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Geomorphic Risk Reduction Using Geospatial Methods and Tools (eBook)

Artikelnummer: 978-981-9977-07-9
Einband: PDF
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This book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards.

In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion, among others. They cause huge loss of human property and lives. Especially in mountainous, coastal, arid and semi-arid regions, these natural hazards are the main barriers for economic development. Furthermore, human pressure and specific human actions such as deforestation, inappropriate land use and farming have increased the danger of natural disasters and degraded the natural environment, making it more difficult for environmental planners and policymakers to develop appropriate long-term sustainability plans. The most challenging task is to develop a sophisticated approach for continuous inspection and resolution of environmental problems for researchers and scientists. However, in the past several decades, geospatial technology has undergone dramatic advances, opening up new opportunities for handling environmental challenges in a more comprehensive manner.

With the help of geographic information system (GIS) tools, high and moderate resolution remote sensing information, such as visible imaging, synthetic aperture radar, global navigation satellite systems, light detection and ranging, Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine and others deliver state-of-the-art investigations in the identification of multiple natural hazards. For a thorough examination, advanced computer approaches focusing on cutting-edge data processing, machine learning and deep learning may be employed. To detect and manage various geomorphic hazards and their impact, several models with a specific emphasis on natural resources and the environment may be created.

accessibilitysupport@springernature.com
This book explores the use of advanced geospatial techniques in geomorphic hazards modelling and risk reduction. It also compares the accuracy of traditional statistical methods and advanced machine learning methods and addresses the different ways to reduce the impact of geomorphic hazards.

In recent years with the development of human infrastructures, geomorphic hazards are gradually increasing, which include landslides, flood and soil erosion, among others. They cause huge loss of human property and lives. Especially in mountainous, coastal, arid and semi-arid regions, these natural hazards are the main barriers for economic development. Furthermore, human pressure and specific human actions such as deforestation, inappropriate land use and farming have increased the danger of natural disasters and degraded the natural environment, making it more difficult for environmental planners and policymakers to develop appropriate long-term sustainability plans. The most challenging task is to develop a sophisticated approach for continuous inspection and resolution of environmental problems for researchers and scientists. However, in the past several decades, geospatial technology has undergone dramatic advances, opening up new opportunities for handling environmental challenges in a more comprehensive manner.

With the help of geographic information system (GIS) tools, high and moderate resolution remote sensing information, such as visible imaging, synthetic aperture radar, global navigation satellite systems, light detection and ranging, Quickbird, Worldview 3, LiDAR, SPOT 5, Google Earth Engine and others deliver state-of-the-art investigations in the identification of multiple natural hazards. For a thorough examination, advanced computer approaches focusing on cutting-edge data processing, machine learning and deep learning may be employed. To detect and manage various geomorphic hazards and their impact, several models with a specific emphasis on natural resources and the environment may be created.

accessibilitysupport@springernature.com
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VerlagSpringer Nature Singapore
EinbandPDF
Erscheinungsjahr2024
Seitenangabe325 S.
AusgabekennzeichenEnglisch
AbbildungenXVII, 325 p. 134 illus., 124 illus. in color.
Masse16'802 KB
PlattformPDF
ReiheDisaster Risk Reduction; Earth and Environmental Science; Earth and Environmental Science
AutorSarkar, Raju (Hrsg.) / Saha, Sunil (Hrsg.) / Adhikari, Basanta Raj (Hrsg.) / Shaw, Rajib (Hrsg.)

Alle Bände der Reihe "Disaster Risk Reduction; Earth and Environmental Science; Earth and Environmental Science (R0)"

Über den Autor Raju (Hrsg.) Sarkar

Prof. Raju Sarkar is a Professor of Civil Engineering at Delhi Technological University and Coordinator of the Centre of Excellence in Disaster Risk Reduction. He previously served at the Royal University of Bhutan, where he established a Disaster Risk Reduction research centre and initiated the Engineering Geology programme. He holds key leadership positions in IASPEI-IUGG and has extensive field experience in the Hindu-Kush Himalayas. His work focuses on geo-hazards risk management, landslides, seismology, construction engineering, and community resilience, with numerous publications and international research projects. Dr. Sunil Saha is an Assistant Professor of Geography at the University of Gour Banga, specializing in Environmental Geography. His research focuses on fluvial landforms, geo-environmental hazards (gully erosion, landslides, floods), and surface-subsurface hydrology and their spatio-temporal impacts on land use. With over eight years of teaching and research experience, he has been listed among the world's top 2% scientists (2021-2025) by Stanford University. He has supervised multiple Ph.D. and M.Phil. scholars and published over 75 research papers, 17 book chapters, and one book. Prof. Prateek Sharma is the Vice Chancellor of Delhi Technological University and an expert in Environmental Engineering. He holds a Ph.D. from IIT Delhi and has over 29 years of teaching and research experience. His research focuses on environmental systems modelling, air quality, statistical applications, and environmental risk assessment. He is a Fellow of the Wessex Institute (UK) and a member of the National Knowledge Network supporting the National Clean Air Programme (NCAP). Prof. Rajib Shaw, a Japanese national of Indian origin, is Professor at Keio University's Shonan Fujisawa Campus (SFC). He previously served as Executive Director of the Integrated Research on Disaster Risk (IRDR) programme and as Professor at Kyoto University. His expertise includes community-based disaster risk management, climate change adaptation, and disaster education. He has authored over 73 books and 450 academic publications and holds key advisory roles with the United Nations and Asian science bodies.

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