Ice Monitoring Research Articles

Multifunctional superhydrophobic composite film with icing monitoring and anti-icing/deicing performance

Icing can cause damage to outdoor equipment such as airplanes, wind turbine blades and power lines, which poses potential safety hazards. Recently, electrothermal superhydrophobic composite film with the synergistic effect of anti-icing and deicing properties can effectively hinder the formation and accumulation of ice. However, these superhydrophobic composite films have no icing condition monitoring property, which is critical to improve deicing efficiency and reduce energy consumption. In this paper, we have designed a multifunctional superhydrophobic composite film using the combination of laser ablation and spraying method, which exhibits excellent comprehensive anti-icing, deicing and icing monitoring properties. The experimental results demonstrated the icing delay time of the film reached 4.0 min at −20 °C. Meanwhile, taking advantage of the outstanding electrothermal effects of the laser induced graphene/carbon nanotubes (LIG/CNTs) composite conductive network, with DC voltage (5 V) excitation, the film temperature rapidly rose from −20 °C to 107 °C in 90 s, thereby effectively removing the ice. More importantly, due to the temperature sensing performance of the LIG/CNTs composite conductive network, it could monitor whole icing and deicing process of droplet with different volumes and temperatures in real time through the change of film resistance. Therefore, the comprehensive anti-icing, deicing and ice monitoring properties allowed it to effectively reduce icing hazards.

Exploring the Use of Fengyun-3 Meteorological Satellite for Monitoring Sea Ice to Provide Services for Polar Navigation

This paper explores the feasibility and effectiveness of using the Fengyun-3 meteorological satellite to monitor sea ice, providing services for ships navigating in polar regions. Firstly, it analyzes the impact of Arctic sea ice changes on ship navigation and the importance of sea ice monitoring in route planning. Next, it provides a detailed introduction to the data sources and processing methods of the Fengyun-3 satellite, including radiometric calibration, geometric correction, image registration, and cropping. Subsequently, it discusses the characteristics of sea ice in the visible spectrum and successfully extracts sea ice information using MERSI-II data with land, cloud, and seawater masking techniques. The study indicates that the comprehensive use of multi-spectral data and other observation methods can significantly enhance sea ice monitoring capabilities. In the future, integrating more advanced technologies is expected to achieve refined identification and short-term prediction of sea ice movement, thereby providing more scientific and efficient support for ships navigating in polar regions, enhancing navigation safety and efficiency, and offering a scientific basis for the development of Arctic shipping routes.

Identifying key safety investments needed for arctic shipping via a fuzzy analytic hierarchy process (FAHP) approach

ABSTRACT The retreating ice coverage and reduced (ice) thickness scientifically recorded in the wider Arctic region, during the last few decades, are transforming it into a more accessible operating environment. Vessels engaged in maritime transport activities can now navigate the already established routes for more extended periods each year. With this background, Arctic shipping routes have attracted the interest of academics and maritime stakeholders because of the potential distance reduction compared to other existing (and often viewed as) conventional routes utilizing the Suez and Panama Canals today. However, the existing Arctic shipping environment requires specific future safety investments. This paper intends to provide input towards better-informed decision-making and identification of priority investments needed to improve the level of safety related to the Northern Sea Route (NSR) and Northwest Passage (NWP). A multi-attribute decision-making methodology, the Fuzzy Analytic Hierarchy Process, is applied to rank the various proposed safety investments. The results indicate that “infrastructure and facility” should come first, followed by “personnel,” “technology,” “measure,” and “management” investments. Ice monitoring and weather forecasting, strengthening Arctic seafaring expertise, and icebreakers stand out as the top three most important future investments required in the region.

Intersensor Calibration of Spaceborne Passive Microwave Radiometers and Algorithm Tuning for Long-Term Sea Ice Trend Analysis Based on AMSR-E Observations

