Ice Monitoring Research Articles

Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015–2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3–10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2–10 days. Mean errors for comparisons of different sections of the lake ranged 0–15 days for the first difference method and 2–17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

River ice monitoring is of great significance for river management, ship navigation and ice hazard forecasting in cold-regions. Accurate ice segmentation is one most important pieces of technology in ice monitoring research. It can provide the prerequisite information for the calculation of ice cover density, drift ice speed, ice cover distribution, change detection and so on. Unmanned aerial vehicle (UAV) aerial photography has the advantages of higher spatial and temporal resolution. As UAV technology has become more popular and cheaper, it has been widely used in ice monitoring. So, we focused on river ice segmentation based on UAV remote sensing images. In this study, the NWPU_YRCC dataset was built for river ice segmentation, in which all images were captured by different UAVs in the region of the Yellow River, the most difficult river to manage in the world. To the best of our knowledge, this is the first public UAV image dataset for river ice segmentation. Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET. Experiments demonstrated that the proposed ICENET outperforms the state-of-the-art methods, achieving a superior result on the NWPU_YRCC dataset.

Sea Ice Automatic Extraction in the Liaodong Bay from Sentinel-2 Imagery Using Convolutional Neural Networks

Sea ice classification is one of the important tasks of sea ice monitoring. Accurate extraction of sea ice types is of great significance on sea ice conditions assessment, smooth navigation and safty marine operations. Sentinel-2 is an optical satellite launched by the European Space Agency. High spatial resolution and wide range imaging provide powerful support for sea ice monitoring. However, traditional supervised classification method is difficult to achieve fine results for small sample features. In order to solve the problem, this paper proposed a sea ice extraction method based on deep learning and it was applied to Liaodong Bay in Bohai Sea, China. The convolutional neural network was used to extract and classify the feature of the image from Sentinel-2. The results showed that the overall accuracy of the algorithm was 85.79% which presented a significant improvement compared with the tranditional algorithms, such as minimum distance method, maximum likelihood method, Mahalanobis distance method, and support vector machine method. The method proposed in this paper, which combines convolutional neural networks and high-resolution multispectral data, provides a new idea for remote sensing monitoring of sea ice.

Внедрение системы автоматизированного мониторинга гололедообразования в распределительных сетях ПАО «Татнефть»

Внедрение системы автоматизированного мониторинга гололедообразования в распределительных сетях ПАО «Татнефть»

Multi-scale observations of the co-evolution of sea ice thermophysical properties and microwave brightness temperatures during the summer melt period in Hudson Bay

Monitoring the trend of sea ice breakup and formation in Hudson Bay is vital for maritime operations, such as local hunting or shipping, particularly in response to the lengthening of the ice-free period in the Bay driven by climate change. Satellite passive microwave sea ice concentration products are commonly used for large-scale sea ice monitoring and predictive modelling; however, these product algorithms are known to underperform during the summer melt period due to the changes in sea ice thermophysical properties. This study investigates the evolution of in situ and satellite-retrieved brightness temperature (TB) throughout the melt season using a combination of in situ passive microwave measurements, thermophysical sampling, unmanned aerial vehicle (UAV) surveys, and satellite-retrieved TB. In situ data revealed a strong positive correlation between the presence of liquid water in the snow matrix and in situ TB in the 37 and 89 GHz frequencies. When considering TB ratios utilized by popular sea ice concentration algorithms (e.g., NASA Team 2), liquid water presence in the snow matrix was shown to increase the in situ TB gradient ratio of 37/19V. In situ gradient ratios of 89/19V and 89/19H were shown to correlate positively with UAV-derived melt pond coverage across the ice surface. Multi-scale comparison between in situ TB measurements and satellite-retrieved TB (by Advanced Microwave Scanning Radiometer 2) showed a distinct pattern of passive microwave TB signature at different stages of melt, confirmed by data from in situ thermophysical measurements. This pattern allowed for both in situ and satellite-retrieved TB to be partitioned into three discrete stages of sea ice melt: late spring, early melt and advanced melt. The results of this study thus advance the goal of achieving more accurate modeled predictions of the sea ice cover during the critical navigation and breakup period in Hudson Bay.

