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• Offers an extensive review of global research on grassland-human activity interactions since 2000, covering key developments and insights. • Unveils the dynamic evolution of research focus across various stages in the study of human activities in grasslands. • Illustrates the expansion of research from small-scale and botanical to multi-scale and multidisciplinary scopes. • Identifies and emphasizes the prevalent use of analytical tools such as R language, PC-ORD, and Google Earth Engine (GEE) in grassland research. Due to their ecological sensitivity and limited resource availability, grasslands are exceptionally vulnerable to environmental changes and human activities. However, comprehensive and in-depth analyses of the interplay between grasslands and human activities remain scarce. This study employed bibliometrics and critical paper analysis to scrutinize 4738 publications from 2000 to 2023. The findings reveal that: (1) Since 2000, research on grassland-human activity interactions has evolved and intensified, spanning diverse disciplines including environmental ecology, plant science, agricultural science, and biodiversity conservation. A notable trend is the relative clustering of scholars and research institutions within Eastern and Western countries and within national boundaries. China leads in publication volume, whereas the United States exerts greater influence in the international collaborative network. (2) Approximately 80% of the highly co-citation literature concentrates on elucidating scientific knowledge, particularly the impact of human activities on vegetation characteristics, grassland communities, and ecosystems. Research hotspots predominantly encompass European grasslands, North America's Great Plains, and the Tibetan Plateau grasslands in China. The remaining 20% focuses on methodological advancements and tools, with frequent utilization of R language, PC-ORD, and Google Earth Engine (GEE). (3) Initial studies (2000–2005) primarily investigated the effects of specific anthropogenic activities on grassland biomes at a community level through experimental observations. Subsequent research (2006–2013) shifted towards exploring the overarching characteristics and transformations of grassland rangelands at regional scales, heavily relying on remote sensing technologies. More recent studies (2014–2023) have emphasized a comprehensive, quantitative appraisal of the multifaceted impacts of internal and external stressors on grassland ecosystems. This study enhances the understanding of the historical trajectory of grassland-anthropogenic relationship research, highlighting pivotal knowledge and collaborative tools. [ABSTRACT FROM AUTHOR]

• This paper analyzes whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. • There is a nonlinear relationship between financial support for ecological restoration and grassland vegetation coverage. • Different fiscal expenditure structure has a heterogeneous response. • The degree of fiscal decentralization has a significant positive regulatory effect on the ecological benefits. Grassland ecological restoration is an important cornerstone to maintain the sustainable development of grassland ecosystem. One of the goals of financial support for grassland ecological protection and restoration is whether the ecological benefits of grassland are fully realized, and grassland vegetation coverage is the main index to measure the ecological benefits. This paper aims to analyze whether the financial support for grassland ecological protection and restoration has improved the vegetation coverage of grassland. and then verify whether there is room for improvement in the fiscal effect of grassland ecological protection and restoration? Based on the panel data of 54 animal husbandry and semi-agricultural and semi-animal husbandry counties in Inner Mongolia Autonomous Region of China, this paper adopts a bidirectional fixed effect model and cross-fixed effect model to introduce natural factors affecting grassland vegetation coverage into the model and systematically examines the financial effects of ecological benefits of financial support for grassland ecological protection and restoration. The main findings are as follows: (1) there is a nonlinear relationship between financial support for grassland ecological protection and restoration and grassland vegetation coverage when natural factors are taken into account in the model. (2) different fiscal expenditure structure has a heterogeneous response to the impact of grassland ecological protection and restoration on ecological benefits. (3) the degree of fiscal decentralization and the relative performance of the government have a significant positive moderating effect on the ecological benefits of fiscal support for grassland ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Highlights • Grassland plays carbon source or sink function of Inner Mongolia? • PD was used to explore the coupling relation between NEP and precipitation. • Precipitation deficit restrains C sequestration in 29% of regions of IM. • Targeted implementation of ecological program can be determined in this paper. • Appropriate measures that can convert a C source to a C sink are necessary. Abstract Grassland carbon (C) sink/source evaluation is important to terrestrial ecosystem C cycling research. In this paper, boreal ecosystem productivity simulator (BEPS) model was used to simulate the net ecosystem productivity (NEP) of Inner Mongolia (IM) from 1982 to 2008. The coupling relation between NEP and climate factors was then explored. Results showed that the mean annual value of grassland NEP is 12.2 gC/m2/yr, and regions with NEP of >0 (C sink) accounts for 73.2% of the total grassland area of IM. Additionally, the total C sequestration reaches 0.35 Pg C yearly. NEP was positively correlated with precipitation (R = 0.31), and the positive correlation percentage accounted for 90% of total grassland area. But NEP was negative correlated with temperature (R = −0.11), and the negative correlation percentage was 72% of case. And the partial correlation coefficient between NEP and precipitation and between NEP and temperature was 0.30 and −0.06 respectively. Meanwhile, the monthly NEP of the grassland is obviously lagging behind the precipitation and temperature, and the lag time is both three months. We defined a precipitation differential (PD) parameter to explore the coupling relation between grassland NEP and precipitation. Generally, areas with positive PD are typically a C sinks (72% of the grassland area of IM). However, regions with negative PD are likely a C source (28% of case). The obtained mean PD value of IM is 18 mm, indicating that IM is a C sink. Further analysis showed that 69% of regions have positive PD and positive NEP, and 20% of regions have negative PD and negative NEP. This result confirmed that precipitation deficit restrains C sequestration. However, the rest of 11% of regions was sensitive area of carbon sink and carbon source transition. Among 8% of the regions (with negative PD but positive NEP) have insufficient precipitation, although other conditions (i.e., ecological restoration program) are favorable to C sink. Moreover, 3% of the regions (with positive PD but negative NEP) have sufficient precipitation, but have negative NEP because of inappropriate management or low temperature. Thus, appropriate measures that can convert a C source to a C sink are necessary. This paper can serve as a reference for policy makers for the efficient targeted implementation of ecological engineering. [ABSTRACT FROM AUTHOR]

