Supervised Method of Landslide Inventory Using Panchromatic SPOT5 Images and Application to the Earthquake-Triggered Landslides of Pisco (Peru, 2007, Mw8.0)

Abstract

Earthquake is one of the dominant triggering factors of landslides. Given the wide areas covered by mega earthquake-triggered landslides, their inventory requires development of automatic or semi-automatic methods applied to satellite imagery. A detection method is here proposed for this purpose, to fit with simple datasets; SPOT5 panchromatic images of 5 m resolution coupled with a freely and globally available DEM. The method takes advantage of multi-temporal images to detect changes based on radiometric variations after precise coregistration/orthorectification. Removal of false alarms is then undertaken using shape, orientation and radiometric properties of connected pixels defining objects. 80% of the landslides and 93% of the landslide area are detected indicating small omission errors but 50% of false alarms remain. They are removed using expert based analysis of the inventory. The method is applied to realize the first comprehensive inventory of landslides triggered by the Pisco earthquake (Peru, 15/08/2007, Mw 8.0) over an area of 27,000 km 2 . 866 landslides larger than 100 m 2 are detected covering a total area of 1.29 km 2 . The area/number distribution follows a power-law with an exponent of 1.63, showing a very particular regime of triggering in this arid environment compared to other areas in the world. This specific triggering can be explained by the little soil cover in the coastal and forearc regions of Peru.Analysis of this database finally shows a major control of the topography (both orientation and inclination) on the repartition of the Pisco-triggered landslides.Remote Sens. 2013, 5 2591

Automated Spatiotemporal Landslide Mapping over Large Areas Using RapidEye Time Series Data

“…Most of the existing automated methods using optical remote sensing data have been developed for one-time landslide mapping after a single major triggering event. These methods are based on either a single post-event classification (e.g., [15,[20][21][22][23]) or a bi-temporal change detection between an image pair acquired before and after the triggering event (e.g., [24][25][26][27][28]). Single post-event classification approaches assume that all of the mapped landslides have been caused by the analyzed triggering event without further specifying the time period of landslide occurrence.…”

Identification of Forested Landslides Using LiDar Data, Object-based Image Analysis, and Machine Learning Algorithms

“…The Three Gorges area is characterized by steep and rugged terrain with heavy vegetation cover, often obscuring or subduing morphologic features indicative of landslides [12,13]. The landscape makes it particularly challenging to identify landslides using optical and aerial photographs, synthetic aperture radar (SAR) images, high spatial resolution multispectral images, moderate resolution digital terrain models (DTMs), and very high resolution (VHR) satellite images [6,[14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Tectonic and climatic controls on the Chuquibamba landslide (western Andes, southern Peru)

“…This preferential localization suggests that tectonic fracturing plays an important role in preconditioning the landscape for landsliding within this arid area. No relationship between a M w 8.0 subduction earthquake and a giant landslide has been previously documented in southern Peru (Lacroix et al, 2013). However, Keefer et al (2003) point out that a close temporal succession of subduction earthquakes and El Niño events produces debris flows in the coastal region (southern Peru).…”