Total Grassland Research Articles

Analysis of Spatiotemporal Variation Characteristics and Influencing Factors of Grassland Vegetation Coverage in the Qinghai–Tibet Plateau from 2000 to 2023 Based on MODIS Data

Changes in grassland fractional vegetation coverage (FVC) are important indicators of global climate change. Due to the unique characteristics of the Tibetan Plateau ecosystem, variations in grassland coverage are crucial to its ecological stability. This study utilizes the Google Earth Engine (GEE) platform to retrieve long-term MODIS data and analyzes the spatiotemporal distribution of grassland FVC across the Qinghai–Tibet Plateau (QTP) over 24 years (2000–2023). The grassland growth index (GI) is used to evaluate the annual grassland growth at the pixel level. GI is an important indicator for measuring grassland growth status, which can effectively measure the changes in grassland growth in each year relative to the base year. FVC trends are monitored using Sen-Mann-Kendall slope estimation, the coefficient of variation, and the Hurst exponent. Geographic detectors and partial correlation analysis are then applied to explore the contribution rates of key driving factors to FVC. The results show: (1) From 2000 to 2023, FVC exhibited an overall upward trend, with an annual growth rate of 0.0881%. The distribution of FVC on the QTP follows a pattern of higher values in the east and lower values in the west; (2) Over the past 24 years, 54.05% of the total grassland area has shown a significant increase, 23.88% has remained stable, and only a small portion has shown a significant decrease. The overall trend is expected to continue with minimal variability, covering 82.36% of the total grassland area. The overall grassland GI suggests a balanced state of growth; (3) precipitation (Pre) and soil moisture (SM) are the main single factors affecting FVC changes in grasslands on the Tibetan Plateau (q = 0.59 and 0.46). In the interaction detection, in addition to the highest interaction between Pre and other factors, the interaction between SM and other factors also showed a significant impact on the changes in FVC of the QTP grassland; partial correlation analysis of hydrothermal factors and FVC of the QTP grassland. It shows that precipitation has a stronger correlation with QTP grassland FVC changes than temperature. This study has enhanced our understanding of grassland vegetation change and its driving factors on the QTP and quantitatively described the relationship between vegetation change and driving factors, which is of great significance for maintaining the sustainable development of grassland ecosystems.

Ecological effects of land use and land cover change in the typical ecological functional zones of Egypt

Evaluation the value of ecosystem services can provide crucial reference for formulating ecological protection plans and making informed management decision. This study utilized Landsat remote sensing images as the data source and employed land-use transfer chains and land use transfer matrix methods to compare the ecological effects of land use and land cover change(LUCC) in the two typical ecological functional zones of Egypt, one area isthe northern region of Egypt with the Priority function of Economic Development, another area is the Nasser Reservoir area with the priority function of water conservation. The key findings are as follows: (1)The northern region of Egypt and the Nasser Reservoir exhibited different trends across the ecological functional zones.From 1985 to 2020, the northern region of Egypt is characterized by an increase in arable land and impervious surface,the Nasser Reservoir area is distinguished by the expansion of water and a reduction in sand.(2) Key land transfer patterns vary significantly between the ecological functional zones of the northern region of Egypt and the Nasser Reservoir. From 1985 to 2020, the Egypt’s northern region experienced significant shifts between arable land, sand, and impervious surfaces. The conversion of arable land to impervious surfaces represented 2.41% of the total arable land transfer area, while sand converted to impervious surfaces accounted for 11.68% of the total sand transfer area. In the Nasser Reservoir region, significant conversions occurred between water, grasslands, and sand. Sand converted to water made up 3.83% of the total sand transfer area, while grassland converted to water compriseed 5.38% of the total grassland transfer area. (3) The ecological benefits of the northern region of Egypt and the Nasser Reservoir have increased significantly. In northern region of Egypt, the primary drivers of this increase in ecological service value are the conversion of arable land and grasslands, along with the ecological development and utilization of sand. In the Nasser Reservoir area,the inflow of water and the continued outflow of sands are the main factors contributing to the rise in ecosystem service value. (4) Differentiated LUCC management should be implemented for different ecological functional zones. In northern region of Egypt, the focus should be on balancing ecological security with human development, while the Nasser Reservoir area should prioritize water resource protection and sustainable development. The research findings are not only significant for optimizing LUCC but also for enhancing ecosystem service functions in Egypt.

