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Metal migration and precipitation in hydrothermal fluids are important topics in economic geology. The Hongling polymetallic deposit comprises one of the most important parts of the Huanggangliang–Ganzhuermiao polymetallic metallogenic belt, which is in eastern Inner Mongolia. Except for lead–zinc skarn, minor cassiterite in the skarn and disseminated W–Sn mineralization in granitic rocks have also been found. The dominant Sn–W mineralization is in the northern part of the deposit, occurring as disseminated wolframite and cassiterite in aplite hosted in Mesozoic granite porphyry. The aplite together with pegmatite K-feldspar–quartz comprises vein dikes hosted in the granite porphyry, providing evidence for the transition from melt to fluid. The veins, dikes, and Sn–W mineralization in the aplite provide an opportunity to investigate fluid exsolution and the mechanics of metal precipitation. Based on field observations, the micrographic and scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) results of the vein dikes, chronology, and the whole-rock geochemistry of the host rock, together with the fluid inclusion results, this paper discusses the characteristics of the causative magma, the mechanics of fluid exsolution and W–Sn precipitation. Our results show that the causative magma is of highly fractionated A-type granite affinity and has an intrusive age of late Mesozoic (133.3 ± 0.86 Ma). The magmatic evolution during shallow emplacement led to immiscibility between highly volatile, high-silica, and W- and Sn-enriched melts from the parent magma, followed by fluid exsolution from the water-rich melt. The alkaline-rich fluid exsolution led to a change in the redox state of the magma and the chilling of the melt. Fluid boiling occurred soon after the fluid exsolution and was accompanied by the degassing of CO2. The boiling and escape of CO2 from the fluid led to changes in fluid redox and W and Sn precipitation; thus, the W and Sn mineralization are mostly hosted in causative intrusions or peripheral wall rocks, which can be used as indicators for Sn–W exploration in the area. [ABSTRACT FROM AUTHOR]

Simple Summary: In recent years, the shortage of cow stocks and the reduction of cow breeders in China have caused a shortage of cattle resources and a high price of calves, which has become a bottleneck restricting the further development of China's beef cattle industry. At present, there are few studies on the decision-making behavior of cow breeding in China. In order to explore the factors that affect the decision-making behavior on cow breeding by beef cattle farmers, this paper uses field survey data to make an empirical analysis through the Logit ISM model. The results show that the age of the breeder, the breeding year, the planting area of forage, market expectations, the loan amount, the breeding method policy publicity and policy subsidies are significant factors affecting the decision-making behavior of beef cattle farmers on cow breeding. The beef cattle industry is an important part of herbivorous animal husbandry and an important industry to enrich the dietary structure of residents and promote the income of farmers and herdsmen. The cow is an important foundation to support the healthy development of the beef cattle industry, which is related to the stability of cattle sources and the sustainable development of the industry. However, in recent years, the shortage of cows in our country has led to the shortage of cattle resources and the high price of calves, which has restricted the further development of our beef cattle industry. In order to explore the factors that affect the decision-making behavior behind cow breeding and to find the right policy to mobilize the enthusiasm for cow breeding, based on the field survey of five provinces (cities and regions), including Inner Mongolia Autonomous Region and Ningxia Hui Autonomous Region, this paper conducts an empirical analysis through the Logit ISM model. The results show that market expectation is the surface factor that directly affects the decision-making behavior of cow breeding. The influencing factors of the middle layer are policy propaganda, policy subsidies, breeding years, and feeding methods. The deep root factors are the age of the breeder, the amount of loan, and whether to plant feed crops. Based on this, this paper proposes that we should actively promote the importance of cow breeding, strengthen policy support for cow breeding, formulate scientific and reasonable subsidy policies for cows, innovate financial support methods to solve the problem of fund shortage of farmers, and develop forage resources and reduce the breeding cost of cows. [ABSTRACT FROM AUTHOR]

