Grassland cover dynamics and their relationship with climatic factors in China from 1982 to 2021

Exploring the spatio-temporal distribution characteristics and the impacts of climate change and human activities on global grassland based on kNDVI

Grasslands provide essential resources and maintain ecological balance, yet about 40 % of the world's grasslands have degraded due to climate change and human activities. To investigate the impact of these factors on global grassland coverage from 2001 to 2023, the Kernel Normalized Difference Vegetation Index (kNDVI) was calculated using the Google Earth Engine (GEE) platform. Spatio-temporal variations in global grassland kNDVI were analyzed with the Mann-Kendall mutation (M-K-M) test, Theil-Sen slope analysis, and the Mann-Kendall test. Partial correlation and residual analysis identified the factors influencing kNDVI changes. Results showed significant spatial heterogeneity in global grassland kNDVI, with higher values in the Southern Hemisphere and lower values in the Northern Hemisphere. Over time, global grassland kNDVI increased at a rate of 0.00043/a, with no significant mutation spots identified. However, significant mutations were detected in most Köppen climate zones except Am. Spatially, 34.57 % of regions showed kNDVI degradation, while 65.43 % improved. Driving factor analysis indicated that kNDVI was negatively correlated with mean annual temperature but positively correlated with total annual precipitation. Human activities positively impacted kNDVI in 73.25 % of cases. The smallest area of degradation (5.80 %) was due to human activity, while the least improvement (6.17 %) resulted from climate change. In summary, we concluded that there has been a rising trend in the global grassland kNDVI. Changes in the vegetation coverage of grasslands around the world were caused by human activities and the effects of climate change. This study offered useful theoretical frameworks and data references for managing grasslands and restoring degraded areas.

Quantifying the relative importance of natural and human factors on vegetation dynamics in China's western frontiers during 2010-2021

Vegetation is a core component of terrestrial ecosystems, thus understanding the drivers of its dynamics is crucial for ecological conservation and management, especially in regions rich in natural resources but ecologically fragile. Here, we examined vegetation cover changes in China's western frontiers and quantified the relative importance of key drivers of vegetation dynamics. First, we employed the Dimidiate Pixel Model (DPM) via Google Earth Engine (GEE) to estimate fractional vegetation cover (FVC), followed by trend analysis using the Theil-Sen median and Mann-Kendall methods to examine FVC dynamics from 2010 to 2021. Next, we applied the optimal parameter-based geographic detector (OPGD) to further assess the impact of natural (ie., temperature, precipitation, and elevation) and human (ie., land use and population density) factors on FVC. Our results revealed that approximately 61.26% of the vegetation-covered regions in China's western frontiers have shown improvement. In Xinjiang, population density, land use, and precipitation were the primary drivers of FVC changes, with Q values exceeding 0.20. In Xizang, precipitation, elevation, temperature, and land use changes were the dominant drivers, with Q values greater than 0.30. Furthermore, interactions between natural and human factors significantly influenced FVC variation. Our findings have the potential to provide references for promoting sustainable vegetation management in western China.

Climate change enhances greening while human activities accelerate degradation in northern China's grasslands

Northern China's grasslands play a pivotal role in livestock production, energy utilization, and ecosystem balance, both domestically and globally. However, they exhibit pronounced temporal variability and marked spatial heterogeneity. Since most existing studies rely on single vegetation indices and regional-scale analyses, they may introduce biases in interpreting grassland dynamics and their underlying drivers. To address this gap, we integrated both functional and structural indices - Gross Primary Productivity (GPP), solar-Induced chlorophyll fluorescence (SIF), Normalized Difference Vegetation Index (NDVI), and Leaf Area Index (LAI) - to systematically investigate spatiotemporal trends across various grassland types in northern China. Using partial derivative analysis, we quantified the relative contributions of climate change and human activities to these observed vegetation trends. Results indicated that over 70 % of grassland areas, especially temperate grasslands, showed an overall increase in vegetation indices, while a decline was observed in the southwestern alpine grasslands. Climate change was the primary driver of grassland greening (56.55 %-63.83 %), primarily through increased precipitation in temperate grasslands and rising temperatures in alpine grasslands. Human activities contributed substantially to greening (36.17 %-43.45 %), especially in desertified temperate grasslands (e.g., Mu Us Sandy Land, Gansu, Ningxia, Xinjiang) and Qinghai alpine meadows, mainly through farmland restoration and desertification control. Conversely, human activities also served as the primary driver of grassland degradation (51.70 %-69.64 %) in certain alpine regions, where overgrazing and population growth - compounded by rising temperatures and declining soil moisture - led to significant vegetation losses. Moreover, 72.66 % of temperate grasslands demonstrated strong coupling between vegetation structure and function, whereas 57.59 % of alpine grasslands exhibited increasing GPP alongside declines in both LAI and SIF. Overall, these findings underscore the spatial heterogeneity of grassland responses to climatic and anthropogenic drivers, highlighting the necessity of employing multiple vegetation indices to guide targeted and effective grassland management strategies.

