高级检索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于随机森林的杉木适生性预测研究

高若楠 苏喜友 谢阳生 雷相东 陆元昌

高若楠, 苏喜友, 谢阳生, 雷相东, 陆元昌. 基于随机森林的杉木适生性预测研究[J]. 北京林业大学学报, 2017, 39(12): 36-43. doi: 10.13332/j.1000-1522.20170260
引用本文: 高若楠, 苏喜友, 谢阳生, 雷相东, 陆元昌. 基于随机森林的杉木适生性预测研究[J]. 北京林业大学学报, 2017, 39(12): 36-43. doi: 10.13332/j.1000-1522.20170260
GAO Ruo-nan, SU Xi-you, XIE Yang-sheng, LEI Xiang-dong, LU Yuan-chang. Prediction of adaptability of Cunninghamia lanceolata based on random forest[J]. Journal of Beijing Forestry University, 2017, 39(12): 36-43. doi: 10.13332/j.1000-1522.20170260
Citation: GAO Ruo-nan, SU Xi-you, XIE Yang-sheng, LEI Xiang-dong, LU Yuan-chang. Prediction of adaptability of Cunninghamia lanceolata based on random forest[J]. Journal of Beijing Forestry University, 2017, 39(12): 36-43. doi: 10.13332/j.1000-1522.20170260

基于随机森林的杉木适生性预测研究

doi: 10.13332/j.1000-1522.20170260
基金项目: 

中央级公益性科研院所基本科研业务费专项 IFRIT201501

林业公益性行业科研专项 201504303

详细信息
    作者简介:

    高若楠。主要研究方向:林业信息处理技术。Email: gao_rn0830@163.com  地址:100083 北京市海淀区清华东路35号北京林业大学信息学院

    责任作者:

    苏喜友,博士,副教授。主要研究方向:林业资源管理、林业信息分析。Email: suxiyou@163.com  地址:同上

  • 中图分类号: S758.5+2;S791.27

Prediction of adaptability of Cunninghamia lanceolata based on random forest

  • 摘要: 以中国林业科学研究院热带林业实验中心杉木树种为研究对象,从森林资源二类调查数据中提取优势树种为杉木的小班,将样本数据按7:3的比例分为训练样本和测试样本。以海拔、地貌类型、坡度、坡向、坡位、土壤种类、成土母岩、土壤厚度、腐殖质层厚度为输入变量,以杉木生长适宜性为输出变量,运用随机森林算法建立杉木适生性预测模型,对不同立地条件下的造林地进行杉木适生性预测。同时,利用随机森林模型的变量重要性评估功能,分析了各立地因子对杉木生长的影响权重。结果表明:基于随机森林的杉木适生性预测模型的训练精度为84.3%,泛化精度达到89.5%,具有较高的预测准确率;研究区域内对杉木生长影响较大的立地因子依次为坡度、坡向、腐殖质层厚、海拔,影响因素较小的是土壤种类、土层厚度;就单因素的影响而言,海拔≥350 m的低山和中山地区,坡度在25°~34°之间比较适宜杉木生长。基于随机森林的杉木适生性预测模型可处理复杂的非线性关系,可将模型应用到无林地的造林决策,实现有林地与无林地对杉木适生性判断的有机统一,也可推广到其他树种,为适地适树提供依据。

     

  • 图  1  随机森林的生成步骤

    Figure  1.  Generation steps for the random forest model

    图  2  模型构建流程图

    Figure  2.  Flowchart of model building

    图  3  模型错误率与ntree的关系

    Figure  3.  Relation between model error rate and ntree

    图  4  立地因子重要性排序

    Figure  4.  Importance ranking of site factors

    图  5  坡度、海拔对杉木生长的影响

    Figure  5.  Effects of slope degree and altitude on growth of Cunninghamia lanceolate