As a raw material for clean energy supply for the new generation, the soybean is conducive to the realization of global energy transition and sustainable development in the context of "carbon neutrality". However, global warming has been affecting soybean yields in recent years. How to clarify the correlation between meteorological factors and soybean yields, so as to ensure the security of soybean growth and development and the stability of renewable energy development, is a key concern of the government and academia. Based on the data of temperature, precipitation, sunshine duration and active accumulated temperature during the soybean growing season in Hulunbuir, Inner Mongolia Autonomous Region from 1951 to 2019, and soybean yield data of the city from 1985 to 2019, this paper adopted statistical methods such as the Trend Analysis Method, the Rescaled Range Analysis Method and so on to analyze the trends of yield changes, characteristics of abrupt changes and periodic patterns of climate factors and soybean yields in Hulunbuir. A Pearson Correlation Analysis and a Grey Relation Analysis were used to explore the correlation between climatic factors and soybean yields, followed by a comprehensive impact model of the combined effect of temperature and precipitation on soybean yields established by the Method of Integral Regression. The results showed that temperature and active accumulated temperature are the dominant factors affecting soybean yields in Hulunbuir, while the decrease in precipitation is unfavorable to the improvement of soybean yields. Meanwhile, temperature and precipitation have different effects on the growth and development of the soybean at different stages. The conclusion of this paper is of great practical significance for Hulunbuir to promote the sustainable development of clean energy. [ABSTRACT FROM AUTHOR]

Simple Summary: In this paper, two new genera with two new species of Dermaptera are described from the Middle Jurassic Jiulongshan Formation of Daohugou, Inner Mongolia, China. The discovery of these two new species enriches the comparatively meagre fossil record of Dermaptera, particularly from the Middle Jurassic. The description of Applanatiforceps angustus is another brick laid in the foundation of protodiplatyid diversity, and the new genus Ekpagloderma gracilentum highlights the diversity of cercal forms among Aglyptodermatinae and the remarkable homogeneity of the general morphology of groups within this clade. Two new genera and species of Dermaptera are described from the Middle Jurassic Jiulongshan Formation of Daohugou, Inner Mongolia, China: Applanatiforceps angustus gen. et sp. nov. in the archidermapteran family Protodiplatyidae, and Ekpagloderma gracilentum gen et sp. nov. in the eodermapteran family Semenoviolidae. Applanatiforceps shares the typical characters of the extinct suborder Archidermaptera (e.g., pentamerous meta tarsi, filiform and multimerous cerci) and externalized ovipositor. The family identity of the Protodiplatyidae can be further distinguished by comparing this new genus with other genera of the Protodiplatyidae. As a result of its large compound eyes, tegmina without venation, body sparsely setose, legs rather short and slender, and shape of the veinless tegmina, Ekpagloderma is classified in the subfamily Aglyptodermatinae. Ekpagloderma not only has the typical features of the Aglyptodermatinae, but also exhibits a more primitive slender segmented cerci, which is different from all other genera of Eodermaptera. In fact, the diversity of Eodermaptera as known today indicates some of the challenges in understanding the suborder and whether or not it is monophyletic as historically construed, or if the separation of Turanodermaptera is justified. [ABSTRACT FROM AUTHOR]

Simple Summary: Osmylopsychopidae, an extinct family of Neuroptera, is closely related to the extant Psychopsidae. The sporadic records of this family and limited preservation of fossil specimens have hindered the understanding of the evolution of Osmylopsychopidae. In this paper, one new genus with four new species of Osmylopsychopidae are described from the Middle Jurassic Jiulongshan Formation of northeastern China. These newly discovered osmylopsychopids with a forewing length of ca. 10 mm (implying small body size), clearly distinguished from the traditional sense of Osmylopsychopidae, suggest that Osmylopsychopidae possibly underwent a significant diversification during the Mesozoic. One new genus with four new species sharing the similar character of a forewing length of ca. 10 mm, namely, Minipsychops spasulus gen. et sp. nov., Minipsychops polychotomus sp. nov., Minipsychops densus sp. nov., and Minipsychops unicus sp. nov., are described from the Middle Jurassic Jiulongshan Formation of Daohugou, Inner Mongolia in China. These new insects can be attributed to Osmylopsychopidae for their distinctive configuration of costal space and the venations of RP1 and Cu. Compared with the typically medium to large body sizes of known osmylopsychopids, these new Middle Jurassic taxa of a particularly miniaturized lineage not only enrich the species diversity of Osmylopsychopidae, but also enhance our understanding of the historical evolution of these poorly known lacewings. [ABSTRACT FROM AUTHOR]

