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人居环境安全视角下的北京市浅山区泥石流防灾林规划研究

蔡怡然 刘阳 李柳意 梁淑榆 郑曦

蔡怡然, 刘阳, 李柳意, 梁淑榆, 郑曦. 人居环境安全视角下的北京市浅山区泥石流防灾林规划研究[J]. 北京林业大学学报, 2021, 43(6): 130-140. doi: 10.12171/j.1000-1522.20200207
引用本文: 蔡怡然, 刘阳, 李柳意, 梁淑榆, 郑曦. 人居环境安全视角下的北京市浅山区泥石流防灾林规划研究[J]. 北京林业大学学报, 2021, 43(6): 130-140. doi: 10.12171/j.1000-1522.20200207
Cai Yiran, Liu Yang, Li Liuyi, Liang Shuyu, Zheng Xi. Planning of debris flow prevention forest in shallow mountain area of Beijing from the perspective of human settlement environment safety[J]. Journal of Beijing Forestry University, 2021, 43(6): 130-140. doi: 10.12171/j.1000-1522.20200207
Citation: Cai Yiran, Liu Yang, Li Liuyi, Liang Shuyu, Zheng Xi. Planning of debris flow prevention forest in shallow mountain area of Beijing from the perspective of human settlement environment safety[J]. Journal of Beijing Forestry University, 2021, 43(6): 130-140. doi: 10.12171/j.1000-1522.20200207

人居环境安全视角下的北京市浅山区泥石流防灾林规划研究

doi: 10.12171/j.1000-1522.20200207
基金项目: 国家重点研发计划项目(2019YFD11004021)
详细信息
    作者简介:

    蔡怡然。主要研究方向:风景园林规划设计与理论。Email:caiyiran1996@126.com 地址:100083北京市海淀区清华东路35号北京林业大学园林学院

    责任作者:

    郑曦,教授,博士生导师。研究方向:风景园林规划设计与理论。Email:zhengxi@bjfu.edu.cn 地址:同上

  • 中图分类号: S715.3

Planning of debris flow prevention forest in shallow mountain area of Beijing from the perspective of human settlement environment safety

  • 摘要:   目的  城市边缘的浅山区是人类生产生活与自然生境的交接缓冲区,也是人居环境面对泥石流等自然灾害风险脆弱性较高的区域。相比通过工程方法降低浅山区中自然灾害威胁,防灾林选址规划作为基于生态系统的方法,可以恢复健康的生态系统,提供多样的生态系统服务并有效降低灾害风险,提高人居环境对自然灾害的抵抗力与适应力。  方法  本研究提出一种人居环境安全视角下的泥石流防灾林造林选址评价体系,并评估规划的防灾林对降低人居环境中泥石流风险的有效性。基于多准则决策法(AHP),将基于最大熵模型(MaxEnt)的泥石流敏感性预测结果作为泥石流风险因子,识别具有降低泥石流灾害发生概率的泥石流流域内适宜造林位置作为环境因子,同时结合造林可行性因子,叠加3类因子分析出高风险且高适宜的造林区域。通过对比造林前后泥石流敏感性与人居点危险性评价造林的减灾效益,指导北京市浅山区泥石流防灾林造林选址。  结果  研究选取了118.50 km2的造林区域,主要分布在密云水库周边地区、怀柔区、昌平区以及房山区,呈现以零散斑块形态分布在人居点上游的特征。造林后泥石流易发区面积减少了117.45 km2,位于泥石流易发区的人居点个数减少了51个。  结论  本研究构建了提升人居环境泥石流抵抗力的防灾林造林选址评价体系,通过基于自然系统的方法降低山区自然灾害风险,提升浅山区人居环境质量,保障生态系统健康与人居环境安全,并为北京市百万亩造林计划提供理论依据。

     

  • 图  1  北京浅山区人居点和泥石流历史灾害点分布

    Figure  1.  Distribution of human settlements and historical debris flow occurrence points in the shallow mountain area of Beijing

