABSTRACT FROM AUTHOR]

This study presents an innovative investigation into the spatiotemporal dynamics of vegetation growth and its response to both individual and composite climatic factors. The Normalized Difference Vegetation Index (NDVI), derived from SPOT satellite remote sensing data, was employed as a proxy for vegetation growth. Multiple analytical methods, including the coefficient of variation, Mann–Kendall trend analysis, and Hurst index, were applied to characterize the spatiotemporal patterns of the NDVI in Sichuan Province from 2000 to 2020. The Standardized Precipitation Evapotranspiration Index (SPEI) was calculated using monthly precipitation and temperature data from 45 meteorological stations to examine the influence of composite climatic factors on vegetation growth, while the time lag effects between the NDVI and various climatic variables were also explored. Our findings unveil three key insights: (1) Vegetation coverage in Sichuan Province exhibited an overall increasing trend, with the highest NDVI values in summer and the lowest in winter. Significant NDVI fluctuations were observed in spring in the western Sichuan plateau and in winter in northern, eastern, and southern Sichuan. (2) A significant upward trend in the NDVI was detected across Sichuan Province, except for Chengdu Plain, where a downward trend prevailed outside the summer season. (3) On shorter time scales, the NDVI was positively correlated with precipitation, temperature, and the SPEI, with a one-month lag. The response of the NDVI to sunlight duration showed a two-month lag, with the weakest correlation and a five-month lag in western Sichuan. This research advances our understanding of the complex interactions between vegetation dynamics and climatic factors in Sichuan Province and provides valuable insights for predicting future vegetation growth trends. [ABSTRACT FROM AUTHOR]

The quantitative assessment of the impact of vegetation restoration on evapotranspiration and its components is of great significance in developing sustainable ecological restoration strategies for water resources in a given region. In this study, we used the Priestley-Taylor Jet Pro-pulsion Laboratory (PT-JPL) to simulate the ET components in the Helong section (HLS) of the Yellow River basin. The effects of vegetation restoration on ET and its components, vegetation transpiration (Et), soil evaporation (Es), and canopy interception evaporation (Ei) were separated by manipulating model variables. Our findings are as follows: (1) The simulation results are compared with the ET calculated by water balance and the annual average ET of MODIS products. The R2 of the validation results are 0.61 and 0.78, respectively. The results show that the PT-JPL model tracks the change in ET in the HLS well. During 2000–2018, the ET, Ei, and Es increased at a rate of 1.33, 0.87, and 2.99 mm/a, respectively, while the Et decreased at a rate of 2.52 mm/a. (2) Vegetation restoration increased the annual ET in the region from 331.26 mm (vegetation-unchanged scenario) to 338.85 mm (vegetation change scenario) during the study period, an increase of 2.3%. (3) TMP (temperature) and VPD (vapor pressure deficit) were the dominant factors affecting ET changes in most areas of the HLS. In more than 37.2% of the HLS, TMP dominated the change affecting ET, and vapor pressure difference (VPD) dominated the area affecting ET in 30.5% of the HLS. Overall, the precipitation (PRE) and VPD were the main factors affecting ET changes. Compared with previous studies that directly explore the relationship between many influencing factors and ET results through correlation research methods, our study uses control variables to obtain results under two different scenarios and then performs difference analysis. This method can reduce the excessive interference of influencing factors other than vegetation changes on the research results. Our findings can provide strategic support for future water resource management and sustainable vegetation restoration in the HLS region. [ABSTRACT FROM AUTHOR]

With global climate change, linking vegetation phenology with net ecosystem productivity (NEP) is crucial for assessing vegetation carbon storage capacity and predicting terrestrial ecosystem changes. However, there have been few studies investigating the relationship between vegetation phenology and NEP in the middle and high latitudes of the Northern Hemisphere. This study comprehensively analyzed vegetation phenological changes and their climate drivers using satellite data. It also investigated the spatial distribution and climate drivers of NEP and further analyzed the sensitivity of NEP to vegetation phenology. The results indicated that the average land surface phenology (LSP) was dominated by a monotonic trend in the study area. LSP derived from different satellite products and retrieval methods exhibited relatively consistent responses to climate. The average SOS and POS for different retrieval methods showed a higher negative correlation with nighttime temperatures compared to daytime temperatures. The average EOS exhibited a higher negative correlation with daytime temperatures than a positive correlation. The correlations between VPD and the average SOS, POS, and EOS showed that the proportion of negative correlations was higher than that of positive correlations. The average annual NEP ranged from 0 to 1000 gC·m−2. The cumulative trends of NEP were mainly monotonically increasing, accounting for 61.04%, followed by monotonically decreasing trends, which accounted for 17.95%. In high-latitude regions, the proportion of positive correlation between VPD and NEP was predominant, while the proportion of negative correlation was predominant in middle-latitude regions. The positive and negative correlations between soil moisture and NEP (48.08% vs. 51.92%) were basically consistent in the study area. The correlation between SOS and POS with NEP was predominantly negative. The correlation between EOS and NEP was overall characterized by a greater proportion of negative correlations than positive correlations. The correlation between LOS and NEP exhibited a positive relationship in most areas. The sensitivity of NEP to vegetation phenological parameters (SOS, POS, and EOS) was negative, while the sensitivity of NEP to LOS was positive (0.75 gC·m−2/d for EVI vs. 0.63 gC·m−2/d for LAI vs. 0.30 gC·m−2/d for SIF). This study provides new insights and a theoretical basis for exploring the relationship between vegetation phenology and NEP under global climate change. [ABSTRACT FROM AUTHOR]

Droughts in the Weihe River Basin are occurring more frequently and are becoming more intense. These events negatively affect industrial production, economic development, and ecosystems. Studying how vegetation changes in response to them is of practical significance. We report temporal and spatial trends in vegetation cover, use a copula function to analyze relationships between drought and vegetation cover, and assess the probability of vegetation loss in different drought scenarios. A vegetation index trends upwards from north to south in this basin; from 2001 to 2017, vegetation cover also trends upward in most areas, although it decreases in areas with high vegetation cover. An escalated susceptibility to drought has been observed in the southern and eastern sectors, where proximity to the riverbank correlates with heightened drought sensitivity, particularly in zones of intensified vegetation density. The probability of vegetation loss at the same vegetation loss preset point gradually increases with increased drought severity. These results will facilitate the formulation of countermeasures to prevent and combat the effects of drought on vegetation and land management. [ABSTRACT FROM AUTHOR]

