Remote Sensing and Machine Learning in Conservation

Applications and Techniques
Erscheinungsjahr 2027
ISBN: 978-0-443-44542-2
Verlag: Elsevier Science

Buch, Englisch, 305 Seiten, Format (B × H): 152 mm x 229 mm, Gewicht: 449 g

Remote Sensing and Machine Learning in Conservation: Applications and Techniques presents a structured, decision-oriented guide to the responsible use of Earth observation, geospatial analysis, and artificial intelligence in biodiversity conservation and ecosystem management. It connects conservation questions with appropriate evidence, data preparation, model development and validation, uncertainty communication, ecological interpretation, and decision-ready outputs.

Beginning with the foundations of remote sensing, it explains major machine-learning and deep-learning approaches, from supervised classification to Vision Transformers and reinforcement learning. Chapters then show how remote sensing and GeoAI support habitat mapping, species distribution modeling, biodiversity assessment, ecosystem-change detection, water and coastal conservation, wildlife monitoring, climate adaptation, invasive-species detection, restoration planning, and threat monitoring.

Dedicated chapters translate theory into practice through project design, cloud-based platforms, open data, reproducible workflows, validation, dashboards, and operational monitoring, and address data governance, privacy, bias, accountability, Indigenous and local knowledge, participation, and responsible use. The closing chapter examines foundation models, multimodal GeoAI, ecosystem digital twins, real-time and edge AI, participatory monitoring, and future research priorities, helping readers move from tool-driven analysis toward scientifically grounded and actionable conservation evidence.

Autoren/Hrsg.

Sharifi, Alireza

Fachgebiete

• Geowissenschaften Umweltwissenschaften Artenschutz
• Naturwissenschaften Agrarwissenschaften Tierhaltung
• Geowissenschaften Umweltwissenschaften Umweltschutz, Umwelterhaltung
• Naturwissenschaften Biowissenschaften Tierkunde / Zoologie
• Geowissenschaften Umweltwissenschaften Biodiversität

Inhaltsverzeichnis

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

Über Autor(innen)

Sharifi, Alireza
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.