The Qilian Mountains (QLMs), an important ecological protective barrier and major water resource connotation area in the Hexi Corridor region, have an important impact on ecological security in western China due to their ecological changes. However, most existing studies have investigated vegetation changes and their main driving forces in the QLMs on the basis of a single scale. Thus, the interactions among multiple environmental factors in the QLMs are still unclear. This study was based on normalised difference vegetation index (NDVI) data from 2000 to 2019. We systematically analysed the spatial and temporal characteristics of the QLMs at multiple time scales using trend analysis, ensemble empirical mode decomposition, Geodetector, and correlation analysis methods. At different time scales under single-factor and multi-factor interactions, we examined the mechanisms of the vegetation changes and their drivers. Our results showed that the vegetation in the QLMs showed a trend of overall improvement in 2000–2019, at a rate of 0.88 × 10⁻³, mainly in the central western regions. The NDVI in the QLMs showed a short change cycle of 3 and 5 years and a long-term trend. Sunshine time and wind speed were the main drivers of the vegetation variation in the QLMs, followed by temperature. Precipitation affected the vegetation spatial variation within a certain altitude range. However, temperature and precipitation had stronger explanatory powers for the vegetation variation in the western QLMs than in the eastern part. Their interaction was the dominant factor in the regional differences in vegetation. The responses of the NDVI to temperature and precipitation were stronger in the long time series. The main drivers of vegetation variation were land surface temperature and precipitation in the east and temperature and evapotranspiration in the west. Precipitation was the main driver of vegetation growth in the northern and southwestern QLMs on both the short- and long-term scales. Vegetation changes were more significantly influenced by short-term temperature changes in the east but by a combination of temperature and precipitation in most parts of the QLMs on a 5-year time scale.

Permafrost is very sensitive to climate change, and the accelerated degradation of permafrost in Northeast China caused by global climate change will change the hydrological and ecological processes in the region and cause significant impacts on natural systems and human activities. In this study, the spatial distribution of permafrost in Northeast China from 2000 to 2020 was simulated using an improved ground freezing number model. The spatial and temporal variations of permafrost thickness and active layer thickness were estimated using the mean ground temperature method based on the obtained permafrost distribution. Based on the above simulation results, the mean annual ground temperature and field monitoring temperature gradient, based on remote sensing estimation and the ice content data of permafrost, were used to calculate the amount of permafrost ice storage in Northeast China for many years and to predict the amount of water released from permafrost in the future to better reveal the influence of permafrost changes on ecohydrological changes in the watershed. The results show that, in the past 20 years, climate warming has led to the degradation of the permafrost area in Northeast China from 3.31 × 10⁵ km² to 2.70 × 10⁵ km², with a degradation rate of 18.43%; the stored ice in the permafrost has been released at an accelerated rate. The total ice storage volume in the permafrost of Northeast China is 3.178 × 10¹¹ m³. The amount of ice storage in the permafrost increases with latitude and altitude, and the ice storage volume decreases to 6.641 × 10¹⁰ m³ after 100 years, which is a decrease of 2.514 × 10¹¹ m³. The amount of water released due to permafrost degradation accounts for 79.11% of the current total ice storage, and the rate of water release reaches 2.51 × 10⁹ m³/a. The release of water from permafrost has an important impact on river runoff whose source is at high altitudes, such as the Greater and Lesser Khingan Mountains in Northeast China.

