Search Results (52)

Grazing affects soil organic carbon (SOC) through plant removal, livestock trampling, and manure deposition. However, the impact of grazing on SOC is also influenced by multiple factors such as climate, soil properties, and management approaches. Despite extensive research, the mechanisms by which grazing intensity influences SOC density in grasslands remain incompletely understood. This study examines the effects of varying grazing intensities on SOC density (0–30 cm) dynamics in temperate grasslands of northern China using field surveys and experimental analyses in a typical steppe ecosystem of Inner Mongolia. Results show that moderate grazing (3.8 sheep units/ha/yr) led to substantial consumption of aboveground plant biomass. Relative to the ungrazed control (0 sheep units/ha/yr), aboveground plant biomass was reduced by 40.5%, 36.2%, and 50.6% in the years 2016, 2019, and 2020, respectively. Compensatory growth failed to fully offset biomass loss, and there were significant reductions in vegetation carbon storage and cover (p < 0.05). Reduced vegetation cover increased bare soil exposure and accelerated topsoil drying and erosion. This degradation promoted the downward migration of SOC from surface layers. Quantitative analysis revealed that moderate grazing significantly reduced surface soil (0–10 cm) organic carbon density by 13.4% compared to the ungrazed control while significantly increasing SOC density in the subsurface layer (10–30 cm). Increased precipitation could mitigate the SOC transfer and enhance overall SOC accumulation. However, it might negatively affect certain labile SOC fractions. Elucidating the mechanisms of SOC variation under different grazing intensities and precipitation regimes in semi-arid grasslands could improve our understanding of carbon dynamics in response to environmental stressors. These insights will aid in predicting how grazing systems influence grassland carbon cycling under global climate change. Full article

As a vital part of the Eurasian temperate grassland, the Chinese temperate grassland is primarily distributed in the Inner Mongolia Plateau. This paper focuses on mapping temperate grassland dynamics from the 1980s to the 2010s in Inner Mongolia, which was divided into temperate meadow steppe (TMS), temperate typical steppe (TTS), temperate desert steppe (TDS), temperate steppe desert (TSD) and temperate desert (TD). Multi-source features, including multispectral reflectance, vegetation growth, topography, water bodies, meteorological data, and soil characteristics, were selected based on their distinct physical properties and remote sensing variations. Then, we applied deep neural network (DNN) models to classify them, achieving an accuracy of 79.4% in the 1980s and 81.1% in the 2000s. Additionally, validation in the 2010s through field reconnaissance demonstrated an accuracy of 72.7%, which was acceptable, confirming that DNN is an effective method for classifying temperate grasslands. The results revealed that TTS had the highest proportion in the study area (39%), while TMS and TSD had the lowest (8.2% and 8.1%, respectively). Grassland types have the distribution law of aggregation; according to statistics, 61.1% of the grassland area remained unchanged, and the transition zone between adjacent grassland classes was highly easy to change. The area variation mainly came from TTS, TDS, and TSD, but not TD. The mutual transformation of different grassland types occurred mainly in adjacent areas between them. This study demonstrates the potential of DNN for long-term grassland mapping and provides the most comprehensive classification maps of Inner Mongolia grasslands to date, which are invaluable for grassland research and conservation efforts in the area. Full article