Sea ice monitoring is key to analyzing the Earth’s climate system. Long-term sea ice extent (SIE) has been continuously monitored using various spaceborne passive microwave radiometers (PMRs) since November 1978. As the lifetime of a satellite is usually approximately 5 years, bias caused by differences in PMRs should be eliminated to obtain objective SIE trends. Most sea ice products have been analyzed for long-term trends with a bias adjustment based on the coarse resolution special sensor microwave imager (SSM/I) in operation for the longest period. However, since 2002, Japanese microwave radiometers of the Advanced Microwave Scanning Radiometer (AMSR) series, which have the highest spatial resolution in PMR, have been available. In this study, we developed standardization techniques for processing SIE including calibration of the brightness temperature (TB), tuning the sea ice concentration (SIC) algorithm, and adjusting the SIC threshold to retrieve a consistent SIE trend based on the AMSR for the Earth Observing System (AMSR-E, one of the AMSR that operated from May 2002 to October 2011). Analysis results showed that the root-mean-square error between AMSR-E SICs and those of moderate resolution imaging spectroradiometer (MODIS) was 15%. In this study, SIE was defined as the sum of the areas where the AMSR-E SIC was >15%. When retrieving SIE, we adjusted the SIC threshold for each PMR to be consistent with the SIE calculated based on the 15% SIC threshold for AMSR-E. We then calculated a time-series of the SIE trends over approximately 45 years using the adjusted SIE data. Therefore, we revealed the dramatic decrease in global sea ice extent since 1978. This technique enables retrieval of more accurate long-term sea ice trends for more than half a century in the future.

Estimation of boreal forest biomass from ICESat-2 data using hierarchical hybrid inference

The ICESat-2, launched in 2018, carries the ATLAS instrument, which is a photon-counting spaceborne lidar that provides profile samples over the terrain. While primarily designed for snow and ice monitoring, there has been a great interest in using ICESat-2 to predict forest above-ground biomass density (AGBD). As ICESat-2 is on a polar orbit, it provides good spatial coverage of boreal forests.The aim of this study is to evaluate the estimation of mean AGBD from ICESat-2 data using a hierarchical modeling approach combined with rigorous statistical inference. We propose a hierarchical hybrid inference approach for uncertainty quantification of the average AGBD of the area of interest estimated directly from a sample of ICESat-2 lidar profiles. Our approach models the errors coming from the multiple modeling steps, including the allometric models used for predicting tree-level AGB. For testing the procedure, we have data from two adjacent study sites, denoted Valtimo and Nurmes, of which Valtimo site is used for model training and Nurmes for validation.The ICESat-2 estimated mean AGBD in the Nurmes validation area was 65.7 ± 1.9 Mg/ha (relative standard error of 2.9%). The local reference hierarchical model-based estimate obtained from wall-to-wall airborne lidar data was 63.9 ± 0.6 Mg/ha (relative standard error of 1.0%). The reference estimate was within the 95% confidence interval of the ICESat-2 hierarchical hybrid estimate. The small standard errors indicate that the proposed method is useful for AGBD assessment. However, some sources of error were not accounted for in the study and thus the real uncertainties are probably slightly larger than those reported.

Dynamic Thresholding Fully Automated sea ice extraction and classification methods based on multi-source remote-sensing data in the Yellow sea and Bohai sea regions

This paper presents the extraction and classification of sea ice in the Yellow and Bohai seas using multi-source remote sensing products, including Landsat 8, HY-1C, and MODIS. We propose a Dynamic Thresholding Fully Automated (DTFA) method for sea ice extraction and classification. The accuracy of sea ice extraction and classification is evaluated using four metrics: confusion matrix accuracy rate, overall accuracy, user accuracy, and kappa coefficient. The DTFA method showed higher extraction and classification accuracy compared to the SVM and k-means++ methods. HY-1C has the most effective extraction, followed by Landsat 8. Landsat 8 is able to extract new ice, grey ice, and grey-white ice with the best effect, while HY-1C has a better effect in extracting white ice. MODIS data has advantages in coverage and data updating. Finally, the fusion of multi-source remote sensing images was used to obtain the sea ice extent of the entire Chinese offshore. This method is also applicable to remote sensing monitoring of sea ice in other sea areas.

Extraction and analysis of the sea ice parameter dataset of the Bohai Sea from 2011 to 2021 based on GOCI