Accurate burn severity mapping is essential for understanding the impacts of wildfires on vegetation dynamics in arid savannas. The frequent wildfires in these biomes often cause topkill, where the vegetation experiences above-ground combustion but the below-ground root structures survive, allowing for subsequent regrowth post-burn. Investigating post-fire regrowth is crucial for maintaining ecological balance, elucidating fire regimes, and enhancing the knowledge base of land managers regarding vegetation response. This study examined the relationship between bush burn severity and woody vegetation post-burn coppicing/regeneration events in the Kalahari Desert of Botswana. Utilizing UAV-derived RGB imagery combined with a Random Forest (RF) classification algorithm, we aimed to enhance the precision of burn severity mapping at a fine spatial resolution. Our research focused on a 1 km2 plot within the Modisa Wildlife Reserve, extensively burnt by the Kgalagadi Transfrontier Fire of 2021. The UAV imagery, captured at various intervals post-burn, provided detailed orthomosaics and canopy height models, facilitating precise land cover classification and burn severity assessment. The RF model achieved an overall accuracy of 79.71% and effectively identified key burn severity indicators, including green vegetation, charred grass, and ash deposits. Our analysis revealed a >50% probability of woody vegetation regrowth in high-severity burn areas six months post-burn, highlighting the resilience of these ecosystems. This study demonstrates the efficacy of low-cost UAV photogrammetry for fine-scale burn severity assessment and provides valuable insights into post-fire vegetation recovery, thereby aiding land management and conservation efforts in savannas. [ABSTRACT FROM AUTHOR]

This study provides a comprehensive analysis of the dynamics of a three-level vertical food chain model, specifically focusing on the interactions between vegetation, herbivores, and predators in a Snowshoe hare-Canadian lynx system. By simplifying the model through dimensional analysis, we determine conditions for equilibrium existence and identify various types of bifurcations, including Saddle-Node and Hopf bifurcations. Additionally, the study explores codimension-two bifurcations such as Bogdanov–Takens (BT) and zero-Hopf bifurcations. Coefficient formulas of normal forms are derived through the use of center manifold reduction and normal form theory. The study also presents an approximation of homoclinic orbits near a BT bifurcation of the system by computing explicit asymptotics based on regular perturbation methods. Utilizing the MATLAB package MATCONT, a family of limit cycles and their associated bifurcations are computed, including limit point cycles, period-doubling bifurcations, cusp points of cycles, fold-flip bifurcations, and various resonance bifurcations (R1, R2, R3, and R4). The biological implications of the findings are discussed in detail, highlighting how the identified bifurcations and dynamics can impact the population dynamics of vegetation, herbivores, and predators in real-world ecosystems. Numerical experiments validate the theoretical results and provide further support for the conclusions. [ABSTRACT FROM AUTHOR]

Accurate assessment of crop vegetative state is crucial for agricultural management, particularly under environmental stress, and detailed field experiments face logistical and cost challenges. This study addresses two key gaps: (1) limited research on the effects of biostimulants, specifically L-alpha amino acids, on sugarcane's metabolism, yield, and quality; and (2) the need for robust, non-invasive methods to evaluate crop performance. A novel approach is proposed using the Vegetation Activity Index (VAI), derived from integrated NDVI data, as an effective tool for assessing vegetation dynamics over the crop cycle. There was no significant difference in VAI between treated and untreated areas during the plant development cycle (p = 0.342), but a significant increase in VAI was observed in treated areas post-treatment (p = 0.009). Polynomial regression models (R2: 0.921–0.959) effectively explained vegetation variability. VAI and Total Recoverable Sugars per Hectare (STH) data followed a normal distribution, with no significant difference in VAI during the plant development cycle (p = 0.342) and no significant impact on STH (p = 0.492). A strong correlation between VAI and STH for treated areas (r = 0.966, p = 0.034) was observed. The L-alpha biostimulant increased vegetation activity during critical growth periods but did not affect sugar yield. Return on investment analysis indicated high profitability for the treatment. [ABSTRACT FROM AUTHOR]

The Indo–Pacific Pollen Database (IPPD) is the brainchild of the late professor Geoffrey Hope, who gathered pollen records from across the region to ensure their preservation for future generations of palaeoecologists. This noble aim is now being fulfilled by integrating the IPPD into the online Neotoma Paleoecology Database, making this compilation available for public use. Here we explore the database in depth and suggest directions for future research. The IPPD comprises 226 fossil pollen records, most postdating 20 ka but with some extending as far back as 50 ka or further. Over 80 % of the records are Australian, with a fairly even distribution between the different Australian geographical regions, with the notable exception being Western Australia, which is only represented by three records. The records are also well distributed in the modern climate space, with the largest gap being in drier regions due to preservation issues. However, many of the records contain few samples or have fewer than five chronology control points, such as radiocarbon, luminescence or Pb-210, for the younger sequences. Average deposition time for the whole database, counted as years per centimetre, is 64.8 yrcm-1 , with 61 % of the records having a deposition time shorter than 50 yrcm-1. The slowest deposition time by geographical region occurs on Australia's east coast, while the fastest times are from the western Pacific. Overall, Australia has a slower deposition time than the rest of the Indo–Pacific region. The IPPD offers many exciting research opportunities to investigate past regional vegetation changes and associated drivers, including contrasting the impact of the first human arrival and European colonisation on vegetation. Examining spatiotemporal patterns of diversity and compositional turnover/rate of change, land cover reconstructions, and plant functional or trait diversity are other avenues of potential research, amongst many others. Merging the IPPD into Neotoma also facilitates inclusion of data from the Indo–Pacific region into global syntheses. [ABSTRACT FROM AUTHOR]