    表  1  杉木生长信息

    Table  1.   Growth information of Cunninghamia lanceolata

    小班号
    No. of
    sub-compartment
    地貌类型
    Physiognomy
    type
    海拔
    Altitude/
    m
    坡向
    Slope
    aspect
    坡度
    Slope degree/
    (°)
    坡位
    Slope
    position
    土壤厚度
    Soil thickness/
    cm
    腐殖质层
    厚度
    Humus layer
    thickness/
    cm
    土壤种类
    Soil type
    成土母岩
    Parent rock
    平均
    年龄/a
    Mean
    age/year
    优势木
    平均高
    Mean
    height of
    dominant
    tree/m
    1 丘陵Hill 290 南South 20 脊Ridge 180 1 赤红壤
    Latosolic
    red soil
    砂岩Sandstone 25 17.5
    2 丘陵Hill 370 西南
    Southwest
    30 中坡Middle
    slope
    100 1 赤红壤
    Latosolic
    red soil
    砂岩
    Sandstone
    25 17.5
    3 低山Lower
    mountain
    350 东北
    Northeast
    28 下坡
    Downhill
    160 1 赤红壤
    Latosolic
    red soil
    砂岩
    Sandstone
    19 17.5
    4 丘陵Hill 200 西West 36 下坡
    Downhill
    70 1 赤红壤
    Latosolic
    red soil
    砂岩
    Sandstone
    19 17.7
    5 低山Lower
    mountain
    860 东East 30 上坡Uphill 70 1 赤红壤
    Latosolic
    red soil
    砂岩
    Sandstone
    25 15.2
    6 中山Middle
    mountain
    660 南South 33 中坡Middle
    slope
    70 2 赤红壤
    Latosolic
    red soil
    砂岩
    Sandstone
    18 12.3
    7 丘陵Hill 273 北North 21 下坡
    Downhill
    80 2 紫色土
    Purple soil
    砂岩
    Sandstone
    19 11.8
    8 丘陵Hill 415 无坡向No
    slope aspect
    30 中坡Middle
    slope
    130 10 赤红壤
    Latosolic
    red soil
    岩浆岩
    Magmatic
    rock
    17 16.5
    9 低山Lower
    mountain
    590 西北
    Northwest
    15 谷地Valley 130 10 红壤
    Red soil
    岩浆岩
    Magmatic
    rock
    21 19.6
    10 低山Lower
    mountain
    580 北North 30 下坡
    Downhill
    130 5 黄红壤
    Yellow-red
    soil
    岩浆岩
    Magmatic
    rock
    23 17.8
    11 低山Lower
    mountain
    880 东南
    Southeast
    37 中坡Middle
    slope
    100 3 黄壤Yellow soil 岩浆岩
    Magmatic
    rock
    33 17.5
    下载: 导出CSV

    表  2  属性分级标准

    Table  2.   Attribute classification standard

    立地因子Site factor 分级标准Classification standard
    坡度
    Slope degree
    平坡:<5°;缓坡:5°~14°;斜坡:15°~24°;陡坡:25°~34°;急坡:35°~44°;险坡:≥45°
    Flat slope:<5°; Gentle slope: 5°-14°; Incline slope: 15°-24°; Steep slope: 25°-34°;
    Sharp slope: 35°-44°; Dangerously steep slope: ≥45°
    土壤厚度
    Soil thickness
    厚:≥80 cm;中:40~79 cm;薄:<40 cm
    Thick: ≥80 cm; Medium: 40-79 cm; Thin:<40 cm
    腐殖质层厚度
    Humus layer thickness
    厚:≥20 cm;中:10~19 cm;薄:<10 cm
    Thick: ≥20 cm; Medium: 10-19 cm; Thin:<10 cm
    海拔
    Altitude
    Ⅰ级:<350 m;Ⅱ级:350~750 m;Ⅲ级:750~1 050 m;Ⅳ级:>1 050 m
    Grade Ⅰ:<350 m; Grade Ⅱ: 350-750 m; Grade Ⅲ: 750-1 050 m; Grade Ⅳ:>1 050 m
    下载: 导出CSV

    表  3  平衡前后各样本构成情况

    Table  3.   Composition of samples before and after balance

    样本类别
    Sample classification
    正样本
    Positive
    sample
    负样本
    Negative
    sample
    合计
    Total
    原始样本Original sample 244 111 355
    平衡后样本Sample after balance 333 333 666
    下载: 导出CSV

    表  4  不同的mtry取值对应误差的大小

    Table  4.   Errors corresponding to different mtry values

    随机特征个数
    Number of random feature(mtry)
    1 2 3 4 5 6 7 8 9
    误差率Error rate 0.263 0.200 0.165 0.156 0.162 0.170 0.161 0.162 0.167
    下载: 导出CSV

