Keywords: satellite image, digital image correlation, landslide monitoring.

History shows that most earthquakes, which cause serious disaster, are induced by recent movement of faults. For example, the Chichi earthquake, happened on September 21 1999, has a magnitude of 7.3 and resulted to a great loss of life and property. The cause of this earthquake is the movement of the Chelungpu fault. The massive earthquake not only destroyed houses and roads, but also induced landslides. Not only earthquakes but also typhoons or heavy rainfall can induce landslides. Because the destruction occurred mostly in remote areas, coupled with the traffic system disruption, investigation and relief are very difficult.

The digital image correlation (DIC) technique is a non-contact-type optical measurement technique. The progress of digital camera and the rapid development of the computer calculation capability has resulted in the digital image correlation technique being widely applied to different research fields. The landslides will change the appearance of the ground surface and the satellite image is already widely used in the remote sensing technology, therefore we can use the DIC method and the satellite image to monitor the occurrence of landslides.

To determine the occurrence of landslides by comparing satellite images is mainly to find out positions, where correlation between two images is low. Although the algorithm for digital image correlation is to find out the highest correlation, it can be modified to agree with our purpose. In this study, digital image correlation and satellite images are used to identify the occurrence of landslides. Preliminary results of the automatic landslide identification are achieved by using DIC. NDVI (normalized difference vegetation index) and DEM (digital elevation model) are also applied to reduce the misidentification rate.

The following conclusions can be drawn according from the analysis results:

1. The size of the identified landslide areas will increase as the grid size decreases. For the same size of grid, more identified landslide areas will be determined by using a larger correlation coefficient threshold. The best result of this experiment is obtained as the grid size is 5x5 pixels, correlation coefficient threshold equals 0.998, and the corresponding accuracy rate is 90.60%.
2. NDVI can be used to distinguish the existence of vegetation. This property can be applied to remove the misidentified landslide areas. But as the misidentification rate reduces the accuracy rate is also negatively influenced. Experimental results show that the highest accuracy rate is 74.38%. To remove the areas with lower slope using the DEM can also reduce the DIC misidentification rate. The corresponding accuracy rate is 75.22%. To filter the DIC results with NDVI and the DEM simultaneously can filter most of the misidentification, but the accuracy rate reduces to 60.54%.
3. The results show that using the DIC technique to identify the region of landslide is feasible. This method can save a lot of time and cost compared with the manual method. But the satellite images are shot in different directions. This means that the images after orthorectification are still lightly different. Therefore, the analysis results will be influenced. In the future, the influence of the orthorectification difference on the identification results can be further studied and discussed.

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