地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (12): 1624-1633.doi: 10.3724/SP.J.1047.2016.01624

• 地理空间分析综合应用 • 上一篇    下一篇

基于DEM的福建省土质滑坡敏感性评价

杨城1(), 林广发1,2,3,*(), 张明锋1,2,3, 张容焱4, 孙笑古5   

  1. 1. 福建师范大学地理研究所,福州 350007
    2. 福建省陆地灾害监测评估工程技术研究中心,福州 350007
    3. 海西地理国情动态监测与应急保障研究中心,福州 350007
    4. 福建省气候中心,福州 350001
    5. 国家土地督察上海局,上海 200032
  • 收稿日期:2015-10-28 修回日期:2015-11-30 出版日期:2016-12-27 发布日期:2016-12-20
  • 通讯作者: 林广发 E-mail:chengyang615@qq.com;GuangFaLin@qq.com
  • 作者简介:

    作者简介:杨 城(1990- ),男,福建福州人,硕士生,研究方向为空间数据挖掘。E-mail:chengyang615@qq.com

  • 基金资助:
    国家重点研发计划重点专项(2016YFC0502905);福建省公益类科研院所专项项目(2015R1034-1);福建省科技厅产学研重大项目(2012Y4001);福建省气象局开放式气象科研基金项目(2004K03)

Soil Landslide Susceptibility Assessment Based on DEM

YANG Cheng1(), LIN Guangfa1,2,3,*(), ZHANG Mingfeng1,2,3, ZHANG Rongyan4, SUN Xiaogu5   

  1. 1. Institute of Geography, Fujian Normal University, Fuzhou 350007, China
    2. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fuzhou 350007, China
    3. Research Center for National Geographical Condition Monitoring and Emergency Support in the Economic Zone on the West Side of the Taiwan Strait, Fuzhou 350007, China
    4. Fujian Climate Center, Fuzhou 350001, China
    5. Shanghai Bureau of State Land Supervision, Shanghai 200032, China.
  • Received:2015-10-28 Revised:2015-11-30 Online:2016-12-27 Published:2016-12-20
  • Contact: LIN Guangfa E-mail:chengyang615@qq.com;GuangFaLin@qq.com

摘要:

已有滑坡敏感性研究中对评价指标的选取可以归结为气象、水文、地形、地质、植被、人类活动等方面,这些因子指标来源不一,在缺少数据资料地区难以完整收集。针对这个问题,考虑到目前DEM数据的广泛可获得性及其对滑坡评价的重要性,本文仅利用DEM数据及其派生因子,研究土质滑坡敏感性评价的可行性。研究中把评价因子分为2组:第1组数据仅由DEM派生,包括高程、坡度、坡向、地形起伏度、曲率、水流强度指数(Stream Power Index, SPI)、沉积运输指数(Sediment Transport Index, STI)、地形湿度指数(Topographic Wetness Index, TWI);第2组数据作为对照组,除了包括上述DEM派生的8个因子外,同时加入植被覆盖度、土地利用、土壤类型、年均降雨量因子。本文分别选取逻辑回归模型和证据权法,基于上述2组评价因子,以德化县为例对比2组因子评价结果,利用第1组和第2组数据进行滑坡敏感性评价,结果精度分别为73%和83%。结果表明,仅利用DEM数据进行土质滑坡敏感性评价方法可行,可以为缺乏资料区滑坡敏感性评价提供借鉴。

关键词: 土质滑坡, 敏感性, DEM, 评价

Abstract:

The assessment factors described in the existing landslide susceptibility studies can be cataloged into the aspects of meteorology, hydrology, topography, geology, vegetation, human activities, and others. These conditioning factors are derived from different sources and are hard to collect completely, especially for the ungauged area. As an important data source for the assessment of landslide susceptibility, DEM is easy to obtain. Therefore, the purpose of this study is to assess the landslide susceptibility using the DEM data and its derived factors only. In this study, The assessment factors were divided into two datasets. The first dataset was derived from DEM, which contains eight landslide conditioning factors, including: altitude, slope, aspect, topographic relief, curvature, stream power index (SPI), sediment transport index (STI) and topographic wetness index (TWI). The second dataset, which is used as the comparison group, was gathered by using the same conditioning factors of the first dataset, but with the addition of some other conditioning factors, including: vegetation coverage, land use, soil type, and average annual precipitation. Based on the above two groups of conditioning factors, the logistic regression model and the weights-of-evidence method are employed to assess the landslide susceptibility in Dehua county of Fujian province in China. The prediction rates of the landslide susceptibility results were 73% and 83% by using the factors of the first dataset and the second dataset, respectively. As a result, the DEM-derived conditioning factors were more efficient in generating an accurate landslide susceptibility map. The conclusions made in this study can be used as a reference for the assessment of landslide susceptibility in the ungauged area.

Key words: soil landslide, susceptibility, DEM, assessment