摘要
近年来,受自然气候条件变化影响,尤其在冰雪融水和降雨作用下,新疆的黄土区域极易发生滑坡灾害。选取新疆伊宁县某滑坡集中区域作为研究区,开展了一个滑坡的编录制图和滑坡空间分布分析工作。基于谷歌地球影像,在谷歌地球平台上采用人工目视进行滑坡解译,把解译的滑坡导入ArcGIS,根据DEM和地质图在ArcGIS上选择高程、坡度、坡向3个影响因子进行统计分析。研究结果表明:(1)在234.4 km 2的范围内,解译滑坡4498个,滑坡总面积3.907 km 2;(2)在高程1300~2100 m区间内、坡度10°~30°区间内、坡向60°~90°和90°~120°区间内滑坡分布数量最多,面积最大。研究结论可为新疆黄土滑坡防灾减灾提供参考,为进一步研究新疆黄土滑坡的空间分布规律和机理、危险性评价及预测提供基础。
In recent years,affected by the change of natural climate conditions,especially under the action of ice and snow melting water and rainfall,the loess region in Xinjiang is prone to landslide disasters.This paper carries out the compilation and mapping of precipitation-induced landslides and the analysis of their spatial distribution rules.Based on Google earth images,the interpretation of landslides is carried out by artificial vision on the Google earth platform.Then the interpreted landslides are imported into ArcGIS,three influencing factors include elevation,slope angle,slope direction according to DEM and geologic map are selected on ArcGIS platform for statistical analysis.The results are:(1)During the study area of 234.4 km 2,4498 landslides are interpreted,with a total landslide area of 3.907 km 2;(2)The landslides have maximum number and area in the elevation of 1300~2100 m,slope angle 10°~30°,slope direction of 60°~90°and 90°~120°.This study provides a basis for further research on the spatial distribution,mechanism,risk assessment,and prediction of landslides in the loess region of Xinjiang.
作者
崔玉龙
傅贵
郭鸿
鲍鹏鹏
胡俊宏
CUI Yu-long;FU Gui;GUO Hong;BAO Peng-peng;HU Jun-hong(School of Civil Engineering and Architecture,Anhui University of Science and Technology,Huainan 232001,China;School of Civil Engineering and Architecture,Shaanxi University of Technology,Hanzhong 723000,China)
出处
《陕西理工大学学报（自然科学版）》
2020年第2期51-56,共6页
Journal of Shaanxi University of Technology:Natural Science Edition
基金
国家自然科学基金资助项目(41807267)
安徽省高校自然科学研究重点资助项目(KJ2018A0075)。
关键词
滑坡分布规律
谷歌地球影像
目视解译
伊宁县
黄土滑坡
distribution law of landslide
Google earth images
visual interpretation
Yining county
loess landslide