Система мониторинга интенсивности гололедообразования на проводах воздушной линии электропередачи

Improving the reliability and efficiency of power supplying by reducing accidents in electrical networks caused by intense ice and wind loads is an important comprehensive task. The solution of this problem consists of several mainly stages: timely detection of the onset of ice formation, impact on the device for removing ice and frost deposits and efficiency of removing of ice monitoring. This article discusses a system for detecting ice formation based on a fundamentally new technique using the technology of «machine vision». The developed system of ice formation makes identify the thickness of ice wall and indirectly, by the angle of deviation of the wire relative to the horizontal position, to determine the mass of ice deposits and wire sag. Considered monitoring system is located on support in close proximity from the wire and consists of the following mainly elements: an electronic unit, an optical sensor, a solar panel and GSM antenna

Our practice of outreach during the Ice Monitoring project in Nunavik: an early-career researcher perspective

Inuit Nunangat, including Nunavik, is seeing an ever-increasing number of research projects. While mainstream approaches to research are colonial in nature and have historically contributed to the oppression of Indigenous peoples, a new paradigm is now emerging from Indigenous recommendations. Researchers are encouraged to collaborate with Inuit or Northern communities, organizations, and governments and to develop communication strategies to keep local populations informed. This paper focuses on outreach activities organized on several occasions throughout the Ice Monitoring project, in which we participated as PhD students. We share details on this periodic outreach program, which included a Facebook page, hosting an information table at the Co-op store, activities with high school classes, and participation in Raglan Mine’s Environmental Forum. We also discuss lessons learned and the transformation of our practice.

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ∼8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ∼10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads.

Transferred Deep Learning for Sea Ice Change Detection From Synthetic-Aperture Radar Images

High-quality sea ice monitoring is crucial to navigation safety and climate research in the polar regions. In this letter, a transferred multilevel fusion network (MLFN) is proposed for sea ice change detection from synthetic-aperture radar (SAR) images. Considering the fact that training data are limited in the task of sea ice change detection, a large data set was used to train the MLFN, and the deep knowledge can be transferred to sea ice analysis. In addition, cascade dense blocks are employed to optimize the convolutional layers. Multilayer feature fusion is introduced to exploit the complementary information among low-, mid-, and high-level feature representations. Therefore, more discriminative feature extraction can be achieved by the MLFN. Furthermore, the fine-tune strategy is utilized to optimize the network parameters. The experimental results on two real sea ice data sets demonstrated that the proposed method achieved better performance than other competitive methods.

On the reducing of the acoustic signal length from the piezoelectric transducers of the sea ice monitoring systems

The solution of navigation problems the ice situation problems in the Arctic region need to study the acoustic parameters and local reflective properties of ice in real conditions. Information about the local sea ice acoustic properties essentially helps to solve the problems of statistical forecasting of reflective and scattering properties of ice cover. The successful solution of these problems mostly depends on the metrological parameters of the probing signals emitted by the primary piezoelectric transducers. This means, that to increase the resolution of the control and measuring equipment it is better to radiate and to receive short acoustic pulses. The article presents the results of analysis the pulse mode of operation of cylindrical piezoelectric transducers designed to measure the velocity of sound in sea ice. To reduce the length of acoustic pulses, an inductive-resistive load is connected to the electrical input of the radiator. The length and the amplitude of the emitted acoustic pulse were estimated using equivalent circuits of piezoelectric transducers and a spectral method based on the Fourier transforms. The optimal parameters of the electrical circuit, ensuring the minimum length of the acoustic pulses are calculated. The technique of possible measurements is described. The results are presented in the most general form, making possible to use them within different frequency ranges.