Mountain grassland ecosystems around the globe are highly sensitive to seasonal extreme climate events, which thus highlights the critical importance of understanding how such events have affected vegetation dynamics over recent decades. However, research on the time-lag of the effects of seasonal extreme climate events on vegetation has been sparse. This study focuses on Tajikistan, which is characterized by a typical alpine meadow–steppe ecosystem, as the research area. The net primary productivity (NPP) values of Tajikistan’s grasslands from 2001 to 2022 were estimated using the Carnegie–Ames–Stanford Approach (CASA) model. In addition, 20 extreme climate indices (including 11 extreme temperature indices and 9 extreme precipitation indices) were calculated. The spatiotemporal distribution characteristics of the grassland NPP and these extreme climate indices were further analyzed. Using geographic detector methods, the impact factors of extreme climate indices on grassland NPP were identified along a gradient of different altitudinal bands in Tajikistan. Additionally, a time-lag analysis was conducted to reveal the lag time of the effects of extreme climate indices on grassland NPP across different elevation levels. The results revealed that grassland NPP in Tajikistan exhibited a slight upward trend of 0.01 gC/(m²·a) from 2001 to 2022. During this period, extreme temperature indices generally showed an increasing trend, while extreme precipitation indices displayed a declining trend. Notably, extreme precipitation indices had a significant impact on grassland NPP, with the interaction between Precipitation anomaly (PA) and Max Tmax (TXx) exerting the most pronounced influence on the spatial variation of grassland NPP (q = 0.53). Additionally, it was found that the effect of extreme climate events on grassland NPP had no time-lag at altitudes below 500 m. In contrast, in mid-altitude regions (1000–3000 m), the effect of PA on grassland NPP had a significant time-lag of two months (p < 0.05). Knowing the lag times until the effects of seasonal extreme climate events on grassland NPP will appear in Tajikistan provides valuable insight for those developing adaptive management and restoration strategies under current seasonal extreme climate conditions. Full article

Resistance, resilience, and recovery time are critical for quantifying the stability of grasslands in response to drought disturbances. Few studies have simultaneously considered both drought intensity and duration to analyze the stability of different grassland types, which may overlook short-term extreme or long-term cumulative effects. This study used the monthly Standardized Precipitation Evapotranspiration Index (SPEI) to identify distinct drought patterns in Inner Mongolia, China, from 1998 to 2020, accounting for both intensity and duration. Grassland stability was assessed using monthly SPOT-VGT Normalized Difference Vegetation Index (NDVI) data. We focused on the vegetation response to short-term climate changes while minimizing the influence of seasonal fluctuations in vegetation growth. Six drought patterns were identified, and the resistance of grassland types under the same drought pattern followed this order: temperate desert steppe (TDS) > temperate typical steppe (TTS) > temperate meadow steppe (TMS). Resilience was ranked as TDS < TTS < TMS, while recovery time followed the reverse trend: TDS > TTS > TMS. A trade-off was observed between resilience and resistance. Most grasslands were able to recover within five months following a drought. These findings provide scientific support for enhancing ecosystem adaptability to climate change and for managing grassland resources more effectively. Full article

The security of water resources in the desert steppe ecosystem faces threats due to large-scale vegetation restoration. Dynamic changes in soil moisture result from the interplay of precipitation replenishment and evapotranspiration depletion, both directly regulated by vegetation and soil. To achieve sustainable vegetation restoration, understanding the quantifiable impacts of precipitation, evapotranspiration, soil, and vegetation on spatiotemporal soil moisture dynamics is crucial. However, these effects remain insufficiently understood. In this study, against the background of an extreme drought from 2020 to 2022, four typical herbaceous plant communities—Agropyron mongolicum, Sophora alopecuroides, Stipa breviflora, and Achnatherum splendens—were selected for investigation in Yanchi County, Ningxia Province, Northwest China. We analyzed dynamic changes in soil moisture at 0–120 cm during depletion, recovery, and stability periods, quantifying the relative contributions of precipitation, evapotranspiration, soil clay/sand ratio (C/S), and biomass to soil moisture dynamics. The results showed that the 0–120 cm soil moisture of the four plant communities in the depletion, recovery, and stability periods decreased from 7.38% to 6.81%, 11.22% to 8.08%, and 11.70% to 5.84%, respectively. In terms of relative importance, precipitation and evapotranspiration accounted for 25% to 50% and 23.6% to 39.6% of the total explanation for the soil moisture in each plant community, respectively. C/S primarily influenced soil moisture in the S. alopecuroides community, demonstrating a significant positive correlation with soil moisture and accounting for 49.1% of the total explanation. Biomass mainly affected soil moisture in the A. mongolicum, S. breviflora, and A. splendens communities and had a significant negative correlation with soil moisture, accounting for 5.7%, 13.1%, and 9.8% of the total interpretation, respectively. The continuous extreme drought caused the soil moisture deficit to extend from the shallow to the deep layers. The effects of C/S and biomass on soil moisture occurred in leguminous and gramineous communities, respectively. Full article

