Permafrost serves as a crucial indicator of global climate change. Its degradation significantly influences Earth’s surface systems, including hydrology, soil, climate, ecosystems, and even civil construction. The distribution of permafrost in the Mongolian Plateau (MP) has an important influence in North Asia and even the Euro-Asia continent. This study provides a comprehensive review of the current state of permafrost degradation and its influence on MP, including climate change and human activities. Remote sensing technologies for permafrost monitoring, including optical remote sensing data models and InSAR technology, are also reviewed. This paper outlines eight future research directions by exploring the latest advancements and technical challenges in permafrost monitoring in the region. These include fundamental investigations of the permafrost zone; evaluation of permafrost effects on ecosystems; hydrology and water resources research; assessment and engineering of freeze–thaw hazards; sustainable regional development in permafrost zones; remote sensing monitoring techniques for permafrost; inter-regional comparative and collaborative research; and data sharing and standardization for permafrost research. This study provides valuable insights into the progress of permafrost degradation not only in the MP but also as a reference for related permafrost studies in other mid-to-high latitudes regions.

The Mongolian Plateau (MP), situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia, plays a significant role as an Ecological Barrier (EB) with crucial implications for ecological and resource security in Northeast Asia. EB is a vast concept and a complex issue related to many aspects such as water, land, air, vegetation, animals, and people, et al. It is very difficult to understand the whole of EB without a comprehensive perspective, that traditional diverse studies cannot cover. Big data and artificial intelligence (AI) have enabled a shift in the research paradigm. Faced with these requirements, this study identified issues in the construction of EB on MP from a big data perspective. This includes the issues, progress, and future recommendations for EB construction-related studies using big data and AI. Current issues cover the status of theoretical studies, technical bottlenecks, and insufficient synergistic analyses related to EB construction. Research progress introduces advances in scientific research driven by big data in three key areas of MP: natural resources, the ecological environment, and sustainable development. For the future development of EB construction on MP, it is recommended to utilize big data and intelligent computing technologies, integrate extensive regional data resources, develop precise algorithms and automated tools, and construct a big data collaborative innovation platform. This study aims to call for more attention to big data and AI applications in EB studies, thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.

Amid global climate change and extreme weather conditions, sudden dzud events in arid grassland regions inflict severe disasters on herders, livestock, transportation, and the economy. In particular, Mongolia experiences frequent dzud events in recent years, bringing devastating consequences. However, studies on the spatiotemporal distribution characteristics of snow cover during dzud events in Mongolia remain relatively scarce and fail to adequately explain the anomalous features and impacts of extreme snowfall. Therefore, this study examined the spatiotemporal distribution characteristics of snow in the five most severe dzud events in Mongolia from 2000 to 2024. We utilized the Normalized Difference Snow Index (NDSI) extraction method based on 500 m resolution MODIS10A1 data, with the results validated against 10 m resolution Sentinel-2 imagery. The study produces several interesting results: (1) Snow cover in Mongolia generally increases from south to north with rising terrain elevation. Although its interannual variation is highly unstable, a slight decreasing trend is observed over the past 25 years. (2) Significant regional differences form a fan-shaped snow distribution pattern centered around 45–52° N, with trend analysis indicating intensification in the west and weakening in the east, except for extreme weather events. (3) During dzud events, the snow cover fraction (SCF) generally exceeds the multi-year average, exhibiting a pronounced and abrupt rise, while snow cover and livestock mortality fluctuate in synchrony. By revealing the spatiotemporal distribution patterns of snow during dzud years in Mongolia, this research provides an evidence-based reference for the understanding of extreme winter climatic events and disaster risk reduction in arid grassland regions.

The permafrost region is one of the most sensitive areas to climate change. With global warming, the Mongolian Plateau permafrost is rapidly degrading, and its vegetation ecosystem is seriously threatened. To address this challenge, it is essential to understand the impact of climate change on vegetation at different permafrost degradation stages on the Mongolian Plateau. Based on the general permafrost distribution, in this study，we divided different permafrost regions and explored the response of vegetation to climate change at different stages of permafrost degradation by the idea of “space instead of time” from 2014 to 2023. The results of the study showed that: (1) Normalized difference vegetation index (NDVI) values showed a decreasing trend, and the proportion of the decreasing region was in the order of sporadic permafrost region > isolated and sparse permafrost region > continuous and discontinuous permafrost regions. (2) The main controlling factors of vegetation growth in permafrost regions are different, air temperature is the main controlling factor of vegetation growth in isolated and sparse permafrost region (r=–0.736) and sporadic permafrost regions (r=–0.522), and precipitation is the main controlling factor of vegetation growth in continuous and discontinuous permafrost region (r=–0.498). (3) The response of NDVI to climate change varies at different stages of permafrost degradation. In the early stages of permafrost degradation, increased land surface temperature (LST) and air temperature favored vegetation growth and increased vegetation cover, whereas increased precipitation impeded vegetation growth; as the permafrost degraded, increased LST and air temperature impeded vegetation growth, whereas increased precipitation promoted vegetation growth.