Search Results (44)

Against the backdrop of global warming, grassland degradation (GD) in arid and semi-arid regions has become a critical issue constraining ecosystem stability and socio-ecological resilience. This study aims to reveal the spatial differentiation of drivers of GD across four functional zones in Inner Mongolia—resource-oriented (RO), center-service-oriented (CSO), agro-pastoral-oriented (APO), and ecological-oriented (EO)—at the county level, using grassland conversion processes as a structural proxy. The results show that land cover changed in 19.29%, 18.69%, 17.28%, and 4.67% of the RO, CSO, APO, and EO regions, respectively, with GR mainly occurring in the RO region, while GD was more prevalent in the other zones. Drivers of GD exhibit significant variations across functional zones. In the RO zone, land use change is primarily associated with human disturbance. In the CSO and APO zones, it is associated with human activities, climatic factors, and urbanization. In the EO zone, the identified drivers show strong spatial heterogeneity, with urbanization, grazing intensity, and climate change emerging as key associated factors in the Hulunbuir, Xilingol, and Arxar regions. Overall, the results reveal a spatial gradient in the relative importance of anthropogenic pressure and climatic stress, with broader implications for adaptive and place-specific dryland governance under ongoing warming and increasing aridification. Full article

Grassland lake basins are mostly located in arid and semi-arid regions and represent typical ecologically fragile zones. As a representative inland lake in the cold and arid region of northern China, Hulun Lake serves as a crucial node for maintaining the ecological balance of the Hulunbuir grassland. Studying its ecological carrying capacity is particularly key to implementing the philosophy of a holistic approach to the management of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts. Based on data from 2018 to 2024 across four cities (banners, districts) in the Hulun Lake basin, this study constructs an evaluation system to measure ecological carrying capacity across three dimensions—ecosystem support, human activity pressure, and socio-economic response—using the Pressure–State–Response (PSR) model. Spatial analysis and geodetector methods are employed to explore its spatiotemporal differentiation and influencing factors. The findings are as follows: (1) The ecological carrying capacity in the Hulun Lake basin exhibits a significant spatial differentiation pattern, characterized by a gradient of “high in the east, low in the west; high in pastoral areas, low in urban areas.” (2) The overall trend in ecological carrying capacity shows a slow increase amid fluctuations, but the carrying capacity level remains relatively low. (3) The core driving forces of ecological carrying capacity primarily stem from the dimensions of population quality and infrastructure, while the direct influence of agricultural production is relatively limited. (4) Transportation infrastructure plays a strongly influential role as a driving mechanism of ecological carrying capacity in the Hulun Lake basin. Its synergy with factors such as education, information, and industry significantly affects both the ecosystem support capacity and the socio-economic responses of the basin. This study provides a reference for ensuring the ecological security of the Hulun Lake basin. Full article

Understanding the ecological drivers of plant-associated microbiota is essential for predicting grassland ecosystem resilience. This study aimed to characterize the community structure, functional potential, and soil environmental drivers of rhizosphere and root endophytic microbiota associated with Polygonum divaricatum across three Hulunbuir Grassland sites. A nested sampling design was applied with three replicated plots per site, from which paired rhizosphere soil and root samples were collected. Each sample represented a composite of 15 plants, yielding six samples per site (total n = 18) and allowing the separation of compartmental and environmental effects on community assembly. P. divaricatum plays a key role in nutrient cycling and soil stability; however, its rhizosphere and root microbiomes remain poorly characterized. Fungal diversity was consistently higher in the root endosphere, whereas bacterial diversity was greater in rhizosphere soils. Fungal assemblages were dominated by Ascomycota and Mortierellomycota, primarily represented by Mortierella and Trichoderma, while bacterial communities were dominated by Actinomycetota and Pseudomonadota, enriched in Bradyrhizobium and Pseudonocardia. Community differentiation reflected strong compartmental filtering and responses to soil pH, organic carbon, nitrogen, and enzyme activities. Functional prediction indicated clear compartmental partitioning: in the rhizosphere, bacterial communities were enriched in pathways related to carbon and nitrogen metabolism and secondary metabolite biosynthesis, whereas in the root endosphere, functional profiles were more associated with transport, uptake, and fermentation; fungal communities were dominated by saprotrophic and symbiotrophic guilds. These findings demonstrate that soil biochemical gradients and host-driven filtering jointly structure the P. divaricatum microbiome, providing ecological insights into plant–microbe–soil interactions and the maintenance of grassland ecosystem stability. Full article

