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利用MIA-HSU方法划分斜坡单元的奉节县滑坡易发性评价

王秀英, 杨红娟, 贾一凡, 张少杰, 宋建洋, 田华

王秀英,杨红娟,贾一凡,等. 利用MIA-HSU方法划分斜坡单元的奉节县滑坡易发性评价[J]. 中国地质灾害与防治学报,2025,36(2): 152-161. DOI: 10.16031/j.cnki.issn.1003-8035.202403016
引用本文: 王秀英,杨红娟,贾一凡,等. 利用MIA-HSU方法划分斜坡单元的奉节县滑坡易发性评价[J]. 中国地质灾害与防治学报,2025,36(2): 152-161. DOI: 10.16031/j.cnki.issn.1003-8035.202403016
WANG Xiuying,YANG Hongjuan,JIA Yifan,et al. Landslide susceptibility evaluation in Fengjie County based on slope units extracted using the MIA-HSU method[J]. The Chinese Journal of Geological Hazard and Control,2025,36(2): 152-161. DOI: 10.16031/j.cnki.issn.1003-8035.202403016
Citation: WANG Xiuying,YANG Hongjuan,JIA Yifan,et al. Landslide susceptibility evaluation in Fengjie County based on slope units extracted using the MIA-HSU method[J]. The Chinese Journal of Geological Hazard and Control,2025,36(2): 152-161. DOI: 10.16031/j.cnki.issn.1003-8035.202403016

利用MIA-HSU方法划分斜坡单元的奉节县滑坡易发性评价

基金项目: 国家重点研发计划项目(2023YFC3007202);中国气象局气象能力提升联合研究专项项目(23NLTSZ009)
详细信息
    作者简介:

    王秀英(1999—),女,四川达州人,硕士研究生,主要从事滑坡识别方向的研究。E-mail:19983475010@163.com

    通讯作者:

    杨红娟(1982—),女,河南许昌人,博士,副研究员,主要从事泥石流灾害的形成机理、预测预报和动力学过程研究。E-mail:yanghj@imde.ac.cn

  • 中图分类号: P642.22

Landslide susceptibility evaluation in Fengjie County based on slope units extracted using the MIA-HSU method

  • 摘要:

    栅格单元难以表征斜坡的形态与边界,以其为制图单元的滑坡易发性评价结果无法精细化描述自然斜坡的滑坡易发程度。而形态图像分析-均匀坡度单元(morphological image analysis-homogeneous slope unit,MIA-HSU)方法提取的斜坡单元可以表征斜坡的形态与边界,并能克服传统方法提取的斜坡单元存在坡度突变的缺陷。文章使用MIA-HSU为滑坡易发性评价提供制图单元。以重庆市奉节县为研究区,选取高程、坡度、坡向、归一化植被指数、归一化建筑指数、起伏度、距河流距离、距道路距离、岩性、剖面曲率、土地利用、地形湿度指数、水流功率指数、泥沙输移指数、地形位置指数等15个指标,采用信息量法评价奉节县的滑坡易发性程度。评价结果表明,滑坡易发性越高的区域灾害点密度越大,1950—2015年参加训练的滑坡点落在极高易发区和高易发区中的比例为 94.13%,成功率曲线法对滑坡易发性评价结果的测试精度为0.764,表明评价结果与实际滑坡分布情况基本吻合;2018年以后发生的未参与模型训练的滑坡点中超过90%落在高易发区和极高易发区,说明易发性评价结果具有较高的泛化性。研究结果可为研究区滑坡隐患点识别和灾害防治提供科学参考。

    Abstract:

