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.

High-resolution permafrost mapping is an important direction in permafrost research. Arxan is a typical area with permafrost degradation and is situated on the southern boundary of the permafrost region in Northeast China. With the help of Google Earth Engine (GEE), the maximum entropy classifier (MaxEnt) is used for permafrost mapping using the land surface temperature (LST) of different seasons, deviation from mean elevation (DEV), solar radiation (SR), normalized difference vegetation index (NDVI), and normalized difference water index (NDWI) as the characteristic variables. The prior data of permafrost distribution were primarily based on 201 borehole data and field investigation data. A permafrost probability (PP) distribution map with a resolution of 30 m was obtained. The receiver operating characteristic (ROC) curve was used to test the distribution results, with an area under the curve (AUC) value of 0.986. The results characterize the distribution of permafrost at a high resolution. Permafrost is mainly distributed in the Greater Khingan Mountains (GKM) in the research area, which run from the northeast to the southwest, followed by low-altitude area in the northwest. According to topographic distribution, permafrost is primarily found on slope surfaces, with minor amounts present in peaks, ridges, and valleys. The employed PP distribution mapping method offers a suggestion for high-resolution permafrost mapping in permafrost degradation areas.

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.

In cold regions, there is a close relationship between vegetation and the underlying permafrost. While the vegetation protects the permafrost, the permafrost also provides the necessary water, nutrients, and physical support for the vegetation. As the most sensitive area of alpine vegetation to environmental changes, alpine timberlines respond rapidly to permafrost degradation. Based on the data from meteorological stations and remote sensing in Mount Wutai, the distribution and change in surface frost numbers under the influence of vegetation and alpine timberlines in Mount Wutai from 2001 to 2021 were produced. The results show that from 2001 to 2021, along with the continuous degradation of permafrost, the alpine timberline showed an upward followed by a slight downward trend. From 2000 to 2014, the alpine timberline consistently moved upward, with the melting of permafrost, which produces water playing a positive role. In 2014–2021, the permafrost near the timberline in the study area disappeared, and the upward trend in the alpine timberline was blocked, even downward in some areas. Analysis of the above phenomena shows that in the process of permafrost degradation, the functions of supporting and fixing roots as well as water storage for overlying forest vegetation by permafrost will be lost sequentially, which will have an impact on the growth of the vegetation and make the upward trend in NDVI in the growing season blocked. The process of permafrost degradation is beneficial to vegetation growth but is unfavorable at the end of the degradation period, which is reflected in the phenomenon of the alpine timberline rising and then falling.

China's permafrost regions are affected by global warming and the thawing of permafrost, and the occurrence of landslide disasters has become more and more frequent, which makes the evaluation of the susceptibility to geologic hazards in China's permafrost regions is an urgent work to be carried out. Most of the existing geohazard susceptibility models have fewer test cases in the permafrost regions. Twelve evaluation factors, such as altitude, slope, slope direction, land use, and lithology, were selected to draw landslide hazard susceptibility maps by using three commonly used landslide susceptibility assessment models, including the information value model, the frequency ratio model and the random forest model, which can be implemented in GIS, taking the Lesser Khingan Mountains area located in the eastern part of permafrost region of northeast China as the study area. The applicability of the above commonly used landslide susceptibility assessment models in the permafrost regions is carried out by fieldwork and comparing the results of and model simulation. The Random Forest Model was also used to assess the importance of the factors that were adopted and to judge the degree of their influence on landslide development. The results show that the Information Value Model has a better applicability in the permafrost region. However, due to factors such as climate warming and permafrost degradation, the accuracy of the prediction results obtained by applying the existing commonly used landslide susceptibility assessment models in permafrost regions are still in need to be improved. Finally, thawing and degradation of permafrost will play a non-negligible role in influencing the occurrence of landslides in permafrost regions.