Download or read book Leveraging High resolution Imagery and New Technologies in Machine Learning to Map Forest Disturbances written by Sarah Wegmueller and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Remotely sensed imagery, satellite and airborne, has great potential to provide increased monitoring and mapping capabilities for applications in forest health and management. In this dissertation, I investigated ways to leverage newly available satellite imagery, machine learning techniques, and recently collected ground data to develop new methods for monitoring and mapping forest health at various scales and for a range of purposes. The efforts my collaborators and I resulted in two new software programs, named Astrape and Tree Condition and Analysis Program (TreeCAP), that collectively map disturbances ranging from large, stand-replacing derechos to individual tree mortality in a mixed forest with accuracies typically over 80%. Both of these systems were designed to be scaled up for operational use across the contiguous US, and maybe internationally. Astrape is capable of using nearly any imagery source but was designed with Sentinel-2 imagery and Dove imagery. It implements a machine learning framework to produce thematic maps of damage severity in four classes (high severity, moderate severity, low severity, and little to no damage) with limited need for ground-truthing. TreeCAP was built to leverage the National Agricultural Imagery Program (NAIP) data that has a spatial resolution of 0.6-1 m, suitable for differentiating individual trees. TreeCAP uses a machine learning model to create thematic maps of healthy, morbid, and dead trees with high accuracy. Further, I pioneered a vital method to normalize the highly radiometrically variable Dove data called LOESS Radiometric Correction for Contiguous Scenes (LORACCS). The output of LORACCS can be used to create seamless Dove imagery mosaics that can then be used with the aforementioned systems, greatly expanding their temporal and spatial potential. Finally, I conducted a reinvestigating of oak wilt spread in Wisconsin using a time series of NAIP imagery and ground-confirmed incidents (courtesy of the Wisconsin Department of Natural Resources Forest Health Team). The results of this study indicate that oak wilt may be far more prevalent on the landscape than is currently known, highlighting the value of using remote sensing to better understand the patterns of insects and disease regionally.
Download or read book Leveraging Multi Sensor Time Series Datasets to Map Short and Long Term Forest Disturbances and Drivers of Change in the Colombian Andes written by Paulo Jose Murillo Sandoval and published by . This book was released on 2017 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: The spatial distribution of forest disturbance is commonly calculated using a satellite imagery-driven bi- or tri-temporal change analysis. Working in Colombia’s Cordillera de los Picachos National Natural Park – a region of consistent cloud cover and dramatic topographic relief – a change assessment with such infrequent observations cannot capture long-term trends of vegetative decline (browning) or improvement (greening) nor the drivers associated with these changes. In recognition of the importance of spatio-temporally explicit information for assessing the effects of socio-environmental change and conservation strategy implementation, I developed a rigorous assessment of vegetation change using MODIS and Landsat time-series data and the Breaks For Additive Season and Trend (BFAST) algorithm to identify the timing, trends, and locations of change as well the associated drivers. First, I measured long-term vegetation trends from 2001-2015 using a Moderate Resolution Imaging Spectroradiometer (MODIS)-based 250m resolution Multi-Angle Implementation of Atmospheric Correction (MAIAC) time-series, and mapped short-term disturbances using all available Landsat images (149 dates from Landsat 5, 7, and 8). BFAST trends based on MAIAC data indicate a net greening in 6% of the park, with a net browning trend of 2.5% in the 10km-wide region surrounding the park. I also identified a 12,500 ha area within Picachos (4% of the park’s total area) that experienced a consecutive vegetative decline or browning during every year of study, a result corroborated with a BFAST Monitor assessment using finer 30m resolution Landsat data. With Landsat, I recorded 12,642 ha (±1440) of disturbed forest within the park at high spatial and temporal accuracy. Spatially, Landsat results had user’s and producer’s accuracies of 0.95±0.02 and 0.83±0.18, respectively. Temporally, a TimeSync-supported temporal validation assessment showed that 75% of Landsat-detected dates of disturbance events were accurate within ± 6 months. With disturbances identified, I characterized disturbances within Picachos’ southeastern foothills and associated drivers using a set of metrics related to the spectral, pattern and trend properties of disturbance patches derived from Landsat time-series data (1996-2015). A training dataset was initially developed to identify drivers of disturbances using Corine Land Cover maps and high-resolution imagery. A Random Forests classifier was used to attribute disturbances to specific drivers of forest cover change: conversion to pasture, conversion to subsistence agriculture, and non-stand replacing disturbance (i.e., thinning). Attribution of changes had high accuracy at patch and area levels with 1-5% commission and 2-14% omission errors, respectively, for regions that were converted to pasture or experienced thinning. Lower agreement was found for agricultural conversion with 43% omission and 9% commission errors. I found that conversion to pasture is the main cause of forest cover loss within Picachos at 9901 ha (±72) corresponding to 14.7% of Picachos’ foothills, and that subtle forest alteration contributed to 1327 ha (±92) of forest degradation. Recognizing the diversity of pressures facing conservation strategy implementation in the region, these results have direct relevance for anticipating future land use pressures within Colombia, as well as across similar regions in the Andes-Amazon transition area. Indeed, since these results reveal the possibility to uncover historical disturbances related to human-incursion in protected landscapes, the methods are well suited to enhancing landscape planning particularly where biodiversity richness is quickly diminishing due to anthropogenic presence.