Studying surface geochemical anomalies is important for detecting the presence of mineral deposits. However, distinguishing inconspicuous geochemical anomalies is a challenge for geochemists. This paper studies geochemical quantitative prediction for Pb–Zn metallic mineral deposits by identifying inconspicuous surface geochemical anomalies mainly associated with the Permian and Jurassic strata in the middle-southern Da Hinggan Mountains metallogenic belt. Some new methods are employed to highlight weak surface geochemical anomalies. The weak surface geochemical anomalies of Pb and Zn are effectively highlighted by the average contrast values of Pb–Zn–Ag–Cd. The similarity coefficient with the large typical discovered deposits is used to identify new Pb–Zn mineralized anomalies and delineate new prospecting target areas. The denudation degree of mineral deposit is determined by the ratio of (W × Sn)/(As × Sb). The analogy method and areal productivity method are employed for resource prediction. Thirty-six prediction areas with Pb–Zn resources of 307.73 million tons are delineated. Five prediction areas are verified, and some new mineral deposits are proven by drilling. The verification results show that the predicted resources are very reasonable and credible. This paper is a successful case of quantitative prediction assessment of mineral resource potential, which can be used as a reference for future prospecting activities. [ABSTRACT FROM AUTHOR]

In this paper, recent research on terrestrial ecosystem predictability using the conditional nonlinear optimal parameter perturbation (CNOP-P) method is summarized. The main findings include the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, the identification of key physical parameters that lead to large uncertainties in terrestrial ecosystem modeling and prediction, and the evaluation of the simulation ability and prediction skill of terrestrial ecosystems by reducing key physical parameter errors. The study areas included the Inner Mongolia region, north–south transect of eastern China, and Qinghai–Tibet Plateau region. The periods of the studies were from 1961 to 1970 for the impacts of uncertainties in climate change on uncertainties in simulated terrestrial ecosystems, and from 1951 to 2000 for the identification of the most sensitive combinations of physical parameters. Climatic Research Unit (CRU) data were employed. The numerical results indicate the important role of nonlinear changes in climate variability due to the occurrences of extreme events characterized by CNOP-P in the abrupt grassland ecosystem equilibrium state and formation of carbon sinks in China. Second, the most sensitive combinations of physical parameters to the uncertainties in simulations and predictions of terrestrial ecosystems identified by the CNOP-P method were more sensitive than those obtained by traditional methods (e.g., one-at-a-time (OAT) and stochastic methods). Furthermore, the improvement extent of the simulation ability and prediction skill of terrestrial ecosystems by reducing the errors of the sensitive physical parameter combinations identified by the CNOP-P method was higher than that by the traditional methods. [ABSTRACT FROM AUTHOR]

