Application of Satellite Images in Landslide Detection and Factors Analysis
Date Issued
2007
Date
2007
Author(s)
Yang, Yong-An
DOI
zh-TW
Abstract
Because of the precipitous terrain and short rapid rivers in Taiwan, the landslides usually happen after heavy rains and the resulting floods. Landslides are natural hazards that often cause series property damages and even life losses. This makes the landslide monitoring and mitigation techniques an important study issue for the related professional disciplines in Taiwan.
As the developing of remote sensing technology, both the spatial and spectral resolution of satellite images become more mature for objection identification and detection. In this research, the landslide areas are detected using the pre- and post-images by the decision trees classifier. In the first stage of the decision tree, the thresholding method according to the variety of vegetation index (VI) is used to separate the areas into vegetated and exposed areas. In the second stage, In the third stage, using the property in statistics ofdiscriminant analysis to strengthen the difference of land features before doing image classification. In the third stage, a supervised classifier is used to distinguish the natural streams from the exposed areas to obtain the areas of landslides. Finally, the result will check with ground truth data which made manually in aerial photos.
Using the ability of artificial neural network(ANN) to predict each factor effect in landslide. After training and testing data by ANN, results of this study indicate that the ANN method can predict with an accuracy up to 93%. Therefore, it is concluded that the ANN method is a good way to predict the hazard before occurring.
The research applies satellite image and hazard analysis to realize the environment in Shihmen reservoir watershed. The detected landslide information could be utilized as a reference for the mitigation of the future landslide.
Subjects
崩塌地判釋
植生指標
判別分析
類神經網路
災因分析
Landslide Detection
Vegetation Index
Discriminant Analysis
Artificial Neural Network
Factors Analysis
Type
thesis
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