Desertification dynamics and future projections in Qaidam Basin, China

The desertification in the Qaidam Basin has significantly impacted the ecological environment and human livelihood. Amidst the backdrop of anomalous climate warming, predicting the dynamic changes and future trends of desertification within the basin is imperative. In this study, we employ a variety of spatio-temporal statistical analyses to examine the evolutionary trend and driving forces of desertification from 2000 to 2021, integrating vegetation coverage (FVC) indices with climatic factors. Furthermore, a predictive model for desertification was developed, utilizing 6th international coupled model comparison programme (CMIP6) model data coupled with a multivariate pixel-based regression approach. The results indicate a 13% reduction, equivalent to 35,766 km2, in the area of severe desertification in the Qaidam Basin from 2000 to 2021. Both non-desertification and mild desertification increased by 7%, indicating a notable reduction in the severity of desertification processes. However, compared to the period from 2000 to 2010, the pace of desertification reversal slowed down between 2011 and 2021, corresponding to the waning upward trend in temperature and precipitation in the upper basin. The desertification prediction model revealed that under the SSP1-26, SSP3-70, SSP2-45, and SSP5-85 scenarios, the vegetation coverage is projected to decline at rates of 0.004/10a, 0.003/10a, 0.002/10a, and 0.002/10a, respectively, from 2015 to 2100. This suggests that desertification in the basin is likely to worsen over time, with greater radiative forcing leading to more pronounced desertification effects. Future FVC projections suggest that desertification mitigation in the Qaidam Basin will plateau around 2040 and then worsen, particularly in the northeast Qilian Mountains. This trend may be due to glacier melting from ongoing climate warming, leading to reduced regional water resources.

Assessment of vegetation restoration potential in central Asia

Vegetation restoration potential (VRP) assessment is an important aspect and foundation of ecological restoration projects. Neglecting the carrying capacity of the natural environment in the formulation and implementation of ecological restoration projects often leads to diminished effectiveness or even environmental damage. Existing models for VRP either overly rely on empirical knowledge, resulting in low efficiency and reproducibility, or fail to consider the nonlinear relationship between the natural environment and vegetation cover, leading to low accuracy in assessment results. Building upon existing models, this study proposes a new Vegetation Restoration Potential Mapping (VRPM) model based on a dual-variable discretization method for habitat similarity division and machine learning. Focused on Central Asia as the research area, the study evaluates the vegetation restoration potential of the region and validates the model. The results demonstrate that this model efficiently produces high-resolution and high-precision vegetation restoration potential maps. The average VRP in Central Asia is relatively low, around 36%, with most areas already having vegetation cover close to or reaching their restoration potential The regions with a higher degree of unrealized vegetation restoration potential (VRPU) are mainly distributed near human settlements, while VRPU is negative in some areas around the desert-oasis boundaries and artificial structures in the desert. The findings of this research demonstrate that the model can provide a basis for planning and implementing ecological restoration projects, thereby aiding in the health and sustainable development of ecosystems in arid regions.

Exploring the impacts of land titling on eco-environment: A case of livestock farming system in Inner Mongolia, China

Livestock farming system (LFS) is an important pillar of the agri-food system. With global environmental problems severer, exploring how to improve the eco-environment of LFS through institutional change is receiving increasingly concern. Based on macro and micro data from Inner Mongolia, this study examines the impacts of land titling on the eco-environment of LFS, explores the potential mechanisms, and analyzes the heterogeneity in terms of farm size by applying GIS spatial analysis and time-varying difference-in-difference (TV-DID) model. Main results showed that overall land titling significantly improved the eco-environment represented by GVC. (1) The GIS spatial analysis indicated that land titling considerably increased GVC. The TV-DID model results further verified that land titling increased the GVC by 0.306 grade, and increased vegetation height by 1.27 cm on average; (2) The effects varied across farm sizes, with the GVC increased by 0.745 grade on farms larger than 373 ha and 0.230 grade otherwise; (3) The effects were mainly induced by the enhancement of farmers' attitudes, management capacities and land use behaviors after land titling. These three effects indicate the net effect, scale effect and security effect of land titling on the eco-environment of LFS, respectively. The study highlights that appropriate institutions are conducive to the sustainable management of livestock farming system, and beneficial for the natural resource-based poor worldwide.

