Search Results (142)

Under the ongoing trend of climate warming and increasing humidity on the Qinghai–Tibet Plateau, the Three River Source Region (TRSR) has shown strong sensitivity to global climate change. Its vegetation change is particularly worthy of attention and research. The Normalized Difference Vegetation Index (NDVI) is a key indicator for assessing the growth status of vegetation. However, the insufficiency of existing NDVI datasets in terms of spatiotemporal continuity has limited the accuracy of long-term vegetation change studies. This study proposed a machine learning-based downscaling framework that integrates the Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI and the Global Inventory Monitoring and Modeling System (GIMMS) NDVI data to reconstruct a long-term, high-resolution NDVI dataset. Unlike conventional statistical fusion approaches, the proposed framework employs machine learning-based nonlinear relationships to generate long-term, high-resolution NDVI data. Three machine learning algorithms—Random Forest (RF), LightGBM, and CatBoost—were evaluated. Their performance was validated using the MODIS NDVI as reference, with the coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and Pearson’s correlation coefficient (R) as evaluation metrics. Based on model comparison, the CatBoost model was identified as the optimal algorithm for spatiotemporal data fusion (R² = 0.9014, RMSE = 0.0674, MAE = 0.0445), significantly outperforming RF and LightGBM models and demonstrating stronger capability for NDVI spatiotemporal reconstruction. Using this model, a long-term, 1 km monthly GIMMS-MODIS NDVI dataset from 1982 to 2014 was successfully reconstructed. On the basis of this dataset, the spatiotemporal variation characteristics of vegetation in the TRSR from 1982 to 2014 were systematically analyzed. The research results show that: (1) The constructed long-series high-resolution NDVI dataset has a high consistency with MODIS NDVI data; (2) From 1982 to 2014, the NDVI in the TRSR showed an increasing trend, with an average growth rate of 0.0020/10a (p < 0.05). NDVI showed obvious spatial heterogeneity, characterized by a decreasing gradient from southeast to northwest. (3) The Yellow River source exhibited the most evident vegetation recovery, the Yangtze River Source area showed a moderate improvement, whereas the Lancang River Source area displayed little noticeable change. (4) Broad-leaved forests experienced the most significant growth, while cultivated vegetation displayed a marked tendency toward degradation. This study provides both a high-accuracy long-term NDVI product for the TRSR and a methodological foundation for advancing vegetation dynamics research in other high-altitude regions. Full article

Vegetation dynamics in the Hulun Lake Basin (HLB), a vulnerable grassland–wetland–forest transition zone in Northeastern Inner Mongolia, North China, are sensitive to climate change, but traditional greenness metrics like the normalized difference vegetation index (NDVI) lack process-level insights. Using the GIMMS NDVI3g dataset (1982–2015) and meteorological data, this study analyzed the spatiotemporal dynamics of the NDVI and vegetation NDVI change rate (VNDVI)—a metric quantifying greening and browning speeds via NDVI temporal variation—employing linear regression and partial correlation analyses. The NDVI exhibited an overall significant upward trend of +0.0028 yr⁻¹ (p < 0.05) across more than 70% of the basin, indicating a persistent greening tendency. The VNDVI revealed an accelerated spring greening rate of +0.8% yr⁻¹ (p < 0.05) and a slowed autumn browning rate of −0.6% yr⁻¹ (p < 0.05), reflecting an extended growing season. Spatial correlation analysis showed that the temperature dominated spring greening (r = 0.52), precipitation governed summer growth (r = 0.64), and solar radiation modulated autumn senescence (r = 0.38). Compared with the NDVI, the VNDVI was more sensitive to both climatic fluctuations and anthropogenic disturbances, highlighting its utility in capturing process-level vegetation dynamics. These findings provide quantitative insights into the mechanisms of vegetation change in the HLB and offer scientific support for ecological conservation in North China’s grassland–forest ecotone. Full article

