Application analysis of remote sensing dynamic monitoring for geological hazards in southwest mountainous areas using domestic high resolution satellite data
FAN Min1, SUN Xiaofei1, SU Fenghuan2, JIANG Huabiao1, HAN Lei1
1. Geology Investigation Institute of Sichuan Province, Chengdu 610081, China; 2. Institute of Mountain Hazards and Environment, CAS, Chengdu 610041, China
Abstract：For the purpose of promoting applications of domestic high resolution satellite products to geological hazard remote sensing survey and dynamic monitoring, the authors chose study areas in Sichuan,Chongqing and Guizhou, where people suffer from high risk and frequent geological hazards. On the basis of domestic high resolution satellite images obtained in 2014 and 2015, the authors conducted comparison and analysis to identify the dynamic changes in geological hazards. The authors also analyzed application results of domestic high resolution satellite products on geological hazard dynamic monitoring in the study areas, along with evaluating and exploring their practicability and prospect. In this way, the authors provide an objective basis for further applications and promotion of domestic high resolution satellite products on geological hazard remote sensing survey and dynamic monitoring in southwest mountainous areas. The results indicate that domestic high resolution satellites can provide good data quality in the remote sensing monitoring of geological hazards, and human-computer interactive interpretations for orthophoto images exhibit excellent results and accuracy. It is therefore held that the domestic satellites deserve application and promotion in the field of geological hazard remote sensing survey and dynamic monitoring in southwest mountainous areas.
范敏, 孙小飞, 苏凤环, 蒋华标, 韩磊. 国产高分卫星数据在西南山区地质灾害动态监测中的应用[J]. 国土资源遥感, 2017, 29(s1): 85-89.
FAN Min, SUN Xiaofei, SU Fenghuan, JIANG Huabiao, HAN Lei. Application analysis of remote sensing dynamic monitoring for geological hazards in southwest mountainous areas using domestic high resolution satellite data. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(s1): 85-89.
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2017, Vol. 29 