The Bohai Sea and its surrounding areas are rich in oil and natural gas and play an important role in industry, agriculture and the economy. However, the Bohai Sea suffers severely from sea ice in the winter. While previous research has predominantly focused on methods for retrieving sea ice parameters in the Bohai Sea, analyses of their long-term statistical patterns have been limited. The Geostationary Ocean Color Imager (GOCI) is the first geostationary satellite for ocean color remote sensing, offering high spatial and temporal resolution, which greatly facilitates the extraction of Bohai Sea ice parameters. Utilizing GOCI data, we systematically extracted relevant sea ice parameters for the Bohai Sea region from 2011 to 2021. These parameters include sea ice concentration, sea ice thickness, and sea ice drift. We conducted a comprehensive statistical analysis of the long-term sea ice changes in the Bohai Sea and found that the development process of winter sea ice area is different from the sea ice thickness, and the direction of sea ice drift is basically unchanged. Then we developed statistical models linking sea ice parameters with ocean dynamic factors such as temperature, wind, and drift currents. Among them, the correlation coefficient between the predicted value and the measured value of the sea ice area model is the highest, reaching 0.8382. Furthermore, we examined the previously unexplored relationship between daily sea ice area, sea ice thickness, and accumulated temperature with their respective starting temperatures and accumulation periods. This study provides critical data to support Bohai Sea ice monitoring and marine environmental research. The results of this study contribute to a better understanding of sea ice change trends in the Bohai Sea and inform the development of disaster prevention and mitigation measures.

A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics

This study introduces a new automated system that blends multi-satellite information and citizen science data for reliable and timely observations of lake and river ice in under-observed northern regions. The system leverages the Google Earth Engine resources to facilitate the analysis and visualization of ice conditions. The adopted approach utilizes a combination of moderate and high-resolution optical data, along with radar observations. The results demonstrate the system’s capability to accurately detect and monitor river ice, particularly during key periods, such as the freeze-up and the breakup. The integration citizen science data showed added values in the validation of remote sensing products, as well as filling gaps whenever satellite observations cannot be collected due to cloud obstruction. Moreover, it was shown that citizen science data can be converted to valuable quantitative information, such as the case of ice thickness, which is very useful when combined with ice extent derived from remote sensing. In this study, citizen science data were employed for the quantitative assessment of the remote sensing product. Obtained results showed a good agreement between the product and observed river status, with a Critical Success Index of 0.82. Notably, the system has shown effectiveness in capturing the spatial and temporal evolution of snow and ice conditions, as evidenced by its application in analyzing specific ice jam events in 2023. The study concludes that the developed system marks a significant advancement in river ice monitoring, combining technological innovation with community engagement.

Forward modelling of synthetic-aperture radar (SAR) backscatter during lake ice melt conditions using the Snow Microwave Radiative Transfer (SMRT) model

Abstract. Monitoring of lake ice is important to maintain transportation routes, but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave sensors, particularly synthetic-aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions have been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the Snow Microwave Radiative Transfer (SMRT) model to conduct forward-modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry-snow experiments support the dominance of surface scattering from the ice–water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

An image augmentation‐based ice monitoring method for safe navigation of polar ships

AbstractThe navigation safety of polar ships depends on accurate monitoring of ice conditions along the route. Complex polar environments but scarce ice datasets pose challenges for deep learning‐based object detection methods. To address this issue, we propose an ice monitoring method based on polar environment adaptive image enhancement. This approach adaptively augments the ice dataset by simulating polar environments such as snowflakes and aurora to improve the training precision of ice detection models in harsh polar environments and meet the needs of safe navigation of polar ships. First, an image augmentation method based on randomly generated snowflake textures is proposed to address the difficulty of covering polar storm scenarios in existing ice datasets, which leads to weak generalization of ice monitoring models. Second, targeting the problem that the boundary between ice and background is confused by aurora phenomena, which severely reduces the ice monitoring accuracy, we propose an image augmentation method based on aurora simulations to improve the ice monitoring performance of trained models under aurora. Finally, an adaptive image augmentation strategy based on the brightness‐contrast threshold is proposed to optimize the snowflake density and aurora intensity in the augmented images to improve the discrimination of ice boundaries. Experimental results demonstrate the effectiveness of the proposed method, which improves the ice state detection accuracy from 0.640 to 0.950, thus providing a reliable guarantee for the safe navigation of polar ships.

Wind Tunnel Characterization of a Graphene-Enhanced PEDOT:PSS Sensing Element for Aircraft Ice Detection Systems.

