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An accurate estimation of canopy cover can provide an important basis for forest ecological management by understanding the forest status and change patterns. The aim of this paper is to investigate the four methods of the random forest (RF), support vector regression (SVR), k-nearest neighbor (KNN), and k-nearest neighbor with fast iterative features selection (KNN-FIFS) for modeling forest canopy cover, and to evaluate three mainstream optical data sources—Landsat8 OLI, Sentinel-2A, Gaofen-1 (GF-1)—and three types of data combined comparatively by selecting the optimal modeling method. The paper uses the Daxinganling Ecological Station of Genhe City, Inner Mongolia, as the research area, and is based on three types of multispectral remote sensing data, extracting spectral characteristics, textural characteristics, terrain characteristics; the Kauth–Thomas transform (K-T transform); and color transformation characteristics (HIS). The optimal combination of features was selected using three feature screening methods, namely stepwise regression, RF, and KNN-FIFS, and the four methods: RF, SVR KNN, and KNN-FIFS, were combined to carry out the evaluation analysis regarding the accuracy of forest canopy cover modeling: (1) In this study, a variety of remote sensing features were introduced, and the feature variables were selected by different parameter preference methods and then employed in modeling. Based on the four modeling inversion methods, the KNN-FIFS model achieves the best accuracy: the Landsat8 OLI with R2 = 0.60, RMSE = 0.11, and RMSEr = 14.64% in the KNN-FIFS model; the Sentinel-2A with R2 = 0.80, RMSE = 0.08, and RMSEr = 11.63% in the KNN-FIFS model; the GF-1 with R2 = 0.55, RMSE = 0.12, and RMSEr = 15.04% in the KNN-FIFS model; and the federated data with R2 = 0.82, RMSE = 0.08, and RMSEr = 10.40% in the KNN-FIFS model; (2) the three multispectral datasets have the ability to estimate forest canopy cover, and the modeling accuracy superior under the combination of multi-source data features; (3) under different optical data, KNN- FIFS achieves the best accuracy in the established nonparametric model, and its feature optimization method is better than that of the random forest optimization method. For the same model, the estimation result of the joint data is better than the single optical data; thus, the KNN-FIFS model, with specific parameters, can significantly improve the inversion accuracy and efficiency of forest canopy cover evaluation from different data sources. [ABSTRACT FROM AUTHOR]

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model's learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

Satellite laser altimetry technology, a novel space remote sensing technique, actively acquires high-precision elevation information about the Earth's surface. However, the accuracy of laser altimetry can be compromised by alterations in the satellite-ground environment, thermal dynamics, and cosmic radiation. These factors may induce subtle variations in the installation and internal structure of the spaceborne laser altimeter on the satellite platform, diminishing measurement precision. In-orbit calibration is thus essential to enhancing the precision of laser altimetry. Through collaborative calculations between satellite and ground stations, we can derive correction parameters for laser pointing and ranging, substantially improving the accuracy of satellite laser altimetry. This paper introduces a sophisticated calibration method for laser altimeter pointing and ranging that utilizes dense control points. The approach interpolates discrete ground control point data into continuous simulated terrain using empirical Bayesian kriging, subsequently categorizing the data for either pointing or ranging calibration according to their respective functions. Following this, a series of calibration experiments are conducted, prioritizing "pointing" followed by "ranging" and continuing until the variation in the ranging calibration results falls below a predefined threshold. We employed experimental data from ground control points (GCPs) in Xinjiang and Inner Mongolia, China, to calibrate the GaoFen-7 (GF-7) satellite Beam 2 laser altimeter as per the outlined method. The calibration outcomes were then benchmarked against those gleaned from infrared laser detector calibration, revealing disparities of 1.12 s in the pointing angle and 2 cm in the ranging correction value. Post validation with ground control points, the measurement accuracy was refined to 0.15 m. The experiments confirm that the proposed calibration method offers accuracy comparable to that of infrared laser detector calibration and can facilitate the updating of 1:10,000 topographic maps utilizing stereo optical imagery. Furthermore, this method is more cost-effective and demands fewer personnel for ground control point collection, enhancing resource efficiency compared to traditional infrared laser detector calibration. The proposed approach surpasses terrain-matching limitations when calibrating laser ranging parameters and presents a viable solution for achieving frequent and high-precision in-orbit calibration of laser altimetry satellites. [ABSTRACT FROM AUTHOR]

Agricultural drought events have become more frequent in the Inner Mongolia inland river basin in recent years, and the spatio-temporal evolution characteristics and development rules can be accurately and comprehensively understood using the three-dimensional identification method. In this paper, standardized soil moisture index (SSMI) was used to characterize agricultural drought, and modified Mann–Kendall trend test (MMK) and 3D recognition of drought events were used to analyze the spatio-temporal evolution characteristics of agricultural drought events in this basin and reveal the drought development law. The relationships between drought and temperature (T), precipitation (P), evapotranspiration (E), and humidity (H) were analyzed using a cross-wavelet method. The results are as follows: (1) When the time scale of agricultural drought was short (monthly scale), the alternations of dry and wet were frequent, but the SSMI index of all scales showed a downward trend; (2) The spatial distribution characteristics of drought change trend in four seasons were similar, but the area with a significant downward trend of drought in spring was the largest, and the area of high frequency region was also the largest, and the drought trend was the most obvious; (3) The most serious agricultural drought event occurred from October 2000 to May 2002, and reached its maximum value in September 2001 (drought area and drought severity of 2.26 × 105 km2 and 3.61 × 105 months·km2, respectively), which mainly experienced five processes—drought onset–intensification–decay–re-intensification–termination—and the migration path of the drought center showed the characteristics of southwest–northeast transmission; (4) All the four meteorological factors were correlated with SSMI, and P had a greater impact on SSMI. This article aims to reveal the spatio-temporal evolution of agricultural drought events in the Inner Mongolia inland river basin, and provide a new way to accurately evaluate the spatio-temporal evolution of drought. [ABSTRACT FROM AUTHOR]

Agricultural insurance is a risk management tool developed to guarantee regional production and national food security. Based on survey data from 629 households in the Inner Mongolia autonomous region, China, this paper examines the impact of agricultural insurance on farmers' planting structure. The results show that: (1) Farmers' participation in agricultural insurance significantly affects their planting structure, especially lowering the level of specialization in production; insured households are about 3–6% lower than the non-insured, and there is a complementary effect between insurance participation and diversified planting. (2) For different types of farmers, participating in insurance has different effects on their agricultural production structure. Farmers purchasing insurance with low or medium levels in proportion to agricultural income significantly reduce the degree of production specialization. The effect of participation in insurance on large-scale farmers' adjustment of planting structure is more obvious. For pure grain farmers, participation in insurance will reduce planting specialization by about 3–4%. (3) Participation in agricultural insurance will encourage farmers to plant grain crops, but the effect is small. Therefore, we need to expand the coverage areas, increase insurance types, and improve coverage levels; the products should be more targeted and differentiated, strengthening farmers' participation in complete cost insurance and income insurance. [ABSTRACT FROM AUTHOR]