The Normalized Difference Vegetation Index (NDVI) is a fundamental metric for monitoring terrestrial ecosystem dynamics and assessing ecological responses to climate change. However, uncertainties persist across NDVI products, and a comprehensive assessment of their consistency is lacking. This study conducts a multi-faceted evaluation of three NDVI products, GIMMS V1.2 NDVI (NDVI_3g+), PKU GIMMS NDVI (NDVI_pku), and MODIS NDVI (NDVI_mod), to elucidate their performance across ecosystem applications. Our analysis encompasses a comparative analysis of NDVI values, trends, sensitivity to root-zone soil moisture (RSM), and performance in tracking photosynthesis benchmarked against solar-induced chlorophyll fluorescence (SIF). Our results reveal that NDVI_3g+ deviates notably from NDVI_pku and NDVI_mod over cold climates and Evergreen Broadleaf Forest (EBF). Additionally, NDVI_3g+ exhibits significant global browning, in contrast to the significant greening observed for NDVI_pku and NDVI_mod. Although their responses to RSM are generally uncertain, consistent positive responses appear in Drylands, with NDVI_mod showing the highest sensitivity. Additionally, the three NDVI products have high seasonality consistency with SIF, except over EBF, and NDVI_pku and NDVI_3g+ achieve the highest and lowest overall anomaly consistency with SIF, respectively. Furthermore, converting NDVI_3g+, NDVI_pku, and NDVI_mod to the corresponding kernel NDVIs improves seasonality consistency with SIF across 85% of the globe. Full article

In the context of increasingly severe climate change, studying the relationship between climate factors and vegetation dynamics is crucial for ecological conservation and sustainable development. This study focuses on the Oujiang River Basin from 1981 to 2022, aiming to quantitatively model the interactions among temperature, precipitation, the NDVI, and the LAI. Addressing the lack of approaches for forecasting high-resolution LAI data and existing LAI data that are usually interpreted from NDVI data, we proposed a two-step inversion framework: first, modeling the response of the NDVI to climate variables; second, predicting the LAI using the NDVI as a mediating variable. By integrating long-term remote sensing datasets (GIMMS and MODIS NDVI) with meteorological data and applying trend analysis, spatial correlation analysis, and clustering techniques (K-Means and Possibilistic C-Means), we identified spatial heterogeneity in vegetation response patterns. The study results showed that (1) climate factors have a distinctly spatially heterogeneous impact on the NDVI and LAI; (2) temperature is identified as the dominant factor in most regions; and (3) the LAI prediction model based on the climate factors NDVI and NDVI–LAI relationships shows good accuracy in the medium-to-high range of the LAI, with an R² value ranging from 0.516 to 0.824. This study provides a scalable approach to improve LAI estimation and monitor vegetation dynamics in complex terrain under changing climate conditions. Full article

Preseason temperature has always been considered the most critical factor influencing vegetation phenology in the northern hemisphere. While numerous studies have examined the impact of daytime and nighttime warming on vegetation phenology in this region, the specific influence of day and night warming on deciduous broad-leaved forests (DBFs) in Northern China, where significant temperature variations occur between day and night, remains unclear. Furthermore, the sensitivity of daytime and nighttime warming during different preseason periods to phenology has not been quantitatively understood. We analyzed GIMMS3g NDVI data from 1985 to 2015 and employed a double logistic regression model to determine the phenological start of the season (SOS) for DBF in Northern China. To control for monthly precipitation effects, we conducted partial correlation analysis between monthly mean maximum daytime temperature (T_{day_max}), monthly mean minimum nighttime temperature (T_{night_min}), diurnal temperature variation (DTR), and SOS. Our findings over the past 31 years indicate that 75.98% of the area exhibited an advanced trend, with an overall advance of 1.7 days per decade. Interestingly, regardless of T_{day_max}, T_{night_min}, or DTR, most areas had a preseason length of 1 month, accounting for 50.26%, 34.45%, and 44.39%, respectively. Furthermore, approximately 50.68% of the area exhibited a significant negative correlation between preseason temperature and SOS for T_{day_max}, 34.02% for T_{night_min}, and 35.80% for DTR. It can be found that the response of the SOS advance to T_{day_max} in DBFs in Northern China is more obvious than that to T_{night_min} and DTR. Our study revealed that the difference in day and night temperature warming on DBFs in Northern China is not pronounced. Specifically, SOS advanced by 1.8 days, 1.98 days, and 1.95 days for every 1 °C increase in T_{day_max}, T_{night_min}, and DTR, respectively. However, it is important to note that the distribution of advanced days resulting from the warming of these three preseason temperature indicators exhibited spatial heterogeneity. Although many studies have already established the influence of various meteorological indicators on spring phenology, determining which meteorological indicators should be employed to quantify their impact on phenology in different regions and vegetation types remains a subject for further exploration and investigation in the future. Full article