Highlights • Forage quality has been estimated both at the species and the community scale. • Wet grasslands show high fodder values compared to regional references. • Leaf traits (SLA, LDMC and LNC) represent good proxies for fodder value assessment. • For a better assessment, functional groups should be considered separately. Abstract Wet grasslands are now widely recognized as providing many ecosystem services. Wet grasslands are still endangered by both conversion to improved grasslands or crops, and abandonment due to the difficulties associated with exploiting such constrained lands. This paper focuses on forage quality, which is one of the most poorly studied provisioning services of wet grasslands. We tested if the relationship between functional traits (Specific Leaf Area (SLA), Leaf Dry Matter Content (LDMC) and Leaf Nitrogen Content (LNC)) and forage quality established for mesophilic grasslands are valid for wet grasslands. This study was conducted both at the species (44 grassland species) and the community scale. Besides evaluation of both the grassland species and the plant community fodder values for cattle feeding, our aim was to look for proxies of fodder quality through plant functional traits. Pepsin cellulase digestibility (dC) and crude protein content (CP), two uncorrelated indices, were chosen to estimate species fodder quality. At the species scale, SLA seems to be the best trait to estimate dC for forbs, sedges and rushes, while LDMC seems more pertinent for grasses. When considering communities, the weighted mean dC, estimated by these two leaf traits according to the species functional group, constitutes an interesting proxy. CP seemed more linked to LNC. This trait allows an estimation of CP at the species scale but is much less relevant at the community scale. [ABSTRACT FROM AUTHOR]

• An ecosystem process model-based NEP was predicted under varying climate scenarios. • Alpine grasslands were projected to play an important part in carbon sink contributions in Qinghai Province in the 21st century. • An earlier peak and faster decreasing NEP projected for a warmer climate scenario (RCP 8.5). • A more stable carbon sink was projected under a less warm, stable precipitation scenario (RCP 4.5). The alpine grasslands of the Qinghai-Tibetan Plateau play an important role in multiple ecosystem functions, all of which are key in regulating regional climate influences and providing pristine headwaters for millions of people downstream from this basin. Alpine grasslands act as a carbon sink, storing carbon dioxide and keeping heat-trapping greenhouse gases out of the atmosphere, but it is not clear how this will change in a warming climate in the future. In this paper, the net ecosystem productivity (NEP) of alpine grasslands in Qinghai province, on the Qinghai-Tibetan Plateau, was predicted for the period from 2010 to 2099. Trends and stability were analyzed under two climate scenarios, Representative Concentration Pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) representing the lower and higher emission scenarios for greenhouse gases. The results suggest that grasslands will continue to contribute as a carbon sink, with a positive NEP through this century. Almost the same magnitude (38 Tg C a-1, 1 T g = 1012 g) of contribution was projected under both scenarios. Grasslands are projected to be the major contributor to NEP in Qinghai province, with more than 89% of NEP in the future. The carbon sink function will increase over more than 69% of the grasslands and peak around 2069 (RCP4.5) or 2066 (RCP8.5). Then the carbon sink function will begin to decrease and it will decrease more quickly and become more variable under the RCP8.5 than the RCP4.5. The impacts of temperature and precipitation changes were analyzed and NEP was found to be more sensitive to temperature than precipitation change. The trend of increasing contribution to NEP is driven by a warming climate, while the stability of NEP is mainly influenced by the precipitation, which results in an upward trend before the peak and a decline due to stresses from limits in available water in a continued warming climate. [ABSTRACT FROM AUTHOR]

