Landslide detection and monitoring remain difficult with conventional differential radar interferometry (DInSAR) because most pixels of radar interferograms around landslides are affected by different error sources. These are mainly related to the nature of high radar viewing angles and related spatial distortions (such as overlays and shadows), temporal decorrelations owing to vegetation cover, and speed and direction of target sliding masses. On the other hand, GIS can be used to integrate spatial datasets obtained from many sources (including radar and non-radar sources). In this paper, a GRID data model is proposed to integrate deformation data derived from DInSAR processing with other radar origin data (coherence, layover and shadow, slope and aspect, local incidence angle) and external datasets collected from field study of landslide sites and other sources (geology, geomorphology, hydrology). After coordinate transformation and merging of data, candidate landslide representing pixels of high quality radar signals were filtered out by applying a GIS based multicriteria filtering analysis (GIS-MCFA), which excludes grid points in areas of shadow and overlay, low coherence, non-detectable and non-landslide deformations, and other possible sources of errors from the DInSAR data processing. At the end, the results obtained from GIS-MCFA have been verified by using the external datasets (existing landslide sites collected from fieldworks, geological and geomorphologic maps, rainfall data etc.).
Beyene, F., Knospe, S., & Busch, W. (2015). Enhancing DInSAR capabilities for landslide monitoring by applying GIS-based multicriteria filtering analysis. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (Vol. 40, pp. 757–764). International Society for Photogrammetry and Remote Sensing. https://doi.org/10.5194/isprsarchives-XL-7-W3-757-2015