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Journal of Geo-information Science >

2016 , Vol. 18 >Issue 12: 1624 - 1633

DOI: https://doi.org/10.3724/SP.J.1047.2016.01624

Orginal Article

Soil Landslide Susceptibility Assessment Based on DEM

  • YANG Cheng , 1 ,
  • LIN Guangfa , 1, 2, 3, * ,
  • ZHANG Mingfeng 1, 2, 3 ,
  • ZHANG Rongyan 4 ,
  • SUN Xiaogu 5
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  • 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.
*Corresponding author: LIN Guangfa, E-mail:

Received date: 2015-10-28

  Request revised date: 2015-11-30

  Online published: 2016-12-20

Copyright

《地球信息科学学报》编辑部 所有
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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

Cite this article

YANG Cheng , LIN Guangfa , ZHANG Mingfeng , ZHANG Rongyan , SUN Xiaogu . Soil Landslide Susceptibility Assessment Based on DEM[J]. Journal of Geo-information Science, 2016 , 18(12) : 1624 -1633 . DOI: 10.3724/SP.J.1047.2016.01624

1 引言

滑坡敏感性或称易发性是指在特殊地形或某些因素作用下发生滑坡的可能性[1]。目前,已有许多学者基于气象、水文、地形、地质、人类活动等数据结合相关评价模型对区域滑坡敏感性进行研究,如王佳佳等利用坡度、坡向、土地利用等因子对三峡库区滑坡敏感性进行评价[2];邱海军等选取相对高差、坡度、植被覆盖度等因子以陕西省宁强县为研究区进行滑坡敏感性分析[3];陶舒等利用高程、岩性、水系等因子对汶川县滑坡敏感性进行研究[4];Van等选取岩性、距道路和居民区距离等因子对意大利阿尔帕戈地区进行滑坡敏感性评价[5]。这些滑坡敏感性研究中,选取的滑坡评价指标来源、标准不一,许多因子随着时间动态变化,同时受数据的获取限制,尤其在偏远地区,全面收集相关滑坡影响因子显得更为困难。数字高程模型（DEM）作为区域地形的重要表征,目前可在网络上免费获取全球各个地区30 m分辨率的数据。已有的研究表明,由其派生的坡度、坡向、地形起伏度、曲率、地形湿度指数（TWI）、地形粗糙度（TRI）、水流强度指数（SPI）、沉积运输指数（STI）等因子与滑坡存在相关性。例如,Gorsevski等研究认为高程、坡度等可以用于滑坡敏感性评价[6];Glenn等利用激光雷达数据派生的地形因子对滑坡活动情况进行评价,结果表明地形因子与滑坡具有高度相关性[7];Oh等把高程、TRI、SPI等因子用于滑坡敏感性研究,提高了评价结果的精度[8];Domínguez-Cuesta等认为影响滑坡的环境因子可以归结为2类：①与地形相关的,如高程、坡度、坡向、曲率等;②与地质、植被要素相关,如岩性、到断层距离、植被覆盖度等[9];Jebur等利用激光雷达点云数据构建的DEM进行滑坡敏感性研究,其结果表明由DEM派生的因子可以用于滑坡敏感性评价[10];胡德勇等选取坡度、坡向、地表曲率等对马来西亚金马伦高原进行滑坡敏感性分析,其评价结果较好地反映了实际滑坡分布情况[11]。从以上研究成果可知,由DEM派生的因子已广泛参与到滑坡敏感性评价研究中,但多是与其它多元环境因子相结合,较少仅利用DEM数据进行滑坡敏感性评价研究。
本文仅利用DEM派生的相关因子进行区域土质滑坡敏感性评价,为缺少资料地区的滑坡敏感性研究提供参考。同时,文中提及的滑坡敏感性评价都是指土质滑坡,并未对岩体滑坡进行讨论,因为,地质构造是岩体滑坡最重要控制因子,DEM的作用并不突出。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、TWI、SPI、STI;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,还加入植被覆盖度、土地利用、土壤类型、年均降雨量因子。为了降低滑坡敏感性评价模型对结果的影响,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,对区域滑坡敏感性进行评价,同时利用受试者工作特征曲线（Receiver Operating Characteristic Curve,ROC）和曲线下面积（Area Under Curve,AUC）精度验证方法对评价结果进行验证。

2 研究区概况

滑坡作为福建省的主要地质灾害之一,具有区域性和群发性的特点。从滑坡类型来看,土质滑坡占全省滑坡数量的90%以上[12-13]。本研究以德化县作为典型区,该区地处福建省中部,位于“闽中屋脊”戴云山脉中段,面积2232 km2,为福建省滑坡高易发区之一。全县地形复杂,中部高耸,向四周呈阶梯状下降,地势西北高、东南低（图1）;境内残坡积土发育,结构松散,多为各类粘性土,大多数为双层土体,厚度在2~4 m之间,局部地段厚度大于 10 m;年平均降雨量在1500~2000 mm之间,雨量充沛,潮湿多雾[14]。目前区内滑坡主要以浅层土质小型滑坡为主,具有群发、多发、频发的特点。
Fig. 1 The study area