The objective of this study was to map and discriminate variations of vegetation net primary production (NPP) from the effects of precipitation variability and thereby identify the effects of grazing on NPP variations in the Xilingol Grassland. Satellite-based simulations of NPP collected over 14-year period and residuals method were used to normalize the effects of interannual precipitation on NPP. A fixed-size moving-window method was developed in this paper to quantify the annual grazing pressure (defined as grazing pressure index, GPI) on the regional scale, using vegetation cover images from 2001 to 2014. Temporal and spatial analysis methods were conducted to show patterns and trends of GPI and NPP residuals (NPP res ), and further to examine the effects of grazing pressure changes on variations of NPP res . Our results showed 80.7% of the study area experienced increasing NPP res and 18.5% with significantly increasing trends ( p  < 0.05). 76.7% of the study area showed decreasing grazing pressure, and 11.8% of areas experienced significantly decreasing trends ( p  < 0.05), with a small patches scattered distribution pattern. The negative correlations between NPP res and GPI were found in 94.6% of the study area, and 41.9% with significant correlation ( p  < 0.05); and long-term decreasing grazing pressure had positive impacts on vegetation NPP. They indicate grazing is a main driving factor of variations in vegetation NPP other than precipitation in Xilingol Grassland. The spatial patterns and temporal trends of NPP res , grazing pressure and responses of NPP res to grazing pressure changes varied among three main grassland types. [ABSTRACT FROM AUTHOR]

[Display omitted] • A vegetation–water–soil–environment ecological restoration evaluation system. • Optimized models were used to evaluate the four-dimensional ecological functions. • The 4D ESF may increase in the grassland in the future, but may not in the forest. • In general, the ecological restoration in the BTSSCP have improved significantly. The Beijing–Tianjin Sandstorm Source Control Project (BTSSCP) has been implemented for more than 20 years (2001–2022), and a scientific, accurate, and complete evaluation of its implementation effect is of great significance for the study of ecosystem evolution. In this study, we present a vegetation–water–soil–environment system comprising the net ecosystem productivity (NEP), water conservation (WC), soil erosion (SE), and habitat quality (HQ) to evaluate the spatiotemporal changes and future trends. The results showed that: (1) In areas with high vegetation coverage, only WC was significantly and negatively correlated with SE (−0.68, p less than 0.01). In areas with low vegetation coverage, NEP, WC, and HQ were all significantly negatively correlated with SE. This indicates that SE is a basic function that affects the performance of other ecological services. (2) From 2000 to 2020, the annual maximum average vegetation coverage increased by about 10 %; the annual average NEP increased by about 60 g C/km2, and the annual average SE decreased by about 500 t/km2. Compared with Phase I (2000–2010), the vegetation condition (coverage, NEP) in summer (July–August) was improved by about 10 %, and SE in winter (December–January) decreased by 3 t/km2 per month in Phase II (2010–2020). (3) In terms of future trends, NEP may continue to increase in the grassland area, especially in the Mu Us Sandland, but it may not change significantly in the forest area. Similarly, SE may continue to decrease in grassland-covered areas, but not in forest-covered areas. Most areas of WC could continue to increase, but the HQ will not change significantly. In general, the ecological restoration effects of the BTSSCP have improved significantly. This paper is intended to provide some key information for the management of ecological projects and regional ecological security. [ABSTRACT FROM AUTHOR]

• The performance of algorithms for biomass in grassland has not been increasing. • Accuracy depends on number of field samples, data source and pasture composition. • No evidence that any one machine learning method is more suited to this problem. • Future studies should assess the generalization error of the algorithms. The study of grasslands using machine learning (ML) methods combined with proximal/remote sensing data (RS) has been steadily increasing in the last decades. Available algorithms range from a primarily academic use to more widespread practical applications intended at helping farm management. Here, we review the use of ML methods applied to aboveground biomass (AGB) estimation in grassland systems. Based on 26 recent papers, we perform a literature review of the topic to identify common practices, namely the relation between estimation performance and the ML method used, data sources, and scale (local/regional). In order to identify the relation between the characteristics of the studies and the estimation accuracy, we use descriptive and correlation analysis. In spite of a surge in the number of papers and application examples, there is no evidence that the estimation performance of the algorithms has been improving over time. In all approaches used by the authors of the papers herein considered, the number of field samples, RS data source, and species composition of the grassland systems are the most relevant variables to explain the estimation accuracy. This accuracy increases with the number of field samples until it plateaus, hinting at the existence of an optimum level for monitoring efforts. Accuracy also increases with the proximity of the sensor to the field, i.e. , on average accuracy is higher using field spectroscopy than using satellite data. There is no evidence that any particular ML method is more suited to this problem. The literature also displays significant limitations in terms of its applications of the ML algorithms. For example, a limited number of papers validated the models, casting doubt on the potential of the models for generalized application. Despite those limitations, and considering the advancements verified, we expect that, in the near future, ML methods combined with RS/proximal data will continue to improve and be helpful for farm management. [ABSTRACT FROM AUTHOR]

