This Special Issue is devoted to the cutting-edge field of advanced remote sensing methods applied to monitor and analyze the intricate impacts of natural disasters on land cover and land use processes. In an era marked by an increasing severity of disasters, such as hurricanes, floods, wildfires, extreme droughts, and landslides, leveraging technological advancements becomes paramount in understanding, assessing, and responding to the dynamic changes in terrestrial ecosystems. The published papers would promote innovative remote sensing techniques, including satellite passive optical and infrared imagery, including hyperspectral, as well as active remote sensing by lidars and radars. These methods offer a comprehensive view of the immediate and long-term effects of natural disasters, using the visible spectrum and beyond to assess alterations in vegetation health, soil composition, built infrastructure and topographical features. The research papers will not only investigate the physical transformations of land cover caused by natural disasters but also delve into the secondary consequences, such as habitat loss, altered hydrological patterns, and changes in land-use practices. The published studies would demonstrate the integration of machine learning algorithms and artificial intelligence in processing Big Data, enabling more efficient and accurate detection of post-disaster changes. This would showcase how advanced remote sensing methods empower us to monitor, analyze, and provide information for decision-makers to respond to the complex impacts of natural disasters on land cover and land use. The ultimate goal of this research is to enhance our ability to monitor recovery processes and inform resilient land-use planning. The research outcome would provide decision-makers concise information to enable timely response to the complex impacts of natural disasters on land cover and land use and contribute to sustainable ecosystem management in disaster-prone regions.
Manuscript submission deadline: 31 December 2024
For more information please visit: https://www.recentadvancesin.com/remote-sensing/research-topics/advanced...