图1 研究区示意图

3 数据处理与研究方法

3.1 数据处理

本文共收集到的403个德化县滑坡点数据,为地质灾害管理部门2011年统计,选取高程、坡度、坡向、TWI、SPI、STI、地形起伏度、土壤类型、多年平均降雨量、土地利用、植被覆盖度等因子参与滑坡敏感性评价。各个因子的获取与处理过程为：① 数字高程模型采用先进星载热发射和反射辐射仪全球数字高程模型（Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model,ASTER GDEM,http://gdem.ersdac.jspacesystems.or.jp/）,分辨率为30 m,利用ArcGIS软件分别提取地形坡度、坡向、曲率,结合窗口分析法计算地形起伏度因子[15];② TWI和SPI作为重要的水文参数被广泛运用于滑坡研究中,其中TWI被用于描述地形和饱和源区产流带来的影响,该因子随着水流汇流累积量的增加而增大,随着剪切强度的增大而减弱,因此该因子在河流区域的取值明显高于其它区域;SPI用于描述地表水流的侵蚀能力,可以用来确定水流汇集而形成的强水流路径和可能出现沟谷侵蚀的地点,地表水流的侵蚀能力越强,该因子值越大[16];STI是表征地表物质随着水流运输与沉积的综合变量,依据式（1）-（3）分别计算TWI、SPI和STI;③ 土壤类型数据,由福建省1:50万土壤类型图数字化得到;④ 基于德化县2008-2014年的自动气象站点的年降雨量数据,在ArcGIS中做空间插值得到多年平均降雨量图层;⑤ 土地利用类型数据,基于2010年Landsat5遥感影像,通过建立典型地物遥感图像判读标志,并结合野外实地考察进行遥感影像解译得到;⑥ 基于2010年Landsat5遥感影像,分辨率为30 m,采用改进的像元二分法模型,提取植被覆盖度[17]。各因子数据结果如图2所示。
TWI = ln As tanβ （1）
SPI = As × tanβ （2）
STI = As 22.13 0.6 × sinβ 0.0896 1.3 （3）
式中：As为单位长度等高线上地表水所流经的上游区域面积,可根据汇流累积量面积与上游水流长度值计算得到;β为地形坡度。
Fig. 2 The conditioning factors

图2 各因子数据

3.2 研究方法

从研究区滑坡点中随机抽取80%个作为训练样本,20%个作为验证样本。把影响滑坡的因子分为2组：第1组数据由DEM派生,包括高程、坡度、坡向、TWI、SPI、STI、曲率、起伏度;在上一组因子的基础上加入土壤类型、土地利用、植被覆盖度、多年平均降雨量作为第2组数据。基于上述2组滑坡影响因子,引入逻辑回归模型和证据权法分别对研究区滑坡敏感性进行评价,利用ROC曲线和AUC方法对评价结果进行精度验证,技术路线如图3 所示。
Fig. 3 Flowchart of the research methodology

图3 技术路线图

（1）逻辑回归模型
逻辑回归模型是滑坡敏感性评价中常用的多元回归分析统计方法,可以用于分析离散或连续型滑坡影响因子变量,同时分析的变量可以是非正态分布的数据。逻辑回归模型公式如（4）-（5）所示。
Y = b 0 + b 1 x 1 + b 2 x 2 + ⋯ + b n x n （4）
p = 1 ( 1 + e - Y ) （5）
式中：Y表示在滑坡影响因子作用下滑坡发生的情况,用0和1值来表示,0代表滑坡不发生,1代表滑坡发生;b0为常数项,bi(i=1,2,…,n)为回归系数, x i (i=1,2,…,n)为影响滑坡的因子;p为滑坡发生的概率,值在0-1之间。
（2）证据权法
证据权法作为一种双变量统计方法,广泛应用于滑坡的定量研究。该方法以贝叶斯统计模型为基础,通过计算影响因子中各等级区间对滑坡发生的贡献权重值,得到滑坡敏感性指数,该指数高低表示了发生滑坡可能性的大小[18]。利用证据权法进行滑坡敏感性评价,先要进行影响因子统计区间的划分。目前,有许多方法用于等级区间的划分,但Tehrany的研究表明基于百分位法（Quantile）较其它方法更为合适[19]。为了便于在GIS中计算,研究中以栅格作为统计单元,证据权法如式（6）-（8）所示。
W i + = ln P B i S P { B i / S _ } = ln N B i ⋂ S × N ( S _ ) N ( B i ⋂ S _ ) × N S （6）
W i - = ln P { B _ i / S } P { B _ i / S _ } = ln N ( B _ i ⋂ S ) × N ( S _ ) N ( B _ i ⋂ S _ ) × N ( S ) （7）
W i = W i + - W i - （8）
式中： N ( B i ) 为影响因子第i个等级区间内的单元数; N ( S ) 为研究区中所有发生滑坡的单元数; N ( S _ ) 为未发生滑坡的单元数; N ( S _ i ) 为其它因子等级区间内的单元数; W i + 为正相关权重; W i - 为负相关权重,当 W i + >0或 W i - <0时,影响因子与滑坡呈正相关,当 W i + <0或 W i - >0时,影响因子与滑坡呈负相关,当 W i + =0或 W i - =0时,影响因子与滑坡不相关; W i 为综合权重,用以表示该因子等级对滑坡的影响权重值,将每个影响因子等级 W i 相加,即得到该栅格单元最终的敏感性系数,其值越大,越利于滑坡的发生,反之则不利于滑坡发生。
（3）精度验证方法
采用ROC曲线和AUC精度验证方法对滑坡敏感性评价结果进行验证。ROC曲线是指根据一系列不同的二分类方式,以真阳性率（灵敏度）为纵坐标,假阳性率（1-特异度）为横坐标绘制的曲线。其中,真阳性率是指实际为滑坡且模型判断为滑坡的概率;假阳性率是指实际上非滑坡但模型判断为滑坡的概率。用AUC表示ROC曲线下的面积值,AUC越接近1,说明模型评价效果越好。当AUC在0.5-0.7之间时,认为评价结果有较低的准确性;在0.7-0.9之间时,有较高的准确性;在0.9以上时,认为有很高的准确性[20]。

4 结果分析与讨论

4.1 因子系数

在已有滑坡点中随机抽取80%滑坡样本,在未发生滑坡区域随机抽取同等数量的样本点,共646个作为训练样本,分别基于第1、2组数据因子进行逻辑回归分析,其中各组因子的回归系数如表1所示。利用证据权法进行滑坡敏感性评价时,首先把高程、坡度、起伏度、SPI、STI、TWI因子,按照百分位法分成10类,植被覆盖度和年均降雨量数据分成5类,曲率则按照值与0的比较分为凹坡、平坡和凸坡3类,坡向按照方位分成9类,其它离散型因子按照各自子类别进行划分,各因子区间的证据权重如表2所示。
Tab. 1 Factor coefficients of the LR model