• Both GPP and GPP max in Chinese grasslands showed increases at different space–time scales. • Trend analysis and breakpoint tests were used for the first time to monitor GPP change simultaneously in Chinese grasslands. • GPP max × CUP is a robust indicator to explain annual-scale variation in GPP. At the ecosystem level, Gross Primary Productivity (GPP) was defined as the organic compounds formed by plants that absorb atmospheric CO 2 through photosynthesis and sequester carbon in plant bodies. How to accurately assess the spatiotemporal evolution of ecosystem carbon indicators has become a critical issue to be addressed. Chinese grasslands are located in a typical arid and semi-arid climate zone and are sensitive to global changes, which will inevitably have serious impacts on the function and structure of Chinese grasslands. Based on this, our paper takes Chinese grassland ecosystems as the research area, and used multi-source GPP dataset from terrestrial ecosystem model simulations and remote sensing satellite observations to quantitatively analyze the spatiotemporal evolution patterns of GPP in Chinese grasslands over the past 40 years. We combined trend analysis and breakpoint test, and then to analyze the mechanism components of GPP space–time variability. The main results are as follows: (1) The model and remote sensing estimated GPP results showed that more than 80% of Chinese grasslands show a significant increasing trend over the past 40 years, with growth rates ranging from 0.68 to 3.13 g C/m−2 year−1. (2) GPP max also shows that more than 80% of Chinese grasslands are growing rapidly, with an overall growth rate of more than 0.1 g C/m−2 year−1 in each region. The overall long-term trends and interannual variability of multi-source GPP and GPP max are generally consistent, yet vegetation dynamics in local areas are still uncertain. (3) The breakpoint test showed that 'monotonically increasing' was the largest breakpoint type of GPP in Chinese grasslands (33.09%), and the direction of change of GPP in Chinese grasslands before and after the breakpoint was also increasing. (4) GPP max × CUP explained 91% of the temporal variability of annual-scale GPP in Chinese grasslands from a mechanistic view, and we found that the peak photosynthetic growth and the length of the phenological period synergistically controlled the interannual variability of GPP in Chinese grasslands. Under the scenario of rapid global change, our study accurately assessed the long-term dynamics of GPP and its mechanism-driven of grassland ecosystems in China, which is helpful for estimating and predicting the carbon budget of grassland ecosystems in China, and has important guiding significance for policy formulation to mitigate climate change. [ABSTRACT FROM AUTHOR]

• DPSIRM model was used to evaluate the ecological security of Xilingol grassland. • Remote sensing, meteorology, socio-economic and survey data are used in this paper. • We use grid GIS method to obtain more accurate evaluation results. • We analyzed the spatial-temporal evolution characteristics of ecological security. • Changes in the center of gravity in different security levels are also explored. The quantitative evaluation of the ecological security can reflect the changes in the quality of ecological environment and provide a scientific basis for the formulation of land-use policy and for the construction of the ecological environment. In this study, we selected Xilingol League in Inner Mongolia, China, as the research area, and established the driving force-pressure-state-impact-response-management (DPSIRM) model to evaluate grassland ecological security. The results were as follows: (1) During 2005–2015, the overall trend of ecological security level in the study area increased, with the western region being relatively ecologically insecure and the eastern region being more secure. (2) The spatial distribution trend of ecological security level from west to east was " very serious alerts " → " serious alerts " → " moderate alerts " → " light alerts "→ " no alert ". (3) The " very serious alerts " and " serious alerts " are mainly concentrated in Sonid Left Banner and Sonid Right Banner, but from the perspective of spatial migration of ecological security, unsafe and less safe tend to shift to the northeast. This study provides a new idea for grassland ecological security evaluation research, has important practical significance for understanding the ecological security level of Xilingol grassland area, and will guide the reasonable development and utilization of Xilingol grassland. [ABSTRACT FROM AUTHOR]

