Quantitative Geomorphology in the Artificial Intelligence Era: Applications of AI for Earth and Environmental Change focuses on bridging the gaps in this emerging discipline, it delves into the complex interplay between landforms and the processes that shape them, offering innovative solutions through AI and data-driven methods. The book addresses the standards, quality assessment of data, spatial and temporal analysis tools, and rigorous validation techniques in geomorphology. It uses computational intelligence as a pivotal tool alongside GIS, remote sensing, and other advanced technologies. Readers will find a holistic resource that fosters collaboration and knowledge exchange among geological fields, aiming to address geomorphological challenges, hazards, and solutions. By harnessing AI, GIS, remote sensing, machine learning, and geophysical techniques, it offers new dimensions to existing assessment methods and techniques.
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.
Spatial Autocorrelation: A Fundamental Property of Geospatial Phenomena offers a comprehensive exploration of one of the most critical concepts in spatial analysis. Beginning with foundational theories and clear definitions, this book thoroughly sets out the concepts and theory of spatial autocorrelation through detailed conceptualisation and practical examples. The detailed case studies illustrate the pervasive influence of spatial patterns in scientific inquiry, with an eye toward future research and innovative techniques. It provides practical methodologies for quantifying spatial autocorrelation, complete with step-by-step guidance and real-world applications. Spatial Autocorrelation equips graduate students, researchers, and professionals with the knowledge and tools to confidently navigate and apply spatial analysis in their respective domains, making it a vital addition to a number of disciplines that utilise spatial analysis.
Geoheritage: Assessment, Protection, and Management covers the breadth of geoheritage including geodiversity, geoconservation, geotourism and geoparks; it also explores the relationship of geoheritage to landscapes, conservation, and tourism. The book includes methodologies for assessment, mapping, and visualization; along with 12 geographically varied case studies, and ~200 figures and maps of some of the most important global geosites. This second edition significantly expands the previous coverage and adds new chapters on relationships of geoheritage and geodiversity with biodiversity, climate change, natural hazards, ecosystem services, education and urbanization. Geoheritage Second Edition is an essential resource for geoscientists and students working in the fields of geoheritage, geosites, geomorphosites, geoconservation, and geotourism.
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. The book includes a wide range of scientific domains that can utilize remote sensing and geographic information systems (GIS) through detailed case studies. It 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.This 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 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.Structured 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 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 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).
Climate and Anthropogenic Impacts on Earth Surface Processes in the Anthropocene outlines our current understanding of the effects of ongoing and accelerated environmental changes on Earth surface processes and details the systematic and quantitative methodology on the actual drivers of these processes. This book covers various geomorphological process domains and a wide range of terrestrial surface environments on Earth. It provides a broad spectrum of advanced techniques and methods of data collection and generation, together with various approaches and methods of data analysis and geomorphologic modelling.This book is a valuable resource for upper-level undergraduates, graduates, and academics studying Earth surface processes, as well as researchers and professionals in needing a comprehensive overview of Earth surface process change and influence during the Anthropocene
Applications of Geospatial Technology and Modeling for River Basin Management, Volume Twelve covers the use of multi-temporal satellite data for accurate estimations of different watershed features. It includes methods and case studies of the use of geographic information systems (GIS) as a valuable tool for criteria-based spatial analysis to manage natural resources and accurately simulate natural phenomena such as the hydrologic response of a watershed to precipitation and susceptibility to water erosion. The book also provides direction on many types of modelling and mapping techniques in geospatial environments based on river basin management challenges. This book will be a useful guide for academics, researchers, and practitioners involved in the use of geospatial technologies for river basin management, as well as those interested in environmental management and Earth surface geomorphology.