Simulation of Pseudostellaria heterophylla distribution in China: assessing habitat suitability and bioactive component abundance under future climate change scenariosplant components

Background

Pseudostellaria heterophylla is used in traditional Chinese medicine, so ensuring an adequate supply of plant material with high levels of bioactive components is important.

Methods

Using an optimized maximum entropy niche model and assays of bioactive components from cultivation samples, this study started from the plant's natural distribution area and estimated correlations of ecological factors with not only abundance of the plant but also abundance of polysaccharides and heterophyllin B. These correlations were combined with the spatial analysis function in ArcGIS to generate maps of the suitability of different habitats in China for cultivating P. heterophylla under current climate conditions and different models of climate change.

Results

The following ecological factors emerged as particularly important for habitat suitability: precipitation of driest month and driest quarter, annual precipitation, annual mean temperature, temperature seasonality, and mean temperature of coldest quarter, contributing to a cumulative total of 87%. Under current climate conditions, optimum habitats of P. heterophylla were mainly distributed in the southwestern region (Guizhou) and eastern regions (Anhui, Zhejiang, Fujian, Jiangsu) of China, and only 0.197×106 km2 of these areas were optimum habitat. In future climate change scenarios, the optimal habitat area of P. heterophylla exhibited an increase across different time periods under the SSP5-8.5 climate scenario. By the 2090s, distribution area of high heterophyllin B content under SSP5-8.5 climate scenarios will increase significantly, distribution area of high polysaccharide content had little change under all three climate scenarios (SSP 1-2.6, 2-4.5, 5-8.5). The center of mass of suitable habitat migrates southwestward under scenario SSP 1-2.6 and SSP 2-4.5, while it migrates northward under scenario SSP 5-8.5. Under the three climate scenarios, the center of mass of suitable habitat migrated consistently with that of high polysaccharide content but differed from that of high heterophyllin B content.

Conclusion

These findings provide a crucial foundation for cultivating P. heterophylla with superior medicinal properties, developing adaptive management strategies to enhance conservation efforts, and ensuring sustainable utilization in the face of global climate change.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

The impact of compound drought and heatwave events from 1982 to 2022 on the phenology of Central Asian grasslands

In the context of global warming, the occurrence and severity of extreme events like atmospheric drought (AD) and warm spell duration index (WSDI) have increased, causing significant impacts on terrestrial ecosystems in Central Asia's arid regions. Previous research has focused on single extreme events such as AD and WSDI, but the effect of compound hot and dry events (CHWE) on grassland phenology in the arid regions of Central Asia remains unclear. This study utilized structural equation modeling (SEM) and the Pettitt breakpoint test to quantify the direct and indirect responses of grassland phenology (start of season - SOS, length of season - LOS, and end of season - EOS) to AD, WSDI, and CHWE. Furthermore, this research investigated the threshold of grassland phenology response to compound hot and dry events. The research findings indicate a significant increasing trend in AD, WSDI, and CHWE in the arid regions of Central Asia from 1982 to 2022 (0.51 day/year, P < 0.01; 0.25 day/year, P < 0.01; 0.26 day/year, P < 0.01). SOS in the arid regions of Central Asia showed a significant advancement trend, while EOS exhibited a significant advance. LOS demonstrated an increasing trend (-0.23 day/year, P < 0.01; -0.12 day/year, P < 0.01; 0.56 day/year). The temperature primarily governs the variation in SOS. While higher temperatures promote an earlier SOS, they also offset the delaying effect of CHWE on SOS. AD, temperature, and CHWE have negative impacts on EOS, whereas WSDI has a positive effect on EOS. AD exhibits the strongest negative effect on EOS, with an increase in AD leading to an earlier EOS. Temperature and WSDI are positively correlated with LOS, indicating that higher temperatures and increased WSDI contribute to a longer LOS. The threshold values for the response of SOS, EOS, and LOS to CHWE are 16.14, 18.49, and 16.61 days, respectively. When CHWE exceeds these critical thresholds, there are significant changes in the response of SOS, EOS, and LOS to CHWE. These findings deepen our understanding of the mechanisms by which extreme climate events influence grassland phenology dynamics in Central Asia. They can contribute to better protection and management of grassland ecosystems and help in addressing the impacts of global warming and climate change in practice.