MACHINE LEARNING FOR SEA ICE MONITORING FROM SATELLITES

Abstract. Today, radar imaging from space allows continuous and wide-area sea ice monitoring under nearly all weather conditions. To this end, we applied modern machine learning techniques to produce ice-describing semantic maps of the polar regions of the Earth. Time series of these maps can then be exploited for local and regional change maps of selected areas. What we expect, however, are fully-automated unsupervised routine classifications of sea ice regions that are needed for the rapid and reliable monitoring of shipping routes, drifting and disintegrating icebergs, snowfall and melting on ice, and other dynamic climate change indicators. Therefore, we designed and implemented an automated processing chain that analyses and interprets the specific ice-related content of high-resolution synthetic aperture radar (SAR) images. We trained this system with selected images covering various use cases allowing us to interpret these images with modern machine learning approaches. In the following, we describe a system comprising representation learning, variational inference, and auto-encoders. Test runs have already demonstrated its usefulness and stability that can pave the way towards future artificial intelligence systems extending, for instance, the current capabilities of traditional image analysis by including content-related image understanding.

PROBLEMS AND PROSPECTS OF DEVELOPMENT OF INTERNATIONAL TRANSIT ON THE NORTHERN SEA WAY

The growth of cargo transportation along the Northern Sea Route is hampered by a complex of technological, organizational and managerial problems, the solution of which requires the full attention of state bodies. In particular, the year-round operation of the NSR is extremely limited and in the winter period requires mandatory icebreaking support (according to climatologists, the navigation period free from ice only by 2040 will exceed 150 days). With an increase in freight traffic in the medium term, there may be a real shortage of icebreaking capacities and vessels of the Arctic class. A solution to the problem can be the creation of a separate Arctic fleet, which will transport between the extreme points of the NSR with subsequent overload. At the same time, there should be a clear understanding of the prospects for loading built ships with cargo and the corresponding reserves of domestic cargo, which potentially replace international volumes if necessary. It is important to emphasize that the existing infrastructure of the NSR is still limited (only one multifunctional deepwater port in Murmansk operates in the Russian Arctic), whereas for the transarctic navigation along the NSR, a system of deep-water ports with modern logistics and service as transport hubs is required. The attractiveness of the NSR is also reduced due to the acute shortage of realtime ice monitoring systems. At the same time, it should be understood that the growth of international transit, the subsequent construction of multifunctional ports with appropriate infrastructure and communications throughout the route will provide, in turn, a multiplier effect for other sectors of the economy, will require the involvement of new labor, which eliminates negative demographic trends in Arctic zone of the Russian Federation.

Exploring the link between microseism and sea ice in Antarctica by using machine learning

The most continuous and ubiquitous seismic signal on Earth is the microseism, closely related to ocean wave energy coupling with the solid Earth. A peculiar feature of microseism recorded in Antarctica is the link with the sea ice, making the temporal pattern of microseism amplitudes different with respect to the microseism recorded in low-middle latitude regions. Indeed, during austral winters, in Antarctica the oceanic waves cannot efficiently excite seismic energy because of the sea ice in the Southern Ocean. Here, we quantitatively investigate the relationship between microseism, recorded along the Antarctic coasts, and sea ice concentration. In particular, we show a decrease in sea ice sensitivity of microseism, due to the increasing distance from the station recording the seismic signal. The influence seems to strongly reduce for distances above 1,000 km. Finally, we present an algorithm, based on machine learning techniques, allowing to spatially and temporally reconstruct the sea ice distribution around Antarctica based on the microseism amplitudes. This technique will allow reconstructing the sea ice concentration in both Arctic and Antarctica in periods when the satellite images, routinely used for sea ice monitoring, are not available, with wide applications in many fields, first of all climate studies.