The grassland ecosystem in the Inner Mongolia Autonomous Region (IMAR) serves as a vital ecological barrier in northern China, and the vegetation productivity in the grasslands exhibits considerable temporal and spatial variations. However, few studies have examined the long-term variations in the NPP in the IMAR and quantified the effects of natural factors and human activities on the NPP. The study modeled the net primary productivity (NPP) of the IMAR’s grasslands using the Carnegie–Ames–Stanford approach (CASA) model and employed linear regression, trend analysis, and spatial statistics to analyze the spatio-temporal patterns in vegetation productivity and explore the impact on the NPP of natural and socio-economic factors over the past two decades. The results reveal that the average NPP value from 2001 to 2021 was 293.80 gC∙m⁻² a⁻¹, characterized by spatial clustering of a relatively high NPP in the east, a low NPP in the west, and an annual increase of 3.26 gC∙m⁻² over the years. The NPP values varied significantly across different vegetation cover types, with meadows having the highest NPP, followed by typical steppe and desert grasslands. The spatial distribution pattern and temporal changes in the grassland productivity are the result of both natural factors and human activities, including topographical properties and socio-economic indicators such as gross domestic product, night-time light, and population. The results for the NPP in the IMAR were based solely on the CASA model and, therefore, to achieve improved data reliability, exact measurements in real field conditions will be conducted in the future. The findings from the spatial clustering and temporal trajectories of the NPP and the impacts from the factors can provide useful guidance to planning grassland vegetation protection policies for the IMAR. Full article

Vegetation phenology serves as a sensitive indicator of climate change. However, the mechanism of the hydrothermal role in vegetation phenology changes is still controversial. Utilizing the data on the Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from MODIS and meteorological data, the study employed the dynamic threshold method to derive the end of the growing season (EOS). The research delved into the spatiotemporal patterns of the EOS for typical steppe vegetation in the semi-arid region of Inner Mongolia spanning the period from 2003 to 2022. Furthermore, the investigation scrutinized the response of EOS to temperature and precipitation dynamics. The results showed that (1) the dynamic threshold method exhibited robust performance in the EOS of typical steppe vegetation, with an optimal threshold of 45% and a Root Mean Square Error (RMSE) of 5.5 days (r = 0.81); (2) the spatiotemporal patterns of the EOS of typical steppe vegetation in the semi-arid region experienced a noteworthy reversal from 2003 to 2022; (3) the lag effects of precipitation and temperature on the EOS were found, and the lag time scales were mainly 1 month and 2 months. The increase in precipitation in August was the key reason for the reversal of the EOS, and satisfying the precipitation was a prerequisite for the temperature to delay the EOS. The study emphasizes the important role of water availability in regulating the response of the EOS to hydrothermal factors and highlights the utility and reliability of FPAR in monitoring the EOS of typical steppe vegetation. Full article

The accurate estimation of aboveground net herbaceous production (ANHP) is crucial in rangeland management and monitoring. Remote and rural rangelands typically lack direct observation infrastructure, making satellite-derived methods essential. When ground data are available, a simple and effective way to estimate ANHP from satellites is to derive the empirical relationship between ANHP and plant-absorbed photosynthetically active radiation (APAR), which can be estimated from the normalized difference vegetation index (NDVI). While there is some evidence that this relationship will differ across rangeland vegetation types, it is unclear whether this relationship will change across grazing management regimes. This study aimed to assess the impact of grazing management on the relationship between ground-observed ANHP and satellite-derived APAR, considering variations in plant communities across ecological sites in the shortgrass steppe of northeastern Colorado. Additionally, we compared satellite-predicted biomass production from the process-based Rangeland Analysis Platform (RAP) model to our empirical APAR-based model. We found that APAR could be used to predict ANHP in the shortgrass steppe, with the relationship being influenced by ecosite characteristics rather than grazing management practices. For each unit of added APAR (MJ m⁻² day⁻¹), ANHP increased by 9.39 kg ha⁻¹, and ecosites with taller structured herbaceous vegetation produced, on average, 3.92–5.71 kg ha⁻¹ more ANHP per unit APAR than an ecosite dominated by shorter vegetation. This was likely due to the increased allocation of plant resources aboveground for C₃ mid-grasses in taller structured ecosites compared to the C₄ short-grasses that dominate the shorter structured ecosites. Moreover, we found that our locally calibrated empirical model generally performed better than the continentally calibrated process-based RAP model, though RAP performed reasonably well for the dominant ecosite. For our empirical models, R² values varied by ecosite ranging from 0.49 to 0.67, while RAP R² values ranged from 0.07 to 0.4. Managers in the shortgrass steppe can use satellites to estimate herbaceous production even without detailed information on short-term grazing management practices. The results from our study underscore the importance of understanding plant community composition for enhancing the accuracy of remotely sensed predictions of ANHP. Full article

