Search Results (27)

Aboveground biomass (AGB) is a critical indicator for assessing carbon sequestration and ecosystem health in transboundary ecologically fragile areas. High-precision estimation and spatiotemporal inversion of AGB are the key to investigating transition zones. However, inadequate feature selection and complex parameter tuning limit accuracy and spatiotemporal representation in the estimation model. An AGB estimation model that integrates SHAP-based feature selection with a particle swarm optimization-enhanced random forest model (RF_PSO) was proposed. Then AGB trajectory clustering was used to characterize the grassland change pattern. The method was applied to grasslands across the China–Mongolia–Russia (CMR) border area from 2000 to 2020. The results show that (1) the SHAP-RF_PSO model achieved the highest accuracy (R² = 0.87, RMSE = 45.8 g/m²), outperforming other estimation models. (2) AGB improvements were observed in 72.13% of the area, mainly in MN_EA, MN_CE, and CN_NMG, while 27.39% showed degradation, concentrated in CN_NMG and MN_CE. The stable area accounts for 0.48%, which is scattered in RU_BU and RU_ZA.CN_NMG. (3) Four change patterns, namely Fluctuating Low, Stable Low, Fluctuating High, and Stable High, were identified, with major shifts in 2007, 2012, and 2014. (4) Projections indicate that 80% of the region may maintain current trends, 13% may reverse, and 7% remain uncertain, requiring targeted interventions. This study offers a robust tool for high-precision AGB estimation and supports dynamic monitoring in the CMR border area. Full article

Castor fiber birulai is a subspecies of the Eurasian beaver that has a relatively small population size compared to other Castor subspecies. There is limited genetic research on this subspecies. In this study, mitochondrial cytochrome b (Cytb) and D-loop sequences were analysed in genetic samples obtained from 19 individuals residing in the Buergen River Basin, Xinjiang, China. The Cytb region presented a single haplotype, whereas three haplotypes were identified in the D-loop region. The genetic diversity within the Chinese population was low (D-loop Hd = 0.444; Pi = 0.0043), markedly lower than that observed in other geographical populations of C. fiber. Phylogenetic reconstructions and haplotype network analyses revealed substantial genetic differentiation between C. f. birulai and other Eurasian lineages (Fst > 0.95), supporting the status of C. f. birulai as a distinct evolutionary lineage. Although the genetic distance between the Chinese and Mongolian populations was relatively small (distance = 0.00269), significant genetic differentiation was detected (Fst = 0.67055), indicating that anthropogenic disturbances—such as hydraulic infrastructure and fencing along the cross-border Bulgan River—may have impeded gene flow and dispersal. Demographic analyses provided no evidence of recent population expansion (Fu’s Fs = 0.19152), suggesting a demographically stable population. In subsequent studies, we recommend increasing nuclear gene data to verify whether the C. f. birulai population meets the criteria for Evolutionarily Significant Unit classification, and strengthening cross-border protection and cooperation between China and Mongolia. Full article