Characterizing the Regional Differences in Carbon Dioxide Concentration Based on Satellite Observations in the Beijing-Tianjin-Hebei Region during 2015–2021

The regional differences in carbon dioxide (CO2) variations from the Orbiting Carbon Observatory-2 (OCO-2) over the Beijing-Tianjin-Hebei (Jing-Jin-Ji) region from 2015 to 2021 are analyzed in this study. This study shows an annual increase and a seasonal cycle; the CO2 annual growth rate was about 2.63 ppm year−1, with the highest value being in spring and the lowest in summer. The spatial distribution is unbalanced, regional differences are prominent, and the CO2 concentration is lower in the north of the Jing-Jin-Ji region (like Zhangjiakou, Chengde, and Qinhuangdao). Land-type structures and population economy distributions are the key factors affecting CO2 concentration. By analyzing the land-type structures over Jing-Jin-Ji in 2020, we find that cropland, woodland, and grassland (CWG) are the main land cover types in Jing-Jin-Ji; the proportion of these three types is about 83.3%. The woodland areas in Zhangjiakou, Chengde, and Qinhuangdao account for about 65% of the total woodland areas in Jing-Jin-Ji; meanwhile, the grassland areas in these three regions account for 62% of the total grassland areas in Jing-Jin-Ji. CO2 concentration variation shows a high negative correlation with CWG land areas (coefficient of determination (R2) > 0.76). The regions with lower population and GDP secondary industry (SI) density also have lower CO2 concentration (like Zhangjiakou, Chengde, and Qinhuangdao), and the regions with higher population and GDP SI density also have higher CO2 concentration (like the southeast of Jing-Jin-Jin).

Analyzing Spatio-Temporal Dynamics of Grassland Resilience and Influencing Factors in the West Songnen Plain, China, for Eco-Restoration.

Grassland plays an indispensable role in the stability and development of terrestrial ecosystems. Quantitatively assessing grassland resilience is of great significance for conducting research on grassland ecosystems. However, the quantitative measurement of resilience is difficult, and research on the spatio-temporal variation of grassland resilience remains incomplete. Utilizing the Global Land Surface Satellite (GLASS) leaf area index (LAI) product derived from MODIS remote sensing data, along with land cover and meteorological data, this paper constructed the grassland resilience index (GRI) in the west Songnen Plain, China, a typical region with salt and alkali soils. This paper analyzed the spatio-temporal changes of the GRI and explored the contribution of climate factors, human activities, and geographical factors to the GRI. The results revealed that from 2000 to 2021, the GRI in the study area ranged from 0.1 to 0.22, with a multi-year average of 0.14. The average GRI exhibited a pattern of high-value aggregations in the north and low-value distributions in the south. Trend analysis indicated that areas with an improved GRI accounted for 59.09% of the total grassland area, but there were still some areas with serious degradation. From 2000 to 2015, the latitude and mean annual temperature (MAT) were principal factors to control the distribution of the GRI. In 2020, the mean annual precipitation (MAP) and MAT played important roles in the distribution of the GRI. From 2000 to 2021, the influence of human activities was consistently less significant compared to geographical location and climate variables.

Using multilayer perceptron and similarity-weighted machine learning algorithms to reconstruct the past: A case study of the agricultural expansion on grasslands in the Uruguayan savannas.