Remote Sensing of Ice Phenology and Dynamics of Europe’s Largest Coastal Lagoon (The Curonian Lagoon)

A first-ever spatially detailed record of ice cover conditions in the Curonian Lagoon (CL), Europe’s largest coastal lagoon located in the southeastern Baltic Sea, is presented. The multi-mission synthetic aperture radar (SAR) measurements acquired in 2002–2017 by Envisat ASAR, RADARSAT-2, Sentinel-1 A/B, and supplemented by the cloud-free moderate imaging spectroradiometer (MODIS) data, are used to document the ice cover properties in the CL. As shown, satellite observations reveal a better performance over in situ records in defining the key stages of ice formation and decay in the CL. Using advantages of both data sources, an updated ice season duration (ISD) record is obtained to adequately describe the ice cover season in the CL. High-resolution ISD maps provide important spatial details of ice growth and decay in the CL. As found, ice cover resides longest in the south-eastern CL and along the eastern coast, including the Nemunas Delta, while the shortest ice season is observed in the northern CL. During the melting season, the ice melt pattern is clearly shaped by the direction of prevailing winds, and ice drift velocities obtained from a limited number of observations range within 0.03–0.14 m/s. The pronounced shortening of the ice season duration in the CL is observed at a rate of 1.6–2.3 days year‒1 during 2002–2017, which is much higher than reported for the nearby Baltic Sea regions. While the timing of the freeze onset and full freezing has not changed much, the dates of the final melt onset and last observation of ice have a clear decreasing pattern toward an earlier ice break-up and complete melt-off due to an increase of air temperature strongly linked to the North Atlantic Oscillation (NAO). Notably, the correlation between the ISD, air temperature, and winter NAO index is substantially higher when considering the lagoon-averaged ISD values derived from satellite observations compared to those derived from coastal records. The latter clearly demonstrated the richness of the satellite observations that should definitely be exploited in regional ice monitoring programs.

Sea Ice Climate Normals for Seasonal Ice Monitoring of Arctic and Sub-Regions

The climate normal, that is, the latest three full-decade average, of Arctic sea ice parameters is useful for baselining the sea ice state. A baseline ice state on both regional and local scales is important for monitoring how the current regional and local states depart from their normal to understand the vulnerability of marine and sea ice-based ecosystems to the changing climate conditions. Combined with up-to-date observations and reliable projections, normals are essential to business strategic planning, climate adaptation and risk mitigation. In this paper, monthly and annual climate normals of sea ice parameters (concentration, area, and extent) of the whole Arctic Ocean and 15 regional divisions are derived for the period of 1981–2010 using monthly satellite sea ice concentration estimates from a climate data record (CDR) produced by NOAA and the National Snow and Ice Data Center (NSIDC). Basic descriptions and characteristics of the normals are provided. Empirical Orthogonal Function (EOF) analysis has been utilized to describe spatial modes of sea ice concentration variability and how the corresponding principal components change over time. To provide users with basic information on data product accuracy and uncertainty, the climate normal values of Arctic sea ice extents (SIE) are compared with that of other products, including a product from NSIDC and two products from the Copernicus Climate Change Service (C3S). The SIE differences between different products are in the range of 2.3–4.5% of the CDR SIE mean. Additionally, data uncertainty estimates are represented by using the range (the difference between the maximum and minimum), standard deviation, 10th and 90th percentiles, and the first, second, and third quartile distribution of all monthly values, a distinct feature of these sea ice normal products.

PR-KELM: Icing level prediction for transmission lines in smart grid

As a hot spot, the diffusion of the smart city has been more and more popular and widespread all over the world. The concept of smart city has supported a higher quality of urban spaces and a better offering of public services. Smart grid is an important component and the energy supply of smart city. The health of the smart grid will directly affect the health of the smart city. Efficient and accurate prediction about icing level of transmission lines provides a reliable basis for anti-icing and deicing of power grids. The prediction can not only significantly enhance the reliability, safety and stability of transmission networks, but also timely provide the state monitoring of ice on wires to the workers to make proper decisions and to prevent freezing disasters so as to effectively protect people’s safety and build a stable and friendly society. To reduce catastrophic damages caused by the iced transmission lines of the grid, this paper proposed a novel hybrid model called PR-KELM, an integration of Principal Component Analysis, ReliefF and Kernel based ELM (Extreme Learning Machine) method to predict the icing level of transmission lines. This hybrid model covered two stages including: 1) structured and unstructured data feature extraction by local binary patterns, principal component analysis and ReliefF; 2) radial basis function Kernel based ELM predicting the processed data. The experimental data adopted in this paper were from the online monitoring system of South China State Grid Online Monitoring System by terminal CC0289 between December 1, 2011 and March 1, 2016. Experimental results indicated: compared with Elman neural network model, random forest model, support vector machine model and Bayesian networks model, PR-KELM approach demonstrated high accuracy and lower prediction error in terms of RMSE (Root Mean Square Error), MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and R (Coefficient of Correlation) while applied to predicting in real datasets.