表1 逻辑回归模型各因子系数

Tab. 2 Factor coefficients of the WOE model

表2 证据权法中各因子系数

因子 类别 Wi 因子 类别 Wi 因子 类别 Wi 因子 类别 Wi
高程
(m)		 179-426 -1.55 坡度
(°)		 0-7.4 0.03 起
伏
度
(m)		 36-112 0.91 SPI 0-27 0.67
426-546 0.21 7.4-12.6 0.47 112-148 0.47 27-64 -0.44
546-646 -0.21 12.6-17.3 0.27 148-180 0.35 64-101 -1.81
646-740 0.26 17.3-21.6 -0.91 180-211 -0.76 101-138 -18.73
740-834 0.33 21.6-25.8 0.30 211-243 0.15 138-175 -0.13
834-933 0.46 25.8-29.9 0.57 243-277 -2.24 175-212 -0.11
933-1045 -0.20 29.9-33.9 -1.66 277-315 -1.68 212-249 -0.85
1045-1190 -0.74 33.9-38.6 -2.08 315-362 -0.50 249-286 -16.95
1190-1385 -20.42 38.6-44.7 -2.06 362-425 -0.09 286-323 -16.65
1385-1828 -19.89 44.7-69.6 -2.14 425-619 -2.21 323-15845 -18.65
STI 0 -14.34 TWI 2.2-3.9 0.02 坡向 平地 -0.08 植
被
覆
盖
度		 0-0.27 0.08
0-2.9 -0.12 3.9-4.5 -0.45 北向 -0.58 0.27-0.45 1.43
2.9-5.9 0.66 4.5-5.0 0.44 东北向 -0.13 0.45-0.57 1.48
5.9-8.9 0.35 5.0-5.7 0.16 东向 0.67 0.57-0.65 -0.78
8.9-11.9 -0.68 5.7-6.3 -1.02 东南向 -0.41 0.65-0.79 -1.79
11.9-14.9 -1.01 6.3-7.1 0.35 南向 0.07 年
均
降
雨
(mm)		 1410-1577 -0.12
14.9-17.9 -0.46 7.1-8.0 0.20 西南向 0.18 1577-1636 0.27
17.9-23.8 -1.84 8.0-9.0 -0.56 西向 -0.33 1636-1683 0.17
23.8-35.7 -1.46 9.0-10.3 -0.27 西北向 0.09 1683-1732 0.01
35.70-758 -2.72 10.3-17.9 -0.39 1732-2200 -0.42
曲率 凹坡 -0.04 土
壤
类
型		 红壤 -0.11 土
地
利
用		 林地 -1.36
平坡 -0.59 黄壤 -0.60 建设用地 -0.44
凸坡 0.10 水稻土 0.43 耕地 1.63
其它 -16.36 园地 0.65
其它 -1.66
表1为各因子数据的逻辑回归系数,其中,因子回归系数为正说明该因子与滑坡呈正相关,反之,系数为负则呈负相关。从表1可看出,在第1组数据中,TWI与滑坡的正相关系数最高,除了曲率、STI和起伏度以及坡向因子中的北坡和东南坡外,其它因子都与滑坡呈正相关;在第2组数据中,土壤类型中的黄壤、土地利用类型中的耕地以及多年平均降雨量因子都与滑坡表现出正相关性,植被覆盖度则与滑坡呈负相关。表2为证据权法中各因子区间证据权重,系数为正说明该区间有利于滑坡的发生,反之系数为负则不利于滑坡的发生。从表2可看出,德化县滑坡易发于834~933 m高程区间带上;当坡度在25.8~29.9°,起伏度在36~112 m之间时滑坡发生敏感性最高,说明德化县滑坡多发生于丘陵地带;从坡向和曲率来看,东向（67.5~122.5°）坡地上滑坡最易发,北向（0~22.5°,337.5~0°）坡地上滑坡最不易发,凸形坡上分布的滑坡数比凹型坡上更多;当SPI在0~27,TWI在4.5~5.0,STI在2.9~5.9时滑坡易发程度高;植被覆盖度在45%~57%,年均降雨量在1577~1636 mm区间,滑坡发生的可能性最大;从土壤类型来看,水稻土发生滑坡的敏感性最高,这与土地利用类型中耕地易发生滑坡相吻合。

4.2 滑坡敏感性评价结果

基于逻辑回归模型和证据权法,结合表1、2中相关因子系数,计算德化县滑坡敏感性指数,其中敏感性指数越大说明滑坡发生的可能性越大,反之则越小。对得到的敏感性指数需要划分成若干个敏感性类别,目前国内外研究中对变量类别的划分,可以归结为基于专家经验划分和基于机器自动划分2大类。其中,基于专家经验的类别划分,需要较强的专业背景知识,同时也带有很强的主观性,这也导致了不同的学者划分的结果不同。而基于机器的自动划分变量类别算法,如标准差分类法（Standard Deviations）、等间距法（Equal Intervals）、自然断点法（Natural Breaks）、百分位法（Quantile）等,则是根据数据固有的特征进行分类[21]。在已有滑坡敏感性结果等级划分中对上述分类方法都有所提及,但Can等认为一个分类方法的好坏要满足2个条件：① 滑坡高敏感性区域应该覆盖已有滑坡点;② 滑坡高敏感性区域面积不能太大[22]。基于上述2点考虑,经过多次实验,认为选择百分位法比较适合本次滑坡敏感性等级的划分,结果如图4所示。
Fig. 4 Landslide susceptibility assessment results