Urbanization and industrialization have led to obvious changes in the ecological environment and landscape pattern in the Beijing–Tianjin–Hebei region. Therefore, it is crucial to clarify the spatial–temporal changes in vegetation cover and its landscape pattern and conduct its analysis with the driving factors for ecological preservation in the Beijing–Tianjin–Hebei region. This study combined AVHRR GIMMS NDVI and MODIS NDVI data based on the ESTARFM model to obtain a high spatial–temporal resolution for vegetation cover; it then analyzed the vegetation cover changes at the type and landscape scales using a landscape index and explored the driving factors of the landscape pattern through principal component analysis. The results show that (1) the vegetation is mainly of medium and higher coverage and is distributed in the northeast, the western part of the Taihang Mountains and the central plains in the study area. From 1985 to 2022, there was no statistically significant difference in the overall change in its coverage. (2) From 1985 to 2022, at the landscape level, the vegetation cover landscape exhibited the following characteristics: increased fragmentation, an increase in the complexity of the landscape shape, a decrease in connectivity, a discrete landscape and a decrease in species diversity. At the type level, the medium vegetation demonstrated the most significant degree of fragmentation. The high-vegetation-cover areas exhibited a more concentrated distribution. Additionally, the low, lower and higher vegetation types displayed an increase in complexity, shape, discreteness and heterogeneity within the landscape. (3) Meanwhile, the principal component analysis showed that the changes in the landscape pattern of vegetation cover were mainly the result of the combined effects of climatic and anthropogenic factors in the Beijing–Tianjin–Hebei region. The human factor played the dominant role; this was followed by larger contributions from climatic factors. In addition to offering pertinent scientific insights for the maximization of the ecological environment and the fostering of regional ecological and sustainable development in the Beijing–Tianjin–Hebei region, the aforementioned analysis and research could serve as the foundation for the sustainable management and planning of vegetation cover. Full article

Understanding how plants adapt to shifting water availability is imperative for predicting ecosystem vulnerability to drought. However, the spatial–temporal dynamics of the plant–water relationship remain uncertain. In this study, we employed the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI4g), an updated version succeeding GIMMS NDVI3g spanning from 1982 to 2022. We integrated this dataset with the multiple scale Standardized Precipitation Evapotranspiration Index (SPEI 1 to 24) to investigate the spatial–temporal variability of sensitivity and lag in vegetation growth in response to water variability across China. Our findings indicate that over 83% of China’s vegetation demonstrates positive sensitivity to water availability, with approximately 66% exhibiting a shorter response lag (lag < 1 month). This relationship varies across aridity gradients and diverges among plant functional types. Over 66% of China’s vegetation displays increased sensitivity to water variability and 63% manifests a short response lag to water changes over the past 41 years. These outcomes significantly contribute to understanding vegetation dynamics in response to changing water conditions, implying a heightened susceptibility of vegetation to drought in a future warming world. Full article

In recent decades, vegetation phenology, as one of the most sensitive and easily observed features under climate change, has changed significantly under the influence of the global warming as a result of the green house effect. Vegetation phenological change is not only highly related to temperature change, but also to precipitation, a key factor affecting vegetation phenological change. However, the response of vegetation phenology to climate change is different in different regions, and the current research still does not fully understand the climate drivers that control phenological change. The study focuses on the Loess Plateau, utilizing the GIMMS NDVI3g dataset to extract vegetation phenology parameters from 1982 to 2015 and analyzing their spatial–temporal variations and responses to climate change. Furthermore, by incorporating emission scenarios of RCP4.5 (medium and low emission) and RCP8.5 (high emission), the study predicts and analyzes the changes in vegetation phenology on the Loess Plateau from 2030 to 2100. The long-term dynamic response of vegetation phenology to climate change and extreme climate is explored, so as to provide a scientific basis for the sustainable development of the fragile Loess Plateau. The key findings are as follows: (1) From 1982 to 2015, the start of the growing season (SOS) on the Loess Plateau shows a non-significant delay (0.06 d/year, p > 0.05), while the end of the growing season (EOS) is significantly delayed at a rate of 0.1 d/year (p < 0.05). (2) In the southeastern part of the Loess Plateau, temperature increases led to a significant advancement of SOS. Conversely, in the Maowusu Desert in the northwest, increased autumn precipitation caused a significant delay in EOS. (3) From 2030 to 2100, under the RCP4.5 and RCP8.5 scenarios, temperatures are projected to rise significantly at rates of 0.018 °C/year and 0.06 °C/year, respectively. Meanwhile, precipitation will either decrease insignificantly at −0.009 mm/year under RCP4.5 or increase significantly at 0.799 mm/year under RCP8.5. In this context, SOS is projected to advance by 19 days and 28 days, respectively, under RCP4.5 and RCP8.5, with advancement rates of 0.049 days/year and 0.228 days/year. EOS is projected to be delayed by 14 days and 27 days (p < 0.05), respectively, with delay rates of 0.084 d/year and 0.2 d/year. Full article

