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Remote Sensing and Machine Learning in Conservation: Applications and Techniques de Alireza Sharifi

Remote Sensing and Machine Learning in Conservation: Applications and Techniques

Autor Alireza Sharifi
en Limba Engleză Paperback – mar 2027
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 conservation
  • Provides practical examples for how these technologies are used to address key conservation challenges
  • Delivers step-by-step guidance on how to implement these technologies, including funding and policy recommendations
  • Outlines the potential applications of these technologies in different conservation settings, from protected areas to urban environments
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Specificații

ISBN-13: 9780443445422
ISBN-10: 0443445427
Pagini: 305
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE

Cuprins

  1. Introduction
  2. Overview of Remote Sensing Techniques
  3. Overview of Machine Learning and Deep Learning Techniques
  4. Integrating Remote Sensing and Machine Learning for Conservation
  5. Applications of Remote Sensing and Machine Learning in Conservation
  6. Wildlife Monitoring and Conservation with Remote Sensing, Sensor Networks, and Machine Learning
  7. Conservation Challenges and Technology-Based Solutions
  8. Practical Case Studies and Project Design in Conservation GeoAI
  9. Cloud-Based Platforms, Open Data, and Reproducible Conservation Workflows
  10. Policy, Governance, Ethics, and Responsible Use of Conservation Technologies
  11. Future Directions in Remote Sensing, Machine Learning, and Conservation Intelligence

Notă biografică

Dr. Alireza Sharifi is an Associate Professor of Remote Sensing at Shahid Beheshti University. His work focuses on remote sensing, geospatial analysis, and machine learning for environmental monitoring, ecosystem assessment, and sustainable resource management. He received his Ph.D. in Remote Sensing Engineering from the University of Tehran, where his doctoral research examined forest biomass modeling using multi-polarized SAR imagery. His earlier graduate research addressed hyperspectral image classification through spectral-signature analysis.

His research interests include GeoAI, deep learning, hyperspectral and multispectral image analysis, SAR applications, anomaly detection, land-cover mapping, crop monitoring, vegetation-health assessment, flood and drought monitoring, environmental-pollution mapping, forest-biomass estimation, and precision agriculture. His experience with optical, radar, and hyperspectral data provides the methodological foundation for the book's practical and scientifically grounded treatment of conservation, ecosystem monitoring, and sustainable environmental management.