    表  5  混淆矩阵

    Table  5.   Confused matrix of predictive results

    实际类别
    Actual type
    预测类别Predictive type
    适宜Adaptability 不适宜Inadaptability
    适宜Adaptability TP FN
    不适宜Inadaptability FP TN
    注:TP代表真正类,即模型预测结果为适宜生长,且实际情况也为适宜;FP代表假正类,即模型预测结果为适宜生长,但实际情况为不适宜;TN代表真负类,即模型预测结果为不适宜生长,且实际情况也为不适宜;FN代表假负类,即模型预测结果为不适宜生长,但实际情况为适宜。Notes:TP(true positive) implies that the predicted result and the reality are both the adaptability;FP(false positive) implies that the predicted result is the adaptability, but the reality is the opposite;TN(true negative) implies that the predicted result and the reality are both the inadaptability;FN(false negative) implies that the predicted result is the inadaptability, but the reality is the opposite.
    下载: 导出CSV

    表  6  随机森林模型混淆矩阵

    Table  6.   Confusion matrix of random forest model

    实际类别
    Actual type
    预测类别Predictive type 分类误差率
    Classification
    error rate/%
    适宜
    Adaptability
    不适宜
    Inadaptability
    适宜Adaptability 202 29 12.5
    不适宜Inadaptability 44 191 18.7
    下载: 导出CSV

    表  7  测试数据预测结果

    Table  7.   Predicted results of test samples

    实际类别
    Actual type
    预测类别Predictive type
    适宜Adaptability 不适宜Inadaptability
    适宜Adaptability 95 16
    不适宜Inadaptability 7 82
    下载: 导出CSV

    表  8  模型判断结果

    Table  8.   Predicted results of models

    地类
    Land
    type
    地貌类型
    Physiognomy
    type
    海拔
    Altitude/
    m
    坡向
    Slope
    aspect
    坡度
    Slope
    degree/
    (°)
    坡位
    Slope
    position
    土壤厚度
    Soil
    thickness/
    cm
    腐殖质层
    厚度Humus
    layer
    thickness/cm
    土壤种类
    Soil type
    成土母岩
    Parent
    rock
    立地
    指数
    Site
    index
    模型预测结果
    Predicted results of model
    不适宜性概率
    Probability of
    inadaptability
    适宜性概率
    Probability align="center" class="table_top_border2" of
    adaptability
    结果
    Result
    有林地
    Forest land
    丘陵Hill 250 西北
    Northwest
    26 中坡
    Middle slope
    95 1 赤红壤Latosolic
    red soil
    砂岩Sandstone 18 0.057 0.948 适宜
    Adaptability
    低山Lower
    mountain
    420 东北
    Northeast
    20 下坡
    Downhill
    90 1 赤红壤
    Latosolic red soil
    岩浆岩
    Magmatic rock
    22 0.008 0.992 适宜
    Adaptability
    低山Lower
    mountain
    290 西北
    Northwest
    32 上坡Uphill 90 1 赤红壤
    Latosolic red soil
    砂岩Sandstone 10 0.935 0.065 不适宜
    Inadaptability
    无林地
    Non-forest land
    低山Lower mountain 780 北North 17 上坡Uphill 70 1 赤红壤
    Latosolic red soil
    砂岩Sandstone 0.843 0.157 不适宜
    Inadaptability
    丘陵Hill 360 东北
    Northeast
    22 中坡
    Middle slope
    80 1 赤红壤
    Latosolic red soil
    岩浆岩
    Magmatic rock
    0.118 0.882 适宜
    Adaptability
    低山Lower
    mountain
    670 西
    West
    15 中坡
    Middle slope
    70 10 赤红壤
    Latosolic red soil
    砂岩Sandstone 0.177 0.823 适宜
    Adaptability
    下载: 导出CSV
  • [1] 黄云鹏.森林培育学[M].北京:高等教育出版社, 2002.