Drought impacts agricultural production and regional sustainable development. Accordingly, timely and accurate drought monitoring is essential for ensuring food security in rain-fed agricultural regions. Alternating drought and flood events frequently occur in the Heilongjiang River Basin, the largest grain-producing area in Far East Asia. However, spatiotemporal variability in drought is not well understood, in part owing to the limitations of the traditional Temperature Vegetation Dryness Index (TVDI). In this study, an Improved Temperature Vegetation Dryness Index (ITVDI) was developed by incorporating Digital Elevation Model data to correct land surface temperatures and introducing a constraint line method to replace the traditional linear regression for fitting dry–wet boundaries. Based on MODIS (Moderate-resolution Imaging Spectroradiometer) normalized vegetation index and land surface temperature products, the Heilongjiang River Basin, a cross-border basin between China, Mongolia, and Russia, exhibited pronounced spatiotemporal variability in drought conditions of the growing season from 2001 to 2023. Drought severity demonstrated clear geographical zonation, with a higher intensity in the western region and lower intensity in the eastern region. The Mongolian Plateau and grasslands were identified as drought hotspots. The Far East Asia forest belt was relatively humid, with an overall lower drought risk. The central region exhibited variation in drought characteristics. From the perspective of cross-national differences, the drought severity distribution in Northeast China and Inner Mongolia exhibits marked spatial heterogeneity. In Mongolia, regional drought levels exhibited a notable trend toward homogenization, with a higher proportion of extreme drought than in other areas. The overall drought risk in the Russian part of the basin was relatively low. A trend analysis indicated a general pattern of drought alleviation in western regions and intensification in eastern areas. Most regions showed relatively stable patterns, with few areas exhibiting significant changes, mainly surrounding cities such as Qiqihar, Daqing, Harbin, Changchun, and Amur Oblast. Regions with aggravation accounted for 52.29% of the total study area, while regions showing slight alleviation account for 35.58%. This study provides a scientific basis and data infrastructure for drought monitoring in transboundary watersheds and for ensuring agricultural production security. Full article

The conflict between exploitation of coal resources and environmental protection is highly pronounced in the Wanli mining area, located in the arid and semi-arid region of Inner Mongolia, China. The impact of mining operations has led to varying degrees of surface subsidence, which further threatens the ecological environment as coal extraction continues. The Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique offers significant advantages over traditional subsidence monitoring methods, particularly in complex terrain with vertical and horizontal valleys. This approach enables large-scale, low-cost, and all-weather monitoring. Based on 64 Sentinel-1A SAR images from 2018 to 2023, this study aims to promptly identify the location, deformation degree, and evolution characteristics of mining-induced subsidence within the study area using SBAS-InSAR techniques. The results indicate that the area affected by mining-induced subsidence covers 109.73 km², with a maximum cumulative subsidence of 283.41 mm and a maximum subsidence velocity of 46.45 mm/y. Additionally, during the field verification, 29 ground fractures, predominantly located along the precipitous borders of subsidence areas, were identified, validating the credibility of the monitoring results. This study demonstrates that SBAS-InSAR technology remains highly effective in the erosional terrain of the Loess Plateau. The monitoring data can help in-production mining to accurately identify the characteristics and patterns of surface subsidence induced by coal mining operations. It provides reliable policymaking data support and makes significant contributions to optimize cost-efficiency and guide targeted monitoring efforts in subsequent management work of the Wanli mining area as well as other mining areas. Full article

Global dust events have become more frequent due to climate change and increased human activity, significantly impacting air quality and human health. Previous studies have mainly focused on determining atmospheric dust pollution levels through atmospheric parameter simulations or AOD values obtained from satellite remote sensing. However, research on the quantitative description of dust intensity and its cross-regional transport characteristics still faces numerous challenges. Therefore, this study utilized Fengyun-4A (FY-4A) satellite Advanced Geostationary Radiation Imager (AGRI) imagery, Cloud-Aerosol Lidar, and Infrared Pathfinder Satellite Observation (CALIPSO) lidar, and other auxiliary data, to conduct three-dimensional spatiotemporal monitoring and a cross-regional transport analysis of two typical dust events in the Beijing–Tianjin–Hebei (BTH) region of China using four dust intensity indices Infrared Channel Shortwave Dust (Icsd), Dust Detection Index (DDI), dust value (DV), and Dust Strength Index (DSI)) and the HYSPLIT model. We found that among the four indices, DDI was the most suitable for studying dust in the BTH region, with a detection accuracy (POCD) of >88% at all times and reaching a maximum of 96.14%. Both the 2021 and 2023 dust events originated from large-scale deforestation in southern Mongolia and the border area of Inner Mongolia, with dust plumes distributed between 2 and 12 km being transported across regions to the BTH area. Further, when dust aerosols are primarily concentrated below 4 km and PM10 concentrations consistently exceed 600 µg/m³, large dust storms are more likely to occur in the BTH region. The findings of this study provide valuable insights into the sources, transport pathways, and environmental impacts of dust aerosols. Full article