图4 滑坡敏感性评价结果

图4(a)、(b)分别为基于第1组数据和其中的显著因子在逻辑回归模型中进行敏感性评价的结果,图4(c)、(d)分别为基于第2组数据和其中的显著因子在逻辑回归模型中进行敏感性评价的结果, 图4(e)、(f)则分别为基于第1组和第2组数据在证据权法中进行滑坡敏感性评价的结果。从上述滑坡敏感性图可以看出,德化县滑坡易发区主要分布于南部、西南部和西北部,中部地带和东部区域滑坡发生可能性较低。分别统计在不同模型和数据组合下滑坡点在各个敏感性等级区间的分布比例,从定量角度对比2组数据敏感性评价结果,如表3所示。
Tab. 3 Distribution of landslide points in each sensitivity level

表3 各敏感性等级滑坡点分布情况(%)

敏感性等级 LR_DEM LR_DEM_SIG LR_All LR_All_SIG WOE_DEM WOE_All
高 40.4 40.9 59.3 57.8 36 57.1
较高 25.6 26.6 21.6 21.1 27 23.2
中 18.4 17.1 8.7 11.4 19.6 11.7
较低 9.9 10.9 8.7 6.5 13.2 5.5
低 5.7 4.5 1.7 3.2 4.2 2.5

注：WOE_DEM和WOE_All,分别为基于第1组和第2组数据因子集合在证据权法中计算得到的结果（下同）;其它因子集合的说明参照表1中的注示

从表3可知,LR_DEM：中高敏感性等级区间（中、较高、高）覆盖的滑坡点比例为84.4%,低敏感性等级区间（低、较低）覆盖的滑坡点比例为15.6%;LR_DEM_SIG：中高敏感性等级区间覆盖的滑坡点比例为84.6%,低敏感性等级区间覆盖的滑坡点比例为15.4%;LR_All：中高敏感性等级区间覆盖的滑坡点比例为89.6%,低敏感性等级区间覆盖的滑坡点比例为10.4%;LR_All_SIG：中高敏感性等级区间覆盖的滑坡点比例为90.3%,低敏感性等级区间覆盖的滑坡点比例为9.7%;WOE_DEM：中高敏感性等级区间覆盖的滑坡点比例为82.6%,低敏感性等级区间覆盖的滑坡点比例为17.4%;WOE_All：中高敏感性等级区间覆盖的滑坡点比例为92%,低敏感性等级区间覆盖的滑坡点比例为8%。从上述分析可知,基于第1组数据因子的滑坡敏感性评价效果虽然不如第2组数据因子评价得到的结果,但中高敏感性等级区间基本覆盖了已有滑坡点,这从一定程度上也说明基于第1组数据的滑坡敏感性评价方法可行。

4.3 评价结果精度验证

利用ROC曲线与AUC方法对图4中6组滑坡敏感性评价结果进行精度验证,从定量角度对比2组数据评价结果。研究中分别抽取20%的滑坡点和同等数量的非滑坡点,共160个作为验证样本。在SPSS中对评价结果进行精度验证,得到的ROC曲线与AUC值,如图5和表4所示。
Fig. 5 ROC verification curve

图5 ROC精度验证曲线

Tab. 4 Accuracy of landslide susceptibility assessment

表4 滑坡敏感性评价结果精度

模型 因子组合 验证精度/（%）
逻辑回归模型 LR_DEM 73
LR_DEM_SIG 74
LR_All 83
LR_All_SIG 83
证据权法 WOE_DEM 72
WOE_All 81
从图5的ROC精度验证曲线和表4中对应的曲线下面积可以得到,基于第1组数据中的所有因子,利用逻辑回归模型和证据权法进行滑坡敏感性评价,结果精度分别为73%和72%;仅利用在逻辑回归模型中表现显著的5个DEM派生因子进行敏感性评价,结果精度为74%,高于利用第1组数据中的所有因子进行敏感性评价的结果。对第2组数据中的所有因子,利用逻辑回归模型和证据权法进行评价,结果精度分别为83%和81%;仅利用第2组数据中在逻辑回归模型里表现显著的7个因子进行敏感性评价,结果精度为83%。
从2种模型的评价效果来看,利用逻辑回归模型进行敏感性评价效果优于证据权方法。同时,利用逻辑回归模型进行敏感性评价时,应选择与模型表现显著的影响因子,否则会降低滑坡评价精度。从评价因子角度来看,仅利用DEM派生的因子（高程、坡度、坡向、地形起伏度、地形曲率、TWI、SPI和STI）进行敏感性评价,精度可达73%,可以为乏资料区的滑坡敏感性评价提供借鉴。

5 结论

本文利用DEM数据进行土质滑坡敏感性评价,分别采用了逻辑回归模型和证据权法,基于DEM派生数据和年均降雨量、植被覆盖度、土壤类型等其他环境因子进行了滑坡敏感性评价的对比分析,得到以下结论：
（1）仅利用DEM派生因子进行土质滑坡敏感性评价,结果精度可以达到73%。鉴于综合各种来源数据的DEM产品已全球覆盖,因此该方法可以为乏资料区域的滑坡敏感性评价提供参考,这对于偏远山区的滑坡风险评估具有较大的应用价值。
（2）运用逻辑回归模型进行滑坡敏感性评价时,应对评价因子进行筛选,剔除与滑坡显著性较低的因子,否则会造成评价因子越多,结果精度反而越低。
（3）逻辑回归模型和证据权法均可以用于滑坡的敏感性评价,通过二者的评价精度可以看出,逻辑回归模型由于可以针对滑坡与环境因子的关系进行优化,便于选出滑坡的主导因子,因此评价结果的精度会比证据权法稍高。
（4）加入年均降雨量、植被覆盖度、土壤类型等其他环境因子后,滑坡的评价精度仅提高了10%,这说明地形是该区土质滑坡敏感性评价的最主要因子。
由于岩体类滑坡的机理不同,本文所用方法及上述结论对岩体滑坡的敏感性评价不能直接套用;对于其它不同地理背景区域的土质滑坡的适用性也有待于进一步对比和验证。

The authors have declared that no competing interests exist.

