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Juan de la Fuente

Publications and source records attributed to Juan de la Fuente.

2 recordsLinked to original sources

Landslide monitoring and runout hazard assessment by integrating multi-source remote sensing and numerical models: An application to the Gold Basin landslide complex, northern Washington

The landslide complex at Gold Basin, Washington, has been drawing considerable attention after a catastrophic runout of the nearby landslide at Oso, Washington, in 2014. To evaluate potential threats of the Gold Basin landslide to the campground down the slope, remote sensing and numerical modeling were integrated to monitor recent landslide activity and simulate hypothetical runout scenarios. Bare-earth LiDAR DEM (digital elevation model) differencing, InSAR (Interferometric Synthetic Aperture Radar), and offset tracking of SAR images reveal that localized collapses at the headscarps have been the primary type of landslide activity at Gold Basin from 2005 to 2019, and currently no signs indicative of movement of a large centralized block or a deep-seated main body were detected. The maximum horizontal deformation rate is 5 m/year occurring primarily from headscarp recession of the middle lobe, and the annual landsliding volume of the whole landslide complex averages 1.03 × 10 5 m 3 . From three-dimensional limit equilibrium analysis of generalized terrace structures, the maximum landslide volume is estimated as 2.0 × 10 6 m 3 . Simulations of hypothetical runout scenarios were carried out using the depth-averaged two-phase model D-claw with above-obtained landslide geometry constraints. The simulation results demonstrate that debris flows with volume less than 10 5 m 3 only pose limited threats to the campground, while volumes over 10 6 m 3 could cause severe damages. Consequently, the estimated maximum landslide volume of 2.0 × 10 6 m 3 suggests a potential risk to the campground nearby. Adaption of our methodology could prove useful for evaluating other similar landslides globally for hazards prevention and mitigation.

Washington

Large-area landslide detection and monitoring with ALOS/PALSAR imagery data over Northern California and Southern Oregon, USA

Multi-temporal ALOS/PALSAR images are used to automatically investigate landslide activity over an area of ~ 200 km by ~ 350 km in northern California and southern Oregon. Interferometric synthetic aperture radar (InSAR) deformation images, InSAR coherence maps, SAR backscattering intensity images, and a DEM gradient map are combined to detect active landslides by setting individual thresholds. More than 50 active landslides covering a total of about 40 km 2 area are detected. Then the short baseline subsets (SBAS) InSAR method is applied to retrieve time-series deformation patterns of individual detected landslides. Down-slope landslide motions observed from adjacent satellite tracks with slightly different radar look angles are used to verify InSAR results and measurement accuracy. Comparison of the landslide motion with the precipitation record suggests that the landslide deformation correlates with the rainfall rate, with a lag time of around 1–2 months between the precipitation peak and the maximum landslide displacement. The results will provide new insights into landslide mechanisms in the Pacific Northwest, and facilitate development of early warning systems for landslides under abnormal rainfall conditions. Additionally, this method will allow identification of active landslides in broad areas of the Pacific Northwest in an efficient and systematic manner, including remote and heavily vegetated areas difficult to inventory by traditional methods.

California, Oregon