In river management, it is important to obtain ice velocity quickly and accurately during ice flood periods. However, traditional ice velocity monitoring methods require buoys, which are costly and inefficient to distribute. It was found that UAV remote sensing images combined with machine vision technology yielded obvious practical advantages in ice velocity monitoring. Current research has mainly monitored sea ice velocity through GPS or satellite remote sensing technology, with few reports available on river ice velocity monitoring. Moreover, traditional river ice velocity monitoring methods are subjective. To solve the problems of existing time-consuming and inaccurate ice velocity monitoring methods, a new ice velocity extraction method based on UAV remote sensing technology is proposed in this article. In this study, the Mohe River section in Heilongjiang Province was chosen as the research area. High-resolution orthoimages were obtained with a UAV during the ice flood period, and feature points in drift ice images were then extracted with the scale-invariant feature transform (SIFT) algorithm. Moreover, the extracted feature points were matched with the brute force (BF) algorithm. According to optimization results obtained with the random sample consensus (RANSAC) algorithm, the motion trajectories of these feature points were tracked, and an ice displacement rate field was finally established. The results indicated that the average ice velocities in the research area reached 2.00 and 0.74 m/s, and the maximum ice velocities on the right side of the river center were 2.65 and 1.04 m/s at 16:00 on 25 April 2021 and 8:00 on 26 April 2021, respectively. The ice velocity decreased from the river center toward the river banks. The proposed ice velocity monitoring technique and reported data in this study could provide an effective reference for the prediction of ice flood disasters.

The density, temperature, and punch head velocity are dominant factors to the variation of the compacted snow hardness measured by penetrometers. This effect is essential to the construction and operation of compacted snow roads. The Improved Motor-driven Snow Penetrometers (IMSP) are utilized in this research to control the penetration speed and measure the true cone hardness during snow penetration. This study employs a multi-method approach combining orthogonal experiments and the Support Vector Regression (SVR) technique to analyze the effects of these three factors on snow hardness. The results of this investigation indicate that, under identical conditions, density is positively correlated with the hardness of compacted snow, and its sensitivity and significance to the compacted snow hardness are the greatest. Temperature and penetration speed have an effect on hardness, which cannot be completely ignored. The hardness of snow close to its melting point, regardless of its density, decreases significantly at high penetration rates. This study investigates the factors that influence the hardness of compacted snow and provides substantial technical support for the design, construction, and maintenance of snow roads.

The multiphase components of natural ice contain gas, ice, unfrozen water, sediment and brine. X-ray computed tomography (CT) analysis of ice multiphase components has the advantage of high precision, non-destructiveness and visualization; however, it is limited by the segmentation thresholds. Due to the proximity of the CT value ranges of gas, ice, unfrozen water, sediment and brine within the samples, there is uncertainty in the artificial determination of the CT image segmentation thresholds, as well as unsuitability of the global threshold segmentation methods. In order to improve the accuracy of multi-threshold segmentation in CT images, a CT system was used to scan the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice. The threshold ranges of multiphase components within the ice were determined by watershed algorithm to construct a high-precision three-dimensional ice model. The results indicated that CT combined with watershed algorithm was an efficient and non-destructive method for obtaining microscopic information within ice, which accurately segmented the ice into multiphase components such as gas, ice, unfrozen water, sediment, and brine. The gas CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −1024 Hu~−107 Hu, −1024 Hu~−103 Hu, and −1024 Hu~−160 Hu, respectively. The ice CT values of the Yellow River ice, the Wuliangsuhai lake ice and the Arctic sea ice ranged from −103 Hu~−50 Hu, −100 Hu~−38 Hu, −153 Hu~−51 Hu. The unfrozen water CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from −8 Hu~18 Hu, −8 Hu~13 Hu. The sediment CT values of the Yellow River ice and the Wuliangsuhai lake ice ranged from 20 Hu~3071 Hu, 20 Hu~3071 Hu, and the brine CT values of the Arctic sea ice ranged from −6 Hu~3071 Hu. The errors between the three-dimensional ice model divided by threshold ranges and measured sediment content were less than 0.003 g/cm³, which verified the high accuracy of the established microscopic model. It provided a scientific basis for ice engineering, ice remote sensing, and ice disaster prevention.