China's coal industry has long faced challenges due to imbalance between supply and demand divisions and the discrepancies between supply and demand prices. Research on optimizing coal transfer amid fluctuating coal prices is crucial for enhancing coal resource allocation, ensuring a balanced and stable supply of coal resources, and promoting the sustainable development of the coal industry. This paper focuses on optimizing coal transfer schemes within a comprehensive transportation system, considering fixed transportation routes and opening times of railroad transportation and waterway transportation, with road transportation serving as a supplementary network. A three-dimensional spatio-temporal network model is established to represent the spatial and temporal paths and transportation modes of coal logistics. The study uses the coal transportation network from Inner Mongolia and Shaanxi to East China as a case study, analyzing factors such as coal price fluctuation, transportation rates, and the ratio of self-produced to purchased coal at the production end via sensitivity analysis. The findings indicate that the proposed model can effectively guide transportation practices under the integrated production, transportation, and sales framework in the energy industry. Additionally, it shows that enterprises can achieve higher profits and maintain revenue and supply stability despite coal price fluctuations, aligning with the coal industry's transformation and development in the current context. [ABSTRACT FROM AUTHOR]

To investigate the spatial and temporal changes in fractional vegetation coverage (FVC) and their driving forces in different regions of the Inner Mongolia section of the Yellow River Basin, this paper observed the spatial trends and stability of FVC in these regions based on the MOD13Q1 information regarding the 2000–2020 period as a data source. It used the dimidiate pixel model to invert FVC, and based on the centre of gravity migration model, the coefficient of variation and the Mann–Kendall and Sen's slope estimator test, it studied the spatial variation trend and stability of FVC in the four relevant areas of the Inner Mongolia section; an attribution analysis using a geodetector was also conducted. The following results were found: (1) in terms of temporal FVC change in the relevant areas, from 2000 to 2020, the overall FVC showed an increasing trend, indicating an obvious hierarchy of change as per different seasonal scales (summer > growing season > fall > spring). There is a mutation point in FVC in different areas, and the FVC sequence is random. (2) Regarding spatial change, the overall FVC showed a trend of being high in the eastern regions and low in the western regions and low–high–low from the north to the south; the stability of the Hetao Irrigation District–Wuliangsuhai Area changed more significantly with the successive seasons, and the degraded areas of FVC were mainly distributed in the city centre of the Kundulun River–Daheihe River Area and in the Hetao Irrigation District in the summer. (3) In terms of driving factors, soil type had a relatively higher explanatory power regarding the Hetao Irrigation District–Wuliangsuhai Area, rainfall had a relatively higher explanatory power regarding the Morin River–Wuding River Area and the Kundulun River–Daheihe River Area, and land use had a relatively higher explanatory power regarding the Ten Kongtui–Heidaigou Area. [ABSTRACT FROM AUTHOR]

Taking the Jiefangzha irrigation area of the Inner Mongolia Autonomous Region as the research area, the response relationships between the backscattering coefficient and radar frequency, radar incidence angle, root-mean-square height, correlation length, and soil water content under different conditions were simulated using advanced integral equations. The backscattering characteristics of exposed surfaces in cold and dry irrigation areas were discussed, and the reasons for the different effects were analyzed. Based on this, surface roughness models and statistical regression moisture inversion models were constructed through co-polarized backscatter coefficients and combined surface roughness. The correlation between the inverted surface roughness values and the measured values was R2 = 0.7569. The correlation between the soil moisture simulation values and the measured values was R2 = 0.8501, with an RMSE of 0.04. The findings showed a strong correlation between the values from the regression simulation and the measured data, indicating that the model can be applied to soil moisture inversion and has a good inversion accuracy. Compared with previous studies in the same area, the inversion model proposed in this paper has a higher accuracy and is more suitable for the inversion of soil moisture in the Jiefangzha irrigation area. These findings can support research on the water cycle and water environment assessment in the region. [ABSTRACT FROM AUTHOR]

Forest fires are a type of disaster with both human and natural factors; they differ from other forest disasters, in that they can cause significant damage not only to the ecological environments but also to the economy and society in many irreversible ways. While the risk factor of forest fires has been large, systematic studies on economic losses caused by forest fires have been lacking in recent years, and there is also a lack of analysis on forest fire economic losses in both spatial and temporal dimensions. Therefore, based on the forest fire data from 2006 to 2018, this paper establishes a forest fire economic loss evaluation system to calculate the economic losses in China and analyzes the spatial distribution characteristics and change trends of the forest fire economic losses in each province through thermal mapping. The results show the following. (1) The economic loss from forest fires in China is generally characterized by a fluctuating decline, but anomalous values due to human factors may occur. (2) The spatial heterogeneity of economic loss in China's provinces is limited by many factors, such as the differences in resource endowments, showing the characteristics of "low in the eastern and western regions and high in the central region". (3) Forest fires in China cause the most serious losses to forest ecological benefits. (4) Forest resources and fires are not independent of each other between regions, and areas with similar economic losses related to forest fires are often found in blocks. (5) Although the overall economic losses caused by forest fires in China are fluctuating and decreasing, some provinces are showing signs of increasing economic losses, most notably in Inner Mongolia. Therefore, this paper suggests targeted recommendations based on forest fires in different regions and with reference to the changing trends of economic loss caused by forest fires. For low-loss areas, we can further reduce the economic loss per unit area while ensuring that the losses do not increase any further. For high-loss areas, the main focus should be to find the weak points in the adaptation to forest fires. The right way to permanently reduce the damage caused by forest fires is to improve the adaptive and symbiotic capacity of the ecosystems and residential communities in relation to fires in a targeted manner and to improve the capacity for quick economic recovery after a fire. [ABSTRACT FROM AUTHOR]