Grasshoppers pose a significant threat to both natural grassland vegetation and crops. Therefore, comprehending the relationship between environmental factors and grasshopper occurrence is of paramount importance. This study integrated machine learning models (Maxent) using the kuenm package to screen MaxEnt models for grasshopper species selection, while simultaneously fitting remote sensing data of major grasshopper breeding areas in Inner Mongolia, China. It investigated the spatial distribution and key factors influencing the occurrence of typical grasshopper species in grassland ecosystems. The modelling results indicate that a typical steppe has a larger suitable area. The soil type, above biomass, altitude, and temperature, predominantly determine the grasshopper occurrence in typical steppes. This study explicitly delineates the disparate impacts of key environmental factors (meteorology, vegetation, soil, and topography) on grasshopper occurrence in typical steppes. Furthermore, it provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be instrumental in formulating future management measures to guarantee grass ecological environment security and the sustainable development of grassland. Full article

Drylands are characterized by unique ecosystem types, sparse vegetation, fragile environments, and vital ecosystem services. The accurate mapping of dryland ecosystems is essential for their protection and restoration, but previous approaches primarily relied on modifying land use data derived from remote sensing, lacking the direct utilization of latest remote sensing technologies and methods to map ecosystems, especially failing to effectively identify key ecosystems with sparse vegetation. This study attempts to integrate Google Earth Engine (GEE), random forest (RF) algorithm, multi-source remote sensing data (spectral, radar, terrain, texture), feature optimization, and image segmentation to develop a fine-scale mapping method for an ecologically critical area in northern China. The results showed the following: (1) Incorporating multi-source remote sensing data significantly improved the overall classification accuracy of dryland ecosystems, with radar features contributing the most, followed by terrain and texture features. (2) Optimizing the features set can enhance the classification accuracy, with overall accuracy reaching 91.34% and kappa coefficient 0.90. (3) User’s accuracies exceeded 90% for forest, cropland, and water, and were slightly lower for steppe and shrub-steppe but were still above 85%, demonstrating the efficacy of the GEE and RF algorithm to map sparse vegetation and other dryland ecosystems. Accurate dryland ecosystems mapping requires accounting for regional heterogeneity and optimizing sample data and feature selection based on field surveys to precisely depict ecosystem patterns in complex regions. This study precisely mapped dryland ecosystems in a typical dryland region, and provides baseline data for ecological protection and restoration policies in this region, as well as a methodological reference for ecosystem mapping in similar regions. Full article

