Search Results (87)

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

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

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

The advent of a range of high-precision NPP products, including MODIS NPP, MOD17 NPP, and GIMMS NPP, has sparked growing interest in the study of Earth’s ecosystems. In order to enhance comprehension of ecosystem health, in order to facilitate the development of rational resource management and environmental conservation policies, this investigation employs the MOD17A3 dataset to analyze historical variations in Net Primary Productivity (NPP) within Shanxi Province from 2001 to 2020, while also exploring future trends. The Theil–Sen median trend analysis and Mann–Kendall test are commonly used methods for analyzing time series data, employed to study the spatiotemporal trends and variations in NPP. The Grey Wolf Optimization–Support Vector Machine (GWO–SVM) model combines optimization algorithms and machine learning methods, enhancing the predictive capacity of the model for future NPP time series changes. Conversely, the Hurst exponent utilizes historical NPP trends to assess the persistence characteristics of NPP and predict future spatial variations in NPP. This study additionally investigates the natural driving factors of NPP using the Geographic Detector approach. The key findings of this study are as follows. (1) Overall, NPP in Shanxi Province exhibits a fluctuating upward trend from 2001 to 2020, with an average value of 206.278 gCm⁻²a⁻¹. Spatially, NPP exhibits a northwest–low and southeast–high pattern, with significant spatial heterogeneity and considerable variability. (2) The average Hurst exponent is 0.86, indicating a characteristic of strong persistence in growth in future NPP. Regions with strong or higher persistent growth account for 95.54% of the total area. (3) According to the CMIP6 climate scenarios, NPP is projected to gradually increase from 2025 to 2030. (4) The interactive effects between natural factors contribute more to NPP variations than individual factors, with the rainfall–elevation interaction having the highest contribution percentage. Full article

Studying vegetation change and its interaction with climate change is essential for regional ecological protection. Previous studies have demonstrated the impact of climate change on regional vegetation in South America; however, studies addressing the fragile ecological environment in Argentina are limited. Therefore, we assessed the vegetation dynamics and their climatic feedback in five administrative regions of Argentina, using correlation analysis and multiple regression analysis methods. The Normalized Difference Vegetation Index 3rd generation (NDVI3g) from Global Inventory Monitoring and Modeling Studies (GIMMS) and climatic data from the Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) were processed. The NDVI of the 1982–2015 period in Argentina showed a downward trend, varying from −1.75 to 0.69/decade. The NDVI in Northeast Argentina (NEA), Northwest Argentina (NWA), Pampas, and Patagonia significantly decreased. Precipitation was negatively correlated with the NDVI in western Patagonia, whereas temperature and solar radiation were positively correlated with the NDVI. Extreme precipitation and drought were essential causes of vegetation loss in Patagonia. The temperature (73.09%), precipitation (64.02%), and solar radiation (73.27%) in Pampas, Cuyo, NEA, and NWA were positively correlated with the NDVI. However, deforestation and farming and pastoral activities have caused vegetation destruction in Pampas, NEA, and NWA. Environmental protection policies and deforestation regulations should be introduced to protect the ecological environment. The results of this study clarify the reasons for the vegetation change in Argentina and provide a theoretical reference for dealing with climate change. Full article

Assessing vegetation phenology is very important for better understanding the impact of climate change on the ecosystem, and many vegetation index datasets from different remote sensors have been used to quantify vegetation phenology from a regional to global perspective. This study mainly analyzes the similarities and differences in phenology derived from GIMMS NDVI3g and MODIS NDVI datasets across different biomes throughout temperate China. We applied three commonly used methods to extract the start and end of the growing season (SOS and EOS) from two datasets between 2000 and 2015, and analyzed the spatio-temporal characteristics and trends of key phenological parameters between these two datasets in temperate China. Results showed that the multi-year mean GIMMS NDVI was higher than MODIS NDVI throughout most of temperate China, and the consistencies between GIMMS NDVI and MODIS NDVI for all biomes in the senescence phase were better than those in the green-up phase. NDVI differences between GIMMS and MODIS resulted in some distinctions between phenology derived from the two datasets. The results of SOS and EOS for three methods also showed wide discrepancies in spatial patterns, especially in SOS. For different biomes, differences of SOS in forests were obviously less than that in shrublands, grasslands-IM, grasslands-QT and meadows, whereas the differences of EOS in forests were relatively greater than that in SOS. Moreover, large differences of phenological trends were found between GIMMS and MODIS datasets from 2000 to 2015 in entire region and different biomes, and it is particularly noteworthy that both SOS and EOS showed a low proportion of the identical significant trends. The results suggested NDVI datasets obtained from GIMMS and MODIS sensors could induce the differences of the inversion of vegetation phenology in some degree due to the differences of instrumental characteristics between these two sensors. These findings highlighted that inter-calibrate datasets derived from different satellite sensors for some biomes (e.g., grasslands) should be needed when analyzing land surface phenology and their trends, and also provided baseline information for choosing different NDVI datasets in subsequent studies on vegetation patterns and dynamics. Full article