Grassland aboveground biomass (AGB) is a key indicator of grassland ecosystem structure and function, and its accurate monitoring is of great importance for assessing grassland ecological conditions and supporting sustainable grassland management. Traditional biomass estimation methods based on vegetation indices (VIs) often suffer from saturation due to canopy shading. However, comparative studies on VI saturation and the saturation height of AGB detectable by different indices remain limited. In this study, we evaluated 12 commonly used VIs based on field-measured AGB and hyperspectral data in the Hulunbuir meadow steppe. Relationships between vertically accumulated biomass and VIs were analyzed to identify optimal AGB fitting models and to determine the saturation height of each index. Results showed that vertical distribution of AGB followed a unimodal pattern, with biomass peaking at approximately 36 cm in this region. This study employed four models (namely the Linear model, the Logarithmic model, the Power Function model and the Gompertz model) to fit the relationship between the vegetation index and AGB. Among them, Gompertz models consistently outperformed other models, indicating saturation across all indices. Based on saturation height, the 12 VIs were classified into two groups: ARVI, GNDVI, NDRE, OSAVI, and SAVI saturated at 40 cm, whereas DVI, EVI, MSAVI, NDPI, NDVI, RVI, and VARI maintained sensitivity up to 50 cm, demonstrating a stronger anti-saturation capacity. NDVI and NDPI exhibited the highest fitting accuracy and resistance to saturation. These findings validate the saturation limitations of VIs and provide guidance for selecting appropriate indices to improve the accuracy of grassland biomass retrieval. Full article

Cattle behavior is closely related to their health status, and monitoring cattle behavior using intelligent devices can assist herders in achieving precise and scientific livestock management. Current behavior recognition algorithms are typically executed on server platforms, resulting in increased power consumption due to data transmission from edge devices and hindering real-time computation. An edge-based cattle behavior recognition method via Graph Neural Network (GNN) compression is proposed in this paper. Firstly, this paper proposes a wearable device that integrates data acquisition and model inference. This device achieves low-power edge inference function through a high-performance embedded microcontroller. Secondly, a sequential residual model tailored for single-frame data based on Inertial Measurement Unit (IMU) and displacement information is proposed. The model incrementally extracts deep features through two Residual Blocks (Resblocks), enabling effective cattle behavior classification. Finally, a compression method based on GNNs is introduced to adapt edge devices’ limited storage and computational resources. The method adopts GNNs as the backbone of the Actor–Critic model to autonomously search for an optimal pruning strategy under Floating-Point Operations (FLOPs) constraints. The experimental results demonstrate the effectiveness of the proposed method in cattle behavior classification. Moreover, enabling real-time inference on edge devices significantly reduces computational latency and power consumption, thereby highlighting the proposed method’s advantages for low-power, long-term operation. Full article

Tourism, a key driver of regional economies and perceived “green industry,” faces challenges from irrational resource allocation and spatial overlaps, undermining sustainability. This study examines 825 tourism resources in China’s Hulunbuir Grassland, analyzing spatiotemporal patterns, influencing factors, and ecological impacts using GPP and NDVI data. Three development phases emerged: essential development, rapid growth, and upgrading. They present a spatial pattern with Hailar and Chen Barag as the center, and multiple other points, mainly affected by ethnic minority population proportions, tourist reception, tourist attraction density, and river networks. Ecological analysis reveals that tourism-induced disturbances cause less environmental stress than other human activities, with grassland NDVI in tourism areas improving during upgrading. However, the NDVI of grasslands under non-tourism disturbance is still superior to that of grasslands under tourism disturbance. The findings emphasize the need for optimized resource allocation and proactive monitoring of tourism’s ecological footprint to advance sustainable grassland tourism. Full article

Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage–livestock balance assessment. These tasks are integral to the modernization and upgrading of the livestock industry and the sustainable development of grasslands. Unmanned aerial vehicles (UAVs) provide significant advantages in flexibility and maneuverability, making them ideal for livestock population surveys. However, grazing livestock in UAV images often appear small and densely packed, leading to identification errors. To address this challenge, we propose an efficient Livestock Network (LSNET) algorithm, a novel YOLOv7-based network. Our approach incorporates a low-level prediction head (P2) to detect small objects from shallow feature maps, while removing a deep-level prediction head (P5) to mitigate the effects of excessive down-sampling. To capture high-level semantic features, we introduce the Large Kernel Attentions Spatial Pyramid Pooling (LKASPP) module. In addition, we replaced the original CIoU with the WIoU v3 loss function. Furthermore, we developed a dataset of grazing livestock for deep learning using UAV images from the Prairie Chenbarhu Banner in Hulunbuir, Inner Mongolia. Our results demonstrate that the proposed module significantly improves the detection accuracy for small livestock objects, with the mean Average Precision (mAP) increasing by 1.47% compared to YOLOv7. Thus, this work offers a novel and practical solution for livestock detection in expansive farms. It overcomes the limitations of existing methods and contributes to more effective livestock management and advancements in agricultural technology. Full article

