Multi-sensor advanced DInSAR monitoring of very slow landslides: The Tena Valley case study (Central Spanish Pyrenees)

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• 作者：G. Herrera^a ; ^{g.herrera@igme.es} ; F. Gutié ; rrez^b ; ^{fgutier@unizar.es} ; J.C. Garcí ; a-Davalillo^a ; ^c ; J. Guerrero^b ; D. Notti^d ; ^{davide.notti@dst.unipv.it} ; J.P. Galve^b ; J.A. Ferná ; ndez-Merodo^a ; G. Cooksley^e ; ^{geraint.cooksley@altamira-information.com}
• 关键词：DinSAR ; Landslides ; Monitoring ; ALOS ; TerraSAR-X ; Envisat ; Ers
• 刊名：Remote Sensing of Environment
• 出版年：2013
• 出版时间：21 January, 2013
• 年：2013
• 卷：128
• 期：Complete
• 页码：31-43
• 全文大小：5223 K

文摘

In this work it is demonstrated how the combination of multi-sensor and multi-temporal SAR data improves the monitoring capacity of very slow landslides. The study area is the Upper Tena Valley (Central Spanish Pyrenees), where mostly very slow earth flows, have caused direct damage estimated at approximately 15 M€ in the last decade. The advanced DInSAR processing of ascending orbit ALOS PALSAR images (2006-2010) and descending orbit ERS & Envisat (2001-2007) and TerraSAR-X (2008) datasets, has provided heterogeneous displacement velocity measurements. The projection along the steepest slope of LOS displacements reduces the geometrical differences introduced by each satellite. These results were compared and validated with available D-GPS measurements. In a second step PS displacement data are combined with a landslide inventory, increasing the number of monitored landslides with sufficient PS from 4 % to 19 % , obtained from C- and X-band data, to 38 % of the total (294). Finally, the retrieved multi-sensor landslides velocities are classified with respect to the magnitude of road damages that occurred in the 2008-2010 period. At local scale, the combination of multi-sensor data was useful to differentiate different landslide displacement directions, to measure different velocity patterns within the same moving mass, and to distinguish the slower (natural) and faster (human induced) landslides. The analysis of displacement time series permitted to evaluate the acceleration caused by a destabilising anthropogenic change, and the correlation between displacement variability and seasonal precipitation and melting.