Mongolia, situated in central Asia and bordered by Russia to the north and China to the south, experiences a semi-arid climate across most of its territory. Grasslands are pivotal in Mongolia’s agricultural sustainability and food security, facing rapid changes in the last two decades that underscore the ongoing need for innovative approaches to assess vegetation conditions. This study aims to evaluate grassland biomass measurement and prediction through the analysis of high-resolution satellite data. By conducting a time series assessment of grazing-induced changes in vegetation dynamics at the long-term monitoring sites of the Botanic Garden and Research Institute, Mongolian Academy of Sciences, we seek to refine our understanding. The investigation covers biomass estimation across various Mongolian grassland landscapes, encompassing desert, steppe, and mountain regions. Spanning the grassland growing season from May 2020 to October 2023, the research leveraged diverse ground data types, including surface reflectance measurements, geographic coordinates for satellite data correction, and aboveground dry biomass. These components were instrumental in developing a biomass estimation model reliant on establishing correlations between the satellite-derived Normalized Difference Vegetation Index and biomass. The predicted biomass facilitated the time series map analysis and dynamic analysis. The PlanetScope surface reflectance correlates strongly at 0.97 with field measurements, indicating robust relations. Biomass and the Normalized Difference Vegetation Index show correlations of 0.82 for dry grassland, 0.80 for mountain grassland, and 0.65 for desert grassland, with a combined correlation coefficient of 0.62, revealing distinct characteristics across these grasslands. Time series dynamic analysis reveals rising biomass differences between grazed and ungrazed areas, suggesting potential grassland degradation. Variations in the slope coefficient of biomass differences among grassland types indicate differing degradation patterns, emphasizing the need for effective grazing management practices to sustain and conserve Mongolian grasslands. This highlights the potential of remote sensing in monitoring and managing grassland ecosystems. Full article

Wildfires spread rapidly and cause considerable ecological and socioeconomic losses. Inner Mongolia is among the regions in China that suffer the most from wildfires. A simple, effective model that uses fewer parameters to simulate wildfire spread is crucial for rapid decision-making. This study presents a region-specific technological process that requires a few meteorological parameters and limited grassland vegetation data to predict fire spreading dynamics in Inner Mongolia, based on cellular automata that emphasize the numeric evaluation of both heat sinks and sources. The proposed method considers a case that occurred in 2021 near the East Ujimqin Banner border between China and Mongolia. Three hypothetical grassland wildfires were developed using GIS technology to test and demonstrate the proposed model. The simulation results suggest that the model agrees well with real-world experience and can facilitate real-time decision-making to enhance the effectiveness of firefighting, fire control, and simulation-based training for firefighters. Full article

In recent years, the conflicts of the human–land coupling system (HLS) in the cross-border transportation corridor areas have become increasingly severe, especially in the China–Mongolia–Russia Cross-Border Transportation Corridor (CMRTC). The vulnerability assessment of the HLS-CMRTC is the key scientific issue for regional sustainable development. Based on the nearly 20 years of a scientific expedition, we set the CMRTC as the study area, constructed the vulnerability assessment index system and quantitative model, assessed the vulnerability of the HLS-CMRTC, revealed the key influencing factors, divided vulnerability risk prevention zones, and proposed the targeted optimization countermeasures. This study found that: (1) The overall vulnerability pattern of the HLS-CMRTC showed a vulnerability level gradually increasing from south to north. (2) Permafrost instability risk, land desertification, temperature increase, and backward social development were key influences. (3) Vulnerability risk prevention zones were divided into four priority and two general zones. The targeted optimization countermeasures were proposed, such as establishing an ecological security barrier, carrying out collaborative ecological risk monitoring, and early warning. The conclusions could provide a decision-making basis for the study area to reduce the vulnerability of the HLS. They could also provide reference and scientific support for achieving sustainable development of the economy and environment in similar regions of the world. Full article