    图  2  研究框架

    Figure  2.  Research framework

    图  3  基于增强人居环境泥石流抵抗力的造林选址

    Figure  3.  Afforestation area selection based on enhancing the resistance of debris flow of human settlements

    图  4  现状研究范围内泥石流易发等级分布以及人居点泥石流危险性

    Figure  4.  Distribution of debris flow prone area and the debris flow risk of human settlements before afforestation

    图  5  造林后研究范围内泥石流易发等级分布以及人居点泥石流危险性

    Figure  5.  Distribution of debris flow prone area and the debris flow risk of human settlements after afforestation

    表  1  数据名称及来源

    Table  1.   Data name and source

    序号
    No.
    数据名称
    Data name
    数据来源
    Data source
    1 泥石流出现点
    Debris flow occurrence point
    北京市国土资源局2016年山地灾害统计
    Statistics of mountain disasters by Beijing Municipal Bureau of Land and Resources in 2016
    2 人居点/道路数据/河流数据
    Human settlement, road data, river data
    国家测绘地理信息局
    National Bureau of Surveying, Mapping and Geographic Information
    3 气象数据 Meteorological data 国家气象科学数据中心 National Meteorological Science Data Center
    4 高程/坡度/坡向/地形起伏度数据
    Elevation, slope degree, slope aspect, terrain relief degree data
    地理空间数据云平台/GIS处理
    Geospatial Data Cloud, GIS processing
    5 基岩岩性/断裂带 Bedrock lithology/fault zone 《北京山洪泥石流》 Beijing Mountain Torres and Debris Flow
    6 土壤类型数据 Soil type data 中国土壤数据库 China Soil Database
    7 土地利用数据 Land use data 地理空间数据云平台/ENVI解译 Geospatial data cloud, ENVI interpretation
    下载: 导出CSV

    表  2  12个环境变量之间的相关系数

    Table  2.   Correlation coefficients among 12 environmental variables

    项目
    Item
    年均降
    雨量
    Average
    annual
    rainfall
    最湿季
    降雨量
    Wettest
    season
    rainfall
    地形
    起伏度
    Terrain
    relief
    degree
    (RDLS)
    坡向
    Slope
    aspect
    坡向
    变化率
    Slope
    aspect
    change
    rate (SOA)
    基岩岩性
    Bedrock
    lithology
    断裂带
    密度
    Density of
    fracture
    zone
    土壤
    类型
    Soil
    type
    河网密度
    River
    network
    density
    林地分布
    Woodland
    distribution
    距人居点
    距离
    Distance to
    human
    settlement
    距道路
    距离
    Distance
    to road
    年均降雨量
    Average annual rainfall
    1
    最湿季降雨量
    Wettest season rainfall
    −0.098 1
    地形起伏度 RDLS 0.295 −0.051 1
    坡向 Slope aspect −0.001 0.007 −0.028 1
    坡向变化率 SOA −0.035 0.003 −0.198 0.059 1
    基岩岩性
    Bedrock lithology
    −0.278 −0.137 −0.138 −0.010 0.047 1
    断裂带密度
    Density of fracture zone
    −0.177 0.168 0.029 0.013 −0.022 0.126 1
    土壤类型 Soil type −0.045 −0.037 −0.075 −0.070 −0.072 0.102 0.045 1
    河网密度
    River network density
    0.094 0.057 0.253 0.046 −0.033 −0.152 0.080 −0.233 1
    林地分布
    Woodland distribution
    0.133 −0.155 0.338 −0.033 −0.102 −0.119 −0.069 −0.104 0.139 1
    距人居点距离
    Distance to human settlement
    0.172 −0.030 0.324 −0.016 −0.163 0.030 0.068 0.230 0.038 0.109 1
    距道路距离
    Distance to road
    0.274 −0.114 0.382 −0.011 −0.140 −0.002 0.001 0.242 0.051 0.130 0.532 1
    下载: 导出CSV