[Display omitted] • As compared to precipitation, soil moisture greatly influences GPP. • There is a solid spatial agreement among PUE and SWUE with a step-wise gradient from southwest to northeast. • PUE and SWUE are relatively low in extremely arid and humid regions. • In relatively moderate areas, PUE and SWUE increase with the abundance of moisture conditions. • Meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Grassland ecosystem dominates in the Mongolian Plateau, mostly located in its arid and semi-arid regions. Although the ecosystem is an important source for agriculture, it is also a fragile system ecologically. This system is one of the most sensitive areas to global climate change. Precipitation (PPT) and soil moisture (SM) are important water sources in the grassland ecosystem, and their changes would greatly affect vegetation growth. This paper generates the precipitation use efficiency (PUE) and soil water use efficiency (SWUE) of Mongolian Plateau grassland based on multi-source remote sensing data to investigate the spatio-temporal distribution pattern and identify the driving factors. Results showed four main findings. Firstly, two water use efficiency (WUE) indicators show a generally increasing trend from 2000 to 2018, with average PUE and SWUE 1.07 gC·m−2·mm−1 and 1.03 gC/kg·H 2 O, respectively. They have similar spatial distribution patterns, consistent with the available water resources, decreasing from northeast to southwest. However, grassland vegetation growth is more sensitive to soil moisture than precipitation, and the dynamic change of SWUE is smoother and more significant than PUE. Secondly, due to the higher species richness, better vegetation biological characteristics and less severe growth environment, meadow grassland has the highest PUE and SWUE, followed by typical grassland and desert steppe. Thirdly, PUE and SWUE are relatively low in extremely arid and humid regions. In areas with relatively moderate water conditions (PPT in 148–360 mm, SM in 0.14–0.35 cm3/cm3), two indicators increase with the abundance of moisture conditions and reach the maximum. Fourthly, there is a positive linear relationship between PUE (SWUE) with precipitation and a unimodal correlation between PUE (SWUE) with temperature across the entire grassland. However, a varying correlation exists in different grassland ecosystems, especially meadow grassland. Together with analyzing past and future trends, this study provides strong evidence to reflect the impact of global climate change and the management and protection of the grassland ecosystems in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

• The relationship between the groundwater depth and grassland ecology was analysed; • Groundwater depth affected the composition and diversity of vegetation species; • A reasonable groundwater depth can buffer the impact of climate change on the vegetation to maintain grassland ecological stability; • The control of grassland degradation should include the potential exploitation of groundwater. Grassland degradation in the semiarid area of China is often attributed to overgrazing and expansion of irrigated agriculture without the consideration of groundwater. However, falling groundwater levels in the region have caused many problems for grassland ecology. In this paper,the typical sandy grassland- the Tongliao part of Horqin grassland was selected as the study area to study the spatial distribution of normalized difference vegetation index (NDVI) and the relationship between the NDVI and groundwater depth (GD) of each grassland pixel was studied. The temporal variation of the NDVI and its correlations with precipitation were investigated. The relationship between GD and vegetation species diversity in grassland was analyzed based on the vegetation survey. The results of the relation between GD and NDVI and vegetation species showed that GD can buffer the impact of increase in drought events on the vegetation because GD affected the composition and diversity of vegetation species. As the GD increases, the mean NDVI showed a trend of increasing first and then decreasing, and basically remained unchanged when the GD was greater than 3.5 m. The species diversity of grassland decreased with increasing GD and was not related to GD when GD was greater than 3.5 ∼ 4 m. Falling groundwater levels will cause great changes in the structure of grassland; specifically, azonal vegetation will gradually die and 3.5 m may be the critical GD needed to maintain the incomplete extinction of grassland azonal vegetation in the aeolian sandy soil. Correlation analysis proved that the correlation between NDVI and precipitation was significantly enhanced when GD increased sharply from 3 m to 6 m after 1999. This research indicates that the control of grassland degradation should pay attention to controlling the groundwater exploitation to recover a reasonable groundwater level, which was found to be necessary in the support of diverse and climate resilient of vegetation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Sentinel-2 and PlanetScope used for the first time for grassland FVC estimation. • Vegetation indices enhance the band spectral separability of bright soils. • Best indices based on NIR and red for Sentinel-2, NIR and red-edge for PlanetScope. • High resolution necessary to effectively describe grassland FVC spatial variability. Grassland fractional vegetation cover (FVC) accurate mapping on a large scale is crucial, since degraded grasslands contribute less to provisioning services, carbon storage, water purification, erosion control and biodiversity conservation. The spatial and temporal resolution of Sentinel-2 (S2) and PlanetScope (PS) data has never been explored for grassland FVC estimation so far and will enable researchers and agencies to quantify and map timelier and more precisely grassland processes. In this paper we compare FVC estimation models developed from Landsat-8 (L8), S2 and PS imagery. The reference grassland FVC dataset was obtained on the Paganella ski runs (46.15°N, 11.01°E, Italy) applying unsupervised classification to nadir grassland RGB photographs taken from 1.35 m above the soil. Fractional Response Models between reference FVC and 18 vegetation indices (VIs) extracted from satellite imagery were fitted and analysed. Then, leave-one-out cross validation and spatiotemporal change analysis were also performed. Our study confirms the robustness of the commonly used VIs based on the difference between NIR and the red wavelength region (R2 = 0.91 for EVI using S2 imagery) and indicate that VIs based on the red-edge spectral region are the best performing for PS imagery (R2 = 0.89 for RECI). Only medium to high spatial resolution imagery (S2 and PS) precisely mapped spatial patterns at the study site, since grasslands FVC varies at a fine scale. Previously available imagery at medium to low spatial and temporal resolution (e.g., L8) may still be interesting for analysis requiring long time-series of data. [ABSTRACT FROM AUTHOR]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