To study the snow microstructure at various metamorphism times and extract the snow neck area, a constant density (200 kg/m³) snow metamorphism experiment was conducted. The findings show that the neck region is mostly influenced by temperature, sun radiation, snow density and specific humidity, with wind speed having little effect. Additionally, we developed a multiple linear regression equation for the neck area under atmospheric forcing: “S = 288T + 2E + 189ρ + 12,194V − 20,443RH − 42,729”. This equation accounts for solar radiation (E), temperature (T), snow density (ρ), specific humidity (RH) and wind speed (V). Notably, the above five factors can account for 84% of the factors affecting the neck area, making it a crucial factor. The relationship between snow hardness and neck area is correlated at 71%, and in later stages of metamorphism, the correlation may increase to 91%. Based on the neck area, the following hardness value prediction is made: “H = 0.002764S + 67.922837”. This study documents the growth variations in the neck region of the metamorphic snow cover and elucidates the process by which outside factors impact the microstructure and macroscopic physical characteristics of the snow cover.

In their natural state, snow crystals are influenced by the atmosphere during formation and multiple factors after landing, resulting in varying particle sizes and unstable particle morphologies that are challenging to quantify. The current research mainly focuses on the relationship between the porosity of compacted snow samples or qualitatively describes snow crystals and their macroscopic physical properties, ignoring that the significant differences in the morphology of snow crystals also affect their physical properties. To quantitatively evaluate the morphology of snow crystals, we employed optical microscopy to obtain digital images of snow crystals in Harbin, utilizing the Sobel and Otsu algorithms to determine the equivalent particle size and fractal dimension of individual snow particles. In addition, the hardness of snow with a density of 0.4 g/cm³ was measured through a penetration test, with an analysis of its correlation relative to particle size and fractal dimension. The results indicated the fractal dimension as an effective parameter for characterizing particle shape, which decreased rapidly over time and then fluctuated within the range of 1.10 to 1.15. During the initial period, natural snow crystals broke down rapidly, leading to an increase in the percentage of natural snow crystals with an equivalent particle size of 0.2–0.4 mm up to 51.86%. After three days, the sintering effect between snow crystals was enhanced, resulting in an even distribution of the equivalent particle size. Finally, multiple linear regression analysis showed a positive correlation between compacted snow hardness and fractal dimension, with a negative correlation between compacted snow hardness and equivalent particle size. These findings offer valuable technical support and data reference for exploring the relationship between snow’s mechanical properties and its microscopic particle shape.

Mathematical models and methods serve as fundamental tools for studying ice-related phenomena in the Yellow River. River ice is driven and constrained by hydrometeorological and geographical conditions, creating a complex system. Regarding the Yellow River, there are some uncertainties that manifest in unique features in this context, including ice–water–sediment mixed transport processes and the distribution of sediment both within the ice and on its surface. These distinctive characteristics are considered to different degrees across different scales. Mathematical models for Yellow River ice developed over the past few decades not only encompass models for the large-scale deterministic evolution of river ice formation and melting, but also uncertainty parameter schemes for deterministic mathematical models reflecting the Yellow River’s particular ice-related characteristics. Moreover, there are modern mathematical results quantitatively describing these characteristics with uncertainty, allowing for a better understanding of the unique ice phenomena in the Yellow River. This review summarizes (a) universal equations established according to thermodynamic and hydrodynamic principles in river ice mathematical models, as well as (b) uncertainty sources caused by the river’s characteristics, ice properties, and hydrometeorological conditions, embedded in parametric schemes reflecting the Yellow River’s ice. The intractable uncertainty-related problems in space–sky–ground telemetric image segmentation and the current status of mathematical processing methods are reviewed. In particular, the current status and difficulties faced by various mathematical models in terms of predicting the freeze-up and break-up times, the formation of ice jams and dams, and the early warning of ice disasters are presented. This review discusses the prospects related to the uncertainties in research results regarding the simulation and prediction of Yellow River ice while also exploring potential future trends in research related to mathematical methods for uncertain problems.