Changes in land use and land cover (LULC) have significant implications for biodiversity, ecosystem functioning, and deforestation. Modeling LULC changes is crucial to understanding anthropogenic impacts on environmental conservation and ecosystem services. Although previous studies have focused on predicting future changes, there is a growing need to determine past scenarios using new assessment tools. This study proposes a methodology for LULC past scenario generation based on transition analysis. Aiming to hindcast LULC scenario in 1970 based on the transition analysis of the past 35 years (from 1985 to 2020), two machine learning algorithms, multilayer perceptron (MLP) and similarity weighted (SimWeight), were employed to determine the driver variables most related to conversions in LULC and to simulate the past. The study focused on the Aristida spp. grasslands in the Uruguayan savannas, where native grasslands have been extensively converted to agricultural areas. Land use and land cover data from the MapBiomas project were integrated with spatial variables such as altimetry, slope, pedology, and linear distances from rivers, roads, urban areas, agriculture, forest, forestry, and native grasslands. The accuracy of the predicted maps was assessed through stratified random sampling of reference images from the Multispectral Scanner (MSS) sensor. The results demonstrate a reduction of approximately 659 934 ha of native grasslands in the study area between 1985 and 2020, directly proportional to the increase in cultivable areas. The MLP algorithm exhibited moderate performance, with notable errors in classifying agriculture and grassland areas. In contrast, the SimWeight algorithm displayed better accuracy, particularly in distinguishing grassland and agriculture classes. The modeled map using SimWeight accurately represented the transitions between grassland and agriculture with a high level of agreement. By modeling the 1970s scenario using the SimWeight model, it was estimated that the Aristida spp. grasslands experienced a substantial reduction in grassland coverage, ranging from 9982.31 to 10 022.32 km2 between 1970 and 2020. This represents a range of 60.8%-61.07% of the total grassland area in 1970. These findings provide valuable insights into the driving factors behind land use change in the Aristida spp. grasslands and offer useful information for land management, conservation, and sustainable development in the region. The study's main contribution lies in the hindcasting of past LULC scenarios, utilizing a tool used primarily for forecasting future scenarios. Integr Environ Assess Manag 2024;20:1140-1155. © 2023 SETAC.

Dynamic of Grassland Degradation and Its Driving Forces from Climate Variation and Human Activities in Central Asia

Central Asia is one of the most sensitive regions to climate changes in the world and the grassland degradation of this region has attracted considerable concern. Quantifying the driving force of grassland degradation is important for understanding the effects of climate variation and human activities on grassland. In this study, net primary productivity (NPP) was selected as an indicator to quantitatively evaluate the relative role of climate variation and human activities in Central Asia from 2000 to 2020. This study used the global NPP product MOD17A3 as actual NPP and estimated the potential NPP using the Thornthwaite memorial model. The potential NPP and the difference between the potential NPP and actual NPP were used to represent the influence of climate variation and human activities. The grassland degradation or restoration can be demonstrated by the slope of actual NPP (SA). A positive slope value (SA) suggested that restoration occurs, whereas a negative slope value suggested that degradation occurred. The results showed that 23.08% of the total grassland area experienced grassland degradation, whereas 2.51% of the whole grassland underwent grassland restoration. Furthermore, 53.8% of the degraded grassland areas were influenced by climate variation, and 14.5% were caused by human activities. By contrast, the relative roles of climate variation and human activities in grassland restoration were 25% and 47.9%, respectively. The NPP variation also could be calculated by assessing the effects of these factors and the results showed that 55.7% of the NPP decrease was caused by climate variation, whereas 9.6% was a result of human activities. On the contrary, climate variation and human activities resulted in 19.8% and 37.3% of grassland restoration, respectively. Therefore, climate variation was the dominant factor of grassland degradation, and human activities were the main driver of grassland restoration in Central Asia.

Untangling the effects of climate variation and human interference on grassland dynamics in North China

AbstractClimatic and anthropogenic disturbances play pivotal roles in shaping the dynamics of grassland. Quantifying their impacts on grassland variation is essential to ensure sustainable grassland management. In this study, we employed the Thornthwaite Memorial and Carnegie–Ames–Stanford‐approach (CASA) models to investigate the spatiotemporal effects of these two variables on grassland variation in northern China from 2000 to 2016, using the net primary productivity (NPP) as a measure. Our findings reveal that approximately 25.92% of the grassland in northern China experienced degradation, while restored grasslands occupied 45% of the total grassland area. The average grassland actual NPP (ANPP) and human‐induced NPP decreased at rates of −0.60 and −5.62 gC m−2 a−1, respectively. Conversely, potential NPP exhibited an upward trend with an average increase of 2.27 gC m−2 a−1. Furthermore, grassland ANPP showed a projected increase in most parts of northern China. Climate change emerged as the primary driver for grassland restoration in Xinjiang, Qinghai, and Inner Mongolia, leading to an increase of 21582.79 Gg C in grassland NPP. In contrast, human activities were the dominant catalysts for grassland degradation, resulting in a reduction of 51932.3 Gg C in grassland NPP. Human‐induced grassland degradation was ubiquitous in northwest and northeast China. With the exception of slope grassland, climate change primarily influenced the restoration of most grassland types, while human activities were the primary cause of degradation. Our analysis indicated a strong correlation between temperature and grassland degradation, while precipitation played a pivotal role in grassland restoration in northern China. Human interference demonstrated both positive and negative impacts on grassland changes. In conclusion, the increase in precipitation and the implementation of ecological restoration plans have effectively promoted the restoration of grasslands in northern China.