The Shuangjianzishan silver polymetallic deposit is located in the copper–tin–lead–zinc–silver polymetallic metallogenic belt in the Southern Great Xing'an Range, with silver resources of more than 18,000 t, which is the largest silver polymetallic deposit in Asia. Early studies concluded that the Shuangjianzishan deposit is typically an epithermal Ag-Pb-Zn deposit that lacks a high-temperature mineralization stage. In recent years, with the deepening of research, a large amount of Cu-Sn mineralization has been found in the deep part of the Shuangjianzishan deposit, but it is less studied. The laser-ablation inductively coupled mass spectroscopy (LA-ICP-MS) technique is used to investigate the distribution and substitution of trace elements in chalcopyrite and cassiterite. In this paper, the trace element study of chalcopyrite and cassiterite from the Shuangjianzishan deposit reveals that Sn, In, As, Se, Sb, and Tl mainly exist in chalcopyrite in isomorphic form, while Pb, Bi, and Ni mainly exist in chalcopyrite in the form of mineral inclusions. The enrichment of the high-temperature elements Sn and Se in chalcopyrite, and the deficit of the middle- and low-temperature elements Ga, Sb, etc., reflect that the chalcopyrite in the Shuangjianzishan deposit was formed in a middle- and high-temperature environment, and it also indicates that the early ore-forming hydrothermal solution may be rich in Sn. Fe, In, Co, and Ni mainly exist in cassiterite in isomorphic form, and the content of W in cassiterite is high. There are two main forms, one is isomorphic and the other is wolframite inclusion. Cassiterite has Fe-rich and W-U-poor characteristics, indicating that cassiterite from the Shuangjianzishan deposit was formed under relatively oxidized conditions, and the relative enrichment of elements such as Fe, W, Zr, and Hf indicates that the temperature of cassiterite formation was high. The elemental content and inter-ionic coupling relationships suggest that the cassiterite from the Shuangjianzishan deposit may have an elemental replacement mechanism of W6+ + Fe2+ ↔ 2Sn4+ or Fe3+ + OH− ↔ Sn4+ + O2−. The trace elements in cassiterite of the Shuangjianzishan deposit are rich in Fe and Mn and depleted in Nb and Ta, according to the Fe-W diagram, and the tin mineralization of the Shuangjianzishan deposit belongs to cassiterite–sulfide-type tin mineralization. Chalcopyrite Co/Ni ratios >1 are consistent with the characteristics of chalcopyrite genesis in hydrothermal deposits. [ABSTRACT FROM AUTHOR]

For the Jiawula lead–zinc deposit, as easily accessible resources become depleted, mines are becoming deeper to replenish ore reserves. Identifying large, continuous, and high-grade ore bodies in deep areas has become a daunting problem. Moreover, separating lead–zinc-bearing complex ore bodies from waste material and extracting them from associated minerals are also difficult. Thus, pioneering exploratory strategies and technological methodologies are required to make breakthroughs in mineral discovery. Based on extensive-scale structural lithofacies mapping, this paper uses short-wave infrared (SWIR) spectroscopy technology to investigate hydrothermal alteration minerals in the mining area. It has identified a total of 16 hydroxyl-bearing alteration minerals, including chlorite, muscovite, illite, calcite, ankerite, kaolinite, and smectite. These minerals establish zoning characteristics around the ore bodies and on their flanks. They comprise a segmented assemblage that follows the pattern of comb-textured quartz–illite–chlorite–carbonate → muscovite–illite–chlorite–ankerite → illite–smectite–chlorite → chlorite–kaolinite–calcite. Deep-zone illitization with a lower Al–OH absorbance peak wavelength (<2206 nm) and higher crystallinity indices (>1.1) and chloritization with higher Fe–OH absorbance peak wavelengths (>2254) and higher crystallinity indices (>3.0) are indicators of potential hydrothermal centers in the deeper regions. By finding hydrothermal centers and connecting their spatial distribution with existing ore bodies, a pertinent relationship between diabase + andesite, Fe-chlorite + illite, and high-grade mineralization has been established. They correspond well with the lithology-alteration mineralization. This research provides a basis for predicting the positioning of concealed ore bodies deep inside a mine or at the periphery. [ABSTRACT FROM AUTHOR]

The general requirements of China's rural revitalization strategy are industrial prosperity, ecological livability and rich life. However, the traditional livestock breeding model has struggled to balance the dual requirements of production development and ecological protection, and it is urgent to inject new impetus and explore new development paths. At present, e-commerce has become a bridge between pastoral areas and cities, herdsmen and consumers. E-commerce participation is not only an important starting point for herdsmen to increase the added value and profit space of livestock products, but also an effective way to change the original breeding behavior based on the premise of destroying grassland. Therefore, this paper presents an in-depth study on the issue of e-commerce enabling grassland ecological restoration, aiming to provide more scientific and effective guidance for e-commerce to be used to achieve a win–win situation in economy and ecology. Therefore, based on the data of 271 herdsmen in pastoral areas of Inner Mongolia, we used the OLS model and the two-stage least squares (2SLS) method to identify the direct impact of herdsmen's e-commerce participation on grassland utilization pressure. The empirical results show that e-commerce participation can significantly decrease the grassland utilization pressure. The conclusion was still valid after alleviating endogeneity and conducting a robustness test. The results of a mechanism analysis suggest that the reduction effect of e-commerce participation on grassland utilization pressure is mainly due to price incentive, reputation incentive and place identity. Subjective norms can strengthen the inhibitory effect of e-commerce participation on grassland utilization pressure. Furthermore, a heterogeneity analysis demonstrates that e-commerce participation has a better, decreased impact on the grassland utilization pressure on the banners of China's rural e-commerce demonstration county program. Under a counterfactual assumption, if herdsmen who can participate in e-commerce choose not to do this, their grassland utilization pressure will increase. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanisation, urban areas are subject to the combined effects of the accumulation of various natural factors, such as changes in temperature leading to the thermal expansion or contraction of surface materials (rock, soil, etc.) and changes in precipitation and humidity leading to an increase in the self-weight of soil due to the infiltration of water along the cracks or pores in the ground. Therefore, the subsidence of urban areas has now become a serious geological disaster phenomenon. However, the use of traditional neural network prediction models has limitations when examining the causal relationships between time series surface deformation data and multiple influencing factors and when applying multiple influencing factors for predictive analyses. To this end, Sentinel-1A data from March 2017 to February 2023 were used as the data source in this paper, based on time series deformation data acquired using the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. A sparrow search algorithm–convolutional neural network–long short-term memory (SSA-CNN-LSTM) neural network prediction model was built. The six factors of temperature, humidity, precipitation, and ground temperature at three different depths below the surface (5 cm, 10 cm, and 15 cm) were taken as the input of the model, and the surface deformation data were taken as the output of the neural network model. The correlation between the spatial and temporal evolution characteristics of the ground subsidence in urban areas and various influencing factors was analysed using grey correlation analysis, which proved that these six factors contribute to some extent to the deformation of the urban surface. The main urban area of Hohhot City, Inner Mongolia Autonomous Region, was used as the study area. In order to verify the efficacy of this neural network prediction model, the prediction effects of the multilayer perceptron (MLP), backpropagation (BP), and SSA-CNN-LSTM models were compared and analysed, with the values of the correlation coefficients of the feature points of A1, B1, and C1 being in the range of 0.92, 0.83, and 0.93, respectively. The results show that compared with the traditional MLP and BP neural network models, the SSA-CNN-LSTM model achieves a higher performance in predicting time series surface deformation data in urban areas, which provides new ideas and methods for this area of research. [ABSTRACT FROM AUTHOR]