Abstract: Shrub expansion in arid and semi-arid environment leads to serious issues to earth system regarding net primary production, nutrient cycling, and global carbon balance. Caragana microphylla is a shrub species distributed in Xilin Gol grassland, China, which is expanding into and degrading grassland areas. The current paper proposed two important indicators from Moderate Resolution Imaging Spectroradiometer (MODIS) data that will help potentially distinguish C. microphylla from grass species around it: Difference of Maximum (DM) NDVI/EVI and Difference of Accumulated (DA) NDVI/EVI between wet and dry years. The two indicators were developed based on relative drought resistance of woody vs. grass species. Accuracy assessment was performed using the field survey data collected in Xilin Gol grassland. The results demonstrated that the proposed indicators were effective for identifying C. microphylla, with the overall accuracy of 81.67% and Kappa coefficient of 0.6300. Due to the mixed pixel effect, the performance of the indicators was less desirable when the coverage of C. microphylla was on low levels. In addition, the proposed indicators would be more effective if two years with significant variation in precipitation could be selected. Furthermore, the indicators developed are anticipated to be sensitive to other similar drought resistant shrubs around the world. [Copyright &y& Elsevier]

• Propose a Modified Landscape Disturbance Index. • Determine the spatial distribution of surface mining impact on grasslands. • The impact of surface mining on grasslands landscape ecology is heterogeneous. Coal mining and utilization have always been a great impact on landscape ecology. Many studies have identified the impact of surface mining through landscape changes, but the spatial distribution and extent of the impacts are considered less in existing studies. The objective of this study is to determine the spatial distribution and extent of the impact of surface mining on the Landscape Ecological Health (LEH) of semi-arid grasslands. The research area of this paper is Shengli surface mines, Xilinhot City, Inner Mongolia Autonomous Region, China. The main steps of the research method include LEH assessment, buffer analysis, Landscape Ecological Function Contribution Rate (LEFCR) and Modified Landscape Disturbance Index (MLDI) measurement. The results indicated that: 1) the theoretical reference range of the impact of Shengli surface mines on the semi-arid grassland LEH is 5 km. 2) The proposed MLDI can effectively monitor the disturbance of surface mining on landscape ecology. 3) The heavily affected area is mainly the open-pit landscape; the moderately affected area is mainly the dumping landscape and the mining area industrial square landscape, and the slightly affected area is mainly a small part of the dumping landscape and the surrounding grassland landscape of the mining area. The spatial distribution of the impact of surface mining on the landscape ecology of semi-arid grasslands is heterogeneous, which should be taken into account in landscape ecological management, restoration, and planning. This paper may serve as a reference for the study of the disturbances and impacts of human activities on landscape ecology. [ABSTRACT FROM AUTHOR]

• Annual maximum NDVI (NDVI max) and time-integrated NDVI (TI-NDVI) were compared. • Spatial and temporal variations of grassland revealed by TI-NDVI differ a lot from that revealed by NDVI max. • TI-NDVI captures more spatial and temporal heterogeneity of high covered grassland. • TI-NDVI also captures more about the close correlations between grassland dynamics and precipitation or temperature than NDVI max. The saturation of NDVI makes the annual maximum NDVI(NDVI max) underestimate vegetation with high coverage. Time-Integrated NDVI(TI-NDVI) accumulates NDVIs of vegetation at different growing stages in one growing season. TI-NDVI might alleviate the saturation effect of NDVI max when vegetation dynamics are assessed at annual time scale. Taking grassland in Ili Valley at the west end of Tianshan Mountain as the study area, this paper compared NDVI max and TI-NDVI systematically for assessing grassland dynamics and also their correlations with precipitation and temperature. Data of time-series Moderate Resolution Imaging Spectroradiometer(MODIS) NDVI were used and methods of Mann-Kendall trend test, Sen's slope estimator test, coefficient of variation(CV) and partial correlation coefficient were employed. We found that there were 28.69% grassland in Ili Valley where the TI-NDVI and NDVI max showed different changing trends over the years of 2000–2019. TI-NDVI not only revealed more about the spatial and temporal heterogeneities of grassland with high coverage than NDVI max at annual time scale, but also captured more about the close relationship between grassland dynamics and precipitation or temperature in Ili Valley. [ABSTRACT FROM AUTHOR]