Change characteristics and influencing factors of grassland degradation in adjacent areas of the Qinghai-Tibet Plateau and suggestions for grassland restoration.

Natural grasslands are being progressively degraded around the world due to climate change and socioeconomic factors. Most of the drivers, processes, and consequences of grassland degradation are studied separately, and it is not yet clear whether the change characteristics and influence factors of adjacent areas of grassland are identical. We analyzed changes in grassland area and quality, and the influences of climate changes and socioeconomic factors from 1980-2018 in Maqu County, Xiahe County and Luqu County on the eastern Qinghai-Tibet Plateau (QTP). We found that areas with high and medium coverage grassland in Maqu County and Luqu County decreased continuously with time, while low coverage grassland areas increased in three counties. In Xiahe County, the medium coverage grassland area reduced with time (except for 2010), while the high and low coverage grassland areas increased. The actual net primary productivity of the three counties showed a downward trend. In Maqu County, the total grassland area had an extremely significant positive correlation with number of livestock going to market, commodity rate, gross domestic product (GDP), primary industry, tertiary industry, household density, and levels of junior middle school education and university education in the area. In Luqu County, the total grassland area high coverage grassland area were significantly negatively correlated with total number of livestock, secondary industry, levels of primary school education, and temperature. Ecological education was positively correlated with high coverage grassland, and negatively correlated with low coverage grassland in all three areas. The results of this study suggest that the best ways to restore the area and quality of grasslands in these areas would be to reduce the local cultivated land area and slow down the development of the primary and tertiary industries in Maqu County, and to control industry development and the total number of livestock in Luqu County. This study also suggests that improving education level and strengthening the level of ecological education are conducive to the restoration of grasslands.

Assessment of multiple factors and interactions affecting grassland degradation on the Tibetan Plateau

The Tibetan Plateau (TP), a vital region for terrestrial ecosystem services and biodiversity, is facing severe grassland degradation influenced by multiple factors. However, it remains unclear how several influential factors interactively influence grassland degradation. Here, we used five land cover products and six vegetation indexes to quantify grassland degradation on the TP. We then applied structural equation modeling (SEM) to quantitatively assess multiple factors and interactions affecting grassland degradation. We found that grassland degradation on the TP was mainly concentrated in the central and southern regions which were under high pressure from human activities, with the area of 325,430 km2, accounting for about 12.63% of the total grassland area on the TP during 2000–2020. The factors such as overgrazing, climate change, and human activities have contributed to the grassland degradation on the TP. Moreover, we found that there is a complex and interrelated relationship between these factors and that addressing one factor alone is unlikely to solve the problem of grassland degradation. Human activities significantly (p < 0.05) influenced grassland degradation on the TP, while climate factors had a greater direct effect (0.384) compared to soil factors (0.298) and human activities (0.263). Soil factors, on the other hand, had a greater total effect (0.388) than human activities (0.263) and climate factors (0.161). The topography of the TP played an indirect role (−0.4) in slowing down the degradation of grasslands by significantly (P < 0.05) affecting human activities and climate. In order to fully understand the truth about grassland degradation and to avoid further grassland degradation, it is necessary to consider multiple influencing factors and their interactions.

Timing and magnitude of drought impacts on carbon uptake across a grassland biome.