    表  3  泥石流敏感性模拟选取因子

    Table  3.   Variables selected for the simulation of debris flow susceptibility

    编号
    No.
    123456
    因子名称
    Factor name
    年均降雨量
    Average annual rainfall
    最湿季降雨量
    Wettest season rainfall
    地形起伏度
    RDLS
    坡向
    Slope aspect
    坡向变化率
    SOA
    基岩岩性
    Bedrock lithology
    编号
    No.
    7 8 9 10 11 12
    因子名称
    Factor name
    断裂带密度
    Density of fracture zone
    土壤类型
    Soil type
    河网密度
    River network density
    林地分布
    Woodland distribution
    距人居点距离
    Distance to human settlement
    距道路距离
    Distance to road
    下载: 导出CSV

    表  4  人居环境安全视角下的泥石流防灾林选址综合评价体系

    Table  4.   Evaluation system for site selection of debris flow disaster prevention forest from the perspective of human settlement security

    因子类型
    Factor type
    评价指标
    Evaluation index
    指标分级
    Index rating
    评分
    Score
    指标权重
    Index weight
    风险因子
    Risk factor
    泥石流敏感性
    Debris flow susceptibility
    高适宜性(高易发区)
    High suitability (high prone area)
    10 0.388
    中适宜性(中易发区)
    Medium suitability (medium prone area)
    6
    低适宜性(不易发区)
    Low suitability (not prone area)
    2
    环境因子
    Environmental factor
    人居点与泥石流流域内各区域关系
    Relationship between human settlement and various area in the debris flow catchment basin
    人居点上游泥石流清水区
    Debris flow catchment area upstream of human settlement
    10 0.421
    人居点上游的泥石流物源区
    Debris flow forming area upstream of human settlement
    8
    其他区域
    Other area
    2
    造林可行性因子
    Feasibility factor of afforestation
    距道路距离
    Distance to road
    高适宜性(0 ~ 500 m)
    High suitability (0−500 m)
    10 0.072
    中适宜性(500 ~ 2 000 m)
    Moderate suitability (500−2 000 m)
    6
    低适宜性(> 2 000 m)
    Low suitability (> 2 000 m)
    2
    坡度适宜性
    Slope degree suitability
    高适宜性(0° ~ 15°)
    High suitability (0°−15°)
    10 0.087
    中等适宜性(15° ~ 30°)
    Moderate suitability (15°−30°)
    6
    低适宜性(> 30°)
    Low suitability (> 30°)
    1
    用地转换林地适宜性
    Suitability of land conversion to woodland
    高适宜性(草地、园地)
    High suitability (grassland, garden plot)
    10 0.032
    中适宜性(一般农田、其他用地)
    Moderate suitability (general farmland, other land)
    4
    低适宜性(城镇村、工矿、交通运输、水域及水利设施用地)
    Low suitability (urban and villages, industrial and mining, transportation, water area and water conservancy facility land)
    1
    下载: 导出CSV

    表  5  造林前后泥石流敏感性评价

    Table  5.   Susceptiblity evaluation of debris flow before and after afforestation

    泥石流敏感性等级
    Susceptibility level of
    debris flow
    现状面积
    Current
    area/km2
    造林后面积
    Area after
    afforestation/km2
    面积变化
    Area change/
    km2
    现状面积占比
    Proportion of
    current area/%
    造林后面积占比
    Proportion of
    area after
    afforestation/%
    面积占比变化
    Change of area
    proportion/%
    泥石流高易发区
    Debris flow high-prone area
    689.04 653.80 −35.24 17.6 16.7 −0.9
    泥石流中易发区
    Debris flow moderate-prone area
    1 041.39 959.18 −82.21 26.6 24.5 −2.1
    泥石流不易发区
    Areas not prone to debris flow
    2 184.57 2 302.02 +117.45 55.8 58.8 +3.0
    小计 Total 3 915.00 3 915.00 0 100 100 0
    下载: 导出CSV