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO₂, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions. Full article

This study analyzes the temporal patterns of rainfed cereal phenology extracted from the GIMMS NDVI3g dataset in the main cereal-growing regions under a Mediterranean climate in Spain, Portugal, France and Italy during the period 1982–2022. The series before and after the beginning of the 21st century were analyzed separately. Phenological parameters were extracted using the modified dynamic threshold method, and their trends were analyzed. Correlation analyses were performed to study the relationships among these parameters and to analyze the influence of hydroclimatic variables on the start (SOS) and end (EOS) of the growing season. Results showed a temporal reversal in phenological trends between both study periods, coinciding with the global warming hiatus. In the first period (1982–2002), SOS and EOS advanced (−7.5 and −3.1 days, respectively), and the length of growing season (LOS) increased. However, during the second stage (2003–2022), SOS and EOS were delayed (7.5 and 1.7 days, respectively), and LOS decreased. Similar dynamics were observed for the influence of the hydroclimatic variables on SOS and EOS, stronger in the first period and weaker in the second. This study provides valuable information on the phenological dynamics of rainfed cereals that may be useful for their management and planning in climate change scenarios. Full article

Exploring the evolution of vegetation cover and its drivers in the Ferghana Basin helps to understand the current ecological status of the Ferghana Basin and to analyze the vegetation changes and drivers, with a view to providing a scientific basis for regional ecological and environmental management and planning. Based on GIMMS NDVI3g and meteorological data, the spatial and temporal evolution characteristics of NDVI were analyzed from multiple perspectives with the help of linear trend and Mann–Kendall (MK) test methods using arcgis and the R language spatial analysis module, combined with partial correlation coefficients and residual analysis methods to analyze the impacts of climate change and human activities on the regional vegetation cover of the Ferghana Basin from 1982 to 2015. NDVI driving forces. The results showed the following: (1) The growing season of vegetation NDVI in the Ferghana Basin showed an increasing trend in the 34-year period, with an increase rate of 0.0044/10a, and the spatial distribution was significantly different, which was high in the central part of the country and low in the northern and southern parts of the country. (2) Temperature and precipitation simultaneously co-influenced the vegetation NDVI growth season, with most of the temperature and precipitation contributing in the spring, most of the temperature in the summer being negatively phased and the precipitation positively correlated, and most of the temperature and precipitation in the fall inhibiting vegetation NDVI growth. (3) The combined effect of climate change and human activities was the main reason for the overall rapid increase and great spatial variations in vegetation NDVI in China, and the spatial distribution of drivers, namely human activities and climate change, contributed 44.6% to vegetation NDVI in the growing season. The contribution of climate change and human activities to vegetation NDVI in the Ferghana Basin was 62.32% and 93.29%, respectively. The study suggests that more attention should be paid to the role of human activities and climate change in vegetation restoration to inform ecosystem management and green development. Full article

Combining tree ring data with remote sensing data can help to gain a deeper understanding of the driving factors that influence vegetation change, identify climate events that lead to vegetation change, and improve the parameters of global vegetation index reconstruction models. However, it is currently not well understood how climate change at different elevations in the central Tianshan Mountains affects radial tree growth and the dynamics of forest canopy growth. We selected Schrenk spruce (Picea schrenkiana) tree core samples from different elevations in the central Tianshan Mountains. We analyzed the relationships of various tree-ring parameters, including tree-ring width, maximum latewood density (MXD), and minimum earlywood density (MID) chronologies, with 1982–2012 GIMMS (Global Inventory Modelling and Mapping Studies) NDVI (Normalized Difference Vegetation Index), 2001–2012 MODIS (moderate resolution imaging spectroradiometer) NDVI, and meteorological data. (1) There were strong correlations between tree-ring width chronologies and the lowest temperatures, especially in July. Tree-ring width chronologies at higher altitudes were positively correlated with temperature; the opposite pattern was observed at lower altitudes. MID chronologies were positively correlated with July temperature in high-altitude areas and mean temperature and highest temperature from May to September in low-altitude areas, and negatively correlated with precipitation during this period. MXD chronologies were mainly negatively correlated with precipitation. MXD chronologies were mainly positively correlated with temperature in April and May. (2) The correlations between MXD chronologies at each sampling point and NDVI in each month of the growing season were strong. Both MID and MXD chronologies were negatively correlated with GIMMS NDVI in July. The overall correlations between tree-ring parameters and MODIS NDVI were stronger than the correlations between tree-ring parameters and GIMMS NDVI in high-altitude areas; the opposite pattern was observed in low-altitude areas. Drought stress may be the main factor affecting tree ring parameters and NDVI. In the future, we should combine tree ring parameters with vegetation index to investigate a larger scale of forests. Full article