The absence of spatial and temporal cropping information in semi-arid regions poses a significant challenge in assessing the dynamics of agricultural systems at river basin scales. Satellite remote sensing provides qualitative and quantitative information to derive vegetation dynamics over extensive areas of inherent complexities due to limitations in the availability of field data and the diverse nature of agricultural cropping practices. Utilizing phenological information derived from MODIS Normalized Difference Vegetation Index (NDVI) time series data from 2003–2004 to 2021–2022, this study derives major crop types, and cropping calendar (sowing, maturity, and harvest dates) for each season and year at 250-m resolution. This study introduces an integrated function-fitting model based on the Fast Fourier Transform (FFT) and a Lagrangian 3-point derivative-based approach to extract phenological events from the NDVI time series automatically. The derived phenological information is used in a hybrid crop classification algorithm that combines a rule-based decision tree approach (using the phenological events/dates) and a random forest classifier (using the phenological metrics) to generate the classified crop map at a river basin scale for multiple seasons and years. The implemented approach successfully captured sowing and harvesting dates for every crop growing season over 19 years, with an RMSE of 9 days, as observed with the available field survey data from 2015 to 2021. The classification results from this hybrid approach demonstrate an overall 82% accuracy. The proposed method shows a substantial improvement in cropping area estimation with a 60% reduction in MAE and RMSE compared to the existing algorithm. From the long time series of derived seasonal crop data (19-year analysis period), the influence of monsoonal activities and shifts on the spatial and temporal dynamics of sowing time and cropping patterns are assessed for a large river basin, demonstrating the utility of this continuous crop calendar. Further, an extensive analysis of results highlights farmers' adaptive strategies in response to dry and wet years, providing foundational insights for future studies on assessing the impacts of climate change. Hence, the hybrid framework adopted in this study for deriving a continuous crop calendar holds immense relevance for parameterizing and utilizing such information for developing river basin scale hydrologic and crop growth models for water resources planning and assessment. [ABSTRACT FROM AUTHOR]

Vegetation is one of the important components of ecosystems that usually changes seasonally. An accurate parameterization of vegetation cover dynamics by developing time series models can strengthen our understanding of vegetation change. This research aims to investigate and model the temporal changes of net primary production (NPP) and normalized difference vegetation index (NDVI) across bioclimatic regions of Iran, including the Khazari, Baluchi, semi-desert, steppe, semi-steppe, and arid forests. We used Moderate Resolution Imaging Spectroradiometer (MODIS) sensor products for NPP and NDVI time series (MOD17A2 and MOD13Q1, respectively). The SARIMA (Seasonal Autoregressive Integrated Moving Average) time series model is developed for NPP and NDVI time series. The investigation of autocorrelation functions (ACF) showed a strong seasonality in NPP and NDVI at the 12-month lag time. Comparing the lag times from 1 to 24 month for different regions shows that the NPP variable has a stronger seasonality. The evaluation of error criteria which showed NPP time series models based on RMSE, R2, MRE, and CE criteria was better, while based on the ME criteria, the models perform better for NDVI time series (for example, in Khazari region for NPP and NDVI time series, respectively, ME = 3.67, 0.05, RMSE = 0.12, 0.18, R2 = 0.87, 0.63, MRE = 0.02, 0.12, and CE = 0.84, 0.12). The selected models provided a short-term forecasting of the NPP and NDVI index for study regions at 24-month time, which is useful for the planning and management to reduce vegetation degradation and preserve ecosystem and biodiversity. [ABSTRACT FROM AUTHOR]

To evaluate the quantitative impacts of land cover change on vegetation greenness in the significantly human-impacted subtropical region, the characteristics of land cover change were explored by land use dynamic degree, transition matrix and normalized entropy. Various methods including Standardized coefficient, LMG (Lindeman-Merenda-Gold), GEN (Genizi measure) and CAR (Correlation-Adjusted Marginal Correlation) were employed to estimate the contributions of land cover changes on vegetation greenness using MODIS data during 2001–2022 in Guangdong. The conclusions revealed that land cover changes exhibited obvious temporal characteristics in Guangdong with a significantly increasing trend of normalized entropy indicating a more balanced distribution of land cover types under human intervention. NDVI (Normalized Difference Vegetation Index) tended to increase likely due to the large-scale increase in evergreen forest. With regard to the contributions of impact factors on vegetation greenness, the contributions evaluated by LMG, GEN and CAR showed that the natural variation of NDVI accounted for the major contribution (> 33%), while the changes of evergreen forest and grassland had the highest contribution (> 37%) according to Standardized coefficient. These differences were mainly due to the characteristics of land cover changes in Guangdong, the correlations among impact factors and the inherent attributions of the methods. Moreover, the expansions of evergreen forest and urban at the expense of the reductions of grassland and cropland also had significant impacts on NDVI (> 10%) according to LMG, GEN and CAR indicating that human-induced land cover changes had remarkable influences on NDVI. [ABSTRACT FROM AUTHOR]

Ecological sluices were constructed along the Tarim River to supplement the ecosystem's water supply. However, the impact of water regulation by these sluices on the surface water area (SWA) and its relationship with the vegetation response remain unclear. To increase the efficiency of ecological water use, it is crucial to study the response of SWA to water control by the ecological gates and its relationship with vegetation restoration. We utilized the Google Earth Engine (GEE) cloud platform, which integrates Landsat-5/7/8 satellite imagery and employs methods such as automated waterbody extraction via mixed index rule sets, field investigation data, Sen + MK trend analysis, mutation analysis, and correlation analysis. Through these techniques, the spatiotemporal variations in SWA in the middle reaches of the Tarim River (MROTR) from 1990–2022 were analyzed, along with the relationships between these variations and vegetation restoration. From 1990–2022, the SWA in the MROTR showed an increasing trend, with an average annual growth rate of 12.47 km2 per year. After the implementation of ecological gates water regulations, the SWA significantly increased, with an average annual growth rate of 28.8 km2 per year, while the ineffective overflow within 8 km of the riverbank notably decreased. The NDVI in the MROTR exhibited an upward trend, with a significant increase in vegetation on the northern bank after ecological sluice water regulation. This intervention also mitigated the downward trend of the medium and high vegetation coverage types. The SWA showed a highly significant negative correlation with low-coverage vegetation within a 5-km range of the river channel in the same year and a significant positive correlation with high-coverage vegetation within a 15-km range. The lag effect of SWA influenced the growth of medium- and high-coverage vegetation. These findings demonstrated that the large increase in SWA induced by ecological gate water regulation positively impacted vegetation restoration. This study provides a scientific basis for water resource regulation and vegetation restoration in arid regions globally. [ABSTRACT FROM AUTHOR]

