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RSS FeedsRemote Sensing, Vol. 11, Pages 1507: Measurement of Road Surface Deformation Using Images Captured from UAVs (Remote Sensing)


26 june 2019 06:03:10

Remote Sensing, Vol. 11, Pages 1507: Measurement of Road Surface Deformation Using Images Captured from UAVs (Remote Sensing)

This paper presents a methodology for measuring road surface deformation due to terrain instability processes. The methodology is based on ultra-high resolution images acquired from unmanned aerial vehicles (UAVs). Flights are georeferenced by means of Structure from Motion (SfM) techniques. Dense point clouds, obtained using the multiple-view stereo (MVS) approach, are used to generate digital surface models (DSM) and high resolution orthophotographs (0.02 m GSD). The methodology has been applied to an unstable area located in La Guardia (Jaen, Southern Spain), where an active landslide was identified. This landslide affected some roads and accesses to a highway at the landslide foot. The detailed road deformation was monitored between 2012 and 2015 by means of eleven UAV flights of ultrahigh resolution covering an area of about 260 m × 90 m. The accuracy of the analysis has been established in 0.02 ± 0.01 m in XY and 0.04 ± 0.02 m in Z. Large deformations in the order of two meters were registered in the total period analyzed that resulted in maximum average rates of 0.62 m/month in the unstable area. Some boundary conditions were considered because of the low required flying height (<50 m above ground level) in order to achieve a suitable image GSD, the fast landslide dynamic, continuous maintenance works on the affected roads and dramatic seasonal vegetation changes throughout the monitoring period. Finally, we have analyzed the relation of displacements to rainfalls in the area, finding a significant correlation between the two variables, as well as two different reactivation episodes. Digg Facebook Google StumbleUpon Twitter
25 viewsCategory: Geology, Physics
Remote Sensing, Vol. 11, Pages 1499: A Review on Deep Learning Techniques for 3D Sensed Data Classification (Remote Sensing)
Remote Sensing, Vol. 11, Pages 1506: Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2 (Remote Sensing)
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