669 research outputs found

    Flood dynamics derived from video remote sensing

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    Flooding is by far the most pervasive natural hazard, with the human impacts of floods expected to worsen in the coming decades due to climate change. Hydraulic models are a key tool for understanding flood dynamics and play a pivotal role in unravelling the processes that occur during a flood event, including inundation flow patterns and velocities. In the realm of river basin dynamics, video remote sensing is emerging as a transformative tool that can offer insights into flow dynamics and thus, together with other remotely sensed data, has the potential to be deployed to estimate discharge. Moreover, the integration of video remote sensing data with hydraulic models offers a pivotal opportunity to enhance the predictive capacity of these models. Hydraulic models are traditionally built with accurate terrain, flow and bathymetric data and are often calibrated and validated using observed data to obtain meaningful and actionable model predictions. Data for accurately calibrating and validating hydraulic models are not always available, leaving the assessment of the predictive capabilities of some models deployed in flood risk management in question. Recent advances in remote sensing have heralded the availability of vast video datasets of high resolution. The parallel evolution of computing capabilities, coupled with advancements in artificial intelligence are enabling the processing of data at unprecedented scales and complexities, allowing us to glean meaningful insights into datasets that can be integrated with hydraulic models. The aims of the research presented in this thesis were twofold. The first aim was to evaluate and explore the potential applications of video from air- and space-borne platforms to comprehensively calibrate and validate two-dimensional hydraulic models. The second aim was to estimate river discharge using satellite video combined with high resolution topographic data. In the first of three empirical chapters, non-intrusive image velocimetry techniques were employed to estimate river surface velocities in a rural catchment. For the first time, a 2D hydraulicvmodel was fully calibrated and validated using velocities derived from Unpiloted Aerial Vehicle (UAV) image velocimetry approaches. This highlighted the value of these data in mitigating the limitations associated with traditional data sources used in parameterizing two-dimensional hydraulic models. This finding inspired the subsequent chapter where river surface velocities, derived using Large Scale Particle Image Velocimetry (LSPIV), and flood extents, derived using deep neural network-based segmentation, were extracted from satellite video and used to rigorously assess the skill of a two-dimensional hydraulic model. Harnessing the ability of deep neural networks to learn complex features and deliver accurate and contextually informed flood segmentation, the potential value of satellite video for validating two dimensional hydraulic model simulations is exhibited. In the final empirical chapter, the convergence of satellite video imagery and high-resolution topographical data bridges the gap between visual observations and quantitative measurements by enabling the direct extraction of velocities from video imagery, which is used to estimate river discharge. Overall, this thesis demonstrates the significant potential of emerging video-based remote sensing datasets and offers approaches for integrating these data into hydraulic modelling and discharge estimation practice. The incorporation of LSPIV techniques into flood modelling workflows signifies a methodological progression, especially in areas lacking robust data collection infrastructure. Satellite video remote sensing heralds a major step forward in our ability to observe river dynamics in real time, with potentially significant implications in the domain of flood modelling science

    Seasonal and Multi-year Variability of Ice Dynamics of South Croker Bay Glacier, Devon Ice Cap, Canadian Arctic from 2015 to 2021

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    The effects of climate change have already been observed across the globe, impacting weather, ecosystems, and society. These effects have been most pronounced in polar regions, which experience warming at a faster rate than other latitudes due to positive feedbacks resulting from reduced ice and snow cover. Compared to the 1.1oC of warming around the globe since the 1980s, the Arctic has warmed by 3oC. Glaciers and ice caps are of particular concern as they have profound impacts on water resources, shipping and travel routes, and global sea level rise. As such, glacier dynamics play a key role in understanding effects on the global system. The Canadian High Arctic in particular has doubled in rates of mass loss since the 1990s, which is of great concern as it is the third largest contributor to global sea level rise after Antarctica and Greenland. While glacier flow within the region has been studied, some glaciers have been observed to not align with current understandings of dynamics. The subject of this study, South Croker Bay Glacier, located on Devon Ice Cap in Nunavut, Canada has exhibited velocity variability on oscillating temporal scales which do not align with surging, pulsing, or consistent acceleration explanations. The primary objective of this thesis was to create a dense record of velocities derived from TerraSAR-X imagery every 11 days from 2015 to 2021 to gain insight into seasonal and multi-annual velocity variability. As a result, a near-continuous velocity record of South Croker Bay Glacier has been created, highlighting a shift in velocities which occurred during the winter of 2018/19. The second objective was to explore the potential drivers of the observed velocity variability, which were hydrology, sea ice buttressing, and bed topography. Looking at the spatial propagation of acceleration and terminus position as well, it is concluded that the variability is not driven by surge- or pulse-type mechanisms. Instead, it is suggested that the driver of the observed variability on the glacier is the result of the evolving configuration of the hydrological network. This is supported by surface air temperature and surface lake area records during the study period. Finally, the third objective was to assess the feasibility of utilizing remote sensing for seasonal variability detection. Based on the analysis, the method was successful in the proposed objectives, creating a record of velocities that was not previously available for South Croker Bay Glacier

