Data-driven Regularization and Uncertainty Estimation to Improve Sea Ice Data Assimilation

Accurate estimates of sea ice conditions such as ice thickness and ice concentration in the ice-covered regions are critical for shipping activities, ice operations and weather forecasting. The need for this information has increased due to the recent record of decline in Arctic ice extent and thinning of the ice cover, which has resulted in more shipping activities and climate studies. Despite the extensive studies and progress to improve the quality of sea ice forecasts from prognostic models, there is still significant room for improvement. For example, ice-ocean models have difficulty estimating the ice thickness distribution accurately. To help improve model forecasts, data assimilation is used to combine observational data with model forecasts and produce more accurate estimates. The assimilation of ice thickness observations, compared to other ice parameters such as ice concentration, is still relatively unexplored since the satellite-based ice thickness observations have only recently become common. Also, preserving sharp features of ice cover, such as leads and ridges, can be difficult, due to the spatial correlations in the background error covariance matrices. At the same time, the current ice concentration assimilation systems do not directly assimilate high resolution sea ice information from synthetic aperture radar (SAR), even though they are the main source of information for operational production of ice chart products at the Canadian Ice Service. The key challenge in SAR data assimilation is automating the interpretation of SAR images. To address the problem of assimilating ice thickness observations while preserving sharp features, two different objective functions are studied. One with a conventional l2-norm and one imposing an additional l1-norm on the derivative of the ice thickness state estimate as a sparse regularization. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. The data fusion and data assimilation experiments are performed over a wide range of background and observation error correlation length scales. Results demonstrate the superiority of using a combined l1-l2 regularization framework especially when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing). The problem of automated information retrieval from SAR images has been explored in a problem of ice/water classification. The selected classification approach takes advantage of neural networks to produce results comparable to a previous study using logistic regression. The employed dataset in both studies is a comprehensive dataset consisting of 15405 SAR images over a seven year period, covering all months and different locations. In addition, recent neural network uncertainty estimation approaches are employed to estimate the uncertainty associated with the classification of ice/water labels, which was not explored in this problem domain previously. These predicted uncertainties can improve the automated classification process by identifying regions in the predictions that should be checked manually by an analyst

Remote Sensing of Plant Biodiversity

This Open Access volume aims to methodologically improve our understanding of biodiversity by linking disciplines that incorporate remote sensing, and uniting data and perspectives in the fields of biology, landscape ecology, and geography. The book provides a framework for how biodiversity can be detected and evaluated—focusing particularly on plants—using proximal and remotely sensed hyperspectral data and other tools such as LiDAR. The volume, whose chapters bring together a large cross-section of the biodiversity community engaged in these methods, attempts to establish a common language across disciplines for understanding and implementing remote sensing of biodiversity across scales. The first part of the book offers a potential basis for remote detection of biodiversity. An overview of the nature of biodiversity is described, along with ways for determining traits of plant biodiversity through spectral analyses across spatial scales and linking spectral data to the tree of life. The second part details what can be detected spectrally and remotely. Specific instrumentation and technologies are described, as well as the technical challenges of detection and data synthesis, collection and processing. The third part discusses spatial resolution and integration across scales and ends with a vision for developing a global biodiversity monitoring system. Topics include spectral and functional variation across habitats and biomes, biodiversity variables for global scale assessment, and the prospects and pitfalls in remote sensing of biodiversity at the global scale

