Radar systems for a polar mission, volume 3, appendices A-D, S, T

Success is reported in the radar monitoring of such features of sea ice as concentration, floe size, leads and other water openings, drift, topographic features such as pressure ridges and hummocks, fractures, and a qualitative indication of age and thickness. Scatterometer measurements made north of Alaska show a good correlation with a scattering coefficient with apparent thickness as deduced from ice type analysis of stereo aerial photography. Indications are that frequencies from 9 GHz upward seem to be better for sea ice radar purposes than the information gathered at 0.4 GHz by a scatterometer. Some information indicates that 1 GHz is useful, but not as useful as higher frequencies. Either form of like-polarization can be used and it appears that cross-polarization may be more useful for thickness measurement. Resolution requirements have not been fully established, but most of the systems in use have had poorer resolution than 20 meters. The radar return from sea ice is found to be much different than that from lake ice. Methods to decrease side lobe levels of the Fresnel zone-plate processor and to decrease the memory requirements of a synthetic radar processor are discussed

Image Processing for Ice Parameter Identification in Ice Management

Various types of remotely sensed data and imaging technology will aid the development of sea-ice observation to, for instance, support estimation of ice forces critical to Dynamic Positioning (DP) operations in Arctic waters. The use of cameras as sensors for offshore operations in ice-covered regions will be explored for measurements of ice statistics and ice properties, as part of a sea-ice monitoring system. This thesis focuses on the algorithms for image processing supporting an ice management system to provide useful ice information to dynamic ice estimators and for decision support. The ice information includes ice concentration, ice types, ice floe position and floe size distribution, and other important factors in the analysis of ice-structure interaction in an ice field. The Otsu thresholding and k-means clustering methods are employed to identify the ice from the water and to calculate ice concentration. Both methods are effective for model-ice images. However, the k-means method is more effective than the Otsu method for the sea-ice images with a large amounts of brash ice and slush. The derivative edge detection and morphology edge detection methods are used to try to find the boundaries of the ice floes. Because of the inability of both methods to separate connected ice floes in the images, the watershed transform and the gradient vector flow (GVF) snake algorithm are applied. In the watershed-based method, the grayscale sea-ice image is first converted into a binary image and the watershed algorithm is carried out to segment the image. A chain code is then used to check the concavities of floe boundaries. The segmented neighboring regions that have no concave corners between them are merged, and over-segmentation lines are removed automatically. This method is applicable to separate the seemingly connected floes whose junctions are invisible or lost in the images. In the GVF snake-based method, the seeds for each ice floe are first obtained by calculating the distance transform of the binarized image. Based on these seeds, the snake contours with proper locations and radii are initialized, and the GVF snakes are then evolved automatically to detect floe boundaries and separate the connected floes. Because some holes and smaller ice pieces may be contained inside larger floes, all the segmented ice floes are arranged in order of increasing size after segmentation. The morphological cleaning is then performed to the arranged ice floes in sequence to enhance their shapes, resulting in individual ice floes identification. This method is applicable to identify non-ridged ice floes, especially in the marginal ice zone and managed ice resulting from offshore operations in sea-ice. For ice engineering, both model-scale and full-scale ice will be discussed. In the model-scale, the ice floes in the model-ice images are modeled as square shapes with predefined side lengths. To adopt the GVF snake-based method for model-ice images, three criteria are proposed to check whether it is necessary to reinitialize the contours and segment a second time based on the size and shape of model-ice floe. In the full-scale, sea-ice images are shown to be more difficult than the model-ice images analyzed. In addition to non-uniform illumination, shadows and impurities, which are common issues in both sea-ice and model-ice image processing, various types of ice (e.g., slush, brash, etc.), irregular floe sizes and shapes, and geometric distortion are challenges in seaice image processing. For sea-ice image processing, the “light ice” and “dark ice” are first obtained by using the Otsu thresholding and k-means clustering methods. Then, the “light ice” and “dark ice” are segmented and enhanced by using the GVF snake-based method. Based on the identification result, different types of sea-ice are distinguished, and the image is divided into four layers: ice floes, brash pieces, slush, and water. This then makes it possible to present a color map of the ice floes and brash pieces based on sizes. It also makes it possible to present the corresponding ice floe size distribution histogram

Comparison of Passive Microwave Data with Shipborne Photographic Observations of Summer Sea Ice Concentration along an Arctic Cruise Path

Arctic sea ice concentration (SIC) has been studied extensively using passive microwave (PM) remote sensing. This technology could be used to improve navigation along vessel cruise paths; however, investigations on this topic have been limited. In this study, shipborne photographic observation (P-OBS) of sea ice was conducted using oblique-oriented cameras during the Chinese National Arctic Research Expedition in the summer of 2016. SIC and the areal fractions of open water, melt ponds, and sea ice (Aw, Ap, and Ai, respectively) were determined along the cruise path. The distribution of SIC along the cruise path was U-shaped, and open water accounted for a large proportion of the path. The SIC derived from the commonly used PM algorithms was compared with the moving average (MA) P-OBS SIC, including Bootstrap and NASA Team (NT) algorithms based on Special Sensor Microwave Imager/Sounder (SSMIS) data; and ARTIST sea ice, Bootstrap, Sea Ice Climate Change Initiative, and NASA Team 2 (NT2) algorithms based on Advanced Microwave Scanning Radiometer 2 (AMSR2) data. P-OBS performed better than PM remote sensing at detecting low SIC (< 10%). Our results indicate that PM SIC overestimates MA P-OBS SIC at low SIC, but underestimates it when SIC exceeds a turnover point (TP). The presence of melt ponds affected the accuracy of the PM SIC; the PM SIC shifted from an overestimate to an underestimate with increasing Ap, compared with MA P-OBS SIC below the TP, while the underestimation increased above the TP. The PM algorithms were then ranked; SSMIS-NT and AMSR2-NT2 are the best and worst choices for Arctic navigation, respectively