    表  6  造林前后人居点泥石流危险性评价

    Table  6.   Risk assessment of debris flow in human settlements before and after afforestation

    人居点泥石流敏感性等级
    Susceptibility of debris flow
    in human settlement
    现状人
    居点个数
    Number of
    current
    settlement
    造林后人
    居点个数
    Number of
    settlement after
    afforestation
    人居点
    数量变化
    Quantity change
    of settlement
    现状人
    居点占比
    Proportion of
    current
    settlement/%
    造林后人
    居点占比
    Proportion of
    settlement after
    afforestation/%
    人居点
    占比变化
    Change of
    settlement
    proportion/%
    泥石流高易发区
    Debris flow high-prone area
    119 75 −44 26.7 16.8 −9.9
    泥石流中易发区
    Debris flow moderate-prone area
    87 80 −7 19.6 18.0 −1.6
    泥石流不易发区
    Areas not prone to debris flow
    239 290 +51 53.7 65.2 +11.5
    小计 Total 445 445 0 100 100 0
    下载: 导出CSV
  • [1] 张新伟, 王建西. 浅谈地质灾害隐患点辨识及数值模拟分析: 以北京山区典型区域为例[J]. 城市地质, 2019, 14(4):72−76.

    Zhang X W, Wang J X. Study on identification and numerical simulation analysis for potential geological hazards-taking Beijing typical mountainous area as an example[J]. Urban Geology, 2019, 14(4): 72−76.
    [2] 倪化勇, 唐川. 中国泥石流起动物理模拟试验研究进展[J]. 水科学进展, 2014, 25(4):606−613.

    Ni H Y, Tang C. Advances in the physical simulation experiment on debris flow initiation in China[J]. Advances in Water Science, 2014, 25(4): 606−613.
    [3] Reichenbach P, Rossi M, Malamud B D, et al. A review of statistically-based landslide susceptibility models[J]. Earth-Science Reviews, 2018, 180: 60−91. doi: 10.1016/j.earscirev.2018.03.001
    [4] 刘希林. 国外泥石流机理模型综述[J]. 灾害学, 2002,17(4):2−7.

    Liu X L. An overview of foreign debris flow mechanism models[J]. Journal of Catastrophology, 2002,17(4): 2−7.
    [5] 王晓朋, 潘懋, 任群智. 基于流域系统地貌信息熵的泥石流危险性定量评价[J]. 北京大学学报(自然科学版), 2007, 43(2):211−215.

    Wang X P, Pan M, Ren Q Z. Hazard assessment of debris flow based on geomorphic information entropy in catchment[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2007, 43(2): 211−215.
    [6] 齐信, 唐川, 铁永波, 等. 基于GIS技术的汶川地震诱发地质灾害危险性评价: 以四川省北川县为例[J]. 成都理工大学学报(自然科学版), 2010, 37(2):160−167.

    Qi X, Tang C, Tie Y B, et al. Hazard assessment of geohazards triggered by the Wenchuan earthquake using GIS technology: Taking Beichuan County of Sichuan Province for example[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2010, 37(2): 160−167.
    [7] 倪树斌, 马超, 杨海龙, 等. 北京山区崩塌、滑坡、泥石流灾害空间分布及其敏感性分析[J]. 北京林业大学学报, 2018, 40(6):81−91.

    Ni S B, Ma C, Yang H L, et al. Spatial distribution and susceptibility analysis of avalanche, landslide and debris flow in Beijing mountain region[J]. Journal of Beijing Forestry University, 2018, 40(6): 81−91.
    [8] 于秀治, 韦京莲. 灰色系统理论在北京山区泥石流危险度评价预测中的应用[J]. 中国地质灾害与防治学报, 2004,15(1):121−123. doi: 10.3969/j.issn.1003-8035.2004.01.026

    Yu X Z, Wei J L. Grey system analysis and its application in the forecast of mud-rock flow criticality of Beijing[J]. The Chinese Journal of Geological Hazard and Control, 2004,15(1): 121−123. doi: 10.3969/j.issn.1003-8035.2004.01.026
    [9] 王子健, 肖盛燮, 戴廷利, 等. 泥石流危险度模糊综合评判方法及应用[J]. 重庆交通大学学报(自然科学版), 2008,27(5):794−798.

