The chapters progress from foundational concepts to practical case studies in spatial data and GIS workflows to real-world applications, including air quality monitoring, water resource management, land-use analysis, biodiversity conservation, and disaster risk assessment.

With a strong focus on real-world implementation, the book bridges theory and practice by offering methodological insights, policy relevance, and data-driven strategies for sustainable environmental management.

Key Features:

-Integration of machine learning with GIS and spatial analysis.
-Coverage of major environmental challenges and applications.
-Real-world case studies for monitoring, prediction, and planning.
-Focus on decision support, policy insights, and sustainability.
-Practical approaches to data-driven environmental management.