秦巴山区地质灾害发育规律研究——以镇巴县幅为例

Abstract
Figure/Table
References
Related Citation (15)

Download: PDF (4388 KB) HTML (1 KB)
Export: BibTeX | EndNote (RIS)

Abstract Taking Zhenba County as an example, the correlation between slope geohazard distribution and landform, gradient, aspect, structure type, water system and road are analyzed through field geohazard survey, UAV aerial photography and indoor remote sensing interpretation. The conclusions are shown as follows:1) The development of slope hazards is closely related to landform, with slope hazards in low and middle mountains accounting for 96.97% of the total disasters. 2) Slope hazards are mainly distributed in gradient of 20-40°, accounting for 75.76% of the total. 3) Of slope hazards, 41% are distributed on the east and west slopes. 4) Bedding slope is the most prone to geohazards, accounting for 33% of the total. 5) Rivers have a strong control on geohazards, with 45.45% of the geohazards occurred within 200 m from the river. 6) Slope hazards in the study area are significantly correlated with human engineering activities, especially road construction. The slope hazards developed within 50 m on both sides of roads account for 55.56% of the total. Moreover, combined with the development regularity of geohazards and characteristics of remote sensing images in the study area, a set of slope disaster identification methods applicable to Qinling-Daba Mountains in southern Shaanxi Province are summarized, which provides technical support for disaster prevention, mitigation and identification.

Key words： Qinling-Daba Mountains geohazard remote sensing interpretation distribution law identification method Shaanxi Province

Received: 06 December 2019

PACS:

P694

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	ZHAO Li-xing
	FAN Wen
	CHAI Xiao-qing
	LI Pei
	NAN Ya-lin

Cite this article:

ZHAO Li-xing,FAN Wen,CHAI Xiao-qing等. STUDY ON THE DEVELOPMENT REGULARITY OF GEOHAZARDS IN QINLING-DABA MOUNTAINS: A Case Study of Zhenba County, Shaanxi Province[J]. GEOLOGY AND RESOURCES, 2020, 29(2): 187-195.

URL:

http://manu25.magtech.com.cn/Jweb_dzyzy/EN/ OR http://manu25.magtech.com.cn/Jweb_dzyzy/EN/Y2020/V29/I2/187

[1]	韩金良,吴树仁,李东林,等. 秦巴地区地质灾害的分布规律与成因[J]. 地质科技情报,2007,26(1):101-108.
[2]	李培,范文,于国强,等. 秦岭矿产资源开采区斜坡灾害发育规律与识别研究——以山阳-商南钒矿开采区为例[J]. 工程地质学报, 2018,26(5):1162-1169.
[3]	房浩,李媛,杨旭东,等. 2010-2015年全国地质灾害发育分布特征分析[J]. 中国地质灾害与防治学报,2018,29(5):1-6.
[4]	宁奎斌,李永红,何倩,等. 2000~2016年陕西省地质灾害时空分布规律及变化趋势[J]. 中国地质灾害与防治学报,2018,29(1):93-101.
[5]	颉海军. 镇巴县地质灾害发育规律分析[C]//陕西环境地质研究——2014年陕西省地质灾害防治学术研讨会论文集. 西安:陕西省地质学会,陕西省地质学会环境地质与灾害地质专业委员会,陕西省地质调查院,2014:6.
[6]	梁鑫,范文,苏艳军,等. 秦岭钒矿集中开采区隐蔽性地质灾害早期识别研究[J]. 灾害学,2019,34(1):208-214.
[7]	苏艳军,梁鑫. 钒矿带矿区地质灾害危险性评价——以陕西山阳-商南钒矿带中村镇钒矿开采区为例[J]. 地质与资源,2019,28(3):280-288.
[8]	侯兴利,陈建勋,张恋. 基于ArcGIS的滑坡空间分布的变维分形研究——以江西省宁都县为例[J]. 地质与资源,2019,28(3):293-297.
[9]	褚宏亮. 三维激光扫描技术在地质灾害调查、形变监测和早期识别方面的研究[D]. 北京:中国地质大学(北京),2016.
[10]	冯雨林,杨佳佳,蔡贺. 基于3D影像模型的长白山天池地质灾害现状与潜在危害分析[J]. 地质与资源,2016,25(4):395-400.
[11]	高永志,初禹,薛广垠,等. 黑龙江省矿山地质灾害遥感调查研究[J]. 地质与资源,2017,26(2):161-164,189.
[12]	刘战庆,裴先治,丁仨平,等. 南大巴山西北段镇巴-下高川地区地质构造解析[J]. 地球科学与环境学报,2011,33(1):54-63.
[13]	李瑞保. 南大巴山镇巴-城口段构造变形特征及构造演化[D]. 西安:长安大学,2009.
[14]	裴先治,李瑞保,丁仨平,等. 陕南镇巴地区大巴山与米仓山构造交接关系[J]. 石油与天然气地质,2009,30(5):576-583.
[15]	王飞. 陕南镇巴地区南华系-震旦系岩石地层划分与区域对比[D]. 西安:长安大学,2009.
[16]	王福恒. 基于GIS的区域公路边坡灾害评价与预测研究[D]. 西安:长安大学, 2011.
[17]	羊秀娟,黎武,杨晓. 不同DEM分辨率下影响三台县水土流失的地形因子分析[J]. 安徽农学通报,2017,23(12):91-93.
[18]	李凯. 陕西山阳县中村钒矿区滑坡形成机理及早期识别研究[D]. 西安:长安大学,2017.
[19]	王佳运,王根龙,石小亚. 陕西山阳特大型滑坡视向滑移-溃屈破坏力学分析[J]. 中国地质,2018,46(2):381-388.
[20]	王慧. 山区地质灾害易发性区划与降雨诱发时空联合预报研究[D]. 重庆:重庆大学,2016.
[21]	邓辉. 高精度卫星遥感技术在地质灾害调查与评价中的应用[D]. 成都:成都理工大学,2007.
[22]	王云南,任光明,王家柱,等. 滑坡遥感解译研究综述[J]. 西北水电, 2017(1):17-21.