    HUANG Y P.Silviculture[M].Beijing:Higher Education Press, 2002.
    [2] 郭艳荣, 刘洋, 吴保国.福建省宜林地立地质量的分级与数量化评价[J].东北林业大学学报, 2014, 42(10):54-59. doi: 10.3969/j.issn.1000-5382.2014.10.012

    GUO Y R, LIU Y, WU B G.Evaluating dividing rank and quantification of site quality of suitable land for forest in Fujian Province, China[J].Journal of Northeast Forestry University, 2014, 42(10):54-59. doi: 10.3969/j.issn.1000-5382.2014.10.012
    [3] CURT T, BOUCHAUD M, AGRECH G.Predicting site index of Douglasfir plantations from ecological variables in the Massif Central area of France[J].Forest Ecology and Management, 2001, 149(1):61-74. http://cn.bing.com/academic/profile?id=33a58ff18489b10367956465b4786ef8&encoded=0&v=paper_preview&mkt=zh-cn
    [4] LOUWA J H, SCHOLES M.Forest site classification and evaluation:a South African perspective[J].Forest Ecology and Management, 2002, 171(1-2):153-168. doi: 10.1016/S0378-1127(02)00469-3
    [5] 张伏全, 魏汗功, 陈远材.滇西南地区立地龙竹质量评价的研究[J].林业科学, 1994, 30(2):104-110. http://www.cnki.com.cn/Article/CJFDTotal-LYKE402.001.htm

    ZHANG F Q, WEI H G, CHEN Y C.Study on site quality evaluation of fragon bamboo in Southwest Yunnan[J].Scientia Silvae Sinicae, 1994, 30(2):104-110. http://www.cnki.com.cn/Article/CJFDTotal-LYKE402.001.htm
    [6] 黄家荣, 马天晓, 王艳梅, 等.基于BP网络的无林地立地质量评价模型研究[J].山地农业生物学报, 2006, 25(6):479-483. doi: 10.3969/j.issn.1008-0457.2006.06.003

    HUANG J R, MA T X, WANG Y M, et al.Forest site evaluation model studies on the basis of BP Neural Network[J].Journal of Mountain Agriculture and Biology, 2006, 25(6):479-483. doi: 10.3969/j.issn.1008-0457.2006.06.003
    [7] IVERSON L R, PRASAD A M, MATTHEWS S N, et al.Estimating potential habitat for 134 eastern US tree species under six climate scenarios[J].Forest Ecology and Management, 2008, 254:390-406. doi: 10.1016/j.foreco.2007.07.023
    [8] PRASAD A M, IVERSON L R, LIAW A.Newer classification and regression tree technique:bagging and random forests for ecological prediction[J].Ecosystems, 2006, 9(2):181-199. doi: 10.1007/s10021-005-0054-1
    [9] DONG L J, LI X B.Prediction of rockburst classification using random forest[J].Transaction of Nonferrous Metals Society of China, 2013, 23(2):472-477. doi: 10.1016/S1003-6326(13)62487-5
    [10] 余坤勇, 姚雄, 邱祁荣, 等.基于随机森林模型的山体滑坡空间预测研究[J].农业机械学报, 2016, 47(10):338-345. doi: 10.6041/j.issn.1000-1298.2016.10.043

    YU K Y, YAO X, QIU Q R, et al.Landslide spatial prediction based on random forest model[J].Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(10):338-345. doi: 10.6041/j.issn.1000-1298.2016.10.043
    [11] 张雷, 王琳, 张旭东, 等.随机森林算法基本思想及其在生态学中的应用:以云南松分布模拟为例[J].生态学报, 2014, 34(3):650-659. http://d.old.wanfangdata.com.cn/Periodical/stxb201403015

    ZHANG L, WANG L, ZHANG X D, et al. The basic principle of random forest and its application in ecology: a case study of Pinus yunnanensis[J].Acta Ecologica Sinica, 2014, 34(3):650-659. http://d.old.wanfangdata.com.cn/Periodical/stxb201403015
    [12] 赖成光, 陈晓宏, 赵仕威, 等.基于随机森林的洪灾风险评价模型及其应用[J].水利学报, 2015, 46(1):59-65. http://d.old.wanfangdata.com.cn/Periodical/slxb201501008

    LAI C G, CHEN X H, ZHAO S W, et al.Flood risk assessment model and its application based on random forest[J].Journal of Hydraulic Engineering, 2015, 46(1):59-65. http://d.old.wanfangdata.com.cn/Periodical/slxb201501008
    [13] 邢海涛, 陆元昌, 刘宪钊, 等.基于近自然改造的马尾松林分竞争强度研究[J].北京林业大学学报, 2016, 38(9):42-53. doi: 10.13332/j.1000-1522.20160023

