Scaling-Up Deformation Monitoring and Analysis

Dear Colleagues,

Remote sensing based on synthetic aperture radar and/or multispectral data for mapping ground deformation has nearly become a commodity in the last few years. Conventional satellite interferometry, multiple aperture interferometry, offset tracking, persistent scatterer interferometry, small baseline subset, co-registration, and correlation of optical imagery, to name a few, have grown to become mature techniques for ground deformation assessment. The deformation signals studied may vary; geophysical phenomena, such as large earthquakes, volcanic activity, tectonic creep, and landslides, for example, cause clear displacement patterns that can be easily identified from space and analyzed further. The footprint of human activity has been also studied, including urban subsidence, groundwater and minerals extraction, construction activity, etc. The applications of these techniques also range in scale and impact from the single facility asset to the global level.

Nowadays, the big challenge is to shift the research focus from studying deformation due to single events and at the local level to systematic, continuous, and large-scale monitoring and analysis of deformations. The scaling-up of deformation monitoring and analysis is not trivial. There are two opportunities that lay ahead in this direction. The first one is the availability of big multi-modal satellite data, considering the assets that are currently on the table: SAR and optical data, diversity in imaging modes, variety in spectral and spatial resolution and revisit times, different carrier frequencies used in radar imaging systems, different deformation mapping techniques, s/w solutions, etc. Most importantly, with the advent of the Copernicus Programme, these large volumes of observations have become available on a free and open basis for studying the dynamically changing surface of the Earth. In parallel, there are now featured IT technologies, such as machine learning/deep learning models, data mining, data fusion, data cube for analytics, and feature extraction techniques on top of high performance and cloud computing environments (e.g., DIAS, the Geohazards Exploitation Platform, etc.), which have started to disrupt the remote sensing community, through efficient big satellite data analysis.

This Special Issue focuses on the implementation of novel techniques, as mentioned above, with high potential for mapping, monitoring, and analyzing deformations on a large scale, on a timely basis, and with high precision in the measured deformation patterns. There are no restrictions on the driver of the deformation, the methodology for the data processing, and/or the data type (radar or optical). The novelty should lay in the scale of the application and the potential to unveil new information about the observed deformations through the analysis of big satellite data.

Dr. Charalampos Kontoes
Dr. Ioannis Papoutsis
Dr. Gregory Giuliani
Guest Editor

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• Deformation monitoring
• Deformations analysis
• Big satellite data
• Scaling-up
• Machine learning
• Deep learning
• Data cube
• Data analytics
• Data fusion