Abstract: The paper presents a methodological framework for quantifying soil ecosystem health with special focus on chemical stressors and ecological integrity as determinant for biological productivity of soil ecosystems. Ecological risk assessment is needed to facilitate the assessment of soil health and the capability of a soil to provide ecosystem services such as e.g. detoxification and decomposition of wastes, soil formation and renewal of soil fertility. We have developed such an approach that is based on systematic enumeration of vulnerable indicators that reflect essential soil ecosystem structures and processes that underlay such soil ecosystem services. The method is illustrated for a shortlist of common chemical stressors, represented by nickel, cadmium, chlorpyriphos, lindane and diazinon, and applied in a comparative assessment of suitability for use of grassland on contaminated soil. A comprehensive and relevant set of ecological integrity indicators has been analysed to derive a smaller core set of indicators highly relevant for all types of grassland use; i.e. reflecting ecological requirements to be fulfilled for any sustainable use of grassland. [Copyright &y& Elsevier]

Abstract: In this study a new field of action for outcome-oriented payment schemes (payments by results) is introduced. That is the promotion of extensive summer grazing activities of livestock. Combining this objective with methods of outcome-oriented rewarding means to tie the payment to vegetation criteria indicating regular grazing activities instead of regularly controlling the presence of livestock on the field. In comparison to action-oriented concepts, the main advantage of this approach is that only one inspection walk is required. The paper describes and evaluates the first attempt to determine suitable indicator species for such a scheme. We pre-tested the approach with existing vegetation data only. A database of relevés, plant species and agriculturally used grassland communities of the regional district Doberan, Mecklenburg-Western Pomerania (Germany) was utilised. Grazing activities can be identified by the existence of a specific number of vascular species indicating grazing activities on a particular grassland field. We utilised the indicator function of grassland communities for management conditions to resolve the deficiency of data base relevés for providing no data about the management regime. The pasture community Lolio perennis-Cynosuretum cristati was used as a reference system to pre-test the suitability of different combinations of potential indicator species for separating pasture from non-pasture grassland. A tentative quantitative selection criterion for pasture grassland was proposed. That is the number of indicator species a relevé/grassland field has to hold to be identified as pasture grassland. Database relevés are not sufficient for calibrating such a concept, though. It is necessary to adjust the indicator species list to other drivers like use characteristics, nutrient availability, and soil moisture level. Here additional fieldwork is needed. As a first evidence for the validity of the concept, the suitability of the indicator list was measured by testing the correct identification of pasture grassland in a long-term grassland experiment built by the treatments “grazing” and “mowing”. ANOVA tests on the precision of the indicator species showed significant results. In the discussion a modification of the inventory method of grassland fields was recommended. The precision of the developed concept could be further enhanced by introducing the species dominance as a second factor deciding about the correct identification of pasture use. [Copyright &y& Elsevier]

• Propose a NCUE index for monitoring grassland dynamic to reduce climate fluctuation. • NCUE is suitable for cold and dry climate characteristics of Tibetan Plateau, China. • Construct an Integrated Meteorological Factor (IMF) • Reveal the spatio-temporal characteristics of grassland change from 1982 to 2012. • NCUE can mitigate the impact of climate fluctuation and reflect grassland changes. Grassland degradation is one of the most pressing challenges in natural environment and anthropogenic society. However, there is yet no effective approach for monitoring the spatio-temporal pattern of large-scale grassland degradation. In particular, the research on grassland changes in the harsh natural environment such as the Qinghai-Tibet Plateau is still in its infancy due to complexity, and it is extremely difficult for humans to reach these remote areas. The annual changes in the grassland biomass might be the results of climate fluctuations or grassland degradation. To test the hypothesis, the impact of inter-annual climate fluctuations needs to be considered when monitoring the grassland degradation based on spatio-temporal change of grassland biomass. In this paper, we propose a Novel Climate Use Efficiency index (NCUE) by considering rainfall, temperature, sunlight time, wind speed, surface temperature, accumulated temperature, time lag effect, light, temperature and water suitability and their coordination climatic factors that mainly affect vegetation growth comprehensively, to monitor grassland change suitable for cold and dry climate characteristics of the Qinghai-Tibet Plateau, and to reduce the effect of inter-annual variability of grassland productivity caused by climate fluctuation. As a consequence, grassland degradation monitoring could be more accurate and objective than existing ecological indicators. Our experiments show that the slope of NCUE over 31 years from 1982 to 2012 is 0.0028, showing a recovery trend in grassland. Degradation and restoration of grassland exist at the same time, and their proportions are 20.49% and 23.89%, respectively. By comparing with in-situ measurements in 2013 and 2009, 68% consistency was achieved with our prediction, and the 70% consistency is achieved by comparing with the positive and negative change trend of accumulated NDVI during the growing season. Moreover, the comparative analysis of land use/cover changes (LUCC) from 1990 to 2010 shows 69% of consistency. The ratio of the area of grassland significantly degradation and recovered predicted by NCUE change trend is 1.41% and 1.43%, respectively. It occupies a very small area of the study area. Yet, that predicted by NDVI change trend is 42.17% and 31.90%, respectively, and about 70% of the area is detected as drastic changes. It shows that NDVI is sensitive to climate fluctuations, while NCUE reduces the impact of climate fluctuations, reflecting change of grassland being affected by human activities and long-term climate change. The novel NCUE has great potential and utility to minify the impact of climate fluctuation and reflect grassland changes over space and time quantitatively. Such ecological index provides a new understanding of spatial and temporal patterns of grassland degradation in the Three River Headwaters Region (TRHR) at the same time. [ABSTRACT FROM AUTHOR]