Polarimetric target decomposition algorithms have played an important role in extracting the scattering characteristics of buildings, crops, and other fields. However, there is limited research on the scattering characteristics of grasslands and a lack of volume scattering models established for grasslands. To improve the accuracy of the polarimetric target decomposition algorithm applicable to grassland environments, this paper proposes an adaptive polarimetric target decomposition algorithm (APD) based on the anisotropy degree (A). The adaptive volume scattering model is used in APD to model volume scattering in forest and grassland regions separately by adjusting the value of A. When A > 1, the particle shape becomes a disk, and the grassland canopy is approximated as a cloud layer composed of randomly oriented disk particles; when A < 1, the particle shape is a needle, simulating the scattering mechanism of forests. APD is applied to an L-band AirSAR dataset from San Francisco, a C-band AirSAR dataset from Hunshandak grassland in Inner Mongolia Autonomous Region, and an X-band COSMO-SkyMed dataset from Xiwuqi grassland in Inner Mongolia Autonomous Region to verify the effectiveness of this method. Comparison studies are carried out to test the performance of APD over several target decomposition algorithms. The experimental results show that APD outperforms the algorithms tested in terms of this study in decomposition accuracy for grasslands and forests on different bands of data. [ABSTRACT FROM AUTHOR]

The durability of concrete is critical for the service life of concrete structures, and it is influenced by various factors. This paper investigates the impact of the relative humidity (RH) of the curing environment on the durability of five different concrete types. The aim is to determine a suitable approach for designing concrete that is well-suited for use in the salt lake region of Inner Mongolia. The concrete types comprise ordinary Portland cement (OPC), high-strength expansive concrete (HSEC), high-strength expansive concrete incorporating silica fume, fly ash, and blast furnace slag (HSEC-SFB), steel fiber-reinforced high-strength expansive concrete (SFRHSEC), and high elastic modulus polyethylene fiber-reinforced high-strength expansive concrete (HFRHSEC). All these concrete types underwent a 180-day curing process at three distinct relative humidities (RH = 30%, 50%, and 95%) before being subjected to freeze–thaw cycles in the Inner Mongolia salt lake brine. The curing environment with a 95% RH is referred to as the standard condition. The experimental results reveal that the durability of OPC and HSEC decreases significantly with increasing relative humidity. In comparison with the control sample cured in 95% RH, the maximum freeze–thaw cycles for concrete cured in lower RHs are only 31% to 76% for OPC and 66% to 77% for HSEC. However, the sensitivity of the durability of HSEC-SFB, SFRHSEC, and HFRHSEC to variations in RH in the curing environment diminishes. In comparison with the corresponding reference value, the maximum freeze–thaw cycles for samples cured in dry conditions increase by 14% to 17% for HSEC-SFB and 21% for SFRHSEC. Specifically, the service life of HFRHSEC cured in a low RH is 25% to 46% higher than the reference value. The durability of HSEC-SFB, SFRHSEC, and HFRHSEC has been proven to be appropriate for structures located in the salt lake region of Inner Mongolia. [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]

Among the methods used to describe soil-available water in terms of yield, field water capacity and permanent wilting coefficient were considered as the upper and lower boundaries, respectively, for normal plant growth. This study investigated the soil properties and groundwater resources of the Hailiutu pasture in Inner Mongolia, China. Soil water characteristics and hydraulic conductivity function curves of the pasture soil were measured based on filter paper method. Experimental results showed that air-entry and residual value were determined to be 34.6% and 13.1%, corresponding to the field water capacity and permanent wilting coefficient, respectively, and the available water capacity was 218.0 mm. The downward velocity of the soil wetting front in the silt is 3.0 mm/h. Site investigation revealed that the pasture was a natural capillary barrier, and its critical suction could optimize the upper boundary of irrigation. Considering the limited underground water resources, shallow water level, and the transpiration of Caragana Korshinskii in the pasture, an available drip irrigation method was designed. The spacing between drip irrigation pipes is 60 cm, the spacing between drip holes is 60 cm, the drip irrigation volume per hole is 475.2~838.8 mL/d, the drip irrigation time is 110~200 min at each night. Each hectare pasture exhausts 13.5~22.5 m3 water each day. Field tests showed that the proposed method is feasible and can be used for irrigation in dryland pastures. [ABSTRACT FROM AUTHOR]

Improving the operating income of farmers and herdsmen is an important starting point for achieving common prosperity. As a common form of learning and an important source of skills training, informal learning has a certain impact on the economy and income level of farmers and herdsmen. This paper takes 439 herdsmen in three cities of the Inner Mongolia Autonomous Region as the research object and tries to explore the comprehensive influence, subdivision difference, and mechanism of informal learning on the operating income of herdsmen on the theoretical and empirical levels. The results show that the impact of informal learning on the operating income of herdsmen is "inverted U-shaped", and there is an informal learning balance point of 2.9776 h, which maximizes the effect of informal learning on the increased operating income of herdsmen. After using the instrumental variable method to deal with endogeneity and conducting robustness tests through winsorizing, quantile regression, and substitution variables, the research conclusions were still valid. Heterogeneity analysis found that informal learning has a significant impact on the increase in operating income of herdsmen in the low human capital group, reflecting the role of "sending charcoal in the snow". However, it has no significant effect on the increase in operating income of herdsmen in the high human capital group, and the effect of "icing on the cake" is not obvious. In view of this result, government departments should speed up the planning of Internet infrastructure construction in pastoral areas, and accurately push the knowledge and skills needs of herdsmen to help increase the operating income of herdsmen. [ABSTRACT FROM AUTHOR]

