A data fusion approach for retrieving 3D slope displacements from satellite InSAR and UAV-based orthoimagery correlation data: application to a reclaimed coal mining area

Detecting and monitoring slope movements in mining areas is essential to better understand their causes and mitigate their adverse consequences. Satellite radar interferometry (InSAR) techniques allow to generate deformation maps at high resolution (both spatial and temporal), especially since 2014, when the European Space Agency's Sentinel-1 mission (6-day revisit frequency) became operational. The application of InSAR is, however, constrained by a number of limitations. One of the most important of them relates to its ability to measure only one component (or, at best, two components, provided that ascending and descending data are available) of the surface displacement (i.e., the line-of-sight component). In addition to this, InSAR offers a very low sensitivity in the north-south (NS) direction, which makes it difficult to study, solely on the basis of InSAR data, phenomena characterized by a strong NS component. In this context, this work discusses the potential role of UAV-based SfM image correlation as a possible data source to resolve the NS component of the motion, which in turn allows resolving, in the strict sense of the term, the three components of the motion from (at least) one ascending and one descending InSAR dataset.

In this work we present the results of a local-scale study carried out in El Feixolín (León), a former open-pit and underground mining area affected by a rapid (1.67 m/year according to in situ measurements), large slope movement. Results include ground displacement velocity data obtained using (i) FASTVEL (and Sentinel-1 ascending and descending imagery), an on-demand, unsupervised InSAR processing service available on the Geohazards Exploitation Platform (GEP) (https://geohazards-tep.eu/), (ii) image correlation techniques (applied on UAV-based SfM orthoimagery) and (iii) DGNSS techniques. Further, this study provides as final result a dataset of 3D displacement velocity values (InSAR 3D dataset) derived by integrating the InSAR data obtained in ascending (InSAR ASC dataset) and descending (InSAR DES dataset) geometry, with the data obtained in NS direction through image correlation (SfM NS dataset). Comparison of the results with the data acquired in situ through DGNSS surveying revealed Root Mean Square Error (RMSE) values of 0.05, 0.23, 0.16 and 0.03 m/year (and relative RMSE values of 34, 67, 13 and 19%), respectively for the InSAR ASC, InSAR DES, SfM NS and InSAR 3D datasets, highlighting the effectiveness of UAV-based SfM image correlation for deriving NS ground deformation data to support InSAR processing and obtain 3D ground deformation vectors.