Diaspore dispersal is crucial in shaping plant population dynamics, biodiversity, and ecosystem functions. The effect of the vegetation structure on the secondary wind dispersal of diaspores with different appendage types is not well understood. Using a wind tunnel and a high-definition video camera, we accurately measured the lift-off and dispersal velocities of diaspores from sixteen plant species across six wind velocities (2, 4, 6, 8, 10, and 12 m s⁻¹) under six simulated vegetation structures. Vegetation structure and appendage type were pivotal factors, explaining 41.1% and 42.3% of the variance in lift-off velocity and accounting for 12.0% and 25.3% of the variability in diaspore dispersal velocity, respectively. Vegetation coverage was the main factor influencing near-surface wind velocity, and the lift-off and dispersal velocities of diaspores changed significantly when vegetation coverage exceeded 40%. Diaspores with one wing, having high lift-off velocities and low dispersal velocities, adopt the anti-long-distance wind dispersal strategy, whereas diaspores with pappus, having low lift-off velocities and high dispersal velocities, adopt the long-distance wind dispersal strategy. In contrast, diaspores with thorn, discoid, balloon, and four wings adopt the non-long-distance wind dispersal strategy, suitable for environments such as low-coverage deserts and desert steppes but not high-coverage typical steppes. This study could help comprehend the effect of the vegetation structure on the dispersal process of diaspores, which facilitate habitat restoration and biodiversity conservation of grassland and forest ecosystems. Full article

Aboveground biomass (AGB) is an important indicator of the grassland ecosystem. It can be used to evaluate the grassland productivity and carbon stock. Satellite remote sensing technology is useful for monitoring the dynamic changes in AGB across a wide range of grasslands. However, due to the scale mismatch between satellite observations and ground surveys, significant uncertainties and biases exist in mapping grassland AGB from satellite data. This is also a common problem in low- and medium-resolution satellite remote sensing modeling that has not been effectively solved. The rapid development of uncrewed aerial vehicle (UAV) technology offers a way to solve this problem. In this study, we developed a method with UAV and satellite synergies for estimating grassland AGB that filled the gap between satellite observation and ground surveys and successfully mapped the grassland AGB in the Hulunbuir meadow steppe in the northeast of Inner Mongolia, China. First, based on the UAV hyperspectral data and ground survey data, the UAV-based AGB was estimated using a combination of typical vegetation indices (VIs) and the leaf area index (LAI), a structural parameter. Then, the UAV-based AGB was aggregated as a satellite-scale sample set and used to model satellite-based AGB estimation. At the same time, spatial information was incorporated into the LAI inversion process to minimize the scale bias between UAV and satellite data. Finally, the grassland AGB of the entire experimental area was mapped and analyzed. The results show the following: (1) random forest (RF) had the best performance compared with simple regression (SR), partial least squares regression (PLSR) and back-propagation neural network (BPNN) for UAV-based AGB estimation, with an R² of 0.80 and an RMSE of 76.03 g/m². (2) Grassland AGB estimation through introducing LAI achieved higher accuracy. For UAV-based AGB estimation, the R² was improved by an average of 10% and the RMSE was reduced by an average of 9%. For satellite-based AGB estimation, the R² was increased from 0.70 to 0.75 and the RMSE was decreased from 78.24 g/m² to 72.36 g/m². (3) Based on sample aggregated UAV-based AGB and an LAI map, the accuracy of satellite-based AGB estimation was significantly improved. The R² was increased from 0.57 to 0.75, and the RMSE was decreased from 99.38 g/m² to 72.36 g/m². This suggests that UAVs can bridge the gap between satellite observations and field measurements by providing a sufficient training dataset for model development and AGB estimation from satellite data. Full article

The northern foothills of Yinshan Mountain are situated in northern China’s agricultural and pastoral ecotone, serving as a crucial ecological barrier. To comprehensively assess the impact of grassland resource restoration in this region since the initiation of the Grain-for-Green conversion project in 2000, this study analyzes the spatiotemporal characteristics of precipitation use efficiency (PUE) and investigates climate-driven factors during 2001–2021. The results showed that the grassland types at the north foot of Yinshan could be divided into four categories: warm-arid, warm subtropical semidesert (WSS), warm temperate-arid, warm temperate zonal semidesert (WZS), warm temperate-semiarid, warm temperate typical steppe (WTS), and warm temperate-subhumid forest steppe (WFT). The NPP of the four grassland species were 151.34 (WSS), 196.72 (WZS), 283.33 (WTS), and 118.06 gC·m⁻² (WFT), and correspondingly, the PUE of the four grassland species were 0.66 (WSS), 0.66 (WZS), 0.80 (WTS), and 0.57 gC·m⁻²·mm⁻¹ (WFT). From 2001 to 2021, PUE in grassland showed an overall upward trend, rising from 0.57 to 0.99 gC·m⁻²·mm⁻¹. The trend analysis found that the vegetation ecological area of the northern foot of Yinshan became better, of which 54.36% was improved and 15.72% was degraded. It is worth pointing out that WSS had the highest degree of improvement, while WFT was in a degraded state. The climate driving force analysis shows that the regional contribution of precipitation is 19.57%, temperature is 28.33%, potential evapotranspiration is 13.65%, wind speed is 10.79%, and saturated vapor pressure is 27.66%. Full article