Although drought is known to negatively impact grassland functioning, the timing and magnitude of these impacts within a growing season remain unresolved. Previous small-scale assessments indicate grasslands may only respond to drought during narrow periods within a year; however, large-scale assessments are now needed to uncover the general patterns and determinants of this timing. We combined remote sensing datasets of gross primary productivity and weather to assess the timing and magnitude of grassland responses to drought at 5km2 temporal resolution across two expansive ecoregions of the western US Great Plains biome: the C4 -dominated shortgrass steppe and the C3 -dominated northern mixed prairies. Across over 700,000 pixel-year combinations covering more than 600,000 km2 , we studied how the driest years between 2003-2020 altered the daily and bi-weekly dynamics of grassland carbon (C) uptake. Reductions to C uptake intensified into the early summer during drought and peaked in mid- and late June in both ecoregions. Stimulation of spring C uptake during drought was small and insufficient to compensate for losses during summer. Thus, total grassland C uptake was consistently reduced by drought across both ecoregions; however, reductions were twice as large across the more southern and warmer shortgrass steppe. Across the biome, increased summer vapor pressure deficit (VPD) was strongly linked to peak reductions in vegetation greenness during drought. Rising VPD will likely exacerbate reductions in C uptake during drought across the western US Great Plains, with these reductions greatest during the warmest months and in the warmest locations. High spatiotemporal resolution analyses of grassland response to drought over large areas provide both generalizable insights and new opportunities for basic and applied ecosystem science in these water-limited ecoregions amid climate change.

Construction and Optimization of an Ecological Network in the Yellow River Source Region Based on MSPA and MCR Modelling.

The source region of the Yellow River (SRYR) is an important water conservation and farming area in China. Under the dual influence of the natural environment and external pressure, ecological patches in the region are becoming increasingly fragmented, and landscape connectivity is continuously declining, which directly affect the landscape patch pattern and SRYR sustainable development. In the SRYR, morphological spatial pattern analysis (MSPA) and landscape index methods were used to extract ecologically important sources. Based on the minimum cumulative resistance model (MCR), Linkage Mapper was used to generate a potential corridor, and then potential stepped stone patches were identified and extracted by the gravity model and betweenness centrality to build an optimal SRYR ecological network. The distribution of patches in the core area of the SRYR was fragmented, accounting for 80.53% of the total grassland area. The 10 ecological sources based on the landscape connectivity index and 15 important corridors identified based on the MCR model were mainly distributed in the central and eastern regions of the SRYR. Through betweenness centrality, 10 stepped stone patches were added, and 45 planned ecological corridors were obtained to optimize the SRYR ecological network and enhance east and west connectivity. Our research results can provide an important reference for the protection of the SRYR ecosystem, and have important guiding significance and practical value for ecological network construction in ecologically fragmented areas.

Modeling carbon uptake by vegetation of grassland ecosystems and its associated factors in China based on remote sensing

In order to reveal the spatial variation characteristics and influencing factors of grassland net primary productivity (NPP) in China, this paper uses remote sensing data, land use data and meteorological data to simulate and estimate China’s grassland net primary productivity from 2001 to 2019 using the Carnegie-Ames-Stanford Approach (CASA). The trend analysis and complex correlation analysis were used to analyze the relationship with the temporal and spatial changes of grassland NPP from the perspectives of climate factors, topography, longitude and latitude. The results show that: 1) In the past 19 years, the China’s grassland NPP has generally shown a fluctuating upward trend, the spatial distribution of NPP variation shows a characteristic of low in the west and high in the east, with the increased area accounting for 70.39% of the total grassland area, and the low NPP values are mainly distributed in the northwestern part of Tibet and Qinghai and the central part of Inner Mongolia, the average annual NPP is 257.13 g C·m−2·a−1. 2) The change of mean NPP value of grassland in China is more dependent on precipitation (p) than air temperature (T). 3) Grassland NPP showed a decreasing trend with the increase of altitude, and the NPP on the gradient with DEM between 200 m and 500 m was the highest (483.86 g·C·m−2·a−1); The maximum annual mean value (448.42 g C·m−2·a−1) is fallen over the sharp slope of 35°–45°; the NPP of grassland increases with the slope (from shade to sunny), and the NPP of grassland on the semi-sunny slope increases. The annual average NPP is the highest (270.87 g C·m−2·a−1). 4) The mean value of grassland NPP was negatively correlated with the change of latitude, and showed a “wave-like” downward trend from south to north; the mean value of grassland NPP was positively related to the change of longitude. The correlation relationship shows a “stepped” upward trend from west to east.