Active vegetation restoration plays an important role in the improvement in soil organic matter (SOM), including the carbon (C), nitrogen (N) and phosphorus (P) sequestration of degraded mining ecosystems. However, there is still a lack of understanding of the key drivers of SOM pool size and dynamics in active vegetation restoration. For this study, soil was collected from five different sites (Xiaoxian, Dingyuan, Chaohu, Tongling and Dongzhi), four habitats (platforms, slopes, steps and native areas) and two soil layers (0–20 cm and 20–40 cm) in limestone mines of Anhui province to quantify the spatial distribution of SOM contents and their stoichiometric characteristics and influential factors. It was found that the top soil in Chaohu had the highest significant C, N and P contents in the ranges of 14.95–17.97, 1.74–2.21 and 0.80–1.24 g/kg, respectively. Comparing the stoichiometric ratios of the different sites revealed significant differences in C:N and N:P ratios, but C:P ratios were relatively consistent. In particular, the C:N and C:P ratios in deep soil were higher than those in top soil, whereas the N:P ratio in deep soil was lower than that in top soil, suggesting that soil N is a major limiting factor in the top soil. The SOM content did not differ significantly between the three reclaimed habitats, but was significantly higher than that in the native habitat, suggesting that mine restoration has significantly enhanced SOM accumulation. Further analysis showed that nutrient availability and enzyme activity are important factors affecting soil C, N and P content in top soil, while the relationship gradually weakens in deep soil. This was attributed to active anthropogenic management and conservation measures during the early stages of reclamation. This study shows that the ecological recovery of the mining area can be enhanced by implementing differentiated vegetation planting strategies and anthropogenic management on different habitats in the mining area. [ABSTRACT FROM AUTHOR]

The Grain for Green Project (GGP) is a major national initiative aimed at ecological improvement and vegetation restoration in China, achieving substantial ecological and socio-economic benefits. Nevertheless, research on vegetation cover trends and the long-term restoration efficacy of the GGP in the Loess Plateau remains limited. This study examines the temporal–spatial evolution and sustainability of vegetation cover in this region, using NDVI data from Landsat (2000–2022) with medium-high spatial resolution. The analytical methods involve Sen's slope, Mann–Kendall non-parametric test, and Hurst exponent to assess trends and forecast sustainability. The findings reveal that between 2000 and 2022, vegetation coverage in the Loess Plateau increased by an average of 0.86% per year (p < 0.01), marked by high vegetation cover expansion (173 × 103 km2, 26.49%) and low vegetation cover reduction (149 × 103 km2, 22.83%). The spatial pattern exhibited a northwest-to-southeast gradient, with a transition from low to high coverage levels, reflecting a persistent increase in high vegetation cover and decrease in low vegetation cover. Approximately 93% of the vegetation cover in the Loess Plateau showed significant improvement, while 5% (approximately 31 × 103 km2) displayed a degradation trend, mainly in the urbanized and Yellow River Basin regions. Projections suggest that 90% of vegetation cover will continue to improve. In GGP-targeted areas, high and medium-high levels of vegetation cover increased significantly at rates of 0.456 ×103 km2/year and 0.304 × 103 km2/year, respectively, with approximately 75% of vegetation cover levels exhibiting positive trends. This study reveals the effectiveness of the GGP in promoting vegetation restoration in the Loess Plateau, offering valuable insights for vegetation recovery research and policy implementation in other ecologically fragile regions. [ABSTRACT FROM AUTHOR]

Forest ecosystems, as the primary component of terrestrial ecosystems, provide essential ecosystem services (ESs) critical for sustainable human development. However, changes in climate and vegetation can alter these forest ESs. Understanding the complex relationships between regional climate, vegetation, and ESs is key to ensuring the sustainable management of forest ESs. Therefore, this study, using Baishanzu National Park as a case example, analyzed the impacts of regional climate and vegetation dynamics (vegetation coverage, forest type, and forest structure) on forest ESs, specifically water yield (WY), soil conservation (SC), net primary productivity (NPP), and habitat quality (HQ). The results indicate that from 2000 to 2020, the forest Composite Index of Ecosystem Services (CIES) in Baishanzu National Park increased. Climate and vegetation dynamics have significant effects on forest ESs. Specifically, changes in WY and SC are primarily influenced by climate change, while changes in NPP and HQ are mainly affected by changes in forest type and structure. Complex trade-offs and synergies exist among different ESs, and the driving mechanisms of climate and vegetation changes on ES variations are also complex, involving both direct and indirect effects, with significant spatial heterogeneity. This study provides important references for the sustainable management and appropriate restoration of regional forest ESs. [ABSTRACT FROM AUTHOR]

Understanding changes in the distribution and coverage of aquatic vegetation (AV) is of great significance for the restoration of lake ecosystems. In this study, the vegetation and bloom indices (VBI) algorithm were used to interpret submerged aquatic vegetation (SAV), floating/emergent aquatic vegetation (FEAV), and algal bloom (AB). The dynamics of AV and their influencing factors in Bao' an Lake, in the middle reaches of the Yangtze River in China, were studied from 2000 to 2023. The results showed that (1) the VBI algorithm can accurately distinguish AV and AB of different life forms with an overall accuracy of 93% and a kappa coefficient of 0.86. (2) Macrophyte coverage decreases. AV grew vigorously in spring, and SAV was the dominant type within it, whereas AV coverage was low in summer, and SAV had no summer species for a long time. In 2000, the coverage of AV was the highest, reaching 64.5%, but a gradual decrease that followed in the coming years finally led to a coverage percentage of less than 5% by 2023. (3) The correlation between SAV coverage and total phosphorus (p < 0.01), total nitrogen (p < 0.05), and water depth/transparency (p < 0.05) in Bao' an Lake were 0.23, 0.28, and 0.32, respectively. (4) The SAV species experienced three stages: richness (before 2003), monotonicity (2004–2020), and final disappearance (2021–present). This study shows that the coverage of AV in Bao' an Lake is too low and the number of SAV species is one (2010–now). Therefore, it is necessary to implement measures to improve vegetation coverage and diversity. [ABSTRACT FROM AUTHOR]

