Proceedings Article | 14 May 2014
KEYWORDS: Landslide (networking), Earthquakes, Remote sensing, Image segmentation, Image analysis, Fuzzy logic, Vegetation, Analytical research, Image classification, Earth observing sensors
As a kind of secondary geological disaster caused by strong earthquake, the earthquake-induced landslide has drawn much attention in the world due to the severe hazard. The high-resolution remote sensing, as a new technology for investigation and monitoring, has been widely applied in landslide susceptibility and hazard mapping. The Ms 8.0 Wenchuan earthquake, occurred on 12 May 2008, caused many buildings collapse and half million people be injured. Meanwhile, damage caused by earthquake-induced landslides, collapse and debris flow became the major part of total losses. By analyzing the property of the Zipingpu landslide occurred in the Wenchuan earthquake, the present study advanced a quick-and-effective way for landslide extraction based on NDVI and slope information, and the results were validated with pixel-oriented and object-oriented methods. The main advantage of the idea lies in the fact that it doesn’t need much professional knowledge and data such as crustal movement, geological structure, fractured zone, etc. and the researchers can provide the landslide monitoring information for earthquake relief as soon as possible. In pixel-oriented way, the NDVI-differential image as well as slope image was analyzed and segmented to extract the landslide information. When it comes to object-oriented method, the multi-scale segmentation algorithm was applied in order to build up three-layer hierarchy. The spectral, textural, shape, location and contextual information of individual object classes, and GLCM (Grey Level Concurrence Matrix homogeneity, shape index etc. were extracted and used to establish the fuzzy decision rule system of each layer for earthquake landslide extraction. Comparison of the results generated from the two methods, showed that the object-oriented method could successfully avoid the phenomenon of NDVI-differential bright noise caused by the spectral diversity of high-resolution remote sensing data and achieved better result with an overall accuracy of 92.16%, while of the pixel-oriented one could only get 71.32%. As the high-resolution remote sensing has been widely utilized in many fields, the object-oriented image analytical technique will have an extensive application.