    Grid units have limitations in accurately delineating the morphology and boundaries of slopes, and when used as mapping units in landslide susceptibility evaluation, they cannot accurately describe the landslide susceptibility of natural slopes. Investigations have shown that the morphological image analysis-homogeneous slope unit(MIA-HSU) method provides slope units that are more homogenous in slope angle and aspect, addressing the deficiencies of traditional methods. In this study, MIA-HSU was applied to provide mapping units for landslide susceptibility evaluation. Taking Fengjie County, Chongqing as the study area, 15 factors including elevation, slope angle, slope aspect, normalized difference vegetation index (NDVI), normalized difference built-up index(NDBI), topographic relief, distance from rivers, distance from roads, lithology, profile curvature, land use, topographic wetness index (TWI), stream power index (SPI), sediment transport index (STI), and topographic position index(TPI) were selected to evaluate landslide susceptibility using the information value method. The evaluation results indicated that areas with higher landslide susceptibility exhibited a greater density of disaster points. During the 1950 to 2015 period, 94.13% of the landslide points used for training fell within the extremely high and high susceptibility zones. The accuracy of landslide susceptibility evaluation was further verified using the success rate curve method. The accuracy of the verification set was 0.764, indicating that the evaluation results were generally consistent with the actual landslide distribution. Over 90% of the landslide points occurring after 2018 (which were not used in training) were located in the high and extremely high susceptibility zones, demonstrating the model’s high generalization ability. The findings provide a scientific basis for identifying potential landslide hazards and for landslide prevention and mitigation in the study area.

  • 强降雨多发生在每年7—8月,降雨因素为该时间段地质灾害发生的主要影响因素。据历史数据统计,中国约60%的突发地质灾害的发生与降雨强度及累计降雨量密切相关,突发地质灾害集中发生在每年暴雨多发的汛期[1]

    降雨型滑坡预测预报研究一直以来受到国内外学者的关注,研究方向可分为滑坡破坏机理分析和预警预报研究两方面。降雨型滑坡预警预报研究方法包括试验分析法、数值模拟法和数学统计法。黄润秋等[2]通过室内模型试验揭示了降雨型滑坡随着降雨量的增大,滑坡岩土体孔隙水压力逐渐升高,最终形成滑坡,揭示了降雨型滑坡存在降雨阈值的根本原因。Guzzetti等[3]、Sharir等[4]认为降雨型滑坡的发生通常与临界降雨量有关,若超过此雨量界限,可能会发生滑坡。

    根据分析对象不同,数学统计法又分为滑坡位移与降雨相关性分析法[5]、滑坡结构与降雨相关性分析法[6]、经验统计模型法[7]。甘肃省地质构造复杂,崩塌、滑坡、泥石流等地质灾害非常发育。按年度统计,降雨引发的地质灾害占比约60%以上,最高可达99%(图1)。甘肃省以区域降雨统计模型、滑坡24 h趋势预警模型、泥石流预警模型均属于以数学统计为主的第一代预警模型,在地质灾害气象风险预警中发挥了重要作用[810]

    图  1  2013—2023年甘肃省降雨引发的地质灾害占年度地质灾害的比例图
    Figure  1.  Proportion of geological disasters caused by rainfall in annual geological disasters from 2013 to 2023 in Gansu Province

    目前,甘肃省的地质灾害气象风险预警模型研究仍处于第一代隐式统计模型向第二代显示统计模型的过渡阶段[11],预警模型研究及应用相对滞后,精度有待提高。近年来,甘肃省地质灾害气象风险预警模型研究主要集中在地质灾害高发、频发的白龙江流域,且以泥石流灾害预警模型研究为主。比如王高峰等[12]选取泥石流危险性评价因子:泥石流沟的规模、主沟纵比降、沟谷发育密度、物源区沟道纵比降,通过综合分析研究,得出了自然沟谷发生泥石流灾害的定量评价模型,为中小型泥石流预警预报提供思路,而针对斜坡类灾害的预警模型研究相对较少。

    本文以白龙江流域降雨型滑坡为研究对象,针对不同岩性特征的滑坡,建立了不同概率等级下的滑坡发生时事件降雨量(event rainfall)与降雨历时(duration of rainfall)之间的关系模型,以下简称ED模型,为不同岩性类型的斜坡在降雨作用下发生滑坡的阈值研究提供新思路。

    白龙江流域甘肃段包含5个县(区),位于长江上游,区内植被发育,受新生代印度—亚洲板块挤压作用影响,断裂构造变形明显[13]。区内山大沟深,地形地貌复杂、岩土体类型多样、新构造活动强烈、生态环境脆弱,加之地震活动频繁、降雨集中、暴雨频发,建设用地紧张、发展与环境保护矛盾突出。滑坡、泥石流灾害的暴发,不仅严重威胁当地人民的生命财产,也严重制约社会经济发展。