    Wang Z J, Xiao S X, Dai T L, et al. Fuzzy comprehensive evaluation method and its application in judging the risk degree of debris flows[J]. Journal of Chongqing Jiaotong University (Natural Science), 2008,27(5): 794−798.
    [10] Chen W, Xie X, Wang J, et al. A comparative study of logistic model tree, random forest, and classification and regression tree models for spatial prediction of landslide susceptibility[J]. Catena, 2017, 151: 147−160. doi: 10.1016/j.catena.2016.11.032
    [11] 刘涌江, 胡厚田, 白志勇. 泥石流危险度评价的神经网络法[J]. 地质与勘探, 2001,11(2):84−87.

    Liu Y J, Hu H T, Bai Z Y. Artificial neural network method for evaluating the dangerous degree of debris flows[J]. Geology and Prospecting, 2001,11(2): 84−87.
    [12] Catani F, Lagomarsino D, Segoni S, et al. Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues[J]. Natural Hazards and Earth System Sciences, 2013, 13(11): 2815−2831. doi: 10.5194/nhess-13-2815-2013
    [13] Shirvani Z. A holistic analysis for landslide susceptibility mapping applying geographic object-based random forest: a comparison between protected and non-protected forests[J]. Remote Sensing, 2020, 12(3): 434. doi: 10.3390/rs12030434
    [14] Xiong K, Adhikari B R, Stamatopoulos C A, et al. Comparison of different machine learning methods for debris flow susceptibility mapping: a case study in the Sichuan Province, China[J]. Remote Sensing, 2020, 12(2): 295. doi: 10.3390/rs12020295
    [15] 田丰, 张军, 冉有华, 等. 甘肃陇南市泥石流灾害危险性及影响因子评价[J]. 灾害学, 2017, 32(3):197−203. doi: 10.3969/j.issn.1000-811X.2017.03.033