Vegetation Growth Trends of Grasslands and Impact Factors in the Three Rivers Headwater Region

Areas of grassland improvement and degradation were mapped and assessed to identify the driving forces of change in vegetation cover in the Three Rivers headwater region of Qinghai, China. Based on linear regression at the pixel level, we analyzed the vegetation dynamics of the grasslands of this region using MODIS NDVI data sets from 2000 to 2010. Correlation coefficients were computed to quantitatively characterize the long-term interrelationship between vegetation NDVI and precipitation/temperature variability during this period. The use of time series residuals of the NDVI/precipitation linear regression to normalize the effect of precipitation on vegetation productivity and to identify long-term degradation was extended to the local scale. Results showed that significant improvements occurred in 26.4% of the grassland area in the Three Rivers Headwater region between 2000 and 2010. The study area, which represents about 86.4% of the total grassland area of this headwater region, showed a general trend of improvement with no obvious trend of degradation.

Characteristics of grassland degradation and its relationship with climate factors on Qinghai-Tibetan Plateau, China

Understanding the distribution, characteristics, and changing trend and persistence of grassland degradation and revealing its mechanism on the Qinghai-Tibetan Plateau can provide scientific basis for effective grassland management and conservation. We selected grassland coverage as the remote sensing monitoring index to establish the remote sensing monitoring and evaluation index system of grassland degradation and evaluate grassland degradation during 2016 to 2020 on the Qinghai-Tibet Plateau. The changing trend and persistence of grassland coverage were analyzed using linear regression and Hurst index analysis on a long time series scale (1982-2020). The partial correlation analysis was used to examine the influence of climate on grassland degradation. The results showed that grassland degradation reached 24.3% during 2016 to 2020, which was mainly light and moderate degradation, and largely distributed in low altitude and high fractional vegetation cover areas. From 1982 to 2020, grassland coverage tended to increase in the north, west and southwest, and decreased in the east and center of the Qinghai-Tibetan Plateau. The Hurst index of grassland coverage was less than 0.5 in 98.1% of the total grassland, indicating grassland coverage showed negatively persistent. The partial correlation coefficient between grassland coverage and precipitation (0.096) was higher than that of temperature (-0.033). About 16.0% area was dominated by temperature, which was mainly distributed in the central and southeast. About 12.2% area was dominated by precipitation, which was distributed in the northeast and west of the Qinghai-Tibetan Plateau.

Predicting the impact of invasive trees from different measures of abundance

Biological invasions produce negative impacts worldwide, causing massive economic costs and ecological impacts. Knowing the relationship between invasive species abundance and the magnitude of their impacts (abundance-impact curves) is critical to designing prevention and management strategies that effectively tackle these impacts. However, different measures of abundance may produce different abundance-impact curves. Woody plants are among the most transformative invaders, especially in grassland ecosystems because of the introduction of hitherto absent life forms. In this study, our first goal was to assess the impact of a woody invader, Pinus contorta (hereafter pine), on native grassland productivity and livestock grazing in Patagonia (Argentina), building abundance-impact curves. Our second goal, was to compare different measure of pine abundance (density, basal area and canopy cover) as predictors of pine's impact on grassland productivity. Our third goal, was to compare abundance-impact curves among the mentioned measures of pine abundance and among different measures of impact: total grassland productivity, palatable productivity and sheep stocking rate (the number of sheep that the grassland can sustainably support). Pine canopy cover, closely followed by basal area, was the measure of abundance that best explained the impact on grassland productivity, but the shape of abundance impact curves differed between measures of abundance. While increases in pine density and basal area always reduced grassland productivity, pine canopy cover below 30% slightly increased grassland productivity and higher values caused an exponential decline. This increase in grassland productivity with low levels of pine canopy cover could be explained by the amelioration of stressful abiotic conditions for grassland species. Different measures of impact, namely total productivity, palatable productivity and sheep stocking rate, drew very similar results. Our abundance-impact curves are key to guide the management of invasive pines because a proper assessment of how many invasive individuals (per surface unit) are unacceptable, according to environmental or economic impact thresholds, is fundamental to define when to start management actions.

