摘要
由于本身特征的复杂差异性和背景环境信息干扰,目前地质灾害自动识别难度较大,尚无法满足应用需求。利用算法自动提取某类地质灾害普遍具备且明显区别于周围地物的特征,是实现地质灾害自动识别的有效途径。一定规模的滑坡堵江事件常通过链式过程在山间河谷形成裂点等诸多微地貌特征,成为其遥感解译和现场调查的重要标志。本文从河流地貌演化角度出发,以河流裂点作为滑坡堵江的普遍地貌特征,提出了基于河长-坡降指数(Stream Length-gradient index,SL)的滑坡堵江事件自动识别方法,并通过GIS程序设计和编写提供了相应功能模块。以DEM、遥感影像和区域地质图为数据源,利用该方法在西藏亚东县康布麻曲上游流域开展应用分析,得到以下结论:针对1︰5万DEM数据,采样点计算间隔设定为300m比较合理,能够同时满足降低数据误差和突出地形差异的要求;研究区中不同因素按照对河流地貌的改造程度,排序为滑坡堵江>基岩变化>构造活动;经验证研究区滑坡堵江事件自动提取的正确率达85.71%,表明在高山峡谷地区,基于河长—坡降指数开展滑坡堵江事件自动识别具有可行性,效果理想。
Because of the complexity and diversity of geohazards,as well as the surroundings interference,their automatic recognitions are in a situation of great difficulty and can't meet the application requirements. Finding an algorithm to extract the common characteristics of a kind of geohazard and to distinguish them from the surroundings is effective for automatic recognition of geohazards. Landslide dams of a certain volume can bring many micro-topographic features by chain processes,such as knickpoints,which is regarded as a key for their remote sensing interpretation and field investigation. From the point of fluvial geomorphology evolution,taking the knickpoints as a common feature of landslide dams,we present a automatic recognition method of landslide dams. It is based on stream length-gradient index,and can provide the computer module by GIS project designing and coding. Using DEM,remote sensing images and geological maps,we apply the method in the upper region of Kangbumaqu,in Yadong Country,Tibet,China,and obtain the following conclusions.( 1) For 1︰50000 DEM,the reasonable computing interval is 300 m,which can reduce data error and highlight terrain differences simultaneously.( 2) Thethree factors with the greatest transformation to fluvial geomorphology in the study area are,in order,landslide dams,lithological changes and tectonic movement.( 3) The accuracy of automatic recognition of landslide dams in the study area is 85. 71%,indicating the proposed method is feasible for automatic recognition of landslide dams in high mountainous areas.
出处
《工程地质学报》
CSCD
北大核心
2017年第2期511-519,共9页
Journal of Engineering Geology
基金
中国地质调查局地质调查项目(12120113010700)资助