In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies.

In recent decades, remote sensing technology has been incorporated in numerous mineral exploration projects in metallogenic provinces around the world. Multispectral and hyperspectral sensors play a significant role in affording unique data for mineral exploration and environmental hazard monitoring. This book covers the advances of remote sensing data processing algorithms in mineral exploration, and the technology can be used in monitoring and decision-making in relation to environmental mining hazard. This book presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling, offering excellent information to professional earth scientists, researchers, mineral exploration communities and mining companies.

Geospatial Analysis Applied to Mineral Exploration: Remote Sensing, GIS, Geochemical, and Geophysical Applications to Mineral Resources presents state-of-the-art approaches on recent remote sensing and GIS-based mineral prospectivity modeling for Earth scientists, researchers, mineral exploration communities and mining companies. This book will help readers solve high complexity issues in remote sensing data processing, geochemical data analysis, geophysical data analysis, and appropriate applications of GIS techniques for data fusion designed for mineral exploration purposes. It contains updated knowledge of remote sensing imagery, geochemistry, geophysics and geospatial techniques that can assist in delineating the signatures and patterns linked to deep-seated, covered, blind or buried mineral deposits. - Covers advances in remote sensing data processing algorithms and geochemical data analysis - Includes sections on geophysical data analysis and machine learning algorithms for mineral exploration - Introduces the suite of geo-spatial tools currently available for mineral exploration - Presents case studies to provide real-world examples of the theories covered

This book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of “target detection→scene classification→semantic segmentation." Taking China’s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research.

Remote sensing and Geographic Information Systems (GIS) have become indispensable tools for understanding our planet's complex systems and addressing a wide range of environmental, social, and economic challenges. With several environmental and social factors coming to a head, it is important that our society utilizes every tool at its disposal in order to identify these issues and make comprehensive action plans in order to avoid negative social and environmental consequences. Applying Remote Sensing and GIS for Spatial Analysis and Decision-Making offers a comprehensive exploration of the applications of remote sensing, Geographic Information Systems (GIS), and emerging technologies in spatial analysis and decision-making across various domains. The book explores fundamental principles, methodologies, and advanced techniques pertinent to remote sensing and GIS, while also discussing the integration of emerging technologies such as unmanned aerial vehicles (UAVs), hyperspectral imaging, LiDAR, machine learning, and artificial intelligence (AI). Covering topics such as climate change modeling, land resources, and spatial data analysis, this book is an excellent resource for researchers and academicians, urban planners, practitioners, professionals, policy makers, postgraduate students and educators, and more.

Hyperspectral Remote Sensing: Theory and Applications offers the latest information on the techniques, advances and wide-ranging applications of hyperspectral remote sensing, such as forestry, agriculture, water resources, soil and geology, among others. The book also presents hyperspectral data integration with other sources, such as LiDAR, Multi-spectral data, and other remote sensing techniques. Researchers who use this resource will be able to understand and implement the technology and data in their respective fields. As such, it is a valuable reference for researchers and data analysts in remote sensing and Earth Observation fields and those in ecology, agriculture, hydrology and geology. - Includes the theory of hyperspectral remote sensing, along with techniques and applications across a variety of disciplines - Presents the processing, methods and techniques utilized for hyperspectral remote sensing and in-situ data collection - Provides an overview of the state-of-the-art, including algorithms, techniques and case studies

In summary, the future of topographic measurement technologies holds immense potential. As researchers, practitioners, and technologists continue to push the boundaries of what is possible, we anticipate the development of even more sophisticated tools and methods. Such advancements will not only enhance our ability to understand and map the terrestrial environment but will also contribute significantly to sustainable development goals worldwide. The journey is ongoing, and it invites active participation from the broader academic and professional community to ensure that the evolution of topographic measurement continues to align with the needs of society.

Land management issues, such as mapping tree species, recognizing invasive plants, and identifying key geologic features, require an understanding of complex technical issues before the best decisions can be made. Hyperspectral remote sensing is one the technologies that can help with reliable detection and identification. Presenting the fundamenta

Geological processes affect the earth itself and human society. Solutions to geological problems, whether natural or man-made, demand close international collaboration. This book presents new approaches to current problems of environmental assessment, demonstrates the interactions between those involved in addressing global problems, and represents a means for the education of others. The book focuses on four major themes: geoenvironmental models, GIS methods and techniques, assessment and resource management, and resource policies and sustainable development. The major topics falling under each theme are introduced, followed by discussions of specific applications. Reports of the discussions of working groups are also presented to round out the individual contributions. The disciplines represented include geology, geophysics, geochemistry, remote sensing, economics, biology, mining engineering, resource analysis, mathematics and statistics.