Impacts of climate change and human activities on different degraded grassland based on NDVI

Grassland degradation has emerged as a serious socio-economic and ecological problem, endangering both long-term usage and the regional biogeochemical cycle. Climate change and human activities are the two leading factors leading to grassland degradation. However, it is unclear what the degradation level caused by these two factors is. Using the normalized difference vegetation index (NDVI) and coefficient of variation of NDVI (CVNDVI), the spatial distribution features of grassland degradation or restoration were analyzed in Qilian County in the northeast of the Qinghai–Tibet Plateau. The dominant climate variables affecting NDVI variation were selected through the combination of random forest model and stepwise regression method to improve the residual trend analysis, and on this basis, twelve possible scenarios were established to evaluate the driving factors of different degraded grasslands. Finally, used the Hurst index to forecast the trend of grassland degradation or restoration. The results showed that approximately 55.0% of the grassland had been degraded between 2000 and 2019, and the area of slight degradation (NDVIslope > 0; CVNDVI (slope) > 0; NDVIvalue > 0.2) accounted for 48.6%. These regions were centered in the northwest of Qilian County. Climate and human activities had a joint impact on grassland restoration or degradation. Human activities played a leading role in grassland restoration, while climate change was primarily a driver of grassland degradation. The regions with slight degradation or re-growing (NDVIslope > 0; CVNDVI (slope) > 0), moderate degradation (NDVIslope < 0; CVNDVI (slope) > 0), and severe degradation or desertification (NDVIslope < 0; CVNDVI (slope) < 0) were dominated by the joint effects of climate and anthropogenic activity accounted for 34.3%, 3.3%, and 1.3%, respectively, of the total grassland area. Grasslands in most areas of Qilian County are forecasted to continue to degrade, including the previously degraded areas, with continuous degradation areas accounting for 54.78%. Accurately identifying the driving factors of different degraded grassland and predicting the dynamic change trend of grassland in the future is the key to understand the mechanism of grassland degradation and prevent grassland degradation. The findings offer a reference for accurately identifying the driving forces in grassland degradation, as well as providing a scientific basis for the policy-making of grassland ecological management.

Research on Service Value and Adaptability Zoning of Grassland Ecosystem in Ethiopia

The evaluation of the ecosystem service value (ESV) and its regionalization toward coordinating ecological protection and socioeconomic development is of great significance. In this study, we developed a classification method based on the Random Forest algorithm and a feature optimization method to identify grassland types. Then, we proposed an approach to quantitatively evaluate the ESV of the grassland ecosystem in Ethiopia, in which net primary production derived from remote sensing was used to evaluate organic matter production value (ESV1), promoting nutrient circulation value (ESV2), and gas regulation value (ESV3), the RUSLE model was used to evaluate soil conservation value (ESV4), and cumulative rainfall was used to calculate water conservation value (ESV5). By integrating the mean ESV under various influencing factors, the zoning map of grassland ecosystem service value was obtained. Our study found that more fine grassland types can be well classified with the overall accuracy of 86.52%. And the classification results are the basis of the ESV analysis. The total ESV of grassland ecosystems was found to be USD 105,221.72 million, of which ESV4 was the highest, accounting for 44.09% of the total ESV. The spatial analysis of ESV showed that the differences were due to the impacts of grassland types, elevation, slope, and rainfall. It was found that the grassland is suitable to grow in the elevation zone between approximately 1000 and 2000 m, and the larger the slope and rainfall are, the greater the mean ESV is. The zoning map was used to conclude that the areas from approximately the fourth to sixth level (only 34.78% of the total grassland area, but 65.94% of the total ESV) have better growth status and development potential. The results provide references and bases to support the local coordination and planning of various grassland resources and form reasonable resource utilization and protection measures.

