Alireza Sharifi – författare

In Remote Sensing and Machine Learning in Conservation: Applications and Techniques, readers will discover how cutting-edge technology is transforming our understanding of the natural world. This book explores how remote sensing and machine learning are being used to monitor wildlife populations, map critical habitats, predict the effects of climate change, and identify poaching hotspots. From the analysis of satellite imagery to the use of drones and artificial intelligence, this book provides a comprehensive overview of the latest tools and techniques available to conservationists. Written by a renowned expert in spatial information research, this book introduces readers to the exciting new possibilities opened by the combination of remote sensing and machine learning. Early chapters provide an overview of both emerging technologies and their integration into habitat mapping, species distribution, environmental monitoring, and climate change adaptation. Central chapters explore real-world applications and case studies for combating poaching, managing urban expansion, controlling invasive species, and more. Final chapters explore the funding and policy landscape and conclude with a reflection on the role of these technologies in transforming conservation practices. By equipping readers with an overview of these technologies, case studies of their real-world applications, and sample code, datasets, and open-source tools, Remote Sensing and Machine Learning in Conservation: Applications and Techniques promotes the innovative use of these technologies to address global environmental challenges. This is an essential resource for conservationists, resource managers, environmental scientists, and academics studying novel technological applications to conservation issues.Summarizes the latest advances in remote sensing and machine learning techniques for conservationProvides practical examples for how these technologies are used to address key conservation challengesDelivers step-by-step guidance on how to implement these technologies, including funding and policy recommendationsOutlines the potential applications of these technologies in different conservation settings, from protected areas to urban environments

Fundamentals and Advances in Remote Sensing: From Principles to AI Applications is an essential scientific reference for researchers, students, and professionals in geospatial sciences. It addresses the need to combine traditional remote sensing principles with modern AI-driven methods. Covering sensor technologies, image calibration, classification, semantic segmentation, InSAR, and data fusion, the book offers both theoretical insights and practical workflows. It features open-source code, real-world case studies, and visual aids like flow diagrams and comparison tables to enhance understanding. The structured chapters facilitate easy navigation and application, empowering users to deploy advanced techniques confidently in environmental monitoring, urban planning, and hazard assessment. By bridging fundamental concepts with cutting-edge tools, this volume supports innovative research and operational solutions to pressing societal and environmental challenges, making it a vital resource for advancing remote sensing science and applications.Offers open-source workflows using Python and Google Earth Engine for practical remote sensing applicationsDemonstrates real-world case studies across diverse sensors including optical, SAR, and UAV dataUtilizes a modular, template-based chapter structure to enhance learning and cross-referencingBridges traditional remote sensing techniques with cutting-edge AI-driven methods like semantic segmentation and InSARIntegrates visual elements such as flow diagrams, step-by-step boxes, comparison tables, and high-resolution figures for clarity