    Tian F, Zhang J, Ran Y H, et al. Assessment of debris flow disaster hazard and influence factors in Longnan District[J]. Journal of Catastrophology, 2017, 32(3): 197−203. doi: 10.3969/j.issn.1000-811X.2017.03.033
    [16] Lombardo L, Bachofer F, Cama M, et al. Exploiting maximum entropy method and ASTER data for assessing debris flow and debris slide susceptibility for the Giampilieri catchment (northeastern Sicily, Italy)[J]. Earth Surface Processes and Landforms, 2016, 41(12): 1776−1789. doi: 10.1002/esp.3998
    [17] Mercer J, Gaillard J C, Crowley K, et al. Culture and disaster risk reduction: lessons and opportunities[J]. Environmental Hazards, 2012, 11(2): 74−95. doi: 10.1080/17477891.2011.609876
    [18] Sandholz S, Lange W, Nehren U. Governing green change: Ecosystem-based measures for reducing landslide risk in Rio de Janeiro[J]. International Journal of Disaster Risk Reduction, 2018, 32: 75−86. doi: 10.1016/j.ijdrr.2018.01.020
    [19] Booth A M, Sifford C, Vascik B, et al. Large wood inhibits debris flow runout in forested southeast Alaska[J]. Earth Surface Processes and Landforms, 2020, 45(7): 1555−1568. doi: 10.1002/esp.4830
    [20] Lancaster S T, Hayes S K, Grant G E. Effects of wood on debris flow runout in small mountain watersheds[J]. Water Resources Research, 2003, 39(6): 1−17.
    [21] Michelini T, Bettella F, D’Agostino V. Field investigations of the interaction between debris flows and forest vegetation in two Alpine fans[J]. Geomorphology, 2017, 279: 150−164. doi: 10.1016/j.geomorph.2016.09.029
    [22] Coelho-Netto A L, Avelar A S, Fernandes M C, et al. Landslide susceptibility in a mountainous geoecosystem, Tijuca Massif, Rio de Janeiro: the role of morphometric subdivision of the terrain[J]. Geomorphology, 2007, 87(3): 120−131.
    [23] McVittie A, Cole L, Wreford A, et al. Ecosystem-based solutions for disaster risk reduction: lessons from European applications of ecosystem-based adaptation measures[J]. International Journal of Disaster Risk Reduction, 2018, 32: 42−54. doi: 10.1016/j.ijdrr.2017.12.014
    [24] García-Feced C, Saura S, Elena-Rosselló R. Improving landscape connectivity in forest districts: a two-stage process for prioritizing agricultural patches for reforestation[J]. Forest Ecology and Management, 2011, 261(1): 154−161. doi: 10.1016/j.foreco.2010.09.047
    [25] Hu W, Wang Y, Dong P, et al. Predicting potential mangrove distributions at the global northern distribution margin using an ecological niche model: determining conservation and reforestation involvement[J]. Forest Ecology and Management, 2020, 478: 118517. doi: 10.1016/j.foreco.2020.118517
    [26] Cimini D, Portoghesi L, Madonna S, et al. Multifactor empirical mapping of the protective function of forests against landslide occurrence: statistical approaches and a case study[J]. iForest-Biogeosciences and Forestry, 2016, 9(3): 383. doi: 10.3832/ifor1740-008
    [27] Moos C, Fehlmann M, Trappmann D, et al. Integrating the mitigating effect of forests into quantitative rockfall risk analysis: two case studies in Switzerland[J]. International Journal of Disaster Risk Reduction, 2018, 32: 55−74. doi: 10.1016/j.ijdrr.2017.09.036
    [28] 张嫱, 马超, 杨海龙, 等. 北京山区典型低频泥石流特征及危险性研究[J]. 北京林业大学学报, 2015, 37(12):92−99.

    Zhang Q, Ma C, Yang H L, et al. Characteristics of low frequency debris flow and risk analysis in Beijing mountainous region[J]. Journal of Beijing Forestry University, 2015, 37(12): 92−99.
    [29] 周亮, 徐建刚, 林蔚, 等. 秦巴山连片特困区地形起伏与人口及经济关系[J]. 山地学报, 2015, 33(6):742−750.

    Zhou L, Xu J G, Lin W, et al. Relationship of terrain relief degree and population economic development and evaluation of development suitability in continuous poor areas[J]. Mountain Research, 2015, 33(6): 742−750.
    [30] 张成奇, 王喜琴, 王喜生. 浅析预防地质灾害防灾林的设计与创新[J]. 农家参谋, 2019(19):113.

    Zhang C Q, Wang X Q, Wang X S. Analysis on the design and innovation of disaster prevention forest to prevent geological disasters[J]. The Farmers Consultant, 2019(19): 113.
    [31] 余新晓, 秦永胜, 陈丽华, 等. 北京山地森林生态系统服务功能及其价值初步研究[J]. 生态学报, 2002, 22(5):783−786.

    Yu X X, Qin Y S, Chen L H, et al. The forest ecosystem services and their valuation of Beijing mountain areas[J]. Acta Ecologica Sinica, 2002, 22(5): 783−786.
    [32] 倪畅, 周凯, 郑曦. 基于景观生态风险评价的景观格局优化:以北京市浅山区为例[J]. 风景园林, 2021, 28(5):80−85.

    Ni C, Zhou K, Zheng X. Landscape pattern optimization based on landscape ecological risk assessment: a case study of shallow mountain area in Beijing[J]. Landscape Architecture, 2021, 28(5): 80−85.
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出版历程
  • 收稿日期:  2020-07-06
  • 修回日期:  2021-05-14
  • 网络出版日期:  2021-06-04
  • 刊出日期:  2021-06-30

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