    Satellite remote sensing of surface winds, waves, and currents: Where are we now?

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    This review paper reports on the state-of-the-art concerning observations of surface winds, waves, and currents from space and their use for scientific research and subsequent applications. The development of observations of sea state parameters from space dates back to the 1970s, with a significant increase in the number and diversity of space missions since the 1990s. Sensors used to monitor the sea-state parameters from space are mainly based on microwave techniques. They are either specifically designed to monitor surface parameters or are used for their abilities to provide opportunistic measurements complementary to their primary purpose. The principles on which is based on the estimation of the sea surface parameters are first described, including the performance and limitations of each method. Numerous examples and references on the use of these observations for scientific and operational applications are then given. The richness and diversity of these applications are linked to the importance of knowledge of the sea state in many fields. Firstly, surface wind, waves, and currents are significant factors influencing exchanges at the air/sea interface, impacting oceanic and atmospheric boundary layers, contributing to sea level rise at the coasts, and interacting with the sea-ice formation or destruction in the polar zones. Secondly, ocean surface currents combined with wind- and wave- induced drift contribute to the transport of heat, salt, and pollutants. Waves and surface currents also impact sediment transport and erosion in coastal areas. For operational applications, observations of surface parameters are necessary on the one hand to constrain the numerical solutions of predictive models (numerical wave, oceanic, or atmospheric models), and on the other hand to validate their results. In turn, these predictive models are used to guarantee safe, efficient, and successful offshore operations, including the commercial shipping and energy sector, as well as tourism and coastal activities. Long-time series of global sea-state observations are also becoming increasingly important to analyze the impact of climate change on our environment. All these aspects are recalled in the article, relating to both historical and contemporary activities in these fields

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Sea Ice Extraction via Remote Sensed Imagery: Algorithms, Datasets, Applications and Challenges

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    The deep learning, which is a dominating technique in artificial intelligence, has completely changed the image understanding over the past decade. As a consequence, the sea ice extraction (SIE) problem has reached a new era. We present a comprehensive review of four important aspects of SIE, including algorithms, datasets, applications, and the future trends. Our review focuses on researches published from 2016 to the present, with a specific focus on deep learning-based approaches in the last five years. We divided all relegated algorithms into 3 categories, including classical image segmentation approach, machine learning-based approach and deep learning-based methods. We reviewed the accessible ice datasets including SAR-based datasets, the optical-based datasets and others. The applications are presented in 4 aspects including climate research, navigation, geographic information systems (GIS) production and others. It also provides insightful observations and inspiring future research directions.Comment: 24 pages, 6 figure

    Copernicus Cal/Val Solution - D3.2 - Recommendations for R&D on Cal/Val Methods

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    This document presents a gap analysis of the methods used in the calibration and validation of Earth Observation satellites relevant to the Copernicus programme and suggests recommendations for the research and developments required to fulfil this gap when/where possible. The document identifies the gaps and limitations of the CalVal methods, used for calibration and validation (CalVal) activities for the current Copernicus missions. It will also address the development needs for future Copernicus missions. Four types of missions are covered based on the division used in the rest of the CCVS project: optical, altimetry, radar and microwave and atmospheric composition. Finally, it will give a prioritized list of recommendations for R&D activities on the CalVal methods. The information included is mainly collected from the deliverables of work packages 1 and 2 in the CCVS project and from the consortium experts in CalVal activities

    Solutions for Sustainable Economic Development - 4th Arctic Science Ministerial Meeting Report