In order to clarify the change characteristics of soil organic carbon and its components in sandy land after restoration of different artificial vegetation measures, this study took 4 common artificial planting measures in Hulunbuir Sandy Land as the research object and took mobile sandy land as the control (CK). The results show the following: (1) The soil organic carbon content of the treatment measures was as follows: arbor-irrigation-grass (22.96 g·kg⁻¹) > single arbor (12.68 g·kg⁻¹) > single shrub (11.17 g·kg⁻¹) > single herb (8.89 g·kg⁻¹) > CK (1.14 g·kg⁻¹). The soil organic carbon showed an increasing trend, and the change was significant. (2) The contents of POC (Particulate Organic Carbon), MBC (Microbial Organic Carbon), DOC (Soluble Organic Carbon) and EOC () in soil were significantly increased by planting treatment measures, and the contents of each component in the combination mode of tree, shrub and grassland were the highest, which were increased by 1541.32%, 302.44%, 340% and 204.88% compared with CK. (3) There Please check that intended meaning has been retained. was a significant positive correlation between SOC and its components, and the correlation coefficients were 0.93, 0.91, 0.93, and 0.93, respectively. TC, TN and TP are all important influencing factors of POC. The correlation coefficient between DOC and MBC, MBC and EOC reached 0.96. (4) The carbon sequestration effect of the combination of trees, shrubs, and grassland is good, and the vegetation growth is good, which is conducive to the accumulation of organic carbon in the surface soil. It is suggested that the combination of treess, shrubs, and grassland should be adopted in the process of sandy land management. Full article

Monitoring the dust retention content in grassland plants around open-pit coal mines is of significant importance for environmental pollution monitoring and the development of dust control strategies. This paper focuses on the HulunBuir grassland in the Inner Mongolia Autonomous Region, China. UAV-borne hyperspectral data and measured dust retention content in plant canopies are used as data sources. The spectral response characteristics of canopy dust retention are analyzed, and four types of optimized spectral indices are constructed, including the difference index (DI), ratio index (RI), normalized difference index (NDI), and inverse difference index (IDI). The spectral index with the highest absolute value of the correlation coefficient with the canopy dust retention is selected as the feature variable for each spectral index. In addition, machine learning methods such as the partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) methods are used to develop models for the inversion of canopy dust retention. The results show that as the dust retention content increases, the canopy reflectance in the visible wavelength initially increases and then decreases, while the reflectance in the near-infrared wavelength gradually decreases. The spectral reflectance values at different dust retention levels exhibit significant differences in the 400–420 nm, 579–698 nm, and 714–1000 nm ranges. The four types of spectral indices constructed exhibit high correlations with the canopy dust retention content, and the spectral index with the highest absolute value of the correlation coefficient is composed of near-infrared bands. The dust retention inversion model established using the RF method is more accurate than those established using the PLSR and SVM methods and yields a higher prediction accuracy. The high canopy dust retention areas are mainly distributed within 900 m of the mining area, and the dust retention gradually decreases with distance. In addition, with increasing dust retention, the fractional vegetation cover (FVC) decreases. The results of this study provide a theoretical basis and technical support for monitoring dust retention in grassland plant canopies and for dust control measures. Full article

Grassland degradation could affect the composition, structure, and ecological function of plant communities and threaten the stability of their ecosystems. It is essential to accurately evaluate grassland degradation and elucidate its impacts on the vegetation–soil relationship. In this study, remote sensing data based on vegetation coverage were used to assess the degradation status of Hulunbuir grassland, and five different grassland degradation degrees were classified. Vegetation community composition, diversity, biomass, soil nutrient status, and their relationships in different degraded grasslands were investigated using field survey data. The results showed that grassland degradation significantly affected the species composition of the vegetation community. As degradation intensified, species richness declined, with the proportion of Gramineae and Legume species decreasing and Asteraceae species increasing. Additionally, the proportion of annual species initially increased and then decreased. Degradation also markedly reduced aboveground, belowground, and litter biomass within the communities. Soil moisture, electrical conductivity, organic carbon, total carbon, total potassium, and hydrolyzable nitrogen contents in non-degraded areas were higher than those in severely degraded areas. Conversely, soil total phosphorus content and bulk density gradually increased with degradation. Nitrate nitrogen and ammonium nitrogen levels in severely degraded soils were significantly higher than those in non-degraded soils. Plant diversity in the study area was significantly positively correlated with aboveground biomass and belowground biomass, and it positively correlated with soil nutrient total carbon and available carbon but negatively correlated with soil bulk density. Results of the partial least squares path model showed that grassland degradation had significant negative effects on plant diversity, soil nutrients, and biomass. Soil nutrients were the main factors affecting ecosystem productivity. The direct effect of plant diversity on biomass was not significant, suggesting that soil nutrients may play a more important role than plant diversity in determining biomass during grassland degradation. The results illustrated the relationships among soil nutrients, plant diversity, and biomass in degraded grasslands and emphasized the importance of an integrated approach in the effective management and restoration of degraded grasslands. Full article

