| Yunnan is one of the hardest hit provinces of geological disasters in China.Especially in recent years,earthquakes frequently occur,which are extremely easy to induce unstable slopes,landslides,collapses and other geological disasters.Lianghe County has complex terrain and landform,strong tectonic activity,and human engineering activities,which easily lead to the development of geological disasters.According to the detailed survey data of geological hazards in Lianghe County in 2015,there are 292 potential geological hazards such as landslide,collapse and debris flow.Geological disasters not just pose a huge threat to the safety of people and material property in the area,but also limit the local urbanization and economic development.Therefore,it is particularly important to carry out effective comprehensive identification and vulnerability evaluation of geological disasters,and it also has important theoretical and practical significance for the prevention and control of geological disasters.In this paper,Lianghe County,Yunnan Province,is selected as the object.Based on the big geological environment background conditions,optical and SAR remote sensing images are used as the main data source to discuss the comprehensive remote sensing recognition of geological disasters at the county scale.According to the comprehensive recognition results,the method of combining the analytic hierarchy process and the information model is used to carry out the vulnerability evaluation of geological disasters.The main contents,research results are as follows:(1)GF-1 and Sentinel-1 radar images are taken as the main data,and SAR technology is used to extract the surface deformation rate of a study area,determine the surface deformation threshold as 10mm/a.In combination with optical remote sensing images,interactive visual interpretation is conducted to identify 398 potential geological hazard points.Through field investigation,only 5 geological hazard hidden danger information were identified.The verification results show what the comprehensive remote sensing identification method combining optical remote sensing and SAR technology has certain reliability in the identification of geological hazard hidden danger;(2)The relationship between geological disasters and geological background of disasters such as elevation,slope,aspect,landform,geological structure,stratum lithology,land cover,etc.is analyzed emphatically.The main influencing factors of the formation of geological disasters are discussed,and the development characteristics and distribution laws of various geological disasters are summarized;(3)The construction of the evaluation index system of geological hazard susceptibility.According to the geological environment background of the study area,the slope,aspect,elevation,distance from rivers,road,stratum lithology,distance from faults,vegetation coverage and land use type factors are selected as the indicator factors for the evaluation of geological disaster vulnerability.The weight of influence factors is determined by the Geo-detector,and the evaluation factors are selected according to the threshold value.The total information quantity of geological hazard vulnerability is calculated quantitatively by information quantity model method,and the vulnerability zoning map is obtained after equidistant classification.(4)On the basis of quantitative calculation,taking into account various factors,taking into account the principle of "Similarity within the zone and difference between the zones",and taking into account as far as possible the setting of administrative territorial entity and the integrity of small watersheds,after revision and improvement,the final result of the geological hazard vulnerability zoning map of Lianghe County was formed.The results showed that the high-risk area was 437.23km2,accounting on 37.72% of the total area,the middle-risk area were 407.28km2,accounting for 35.14% of the total area,and the low-risk area were 314.49 km2,accounting for 27.14% of the area.The rationality of the evaluation results was tested by using ROC curve.The results showed that the significance was less than 0.05 and the precision was good. |
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