Unregulated livestock breeding and grazing can degrade grasslands and damage the ecological environment. The combination of remote sensing and artificial intelligence techniques is a more convenient and powerful means to acquire livestock information in a large area than traditional manual ground investigation. As a mainstream remote sensing platform, unmanned aerial vehicles (UAVs) can obtain high-resolution optical images to detect grazing livestock in grassland. However, grazing livestock objects in UAV images usually occupy very few pixels and tend to gather together, which makes them difficult to detect and count automatically. This paper proposes the GLDM (grazing livestock detection model), a lightweight and high-accuracy deep-learning model, for detecting grazing livestock in UAV images. The enhanced CSPDarknet (ECSP) and weighted aggregate feature re-extraction pyramid modules (WAFR) are constructed to improve the performance based on the YOLOX-nano network scheme. The dataset of different grazing livestock (12,901 instances) for deep learning was made from UAV images in the Hadatu Pasture of Hulunbuir, Inner Mongolia, China. The results show that the proposed method achieves a higher comprehensive detection precision than mainstream object detection models and has an advantage in model size. The m A P of the proposed method is 86.47%, with the model parameter 5.7 M. The average recall and average precision can be above 85% at the same time. The counting accuracy of grazing livestock in the testing dataset, when converted to a unified sheep unit, reached 99%. The scale applicability of the model is also discussed, and the GLDM could perform well with the image resolution varying from 2.5 to 10 cm. The proposed method, the GLDM, was better for detecting grassland grazing livestock in UAV images, combining remote sensing, AI, and grassland ecological applications with broad application prospects. [ABSTRACT FROM AUTHOR]

With the centralization of wind power development, power-prediction technology based on wind power clusters has become an important means to reduce the volatility of wind power, so a large-scale power-prediction method of wind power clusters is proposed considering the prediction stability. Firstly, the fluctuating features of wind farms are constructed by acquiring statistical features to further build a divided model of wind power clusters using fuzzy clustering algorithm. Then the spatiotemporal features of the data of wind power are obtained using a spatiotemporal attention network to train the prediction model of wind power clusters in a large scale. Finally, the stability of predictive performance of wind power is analyzed using the comprehensive index evaluation system. The results show that the RMSE of wind power prediction is lower than 0.079 at large-scale wind farms based on the prediction method of wind power proposed in this paper using experience based on the data of 159 wind farms in the Nei Monggol Autonomous Region in China and the extreme error is better than 25% for the total capacity of wind farms, which indicates high stability and accuracy. [ABSTRACT FROM AUTHOR]

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry. [ABSTRACT FROM AUTHOR]

Groundwater is a primary freshwater resource for human consumption and an essential source for industry and agriculture. Therefore, understanding its spatial and temporal trends and drivers is crucial for governments to take appropriate measures to manage water resources. This paper uses Gravity Recovery and Climate Experiment (GRACE) satellite data and the Global Land Data Assimilation System (GLDAS) to derive groundwater storage anomalies (GWSAs) and to analyze the spatial and temporal trends of GWSA in different regions of China (Xinjiang, Tibet, Inner Mongolia, North China Plain, South China, and Northeast China). It used groundwater-level observation data to verify the accuracy of GWSA estimates and analyzed the drivers of regional GWSA changes. The results showed that: (1) GWSA in South China increased at a rate of 4.79 mm/a from 2003 to 2016, and GWSA in other regions in China showed a decreasing trend. Among them, the decline rates of GWSA in Xinjiang, Tibet, Inner Mongolia, North China Plain, and Northeast China were −6.24 mm/a, −3.33 mm/a, −3.17 mm/a, −7.35 mm/a, and −0.75 mm/a, respectively. (2) The accuracy of the annual-scale GWSA estimates was improved after deducting gravity losses due to raw coal quality, and the correlation coefficient between GWSA and groundwater levels monitored by observation wells increased. (3) In Xinjiang, the annual water consumed by raw coal mining, industrial, and agricultural activities had a greater impact on GWSA than rainfall and temperature, so these human activities might be the main drivers of the continued GWSA decline in Xinjiang. Water consumption by raw coal mining and industry might be the main drivers of the continued decline in GWSA in Inner Mongolia and the North China Plain. The increase in groundwater storage in South China was mainly due to the recharge of rainfall. [ABSTRACT FROM AUTHOR]