References

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Gorsevski P V, Gessler P E, Boll J, et al. Spatially and temporally distributed modeling of landslide susceptibility[J]. Geomorphology, 2006,80(3):178-198.Mapping of landslide susceptibility in forested watersheds is important for management decisions. In forested watersheds, especially in mountainous areas, the spatial distribution of relevant parameters for landslide prediction is often unavailable. This paper presents a GIS-based modeling approach that includes representation of the uncertainty and variability inherent in parameters. In this approach, grid-based tools are used to integrate the Soil Moisture Routing (SMR) model and infinite slope model with probabilistic analysis. The SMR model is a daily water balance model that simulates the hydrology of forested watersheds by combining climate data, a digital elevation model, soil, and land use data. The infinite slope model is used for slope stability analysis and determining the factor of safety for a slope. Monte Carlo simulation is used to incorporate the variability of input parameters and account for uncertainties associated with the evaluation of landslide susceptibility. This integrated approach of dynamic slope stability analysis was applied to the 72-km2 Pete King watershed located in the Clearwater National Forest in north-central Idaho, USA, where landslides have occurred. A 30-year simulation was performed beginning with the existing vegetation covers that represented the watershed during the landslide year. Comparison of the GIS-based approach with existing models (FSmet and SHALSTAB) showed better precision of landslides based on the ratio of correctly identified landslides to susceptible areas. Analysis of landslide susceptibility showed that (1) the proportion of susceptible and non-susceptible cells changes spatially and temporally, (2) changed cells were a function of effective precipitation and soil storage amount, and (3) cell stability increased over time especially for clear-cut areas as root strength increased and vegetation transitioned to regenerated forest. Our modeling results showed that landslide susceptibility is strongly influenced by natural processes and human activities in space and time; while results from simulated outputs show the potential for decision-making in effective forest planning by using various management scenarios and controlling factors that influence landslide susceptibility. Such a process-based tool could be used to deal with real-dynamic systems to help decision-makers to answer complex landslide susceptibility questions.

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Glenn N F, Streutker D R, Chadwick D J, et al. Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity[J]. Geomorphology, 2006,73(1):131-148.This study used airborne laser altimetry (LiDAR) to examine the surface morphology of two canyon-rim landslides in southern Idaho. The high resolution topographic data were used to calculate surface roughness, slope, semivariance, and fractal dimension. These data were combined with historical movement data (Global Positioning Systems (GPS) and laser theodolite) and field observations for the currently active landslide, and the results suggest that topographic elements are related to the material types and the type of local motion of the landslide. Weak, unconsolidated materials comprising the toe of the slide, which were heavily fractured and locally thrust upward, had relatively high surface roughness, high fractal dimension, and high vertical and lateral movement. The body of the slide, which predominantly moved laterally and consists mainly of undisturbed, older canyon floor materials, had relatively lower surface roughness than the toe. The upper block, consisting of a down-dropped section of the canyon rim that has remained largely intact, had a low surface roughness on its upper surface and high surface roughness along fractures and on its west face (unrelated to landslide motion). The upper block also had a higher semivariance than the toe and body. The topographic data for a neighboring, older and larger landslide complex, which failed in 1937, are similarly used to understand surface morphology, as well as to compare to the morphology of the active landslide and to understand scale-dependent processes. The morphometric analyses demonstrate that the active landslide has a similar failure mechanism and is topographically more variable than the 1937 landslide, especially at scales > 20 m. Weathering and the larger scale processes of the 1937 slide are hypothesized to cause the lower semivariance values of the 1937 slide. At smaller scales (< 10 m) the topographic components of the two landslides have similar roughness and semivariance. Results demonstrate that high resolution topographic data have the potential to differentiate morphological components within a landslide and provide insight into the material type and activity of the slide. The analyses and results in this study would not have been possible with coarser scale digital elevation models (10-m DEM). This methodology is directly applicable to analyzing other geomorphic surfaces at appropriate scales, including glacial deposits and stream beds.

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Oh H J, Pradhan B.Application of a neuro-fuzzy model to landslide-susceptibility mapping for shallow landslides in a tropical hilly area[J]. Computers & Geosciences, 2011,37(9):1264-1276.This paper presents landslide-susceptibility mapping using an adaptive neuro-fuzzy inference system (ANFIS) using a geographic information system (GIS) environment. In the first stage, landslide locations from the study area were identified by interpreting aerial photographs and supported by an extensive field survey. In the second stage, landslide-related conditioning factors such as altitude, slope angle, plan curvature, distance to drainage, distance to road, soil texture and stream power index (SPI) were extracted from the topographic and soil maps. Then, landslide-susceptible areas were analyzed by the ANFIS approach and mapped using landslide-conditioning factors. In particular, various membership functions (MFs) were applied for the landslide-susceptibility mapping and their results were compared with the field-verified landslide locations. Additionally, the receiver operating characteristics (ROC) curve for all landslide susceptibility maps were drawn and the areas under curve values were calculated. The ROC curve technique is based on the plotting of model sensitivity 鈥 true positive fraction values calculated for different threshold values, versus model specificity 鈥 true negative fraction values, on a graph. Landslide test locations that were not used during the ANFIS modeling purpose were used to validate the landslide susceptibility maps. The validation results revealed that the susceptibility maps constructed by the ANFIS predictive models using triangular, trapezoidal, generalized bell and polynomial MFs produced reasonable results (84.39%), which can be used for preliminary land-use planning. Finally, the authors concluded that ANFIS is a very useful and an effective tool in regional landslide susceptibility assessment.