Russia has considerable experience in the development of winter sports and ski resorts. The region around Lake Baikal possesses unique landscapes and cultural unity, making it a hot spot for winter tourism in Russia. The ski resorts around Lake Baikal are among the most attractive tourist destinations during the winter season, attracting a large number of domestic and international tourists. Based on the experience of the Northeast Asia Sustainable Development Research Centre, this study includes a survey of experts from China and Russia. The study focuses on five major ski resorts near Lake Baikal. A comprehensive competitiveness assessment index system and a quantitative evaluation model for winter tourism resorts have been established, which enable a scientific evaluation of the level of comprehensive competitiveness of winter tourism regions near Lake Baikal. The study showed that the Sobolinaya ski resort has excellent competitiveness among the resorts studied, while Bychya and Istland have average competitiveness and Davan and Mamai have low competitiveness. Local natural resources and the level of infrastructure development make the most significant contributions to the overall competitiveness of a resort. The study proposes development measures, such as the creation of a winter tourism complex with the Sobolinaya resort as its core and the establishment of an international special zone for winter tourism along the China–Mongolia–Russia economic corridor. The research results can serve as a basis for decision making to improve the overall competitiveness of the winter tourism industry around Lake Baikal and provide scientific and technical support for cross-border international cooperation in the winter tourism industry between China and Russia. Full article

This article discusses the efficiency of transport infrastructure and cooperation of neighboring regions located in Asian Russia, China, Mongolia, and Kazakhstan in the context of creating new international economic corridors from the Silk Road and trans-Eurasian transport corridors. This study aims to highlight the possible ways of strengthening cross-border cooperation in the field of transport infrastructure. We evaluated the current state of the transport infrastructure, the dynamics of its development, and its influence on the territorial–production complex. Using quantitative data and the unified indicator for the efficiency of transport infrastructure, we also characterized the territorial differentiation, its causes, and prerequisites for further economic and trade cooperation between these countries. The main results are as follows: (1) The lowest levels of the efficiency of transport infrastructure are typical for the northeast of Asian Russia, as well as for the border regions of China, Mongolia, and Kazakhstan. (2) For Asian Russia, Kazakhstan, and Mongolia, the highest levels of the unified indicator are typical for regions located along the main transport routes and for regions with a developed mining industry. This is due to the strong unevenness of the socio-economic development of the territories. (3) The largest industrial and economic centers have been developing along the main transport corridors primarily due to the accumulated potential of equivalent freight turnover and export potential. This study can be useful for authorities and business, as well as for other users of transport infrastructure to improve its regulation and efficiency. Full article

The frequency and intensity of fires are increasing because of warmer temperatures and increased droughts, as well as climate-change induced fuel distribution changes. Vegetation in environments, such as those in the mid-to-high latitudes and high elevations, moves to higher latitudes or elevations in response to global warming. Over the past 40 years, the Mongolian Plateau has been arid and semi-arid, with a decrease in growing season vegetation in the southwest and an increase in growing season vegetation in the northeast. The northward movement of vegetation has brought fires, especially in the Dornod, Sukhbaatar, and Kent provinces near the Kent Mountains, and has become more obvious in the past 20 years. The occurrence of a dead fuel index (DFI) with high probability is distributed in northern Mongolia, the border area between China and Mongolia, and the forest-side meadow-steppe region of the Greater Khingan Mountains. These findings suggest that vegetation is moving northward because of climate change and this presents a challenge of future warming spreading fire northward, adding material to the study of the relationship between the northward movement of global vegetation and fires. Full article

