融合多源数据的稀土矿区滑坡危险性定量识别方法
Quantitative identification method of landslide hazard in rare earth mining area based on multi-source data
戴妹谊 1李恒凯 1龙北平 2王秀丽3
作者信息
- 1. 江西理工大学土木与测绘工程学院,江西赣州 341000
- 2. 江西省煤田地质局测绘大队,江西南昌 330001
- 3. 江西理工大学经济管理学院,江西赣州 341000
- 折叠
摘要
为了提前对离子吸附型稀土矿区潜在的滑坡危险性进行识别,本研究以岭北矿区为例,提出了融合多源数据的稀土矿区滑坡危险性定量识别方法.首先基于时序哨兵一号(Sentinel-1A)数据,采用小基线集(Small Baseline Subset InSAR,SBAS-InSAR)技术获取研究区地表形变;再以数字高程模型(DEM)、Landsat 8陆地成像仪(OLI)等多源遥感数据为辅,提取潜在滑坡点,构建矿区滑坡危险性信息量模型;最后再结合同时期高空间分辨率遥感影像进行潜在滑坡危险性识别.结果表明:矿区整体地表年平均形变速率在-20.28～20.08 mm/a,共提取183个潜在滑坡点;构建的稀土矿区滑坡灾害危险性信息量模型是可行的,总结了研究区诱发滑坡的最佳信息量组合方式;对甲子背和大坑2个典型矿点进行具体分析,发现稀土开采活动会加速滑坡的产生,开采之后即使进行复垦,也容易诱发滑坡.
Abstract
To identify the potential landslide hazard of ion-adsorption rare earth mining areas in advance,the Lingbei mining area is used as a case study,and a quantitative landslide hazard i-dentification method for rare earth mining areas based on multi-source data was proposed.Based on the Sentinel-1A time series data,the study employed the Small Baseline Subset In-SAR(SBAS-InSAR)technology to identify surface deformation in the study area.Supplemen-ted by multi-source remote sensing data,such as the Digital Elevation Model(DEM)and Landsat 8 Operational Land Imager(OLI),potential landslide points were extracted,and an information value model of landslide hazard in the mining area was constructed.Finally,the potential landslide hazard was identified by combining high-resolution remote sensing images from the same period.The results show that the mining area's average annual surface deforma-tion rate is-20.28～20.08 mm/a and 183 potential landslide points are extracted.The infor-mation value model of landslide hazard in the mining area is feasible and summarizes the opti-mal combination mode of information value for inducing landslides in the study area.Having analyzed the two typical mining sites of Jiazibei and Dakeng,we can find that rare earth mining activities contribute to an increased likelihood of landslides.In addition,the susceptibility to induced landslides remains significantly high,even with post-mining efforts to restore vegeta-tion.
关键词
滑坡识别/稀土矿区/多源数据/SBAS-InSAR技术/信息量模型Key words
landslide identification/rare earth mining area/multi-source data/SBAS-InSAR technology/information value model引用本文复制引用
出版年
2024
NETL
NSTL
万方数据