    研究区是中国滑坡、泥石流四大高易发区之一,开展区内降雨引发滑坡灾害预警模型研究意义重大。据统计,截至2023年底,研究区共发育地质灾害隐患点5035处,占当年全省隐患点总数的22.97%,隐患点密度为0.24处/km2;按类型划分,滑坡2776处,崩塌910处,泥石流1341处,地裂缝1处,地面塌陷7处;按行政区划分,宕昌380处,武都2251处,文县1530处,迭部401处,舟曲473处。

    本研究以2000—2019年研究区发生的滑坡灾害数据为基础。结合自然资源部门地质灾害隐患点台账、县(区)地质灾害区划报告资料、地质灾害调查报告、县志等资料,同时采用多期遥感数据对比分析、室内解译、野外调查、访问、取样、测试等手段,修正补充已有的滑坡灾害台账数据,结合前期降雨事件分析比对,最终整理形成128个因降雨引发滑坡的记录,资料详细记录了滑坡事件发生的地点、时间、类型、成因等,数据较为可靠,成为本次研究的对象。经分析,该类滑坡主要分布在白龙江两岸的山坡地带,在6—9月多发,占比为74%,7月下旬及8月上旬,滑坡发生数量达到峰值,其余月份滑坡发生的数量较少,滑坡的发生数量与降雨量和降雨强度基本吻合[14],与当地灾害发生的规律相符(图23)。

    图  2  累计月降雨量与滑坡数量的相关关系
    Figure  2.  Correlation between cumulative monthly average rainfall and number of landslides
    图  3  累计月降雨量与滑坡数量的相关性分析图
    Figure  3.  Correlation analysis diagram between cumulative monthly rainfall and number of landslides

    按照滑坡岩性特征将滑坡分为较硬、极软、软硬相间三种类型。对于每种类型的滑坡,采用频数法,分别计算不同降雨事件下滑坡发生的概率,即不同岩性类型的滑坡发生前的事件雨量和降雨历时关系,基于频率法分别获得不同概率条件下ED降雨阈值曲线[1516],建成滑坡发生概率预警模型。

    2010年Brunetti等[15]发表的论文中指出基于频率法的ED降雨阈值符合幂律法则:

    E=(α±Δα)D(γ±Δγ)

    式中:E——事件雨量/mm;

    D——降雨持续时间/d;

    α——截距,Δα为与α相关的变量;

    γ——指数,Δγ为与γ相关的变量。

    假设选择一组滑坡数据,以滑坡发生的概率为5%、20%和50% 3种情况下,获得对应的截距和指数,绘制3条曲线,将滑坡事件分为4个区间。即,位于5%概率线以下的点表示该降雨事件下的雨量及降雨历时引发滑坡的概率小于5%。如图4所示,通过对滑坡降雨事件数据统计分析,分别获取滑坡发生概率为5%、20%和50%的ED关系曲线,其中,RLs为诱发滑坡的降雨事件,NRLs为未诱发滑坡的降雨事件。根据样本数据中滑坡发生概率,采用数据拟合方法,计算获得截距和指数γ±Δγ取0.64±0.09,概率为5%的直线关系(T5)为E=(5.01±0.06)·D(0.64±0.09),概率为20%的直线关系为(T20)E=(7.08±0.67)·D(0.64±0.09),概率为50%的直线关系(T50)为E=(15.14±1.15)·D(0.64±0.09)ɑ取值介于2.07~16.29,D取值介于1~40 d,随着滑坡事件概率的增大,相对不确定性增加,ED之间的关系趋于离散[17]。通过对本次研究中数据的分析,在概率为50%的直线关系中,其相对不确定度为7.6%;在概率为20%的直线关系中,其相对不确定度为9.5%;在概率为5%的直线关系中,其相对不确定度为1.2%。从图2中可以看出,概率为50%的曲线关系中,其相对不确定度较低,说明数据较为集中[18]

    图  4  概率为T5、T20、T50的阈值曲线
    Figure  4.  Threshold curves for T5,T20,and T50 obtained by frequency method