This study details the development and validation of a graphene-based ice detection system, designed to enhance flight safety by monitoring ice accumulation on aircraft surfaces. The system employs a semiconductive polymer (PEDOT:PSS) with graphene electrodes, interpreting resistance changes to detect water impact and ice formation in real time. The sensor's performance was rigorously tested in a wind tunnel under various temperature and airflow conditions, focusing on resistance signal dependency on air temperature and phase change. The results demonstrate the sensor's ability to distinguish water droplet impacts from ice formation, with a notable correlation between resistance signal amplitude and water droplet impacts leading to ice accretion. Further analysis shows a significant relationship between air temperature and the resistance signal amplitude, particularly at lower temperatures beneficial to ice formation. This underlines the sensor's precision in varied atmospheric conditions. The system's compact design and accurate detection highlight its potential for improving aircraft ice monitoring, offering a path toward a robust and reliable ice detection system.

Characterization of north Greenland polynyas with super-resolved passive microwave sea ice concentration

ABSTRACT Located at the easternmost sector of the Last Ice Area (LIA) with the oldest and thickest sea ice, the north of Greenland has witnessed some unprecedented polynyas since 2018, receiving considerable attention. Sea ice concentration (SIC) derived from passive microwave data can provide near daily observations of these polynyas. However, these SIC products are limited by the coarse spatial resolution of passive microwave data, with mixed pixels and large uncertainties at the ice–water divide. It is especially difficult in the case of newly formed polynyas in the form of leads with narrow openings, preventing accurate estimation of the regional energy budget and heat fluxes as well as the hindering causal analysis of the polynyas. In this paper, a novel state-of-the-art deep-learning-based super-resolution (SR) method is adopted, and passive microwave SR images, with spatial resolutions of up to 4 times the original, are generated and employed to derive super-resolved SIC (SR-SIC). Experimental results confirm the benefits of SR-SIC for finer monitoring of sea ice at both the polynya and lead scale compared to the original lower-resolution SIC, with an average 7.55% improvement in F1-Score for polynya extent and 31.17% for lead width. Using the SR-SIC, more accurate quantitative parameters of polynyas and leads are extracted to present more detailed spatial distributions at their different development stages. For example, SR-SIC with reduced mixed pixels can distinguish more SIC values below 40%, advantageous for detecting open water pixels within polynya areas and improving the accuracy of heat fluxes estimation and ice production calculation. Moreover, SR-SIC has the capability to identify leads with openings less than 3 km and to locate the finer boundaries of the polynyas. Finally, it is found that the lead development along the 2018 winter polynya is associated with regional wind, air temperature, and ice drift. The prominent summer polynya events in 2018, 2020, and 2021 may be in part caused by the thinning sea ice in the north of Greenland.

Spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea from 2001 to 2020

The sea ice disaster is one of the major marine disasters in China. The presence of sea ice poses a potential threat to the social-environmental system in the Northern China Sea during winter. The study focused on investigating the spatiotemporal distribution characteristics of sea ice disasters in the Northern China Sea during 2001–2020, including exploring the variation of sea ice hazards, climate drivers influencing sea ice occurrence, and the subsequent impacts on maritime activity and marine aquaculture. The results show that the sea ice grade in the Bohai and Northern Yellow Seas (BNYS) exhibited a slight downward trend (−0.40/decade, P > 0.10) during the past two decades. The winter temperature and the frequency of cold waves are the primary local climate factors for sea ice variation. The decrease in sea ice grade in the BNYS due to global climate change does not eliminate the occurrence of extreme events. However, the potential for more severe damages remains. It is worth noting that the winters of 2010, 2011, and 2013 stand out as periods with the highest recorded direct economic losses caused by sea ice hazards. In terms of spatial distribution, Liaoning and Shandong provinces have experienced the most severe impacts of sea ice hazards. Sea ice has a negative impact on the throughput of ports that are influenced by sea ice. Significant moderate negative correlations (r = −0.40, P < 0.10) have been observed between port throughput and the sea ice index in Yingkou, Jinzhou, and Tianjin ports. Moreover, the mariculture industry emerged as the primary sector affected by sea ice disasters in the Northern China Sea between 2001 and 2020. Attention should be paid to Haizhou Bay shortly as marine aquaculture in this area has been seriously affected by sea ice in the past five years. In the future, it is crucial to prioritize more accurate and refined monitoring and forecasting of sea ice, enhance the provision of effective icebreaking and pilot services, and optimize the fishery industry structure to prevent sea ice disasters in the Northern China Sea.