Background: Desertification is one of the main obstacles to global sustainable development. Monitoring, evaluating and mastering its driving factors are very important for the prevention and control of desertification. As one of the largest deserts in China, the development of desertification in Otindag Sandy Land (OSL) resulted in the reduction in land productivity and serious ecological/environmental consequences. Although many ecological restoration projects have been carried out, the vegetation restoration of OSL and the impact mechanism of climate and human activities on desertification remain unclear. Methods: Taking OSL as the research area, this paper constructs the desertification index by using the remote sensing images and meteorological and socio-economic data, between 1986 and 2016, and analyzes the spatio-temporal evolution process and driving factors of desertification by using trend analysis and spearman rank correlation. Results: The results showed that: (1) Desertification in the OSL has fluctuated greatly during the past 30 years. Desertification recovered between 1986 and 1990, expanded and increased between 1990 and 2000, reduced between 2000 and 2004, developed rapidly between 2004 and 2007, and recovered again between 2007 and 2016; (2) The desertification of OSL is dominated by a non-significant change trend, accounting for 73.27%. In the significant change trend, the area of desertification rising trend is 20.32%, which is mainly located in the north and east, and the area of declining trend is 6.41%, which is mainly located in the southwest; (3) Desertification is the result of the superposition of climate and human activities. Climate change is the main influencing factor, followed by human activities, and the superposition effects of the two are spatio-temporal differences. Conclusions: These results shed light on the development of desertification in OSL and the relative importance and complex interrelationship between human activities and climate in regulating the process of desertification. Based on this, we suggest continuing to implement the ecological restoration policy and avoid the destruction of vegetation by large-scale animal husbandry in order to improve the situation of desertification. [ABSTRACT FROM AUTHOR]

Aeromagnetic gradient data have higher horizontal resolution on shallow sources, a magnetic anomaly that can better reflect the features of deeper sources. Therefore, we used a joint survey of aeromagnetic and gradient data to obtain the distribution of sources with different depths. In this paper, we propose an intersection constraint weighting (ICW) method based on aeromagnetic and gradient data for original and gradient anomalies with inconsistent high-low variation characteristics. The ICW method can effectively improve the resolution of inversion results and can more accurately obtain the distribution of magnetic bodies via cross-gradient by gradually adding a gradient component and applying a normalized property weighting function. Our theoretical model tests indicate that the distribution of the recovered magnetic susceptibility model of the ICW method was similar to that of the true model. In addition, the anomaly containing noise with different signal-to-noise ratios verified that the ICW method had a stronger anti-noise ability than other methods. We also inverted real data in the Zhurihe area of the Inner Mongolia Autonomous Region in northern China. The inversion result showed that the main trend of high magnetic bodies was in the northeast direction, that the shallowest depth of high magnetic bodies was 100 m, and that the greatest depth was 960 m. [ABSTRACT FROM AUTHOR]

Simple Summary: Grasshoppers are among the most dangerous agricultural pests of China. However, the monitoring, prediction and control of grasshoppers are complex and difficult. Therefore, it is crucial to detect the key factors affecting the spatial distribution of grasshopper occurrence, understand the role of the environmental factors in grasshopper occurrence, and study whether different laws exist between different grass types. Here we conduct a species–environmental matching model integrated by Maxent model and remote sensing data to identify the spatial distribution of grasshopper occurrence in Inner Mongolia of China, analyze the related environmental variables and detect the most relevant environmental factors for grasshopper occurrence both in typical steppe and meadow steppe. Grasshoppers mainly threaten natural grassland vegetation and crops. Therefore, it is of great significance to understand the relationship between environmental factors and grasshopper occurrence. This paper studies the spatial distribution and key factors of grasshopper occurrence in two grass types by integrating a machine learning model (Maxent) and remote sensing data within the major grasshopper occurrence areas of Inner Mongolia, China. The modelling results demonstrate that the typical steppe has larger suitable area and more proportion for grasshopper living than meadow steppe. The soil type, above biomass, altitude and temperature mainly determine the grasshopper occurrence in typical steppe and meadow steppe. However, the contribution of these factors in the two grass types is significantly different. In addition, related vegetation and meteorological factors affect the different growing stages of grasshoppers between the two grass types. This study clearly defines the different effects of key environmental factors (meteorology, vegetation, soil and topography) for grasshopper occurrence in typical steppe and meadow steppe. It also provides a methodology to guide early warning and precautions for grasshopper pest prevention. The findings of this study will be helpful for future management measures, to ensure grass ecological environment security and the sustainable development of grassland. [ABSTRACT FROM AUTHOR]

Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering algorithm and the center-of-gravity method is proposed to deal with the multi-facility location problem arising from a real-world case. First, a distance function used in clustering is redefined to include both the spatial indicator and the socio-economic indicator. Then, an improved clustering algorithm is used to determine the optimal number of distribution centers needed and the coverage of each center. Third, the center-of-gravity method is used to determine the final location of each center. Finally, the improved method is compared with the traditional clustering method by testing data from 12 cities in Inner Mongolia Autonomous Region in China. The comparison result proves the proposed method's effectiveness. [ABSTRACT FROM AUTHOR]

Coal is one of the fundamental fossil energy supporting the world's economy. The synergistic development between efficient coal mining and ecological environment protection is the inevitable requirement for the preservation of global harmony. As the world's largest coal producer, China has conducted a strategic shift from east to west in terms of the exploitation of its energy resources, posing a serious threat to the fragile ecological environment of the western region. In particular, the surface subsidence caused by coal mining is the root of the ecological deteriotation and the destruction of ground structures. However, it is difficult to reveal the law of large-scale surface subsidence in western mining areas merely by conventional measurement methods such as leveling, on account of the high intensity of coal seam mining, the weakness of the lithology of overlying rock and the large thickness of wind-blown sand strata. In view of this, small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used in this study to obtain the time series of surface vertical displacement during the whole mining process of the 2401 working face in the Yingpanhao coal mine, Inner Mongolia. Based on the deformation data, the dynamic evolution characteristics of surface subsidence under high intensity mining in the western mining area were analyzed exhaustively. It was found that the surface subsidence is characterized by an extensive coverage range (48.52 km2) with minimal ground settlement (250 mm) in the study area. Meanwhile, the boundary shape of the subsidence basin followed a "circular-parallelogram-trapezoid" changeable process and the coverage area of the basin experienced three stages: a linear increasing period, a temporary stagnation period, and a re-expansion period. Furthermore, there existed an abnormal uplift phenomenon on the east side of the open-off cut in the 2401 working face. Combined with the structure of overlying strata, this paper carried out a preliminary analysis on the reasons of the abovementioned phenomenon. The research results are of vital realistic significance for ground buildings and ecological environmental protection in the aeolian sand mining area in Western China. [ABSTRACT FROM AUTHOR]

This paper presents scientific analyses of the wall paintings in Wudang Lamasery, which is located in the Baotou area of Inner Mongolia, China. Raman spectroscopy was used to analyze the pigments of the mural of the Lam rim Hall. The results show that vermilion, red lead, chrome yellow, emerald green and synthetic ultramarine were used. The existence of synthetic pigments provides a clue for the date the mural was painted. [ABSTRACT FROM AUTHOR]

Socio-economic development is undergoing changes in China, such as the recently proposed carbon peak and carbon neutral targets, new infrastructure development strategy and the Coronavirus disease 2019 (COVID-19) pandemic. Meanwhile, the new-round marketization reform of the electricity industry has been ongoing in China since 2015. Therefore, it is urgent to evaluate the risk of electric power grid investment in China under new socio-economic development situation, which can help the investors manage risk and reduce risk loss. In this paper, a hybrid novel multi-criteria decision making (MCDM) method combining the latest group MCDM method, namely, Bayesian best–worst method (BBWM) and improved matter-element extension model (IMEEM) is proposed for risk evaluation of electric power grid investment in China under new socio-economic development situation. The BBWM is used for the weights' determination of electric power grid investment risk criteria, and the IMEEM is employed to rank risk grade of electric power grid investment. The risk evaluation index system of electric power grid investment is built, including economic, social, environmental, technical and marketable risks. The risk of electric power grid investment under new socio-economic development situation in Inner Mongolia Autonomous Region of China is empirically evaluated by using the proposed MCDM method, and the results indicate that it belongs to "Medium" grade, but closer to "High" grade. The main contributions of this paper include: (1) it proposes a hybrid novel MCDM method combining the BBWM and IMEEM for risk evaluation of electric power grid investment; and (2) it provides a new view for risk evaluation of electric power grid investment including economic, social, environmental, technical and marketable risks. The proposed hybrid novel MCDM method for the risk evaluation of electric power grid investment is effective and practical. [ABSTRACT FROM AUTHOR]