Remote Sensing of Plant Biodiversity

At last, here it is. For some time now, the world has needed a text providing both a new theoretical foundation and practical guidance on how to approach the challenge of biodiversity decline in the Anthropocene. This is a global challenge demanding global approaches to understand its scope and implications. Until recently, we have simply lacked the tools to do so. We are now entering an era in which we can realistically begin to understand and monitor the multidimensional phenomenon of biodiversity at a planetary scale. This era builds upon three centuries of scientific research on biodiversity at site to landscape levels, augmented over the past two decades by airborne research platforms carrying spectrometers, lidars, and radars for larger-scale observations. Emerging international networks of fine-grain in-situ biodiversity observations complemented by space-based sensors offering coarser-grain imagery—but global coverage—of ecosystem composition, function, and structure together provide the information necessary to monitor and track change in biodiversity globally. This book is a road map on how to observe and interpret terrestrial biodiversity across scales through plants—primary producers and the foundation of the trophic pyramid. It honors the fact that biodiversity exists across different dimensions, including both phylogenetic and functional. Then, it relates these aspects of biodiversity to another dimension, the spectral diversity captured by remote sensing instruments operating at scales from leaf to canopy to biome. The biodiversity community has needed a Rosetta Stone to translate between the language of satellite remote sensing and its resulting spectral diversity and the languages of those exploring the phylogenetic diversity and functional trait diversity of life on Earth. By assembling the vital translation, this volume has globalized our ability to track biodiversity state and change. Thus, a global problem meets a key component of the global solution. The editors have cleverly built the book in three parts. Part 1 addresses the theory behind the remote sensing of terrestrial plant biodiversity: why spectral diversity relates to plant functional traits and phylogenetic diversity. Starting with first principles, it connects plant biochemistry, physiology, and macroecology to remotely sensed spectra and explores the processes behind the patterns we observe. Examples from the field demonstrate the rising synthesis of multiple disciplines to create a new cross-spatial and spectral science of biodiversity. Part 2 discusses how to implement this evolving science. It focuses on the plethora of novel in-situ, airborne, and spaceborne Earth observation tools currently and soon to be available while also incorporating the ways of actually making biodiversity measurements with these tools. It includes instructions for organizing and conducting a field campaign. Throughout, there is a focus on the burgeoning field of imaging spectroscopy, which is revolutionizing our ability to characterize life remotely. Part 3 takes on an overarching issue for any effort to globalize biodiversity observations, the issue of scale. It addresses scale from two perspectives. The first is that of combining observations across varying spatial, temporal, and spectral resolutions for better understanding—that is, what scales and how. This is an area of ongoing research driven by a confluence of innovations in observation systems and rising computational capacity. The second is the organizational side of the scaling challenge. It explores existing frameworks for integrating multi-scale observations within global networks. The focus here is on what practical steps can be taken to organize multi-scale data and what is already happening in this regard. These frameworks include essential biodiversity variables and the Group on Earth Observations Biodiversity Observation Network (GEO BON). This book constitutes an end-to-end guide uniting the latest in research and techniques to cover the theory and practice of the remote sensing of plant biodiversity. In putting it together, the editors and their coauthors, all preeminent in their fields, have done a great service for those seeking to understand and conserve life on Earth—just when we need it most. For if the world is ever to construct a coordinated response to the planetwide crisis of biodiversity loss, it must first assemble adequate—and global—measures of what we are losing

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Sparsity-based restoration of SMOS images in the presence of outliers

International audienc

Australasian Groundwater Conference: Groundwater in a Changing World

© Copyright is retained by the author/s of each abstract.The Australasian Groundwater Conference (AGC) was held in Brisbane Queensland, 24-27 November 2019. This conference was an epic event filled with informative presentations, entertaining networking events and stunning field trips exploring the sights and sounds that this subtropical dynamic region has to offer. The AGC 2019 featured a stimulating technical program around the theme of “Groundwater in a Changing World” that covered a broad range of applications to resources, infrastructure and environment. The program included stimulating plenary speakers, engaging panel discussions and enticing social events. Over 600 groundwater researchers, industry professionals and policy development specialists from around the region attended this unique event. There were many opportunities on offer for delegates to share their experiences, inform best practice, and identify the steps they can take to bring about lasting improvements to the management of our vital groundwater resources. Our hard working volunteer organisational team wishes to thank sponsors, speakers, delegates, exhibitors and volunteers for making the conference such a huge success

Climate change 2013: the physical science basis

This report argues that it is extremely likely that human influence has been the dominant cause of the observed warming since the mid-20th century. This is an an unedited version of the Intergovernmental Panel on Climate Change\u27s Working Group I contribution to the Fifth Assessment Report following the release of its Summary for Policymakers on 27 September 2013.  The full Report is posted in the version distributed to governments on 7 June 2013 and accepted by Working Group I and the Panel on 27 September 2013. It includes the Technical Summary, 14 chapters and an Atlas of Global and Regional Climate Projections. Following copy-editing, layout, final checks for errors and adjustments for changes in the Summary for Policymakers, the full Report will be published online in January 2014 and in book form by Cambridge University Press a few months later