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[9]
Domínguez-Cuesta M J, Jiménez-Sánchez M, Berrezueta E. Landslides in the Central Coalfield (Cantabrian Mountains, NW Spain): geomorphological features, conditioning factors and methodological implications in susceptibility assessment[J]. Geomorphology, 2007,89(3):358-369.A geomorphological study focussing on slope instability and landslide susceptibility modelling was performed on a 278km 2 area in the Nalón River Basin (Central Coalfield, NW Spain). The methodology of the study includes: 1) geomorphological mapping at both 1:5000 and 1:25,000 scales based on air-photo interpretation and field work; 2) Digital Terrain Model (DTM) creation and overlay of geomorphological and DTM layers in a Geographical Information System (GIS); and 3) statistical treatment of variables using SPSS and development of a logistic regression model. A total of 603 mass movements including earth flow and debris flow were inventoried and were classified into two groups according to their size. This study focuses on the first group with small mass movements (10 0 to 10 1 m in size), which often cause damage to infrastructures and even victims. The detected conditioning factors of these landslides are lithology (soils and colluviums), vegetation (pasture) and topography. DTM analyses show that high instabilities are linked to slopes with NE and SW orientations, curvature values between 61026 and 61020.7, and slope values from 16° to 30°. Bedrock lithology (Carboniferous sandstone and siltstone), presence of Quaternary soils and sediments, vegetation, and the topographical factors were used to develop a landslide susceptibility model using the logistic regression method. Application of “zoom method” allows us to accurately detect small mass movements using a 5-m grid cell data even if geomorphological mapping is done at a 1:25,000 scale.

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[10]
Jebur M N, Pradhan B, Tehrany M S.Optimization of landslide conditioning factors using very high-resolution airborn laser scanning (LIDAR) data at catchment scale[J]. Remote Sensing of Environment, 2014,152:150-165.Landslide susceptibility, hazards, and risks have been extensively explored and analyzed in the past decades. However, choosing relevant conditioning factors in such analyses remains a challenging task. Landslide susceptibility mapping employs topological, environmental, geological, and hydrological parameters. Some researchers assume that as the number of conditioning factors increases, the precision of the generated susceptibility map increases. By contrast, other case studies prove that a small number of conditioning factors are sufficient to produce landslide susceptibility maps with a reasonable quality. This study investigates the effects of conditioning factors on landslide susceptibility mapping. Bukit Antarabangsa, Ulu Kiang, Malaysia was selected as the study area, because it is a catchment area with a high potential of landslide occurrence. A spatial database of 31 landslide locations was evaluated to map landslide-susceptible areas. Two datasets of conditioning factors were constructed in GIS environment. The first dataset was derived from high-resolution airborne laser scanning data (LiDAR), which contains eight landslide conditioning factors: altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and sediment transport index (STI). The second dataset was gathered by using the same conditioning factors of the first dataset, but with the addition of other conditioning factors: geological and environmental factors of soil, geology, land use/cover (LULC), distance from river, and distance from road. Two different datasets were constructed to compare the efficiency of one over the other in landslide susceptibility zonation. Three methods were implemented to recognize the importance of different conditioning factors in landslide susceptibility mapping. Three different types of models such as weights-of-evidence (WoE) (bivariate statistical analysis), logistic regression (LR) (multivariate statistical analysis), and data-driven support vector machine (SVM) were used to determine the optimal landslide conditioning factors. The area under curve (AUC) was used to assess the obtained results. The prediction rates of WoE, LR, and SVM obtained from only the LiDAR-derived conditioning factors were 59%, 86%, and 84%, respectively. The prediction rates of the WoE, LR, and SVM obtained ffom the second dataset were 65%, 66%, and 69%, respectively. The LiDAR-derived conditioning factors were more sufficient in generating an accurate landslide susceptibility map. Using additional factors, such as geology, LULC, and so on, does not significantly increase the accuracy of the map. The findings of this study can be used as reference for future analysis in selecting data for landslide conditioning factors. (C) 2014 Elsevier Inc. All rights reserved.

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[11]
胡德勇,李京,陈云浩,等.基于GIS的热带雨林地区滑坡敏感性分析—马来西亚金马伦高原个案研究[J].自然灾害学报,2008,17(6):147-152.滑坡敏感性分析对灾害评价和预测具有重要的作用。以马来西亚金马伦高原为研究区,选择坡度、坡向、地表曲率、岩性、构造、土地覆盖、地貌类型、道路和排水系统等9个要素作为评价因子,探讨运用G IS和RS技术获取、管理滑坡灾害信息,和热带雨林地区湿热环境下滑坡灾害敏感性的分析方法。条件概率模型和逻辑回归模型分别应用于滑坡灾害敏感性分析与制图,通过比较滑坡敏感性的计算结果与历史滑坡信息,验证了两种方法的有效性,结果显示,条件概率模型和逻辑回归模型的预测精度分别为77.3%和83.6%,逻辑回归法具有较好的描述精度;滑坡敏感性分析中土地利用和土地覆盖、道路设施等因素具有较高权重,人类对雨林的垦殖和开发提升了该地区滑坡发生的敏感度。

DOI

[ Hu D Y, Li J, Chen Y H, et al. GIS-based susceptibility analysis of landslide in tropical rainforest area: a case study of Cameron Highland, Malaysia[J]. Journal of Natural Disasters, 2008,17(6):147-152. ]

[12]
陆求裕. 基于统计学方法福建滑坡灾害与其影响因子的相关性分析[J].福建地质,2010,29(A01):52-56.滑坡为福建省主要地质灾害之一,受自然地理、地质条件的制约,以及人类工程活动及降雨的诱发影响,发生频率较高。本文应用统计学方法,对近几年来福建省所发生滑坡的地质灾害点进行相关性研究,提出了福建省内山体滑坡主要受地形、地质、气象及人类工程活动因素的综合影响。

[ Lu Q Y.Study of the correlation analysis between landslide with its influence factors based on the statistical methods in Fujian province[J]. Geology of Fujian, 2010,29(A01):52-56. ]