    根据地层年代、岩体工程性质特征等因素综合考虑,将区域内地层岩性按照软弱程度进行分类,分类标准详见表1。通过岩性分类结果与128处滑坡样本空间分布进行对比,得出松散物质、软硬相间、极软三种类型的岩性中滑坡灾害多发,其中,有72起滑坡分布在松散层内,岩土体类型主要为第四系残坡积碎石土、粉质黏土、强风化千枚岩、砾石,占比约56.25%;有37起滑坡分布在软硬相间的岩性中,岩体类型主要有板岩、千枚岩、浅变质砂岩、砂岩与千枚岩互层岩体,占比约28.91%;有12起滑坡分布极软的岩组中,岩性主要是新近系砾岩、页岩、泥质砂岩等,占比9.38%[1920]。而坚硬、较软、较硬三种岩性类型中滑坡分布数量为7起,数量较少,滑坡降雨阈值曲线的拟合效果差,因此此处不做分析(图5)。

    表  1  岩性类型的划分标准
    Table  1.  Classification Standards for Lithological Types
    软硬类型 主要岩性类型
    极软 层状碎屑岩:古近系砾岩;新近系砾岩、页岩、泥质砂岩
    坚硬 块状岩浆岩:花岗岩、辉绿岩、辉长岩、闪长岩、
    闪长玢岩、闪斜煌斑岩等
    较软 层状碎屑岩:白垩系砾岩、砂岩、泥岩
    较硬 层状碳酸盐岩:三叠系、二叠系灰岩、砂岩、页岩等
    泥盆系板岩、砂岩、页岩、灰岩等
    软硬相间 ①层状变质岩:二叠系砂岩、砂质板岩、凝灰岩、千枚岩;
    志留系砂岩、石灰岩、千枚岩、板岩
    ②层状碳酸盐岩:泥盆系板岩、千枚岩、灰岩
    ③层状碎屑岩:侏罗系砂岩、泥岩、砾岩、页岩
    松散物质 第四系残坡积碎石土、粉质黏土、强风化千枚岩、砾石
    下载: 导出CSV 
    | 显示表格
    图  5  降雨型滑坡岩土类型分类图
    Figure  5.  Classification diagram of rainfall-induced landslides in different rock and soil types

    采用频数法对不同岩体类型的滑坡进行分析,得到概率分别为15%(低)、25%(中)、40%(高)、60%(极高)时,降雨ED曲线(图6),以曲线为下限,将曲线上部4个区间自下而上依次对应定义为低风险区、中风险区、高风险区、极高风险区4个预警等级,即降雨事件雨量与降雨历时所对应的点落入4个区间中的某一个,即判定该滑坡的风险等级为该区间的风险等级。

    图  6  不同岩性类型滑坡不同预警等级的降雨阈值曲线
    Figure  6.  Rainfall threshold curves for different warning levels of landslides with different lithological types

    3种岩性类型的滑坡不同预警等级下限的降雨阈值曲线分别如下:

    松散物质:E=6.43D0.72P=15%,蓝色预警)、E=7.94D0.72P=25%,黄色预警)、E=10.91D0.72P=40%,橙色预警)、E=18.79D0.72P=60%,红色预警)。

    极软岩类:E=9.25D0.54P=15%,蓝色预警)、E=12.30D0.54P=25%,黄色预警)、E=18.88D0.54P=40%,橙色预警)、E=31.48D0.54P=60%,红色预警)。

    软硬相间:E=9.79D0.46P=15%,蓝色预警)、E=11.16D0.54P=25%,黄色预警)、E=15.00D0.46P=40%,橙色预警)、E=21.09D0.46P=60%,红色预警)。

    图6中可知,松散层滑坡降雨阈值曲线的间距较小,不同预警等级临界累计降雨量差值最小,降雨量对松散层滑坡作用较快。极软岩类滑坡降雨阈值曲线的间距较大,不同预警等级临界累计降雨量差值较大,降雨量对滑坡发生反映慢。软硬相间岩类滑坡降雨阈值曲线的间距中等,不同预警等级临界累计降雨量差值中等,降雨量对滑坡发生反映中等。按照12 d降雨历时,计算得不同岩性类型斜坡分别在4种预警等级下的下限临界累计雨量值(表2)。

