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Almost all preparatory and triggering causal factors are well set in place in the case of Philippines to make its sloping terrains vulnerable to landslides; the history of landslide occurrences stands as foolproof evidence. A review of the past events however indicates that rainfall has, inter alia, been offering the triggering effect to cause most of the devastating landslides. A number of such landslides have occurred in the Southern Leyte province of Philippines causing considerable loss of life and damage to properties. Facilitating towards managing the associated landslide risk in Southern Leyte it was aimed at delineating the areas that are prone to rain-triggered landslides. The SINMAP model which combines a mechanistic slope stability model with a steady-state hydrology model was employed in way accomplishing this, much needed, task. Elevation data in the form of a raster GIS layer was the main input for the model. Mapping of past landslide initiation points…

Large landslides are one of the main natural hazards in Kyrgyzstan. Because of the high number of landslides and their relative inaccessibility in mountainous terrain the potential of satellite remote sensing in combination with GIS-based analysis has been investigated in the frame of a collaboration with the Ministry of Emergency and Environment (MEE) in Kyrgyzstan. Emphasis is put on the development of multi-temporal remote sensing techniques to support landslide inventories and characterisation of main terrestrial factors preconditioning landslides on a regional scale. The database consists of Landsat-TM, stereoscopic MOMS-2P and radar (ERS-1/2) satellite imagery. MOMS-2P and ERS-1/2 data allowed the generation of high-resolution Digital Elevation Models (DEM). The derived relief parameters have been analysed in a GIS in combination with other information obtained from remote sensing data, thematic maps and field observation for a spatially differentiated…

The aim of the research was to monitor and assess landslide hazards by remote sensing data processing and GIS spatial analysis. The automatic classification of remote sensing images pro-vides many useful land use information to combine in a GIS environment with other spatial factors influencing the occurrence of landslide.
The upper part of Susa Valley, in the Italian Western Alps, was chosen as test area because of a large variety of remote sensing data available by ISPRS WG VIII/2 with the aim to exchange infor-mation and experience in the field of geomatic techniques.
It is well known that the occurrence of landslides is controlled by a lot of morphological, geological, and human factors. We have chosen, regarding the available data, the following factors: acclivity, aspect, lithology, land use and precipitations. We have built up a mathematical predictive model enabling ac-tual/potential unstable slopes. It is a linear model where the hazard score depends…

This study investigates and demonstrates the state of the art in remote sensing techniques for detailed landslide hazard assessment applicable to large areas. Since the most common methods of landslide hazard assessment using simple inventories and weighted overlays are heavily dependent on three-dimensional terrain visualization and analysis, stereo satellite images from the IKONOS Very High Resolution (VHR) sensor are used for this study. The DEMs created from IKONOS stereo images appear to be much more accurate and sensitive to micro-scale terrain features than a DEM created from digital contour data with a 2 m contour interval. Pan-sharpened stereo IKONOS images permit interpretation of recent landslides as small as 2–3 m in width as well as relict landslides older than 50 years. A cost–benefit analysis comparing stereo air photo interpretation with stereo satellite image interpretation suggests that stereo satellite imagery is usually more cost-…

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 are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally,…

Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of sufficient ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of highresolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time prediction system to identify where rainfall-triggered landslides will occur is proposed by combining two necessary components: surface landslide susceptibility (LS) and a real-time space-based rainfall analysis system. First, a global LS map is derived from a combination of semistatic global surface…

Floods and associated landslides account for the largest number of natural disasters and affect more people than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this article, a framework to detect floods and landslides related to heavy rain events in near-real-time is proposed. Key components of the framework are: a fine resolution precipitation acquisition system; a comprehensive land surface database; a hydrological modeling component; and landslide and debris flow model components. A key precipitation input dataset for the integrated applications is the NASA TRMM-based multi-satellite precipitation estimates. This dataset provides near real-time precipitation at a spatial-temporal resolution of 3 h and 0.258 x 0.258. In combination with global land surface datasets it is now possible to expand regional hazard…

Heavy rains have triggered massive rockslides on a coastal highway, trapping up to 400 travelers.

Source: GLIDEnumber

Hundreds of families have been affected by mudslides triggered by heavy rains in Urubamba Province.

Source: GLIDEnumber

A landslide buried about 30 people on a highway in northwestern Colombia caused by heavy rains in the midst of a deadly rainy season.

Source: http://www.glidenumber.net/