Remote Sensing and Machine Learning in Conservation - by Alireza Sharifi (Paperback)

Book Synopsis

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.