In this study, a machine learning model was used to investigate the potential consequences of climate change on vegetation growth. The methodology involved analyzing the historical Normalized Difference Vegetation Index (NDVI) data and future climate projections under four Shared Socioeconomic Pathways (SSPs). Data from the Global Inventory Monitoring and Modeling System (GIMMS) dataset for the period 1981–2000 were used to train the machine learning model, while CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate projections from 2021–2100 were employed to predict future NDVI values under different SSPs. The study results revealed that the global mean NDVI is projected to experience a significant increase from the period 1981–2000 to the period 2021–2040. Following this, the mean NDVI slightly increases under SSP126 and SSP245 while decreasing substantially under SSP370 and SSP585. In the near-term span of 2021–2040, the average NDVI value of SSP585 slightly exceeds that of SSP245 and SSP370, suggesting a positive vegetation development in response to a more pronounced temperature increase in the near term. However, if the trajectory of SSP585 persists, the mean NDVI will commence a decline over the subsequent three periods (2041–2060, 2061–2080, and 2080–2100) with a faster speed than that of SSP370. This decline is attributed to the adverse effects of a rapid temperature rise on vegetation. Based on the examination of individual continents, it is projected that the NDVI values in Africa, South America, and Oceania will decline over time, except under the scenario SSP126 during 2081–2100. On the other hand, the NDVI values in North America and Europe are anticipated to increase, with the exception of the scenario SSP585 during 2081–2100. Additionally, Asia is expected to follow an increasing trend, except under the scenario SSP126 during 2081–2100. In the larger scope, our research findings carry substantial implications for biodiversity preservation, greenhouse gas emission reduction, and efficient environmental management. The utilization of machine learning technology holds the potential to accurately predict future changes in vegetation growth and pinpoint areas where intervention is imperative. Full article

Phenology is an important indicator for how plants will respond to environmental changes and is closely related to biomass production. Due to global warming and the emergence of intermittent warming, vegetation in northeast Asia is undergoing drastic changes. Understanding vegetation phenology and its response to climate change is of great significance to understanding the changes in the sustainable development of ecosystems. Based on Global Inventory Modelling and Mapping Studies (GIMMS), normalized difference vegetation index (NDVI)3g data, and the mean value of phenological results extracted by five methods, combined with climatic data, this study analyzed the temporal changes in phenology and the responses to climatic factors of five vegetation types of broad-leaved, needle-leaf, mixed forests, grassland, and cultivated land in northeast Asia over 33 years (1982–2014). The results showed that, during the intermittent warming period (1999–2014), the start of the growing season (SOS) advancement (Julian days) trend of all vegetation types decreased. During 1982–2014, the average temperature sensitivity of the SOS was 1.5 d/°C. The correlation between the SOS and the pre-season temperature is significant in northeast Asia, while the correlation between the EOS and the pre-season precipitation is greater than that between temperature and radiation. The impact of radiation changes on the SOS is relatively small. Full article

Recent research has revealed that the dynamics of autumn phenology play a decisive role in the inter-annual changes in the carbon cycle. However, to date, the shifts in autumn phenology (EGS) and the elements that govern it have not garnered unanimous acknowledgment. This paper focuses on the Yellow River Basin (YRB) ecosystem and systematically analyzes the dynamic characteristics of EGS and its multiple controls across the entire region and biomes from 1982 to 2015 based on the long-term GIMMS NDVI3g dataset. The results demonstrated that a trend toward a significant delay in EGS (p < 0.05) was detected and this delay was consistently observed across all biomes. By using the geographical detector model, the association between EGS and several main driving factors was quantified. The spring phenology (SGS) had the largest explanatory power among the interannual variations of EGS across the YRB, followed by preseason temperature. For different vegetation types, SGS and preseason precipitation were the dominant driving factors for the EGS in woody plants and grasslands, respectively, whereas the explanatory power for each driving factor on cultivated land was very weak. Furthermore, the EGS was controlled by drought at different timescales and the dominant timescales were concentrated in 1–3 accumulated months. Grasslands were more significantly influenced by drought than woody plants at the biome level. These findings validate the significance of SGS on the EGS in the YRB as well as highlight that both drought and SGS should be considered in autumn fall phenology models for improving the prediction accuracy under future climate change scenarios. Full article