Download or read book Grasslands and Climate Change written by David J. Gibson and published by Cambridge University Press. This book was released on 2019-03-21 with total page 363 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive assessment of the effects of climate change on global grasslands and the mitigating role that ecologists can play.
Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Download or read book Deep Learning for the Earth Sciences written by Gustau Camps-Valls and published by John Wiley & Sons. This book was released on 2021-08-18 with total page 436 pages. Available in PDF, EPUB and Kindle. Book excerpt: DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
Download or read book Remote Sensing Imagery written by Florence Tupin and published by John Wiley & Sons. This book was released on 2014-02-19 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dedicated to remote sensing images, from their acquisition to their use in various applications, this book covers the global lifecycle of images, including sensors and acquisition systems, applications such as movement monitoring or data assimilation, and image and data processing. It is organized in three main parts. The first part presents technological information about remote sensing (choice of satellite orbit and sensors) and elements of physics related to sensing (optics and microwave propagation). The second part presents image processing algorithms and their specificities for radar or optical, multi and hyper-spectral images. The final part is devoted to applications: change detection and analysis of time series, elevation measurement, displacement measurement and data assimilation. Offering a comprehensive survey of the domain of remote sensing imagery with a multi-disciplinary approach, this book is suitable for graduate students and engineers, with backgrounds either in computer science and applied math (signal and image processing) or geo-physics. About the Authors Florence Tupin is Professor at Telecom ParisTech, France. Her research interests include remote sensing imagery, image analysis and interpretation, three-dimensional reconstruction, and synthetic aperture radar, especially for urban remote sensing applications. Jordi Inglada works at the Centre National d’Études Spatiales (French Space Agency), Toulouse, France, in the field of remote sensing image processing at the CESBIO laboratory. He is in charge of the development of image processing algorithms for the operational exploitation of Earth observation images, mainly in the field of multi-temporal image analysis for land use and cover change. Jean-Marie Nicolas is Professor at Telecom ParisTech in the Signal and Imaging department. His research interests include the modeling and processing of synthetic aperture radar images.
Download or read book Machine Learning written by Kevin P. Murphy and published by MIT Press. This book was released on 2012-08-24 with total page 1102 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.
Download or read book Google Earth Engine Applications written by Lalit Kumar and published by MDPI. This book was released on 2019-04-23 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: In a rapidly changing world, there is an ever-increasing need to monitor the Earth’s resources and manage it sustainably for future generations. Earth observation from satellites is critical to provide information required for informed and timely decision making in this regard. Satellite-based earth observation has advanced rapidly over the last 50 years, and there is a plethora of satellite sensors imaging the Earth at finer spatial and spectral resolutions as well as high temporal resolutions. The amount of data available for any single location on the Earth is now at the petabyte-scale. An ever-increasing capacity and computing power is needed to handle such large datasets. The Google Earth Engine (GEE) is a cloud-based computing platform that was established by Google to support such data processing. This facility allows for the storage, processing and analysis of spatial data using centralized high-power computing resources, allowing scientists, researchers, hobbyists and anyone else interested in such fields to mine this data and understand the changes occurring on the Earth’s surface. This book presents research that applies the Google Earth Engine in mining, storing, retrieving and processing spatial data for a variety of applications that include vegetation monitoring, cropland mapping, ecosystem assessment, and gross primary productivity, among others. Datasets used range from coarse spatial resolution data, such as MODIS, to medium resolution datasets (Worldview -2), and the studies cover the entire globe at varying spatial and temporal scales.
Download or read book Medical Imaging Informatics written by Alex A.T. Bui and published by Springer Science & Business Media. This book was released on 2009-12-01 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Imaging Informatics provides an overview of this growing discipline, which stems from an intersection of biomedical informatics, medical imaging, computer science and medicine. Supporting two complementary views, this volume explores the fundamental technologies and algorithms that comprise this field, as well as the application of medical imaging informatics to subsequently improve healthcare research. Clearly written in a four part structure, this introduction follows natural healthcare processes, illustrating the roles of data collection and standardization, context extraction and modeling, and medical decision making tools and applications. Medical Imaging Informatics identifies core concepts within the field, explores research challenges that drive development, and includes current state-of-the-art methods and strategies.