Under the background of global warming, understanding the dynamic of vegetation plays a key role in revealing the structure and function of an ecosystem. Assessing the impact of climate change and human activities on vegetation dynamics is crucial for policy formulation and ecological protection. Based on the Global Inventory Monitoring and Modeling System (GIMMS) third generation of Normalized Difference Vegetation Index (NDVI3g), meteorological data and land cover data, this study analyzed the linear and nonlinear trends of vegetation in northern China from 1982 to 2015, and quantified the relative impact of climate change and human activities on vegetation change. The results showed that more than 53% of the vegetation had changed significantly, and 36.64% of the vegetation had a reverse trend. There were potential risks of vegetation degradation in the southwestern, northwestern and northeastern parts of the study’s area. The linear analysis method cannot disclose the reversal of the vegetation growth trend, which will underestimate or overestimate the risk of vegetation degradation or restoration. Climate change and human activities promoted 76.54% of the vegetation growth in the study area, with an average contribution rate of 51.22% and 48.78%, respectively, while the average contribution rate to the vegetation degradation area was 47.43% and 52.57%, respectively. Vegetation restoration of grassland and woodland was mainly affected by climate change, and human activities dominated their degradation, while cropland vegetation was opposite. The contribution rate of human activities to vegetation change in the southeastern and eastern parts of the study area was generally higher than that of climate change, but it was the opposite in the high altitude area, with obvious spatial heterogeneity. These results are helpful to understand the dynamic mechanism of vegetation in northern China, and provide a scientific basis for vegetation restoration and protection of regional ecosystems. Full article

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

The Loess Plateau (LP) of China, which is the pilot region of the “Grain to Green Project” (GGP), has received worldwide attention due to its significant changes in the natural and social environment. Investigation of vegetation variations in response to climate change and human activities is vital for providing support for further ecological restoration planning. This paper aimed to monitor vegetation dynamics of the LP with trend comparisons of various vegetation types, disentangle the effects of climate variations and ecological programs on vegetation variations, and detect the consistency of vegetation variations. More specifically, vegetation dynamics during 1982–2015 were analyzed using the Global Inventory Modelling and Mapping System third-generation Normalized Difference Vegetation Index (GIMMS NDVI3g) data with the application of Breaks for Additive Season and Trend (BFAST) and Hurst Exponent. The results showed that: (1) Vegetation manifested a significant greening trend (0.013 decade⁻¹p < 0.01) in the LP during 1982–2015, and a breakpoint (BP) was detected in 1999, which was the beginning of the GGP. Interannual NDVI after the BP (ABP) showed more than 3.5 times greening rates compared to the NDVI before the BP (BBP). (2) Human activities dominated the vegetation variation (accounted for 59.46% of vegetation variation), among which reforestation and land-use change with steep slopes (i.e., ≥15°) lead to the greening after the GGP implementation. (3) Future trends should be noticed in the Forest Zone and Forest-Grass Zone, where the greening trends tend to slow down or even reverse in the southern LP. The long-term GIMMS NDVI3g time series and multiple geospatial analyses of this study might facilitate a better understanding of the mechanisms of vegetation variations for the assessment of the large restoration programs in fragile ecosystems. Full article