[13]
何永金. 福建省主要地质灾害的特点,成因及其对策[J].福建地质,1995,14(4):263-271.本文主要介绍福建省主要地质灾害类型－崩塌，滑坡，泥石流，岩溶塌陷，地面沉降的主要活动表现，特点，形成机理及其主要控制，影响因素，并从管理上和技术上提出防治的对策建议。此外，文中还扼要地介绍了福建发生地质灾害的背景条件和地质灾害的危害。

[ He Y J.The characteristics, causes and countermeasures of main geological hazards in Fujian Province[J]. Geology of Fujian, 1995,14(4):263-271. ]

[14]
黄俊宝. 德化县滑坡成灾临界降雨量研究[J].福建地质,2013,32(1):65-69.德化县位于闽东南地区,是台风强降雨三大高值区之一,为地质灾害 多发区.地质灾害以浅层土质滑坡为主,具有群发性、多频发性.根据德化县滑坡发育特点和气象雨量数据,采用二项分类Logistic回归模型,计算得出研 究区滑坡临界降雨量的表达式,为区域地质灾害预警预报提供技术支持.

DOI

[ Huang J B.Preliminary studying of landslide critical rainfall in Dehua County[J]. Geology of Fujian, 2013,32(1):65-69. ]

[15]
郎玲玲,程维明,朱启疆,等.多尺度DEM提取地势起伏度的对比分析——以福建低山丘陵区为例[J].地球信息科学,2007,9(6):1-6.坡度和起伏度是地形描述中最常用的参数，它们能快速、直观地反映地势起伏特征；坡度是划分平 原和非平原的重要依据之一，地势起伏度可进一步划分台地、丘陵、小起伏山地、中起伏山地和大起伏山地等类型，基本地貌类型就是由海拔和起伏度两个指标确定 的形态类型，它是遥感解译划分更详细地貌类型的基础。本研究以福建省1：25万和1：10万的DEM为实验数据，计算坡度划分平原和山地大区，其临界坡度 值约为3°；利用ArcGIS空间分析中栅格窗口递增方法，对应不同尺度的DEM，计算地势起伏度，确定研究区的最佳分析窗口面积为4．41km^2，得 出中国低山丘陵区计算基本地貌形态类型的最佳尺度DEM为1：25万比例尺，而1：10万比例尺DEM适用于没有连绵起伏的更小范围的低山丘陵区；利用已 有研究成果得出不同尺度DEM计算地势起伏度与最佳格网单元之间的函数关系。该研究对提取我国低山丘陵区基本地貌形态类型具有一定的借鉴作用。

DOI

[ Lang L L, Cheng W M, Zhu Q J, et al. A comparative analysis of the multicriteria DEM extracted relief - taking Fujian low mountainous region as an example[J]. Journal of Geo-information Science, 2007,9(6):1-6. ]

[16]
江青龙,谢永生,张应龙,等.基于GIS与RS小流域空间数据挖掘[J].水土保持研究,2010,17(6):64-67.针对小流域基础空间数据复杂多样,且难以管理等问题,选取河北省平泉县东北沟小流域为研究对象,运用GIS与RS技术,利用1:10 000比例尺、5 m分辨率的DEM与空间分辨率为0.61 m的Quiekbird影像,对水文、地形、土地利用等多种流域空间数据进行挖掘,并结合二者建立了小流域三维空间模型,同时创建了空间数据库对其进行统一高效管理.结果表明:GIS与RS技术能够为小流域数据空间获取,流域规划建设及管理提供高效的技术手段.

[ Jiang Q L, Xie Y S, Zhang Y L, et al. The spatial data mining for small watershed based on GIS and RS[J]. Research of Soil and Water Conservation, 2010,17(6):64-67. ]

[17]
李苗苗,吴炳方,颜长珍,等.密云水库上游植被覆盖度的遥感估算[J].资源科学,2004,26(4):153-159.该文在对像元二分模型的两个重要参数推导的基础上，对已有模型的参数估算方法进行改进，建立了用NDVI归一化植被指数定量估算植被覆盖度的模型，并根据实际运用时的二种情况，提出了估算植被覆盖度的方案。然后根据研究区密云水库上游的具体特点并结合实际情况设计了模型应用的技术路线和实施方法，对研究区植被覆盖度进行了估算。通过密云流域的实地考察，利用照相法对植被覆盖度的估算结果进行了验证，估算精度达85%，表明使用此改进模型进行植被覆盖度遥感监测是可行的。

DOI

[ Li M M, Wu B F, Yan C Z, et al. Estimation of vegetation fraction in the upper basin of Miyun reservoir by remote sensing[J]. Resources Science, 2004,26(4):153-159. ]

[18]
范强,巨能攀,向喜琼,等.证据权法在区域滑坡危险性评价中的应用——以贵州省为例[J].工程地质学报,2014,22(3):474-481.以GIS为技术平台，采用证据权法对研究区进行了滑坡地质灾害危险性分析。综合分析历史滑坡数据及其环境因素和触发因素，数据源主要有地形图、DEM、地质图，选取地层岩性、构造、高程、坡度、坡向、地形起伏度、道路、水系作为危险性评价因子。首先应用ArcGIS软件对数据源进行处理，提取各个评价因子图层，并对每个图层进行分级、缓冲区分析等处理，建立若干证据层。然后将历史灾害点与评价因子进行空间关联分析，计算每个评价因子等级的权重，最后计算出评价单元的危险性指数，并将危险性分为极高危险区、高危险区、中等危险区、低危险区。采用成功率曲线法对证据权法评价精度进行验证，结果表明本次评价的精度为71%。利用历史滑坡数据对评价结果进行验证，结果显示评价结果与实际情况较为吻合，说明证据权可以客观定量地评价各影响因子对滑坡的影响程度，该方法应用于区域地质灾害危险性评价比较有效。

DOI

[ Fan Q, Ju N P, Xiang X Q, et al. Landslides hazards assessment with weights of evidence: a case study in Guizhou, China[J]. Journal of Engineering Geology, 2014,22(3):474-481. ]

[19]
Tehrany M S, Pradhan B, Jebuv M N.A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT5 imagery[J]. Geocarto International, 2014,29(4):351-369.Land-use/land-cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) among many others. In this study, LULC types of Selangor area were analysed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. Satellite Pour l'Observation de la Terre (SPOT) 5 satellite images with four spectral bands from 2003 to 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently, SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5 and 91%) performed better results than the pixel-based DT (68.6 and 68.4%) and object-based SVM (80.6 and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.