Download or read book The Routledge Companion to Management Information Systems written by Robert D. Galliers and published by Routledge. This book was released on 2017-08-15 with total page 691 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of Information Systems has been evolving since the first application of computers in organizations in the early 1950s. Focusing on information systems analysis and design up to and including the 1980s, the field has expanded enormously, with our assumptions about information and knowledge being challenged, along with both intended and unintended consequences of information technology. This prestige reference work offers students and researchers a critical reflection on major topics and current scholarship in the evolving field of Information Systems. This single-volume survey of the field is organized into four parts. The first section deals with Disciplinary and Methodological Foundations. The second section deals with Development, Adoption and Use of MIS – topics that formed the centrepiece of the field of IS in the last century. The third section deals with Managing Organizational IS, Knowledge and Innovation, while the final section considers emerging and continuing issues and controversies in the field – IS in Society and a Global Context. Each chapter provides a balanced overview of current knowledge, identifying issues and discussing relevant debates. This prestigious book is required reading for any student or researcher in Management Information Systems, academics and students covering the breadth of the field, and established researchers seeking a single-volume repository on the current state of knowledge, current debates and relevant literature.
Download or read book Deep Learning for Medical Image Analysis written by S. Kevin Zhou and published by Academic Press. This book was released on 2023-12-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis. · Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache
Download or read book MATLAB for Machine Learning written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2017-08-28 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.
Download or read book Advanced Remote Sensing written by Shunlin Liang and published by Academic Press. This book was released on 2012-12-06 with total page 821 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Remote Sensing is an application-based reference that provides a single source of mathematical concepts necessary for remote sensing data gathering and assimilation. It presents state-of-the-art techniques for estimating land surface variables from a variety of data types, including optical sensors such as RADAR and LIDAR. Scientists in a number of different fields including geography, geology, atmospheric science, environmental science, planetary science and ecology will have access to critically-important data extraction techniques and their virtually unlimited applications. While rigorous enough for the most experienced of scientists, the techniques are well designed and integrated, making the book’s content intuitive, clearly presented, and practical in its implementation. Comprehensive overview of various practical methods and algorithms Detailed description of the principles and procedures of the state-of-the-art algorithms Real-world case studies open several chapters More than 500 full-color figures and tables Edited by top remote sensing experts with contributions from authors across the geosciences
Download or read book Advances in Unmanned Aerial Vehicles written by Kimon P. Valavanis and published by Springer Science & Business Media. This book was released on 2008-02-26 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen tremendous interest in the production and refinement of unmanned aerial vehicles, both fixed-wing, such as airplanes and rotary-wing, such as helicopters and vertical takeoff and landing vehicles. This book provides a diversified survey of research and development on small and miniature unmanned aerial vehicles of both fixed and rotary wing designs. From historical background to proposed new applications, this is the most comprehensive reference yet.
Download or read book Invasive Species in Forests and Rangelands of the United States written by Therese M. Poland and published by Springer Nature. This book was released on 2021-02-01 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book describes the serious threat of invasive species to native ecosystems. Invasive species have caused and will continue to cause enormous ecological and economic damage with ever increasing world trade. This multi-disciplinary book, written by over 100 national experts, presents the latest research on a wide range of natural science and social science fields that explore the ecology, impacts, and practical tools for management of invasive species. It covers species of all taxonomic groups from insects and pathogens, to plants, vertebrates, and aquatic organisms that impact a diversity of habitats in forests, rangelands and grasslands of the United States. It is well-illustrated, provides summaries of the most important invasive species and issues impacting all regions of the country, and includes a comprehensive primary reference list for each topic. This scientific synthesis provides the cultural, economic, scientific and social context for addressing environmental challenges posed by invasive species and will be a valuable resource for scholars, policy makers, natural resource managers and practitioners.
Download or read book Artificial Intelligence in Asset Management written by Söhnke M. Bartram and published by CFA Institute Research Foundation. This book was released on 2020-08-28 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence (AI) has grown in presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and return forecasts and more complex constraints. Trading algorithms use AI to devise novel trading signals and execute trades with lower transaction costs. AI also improves risk modeling and forecasting by generating insights from new data sources. Finally, robo-advisors owe a large part of their success to AI techniques. Yet the use of AI can also create new risks and challenges, such as those resulting from model opacity, complexity, and reliance on data integrity.
Download or read book Medical Image Analysis written by Alejandro Frangi and published by Academic Press. This book was released on 2023-09-20 with total page 700 pages. Available in PDF, EPUB and Kindle. Book excerpt: Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. Provides an authoritative description of key concepts and methods Includes tutorial-based sections that clearly explain principles and their application to different medical domains Presents a representative selection of topics to match a modern and relevant approach to medical image computing