With the wide application of remote sensing technology, target detection based on deep learning has become a research hotspot in the field of remote sensing. In this paper, aimed at the problems of the existing deep-learning-based desert land intelligent extraction methods, such as the spectral similarity of features and unclear texture features, we propose a multispectral remote sensing image desert land intelligent extraction method that takes into account band information. Firstly, we built a desert land intelligent interpretation dataset based on band weighting to enhance the desert land foreground features of the images. On this basis, we introduced the deformable convolution adaptive feature extraction capability to U-Net and developed the Y-Net model to extract desert land from Landsat remote sensing images covering the Inner Mongolia Autonomous Region. Finally, in order to analyze the spatial and temporal trends of the desert land in the study area, we used a structural equation model (SEM) to evaluate the direct and indirect effects of natural conditions and human activities, i.e., population density (PD), livestock volume (LS), evaporation (Evp), temperature (T), days of sandy wind conditions (LD), humidity (RH), precipitation (P), anthropogenic disturbance index (Adi), and cultivated land (CL). The results show that the F1-score of the Y-Net model proposed in this paper is 95.6%, which is 11.5% more than that of U-Net. Based on the Landsat satellite images, the area of desert land in the study area for six periods from 1990 to 2020 was extracted. The results show that the area of desert land in the study area first increased and then decreased. The main influencing factors have been precipitation, humidity, and anthropogenic disturbance, for which the path coefficients are 0.646, 0.615, and 0.367, respectively. This study will be of great significance in obtaining large-scale and long-term time series of desert land cover and revealing the inner mechanism of desert land area change. [ABSTRACT FROM AUTHOR]

There are a large number of tunnels built in permafrost regions every year in China. When investigating the road tunnels that have been in operation in cold regions, it was found that most of these tunnels have various freezing damages. Therefore, in the design, construction, operation, and maintenance of tunnels in cold regions, there are still many technical problems that need to be solved urgently. Among them, the problems of heat preservation, anti-freezing, and anti-drainage are the key technical problems to determine whether the tunnels in cold regions are affected by freezing damage. The project was based on the completed highway tunnels in Inner Mongolia. Given a series of special problems faced by the construction and management of tunnels in cold regions, a large number of reference documents were consulted. This paper designed thermal insulation, antifreeze, and anti-drainage technology for tunnels in the cold area of Jinpen Bay. The research in this paper can effectively reduce the probability of the occurrence of tunnel freezing damage, ensure the safety of the long-term operation of the tunnel lining structure, and greatly reduce maintenance costs, which have good economic and social benefits. [ABSTRACT FROM AUTHOR]

The net primary productivity (NPP) of vegetation is an important indicator reflecting the vegetation dynamics and carbon sequestration capacity in a region. In recent years, China has implemented policies to carry out ecological protection. To understand the changes in the distribution of vegetation NPP in China and the influence of climate factors, the Carnegie–Ames–Stanford approach (CASA) model was used to estimate the NPP from 2001 to 2020. In this paper, several sets of measurement datasets and products were collected to evaluate the effectiveness of the model and suggestions were provided for the modification of the CASA model based on the evaluation results. In addition to the correlation analysis, this paper presents a statistical method for analyzing the quantitative effects in individual climatic factors on NPP changes in large regions. The comparison found that the model has a better estimation effect on grassland and needleleaf forest. The estimation error for the evergreen needleleaf forest (ENF) and deciduous broadleaf forest (DBF) decreases with the warming of the climatic zone, while the evergreen broadleaf forest (EBF) and deciduous needleleaf forest (DNF) do the opposite. The changes in total CASA NPP were consistent with the trends of other products, showing a dynamic increasing trend. In terms of the degree of correlation between the NPP changes and climatic factors, the NPP changes were significantly correlated with temperature in about 10.39% of the vegetation cover area and with precipitation in about 26.92% of the vegetation cover area. It was found that the NPP variation had a negative response to the temperature variation in Inner Mongolia grasslands, while it had a positive but small effect (±10 g C) in the Qinghai–Tibet Plateau grasslands. Precipitation had a facilitative effect on the grassland NPP variation, while an increase in the annual precipitation of more than 200 mm had an inhibitory effect in arid and semi-arid regions. This study can provide data and methodological reference for the ecological assessment of large-scale regional and climate anomalous environments. [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]

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]

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]

Highwall mining is a method that involves using a continuous highwall miner system (CHM) to extract coal from the remaining coal seams, which has proven to be an effective and safe method for extracting coal after open-pit mining. However, application cases globally have shown that the feasibility of highwall mining in open-pit coal mines is subject to geological conditions, mining techniques, and other factors. If application conditions are not suitable, equipment may be trapped under collapsed coal–rock masses and unable to be retrieved, resulting in severe safety issues for slope stability. To meet the real-world demand for extracting the remaining coal in open-pit coal mines in China, it is urgent to conduct a feasibility evaluation of highwall mining in these areas. This paper establishes a mathematical evaluation framework for assessing the feasibility of highwall mining by summarizing a large number of engineering application cases globally and analyzing various technical characteristics such as geological deposit conditions, mining techniques, and technical equipment. The analytic hierarchy process (AHP), fuzzy comprehensive evaluation (FCE) and variable weight theory (VWT) are utilized in conjunction to form this framework, which includes four secondary indicators: geological deposit factors, mining technique factors, safety impact factors, and economic evaluation factors, and 20 tertiary sub-indicators, along with their corresponding characteristic values. The feasibility sub-set is divided into four categories: infeasible, basically feasible, relatively feasible, and highly feasible, and the values of the sub-indicators strictly follow and represent these four levels of feasibility. Weight vectors for the sub-indicators are obtained through a judgment matrix established within the mathematical evaluation framework. The fuzzy relationship matrix of the sub-indicators is constructed using fuzzy mathematical membership functions, and the final feasibility evaluation is determined through two-level comprehensive evaluation. The accuracy of the model is verified using the characteristic parameters of open-pit coal mines under two different conditions (JZT coal mine in Inner Mongolia, China, and GC coal mine in Australia). The results demonstrate that the maximum evaluation membership degree for the JZT mine is 0.7113, belonging to the "highly feasible" level, while the GC mine is 0.3304, belonging to the "basically feasible" level, which aligns well with real-world usage, proving that the evaluation model can effectively reveal the performance and membership degree of each indicator in different application cases. By quantitatively characterizing the feasibility level of highwall mining technology under different application conditions, this evaluation model can provide scientific guidance for coal mining enterprises to introduce CHM for highwall mining operation in open-pit coal mines. [ABSTRACT FROM AUTHOR]

