Ground Deformation Source Modeling Using Remote Sensing Techniques

Dear Colleagues,

The application of remote sensing technology has greatly changed the access to surface data and greatly improved the accuracy of data. The development of remote sensing technology provides a new technical means for the research of earth science and engineering geology. However, how to combine remote sensing technology with ground deformation research to effectively improve the accuracy and timeliness of stability evaluation model is still a problem at the exploratory stage and needs to be solved.

This topic aims to improve the application prospect of remote sensing technology, summarize the frontier scientific achievements in ground deformation and stability modeling based on remote sensing technology, and promote the development of interdisciplinary fields centered on remote sensing to provides a satisfactory solution to the problem of the timeliness of ground deformation and stability evaluation. This topic covers the application of remote sensing technology in geoscience, geomorphology, engineering geology and other disciplines, which is in line with the objectives and themes of Remote Sensing journal.

This Topic collects original papers and inherent studies of application of remote sensing technology in earth science research (natural hazards, ground deformation, landslides prediction, stability modeling, risk assessment, etc.), but also manuscripts whose contents can help to promote the development of interdisciplinary fields centered on remote sensing technology. The following two directions are encouraged: prediction of ground deformation by remote sensing, and establishing stability model by remote sensing technology. Numerical and experimental investigations for basic or application research and representative case studies, as well as research on models and methods for geological hazards coupled with deep learning-driven remote sensing techniques are welcome too. Interdisciplinary and multidisciplinary approaches are considered added values to contribute to progress in the field of remote sensing.

Prof. Dr. Yiping Wu
Prof. Dr. Török Ákos
Dr. Fasheng Miao
Prof. Dr. Abdolreza Ghods
Guest Editors

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