    表  2  不同岩性类型的斜坡在各预警等级下发生滑坡前不同降雨历时下的累计雨量
    Table  2.  Duration and cumulative rainfall before landslides occur on slopes of different rock types at different warning levels /mm
    滑坡类型 预警等级 降雨历时/d
    1 2 3 4 5 6 7 8 9 10 11 12
    松散物质 低(P=15%) 6.43 10.59 14.18 17.45 20.49 23.36 26.10 28.74 31.28 33.75 36.14 38.48
    中(P=25%) 7.94 13.08 17.51 21.54 25.30 28.85 32.23 35.49 38.63 41.67 44.63 47.52
    高(P=40%) 10.91 17.97 24.06 29.60 34.76 39.64 44.29 48.76 53.07 57.26 61.32 65.29
    极高(P=60%) 18.79 30.95 41.44 50.98 59.87 68.26 76.28 83.98 91.41 98.61 105.62 112.44
    极软岩类 低(P=15%) 9.25 13.45 16.74 19.55 22.06 24.34 26.45 28.43 30.30 32.07 33.77 35.39
    中(P=25%) 12.30 17.88 22.26 26.00 29.33 32.37 35.18 37.81 40.29 42.65 44.90 47.06
    高(P=40%) 18.88 27.45 34.17 39.91 45.02 49.68 54.00 58.03 61.84 65.46 68.92 72.24
    极高(P=60%) 31.48 45.77 56.97 66.55 75.07 82.84 90.03 96.76 103.12 109.15 114.92 120.45
    软硬相间 低(P=15%) 9.79 13.47 16.23 18.52 20.53 22.32 23.96 25.48 26.90 28.23 29.50 30.70
    中(P=25%) 11.61 15.97 19.24 21.97 24.34 26.47 28.42 30.22 31.90 33.48 34.98 36.41
    高(P=40%) 15.00 20.63 24.86 28.38 31.45 34.20 36.71 39.04 41.21 43.26 45.20 47.05
    极高(P=60%) 21.09 29.01 34.96 39.90 44.22 48.09 51.62 54.89 57.95 60.82 63.55 66.15
    下载: 导出CSV 
    | 显示表格

    本文收集了2020年陇南“8•17”暴洪灾害期间59起滑坡信息及前期降雨资料,其中,滑坡发生与8月11—17日,降雨数据为滑坡附近雨量站点8月5—18日逐日累计降雨数据,共计372条。按照3种岩性类型,分别与上述不同预警等级的降雨阈值曲线对比分析,检验模型的准确性。

    根据滑坡事件分析,滑坡多在降雨持续6 d后集中暴发。松散物质类滑坡共计38起,其中,25起滑坡发生前降雨事件位于极高风险区,占比约65.79%;10起位于高风险区(40%≤P<60%),占比约7.89%;3起位于中风险区(25%≤P<40%),占比约0。极软岩类滑坡事件共计9起,其中,8起滑坡时降雨事件位于极高风险区(P≥60%),占比约88.89%;1起位于高风险区(40%≤P<60%),占比约11.11%;中、低风险区(P<40%)无滑坡发生。软硬相间滑坡事件共计12起,其中,10起滑坡降雨事件位于极高风险区(P≥60%),占比约83.33%;1起位于高风险区(40%≤P<60%),占比约8.33%;1起位于低风险区(40%≤P<60%),占比约8.33%;中风险区无滑坡发生(表3图7)。

    表  3  不同岩性类型滑坡事件对应的预警等级
    Table  3.  Warning levels corresponding to landslide events of various lithologic types
    滑坡类型 预警等级 滑坡/处 事件比例/%
    松散物质 低(P<25%) 0 0
    中(25%≤P<40%) 3 7.89
    高(40%≤P<60%) 10 26.32
    极高(P≥60%) 25 65.79
    极软岩类 低(P<25%) 0 0
    中(25%≤P<40%) 0 0
    高(40%≤P<60%) 1 11.11
    极高(P≥60%) 8 88.89
    软硬相间 低(P<25%) 1 8.33
    中(25%≤P<40%) 0 0
    高(40%≤P<60%) 1 8.33
    极高(P≥60%) 10 83.33
    下载: 导出CSV 
    | 显示表格