An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery

This study presents an innovative, automated deep learning-based technique for near real-time satellite monitoring of river ice conditions in northern watersheds of the United States and Canada. The method leverages high-resolution imagery from the VIIRS bands onboard the NOAA-20 and NPP satellites and employs the U-Net deep learning algorithm for the semantic segmentation of images under varying cloud and land surface conditions. The system autonomously generates detailed maps delineating classes such as water, land, vegetation, snow, river ice, cloud, and cloud shadow. The verification of system outputs was performed quantitatively by comparing with existing ice extent maps in the northeastern US and New Brunswick, Canada, yielding a Probability of Detection of 0.77 and a False Alarm rate of 0.12, suggesting commendable accuracy. Qualitative assessments were also conducted, corroborating the reliability of the system and underscoring its utility in monitoring hydraulic and hydrological processes across northern watersheds. The system’s proficiency in accurately capturing the phenology of river ice, particularly during onset and breakup times, testifies to its potential as a valuable tool in the realm of river ice monitoring.

Nondestructive Detection and Early Warning of Pavement Surface Icing Based on Meteorological Information.

Monitoring and warning of ice on pavement surfaces are effective means to improve traffic safety in winter. In this study, a high-precision piezoelectric sensor was developed to monitor pavement surface conditions. The effects of the pavement surface temperature, water depth, and wind speed on pavement icing time were investigated. Then, on the basis of these effects, an early warning model of pavement icing was proposed using an artificial neural network. The results showed that the sensor could detect ice or water on the pavement surface. The measurement accuracy and reliability of the sensor were verified under long-term vehicle load, temperature load, and harsh natural environment using test data. Moreover, pavement temperature, water depth, and wind speed had a significant nonlinear effect on the pavement icing time. The effect of the pavement surface temperature on icing conditions was maximal, followed by the effect of the water depth. The effect of the wind speed was moderate. The model with a learning rate of 0.7 and five hidden units had the best prediction effect on pavement icing. The prediction accuracy of the early warning model exceeded 90%, permitting nondestructive and rapid detection of pavement icing based on meteorological information.

Experimental study on ice monitoring method for 10 kV transmission line with tangent tower in alpine landform

AbstractIce monitoring methods were applied for 110 kV and above transmission lines with tangent towers. However, the change in the vertical span is not considered, and a significant difference lies in the tower‐conductor structure of 10 kV transmission lines. For this reason, a proposal is made about the ice monitoring method for the 10 kV transmission line with tangent tower in alpine landform, which includes the ice monitoring system based on pressure measurement and corresponding equivalent ice thickness calculating methods. Different methods calculate the vertical span under different height difference coefficients. A finite element simulation model and a simulated ice load experiment system are established based on real conductors and insulators. Experiments and simulations under four simulated terrains are conducted within 2.5–20 mm ice thickness range. The comparison is made between this method and the method without considering the change in vertical span. The results show that the two methods are consistent and the relative errors are lower than ±4% in simulation and ±10% in experiment when the height difference coefficient is 0. When it is not 0, the relative errors of this method fall between +0.38% and +6.78% in simulation and −6.40% to +6.60% in experiment, while the relative errors of the method without considering the change in vertical span ranges between −11.13% and −20.23% in simulation and −11.65% to −23.20% in experiment.

Improved Sea Ice Image Segmentation Using U2-Net and Dataset Augmentation

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Traditional image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning-based semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and severe weather conditions affect image quality, which affects the accuracy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multiscale inflation convolution and a multilayer convolutional block attention module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insufficient number of training samples to a certain extent and improved the accuracy of image segmentation; (2) this study designed a multilevel Gaussian noise data augmentation scheme to improve the network’s ability to resist noise interference and achieve a more accurate segmentation of images with different degrees of noise pollution; (3) the inclusion of a multiscale inflation perceptron and multilayer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

