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Books in Earth surface processes

Covering erosion, sediment transport, landscape evolution, and hydrology, this collection provides in-depth insights into dynamic surface phenomena. It supports geoscientists, geomorphologists, and environmental engineers seeking to understand and manage Earth's surface changes. Featuring innovative research, field studies, and modelling approaches, these resources help address environmental challenges, natural hazards, and land use planning, fostering sustainable interactions with our planet’s surface environments.

  • Deep Learning for Synthetic Aperture Radar Remote Sensing

    • 1st Edition
    • Michael Schmitt + 1 more
    • English
    Deep Learning for Synthetic Aperture Radar Remote Sensing delves into the transformative synergy between synthetic aperture radar (SAR) and cutting-edge machine learning techniques. Traditionally rooted in signal processing, SAR's active imaging capabilities rise above optical limitations, offering resilience to environmental factors like cloud cover. This book showcases how machine learning augments every stage of SAR image processing, from raw data refinement to advanced information extraction. Through comprehensive coverage of acquisition modes and processing methodologies, including polarimetry and interferometry, this book equips readers with the tools to harness SAR's full potential. Aiming to further enhance remote sensing imaging, it serves as a vital resource for those seeking to integrate SAR data seamlessly into the modern machine learning landscape. Deep Learning for Synthetic Aperture Radar Remote Sensing addresses a critical gap in the intersection of SAR technology and machine learning, offering a pioneering roadmap for researchers and practitioners alike. With its emphasis on modern techniques, it serves as a catalyst for unlocking SAR's untapped potential and shaping the future of Earth observation.

  • Supervised Learning in Remote Sensing and Geospatial Science

    • 1st Edition
    • Aaron E Maxwell + 2 more
    • English
    Supervised Learning in Remote Sensing and Geospatial Science is an invaluable resource focusing on practical applications of supervised learning in remote sensing and geospatial data science. Emphasizing practicality, the book delves into creating labeled datasets for training and evaluating models. It addresses common challenges like data imbalance and offers methods for assessing model performance. This guide bridges the gap between theory and practice, providing tools and techniques for extracting actionable information from raw geospatial data.The book covers all aspects of supervised learning workflows, including preparing diverse remotely sensed and geospatial data inputs. It equips researchers, practitioners, and students with essential knowledge for applied mapping and modeling tasks, making it an indispensable reference for advancing geospatial science.

  • Geoheritage

    Assessment, Protection, and Management
    • 2nd Edition
    • Emmanuel Reynard + 1 more
    • English
    Geoheritage: Assessment, Protection, and Management, Second Edition provides a comprehensive exploration of geoheritage, beginning with an introduction to geodiversity and progressing to the characterisation of in situ and ex situ geoheritage, its protection and sustainable use. It also offers advanced concepts and methodologies for site assessment, mapping, conservation, visualisation and management, and features 12 case studies spanning five continents.Authored by 75 experts from 22 countries, this edition includes nearly 200 figures and maps. New chapters expand the scope of the first edition to address geoheritage’s links to biodiversity, climate change, natural hazards, ecosystem services, education and cities.This essential resource is perfect for geoscientists and students in the fields of geodiversity, geoheritage, geoconservation and geotourism, as well as professionals involved in nature conservation, protected areas and geoparks.

  • Spatial Autocorrelation

    A Fundamental Property of Geospatial Phenomena
    • 1st Edition
    • Daniel Griffith + 1 more
    • English
    Spatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a state-of-the-art exploration of one of the most pivotal spatial analysis concepts. Beginning with foundational theories and clear definitions, it sets out the concepts and basic theory of spatial autocorrelation through elaborated conceptualizations and practical examples. In-depth case studies reveal the pervasive influence of spatial patterns in scientific inquiry while anticipating emerging research and innovative techniques. It offers practical methods for quantifying spatial autocorrelation, complete with step-by-step instructions and real-world examples.Spatial Autocorrelation equips graduate students, researchers, and professionals with the knowledge and tools to confidently comprehend, navigate, and apply spatial analysis in their respective domains, making it an ideal companion for technical reference books, and a vital addition to the libraries of any discipline utilizing spatial analysis.

  • Machine Learning in Geohazard Risk Prediction and Assessment

    From Microscale Analysis to Regional Mapping
    • 1st Edition
    • Biswajeet Pradhan + 2 more
    • English
    Machine Learning in Geohazard Risk Prediction and Assessment: From Microscale Analysis to Regional Mapping presents an overview of the most recent developments in machine learning techniques that have reshaped our understanding of geo-materials and management protocols of geo-risk. The book covers a broad category of research on machine-learning techniques that can be applied, from microscopic modeling to constitutive modeling, to physics-based numerical modeling, to regional susceptibility mapping. This is a good reference for researchers, academicians, graduate and undergraduate students, professionals, and practitioners in the field of geotechnical engineering and applied geology.