Precisely estimating the grassland biomass carbon storage is vital for evaluating grassland carbon sequestration potential and the monitoring and management of grassland resources. With the increasing intensity of climate change (CC) and human activities (HA), it is necessary to explore spatiotemporal variations in biomass carbon storage and its response to CC and HA. In this study, we focused on the Hulunbuir Grassland, utilizing sample plots data, MODIS data, environmental factors (terrain, soil, and climate), location factor, and texture characteristics to assess the performance of four machine learning algorithms: random forest, support vector machine, gradient boosting decision tree, and extreme gradient boosting in estimating grassland aboveground biomass (AGB). Based on the optimal model combined with root-shoot ratio data, grassland distribution data, and carbon content coefficients, the spatiotemporal characteristics and driving factors of biomass carbon storage from 2001–2022 were analyzed. The results showed that (1) the random forest achieved the highest prediction accuracy for grassland AGB, making it appropriate for AGB estimation in the Hulunbuir Grassland. (2) The spectral indices were the key variables of the grassland AGB, especially the enhanced vegetation index and difference vegetation index. (3) The 22-year average total biomass (TB) of the study area was 1037.10 gC/m², of which the 22-year average AGB was 48.73 gC/m² and 22-year average belowground biomass was 988.37 gC/m², showing a spatial distribution feature of gradual increase from west to east. (4) From 2001–2022, TB carbon storage showed an insignificant growth trend (p > 0.05). The 22-year average carbon storage of TB was 72.34 ± 18.07 gC. (5) Climate factors were the main driving factors for the spatial pattern of grassland TB carbon density, while the combined effects of CC and HA were the main contributors to the interannual increase in grassland TB carbon density. Full article

Accurate comprehension of grassland fires is imperative for maintaining ecological stability. In this study, we propose a novel fire model that incorporates real-time meteorological conditions. Our methodology integrates key meteorological factors including relative humidity, temperature, degree of solidification of combustible materials, and wind speed. These factors are embedded into a comprehensive function that determines both the downwind and upwind spreading speeds of the fire. Additionally, the model accommodates fire spread in the absence of wind by incorporating the direction perpendicular to the wind, with wind speed set to zero. By precisely determining wind speed, the model enables real-time calculation of fire spread speeds in all directions. Under stable wind conditions, the fire spread area typically adopts an elliptical shape. Leveraging ellipse properties, we define the aspect ratio as a function related to wind speed. Consequently, with knowledge of the fire duration, the model accurately estimates the area of fire spread. Our findings demonstrate the effectiveness of this model in predicting and evaluating fires in the Hulunbuir Grassland. The model offers an innovative method for quantifying grassland fires, contributing significantly to the understanding and management of grassland 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 Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera’s NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products. Full article

Grassland management affects soil respiration (Rs, consists of heterotrophic respiration and autotrophic respiration) through soil micro-ecological processes, such as hydrothermal, plant root, organic carbon decomposition and microbial activity. Flooding, an irregular phenomenon in grasslands, may strongly regulate the response of soil respiration and its components to grassland management, but the regulatory mechanism remains unclear. We conducted a 3-year experiment by grassland management (fencing and grazing) and flooding conditions (no flooding (NF), short-term flooding (STF) and long-term flooding (LTF)) to study their effects on Rs and its components in a meadow steppe in the Hui River basin of Hulunbuir. We found differences in the patterns of Rs and its components under grassland management and flooding conditions. In 2021–2023, the temporal trends of Rs, heterotrophic respiration (Rh) and autotrophic respiration (Ra) were generally consistent, with peaks occurring on days 190–220, and the peaks of grazing were higher than that of fencing. In NF, Rs of grazed grassland was significantly higher than that of fenced grassland in 2021–2022 (p < 0.05). In STF and LTF, there was no significant difference in Rs between fenced and grazed grassland (p > 0.05). The dependence of Rs on soil temperature (ST) decreased with increasing flooding duration, and the dependence of Rs on ST of grazed grassland was higher than fenced grassland under NF and STF, but there was no difference between fenced grassland and grazed grassland under LTF. In addition, Rh was more sensitive to ST than Ra. This may be due to the different pathways of ST effects on Rs under grazing in different flooding conditions. Our study indicates that the effect of flooding on Rs is the key to the rational use of grassland under future climate change. To reduce regional carbon emissions, we recommend grazing on flooding grassland and fencing on no-flooding grassland. Full article