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors' influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10−3 yr−1 (p < 0.01), 1.30 g C m−2 yr−2 (p < 0.01) and 1.00 × 10−3 Wm−2μm−1sr−1yr−1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia. [ABSTRACT FROM AUTHOR]

Under the dual impacts of global climate change and human activities, how to accurately and quantitatively assess and identify changes in watershed runoff is crucial to the rational development and utilization of water resources. Different methods of identifying runoff change attribution are applicable to different environments. In this study, we used five different methods, including the double mass curve (DMC), the soil and water assessment tool (SWAT), and the three Budyko hypotheses based on Fu, Choudhury‒Yang, and Milly‒Zhang formulas to explore the impact of human activities and climate change on runoff change in the Jinghe River basin (JRB). The results show that annual runoff of the JRB decreased considerably, and human activities accounted for more than 90%. Specifically, the results of the SWAT model and the three Budyko hypotheses are highly consistent except for the DMC method, which is not recommended in basins with significant changes in factors other than precipitation. More importantly, the Budyko hypothesis based on the Milly‒Zhang formula uses the vegetation water utilization coefficient to consider the impact of vegetation change on runoff, which is most consistent with the simulation results of the SWAT model and is recommended for the attribution analysis of changes in the Loess Plateau represented by the JRB. [ABSTRACT FROM AUTHOR]

Due to spatial scarcity and uncertainties in sediment data, initial and boundary conditions in deep-time climate simulations are not well constrained. On the other hand, depending on these conditions, feedback mechanisms in the climate system compete and balance differently. This opens up the possibility to obtain multiple steady states in numerical experiments. Here, we use the MIT general circulation model to explore the existence of such alternative steady states around the Permian–Triassic Boundary (PTB). We construct the corresponding bifurcation diagram, taking into account processes on a timescale of thousands of years, in order to identify the stability range of the steady states and tipping points as the atmospheric CO2 content is varied. We find three alternative steady states with a difference in global mean surface air temperature of about 10 °C. We also examine how these climatic steady states are modified when feedbacks operating on comparable or longer time scales are included, namely vegetation dynamics and air-sea carbon exchanges. Our findings on multistability provide a useful framework for explaining the climatic variations observed in the Early Triassic geological record, as well as some discrepancies between numerical simulations in the literature and geological data at PTB and its aftermath. [ABSTRACT FROM AUTHOR]

Vegetation health forecasting (NDVI as a proxy) informs decision‐makers about the end of season crop yield productivity but is not well‐documented. This study tests improvements in vegetation health forecasting by developing a data‐driven Dynamic Agricultural Productivity Indicator (DAPI), which simultaneously incorporates satellite‐based root zone soil moisture (RZSM) and satellite‐based NDVI data. RZSM is estimated via data assimilation of satellite based SMAP SM dataset. We employ the proposed DAPI forecast across four cropland types in the CONUS, including corn, cotton, soybeans, and wheat. Results demonstrate superior performance of the DAPI forecasts compared to climatology‐based NDVI forecasts, with the largest improvements in water‐limited regions. DAPI shows particularly good performance during hydrologic disturbances such as floods and droughts. To this end, the DAPI approach is useful in estimating future vegetation health for identifying potential food‐insecure areas, predicting crop price changes, and projecting expected commodities market trends. Plain Language Summary: Crop vegetation health can impact crop productivity. Monitoring the dynamics of vegetation health during the growing season informs decision‐makers about potential yield productivity. However, research in this area is not well‐documented—most crop forecasting models employ stochastic modeling approaches or are based on simple regression of observed NDVI climatology. This study proposes a method to forecast vegetation health by leveraging satellite‐based soil moisture, vegetation indices, and a strategic regression approach. We use the NDVI from satellite data as a proxy for vegetation health. Additionally, we utilize NASA Soil Moisture Active Passive (SMAP) satellite data to enhance a water balance model that simulates root zone soil moisture (RZSM) in deeper soil layers. We combine RZSM with NDVI values to predict NDVI during the growing season at 16 day intervals. Compared to the long‐term average NDVI, our NDVI forecasting approach shows the largest improvements in water‐limited regions and during extreme events such as floods and droughts. Key Points: SMAP‐based root zone soil moisture is a useful predictor for improving NDVI‐based crop forecastingCompared to long‐term average NDVI, the greatest forecast skill improvements are found in water‐limited environmentsSMAP‐based NDVI forecasting demonstrates significant skill in tracking NDVI dynamics during hydrologic disturbances [ABSTRACT FROM AUTHOR]

Fog is an important water source that alleviates vegetation water stress, especially for dryland ecosystems. Comprehensive knowledge of fog and rainfall effects can help us better understand dryland vegetation responses to current and future climates. However, the differences between fog and rainfall effects on vegetation are poorly understood. This study compared the effects of fog and rainfall on vegetation greenness changes based on the ground‐level meteorological observations in the Namib Desert and the satellite vegetation index. The vegetation index and its first derivative were utilized to indicate vegetation greenness and its change rate, respectively. Results showed that fog played a more significant role than rainfall in explaining vegetation greenness change rates, while accumulated rainfall was more important than fog in determining vegetation greenness. Soil temperature was an important factor in explaining vegetation greenness changes. These findings offer key insights into how fog and rainfall differentially contribute to vegetation greenness changes. Plain Language Summary: Dryland plant significantly impacts the global carbon budget, making up about 40% of global net primary productivity. The importance of fog on dryland productivity is highlighted since plant growth in dry areas is strongly constrained by water resources. This study showed that fog consistently appeared among the top five most critical factors in explaining variations in plant greenness and its change rates for both herbaceous and woody areas. Notably, it was found that plant greenness change rates were more influenced by fog than rainfall, although plant greenness was more sensitive to accumulated rainfall than fog. Soil temperature consistently showed large effects on plant greenness changes, potentially limiting plant growth in the context of future global warming. Key Points: The first study to simultaneously quantify the effects of fog and rainfall on vegetation greenness changes in a dryland ecosystemFog was more important than rainfall in explaining vegetation greenness change ratesSoil temperature played an important role in vegetation greenness changes with a negative effect [ABSTRACT FROM AUTHOR]

Several high-elevation plant species would experience an increased risk of regional extinction due to various climatic and anthropogenic factors. Information about the effects of climate change is urgently needed for modeling vegetation dynamics because it influences the various seed parameters like seed germination, seed maturation, seed mass, and seed bank in the soil. The present study was conducted at an elevation of 3145-3560 m in the treeline area of the western Himalayan region of India. The change in seed color is correlated with other seed parameters such as seed moisture content, seed germination, seed mass, and seed fall density. A decline in moisture content in maturing seeds is closely related to seed maturity (p < 0.05). Quercus semecarpifolia contains the highest seed mass followed by Abies spectabilis. Reportedly, the species with higher seed mass have an advantage in light-restricted environments for seed germination and seedling development. In addition, the fruit mass was observed to be the highest for Rhododendron campanulatum, while both Betula utilis and R. arboreum had similar fruit mass. The seed fall density varied between 1.55 and 7.85 seeds m-2 and the maximum mortality of up to 32% of seedlings was observed during post-monsoon season from November to February. The potential disruption in the timing of seed fall, soil seed bank, and seed germination due to climatic irregularities has broader implications for forest ecosystems. Generally, the soil in treeline areas gets frozen during winter, resulting in seedlings facing severe water stress and a high rate of transpiration. The present study addresses the issue regarding the survival and proliferation of important treeline species in the western Himalayan region of India. [ABSTRACT FROM AUTHOR]

Cold ecosystems are experiencing a warming rate that is twice as fast as the global average and are particularly vulnerable to the consequences of climate change. In mountain ecosystems, it is particularly important to monitor vegetation to understand ecosystem dynamics, biodiversity conservation, and the resilience of these fragile ecosystems to global change. Hence, we used satellite data acquired by Sentinel-2 to perform a comparative assessment of the Normalized Difference Vegetation Index (NDVI) and the Plant Phenology Index (PPI) in mountainous regions (canton of Valais-Switzerland in the European Alps) for monitoring vegetation dynamics of four types: deciduous trees, coniferous trees, grasslands, and shrublands. Results indicate that the NDVI is particularly noisy in the seasonal cycle at the beginning/end of the snow season and for coniferous trees, which is consistent with its known snow sensitivity issue and difficulties in retrieving signal variation in dense and evergreen vegetation. The PPI seems to deal with these problems but tends to overestimate peak values, which could be attributed to its logarithmic formula and derived high sensitivity to variations in near-infrared (NIR) and red reflectance during the peak growing season. Concerning seasonal parameters retrieval, we find close concordance in the results for the start of season (SOS) and end of season (EOS) between indices, except for coniferous trees. Peak of season (POS) results exhibit important differences between the indices. Our findings suggest that PPI is a robust remote sensed index for vegetation monitoring in seasonal snow-covered and complex mountain environments. [ABSTRACT FROM AUTHOR]

Climate change and human activities have significantly impacted the long-term growth of vegetation, thereby altering the ecosystem's response mechanisms. The Yellow River Water Conservation Area (YRWCA) is a critical ecological functional zone in China. Since 1982, the vegetation in the YRWCA has changed significantly, and the primary drivers of vegetation which changed before and after 2000 were identified as climate change and human activities, respectively. However, the extent to which different drivers contribute to the vegetation dynamics of the YRWCA remains uncertain. In this study, we introduced a modified deep Convolutional Long Short-Term Memory (ConvLSTM) model to quantify the contributions of climate change and human activities to vegetation change while considering the spatiotemporal heterogeneity. We identified areas with minimal human activity before 2000 using the residual trend method, and used the regional data from these areas to train the model. Subsequently, we applied the trained deep ConvLSTM model to perform an attribution analysis after 2000. The results show that the deep ConvLSTM effectively captures the impacts of climate change on vegetation growth and outperforms the widely used Random Forest model (RF). Despite the fact that the input data of RF were optimized, ConvLSTM still distinctly outperformed RF, achieving R2, MAE, and RMSE values of 0.99, 0.013, and 0.018, respectively, compared to RF's corresponding values of 0.94, 0.038, and 0.045. Since 2000, the regional normalized difference vegetation index (NDVI) has shown a broad increasing trend, particularly in dryland, primarily induced by human activities from 2006 to 2015. Furthermore, an analysis of changes in regional land use, particularly in drylands, revealed that the highest magnitude of conversion of farmland back to forest or grass was recorded from 2000 to 2005. However, the most significant contributions from human activities occurred from 2006 to 2015, indicating a time lag in vegetation recovery from these ecological programs. The attribution results provide valuable insights for the implementation of ecological programs, and the introduced deep ConvLSTM proves the suitability of deep learning models that capture spatiotemporal features in vegetation growth simulations, allowing for broader applications. [ABSTRACT FROM AUTHOR]