    综上所述,位于极高风险预区的降雨事件,比例最低的为松散物质类滑坡,占比65.79%,其次为软硬相间滑坡,占比83.33%,最高为极软岩类滑坡,占比88.89%,但都大于60%,因此,极高风险阈值曲线基本准确。

    图  7  滑坡发生前降雨事件与降雨阈值曲线对应关系
    Figure  7.  Correspondence between rainfall events and rainfall threshold curves before landslides

    (1)白龙江流域甘肃段地质灾害数量多,严重威胁当地群众的生命财产安全,制约社会经济发展,针对降雨引发斜坡类灾害研究较少,本文为开展该区地质灾害预警预报模型研究提供了新思路。

    (2)基于频率法建立了白龙江流域不同岩性特征的滑坡降雨阈值ED模型,并给出了累计雨量下限阈值,对白龙江流域斜坡类灾害预警预报具有指导意义。

    (3)通过2020年陇南“8•17”暴洪灾害期间,降雨引发的59起滑坡事件前期降雨量分析对比,引发滑坡的降雨事件约65.79%以上均位于极高风险预警区,与极高风险(P>60%)阈值曲线一致。

    (4)本文获取的滑坡下限降雨预警曲线,只能通过已发生的滑坡灾害结合前期降雨事件来验证模型准确性,对滑坡发生前的预警曲线校验存在困难,下一步研究中应考虑滑坡发生前不同风险等级预警模型或阈值,为斜坡类地质灾害降雨预警预报提供依据。

  • 图  1   奉节县位置与地形特征

    Figure  1.   Location and terrain features of Fengjie County

    图  2   奉节县斜坡单元划分结果

    Figure  2.   Division of slope units in Fengjie County

    图  3   相关性热图

    Figure  3.   Correlation heat map

    图  4   奉节县滑坡易发性评价结果

    Figure  4.   Landslide susceptibility map of Fengjie County

    图  5   信息量模型的 ROC 曲线

    Figure  5.   ROC curve of the information quantity model

    表  1   数据源

    Table  1   Date sources

    数据名称 数据
    类型
    数据
    分辨率
    数据来源
    GDEM V3 栅格 30 m 地理空间数据云
    土地利用 栅格 30 m 全国地理信息资源目录服务系统
    1960—2021年
    平均降雨量
    栅格 1 km 资源环境科学与数据中心
    1∶25万道路图 矢量 全国地理信息资源目录服务系统
    NDVI/NDBI 栅格 30 m 地理空间数据云, Landsat-8
    1∶25万岩性 矢量 地理空间数据云
    下载: 导出CSV

    表  2   各因子图层分类情况及其对应的信息量值

    Table  2   Classification and corresponding information values of each factor layer