Spatiotemporal response of the optical characteristics of dissolved organic matter to seasonality and land use in tropical island rivers

Dissolved organic matter (DOM), a pivotal component in the global carbon cycle, plays a crucial role in maintaining the productivity and functionality of aquatic ecosystems. However, the driving factors of variations in the properties of riverine DOM in tropical islands still remain unclear. In this study, the spatiotemporal response of the optical characteristics of riverine DOM to seasonality and land use on Hainan Island in southern China was investigated. Our results revealed that DOM in the rivers of Hainan Island exhibited a relatively high proportion of fulvic acid and demonstrated strong terrestrial sources. The optical properties of DOM exhibited significant variations both seasonally and spatially. Land use exerted a dominant influence on riverine DOM. Specifically, during the wet season, riverine DOM exhibited larger molecular weight, increased chromophoric DOM (CDOM) abundance, and higher Fmax compared to the dry season. Furthermore, riverine DOM influenced by grassland and farmland showed higher CDOM abundance, Fmax, and humification degree in contrast to those impacted by forest and urban. Random forest and correlation analysis results indicated that grassland and farmland enhanced the Fmax of DOM by increasing levels of TP, NO3--N, Chl a, and NH4+-N in the dry season. However, during the wet season, the increased Fmax of DOM induced by grassland and farmland relied on the increments of Chl a and TP concentrations. This study improves our understanding of the spatiotemporal fluctuations of DOM in the rivers of Hainan Island, highlighting the effects of season and land use on DOM. It offers valuable support for improving water quality and contributes to enhancing human comprehension of the global carbon cycle.

Study on the change of global ecological distribution of Nicotiana tabacum L. based on MaxEnt model

Nicotiana tabacum L. (tobacco) has extremely high economic value, medicinal value, scientific research value and some other uses. Though it has been widely cultivated throughout the world, classification and change of its suitable habitats is not that clear, especially in the context of global warming. In order to achieve rational cultivation and sustainable development of tobacco, current (average from 1970-2000) and future (2070, average from 2061-2080) potential suitable habitats of Nicotiana tabacum L. were forecasted with MaxEnt model and ArcGIS platform based on 854 occurrence data and 22 environmental factors in this study. The results revealed that mean temperature of warmest quarter (bio10), annual precipitation (bio12), solar radiation in September (Srad9), and clay content (CLAY) were the four decisive environment variables for the distribution of Nicotiana tabacum L. Under current climate conditions, suitable habitats of Nicotiana tabacum L. were mainly distributed in south-central Europe, south-central North America, most parts of South America, central Africa, south and southeast Asia, and southeast coast of Australia, and only 13.7% of these areas were highly suitable. By the year 2070, suitable habitats under SSP1-2.6, SSP3-7.0, and SSP5-8.5 climate scenarios would all increase with the largest increase found under SSP3-7.0 scenario, while suitable habitats would reduce under SSP2-4.5 climate scenario. Globally, the center of mass of suitable habitats would migrate to southeast to varying degrees within Libya under four different climate scenarios. The emergence of new habitats and the disappearance of old habitats would all occur simultaneously under each climate scenario, and the specific changes in each area, combined with the prediction results under current climate conditions, will provide an important reference for the adjustment of agronomic practices and rational cultivation of Nicotiana tabacum L. both currently and in the future.

Evaluation of vegetation restoration effectiveness along the Yangtze River shoreline and its response to land use changes

Assessing the effectiveness of vegetation restoration along the Yangtze River shoreline and exploring its relationship with land use changes are imperative for providing recommendations for sustainable management and environmental protection. However, the impact of vegetation restoration post-implementation of the Yangtze River Conservation Project remains uncertain. In this study, utilizing Sentinel-2 satellite imagery and Dynamic World land use data from pre- (2016) and post- (2022) Yangtze River Conservation Project periods, pixel-based binary models, transition matrices, and geographically weighted regression models were employed to analyze the status and evolution of vegetation coverage along the Yangtze River shoreline. The results indicated that there had been an increase in the area covered by high and high-medium vegetation levels. The proportion of vegetation cover shifting to better was 4201.87 km2 (35.68%). Hotspots of vegetation coverage improvement were predominantly located along the Yangtze River. Moreover, areas witnessing enhanced vegetation coverage experienced notable land use changes, notably the conversion of water to crops (126.93 km2, 22.79%), trees to crops (59.93 km2, 10.76%), and crops to built area (59.93 km2, 10.76%). Notably, the conversion between crops and built area emerged as a significant factor influencing vegetation coverage improvement, with average regression coefficients of 0.68 and 0.50, respectively. These outcomes underscore the significance of this study in guiding ecological environmental protection and sustainable management along the Yangtze River shoreline.