In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velo...In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.展开更多
Disasters including natural and manmade make heavy losses in life and property each year. This subject can affect society, economy, and environment and can be a serious threat for development. In 10 years ago over 200...Disasters including natural and manmade make heavy losses in life and property each year. This subject can affect society, economy, and environment and can be a serious threat for development. In 10 years ago over 200 million people are have been effected both life and property. This figure is seven times more than losses in war. After the earthquake in Bam （a city in south Iran）, tsunami in south-eastern of Asia, fire in Australia, and other disasters, the management of disaster has been considered more than before. They have tried to use all facilities and equipment for reduction of disaster damage. Over 80% of necessary data in disaster management are spatial data. Spatial data and advanced technologies have an important role in disaster management because Geographic Information System （GIS） can help in identifying disaster points. GIS combines geospatial data, and hardware, software that can analyze data to produce information. GIS mainly involves saving and analysis of data according to spatial and attribute data. GIS can combine and analyze spatial and non-spatial data .We have made an attempt to consider disasters management according to facilities and role of Geospatial Technology in control of disaster （especially earthquake）.展开更多
基金CONACyT for financial support for the research project 156242for providing a post-graduate scholarship
文摘In this paper, an attempt to analyse landslide hazard and vulnerability in the municipality of Pahuatlfin, Puebla, Mexico, is presented. In order to estimate landslide hazard, the susceptibility, magnitude （area-velocity ratio） and landslide frequency of the area of interest were produced based on information derived from a geomorphological landslide inventory; the latter was generated by using very high resolution satellite stereo pairs along with information derived from other sources （Google Earth, aerial photographs and historical information）. Estimations of landslide susceptibility were determined by combining four statistical techniques： （i） logistic regression, （ii） quadratic discriminant analysis, （iii） linear discriminant analysis, and （iv） neuronal networks. A Digital Elevation Model （DEM） of lo m spatial resolution was used to extract the slope angle, aspect, curvature, elevation and relief. These factors, in addition to land cover, lithology anddistance to faults, were used as explanatory variables for the susceptibility models. Additionally, a Poisson model was used to estimate landslide temporal frequency, at the same time as landslide magnitude was obtained by using the relationship between landslide area and the velocity of movements. Then, due to the complexity of evaluating it, vulnerability of population was analysed by applying the Spatial Approach to Vulnerability Assessment （SAVE） model which considered levels of exposure, sensitivity and lack of resilience. Results were expressed on maps on which different spatial patterns of levels of landslide hazard and vulnerability were found for the inhabited areas. It is noteworthy that the lack of optimal methodologies to estimate and quantify vulnerability is more notorious than that of hazard assessments. Consequently, levels of uncertainty linked to landslide risk assessment remain a challenge to be addressed.
文摘Disasters including natural and manmade make heavy losses in life and property each year. This subject can affect society, economy, and environment and can be a serious threat for development. In 10 years ago over 200 million people are have been effected both life and property. This figure is seven times more than losses in war. After the earthquake in Bam （a city in south Iran）, tsunami in south-eastern of Asia, fire in Australia, and other disasters, the management of disaster has been considered more than before. They have tried to use all facilities and equipment for reduction of disaster damage. Over 80% of necessary data in disaster management are spatial data. Spatial data and advanced technologies have an important role in disaster management because Geographic Information System （GIS） can help in identifying disaster points. GIS combines geospatial data, and hardware, software that can analyze data to produce information. GIS mainly involves saving and analysis of data according to spatial and attribute data. GIS can combine and analyze spatial and non-spatial data .We have made an attempt to consider disasters management according to facilities and role of Geospatial Technology in control of disaster （especially earthquake）.