    XING H T, LU Y C, LIU X Z, et al.Competiiton intensity of Pinus massoniana stand based on close-to-nature management[J].Journal of Beijing Forestry University, 2016, 38(9):42-53. doi: 10.13332/j.1000-1522.20160023
    [14] BREIMAN L. Random forests[J].Machine Learning, 2001, 45(1):5-32. doi: 10.1023/A:1010933404324
    [15] 张洪强, 刘光远, 赖祥伟.随机森林在肌电的重要特征选择中的应用[J].计算科学, 2013, 40(1):200-202. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjkx201301046

    ZHANG H Q, LIU G Y, LAI X W. Application of random forest algorithm in important feature selection form EMG signal[J].Computer Science, 2013, 40(1):200-202. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=jsjkx201301046
    [16] 李新海.随机森林模型在分类与回归分析中的应用[J].应用昆虫学报, 2013, 50(4):1190-1197. http://d.old.wanfangdata.com.cn/Periodical/kczs201304041

    LI X H.Using"random forest" for classification and regression[J].Chinese Journal of Applied Entomology, 2013, 50(4):1190-1197. http://d.old.wanfangdata.com.cn/Periodical/kczs201304041
    [17] 齐颜君, 王丹, 孙喜林, 等.适地适树的意义与数量标准[J].现代农业科技, 2013(1):169. doi: 10.3969/j.issn.1007-5739.2013.01.104

    QI Y J, WANG D, SUN X L, et al. The meaning and quantity standard of matching species with the site[J].Modern Agriculture Science and Technology, 2013(1):169. doi: 10.3969/j.issn.1007-5739.2013.01.104
    [18] 孟宪宇.测树学[M].北京:中国林业出版社, 2006.

    MENG X Y. Forest measurement[M]. Beijing: China Forestry Publishing House, 2006.
    [19] 姚山.基于数据挖掘技术的造林决策研究[D].北京: 北京林业大学, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10022-2008085015.htm

    YAO S.Study on afforestation decision based on data mining[D]. Beijing: Beijing Forestry University, 2008. http://cdmd.cnki.com.cn/Article/CDMD-10022-2008085015.htm
    [20] 曹正凤.随机森林算法优化研究[D].北京: 首都经济贸易大学, 2014. http://cdmd.cnki.com.cn/Article/CDMD-11912-1014220587.htm

    CAO Z F.Study on optimization of random forests algorithm[D]. Beijing: Capital University of Economics and Business, 2014. http://cdmd.cnki.com.cn/Article/CDMD-11912-1014220587.htm
    [21] CHWLA N V, BOWYER K W, HALL L O, et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artificial Intelligence Research, 2011, 16(1):321-357. http://d.old.wanfangdata.com.cn/Periodical/dianzixb200911024
    [22] 李婉华, 陈宏, 郭坤, 等.基于随机森林算法的用电负荷预测研究[J].计算机工程与应用, 2016, 52(23):236-243. doi: 10.3778/j.issn.1002-8331.1606-0203

    LI W H, CHEN H, GUO K, et al.Research on electrical load prediction based on random forest algorithm[J].Computer Engineering and Applications, 2016, 52(23):236-243. doi: 10.3778/j.issn.1002-8331.1606-0203
    [23] 郭颖婕, 刘晓燕, 郭茂祖, 等.植物抗性基因识别中的随机森林分类方法[J].计算科学与探索, 2012, 6(1):67-77. http://d.old.wanfangdata.com.cn/Periodical/jsjkxyts201201005

    GUO Y J, LIU X Y, GUO M Z, et al.Identification of plant resistance gene with random forest[J].Journal of Frontiers of Computer Science and Technology, 2012, 6(1):67-77. http://d.old.wanfangdata.com.cn/Periodical/jsjkxyts201201005
  • 加载中
图(5) / 表(8)
计量
  • 文章访问数:  851
  • HTML全文浏览量:  236
  • PDF下载量:  25
  • 被引次数: 0
出版历程
  • 收稿日期:  2017-07-19
  • 修回日期:  2017-11-28
  • 刊出日期:  2017-12-01

目录

    /

    返回文章
    返回