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Journal of Geo-information Science, Volume. 22, Issue 3, 452(2020)

Loess Landform Classification based on Random Forest

Zetao CAO1,1,2,2,3,3, Zidong FANG1,1,2,2,3,3, Jin YAO4,4, and Liyang XIONG1,1,2,2,3,3、*
Author Affiliations
  • 1School of Geography, Nanjing Normal University, Nanjing 210023, China
  • 1南京师范大学地理科学学院,南京 210023
  • 2Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Nanjing 210023, China
  • 2虚拟地理环境教育部重点实验室（南京师范大学）,南京 210023
  • 3Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • 3江苏省地理信息资源开发与利用协同创新中心,南京 210023
  • 4The First Institute of Geoinformation Mapping, Ministry of Natural Resources of the People's Republic of China, Xi'an 710054, China
  • 4自然资源部第一地理信息制图院,西安 710054
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    AbstractGet PDF(in Chinese)
    Figures&Tables (13)
    Equations (4)
    References (56)
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    Figures & Tables(13)
    Elevation of the study area and distribution of sampled geomorphic types

    Fig. 1. Elevation of the study area and distribution of sampled geomorphic types

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    Result of small watershed division

    Fig. 2. Result of small watershed division

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    Principle of Random Forest[56]

    Fig. 3. Principle of Random Forest[56]

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    Importance ranking of each feature of watershed in training area

    Fig. 4. Importance ranking of each feature of watershed in training area

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    Relationship between prediction accuracy and features number

    Fig. 5. Relationship between prediction accuracy and features number

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    Calibration results of the nTrees parameter

    Fig. 6. Calibration results of the nTrees parameter

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    Classification result of watershed based on random forest

    Fig. 7. Classification result of watershed based on random forest

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    Result of fusion processing of classified watershed

    Fig. 8. Result of fusion processing of classified watershed

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    Spatial distribution of randomly sampled watersheds （used for accuracy verification）

    Fig. 9. Spatial distribution of randomly sampled watersheds （used for accuracy verification）

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    • Table 1. Preliminary selection of basin features

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      Table 1. Preliminary selection of basin features

      类型小流域特征数量/个
      基本地形因子统计量平均高程、高程标准差、平均坡度、坡度标准差、平均起伏度、起伏度标准差、平均切割深度、切割深度标准差、平均平面曲率、平面曲率标准差、平均剖面曲率、剖面曲率标准差、平均坡向、坡向标准差14
      地形特征点线统计量山顶点密度、山顶点高程标准差、沟沿线密度、沟沿线平均高程、割裂度、沟谷线密度、沟谷线平均高程7
      小流域特征相对高程差、沟谷深度、面积高程积分、坡谱信息熵4
      纹理特征纹理对比度、纹理角二阶矩、纹理信息熵、纹理逆差矩4

    • Table 2. Number of sample areas per geomorphological type

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      Table 2. Number of sample areas per geomorphological type

      地貌类型样区小流域数量/个
      沙丘草滩盆地30
      黄土低丘30
      黄土峁状丘陵沟壑30
      黄土梁状丘陵沟壑30
      黄土残塬丘陵沟壑30
      石质山地30
      黄土塬16
      黄土台塬14
      汇总210

    • Table 3. Confusion Matrix between RF classification result and manual interpretation result

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      Table 3. Confusion Matrix between RF classification result and manual interpretation result

      RF分类结果
      T1T2T2/T1T3T4T4/T3T5T6T7T8
      人工判读结果T113100000000
      T201000000000
      T2/T11200000000
      T301012000000
      T401001000000
      T4/T30003100000
      T500000013010
      T600001021300
      T70000001090
      T80000000005

    • Table 4. Classification accuarcies of different landforms

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      Table 4. Classification accuarcies of different landforms

      地貌类型正确分类数量/个错误分类数量/个分类精度/%
      T113192.9
      T210566.7
      T312380.0
      T410283.3
      T513381.3
      T6130100.0
      T79190.0
      T850100.0
      汇总851585.0
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    Zetao CAO, Zidong FANG, Jin YAO, Liyang XIONG. Loess Landform Classification based on Random Forest[J]. Journal of Geo-information Science, 2020, 22(3): 452

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    Paper Information

    Received: May. 23, 2019

    Accepted: --

    Published Online: Sep. 16, 2020

    The Author Email: XIONG Liyang (xiongliyang@163.com)

    DOI:10.12082/dqxxkx.2020.190247

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