Detailed information from global remote sensing has greatly advanced our understanding of Earth as a system in general and of agricultural processes in particular. Vegetation monitoring with global remote sensing systems over long time periods is critical to gain a better understanding of processes related to agricultural change over long time periods. This specifically relates to sub-humid to semi-arid ecosystems, where agricultural change in grazing lands can only be detected based on long time series. By integrating data from different sensors it is theoretically possible to construct NDVI time series back to the early 1980s. However, such integration is hampered by uncertainties in the comparability between different sensor products. To be able to rely on vegetation trends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent across products. In this paper we analyzed several indicators of vegetation change for a range of agricultural systems in Inner Mongolia, China, and compared the results across different satellite archives. Specifically, we compared two of the prime NDVI archives-AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT) NDVI. Because a true accuracy assessment of long time series is not possible, we further compared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We found high similarities in interannual trends, and also in trends of the seasonal amplitude and integral between SPOT VGT and MODIS Terra (r > 0.9). However, we observed considerable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOT VGT. We detected similar discrepancies for trends based on phenological parameters, such as amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles. Inconsistencies were partially related to land cover and vegetation density. Different pre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introduced further uncertainties. Our results corroborate findings from other studies that vegetation trends derived from AVHRR GIMMS data not always reflect true vegetation changes. A more thorough understanding of the factors introducing uncertainties in AVHRR GIMMS time series is needed, and we caution against using AVHRR GIMMS data in regional studies without applying regional sensitivity analyses. [ABSTRACT FROM AUTHOR]

Airborne eddy covariance (EC) measurement is one of the most effective methods to directly measure the surface mass and energy fluxes at the regional scale. It offers the possibility to bridge the scale gap between local- and global-scale measurements by ground-based sites and remote-sensing instrumentations, and to validate the surface fluxes estimated by satellite products or process-based models. In this study, we developed an unmanned aerial vehicle (UAV)-based EC system that can be operated to measure the turbulent fluxes in carbon dioxides, momentum, latent and sensible heat, as well as net radiation and photosynthetically active radiation. Flight tests of the developed UAV-based EC system over land were conducted in October 2020 in Inner Mongolia, China. The in-flight calibration was firstly conducted to correct the mounting error. Then, three flight comparison tests were performed, and we compared the measurement with those from a ground tower. The results, along with power spectral comparison and consideration of the differing measurement strategies indicate that the system can resolve the turbulent fluxes in the encountered measurement condition. Lastly, the challenges of the UAV-based EC method were discussed, and potential improvements with further development were explored. The results of this paper reveal the considerable potential of the UAV-based EC method for land surface process studies. [ABSTRACT FROM AUTHOR]

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas. [ABSTRACT FROM AUTHOR]

Simple Summary: The Eurasian lynx has a wide distribution range in China, but lynx field studies in China are rare compared to those for European populations. Using camera trapping data, this paper reports the lynx population size, activity patterns, and variation within the prey community at a nature reserve in Inner Mongolia. The results found that there were at least 20 lynx in this reserve, and the population has increased steadily over years. There is a seasonal difference in the daily activity rhythms of lynx. The total activity rhythm of lynx is synchronous with five mammal species: hare, red deer, wild boar, raccoon dog, and badger. Red fox and roe deer showed a timed avoidance of lynx to some extent. The present study sets an example for biodiversity conservation; the recovery of prey communities through habitat management should be a priority in determining the restoration of large carnivore populations in China. Revealing the behavioral relationships between predators and their prey is fundamental in understanding the community structure and ecosystem functions of such animals. This study aimed at detecting the population size and activity patterns of Eurasian lynx (Lynx lynx) (along with its prey) by camera trapping monitoring from 2014 to 2017 at the Saihanwula nature reserve in central Inner Mongolia. The total effective trapping days were 29,892 and 20 lynx were identified from 343 trapping photos based on the inner side patterns of their forelimbs. The daily activity rhythms of the lynx overlapped with those of different prey in different seasons. The yearly activity pattern of the lynx was influenced by its main prey's biology. In conclusion, this study reveals that the activity patterns of the top predator matched those of its prey in different time periods. Habitat management strategies promoting the restoration of prey communities would benefit the lynx in maintaining a stable community structure. [ABSTRACT FROM AUTHOR]