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index η with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index η. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003–2007, 2008–2012, 2013–2017, and 2018–2021, and the permafrost area is, respectively, 28.35 × 10⁴ km², 35.14 × 10⁴ km², 28.96 × 10⁴ km², and 25.21 × 10⁴ km². The proportion of extremely stable permafrost (<−5.0 °C), stable permafrost (−3.0~−5.0 °C), sub-stable permafrost (−1.5~−3.0 °C), transitional permafrost (−0.5~−1.5 °C), and unstable permafrost (0~−0.5 °C) is 0.50–1.27%, 6.77–12.45%, 29.08–33.94%, 34.52–39.50%, and 19.87–26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of ±1 °C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

The permafrost in Northeast China is experiencing rapid degradation due to the influence of climate change and human activities, profoundly impacting the local ecological environment and engineering construction. Understanding the spatiotemporal dynamics of long-term permafrost in this region is crucial; however, systematic research on this topic remains scarce. This study combines meteorological station data, MODIS land surface temperature (LST) datasets, and borehole locations to apply the surface frost number (SFn) model. This approach enables the simulation and estimation of the spatial distribution and changes in the area of the surface frost number without vegetation effects (SFnv) and permafrost temperature (PT) in Northeast China from 1971 to 2020. The area of the SFnv > 0.49 within the permafrost region decreased substantially from approximately 44.353 × 10⁴ km² to 19.909 × 10⁴ km² between 1971 and 2020, with a notable change in 1988. The area of permafrost calculated using PT < 0 was slightly smaller, declining from 39.388 × 10⁴ km² to 29.852 × 10⁴ km². There was also a significant increase in the area with PT ranging from −1 °C to 0 °C, indicating a decline in permafrost stability. Approximately 10.926 × 10⁴ km² of stable permafrost has been transformed into semi-stable and unstable permafrost. Moreover, from 1982 to 2020, the NDVI was negatively correlated with the area of stable permafrost and positively correlated with the area of transitional or unstable permafrost. Vegetation cover decreased as transitional or unstable permafrost degraded. These findings provide valuable information for permafrost research and engineering development in cold regions, as well as for future planning and adaptation strategies.

In the permafrost zone, the vegetation growth condition is closely related to the permafrost occurrence state. Changbai Mountain is the highest peak in Northeast China and is also a permafrost distribution area, where the vegetation shows an obvious vertical distribution pattern, and the alpine timberline has a clear boundary. The intersecting zone of alpine timberline is an ecologically fragile area that can be used as an external signal amplifier and is an important site for monitoring climate change. In this study, the surface frost number and alpine timberline in the Changbai Mountain area were analyzed through combining ground and remote-sensing data, using the freezing number model and support vector machine method. The results show that the distribution characteristics of alpine timberline and permafrost at elevation are correlated, there is a response mechanism of alpine timberline to the degradation of permafrost, and the upward migration rate of alpine timberline has increased in the last 20 years. The continuous degradation of permafrost will change the environment of vegetation growth, which, in turn, will affect the global carbon cycle process. Focusing on the state of permafrost will help us to understand climate change in depth, and we can respond to a series of impacts caused by ecological changes in cold regions in advance.

The Arctic is the fastest-warming region on Earth, exhibiting a pronounced “amplifying effect”, which has triggered widespread permafrost thaw and increased the risk of surface deformation. In the Arctic coastal lowlands, permafrost is also affected by shoreline retreat. The impact of these dual stressors on surface deformation processes in the Arctic coastal lowlands remains poorly understood, particularly in terms of how permafrost thaw and shoreline retreat interact to influence surface stability. To address this gap, we employed PS-InSAR technology to monitor surface deformation from 2017 to 2021 at Tiksi Airport, the northernmost airport on the Siberian mainland, situated adjacent to the Laptev Sea. The results show that Tiksi Airport experiences localized significant surface subsidence, with deformation velocity ranging from −42 to 39 mm/yr. The near-coastal area of Tiksi Airport is strongly influenced by the ocean. Specifically, for extreme subsidence deformation (around –40 mm/yr), the surface subsidence velocity increases by 0.2 mm/yr for every 100 m closer to the coastline. Analysis of these deformation characteristics suggests that the primary causes of subsidence are land surface temperature (LST) warming and erosion by the Laptev Sea, which together lead to increased permafrost thaw. By revealing the combined effects of climate warming and coastal erosion on permafrost stability, this study contributes to enhancing the understanding of infrastructure safety and quality of life for residents in Arctic coastal subsidence areas.