DOI

[20]
方苗,张金龙,徐瑱.基于GIS和Logistic回归模型的兰州市滑坡灾害敏感性区划研究[J].遥感技术与应用,2011,26(6):845-854.针对兰州市脆弱的地质环境和频繁发生的滑坡灾害,采用Logistic回归模型,以ArcGIS和SPSS软件为工具,选取地层岩性、断层构造、坡度、地貌、植被覆盖度、7~9月平均降水、道路(公路、铁路)作为滑坡灾害影响因子。首先对每个影响因子分级并计算每个因子指标值,然后在ArcMap中对影响因子图层进行叠加操作,最后在SPSS软件中运用Logistic回归方法,计算出每个影响因子的系数值并建立Logistic回归模型。根据Logistic回归模型在ArcMap中绘制兰州市滑坡灾害敏感性区划图,区划图和实际的滑坡分布情况基本吻合。模型的Kappa系数值和ROC曲线下面积值(AUC值)分别为0.623和0.709,两种方法的检验结果均表明模型模拟效果较好,能应用于兰州市滑坡灾害敏感性区划研究中。

[ Fang M, Zhang J L, Xu Z.Landslide susceptibility zoning study in Lanzhou city based on GIS and logistic regression model[J]. Remote Sensing Technology and Application, 2011,26(6):845-854. ]

[21]
Ayalew L, Yamagishi H.The application of GIS-based logistic regression for landslide susceptibility mapping in the Kakuda-Yahiko Mountains, Central Japan[J]. Geomorphology, 2005,65(1):15-31.As a first step forward in regional hazard management, multivariate statistical analysis in the form of logistic regression was used to produce a landslide susceptibility map in the Kakuda-Yahiko Mountains of Central Japan. There are different methods to prepare landslide susceptibility maps. The use of logistic regression in this study stemmed not only from the fact that this approach relaxes the strict assumptions required by other multivariate statistical methods, but also to demonstrate that it can be combined with bivariate statistical analyses (BSA) to simplify the interpretation of the model obtained at the end. In susceptibility mapping, the use of logistic regression is to find the best fitting function to describe the relationship between the presence or absence of landslides (dependent variable) and a set of independent parameters such as slope angle and lithology. Here, an inventory map of 87 landslides was used to produce a dependent variable, which takes a value of 0 for the absence and 1 for the presence of slope failures. Lithology, bed rock-slope relationship, lineaments, slope gradient, aspect, elevation and road network were taken as independent parameters. The effect of each parameter on landslide occurrence was assessed from the corresponding coefficient that appears in the logistic regression function. The interpretations of the coefficients showed that road network plays a major role in determining landslide occurrence and distribution. Among the geomorphological parameters, aspect and slope gradient have a more significant contribution than elevation, although field observations showed that the latter is a good estimator of the approximate location of slope cuts. Using a predicted map of probability, the study area was classified into five categories of landslide susceptibility: extremely low, very low, low, medium and high. The medium and high susceptibility zones make up 8.87% of the total study area and involve mid-altitude slopes in the eastern part of Kakuda Mountain and the central and southern parts of Yahiko Mountain.

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[22]
Can T, Nefeslioglu H A, Gokceoglu C, et al. Susceptibility assessments of shallow earthflows triggered by heavy rainfall at three catchments by logistic regression analyse[J]. Geomorphology, 2005,72(1):250-271.Sometimes regional meteorological anomalies trigger different types of mass movements. In May 1998, the western Black Sea region of Turkey experienced such a meteorological anomaly. Numerous residential and agricultural areas and engineering lifelines were buried under the flood waters. Besides the reactivation of many previously delineated landslides, thousands of small-scale landslides (mostly the earthflow type) occurred all over the region. The earthflows were mainly developed in flysch-type units, which have already presented high landslide concentrations. In this study, three different catchments 鈥 namely Agustu, Egerci, and Kelemen 鈥 were selected because they have the most landslide-prone geological units of the region. The purposes of the present study are to put forward the spatial distributions of the shallow earthflows triggered, to describe the possible factors conditioning the earthflows, and to produce the shallow earthflow susceptibility maps of the three catchments. The unique condition units (UCU) were employed during the production of susceptibility maps and during statistical analyses. The unique condition units numbered 4052 for the Agustu catchment, 13,241 for the Egerci catchment and 12,314 for the Kelemen catchment. The earthflow intensity is the highest in the Agustu catchment (0.038 flow/UCU) and lowest in the Egerci catchment (0.0035 flow/UCU). Logistic regression analyses were also employed. However, during the analyses, some difficulties were encountered. To overcome the difficulties, a series of sensitivity analyses were performed based on some decision rules introduced in the present study. Considering the decision rules, the proper ratios of UCU free from earthflow (0) / UCU including the earthflow (1) for the Agustu, Egerci and Kelemen catchments were obtained as 3, 6, and 5, respectively. Also, a chart for the proper ratio selection was developed. The regression equations from the selected ratios were then applied to the entire catchment and the earthflow susceptibility maps were produced. The landslide susceptibility maps revealed that 15% of the Agustu catchment, 8% of the Egerci catchment, and 7% of the Kelemen catchment have very high earthflow susceptibility; and most of the earthflows triggered by the May 1998 meteorological event were found in the very high susceptibility zones.

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