This paper takes 207 traditional villages in Inner Mongolia as the research object and uses the ArcGIS10.7 software platform, using the nearest neighbor index, coefficient of variation analysis, spatial autocorrelation analysis, imbalance index, kernel density estimation method, geographical detector, and other methods to explore the spatial distribution characteristics and influencing factors of traditional villages in Inner Mongolia. The research shows that: (1) the spatial distribution of traditional villages in Inner Mongolia is condensed; the distribution of cities is uneven; and the overall distribution pattern of 'two main and two vice' is presented. (2) The traditional villages are mainly distributed in the altitude area of 500–1500 m, and their spatial distribution characteristics are positively correlated with the annual average temperature, annual precipitation, total population, the proportion of the primary industry, and the number of intangible cultural heritage, and negatively correlated with the slope, river distance, highway density, per capita GDP, urbanization, and the proportion of the secondary industry. (3) The results of GeoDetector2018 software show that socio-economic factors are the primary factors affecting the spatial distribution of traditional villages in Inner Mongolia, followed by natural geographical factors. The interaction and synergy between the influencing factors have increased significantly, which jointly affects the spatial pattern of the distribution of traditional villages in Inner Mongolia. The purpose of the study is to provide reference for the protection and development of traditional villages in Inner Mongolia and the implementation of the national rural revitalization strategy. [ABSTRACT FROM AUTHOR]

In order to deal with the threat of forest fire to the original forest resources, improve the efficiency of firefighting, and better protect the forest ecological resources in the original forest area. In this paper, the ant colony algorithm is used as the search algorithm for the firefighting path, and the Qiqian Forestry Bureau in the northern primitive forest region of the Great Xing'an Range in Inner Mongolia is taken as the research object to select road entry points with different linear distances from the fire poin. By adjusting the pheromone volatility coefficient, the improved ant colony algorithm can better deal with the off-road firefighting path planning in forest environment. The simulation results show that using the road entry point with linear distance of 24.5 km as the starting point for the path planning of off-road firefighting in the northern the Great Xing'an Range can effectively shorten the path and minimize the loss caused by forest fire. At the same time, it verifies the effectiveness and rationality of the path planning of the improved ant colony algorithm under such environmental conditions. The optimization scheme meets the needs of forest fire fighting in the original forest area, and can play a guiding role in design of the forest fire path planning in the original forest area in the northern Great Xing'an Range of Inner Mongolia and a reference for fire path planning in other forest areas in China. [ABSTRACT FROM AUTHOR]

To address the severity of the wind and light abandonment problem and the economics of hydrogen energy production and operation, this paper explores the problem of multi-cycle resource allocation optimization of hydrogen storage systems for coal–wind–solar power generation. In view of the seriousness of the problem of abandoning wind and photovoltaic power and the economy of hydrogen production and operation, the node selection and scale setting issues for hydrogen production and storage, as well as decision-making problems such as the capacity of new transmission lines and new pipelines and route planning, are studied. This research takes the satisfaction of energy supply as the basic constraint and constructs a multi-cycle resource allocation optimization model for an integrated energy system, aiming to achieve the maximum benefit of the whole system. Using data from Inner Mongolia, where wind abandonment and power limitation are severe, and Beijing and Shanxi provinces, where hydrogen demand is high, this paper analyzes the benefits of the hydrogen storage system for coal–wind–solar power generation, and explores the impact of national subsidy policies and technological advances on system economics. [ABSTRACT FROM AUTHOR]

As a typical desert in the Inner Mongolia Autonomous Region, the Ulan Buh Desert has a dry climate and scarce precipitation all year round. Groundwater has become the main factor limiting the growth of vegetation in this region. It is of great significance to study the influence of groundwater depth on the spatial distribution pattern of vegetation in this region. Based on the PIE-Engine platform and using long-term time-series Landsat data, this paper analyzed the spatial–temporal distribution characteristics and trends in vegetation coverage in the Ulan Buh Desert in the last 20 years using a pixel dichotomy model and the image difference method. The Kriging interpolation method was used to interpolate the groundwater depth data from 106 monitoring wells in the Ulan Buh Desert over the past 20 years, and the spatial distribution characteristics of groundwater depth in the Ulan Buh Desert were analyzed. Finally, the correlation coefficient between changes in vegetation coverage and changes in groundwater depth was calculated. The results showed the following: (1) The vegetation coverage in the Ulan Buh Desert was higher in the periphery and lower in the center of the desert. The overall vegetation level showed an increasing trend year by year; the growth rate was 4.73%/10 years, and the overall vegetation cover showed an improving trend. (2) The overall groundwater depth in the Ulan Buh Desert was deep in the southwest and shallow in the northeast. In the past 20 years, the groundwater depth in the Ulan Buh area has become shallower, and the ecological condition has gradually improved. (3) On the whole, the vegetation coverage varied with the groundwater depth, and the shallower the groundwater depth, the greater the vegetation coverage. When the groundwater depth increased to more than 4 m, the change in the groundwater depth had a significant effect on the vegetation coverage. However, when the groundwater depth was greater than 6 m, the change in the groundwater depth had no significant effect on the change in vegetation coverage. [ABSTRACT FROM AUTHOR]

Following the launch of the ZY1-02E satellite, the thermal infrared sensor aboard the satellite experienced alterations in the space environment, leading to varying degrees of attenuation in some components. The laboratory calibration accuracy could not satisfy the demands of quantitative production, and a certain degree of deviation was observed in on-orbit calibration. To accurately characterize the on-orbit radiation properties of thermal infrared remote sensing payloads, an absolute radiometric calibration campaign was carried out at the Ulansuhai Nur and Baotou calibration sites in Inner Mongolia in July 2022. This paper outlines the processes of onboard calibration and vicarious calibration for the ZY1-02E satellite, comparing the outcomes of onboard calibration with those of vicarious calibration. The onboard calibration method involved internal calibration, while the vicarious calibration method utilized an on-orbit absolute radiometric calibration technique based on various natural features that were not constrained by satellite–Earth spectrum matching requirements. Calibration coefficients were acquired, and the absolute radiometric calibration results of on-orbit vicarious and onboard calibration were compared, analyzed, and verified using the radiance computed from measured data and the reference sensor data. The accuracy of on-orbit absolute vicarious calibration was determined, and the causes for the decline in the radiation calibration accuracy on the orbiting satellite were examined. The findings revealed that the vicarious calibration results exhibited a lower percentage of radiance deviation compared with the onboard calibration results, meeting the quantitative requirements of remote sensing data. These results were significantly better than those obtained from onboard blackbody calibration, offering a data foundation for devising satellite calibration plans and enhancing calibration algorithms. In the future, the developmental trend of on-orbit radiometric calibration technology will encompass high-precision and slow-attenuation onboard calibration techniques, as well as high-frequency and simplified-step vicarious calibration methods. [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]