The driving factors of grassland water use efficiency along degradation gradients on the Qinghai-Tibet Plateau, China

Ecosystem water use efficiency (WUE) is an important parameter of carbon-water coupling, which connects the carbon cycle and water cycle. It is crucial to understand the spatial and temporal distribution of WUE in degraded grassland and its influencing factors for predicting land surf-atmosphere interactions and the dynamic changes of terrestrial ecosystems. Thus, combining the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI), gross primary production (GPP) and evapotranspiration (ET), this study investigated the spatial and temporal variation of grassland degradation and WUE from 2001 to 2020 on the Qinghai-Tibet plateau (QTP) by Theil-Sen trend method and Mann-Kendall test, and identified the sensitivity response of grassland WUE to degradation, temperature, wind speed and precipitation within different degradation gradients by ridge regression analysis and generalized linear model. The results showed that (1) the WUE gradually decreases from southeast to northwest, and the interannual variation showed an increasing trend. The grassland degradation on the QTP gradually increases from southeast to northwest, and the grievous severe degradation (GSD) and severely degradation (SD) grassland accounted for 61.59% of the total grassland area. (2) The sensitivity of grassland WUE to temperature (γTEM) and precipitation (γPRE) were generally negative in the whole region, the opposite results were found in wind speed and degradation. (3) Temperature was the main factor affecting WUE in degraded grassland, and the growth trend of WUE becomes faster with the increase of the degree of degradation.

Global trends in grassland carrying capacity and relative stocking density of livestock.

Although the role of livestock in future food systems is debated, animal proteins are unlikely to completely disappear from our diet. Grasslands are a key source of primary productivity for livestock, and feed‐food competition is often limited on such land. Previous research on the potential for sustainable grazing has focused on restricted geographical areas or does not consider inter‐annual changes in grazing opportunities. Here, we developed a robust method to estimate trends and interannual variability (IV) in global livestock carrying capacity (number of grazing animals a piece of land can support) over 2001–2015, as well as relative stocking density (the reported livestock distribution relative to the estimated carrying capacity [CC]) in 2010. We first estimated the aboveground biomass that is available for grazers on global grasslands based on the MODIS Net Primary Production product. This was then used to calculate livestock carrying capacities using slopes, forest cover, and animal forage requirements as restrictions. We found that globally, CC decreased on 27% of total grasslands area, mostly in Europe and southeastern Brazil, while it increased on 15% of grasslands, particularly in Sudano‐Sahel and some parts of South America. In 2010, livestock forage requirements exceeded forage availability in northwestern Europe, and southern and eastern Asia. Although our findings imply some opportunities to increase grazing pressures in cold regions, Central Africa, and Australia, the high IV or low biomass supply might prevent considerable increases in stocking densities. The approach and derived open access data sets can feed into global food system modelling, support conservation efforts to reduce land degradation associated with overgrazing, and help identify undergrazed areas for targeted sustainable intensification efforts or rewilding purposes.

Grassland Variation and Its Driving Factors from 2000 to 2016: A Comparative Assessment between Qinghai-Tibet Plateau and Inner Mongolia Plateau

The grassland of Qinghai-Tibet Plateau (QTP) and Inner Mongolia Plateau (IMP), accounting for 73.9% of the total grassland area in China, is significant to food and ecological safety. Due to climate change and irrational human activities, grasslands on the two plateaus have severely degraded over recent decades. Understanding the dynamic changes of grassland and its driving forces is necessary to make effective measurements to prevent grassland degradation. Here, we selected the net primary productivity (NPP) as an indicator to quantitatively assess the dynamic variation of grassland and the relative roles of climate change and human activities on QTP and IMP from 2000 to 2016. The results found significant spatial variability of grassland on QTP. 28.3% of the grassland experienced degradation and was mainly distributed in the southern QTP, versus 71.7% of the grassland was restored and mainly distributed in the central and northern QTP. In contrast, grassland on IMP didn’t show significant spatial variability. Most of the grassland on IMP was restored during the study period. Climate change (i.e. increased precipitation) was the dominant factor and could explain 72.8% and 84.4% of the restored grassland in QTP and IMP. Irrational human activities (i.e. overgrazing) were the main driving factors and could explain 72.9% and 100.0% of the degraded grassland on the two plateaus during the study period. Ecological restoration projects were favorable for grassland restoration on the two plateaus, and they contributed to 27.2% and 15.6% of the restored grassland in QTP and IMP, respectively. Therefore, climate changes on IMP were more favorable for grassland restoration, and human activities have a greater impact on the grassland variation on QTP.