The large variety of the semi-automated methods in mapping the surface water bodies and the frequent omission of ponds (< 10,000 m²) from permafrost regions inventories motivates this work. Based on the correlation matrix, several widely used classification methods for mapping the surface water bodies were assessed. Water bodies inventory was generated at a local and landscape scale in the Headwater Area of the Yellow River (HAYR) on a Sentinel-2 satellite image from 23 November 2015. The assessed methods are: spectral water indices, supervised and unsupervised classifiers (k-means, Density Slicing, Maximum Likelihood Classification ˗ MLC), and machine learning algorithms (Random Forest and Support Vector Machines). The MLC method applied on visible and near-infrared (NIR) bands represents the best ratio regarding the accuracy (96%), Kappa coefficient (0.87), and water surface (14.87 km²). However, misclassifications are still present, which requires manual editing. Based on the MLC approach, 651 more water bodies were identified than previous inventories. Ponds are account for up to 93% of the 966 of individual water bodies and contribute to 42% of the total water surface, in the context that were omitted before. This analysis emphasize the importance of method settings for the classifier performance, as well as the ponds abundance and substantial contribution to the total water surface. This inventory will improve the general circulation models and provides baseline information for sustainable management of water resources in the HAYR, one of the core Asian Water Towers.

Land use and cover changes (LUCC) in permafrost regions have significant consequences on ecology, engineered systems, and the environment. Obtaining more details about LUCC is crucial for sustainable development, land conservation, and environment management. The Hola Basin (957 km²) in the northernmost part of Northeast China, a boreal forest landscape underlain by discontinuous, sporadic, and isolated permafrost, was selected for the case study. The LUCC was analyzed using the Landsat archive of satellite images from 1973 to 2019. A thematic change detection analysis was performed by combining the object-based image analysis (OBIA) and the Support Vector Machine (SVM) algorithm. Four types of LUCC (forest, grass, water, and anthropic) were extracted with an overall accuracy of 80% for 1973 and >90% for 1986, 2000, and 2019. Forest, the dominant class (750 km² in 1973), declined by 88 km² (11.8%) from 1973 to 1986 but had a recovery of 78 km² (12.5%) from 2000 to 2019. Grass, the second-largest class (187 km² in 1973), increased by 86 km² (46.5%) between 1973 and 1986 and decreased by 90 km² (40%) between 2000 and 2019. The anthropic class continuously increased from 10 km² (1973) to 37 km² (2019). Major features in LUCC are attributed to rapid population growth, resource exploitation, agriculture intensification, economic development, and frequent forest fires. Under a pronounced climate warming, these drivers have been accelerating the degradation of permafrost, subsequently triggering natural hazards and deteriorating the ecological environment. This study represents a benchmark for sustainable LUCC management in the Hola Basin, Northeast China.

Under a warming climate, the occurrence of wildfires has been becoming increasingly more frequent in boreal forests and Arctic tundra over the last few decades. Wildfires can cause radical changes in forest ecosystems and the permafrost environment, such as the irreversible degradation of permafrost, succession of boreal forests, rapid and massive losses of soil carbon stock, and increased periglacial geohazards. Since 2016, we have gradually and more systematically established a network for studying soil nutrients and monitoring the hydrothermal state of the active layer and near-surface permafrost in the northern Da Xing'anling Mountains in Northeast China. Soil moisture content (depth of 0–9.4 m), soil organic carbon content (0–3.6 m), total nitrogen content (0–3.6 m), and total phosphorus and potassium content (0–3.6 m) datasets were obtained in 2016 via field sampling and subsequent laboratory tests. Ground temperature (0–20 m) and active layer thickness (2017–2022) datasets were obtained using thermistor cables that were permanently installed in boreholes or interpolated with these temperatures. The present data can be used to simulate changes in permafrost features under a changing climate and wildfire disturbances and to explore the changing interactive mechanisms of the fire–permafrost–carbon system in hemiboreal forests. Furthermore, they can provide baseline data for studies and action plans to support the carbon neutralization initiative and assessment of the ecological safety and management of the permafrost environment. These datasets can be easily accessed via the National Tibetan Plateau/Third Pole Environment Data Center (https://doi.org/10.11888/Cryos.tpdc.300933, Li and Jin, 2024).