Sea ice segmentation in SAR images using Deep Learning

Sea ice covers over seven percent of the world's oceans. Due to the effect of global warming, Arctic's ice extent has decreased significantly in the past decades. This reduction in sea ice cover is opening new pathways for the international shipping community through the Arctic. Due to the lengthening of the open water season, the Canadian Arctic has also observed a three-fold increase in the shipping traffic in the past few years. Although the ice extent has reduced, the risks and hazards involved in shipping through these regions are still significant. To promote safe and efficient maritime activities in the Canadian Arctic, Canadian Ice Service (CIS) provides information about ice in Canada's navigable waters. CIS uses Synthetic Aperture Radar (SAR) images as one of the prominent sources to gain insights about the ice conditions in Canadian waters. Automated SAR image interpretation is a complex task and requires algorithms to learn complex and rich features. Convolutional neural networks (CNNs) have demonstrated their ability to learn such features and have been used in various image classification, segmentation and object detection tasks. In this thesis, we first propose a method to detect marginal ice zones (MIZs) in SAR images. This method uses transfer learning combined with a multi-scale patch technique to detect the MIZs. The multi-scale patch technique involves generating the segmentation masks over different patch sizes for the same image. These masks are later stacked together and thresholded to generate the final MIZ prediction mask for an image. Later we dive deep into the MIZs and focus on segmenting sea ice floes. We propose a segmentation model optimized for the task of ice floe segmentation in SAR images. The model is based on a fully convolutional architecture with residual connections. In addition to this, a conditional random field is also used as a post-processing step. The whole network is trained end-to-end using a dual loss function. Qualitative and quantitative analysis suggests that our model beats the conventional segmentation architectures for the task of ice floe detection

Thickness distribution and extent of sea ice and snow in Prydz Bay and surrounding waters observed in 2012/2013 austral summer

Three ship-based observational campaigns were conducted to survey sea ice and snow in Prydz Bay and the surrounding waters (64.40°S–69.40°S, 76.11°E–81.29°E) from 28 November 2012 to 3 February 2013. In this paper, we present the sea ice extent and its variation, and the ice and snow thickness distributions and their variations with time in the observed zone. In the pack ice zone, the southern edge of the pack ice changed little, whereas the northern edge retreated significantly during the two earlier observation periods. Compared with the pack ice, the fast ice exhibited a significantly slower variation in extent with its northernmost edge retreating southwards by 6.7 km at a rate of 0.37 km∙d-1. Generally, ice showed an increment in thickness with increasing latitude from the end of November to the middle of December. Ice and snow thickness followed an approximate normal distribution during the two earlier observations (79.7±28.9 cm, 79.1±19.1 cm for ice thickness, and 11.6±6.1 cm, 9.6±3.4 cm for snow thickness, respectively), and the distribution tended to be more concentrated in mid-December than in late November. The expected value of ice thickness decreased by 0.6 cm, whereas that of snow thickness decreased by 2 cm from 28 November to 18 December 2012. Ice thickness distribution showed no obvious regularity between 31 January and 3 February, 2013

Image based real-time ice load prediction tool for ship and offshore platform in managed ice field

The increased activities in arctic water warrant modelling of ice properties and ice-structure interaction forces to ensure safe operations of ships and offshore platforms. Several established analytical and numerical ice force estimation models can be found in the literature. Recently, researchers have been working on Machine Learning (ML) based, data-driven force predictors trained on experimental data and field measurement. Application of both traditional and ML-based image processing for extracting information from ice floe images has also been reported in recent literature; because extraction of ice features from real-time videos and images can significantly improve ice force prediction. However, there exists room for improvement in those studies. For example, accurate extraction of ice floe information is still challenging because of their complex and varied shapes, colour similarities and reflection of light on them. Besides, real ice floes are often found in groups with overlapped and/or connected boundaries, making detecting even more challenging due to weaker edges in such situations. The development of an efficient coupled model, which will extract information from the ice floe images and train a force predictor based on the extracted dataset, is still an open problem. This research presents two Hybrid force prediction models. Instead of using analytical or numerical approaches, the Hybrid models directly extract floe characteristics from the images and later train ML-based force predictors using those extracted floe parameters. The first model extracted ice features from images using traditional image processing techniques and then used SVM and FFNN to develop two separate force predictors. The improved ice image processing technique used here can extract useful ice properties from a closely connected, unevenly illuminated floe field with various floe sizes and shapes. The second model extracted ice features from images using RCNN and then trained two separate force predictors using SVM and FFNN, similar to the first model. The dataset for training SVM and FFNN force predictors involved variables extracted from the image (floe number, density, sizes, etc.) and variables taken from the experimental analysis results (ship speed, floe thickness, force etc.). The performance of both Hybrid models in terms of image segmentation and force prediction, are analyzed and compared to establish their validity and applicability. Nevertheless, there exists room for further development of the proposed Hybrid models. For example, extend the current models to include more data and investigate other machine learning and deep learning-based network architectures to predict the ice force directly from the image as an input

Earth resources: A continuing bibliography with indexes (issue 51)

This bibliography lists 382 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1986. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

NASA Oceanic Processes Program annual review

Current flight projects and definition studies, descriptions of individual research activities, and a bibliography of referred journal articles appearing within the past two years are contained

Earth Resources, A Continuing Bibliography with Indexes

This bibliography lists 460 reports, articles and other documents introduced into the NASA scientific and technical information system between July 1 and September 30, 1984. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economical analysis

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing