The Mongolian plateau is a hotspot of global desertification because it is heavily affected by climate change, and has a large diversity of vegetation cover across various regions and seasons. Within this arid region, it is difficult to distinguish desertified land from other land cover types using low-quality vegetation information. To address this, we analyze both the effects and the applicability of different feature space models for the extraction of desertification information with the goal of finding appropriate approaches to extract desertification data on the Mongolian plateau. First, we used Landsat 8 remote sensing images to invert NDVI (normalized difference vegetation index), MSAVI (modified soil adjusted vegetation index), TGSI (topsoil grain size index), and albedo (land surface albedo) data. Then, we constructed the feature space models of Albedo-NDVI, Albedo-MSAVI, and Albedo-TGSI, and compared their extraction accuracies. Our results show that the overall classification accuracies of the three models were 84.53%, 85.60%, and 88.27%, respectively, indicating that the three feature space models are feasible for extracting information relating to desertification on the Mongolian plateau. Further analysis indicates that the Albedo-NDVI model is suitable for areas with a high vegetation cover or a high forest ratio, whilst the Albedo-MSAVI model is suitable for areas with relatively low vegetation cover, and the Albedo-TGSI model is suitable for areas with extremely low vegetation cover, including the widely distributed Gobi Desert and other barren areas. This study provides a technical selection reference for the investigation of desertification of different zones on the Mongolian plateau.

Mongolia is a globally crucial region that has been suffering from land desertification. However, current understanding on Mongolia’s desertification is limited, constraining the desertification control and sustainable development in Mongolia and even other parts of the world. This paper studied spatiotemporal patterns, driving factors, mitigation strategies, and research methods of desertification in Mongolia through an extensive review of literature. Results showed that: (i) remote sensing monitoring of desertification in Mongolia has been subject to a relatively low spatial resolution and considerable time delay, and thus high-resolution and timely data are needed to perform a more precise and timely study; (ii) the contribution of desertification impacting factors has not been quantitatively assessed, and a decoupling analysis is desirable to quantify the contribution of factors in different regions of Mongolia; (iii) existing desertification prevention measures should be strengthened in the future. In particular, the relationship between grassland changes and husbandry development needs to be considered during the development of desertification prevention measures; (iv) the multi-method study (particularly interdisciplinary approaches) and desertification model development should be enhanced to facilitate an in-depth desertification research in Mongolia. This study provides a useful reference for desertification research and control in Mongolia and other regions of the world.

Rivers and ponds near the Erdenet mine, one of the world’s largest copper-molybdenum mines, exhibit high concentrations of molybdenum (Mo). This study evaluates the distribution and chemical speciation of Mo in surface sediments from ponds and rivers in Erdenet city to elucidate the mobility and solubility of Mo in the surface aquatic environments in the area. The waters and sediments were collected in two shallow ponds connected to the tailing pond and from three rivers flowing through Erdenet city. The distribution and chemical speciation of Mo in the sediments were examined using five-step sequential extraction and X-ray absorption fine structure (XAFS) analyses. The XAFS spectra of the sediments showed that large amounts of Mo in the sediments are molybdate or polymeric molybdate, weakly adsorbed onto ferrihydrite. Sequential extraction consistently showed a large amount of Mo distributed in the labile fractions. Results suggest that the surface sediments from ponds and rivers play a role as secondary contamination sources of Mo rather than as sinks of Mo in the area.

The aim of the present study was to evaluate human health and potential ecological risk assessment in the ger district of Ulaanbaatar city, Mongolia. To perform these risk assessments, soil samples were collected based on reference studies that investigated heavy element distribution in soil samples near the ger area in Ulaanbaatar city. In total, 42 soil samples were collected and 26 heavy metals were identified by inductively coupled plasma optical emission spectrometry (ICP-OES) and inductively coupled plasma mass spectrometry (ICP-MS) methods. The measurement results were compared with the reference data in order to validate the soil contamination level. Although there was a large difference between the measurement results of the present and reference data, the general tendency was similar. Soil contamination was assessed by pollution indexes such as geoaccumulation index and enrichment factor. Mo and As were the most enriched elements compared with the other elements. The carcinogenic and noncarcinogenic risks to children exceeded the permissible limits, and for adults, only 12 out of 42 sampling points exceeded the permissible limit of noncarcinogenic effects. According to the results of the ecological risk assessment, Zn and Pb showed from moderate to considerable contamination indexes and high toxicity values for ecological risk of a single element. The Cr and As ranged as very high ecological risk than that of the other measured heavy metals.

The changes in land cover patterns in the Mongolian Plateau can reveal the regional status of sustainable development. Based on land cover data from 1990–2020, the study reveals the process of land cover change on the Mongolian plateau and integrates those changes with UN Sustainable Development Goals (SDGs) to further evaluate regional sustainable development status. Result revealed there is a stable rate of land cover change (0.16%) for the Mongolian Plateau, but with diverse shifting trends for various land cover types and SDGs indicators in past 30 years. Croplands (SDG2) showed a growth trend in the last five years, which was different from its initial obviously decreasing trend. The status of water (SDG6) showed a clear decreasing trend, which presents a major threat to this arid-to-semi-arid region. The built area (SDG11) increased continuously, but the long upward trend has slowed in recent years. The forest area (SDG15) declined, but it has recently recovered. Grasslands showed diverse changes in various steppe types (including real, meadow, and desert steppe types) while still experiencing land degradation. The expansion of sand areas presents a hidden risk of increasing sandstorms. Comparative analysis revealed that there have clear differences between Mongolia and Inner Mongolia due to the various government policies. In general, the land use degree in Mongolian Plateau increased annually. This indicated that the climate change and human activities have more and more influences, and it is still facing severe challenges for specific SDGs indicators in the region.

Grassland biomass is the embodiment of grassland productivity, and the material basis for the maintenance of the grassland ecosystem. Grassland is the main vegetation type in the Mongolian Plateau. Grassland changes in the core region of the China–Mongolia–Russia Economic Corridor of the Belt and Road Initiative have an important impact on regional ecology, environmental conservation, and sustainable development. This study established three types of models for estimating grassland production through statistical analysis methods using NDVI, EVI, MSAVI, and PsnNet remote sensing indices retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) dataset. This was combined with ground-measured grassland data and meteorological data. Based on model evaluation, the spatial and temporal distribution and variation characteristics of grassland along the Mongolia part of the China–Mongolia Railway were obtained through inversion for the period from 2006 to 2015. The results showed that all the models had good simulation effects. The optimal model was an exponential model based on MSAVI—with its simulation accuracy reaching 78%. Grassland production in the study area has increased slightly in the past ten years, with little change in the first five years and a fluctuating increase in the next five years. The average grassland production (per unit production) in the past ten years was 3400.39 kg/ha and the average total production was 9707.88 × 10⁴ t. Grassland production increased slightly in most areas along the railway, and in some areas it continued to decline. The regional spatial distribution of increased and decreased grassland production was significantly different. With better grassland resources in the northeastern part of the study area—the area around Chinggis City and the capital of Hentiy Province—had the most significant growth. However, the southern Gobi area—with its trend towards land degradation in the area where the southern Gobi and desert steppe transitions to steppe and dry steppe—had a significant decrease. This meant that the risk of grassland degradation still existed. There were also quantitative and spatial differences in the areas where grassland production decreased on both sides of the railway. The decrease in grassland production on the western side of the railway was more obvious than on the eastern side, and the reduction area was dispersed on the western side and relatively concentrated on the eastern side. In future research, the identification of key areas of grassland degradation along the China–Mongolia Railway as well as its driving forces should be investigated further.