鍏抽敭璇�: 鐏惧淇℃伅鎻愬彇, 涔濆娌熸粦鍧�, 褰掍竴鍖栨琚寚鏁�, 婊戝潯璇嗗埆, 澶氭椂鐩搁仴鎰�, 鍙樺寲妫�娴�

Abstract:

With the development of the remote sensing technology锛� the high-resolution satellite data is gradually enriched锛� and the information extraction of landslide disaster is further promoted. The current emergency investigation of the landslide disaster mainly focuses on the visual interpretation and field investigation锛� which is time-consuming锛� laborious锛� and difficult to meet the urgent need of the rescue after disaster. The single-phase landslide information extraction methods by using remote sensing based on the pixel-oriented or object-oriented have problems of over-recognition or mis-recognition of landslides. Therefore锛� the multi-temporal landslide information extraction method is worth studying and is expected to achieve good results锛� especially through the notable NDVI change in landslide. First锛� multi-temporal remote sensing images before and after the landslide are used as the data source. The landslide pre-selection area is determined using the pixel-oriented NDVI change detection. Then锛� the object-oriented geometric rules are used to complete the fine identification of landslides. This method based on the combination of the change detection and geometric rules effectively eliminates non-landslide parts which are with the spectral characteristics similar to landslides锛� such as roads锛� buildings锛� and bare land. Taking Jiuzhaigou landslide as the study case锛� the Gaofen-1 multi-spectral images of August 1锛� 2015 锛坆efore Jiuzhaigou earthquake锛� and the images of August 16锛� 2017 锛坅fter the earthquake锛� are used as data sources to conduct landslide identification experiments. The experimental results show that the multi-phase method has high accuracy in landslide identification. Compared with the object-oriented single-phase method锛� the former method has a mapping accuracy of up to 88.80% and the user accuracy up to 81.19%锛� both of which greatly exceed the accuracy of the object-oriented single-phase method. Moreover锛� the omission error and the mis-classification error decreased by 23.22% and 11.72%锛� respectively. This method determines landslides through the change of NDVI and has high timeliness in landslide identification锛� which does not need to consider the restrictions of excessive topographic and geomorphic factors and can be applied to most areas. It is believed that our method can provide a reliable basis for the effective organization of rescue and reconstruction work after landslide disaster.

Key words: Disaster information extraction, Jiuzhaigou landslide, NDVI, Landslide identification, Multi-temporal remote sensing, Change detection