Abstract: Vegetation or habitat types are ecological phases, which can assume multiple states, and transformations from one type of phase to another are ecological phase transitions. If an ecological phase maintains its condition of normality in the linked processes and functions that constitute ecosystems then is believed healthy. An adaptive cycle, such as in Holling''s model, has been proposed as a fundamental unit for understanding complex systems and their dynamics. Such model alternates between long periods of aggregation and transformation of resources and shorter periods that create opportunities for innovation. The likelihood of shifts among different phases largely depends on resilience; thereby, a clear and measurable definition of resilience has become paramount. Different resilience levels are expected to be intertwined with different scale ranges of real habitats, in relation to the kind and intensity of natural and human disturbances. We argue that the type, magnitude, length and timing of external pressure, its predictability, the exposure of habitats, and the habitat''s inherent resistance have important interactive relationships which determine resilience at multiple scales. In this paper, we provide an operational framework to derive operational indices of short-term retrospective resilience of real grasslands in a northern Italy watershed, from multi-scale analysis of landscape patterns, to find scale domains for habitat edges where change is most likely, i.e. resilience is lowest and fragility highest. That is achieved through cross-scale algorithms like fractal analysis coupled with change detection of ecological response indices. The framework implements the integration of habitat edge fractal geometry, the fitting of empirical power functions by piecewise regressions, and change detection as a procedure to find scale domains for grassland habitat where retrospective resilience is lowest. The effects of external pressure are significantly related to habitat scale domains, resulting from the interactions among ecological, physical, and social controls shaping the systems. Grassland scale domains provide evidence and support for identifying and explaining scale invariant ecological processes at various scales, from which much insight can be gained for characterizing grassland adaptive cycles and capabilities to resist disturbances. [Copyright &y& Elsevier]

• Thresholds define when biodiversity conservation regulations do or don't apply. • Conservation thresholds are often based on judgment and opinion. • Structured expert elicitation and hierarchical modelling can identify thresholds. • Simple floristic indicators can predict expert derived thresholds in grassland. Conservation decision thresholds have most often been described within the context of triggering management actions necessary to maintain an ecological system within a desired state. Thresholds are also applied within conservation legislation, often as triggers to determine when certain regulations may or may not apply. In many applications, thresholds have been criticized for being subjective, inconsistent and open to bias and rather than being derived from sound and objective analysis of ecological data they can result from undocumented judgement and opinion. Although judgement and opinion may be necessary for setting decision thresholds in circumstances where appropriate data are lacking, a failure to use structured processes for obtaining, analysing and presenting these can result in contentious, unsubstantiated thresholds. This paper demonstrates how thresholds for identifying grassland conservation values, for the purposes of decision making under vegetation clearing legislation, can be estimated from simple floristic indicators after combining the judgment of multiple experts through hierarchical modelling. Using two stages of elicitation and modelling, the resulting conservation value states were found to be consistent and predictable, correlated with known paddock-level disturbance history and congruent with prior condition states that define an endangered grassland ecosystem. While the thresholds, and the underpinning coefficients, may not reflect the opinion of all grassland ecologists, they are transparent, testable and can be updated or modified to incorporate additional criteria and a greater diversity of stakeholders. The approach could be adopted to similar contexts in other vegetation types where policy or regulatory decision triggers are required. [ABSTRACT FROM AUTHOR]