The Pan-Third Pole (PTP) region, one of the areas with the most intense global warming, has seen substantial changes in vegetation cover. Based on the GIMMS NDVI3g and meteorological dataset from 1982 to 2015, this study evaluated the spatio-temporal variation in fractional vegetation coverage (FVC) by using linear regression analysis, standard deviation, correlation coefficient, and multiple linear regression residuals to explore its response mechanism to climate change and human activities. The findings showed that: (1) the FVC was progressively improved, with a linear trend of 0.003•10a⁻¹. (2) The largest proportion of the contribution to FVC change was found in the unchanged area (39.29%), followed by the obvious improvement (23.83%) and the mild improvement area (13.53%). (3) The impact of both climate change and human activities is dual in FVC changes, and human activities are increasing. (4) The FVC was positively correlated with temperature and precipitation, with a stronger correlation with temperature, and the climate trend was warm and humid. The findings of the study serve to understand the impacts of climate change and human activities on the dynamic changes in the FVC and provide a scientific foundation for ecological conservation and sustainable economic development in the PTP region. Full article

Drought events cause ecological problems, including reduced water resources and degraded vegetation. Quantifying vegetation responses to drought is essential for ecological management. However, in existing research, the response relationships (correlations and lags) were typically determined based on Pearson correlation coefficient and the resultant lag times were constrained by the spatial and temporal resolutions of the analyzed data. Inner Mongolia is an important ecological barrier in northern China. Ecological security is one of the most concerned issues of the region’s sustainable development. Herein, we combined Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index (NDVI3g) with Systeme Probatoire d’Observation de la Terra-vegetation (SPOT-VGT) NDVI data through spatial downscaling. The obtained 1 km-resolution NDVI dataset spanning Inner Mongolia from 1982 to 2019 was used to represent the refined vegetation distribution. The standardized precipitation evapotranspiration index (SPEI) derived from gridded meteorological data was used to measure drought over the same period. We investigated the spatiotemporal characteristics of vegetation and drought in the region in the past 38 years. We then discussed changes in different vegetation responses to drought across eastern Inner Mongolia using cross wavelet transform (XWT) and wavelet coherence (WTC). The results reveal that in 82.4% of the study area, NDVI exhibited rising trends, and the SPEI values exhibited declining trends in 78.5% of the area. In eastern Inner Mongolia, the grassland NDVI was positively correlated with SPEI and significantly affected by drought events, while NDVI in forestlands, including shrubs, broad-leaved forests, and coniferous forests, was negatively correlated with SPEI in the short term and weakly affected by drought. The NDVI lag times behind SPEI in grasslands, coniferous forests, and broad-leaved forests were 1–1.5, 4.5, and 7–7.5 months, respectively. These findings provide a scientific foundation for environmental preservation in the region. Full article

As one of the most sensitive areas to global environmental change, especially global climate change, the Qinghai–Tibet Plateau is an ideal area for studying global climate change and ecosystems. There are few studies on the analysis of the vegetation’s driving factors on the Qinghai–Tibet Plateau based on large-scale and high-resolution data due to the incompetence of satellite sensors. In order to study the long-term vegetation spatiotemporal pattern and its driving factors, this study used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) to improve the spatial resolution of the GIMMS NDVI3g (8 km) data of the Qinghai–Tibet Plateau in 1990 and 1995 based on the MODIS NDVI (500 m) data. The research on the spatiotemporal pattern and driving factors of vegetation on the Qinghai–Tibet Plateau from 1990 to 2015 was carried out afterward, with combined data including topographic factors, annual average temperature, and annual precipitation. The results showed that there was a strong correlation between the actual MODIS NDVI image and the fused GIMMS NDVI3g image, which means that the accuracy of the fused GIMMS NDVI3g image is reliable and can provide basic data for the accurate evaluation of the spatial and temporal patterns of vegetation on the Qinghai–Tibet Plateau. From 1990 to 2015, the overall vegetation coverage of the Qinghai–Tibet Plateau showed a degrading trend at a rate of −0.41%, and the degradation trend of vegetation coverage was the weakest when the slope was ≥25°. Due to the influence of the policy of returning farmland to forests, the overall degradation trend has gradually weakened. The significant changes in vegetation in 2010 can be attributed to the difference in the spatial distribution of climatic factors such as temperature and precipitation. The area with reduced vegetation in the west was larger than the area with increased vegetation in the east. The effects of temperature and precipitation on the distribution, direction, and degradation level of vegetation coverage were varied by the areal differentiation in different zones. Full article