The Landslide,which is caused by mining activities, has become an important factor which constrains the sustainable development of mining area. Thus it becomes very important to predict the landslide in order to reduce and even to avoid the loss in hazards. The paper is to address the landslide prediction problem in the environment of GIS by establishing the landslide prediction model based on ...

Most distributed slope-stability models for shallow landslide prediction neglect the forces acting on the sidewalls of landslide scars. Back-calculations and field observations, however, show that lateral root strength is a primary control on size and location of shallow landslides in soil mantled hillslopes. Here we report a theory that estimates landslide width, assuming that root strength ac...

[1] We use regionally available digital elevation models and land cover data, calibrated with groundand photo-based landslide inventories, to produce spatially distributed estimates of shallow, translational landslide density (number/unit area). To discern effects of land use, we focus on resolving landslide density relationships with forest cover. We account for topographic variability between...

The evolution of landslide displacement is affected by many factors. This paper studied the displacement monitoring data of Liangshuijing Landslide with Factor Analysis Method and found that the dominant factors influencing landslide displacement were in decreasing sequence: cumulative rainfall of anterior two months> rainfall of current month> the average reservoir level of current month>reser...

As the global climate changes, the threat from landslides and debris flows increases. Learning how a watershed initiates landslides under abnormal rainfall conditions and predicting landslide magnitude and frequency distribution is thus important. Landslides show a power-law distribution in the frequency-area distribution. The distribution curve shows an exponent gradient 1.0 in the Sandpile mo...

Most landslide hazard assessment algorithms in common use are applied to small regions, where highresolution, in situ, observables are available. A preliminary global landslide hazard algorithm has been developed to estimate areas of potential landslide occurrence in near real-time by combining a calculation of landslide susceptibility with satellite derived rainfall estimates to forecast areas...

Landslides often cause economic losses, property damage, and loss of lives. Monitoring landslides using high spatial and temporal resolution imagery and the ability to quickly identify landslide regions are the basis for emergency disaster management. This study presents a comprehensive system that uses unmanned aerial vehicles (UAVs) and Semi-Global dense Matching (SGM) techniques to identify ...

Pi-SAR-L2 full polarimetic data observed in four different observational directions over a landslide area on Izu Oshima Island, induced by Typhoon Wipha on 16 October 2013, were analyzed to clarify the most appropriate L-band full polarimetric parameters and observational direction to detect a landslide area. Japanese airborne Pi-SAR-L2 and PiSAR-L data were used in this analysis. Several L-ban...

Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors ...

The size of a shallow landslide is a fundamental control on both its hazard and geomorphic importance. Existing models are either unable to predict landslide size or are computationally intensive such that they cannot practically be applied across landscapes. We derive a model appropriate for natural slopes that is capable of predicting shallow landslide size but simple enough to be applied ove...