Multi-sensor detection of spring breakup phenology of Canada's lakes

The ice phenology of freshwater lakes throughout the Northern Hemisphere has undergone important climate-induced shifts over the past century. In Canada's North, where freshwater lakes and wetlands cover 15 to 40% of the landscape, monitoring ice phenology is vital to understand its impacts on climate, socio-economic, ecological, and hydrological systems. The rapid and dynamic nature of ice phenology events has restricted monitoring efforts to the use of satellite sensors with frequent revisit times (e.g., MODIS, AVHRR), but their low resolution (e.g., > 500 m) limits observations to larger water bodies. However, the increased abundance of high-resolution open-access satellite imagery combined with the rise of cloud-computing technologies has provided opportunities to reduce the trade-off between temporal (i.e., revisit time) and spatial (i.e., pixel size) resolution allowing for lake ice monitoring over broad scales. In this study, we present the Open Pixel-based Earth eNgine Ice (OPEN-ICE) algorithm implemented in Google Earth Engine (GEE), which classifies imagery from multiple open-access optical sensors, then combines them to construct dense annual time series of ice-water observations and estimate pixel spring breakup dates at a 30-m resolution. Using Landsat 7 ETM+, Landsat 8 OLI, and Sentinel-2 MSI scenes over lakes spanning northern latitudes, we build reference datasets to train decision trees that discriminate between ice, water, and clouds. We combine ice-water classifications from each sensor into annual time series and remove misclassifications with a temporal filter applied using a pixel-wise logistic regression. We then detect the sequence of transition from ice to water in each pixel's time series to estimate the occurrence of breakup each year. We deploy the OPEN-ICE algorithm over all freshwater pixels of Canada for the period of 2013 to 2021. Spring ice phenology events estimated by OPEN-ICE show high accuracy when compared to whole-lake breakup dates measured by the Canadian Ice Service in 105 lakes across 9 years, with mean bias errors of −1.10 and − 0.69 days for breakup start and end, respectively. We apply the OPEN-ICE algorithm to 4000 lakes across Canada and evaluate differences in breakup dates across ecozones and lake sizes. Our new OPEN-ICE tool provides accurate estimates of annual spring breakup events applicable across all boreal and arctic regions to monitor the rapid changes taking place in these vulnerable ecosystems.

Comparison of methods for monitoring icing of high-voltage power lines and an overview of measuring equipment used to diagnose such lines

The low structural reliability of overhead lines, due to continuous climatic influence, becomes the cause of electrical network accidents and unreliable power supply. Ensuring the trouble-free operation of overhead lines is a complex task, the solution of which is of the utmost importance for the normal functioning of the entire infrastructure. In the autumn-winter period, difficult weather conditions almost completely exclude the quick completion of repair works. A fairly large share of accidents occurs during the formation of icy deposits on wires and lightning protection cables. Today, visual inspections of high-voltage power lines remain the main way of diagnosing icy deposits. In practice, it is necessary to equip overhead lines with ice load telemetry devices, which will allow monitoring the formation of ice deposits on wires in real time. The following diagnostic methods are used in modern ice monitoring devices: strainometric, fiber-optic, aerodynamic, locational, and instrumental-parametric. The tensiometric method of diagnosis is implemented by means of direct measurement of the icy load on the wire with subsequent comparison of the measured values with predetermined values of the threshold loads. To implement this diagnostic method, magnetoelastic force measuring sensors are used. Such a sensor is connected between the traverse of the U-shaped support and the upper end of the corresponding garland of insulators with a phase wire. Ice, wind and ice-wind loads are measured separately. This method has a limited scope of application, namely for diagnosing the formation of ice on the phase wires of intermediate spans of single-circuit lines with two-post U-shaped resistances. The fiber-optic method of strain gauge measurements is implemented in object control systems under difficult operating conditions, at nuclear power plants. The mechanical effort sensor (fiber-optic strain sensor) implements the fiber-optic method of strain measurement. Fiber-optic strain sensors are characterized by high accuracy due to resistance to interference and complexity of execution.

Investigation of the sensitivity response of Touzi target scattering decomposition to modeled early ice growth

ABSTRACT Touzi’s scattering vector model allows for a unified decomposition of both coherent and incoherent target scattering. Based on this model, a unique and roll-invariant model can be developed for target decomposition. Our study aims at the investigation of the Touzi decomposition for lake and sea ice monitoring. Thus, we investigate the sensitivity of the target parameters obtained from the Touzi incoherent decomposition to modeled ice growth. Our study focuses on thermodynamically-grown fast lake and sea ice during the early ice growth. A time-series quad pol synthetic aperture radar (SAR) imagery was acquired over a study site around the Resolute Bay area. Results indicate that for lake ice, the scattering type magnitude () decreases within a thickness up to 20 cm. This means switching from volume scattering to dominant surface scattering. The same sensitivity is observed for young sea ice with thickness up to 30 cm, which corresponds to modeled ice bulk salinity of 8.5‰. Remarkably, we found a trend of increasing target orientation angle () for thin sea ice up to 20 cm, which corresponds to a bulk salinity of 10.3‰. This trend is unique to the case of sea ice.