    评价因子 各因子图层各类别对应值 信息增益
    高程 分类范围 61~382 382~613 613~816 816~1006 10061204 12041423 14231694 16942123 0.0318
    信息量 0.5766 0.5850 0.4827 0.1558 0.3198 1.5944 2.2642 3.9106
    坡度 分类范围 0~9 9~15 15~20 20~25 25~31 31~38 38~46 46~76 0.0033
    信息量 0.0344 0.2191 0.2352 0.0776 0.1427 0.3473 0.4424 0.6035
    坡向 分类范围 平面 东北 东南 西南 西 西北 0.0014
    信息量 0.1360 0.0340 0.0273 0.0882 0.0429 0.1840 0.2527 0.0528 0.2337
    NDVI 分类范围 <−0.12 −0.12~0.12 0.12~0.26 0.26~0.37 0.37~0.47 0.47~0.55 0.55~0.63 >0.63 0.0004
    信息量 0.0866 0.4897 0.0491 0.0943 0.0181 0.0870 0.0361 0.0686
    NDBI 分类范围 <−0.48 −0.48~−0.4 −0.4~−0.32 −0.32~−0.25 −0.25~−0.18 −0.18~−0.11 −0.11~0 >0 0.0023
    信息量 0.2254 0.3748 0.1986 0.0108 0.1399 0.2043 0.0449 0.5934
    地形
    起伏度
    分类范围 119~303 303~403 403~492 492~577 577~665 665~773 773~932 932~1365 0.0084
    信息量 1.1694 0.1943 0.1916 0.2921 0.1689 0.1057 0.9061 1.5700
    距河流
    距离
    分类范围 0~300 300~600 600~900 900~1200 12001500 >1500 0.0038
    信息量 0.2621 0.2779 0.1386 0.0197 0.0923 0.3525
    距道路
    距离
    分类范围 0~300 300~600 600~900 900~1200 12001500 >1500 0.0041
    信息量 0.2206 0.0500 0.1985 0.5006 0.6647 0.8397
    岩性 分类范围 黏土、砂砾石
    多层土体
    较软弱岩组 较坚硬岩组 较软弱碳酸
    盐岩组
    坚硬碳酸
    盐岩组
    0.1667
    信息量 0.8507 0.6779 0.5356 2.3212 0.5198
    年平均
    降雨量
    分类范围 10691151 11511207 12071256 12561302 13021362 13621433 14331508 15081597 0.03261
    信息量 0.5001 0.6229 0.4243 0.0279 0.6333 2.2480 2.8662 2.6848
    平面曲率 分类范围 −15.08~−2 −2~−1.01 −1.01~−0.44 −0.44~0.13 0.13~0.7 0.7~1.41 1.41~3.54 3.54~21.31 0.0021
    信息量 0.7206 0.3646 0.0618 0.1533 0.0563 0.2797 0.6510 1.2357
    剖面曲率 分类范围 −19.99~−3.81 −3.81~−1.7 −1.7~−0.79 −0.79~−0.18 −0.18~0.42 0.42~1.33 1.33~3.44 3.44~18.71 0.0023
    信息量 0.8830 0.5894 0.2641 0.0725 0.1688 0.0846 0.4615 0.7636
    土地利用 分类范围 耕地 森林 草丛 水体 人造表面 0.0166
    信息量 0.5734 0.4913 0.0324 0.0888 1.5409
    TRI 分类范围 1~1.05 1.05~1.11 1.11~1.18 1.18~1.28 1.28~1.42 1.42~1.64 1.64~2.09 2.09~4.14 0.0029
    信息量 0.1731 0.1071 0.1787 0.3670 0.4465 0.5403 0.8319 0.3162
    TWI 分类范围 1.83~4.36 4.36~5.58 5.58~6.99 6.99~8.77 8.77~11.11 11.11~13.64 13.64~17.1 17.11~25.8 0.0012
    信息量 0.1798 0.0429 0.1188 0.1837 0.1147 0.2764 0.3209 0.0633
    SPI 分类范围 −3.84~0.39 0.39~2.16 2.16~3.31 3.31~4.54 4.54~5.96 5.96~7.81 7.81~10.73 10.73~18.76 0.0006
    信息量 0.5358 0.0877 0.0630 0.0048 0.0155 0.0983 0.1487 0.2851
    STI 分类范围 0~6 6~26 26~58 58~102 102~163 163~246 246~371 371~818 0.0001
    信息量 0.0149 0.0602 0.1703 0.2320 0.3209 0.4030 0.6023 0.9031
    TPI 分类范围 −256~−70 −70~−44 −44~−26 −26~−10 −10~7 7~23 23~45 45~211 0.0023
    信息量 0.4933 0.1900 0.0932 0.0435 0.2009 0.0390 0.2395 0.6485
    下载: 导出CSV

    表  3   研究区滑坡易发性区划统计表

    Table  3   Landslide susceptibility zoning statistics for the study area

    易发性区 面积
    /km2
    面积占比
    /%
    灾害点
    个数/处
    灾害占比
    /%
    灾害点密度
    /(处·km−2)
    极低易发区 299.05 7.29 4 0.38 0.01
    低易发区 391.29 9.54 10 0.95 0.03
    中易发区 864.88 21.10 48. 4.55 0.06
    高易发区 1376.22 33.57 317 30.05 0.23
    极高易发区 1168.38 28.50 676 64.08 0.58
    下载: 导出CSV

    表  4   研究区灾害点统计表

    Table  4   Statistical table of disaster sites in the study area

    易发性区灾害点个数/处灾害占比/%
    极低易发区00
    低易发区21.82
    中易发区43.64
    高易发区3229.09
    极高易发区7265.45
    下载: 导出CSV
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  • 收稿日期:  2024-03-11
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