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A total of more than 50 000 landslides has occurred in Sichuan province since the "5·12" Wenchuan earthquake, resulting in serious damage to the surface vegetation in southwestern China. In this study,we select Yingxiu, the epicenter of Wenchuan earthquake, as the experimental area. The vegetation coverage information of the experimental area is extracted from the remote sensing images collected in the year of 2005, 2011 and 2013, respectively. The surface vegetation coverage in different periods is analyzed, and the vegetation recovery rate of the whole area is calculated. The experimental results show that in the first three years after the earthquake, the speed of vegetation restoration is slow, and the vegetation coverage rate is less than 20% better than 0.241, while in 2013, the vegetation coverage increases significantly.  相似文献
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A large number of debris flow disasters (called Seismic debris flows) would occur after an earthquake, which can cause a great amount of damage. UAV low-altitude remote sensing technology has become a means of quickly obtaining disaster information as it has the advantage of convenience and timeliness, but the spectral information of the image is so scarce, making it difficult to accurately detect the information of earthquake debris flow disasters. Based on the above problems, a seismic debris flow detection method based on transfer learning (TL) mechanism is proposed. On the basis of the constructed seismic debris flow disaster database, the features acquired from the training of the convolutional neural network (CNN) are transferred to the disaster information detection of the seismic debris flow. The automatic detection of earthquake debris flow disaster information is then completed, and the results of object-oriented seismic debris flow disaster information detection are compared and analyzed with the detection results supported by transfer learning.  相似文献
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