Vegetation dynamics are critical to the terrestrial carbon and water cycle, with China recognized as one of the largest contributors to global greening due to significant variations in forest coverage. However, distinguishing the effects of vegetation changes from those of climate factors on vegetation productivity remains challenging. This study conducted a comprehensive analysis of vegetation productivity in Northwest China over the past two decades, focusing on the spatiotemporal patterns and drivers of gross primary production (GPP) within ecological restoration areas. Using trend analysis and ridge regression models, we assessed the relative contributions of climate factors and vegetation coverage changes to GPP dynamics. The results revealed a significant increase in both the GPP and vegetation coverage in Northern China from 2001 to 2020, with GPP rising by 6.7 g C m−2 yr−1 and forest coverage increasing by 0.08% per year. A strong positive correlation (r = 0.9) was observed between vegetation coverage changes and GPP. The increase in GPP was driven by both climate factors and changes in forest coverage, with climate factors contributing 61.0% and vegetation coverage changes contributing 39.0%. Among the climate factors, radiation, temperature, and precipitation contributed 15.4%, 6.4%, and 39.2%, respectively. The study highlights the critical role of ecological restoration efforts, particular in regions like the Less Plateau and Inner Mongolian Plateau, in enhancing vegetation productivity. These findings provide valuable insights for addressing desertification and inform strategies for ecological restoration and sustainable development in Northern China. [ABSTRACT FROM AUTHOR]

Despite its significance, net primary productivity is rarely used as an indicator of climate change. The purpose of this study was to assess the spatiotemporal dynamics of vegetation net primary productivity and its response to climate variability in the Welmel watershed, southeastern Ethiopia. The investigation utilized data from the Moderate Resolution Imaging Spectroradiometer, and the Climate Hazards Group Infrared Precipitation with Stations accessed through the Google Earth Engine cloud computing platform. To evaluate the trend and response of net primary productivity to climatic factors, the Mann-Kendall and Theil Sen's tests, the Pearson correlation coefficient, and stability analysis through the coefficient of variation of net primary productivity were utilized. The analysis of the temporal trend of net primary productivity revealed that the entire watershed, forest, wood, and cultivated land areas exhibited decreasing trends with net primary productivity values of -0.681, -4.361, -1.41, and − 3.25 g C m− 2/year, respectively. On the other hand, shrubs and grazing land displayed increasing trends of 1.15 and 2.04 g C m− 2/year, respectively. The wood and shrubland trends were statistically significant (p < 0.05). In the spatial analysis, an area of 64.6% showed a decreasing trend, whereas 33.78% displayed an increasing trend in net primary productivity values. The areas with stable and unstable variations in net primary productivity accounted for 26.47% and 8.02%, respectively. The net primary productivity of the land cover types and entire watershed were positively correlated with rainfall, however, only forests and woodlands were statistically significantly correlated (p < 0.05). The net primary productivity and land surface temperature were negatively correlated, except for forests and cultivated land, which were positively correlated. The results of this study provide essential information for managing ecosystems, supporting agricultural practices, and enhancing community resilience in a changing climate. [ABSTRACT FROM AUTHOR]

This study integrates various remote sensing datasets to analyze environmental changes and their impacts on ecosystems across Pakhtunkhwa Province in Pakistan. Precipitation data from the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) dataset, along with vegetation health assessments using Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data from the Landsat dataset, were used to comprehensively analyze the impact of vegetation dynamics on environmental footprints (i.e., temperature, precipitation, and LST). Land use maps, generated through supervised classification of Landsat images from 1985 to 2023, highlight significant changes in different land use classes, including vegetation and forest cover. Bayesian Network Modelling (BNM) and Dummy Variable Regression (DVR) methods were employed to assess the impact of vegetation (using NDVI time series) on environmental footprint and forest cover in particular. The results suggest that the NDVI generally increase the cooling effect across most of the study area, indicating that higher vegetation density is linked to a decrease in temperatures. This inverse relationship is also apparent in the connection between the NDVI and the LST, depicting a negative trend in surface temperature over most of the pixels/districts. The regression coefficients for the NDVI and the LST vary across different pixels, ranging from −5.3839 °C to 5.2697 °C, with standard deviations from 2.057 °C to 5.138 °C, reflecting a variability in the strength of this cooling effect. Similarly, for the relationship between the NDVI and the LST, coefficients range from −7.1513 °C to 6.6322 °C, with standard deviations between 1.612 °C and 4.155 °C. In contrast, NDVI and precipitation show a positive relationship, with regression coefficients ranging from 4.1686 °C to 44.3932 °C and standard deviations between 2.242 °C and 8.224 °C, suggesting greater variability in precipitation corresponding to vegetation dynamics. Additionally, forest cover generally correlates positively with precipitation in most pixels, but the variability across pixels emphasizes the complex nature of these relationships. The study identified substantial fluctuations in land use categories over the decades, indicating environmental shifts driven by both natural and human factors. BNM demonstrated a positive impact of vegetation dynamics on precipitation and a negative impact on both temperature and LST. On the other hand, the increase in forest cover, particularly due to the Billion Tree Tsunami Project, has a significant impact on the environmental footprint identified through DVR. By combining high-resolution datasets with advanced statistical techniques, this study offers key insights into the dynamic interactions between land cover, vegetation, and climate in the study region, providing valuable information for sustainable environmental management. [ABSTRACT FROM AUTHOR]

In managing ecosystem services (ESs), it is vital to understand and effectively regulate the trade-offs and synergies (ToSs) involved. This study investigates the Tianshan Mountains (TSMs), utilizing the InVEST (Integrated Valuation of ESs and Tradeoffs) model to evaluate ecosystem service changes from 2000 to 2020, while employing univariate linear regression to examine their spatiotemporal dynamics. Pearson correlation analysis was also conducted to assess how climatic variables (temperature and precipitation) and vegetation indicators (NDVI, normalized difference vegetation index) influence the overall ecosystem service benefits. The findings reveal notable spatial heterogeneity and dynamic shifts in ESs across the TSMs, with strong synergies observed between carbon storage (CS) and other services (such as habitat quality, HQ; soil conservation, SC; and water yield, WY), especially in areas experiencing increased vegetation. However, the connection between HQ and WY was comparatively weaker and occasionally exhibited negative correlations during specific periods, highlighting the intricate trade-offs among various services. The correlation analysis further showed that climate and vegetation changes significantly impact ecosystem service benefits, with declining precipitation and rising temperatures reducing these benefits, whereas higher NDVI was associated with improved service functions. Ultimately, this study emphasizes the necessity of recognizing and managing ToSs in ESs to promote sustainable regional ecosystem development. [ABSTRACT FROM AUTHOR]