This paper established a mathematical method for the spectral feature extraction of ratholes, based on UAV hyperspectral imaging technology. The degradation of grasslands is a major challenge to terrestrial ecosystems. Rodents not only promote soil erosion and accelerate the process of grassland degradation, but also carry diseases that can easily cause epidemics. The calculation of the number of rodent holes and grassland vegetation cover is an important indicator for monitoring and evaluating grassland degradation. Manual surveys have drawbacks in efficiently monitoring large areas and are human- and material-costly, hardly meeting the current needs of grassland degradation monitoring. Therefore, there is an urgent need to conduct real-time dynamic monitoring of grassland rathole distributions and grassland degradation processes. In this study, a low-altitude remote sensing platform was constructed by integrating a hyperspectral imager with a UAV to collect spectral data of the desert steppes in central Inner Mongolia Autonomous Region, China. Then, the spectral features of ratholes were extracted via radiation correction, noise reduction, and principal component analysis (PCA). Meanwhile, the spectral features of vegetation and bare soil were extracted based on the normalized difference vegetation index (NDVI), which was inputted to calculate the vegetation cover. The results showed that the single-band map extracted based on PCA could effectively determine the location of ratholes, where the overall accuracy and kappa coefficient were 97% and 0.896, respectively. Therefore, the method proposed in this study can accurately identify the location of desert steppe rodent holes. It provides a high-precision technical means for scientific and effective control of grassland rodent infestation and also provides a higher technical means for grassland degradation. [ABSTRACT FROM AUTHOR]

Agate attracts the attention of gem mineralogists because of its variable colors. The color of agate is closely related to its naming and classification, so it is necessary to study the color and mineral origin of agate. In this paper, the mineralogical characteristics and color origin of red, yellow and green Moqi agates from Inner Mongolia were systematically studied by means of Fourier transform infrared spectrometer, Raman spectrometer, X-ray powder diffractometer, electron probe microanalyzer and ultraviolet–visible spectrophotometer. It is found that the color of Moqi agate is related to the minerals and trace elements contained in it, and is associated with the electron transition or charge transfer of Fe ions in the contained minerals. Green agate has the highest Fe content, and its color is caused by celadonite inclusions. The red and yellow color in Moqi agate is mainly caused by hematite and goethite, while red agate contains more Fe than yellow agate. Raman spectroscopy and X-ray diffraction analysis show that the content of moganite in Moqi agate is 0–30%. It is calculated that the crystallinity of Moqi agate is 1.5–3.5. This work provides a theoretical basis for future research on the identification of Moqi agate. [ABSTRACT FROM AUTHOR]

China's state-owned forest farms are the basic sectors of forestry production, and their carbon cycle functions, such as timber processing and forest carbon sequestration, are of great significance to the national emission reduction strategy. By three-stage DEA and Tobit models, this paper measures the carbon sequestration output efficiency of 3706 state-owned forest farms involved in China's National Forestry and Grassland Administration's 2008–2018 survey. We figure out how the mechanism on the carbon sequestration impacts output efficiency of these forest farms and analyze the temporal trends and spatial distributions of their outputs in various regions. Our results indicate that the overall output efficiency of state-owned forest farms in China is relatively low compared with the international advanced level and show that distinctive north-south regional differences exist. Specifically, the carbon storage of the state-owned forest farms in Northeast China and Inner Mongolia occupies more than half of the carbon storage of total amounts, but their output efficiency is unsatisfactory. Conversely, the forest farms in Southwest China have a medium amount of carbon storage and the highest output efficiency. After improving the external environments of these farms, the efficiency value in each province appears as a significant increment. Moreover, the effects of afforestation, timber harvests, the under-forest economy, and other operating behaviors exhibit regional heterogeneity to some extent. Therefore, this paper advocates reforming the current forest cultivation strategy that emphasizes afforestation and neglects management, and relevant government departments are supposed to adjust operations according to local conditions to promote sustainable forest management. [ABSTRACT FROM AUTHOR]

Tracing the historical development of soil salinization and monitoring its current status are crucial for understanding the driving forces behind it, proposing strategies to improve soil quality, and predicting future trends. To comprehensively understand the evolution of research on the remote sensing inversion of soil salinity, a scientific bibliometric analysis was conducted on research from the past two decades indexed in the core scientific databases. This article analyzes the field from various perspectives, including the number of publications, authors, research institutions and countries, research fields, study areas, and keywords, in order to reveal the current state-of-the-art and cutting-edge research in this domain. Special attention was given to topics such as machine learning, data assimilation methods, unmanned aerial vehicle (UAV) remote sensing technology, soil inversion under vegetation cover, salt ion inversion, and remote sensing model construction methods. The results indicate an overall increase in the volume of publications, with key authors such as Metternicht, Gi and Zhao, Gengxing, and major research institutions including the International Institute for Geoinformatics Science and Earth Observation and the Chinese Academy of Sciences making significant contributions. Notably, China and the USA have made substantial contributions to this field, with research areas extending from Inner Mongolia's Hetao irrigation district to the Mediterranean region. Research in the remote sensing domain focuses on various methods, including hyperspectral imaging for salinized soil inversion, with an increasing emphasis on machine learning. This study enriches researchers' knowledge of the current trends and future directions of remote sensing inversion of soil salinization. [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]

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]

Interferometric synthetic aperture radar (InSAR) is an integrated navigation technique that can be used for aircraft positioning and attitude retrieval, regardless of weather conditions. The key aspect of the entire system is interference fringe matching, which has not been extensively researched in existing literature. To address this gap, this paper proposes a terrain-feature-based interference fringe-matching algorithm. The proposed algorithm first extracts mountain line features from the interference fringes and identifies mountain branch points as key points for feature matching. A threshold is set to eliminate false detections of mountain branch points caused by phase mutation. Matching is then carried out by combining the mountain line features and curvature design feature descriptors of the area around a branch point. The proposed algorithm is verified using interference fringe data obtained from an actual flight experiment in Inner Mongolia, China, compared to a reference interference fringe dataset with errors. The results show that, under the condition of position error, the proposed algorithm yields 72 matching inliers with a precision of 0.103, a recall of 0.128, and an F1-Score of 0.144. Compared to traditional algorithms, our proposed algorithm significantly improves the problem of mismatch and opens up new possibilities for downstream interference fringe matching navigation technology. Furthermore, the proposed algorithm provides a new approach for remote sensing image matching using terrain features. [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]

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]

