Integrating GIS and Remote Sensing in Early Warning Systems for Real-Time Disaster Preparedness

This chapter per the authors explores how Geographic Information Systems (GIS) and Remote Sensing (RS) are transforming early warning systems for hydrometeorological disasters through real-time, data-driven approaches. It examines the integration of multi-source datasets topographic, climatic, and socio-economic to assess flood and drought risks with high spatial and temporal precision. The discussion highlights how cloud-based platforms such as Google Earth Engine and Copernicus Open Access Hub, combined with artificial intelligence and Internet of Things (IoT) networks, enable dynamic monitoring and predictive modeling of environmental hazards. Methodological frameworks including multi-criteria decision analysis, machine learning, and digital twin technologies are reviewed for their capacity to support adaptive management and policy decision-making. The chapter concludes by emphasizing the role of open data, interoperability, and ethical governance in advancing global disaster preparedness and climate resilience.