With increasing consumption, plastic mulch benefits agriculture by promoting crop quality and yield, but the environmental and soil pollution is becoming increasingly serious. Therefore, research on the monitoring of plastic mulched farmland (PMF) has received increasing attention. Plastic mulched farmland in unmanned aerial vehicle (UAV) remote images due to the high resolution, shows a prominent spatial pattern, which brings difficulties to the task of monitoring PMF. In this paper, through a comparison between two deep semantic segmentation methods, SegNet and fully convolutional networks (FCN), and a traditional classification method, Support Vector Machine (SVM), we propose an end-to-end deep-learning method aimed at accurately recognizing PMF for UAV remote sensing images from Hetao Irrigation District, Inner Mongolia, China. After experiments with single-band, three-band and six-band image data, we found that deep semantic segmentation models built via single-band data which only use the texture pattern of PMF can identify it well; for example, SegNet reaching the highest accuracy of 88.68% in a 900 nm band. Furthermore, with three visual bands and six-band data (3 visible bands and 3 near-infrared bands), deep semantic segmentation models combining the texture and spectral features further improve the accuracy of PMF identification, whereas six-band data obtains an optimal performance for FCN and SegNet. In addition, deep semantic segmentation methods, FCN and SegNet, due to their strong feature extraction capability and direct pixel classification, clearly outperform the traditional SVM method in precision and speed. Among three classification methods, SegNet model built on three-band and six-band data obtains the optimal average accuracy of 89.62% and 90.6%, respectively. Therefore, the proposed deep semantic segmentation model, when tested against the traditional classification method, provides a promising path for mapping PMF in UAV remote sensing images. [ABSTRACT FROM AUTHOR]

Coal is an important energy resource in the world, especially in China. Extensive coal exploitation seriously damaged the grassland and its fragile ecosystem. However, temporal and spatial impact laws of open-pit coal exploitation on Landscape Ecological Health (LEH) of semi-arid grasslands are still not clear. Therefore, the main objective of this paper is to study impact of Large-scale Open-pit Coal Base (LOCB) on the LEH of semi-arid grasslands from the perspectives of temporal and spatial. Taking Shengli LOCB of Xilinguole grassland in Inner Mongolia as an example, we demonstrate a conceptual model of LOCB impact on LEH of semi-arid grasslands, and establish a research system called landscape Index-pattern Evolution-Driving force-Spatial statistics (IEDS). A complete process integrated from investigation, monitoring, and evaluation to the analysis of impact laws was developed. Result indicated that coal mining causes gradual increase of landscape patches, landscape fragmentation, gradual decline of landscape connectivity, complexity and irregularity of landscape shape, enhancement of landscape heterogeneity and complexity, gradual decline of landscape stability, gradual decrease of grassland landscape and annual increase of unhealthy grassland landscape. The LEH of grassland basically belongs to the state of slight deterioration. In the past 15 years, the spatial and temporal distribution characteristics of LEH in the study area are similar. This study provides scientific reference for ecological disturbance research, environmental protection, landscape planning, restoration and renovation of ecological environment in mining areas. At the same time, future research should integrate geological, hydrological, soil, vegetation, microorganisms, animals, climate, and other perspectives to study the impact of mining on landscape ecology deeply. [ABSTRACT FROM AUTHOR]