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. Full article

Burned area is a critical input to biomass burning carbon emissions algorithms and for understanding variability in fire activity due to climate change. This study presents the spatial and temporal patterns of wildland fires in the Mongolian Plateau (MP) using Collection 6 NASA MCD64A1 500 m global Burned Area product from 2001 to 2021. Both inter- and intra-annual fire trends and variations in two subregions, Mongolia and China’s Inner Mongolia, were analyzed. The results indicated that an average area of 1.3 × 10⁴ km² was consumed by fire per year in the MP. The fire season has two peaks: spring (March, April, and May) and autumn (September, October, and December). The profiles of the burnt area for each subregion exhibit distinct seasonality. The majority of wildfires occurred in the northeastern and southwestern regions of the MP, on the border between Mongolia and China. There were 2.7 × 10⁴ km² of land burned by wildfires in the MP from 2001 to 2021, 57% of which occurred in spring. Dornod aimag (province) of Mongolia is the most fire-prone region, accounting for 51% of the total burned area in the MP, followed by Hulunbuir, at 17%, Sukhbaatar, at 9%, and Khentii at 8%. The changing patterns of spatiotemporal patterns of fire in the MP were analyzed by using a spatiotemporal cube analysis tool, ArcGIS Pro 3.0.2. The results suggested that fires showed a decreasing trend overall in the MP from 2001 to 2021. Fires in the southern region of Dornod aimag and eastern parts of Great Xing’an Mountain showed a sporadic increasing trend. Therefore, these areas should be priorities for future fire protection for both Mongolia and China. Full article

Wildfire is essential in altering land ecosystems’ structures, processes, and functions. As a critical disturbance in the China–Mongolia–Russia cross-border area, it is vital to understand the potential drivers of wildfires and predict where wildfires are more likely to occur. This study assessed factors affecting wildfire using the Random Forest (RF) model. No single factor played a decisive role in the incidence of wildfires. However, the climatic variables were most critical, dominating the occurrence of wildfires. The probability of wildfire occurrence was simulated and predicted using the Adaptive Network-based Fuzzy Inference System (ANFIS). The particle swarm optimization (PSO) model and genetic algorithm (GA) were used to optimize the ANFIS model. The hybrid ANFIS models performed better than single ANFIS for the training and validation datasets. The hybrid ANFIS models, such as PSO-ANFIS and GA-ANFIS, overcome the over-fitting problem of the single ANFIS model at the learning stage of the wildfire pattern. The high classification accuracy and good model performance suggest that PSO-ANFIS can be used to predict the probability of wildfire occurrence. The probability map illustrates that high-risk areas are mainly distributed in the northeast part of the study area, especially the grassland and forest area of Dornod Province of Mongolia, Buryatia, and Chita state of Russia, and the northeast part of Inner Mongolia, China. The findings can be used as reliable estimates of the relative likelihood of wildfire hazards for wildfire management in the region covered or vicinity. Full article

Desertification is one of the most harmful ecological disasters on the Mongolian Plateau, placing the grassland ecological environment under great pressure. Remote-sensing monitoring of desertification and exploration of the drivers behind it are important for effectively combating this issue. In this study, four banners/counties on the border of China and Mongolia on the Mongolian Plateau were selected as the target areas. We explored desertification dynamics and their drivers by using remote sensing imagery and a product dataset for the East Ujimqin Banner and three counties in Mongolia during the period 2000–2015. First, remote sensing information on desertification in the fourth phase of the study area was extracted using the visual interpretation method. Second, the dynamic change characteristics of desertification were analyzed using the intensity analysis method. Finally, the drivers of desertification and their explanatory powers were identified using the geographical detector method. The results show that the desertification of the East Ujimqin Banner has undergone a process of reversion, development, and mild development, with the main transition occurring between slight (SL) and non-desertified land (N), very serious desertified land (VS), and water areas. The dynamics of desertification in this region are influenced by a combination of natural and anthropogenic factors. Desertification in the three counties of Mongolia has undergone processes of development, mild development and mild development with SL and vs. as the main types. Desertification in Mongolia is mainly concentrated in Matad County, which is greatly affected by natural conditions and has little impact from anthropogenic activities. In addition, the change intensity of desertification dynamics in the study area showed a decreasing trend, and the interaction between natural and anthropogenic drivers could enhance the explanatory power of desertification dynamics. The research results provide a scientific basis for desertification control, ecological protection, and ecological restoration on the Mongolian Plateau. Full article