The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification based on time series Sentinel images and object-oriented methods and takes the crop recognition and classification of the National Modern Agricultural Industrial Park in Jalaid Banner, Inner Mongolia, as the research object. It uses the Google Earth Engine (GEE) cloud platform to extract time series Sentinel satellite radar and optical remote sensing images combined with simple noniterative clustering (SNIC) multiscale segmentation with random forest (RF) and support vector machine (SVM) classification algorithms to classify and identify major regional crops based on radar and spectral features. Compared with the pixel-based method, the combination of SNIC multiscale segmentation and random forest classification based on time series radar and optical remote sensing images can effectively reduce the salt-and-pepper phenomenon in classification and improve crop classification accuracy with the highest accuracy of 98.66 and a kappa coefficient of 0.9823. This study provides a reference for large-scale crop identification and classification work. [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]

This paper aims to quantitatively analyze the influence of natural groundwater flowing into the flow field of in situ leaching mining. The computational method was built to evaluate the effect of natural groundwater on the production efficiency of pumping wells for the in situ leaching of uranium, and the "flow ratio of groundwater" and related formulas were defined. C1 and C2 mining areas of an in situ leaching uranium mine in Inner Mongolia were taken as an example, and the effect on the "flow ratio of groundwater" when changing the flow quantity of injection wells and the position and length of the filter in the pumping and injection wells were compared. The results show that the variation in the "flow ratio of groundwater" of a whole mining area or a single pumping well in different production stages can be obtained by the neutral solution concentration value from the mining area's numerical simulation. Regulating the position, length of the filter, and mode of fluid injection in an in situ leaching mine can control the quantity of natural groundwater flowing into the mining area and reduce the fluid exchange between the flow field of in situ leaching uranium mining and natural groundwater. [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]

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]

Drought refers to a meteorological disaster that results in insufficient soil moisture due to a long-term lack of rainfall and disrupts the moisture balance of crops. Yinshanbeilu in Inner Mongolia is an arid and semi-arid region, and the onset of drought and its subsequent transmission is a key challenge in water resource management. This study takes Yinshanbeilu as the study area, analyses the changing characteristics of multi-timescale drought from 1971 to 2020 based on the Standardised Precipitation Index (SPI), and analyses the stochasticity and stability of the drought by using a cloud model. Finally, the cross-wavelet transform method and Pearson's test are used to explore the correlation between atmospheric circulation factors, PRE and PET, and drought. The results indicate that (1) on the annual scale, the frequency of drought in Yinshanbeilu mainly ranges from 22% to 28%, with a high frequency of light droughts, a low frequency of severe droughts, a high frequency of droughts in the east and west, and a low frequency of droughts in the north and south; on the seasonal scale, the frequency of droughts in winter is the highest, with a rate of 34.6%, and the lowest frequency of droughts is in autumn, with a rate of 28.3%. (2) There is a decreasing trend in Entropy (En) and Hyper-Entropy (He), and an increasing trend in Expectation (Ex) for the inter-annual SPI-12 cloud model. Spatially, Ex and He are negatively correlated, while En and He are positively correlated. The inter-annual variation in cloud eigenvalues is greater than the inter-site variation, so the cloud model better reflects the spatial stochasticity and stability of regional inter-annual SPI. For the seasonal-scale SPI-3 cloud model, Ex is smaller in all seasons, En is also smaller, and He is larger. (3) Sunspot, PRE (precipitation), and PET (Potential Evapotranspiration) are all positively correlated with SPI and have the highest correlation. This study reveals the characteristics and causes of variations of drought in Yinshanbeilu, which can be applied to future research areas related to regional drought risk management. [ABSTRACT FROM AUTHOR]

Wind erosion is a key global environmental problem. As an important protective measure to provide services to the ecosystems in wind-eroded areas, the wind erosion prevention service is of great significance to the management of wind and sand hazards and ecological environment restoration in the wind-eroded areas and the neighboring areas. Taking the Shiyang River basin as the study area, the quality of supplies for wind erosion prevention services was estimated using the RWEQ model for the years 2005, 2010, 2015, and 2020; the trajectories of air masses at wind speeds higher than the sand-causing wind speeds were simulated based on the forward trajectory module of the HYSPLIT model for a 24 h period; the spatial simulation of the flow of wind erosion prevention services on a daily scale with Minqin Station as the sand source was carried out; and the beneficiary areas of wind erosion prevention services were identified. Based on the RWEQ model, the spatial patterns of potential wind erosion, actual wind erosion, and wind and sand stabilization services were obtained, and the supply areas were divided. From 2005 to 2020, the wind erosion prevention service flow in the Shiyang River basin was distributed along a northwest–southeast direction, with a radial decrease from the center to the periphery, and with an extremely strong extraterritorial effect. The amount of wind erosion in the basin has a variable downward tendency over time and a spatial distribution pattern of high in the north and low in the south. The area of higher sand fixation is distributed in the eastern oasis area and desert junction zone. The HYSPLIT model was used to simulate the transport paths of wind and sand within 24 h during 2005–2020, the transmission paths of the wind erosion prevention service flow were obtained to be 59–134, and the flows were 2.55 × 104–3.85 × 106 t, displaying a changing trend of first decreasing, then increasing, and then decreasing. Gansu Province, Ningxia Hui Autonomous Region, and Inner Mongolia Autonomous Region are the most important areas benefiting from the wind erosion prevention service flow in the Shiyang River basin. The wind erosion prevention service flows in the basin benefit 47 cities in 9 provinces. [ABSTRACT FROM AUTHOR]

Effective high slope risk assessment plays an important role in the safety management and control of the open-pit coal mining process. Traditional slope stability risk assessment methods rarely consider the time factor or evaluate the dynamic change of high slope in an open-pit mine at a certain time in a sensitivity assessment. This paper develops an interval trapezoidal fuzzy soft set method to achieve the high slope dynamic risk evaluation. The proposed dynamic interval trapezoidal fuzzy soft set method for risk assessment of high slope in an open-pit coal mine is developed by integrating the time points and weights of slope risk factors. The extended interval trapezoidal fuzzy soft set was used to calculate the weights of risk factors at different times, and the Fuzzy Analytical Hierarchy Process (FAHP) method was applied to determine the weights of risk factors. The weight change of different risk factors with time can be easily achieved with the proposed method. As a case study, this approach is implemented into a risk assessment model for the north high slope in Shengli #1 open-pit mine located in Xilinhot, Inner Mongolia. The model complies with three time points and contains 4 primary risk factors (S) and 17 secondary risk factors. The results indicated that the hydrological climate conditions and slope geometry conditions were the high risk factors affecting this open-pit coal mine slope. The reasonability and effectiveness of the evaluation results were verified with in-situ observations and measurements. This dynamic risk assessment method is helpful for improving safety management and control for the high slopes of open-pit mines in the coal mining process. [ABSTRACT FROM AUTHOR]