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    Arctic Science Ministerial is a unique form of scientific cooperation that traditionally advocates preserving the Arctic region as a territory of peace, stability and constructive interaction focused on achieving concrete, practical results in the interests of all people in the northern latitudes, including indigenous peoples. The Russian Federation continues the coordinating functions within the ASM adopted from previous coordinators on June 16, 2021 at the final ASM3 webinar, and on October 14, 2021 in Reykjavik, Iceland at the annual international Arctic Circle Assembly, based on the continuity of previous ASM and the increasing relevance of scientific research in the Arctic. This book provides an overview of past events - webinars, participation in conference roundtables - with the aim of sharing scientific experience of Arctic research and forming informational materials to support science and higher education activities through international organizations and forums in the Arctic zone, supporting and updating the database of Arctic research projects carried out by scientific and educational organizations, including jointly, as well as through international. The information base for this work was the results of feedback assessment from Russian and foreign scientific and educational organizations, data on international projects in the Arctic, materials from the websites of the Arctic Council https://arctic-council.org/ and the working groups of the Arctic Council. In addition, climate, geological, biological, sociological, and technological research was used as the basis for developing strategies for sustainable economic development in the Arctic that take into account the interests of all stakeholders, including indigenous peoples, environmental organizations, industry, and government agencies

    GAC-MAC-SGA 2023 Sudbury Meeting: Abstracts, Volume 46

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    Realising Global Water Futures: a Summary of Progress in Delivering Solutions to Water Threats in an Era of Global Change

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    Canada First Research Excellence FundNon-Peer ReviewedOver the past six years the Global Water Futures program has produced a wide range of scientific findings and engagements with multiple types of potential users of the research. This briefing book provides a snapshot of some of the science advancements and user engagement that have taken place to date. Annual reports to the funding agency are the most up to date source of information: this compilation has been created from reports submitted by projects in 2022, representing both completed and current project work. The briefing book aims to provide quick access to information about GWF projects in a single place for GWF’s User Advisory Panel: we hope that knowing more about the research being produced will spark conversations about how to make the best use of the new knowledge in both policy and practice

    Intra-annual and Long-term Dynamic Behaviour of Hubbard and Valerie Glaciers, Alaska

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    Western North American mountain regions are warming at a faster rate than the global average, which is influencing the retreat and melting of glaciers, with a 75% disappearance of glacier volume in Western North America possible by 2100. The impacts of this are wide reaching, including increasing contributions to sea level rise, decreased freshwater availability, loss of stability of mountain slopes and changing aquatic ecosystems. Hubbard and Valerie glaciers are in the St. Elias Mountains of Alaska/Yukon, which is an important area of study as Alaskan glaciers are likely to respond to climate change differently than glaciers in other regions of the world. The studies on seasonal velocity flow of both glaciers have been limited, with few recent reports of dynamics and mass balance. The goals of this study were to 1) determine the seasonality of Hubbard and Valerie glaciers by creating the densest record of flow to date from July 2013-April 2022; 2) analyze the long-term velocity trend from 1985-2022 to confirm if both glaciers are decelerating; and 3) use surface elevation change and temperature data to analyze potential drivers of the determined velocity patterns. The velocity record of Hubbard and Valerie glaciers was created using ITS_LIVE, RADARSAT-2, RADARSAT Constellation Mission, and TerraSAR-X/TanDEM-X derived measurements. Valerie glacier had an expected seasonal pattern of peak velocities in May and minimum velocities between August-November. Hubbard Glacier had a seasonal pattern that had never been identified in previous studies, with peak velocities between December-February, velocities dropping slightly between January-April, a second velocity peak in May, and minimum velocities in August/September. The May peak and late summer minimum of both glaciers was determined to be from surface melt reaching the bed, increasing flow speeds with an inefficient drainage system before changing to a channelized subglacial hydrological system that causes a velocity drop. It is likely Hubbard Glacier’s winter velocity peak and slowdown before its May peak is internally driven, however the exact driver was not identified. The long-term velocity trend revealed Hubbard Glacier is decelerating, with a minimal deceleration near its terminus that was similar to the minimal deceleration on Valerie Glacier, while there was increased deceleration further up-glacier. For both glaciers, the deceleration did not match the expected patterns of thinning/thickening. Previous instances of pulsing were not resolved in this data. Overall, this study helps improve the knowledge of tidewater glacier dynamics through the identification of a unique intra-annual velocity pattern and can assist in improving sea level rise, ice dynamics, and mass loss models
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