Review of methods to retrieve sea ice parameters from satellite microwave radiometer data

Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.

Synoptic climate evidence of a late-twentieth century change to earlier spring ice-out on Maine Lakes, USA

Trend analysis of spring ice-out on eight lakes within the state of Maine of the northeastern USA reveals a change to earlier occurrence by 1 to 2 weeks over the period 1956–2015. Much of the trend occurred from the late 1970s through the 1980s, but a secondary trend toward earlier ice-out appears to have begun in the late 1990s. Synoptic climate data support local and hemispheric climate evidence of increasingly earlier ice-out, particularly during the earlier period of pronounced change. Local spring and winter maximum daily air temperatures increased while winter precipitation decreased; synoptic weather types of moderate temperature character increased in frequency, while polar types became less frequent; synoptic weather types became warmer in spring and winter, and in spring, warmer weather types became wetter, while cooler weather types became drier; and two key climate teleconnections, the Pacific-North American pattern and the El Nino/La Nina pattern, changed significantly toward a phase historically associated with earlier ice-out. While the results underscore the value in monitoring and study of lake ice as a climate proxy, they also demonstrate the value of synoptic climate data for filling the spatial gap between local and large-scale climate data in studies of lake ice phenology.

Selection of the Key Earth Observation Sensors and Platforms Focusing on Applications for Polar Regions in the Scope of Copernicus System 2020–2030

An optimal payload selection conducted in the frame of the H2020 ONION project (id 687490) is presented based on the ability to cover the observation needs of the Copernicus system in the time period 2020–2030. Payload selection is constrained by the variables that can be measured, the power consumption, and weight of the instrument, and the required accuracy and spatial resolution (horizontal or vertical). It involved 20 measurements with observation gaps according to the user requirements that were detected in the top 10 use cases in the scope of Copernicus space infrastructure, 9 potential applied technologies, and 39 available commercial platforms. Additional Earth Observation (EO) infrastructures are proposed to reduce measurements gaps, based on a weighting system that assigned high relevance for measurements associated to Marine for Weather Forecast over Polar Regions. This study concludes with a rank and mapping of the potential technologies and the suitable commercial platforms to cover most of the requirements of the top ten use cases, analyzing the Marine for Weather Forecast, Sea Ice Monitoring, Fishing Pressure, and Agriculture and Forestry: Hydric stress as the priority use cases.

Review of Methods to Retrieve Sea-Ice Parameters from Satellite Microwave Radiometer Data

Sea ice monitoring using long-term data of satellite passive microwave instruments enables climate change estimates. These numerical estimates depend on the methods used for sea ice parameter retrievals. This work presents a review of methods to retrieve sea ice parameters from the data of satellite microwave radiometers. Physical modeling of the sea ice–ocean–atmosphere microwave radiation provides the means to identify the general sources of the retrieval errors and to classify the methods by used approach. The basics of the algorithms are formulated along with assumptions and approximations as well as the data used for the algorithm verification. Weather filters are considered to identify the areas of open water. A comparative analysis of method advantages and limitations is given related to sea ice concentration retrievals from such satellite instruments as the series of Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR). A review of the basic satellite sea ice products based on SSM/I, AMSR-E and AMSR2 data is complemented by the list of the essential internet resources for operational and historical sea ice data.