  • Carbon Fluxes and Biophysical Variables from Earth Observation

    Methods for Ecosystem Assessment
    • 1st Edition
    • Manuel Campos-Taberner + 2 more
    • English
    Carbon Fluxes and Biophysical Variables from Earth Observation: Methods for Ecosystem Assessment transforms the way remote sensing data can be used to approach monitoring of carbon fluxes (CF) and biophysical variables (BV) in ecosystem and global vegetation monitoring. In a field where these two subjects have traditionally been treated as distinct entities, this book offers an integrated exploration of CF and BV retrieval through remote sensing. It not only delves into a wide array of approaches and methodologies but also assists readers in selecting the most suitable models based on available inputs and spatiotemporal scales. Carbon Fluxes and Biophysical Variables from Earth Observation is a useful resource for Earth Observation specialists, particularly in Remote Sensing, machine learning, ecology, and plant physiology, to enhance and adapt their approaches and methodologies.

  • Google Earth Engine and Artificial Intelligence for Earth Observation

    Algorithms and Sustainable Applications
    • 1st Edition
    • Vishakha Sood + 3 more
    • English
    Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications explores a wide range of transformative data fusion techniques of Artificial Intelligence (AI) technologies applied to Google Earth Engine (GEE) techniques. It includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. This book delves into the challenges of AI-driven tools and technologies for Earth observation data analysis, offering possible solutions and directly addressing current and upcoming needs within Earth observation. Google Earth Engine and Artificial Intelligence for Earth Observation: Algorithms and Sustainable Applications is a useful reference for geospatial scientists, remote sensing experts, and environmental scientists utilizing remote sensing to apply the latest AI techniques to data obtained from GEE for their research and teaching.

  • Deep Learning for Multi-Sensor Earth Observation

    • 1st Edition
    • Sudipan Saha
    • English
    Deep Learning for Multi-Sensor Earth Observation addresses the need for transformative Deep Learning techniques to navigate the complexity of multi-sensor data fusion. With insights drawn from the frontiers of remote sensing technology and AI advancements, it covers the potential of fusing data of varying spatial, spectral, and temporal dimensions from both active and passive sensors. This book offers a concise, yet comprehensive, resource, addressing the challenges of data integration and uncertainty quantification from foundational concepts to advanced applications. Case studies illustrate the practicality of deep learning techniques, while cutting-edge approaches such as self-supervised learning, graph neural networks, and foundation models chart a course for future development.Structur... for clarity, the book builds upon its own concepts, leading readers through introductory explanations, sensor-specific insights, and ultimately to advanced concepts and specialized applications. By bridging the gap between theory and practice, this volume equips researchers, geoscientists, and enthusiasts with the knowledge to reshape Earth observation through the dynamic lens of deep learning.

  • Digital Terrain Analysis, Third Edition

    • 3rd Edition
    • Igor Florinsky
    • English
    Digital Terrain Analysis, Third Edition synthesizes knowledge on methods and applications of digital terrain analysis and geomorphometry in the context of multi-scale problems in soil science, geology, and polar research. Divided into four parts, the book examines the main concepts, principles, and methods of digital terrain modeling, methods for analysis, modeling, and mapping of spatial distribution of soil properties, techniques for recognition, analysis, and interpretation of topographically manifested geological features, and finally, polar research. This new release provides a theoretical and methodological basis for understanding and applying geographical modeling techniques.

  • Probabilistic Tsunami Hazard and Risk Analysis

    Towards Disaster Risk Reduction and Resilience
    • 1st Edition
    • Katsuichiro Goda + 3 more
    • English
    Probabilistic Tsunami Hazard and Risk Analysis: Towards Disaster Risk Reduction and Resilience covers recent calls for advances in quantitative tsunami hazard and risk analyses for the synthesis of broad knowledge basis and solid understanding of interdisciplinary fields, spanning seismology, tsunami science, and coastal engineering. These new approaches are essential for enhanced disaster resilience of society under multiple hazards and changing climate as tsunamis can cause catastrophic loss to coastal cities and communities globally.This is a low-probability high-consequence event, and it is not easy to develop effective disaster risk reduction measures. In particular, uncertainties associated with tsunami hazards and risks are large. The knowledge and skills for quantitative probabilistic tsunami hazard and risk assessments are in high demand and are required in various related fields, including disaster risk management (governments and local communities), and the insurance and reinsurance industry (catastrophe model).

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