One of the most noticeable environmental discontinuities in mountains is the transition that exists in vegetation below and above the treeline. In the North Patagonian Andean lakes (between 900 and 1950 m a.s.l.), we analyzed the bacterial community composition of lakes in relation to surrounding vegetation (erected trees, krummholz belt, and bare rocks), dissolved organic carbon (DOC), and total dissolved nutrients (nitrogen, TDN and phosphorus, TDP). We observed a decrease in DOC, TDP, and TDN concentrations with altitude, reflecting shifts in the source inputs entering the lakes by runoff. Cluster analysis based on bacterial community composition showed a segregation of the lakes below treeline, from those located above. This first cluster was characterized by the cyanobacteria Cyanobium PCC-6307, while in the krummholz belt and bare rocks, bacterial communities were dominated by Actinobacteria hgcl-clade and Proteobacteria (Sandarakinorhabdus and Rhodovarius), with the presence of pigments such as actinorhodopsin, carotenoids, and bacteriochlorophyll a. The net relatedness index (NRI), which considers the community phylogenetic dispersion, showed that lakes located on bare rocks were structured by environmental filtering, while communities of lakes below treeline were structured by species interactions such as competition. Beta-diversity was higher among lakes below than among lakes located above the treeline. The contribution of species turnover was more important than nestedness. Our study brings light on how bacterial communities may respond to changes in the surrounding vegetation, highlighting the importance of evaluating different aspects of community structure to understand metacommunity organization. [ABSTRACT FROM AUTHOR]

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[Objective] In order to accurately estimate net primary productivity (NPP) of vegetation under the context of rising atmospheric C*O_{2} concentrations. Methods] A C*O_{2} concentration factor was introduced into he Carnegie-Ames-Stanford Approach (CASA) model to simulate the maximum light use efficiency of different vegetation types. Based on this, the potential of the improved model for NPP estimation was explored. [Results] From 2000 to 2020, the annual average atmospheric C*O_{2} concentration in China's errestrial areas showed a significant increasing trend, with an average increase of approximately 2.14mu*mol / mona). After incorporating the C*O_{2} stress factor, the vegetation types were ranked by maximum light use efficiency in the following order cropland evergreen broadleaf forest mixed forest needleleaf forest> shrub wetland grassland (1.85, 1.69, 1.30, 0.87, 0.52, 0.40, 0.40 g/MJ). The estimation accuracy of NPP by the C*O_{2} model was improved compared to the original CASA model. The R 2 between the simulated and measured NPP values at the vegetation level and the comprehensive level increased by -0.1% to 3.7% and 0.5%, respectively, and RMSE decreased by 0.3% to 9.2% and 0.7%, respectively. The RMSE of annual NPP estimation results decreased by 1.9%. The C*O_{2} model improved the underestimation of overall NPP by the CASA model. reducing it from 10.62% to 9.81%. The C*O_{2} model underestimated the overall NPP for spring, autumn and winter by 11% 72 * 0/0 and 51 * 0/0, respectively, while overestimating it for summer by 0.53% [Conclusion] Considering the impact of C*O_{2} concentration changes on vegetation in the model can improve the accuracy of NPP estimation. [ABSTRACT FROM AUTHOR]

Long-term, reliable datasets of satellite-based vegetation condition are essential for understanding terrestrial ecosystem responses to global environmental change, particularly in Australia, which is characterised by diverse ecosystems and strong interannual climate variability. We comprehensively evaluate several existing global Advanced Very High Resolution Radiometer (AVHRR) normalised-difference vegetation index (NDVI) products for their suitability for long-term vegetation monitoring in Australia. Comparisons with the MODIS NDVI highlight significant deficiencies, particularly over densely vegetated regions. Moreover, all the assessed products failed to adequately reproduce the interannual variability in the pre-MODIS era as indicated by Landsat NDVI anomalies. To address these limitations, we propose a new approach to calibrating and harmonising NOAA's Climate Data Record of AVHRR NDVI to the MODIS MCD43A4 NDVI for Australia using a gradient-boosting decision tree ensemble method. Two versions of the datasets are developed, one incorporating climate data in the predictors ("AusENDVI-clim": Australian Empirical NDVI-climate) and another that is independent of climate data ("AusENDVI-noclim"). These datasets, spanning 1982–2013 at a spatial resolution of 0.05° and with a monthly time step, exhibit strong correlations (r2=0.89 –0.94) and low mean errors compared with MODIS MCD43A4 NDVI (mean absolute error (MAE) = 0.014–0.028, RMSE = 0.021–0.046), accurately reproducing seasonal cycles over densely vegetated regions. Furthermore, they closely replicate the interannual variability in vegetation condition in the pre-MODIS era. A reliable method for gap-filling the AusENDVI record is also developed that leverages climate, atmospheric CO 2 concentration, and woody-cover fraction predictors. The resulting synthetic NDVI dataset shows excellent agreement with the MODIS MCD43A4 NDVI and the recalibrated AVHRR NDVI time series (r2=0.82 –0.95, MAE = 0.016–0.029, RMSE = 0.039–0.041). Finally, we provide a complete 41-year dataset where the gap-filled AusENDVI-clim from January 1982 to February 2000 is joined with the MODIS MCD43A4 NDVI from March 2000 to December 2022. Analysing 40-year per-pixel trends in Australia's annual maximum NDVI revealed increasing values, and shifts in the timing, of the annual peak NDVI across most of the continent, underscoring the dataset's potential to address crucial questions regarding the changing vegetation phenology and its drivers. The AusENDVI dataset can be used for studying Australia's changing vegetation dynamics and downstream impacts on the terrestrial carbon and water cycles, and it provides a reliable foundation for further research into the drivers of vegetation change. AusENDVI is open access and available at 10.5281/zenodo.10802703 (Burton et al., 2024). [ABSTRACT FROM AUTHOR]