To reveal the changes in the life history characteristics of grassland plants due to vegetation restoration, plant species and communities were analyzed for their competitor, stress tolerator, and ruderal (CSR) ecological strategies after the introduction of woody plants in the damaged steppe grassland and were compared with those in reference sites in Hulunbuir, Inner Mongolia. As a result, it was found that the introduction of the woody plants (Corethrodeneron fruticosum, Caragana microphylla, Populus canadensis, and Pinus sylvestris var. mongolica) into the damaged land greatly increased the plant species diversity and CSR eco-functional diversity as the succession progressed. The plant strategies of the temperate typical steppe (TTS) and woodland steppe (WS) in this Asian steppe are CSR and S/SR, respectively, which means that the plants are adapted to disturbances or stress. As the restoration time elapsed in the damaged lands exhibiting (R/CR) (Corispermum hyssopifolium), the ecological strategies were predicted to change in two ways: (1) →R/CSR (Cynanchum thesioides, Astragalus laxmannii, etc.) → CSR in places (TSS) (Galium verum var. asiaticum, Saussurea japonica, etc.) where only shrubs were introduced, and (2) → S/SR (Allium mongolicum, Ulmus pumila, etc.) → S/SR in sites (WS) (Ulmus pumila, Thalictrum squarrosum, etc.) where trees and shrubs were planted simultaneously. The results mean that the driving force that causes succession in the restoration of temperate grasslands is determined by the life-form (trees/shrubs) of the introduced woody plants. This means that for the restoration of these grasslands to be successful, it is necessary to introduce woody tree species at an early stage. Full article

Leaf area index (LAI) is a key indicator of vegetation structure and function, and its products have a wide range of applications in vegetation condition assessment and usually act as important input parameters for ecosystem modeling. Grassland plays an important role in regional climate change and the global carbon cycle and numerous studies have focused on the product-based analysis of grassland vegetation changes. However, the performance of various LAI products and their discrepancies across different grassland types in drylands remain unclear. Therefore, it is critical to assess these products prior to application. We evaluated the accuracy of four commonly used LAI products (GEOV2, GLASS, GLOBMAP, and MODIS) using LAI reference maps based on both bridging and cross-validation approaches. Under different grassland types, the GLASS LAI performed better in meadow steppe (R² = 0.26, RMSE = 0.41 m²/m²) and typical steppe (R² = 0.32, RMSE = 0.38 m²/m²); the GEOV2 LAI performed better in desert steppe (R² = 0.39, RMSE = 0.30 m²/m²). When we assessed their spatial and temporal discrepancies during the period from 2010 to 2019, the four LAI products overall showed a high spatial and temporal consistency across the region. Compared with GLASS LAI, the most consistent to least consistent correlations can be ordered by GEOV2 LAI (R² = 0.94), MODIS LAI (R² = 0.92), and GLOBMAP LAI (R² = 0.87). The largest differences in LAI throughout the year occurred in July for all grassland types. Limited by the location and number of sample plots, we mainly focused on spatial and temporal variations. The spatial heterogeneity of land surface is pervasive, especially in vast grassland areas with rich grassland types, and the results of this study can provide a basis for the application of the product in different grassland types. Furthermore, it is essential to develop highly accurate and reliable satellite-based LAI products focused on grassland from the regional to the global scale according to these popular approaches, which is the next step in our work plan. Full article