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基于可见光植被指数的乌海市矿山排土场坡面植被覆盖信息提取研究

李鹏飞 郭小平 顾清敏 张昕 冯昶栋 郭光

李鹏飞, 郭小平, 顾清敏, 张昕, 冯昶栋, 郭光. 基于可见光植被指数的乌海市矿山排土场坡面植被覆盖信息提取研究[J]. 北京林业大学学报, 2020, 42(6): 102-112. doi: 10.12171/j.1000-1522.20190252
引用本文: 李鹏飞, 郭小平, 顾清敏, 张昕, 冯昶栋, 郭光. 基于可见光植被指数的乌海市矿山排土场坡面植被覆盖信息提取研究[J]. 北京林业大学学报, 2020, 42(6): 102-112. doi: 10.12171/j.1000-1522.20190252
Li Pengfei, Guo Xiaoping, Gu Qingmin, Zhang Xin, Feng Changdong, Guo Guang. Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index[J]. Journal of Beijing Forestry University, 2020, 42(6): 102-112. doi: 10.12171/j.1000-1522.20190252
Citation: Li Pengfei, Guo Xiaoping, Gu Qingmin, Zhang Xin, Feng Changdong, Guo Guang. Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index[J]. Journal of Beijing Forestry University, 2020, 42(6): 102-112. doi: 10.12171/j.1000-1522.20190252

基于可见光植被指数的乌海市矿山排土场坡面植被覆盖信息提取研究

doi: 10.12171/j.1000-1522.20190252
基金项目: 国家重点研发计划“西北干旱荒漠区煤炭基地生态安全保障技术”项目(2017YFC0504400),“矿区生态修复与生态安全保障技术集成示范研究”(2017YFC0504406)
详细信息
    作者简介:

    李鹏飞。主要研究方向:水土保持与无人机遥感。Email:lpf132@bjfu.edu.cn  地址:100083北京市海淀区清华东路35号北京林业大学水土保持学院

    责任作者:

    郭小平,教授,博士生导师。主要研究方向:工程绿化。Email:guoxp@bjfu.edu.cn 地址:同上

  • 中图分类号: S157

Vegetation coverage information extraction of mine dump slope in Wuhai City of Inner Mongolia based on visible vegetation index

  • 摘要: 目的利用可见光植被指数快速准确获取矿山排土场坡面植被盖度,为乌海矿区排土场坡面植被调查提供有效方法。方法选取乌海市典型矿山排土场,通过样方调查法、无人机遥感及可见光植被指数计算筛选适于研究区排土场坡面植被提取的可见光植被指数,并估算其植被盖度,试为排土场坡面植被盖度提取提供新方法。结果结果表明:(1)不同可见光植被指数提取植被效果存在一定差异,其中绿红比值指数(RGRI)和绿蓝比值指数(BGRI)的灰度图中越暗的部分代表植被指数越大,而其他常见可见光植被指数是越亮的部分代表植被指数越大。(2)研究区中不同可见光植被指数灰度图像特征值基本分布在[− 1,1]范围内,由蓝、绿波段构建的归一化绿蓝差异指数(NGBDI)和绿蓝比值指数(BGRI)的灰度图中植被与裸地像元值范围有较大重叠,即存在部分混淆。(3)常见可见光植被指数中,可见光波段差异植被指数(VDVI)可以快速准确提取研究区排土场坡面植被,通过人工目视解译及误差矩阵得到VDVI植被指数提取结果平均识别精度在93.4%,表明VDVI植被指数更加适用于乌海市矿山排土场坡面植被提取,优于其他常见可见光植被指数,利用该方法估算可得研究区坡面植被盖度约20.4%。结论可见光植被指数作为一种非监督分类方法,无需人工选择参考地物即可提取植被,可以作为矿山排土场坡面植被盖度调查的一种新方法,具有广阔的应用前景,同时研究表明VDVI植被指数在提取乌海市矿山排土场坡面植被盖度时具有较高提取精度,对指导当地矿山排土场植被恢复具有实际意义。

     

  • 图  1  研究区地理位置

    Figure  1.  Location of study area

    图  2  研究区无人机正射影像图

    Figure  2.  UAV orthophotos of study area

    图  3  目视解译与监督分类结果

    A-1、B-1、C-1分别为典型样方A、B、C的航拍原始图像;A-2、B-2、C-2分别为典型样方A、B、C的目视解译结果;A-3、B-3、C-3分别为典型样方A、B、C的最大似然监督分类结果。A-1, B-1, C-1 represent original images of quadrat A, B and C, respectively; A-2, B-2, C-2 represent visual interpretation results of quadrat A, B and C, respectively; A-3, B-3, C-3 represent maximum likelihood supervised classification calculating results of quadrat A, B and C, respectively.

    Figure  3.  Results of visual interpretation and supervised classification

    图  4  可见光植被指数提取结果(样方A)

    Figure  4.  Extraction results of vegetation indices (quadrat A)

    图  5  Otsu法和双峰直方图法分割结果对比图

    Figure  5.  Comparison of different indices by Otsu and histogram threshold methods

    图  6  VDVI验证结果

    Figure  6.  Verifying results of VDVI

    图  7  排土场坡面植被分类结果图

    Figure  7.  Classification results of slope in mine junkyard of the study area

    表  1  常见的可见光植被指数

    Table  1.   Common vegetation indices of visible bands

    可见光植被指数
    Visible vegetation index
    全称 Full name计算公式 Equation参考文献Reference
    NGRDI 归一化绿红差异指数
    Normalized green-red difference index
    G − R)/(G + R [20]
    NGBDI 归一化绿蓝差异指数
    Normalized green-blue difference index
    G − B)/(G + B [21]
    EXG 超绿指数
    Excess green index
    2g − r − b [22]
    EXGR 超绿超红差异指数
    Excess green minus excess red index
    EXG − 1.4rg [23]
    VEG 植被指数
    Vegetation index
    $g / r^{0.67} b^{0.33}$ [24]
    VDVI 可见光波段差异植被指数
    Visible-band difference vegetation index
    (2G − R − B)/(2G + R + B [11]
    RGRI 绿红比值指数
    Red-green ratio index
    R/G [25]
    BGRI 绿蓝比值指数
    Blue-green ratio index
    B/G [26]
    注:G. 绿光通道;R. 红光通道;B. 蓝光通道;g. 绿光通道标准化结果;r. 红光标准化结果;b. 蓝光标准化结果;Notes: G, green channel; R, red channel; B, blue channel; g, standardization of green channel; r, standardization of red channel; b, standardization of blue channel; g = G/(G + R + B),r = R/(G + R + B),b = B/(G + R + B).
    下载: 导出CSV

    表  2  地物在红、绿、蓝波段及各可见光植被指数波段的像元值差异表

    Table  2.   Differences in pixel values in red, green, blue bands and vegetation indices of land cover

    波段类型 Band type植被 Vegetation裸地 Bare land
    均值 Mean标准差 Standard deviation均值 Mean标准差 Standard deviation
    红光波段像元值 Pixel value for red band 164.470 26.898 212.602 21.997
    绿光波段像元值 Pixel value for green band 177.432 27.016 204.327 19.403
    蓝光波段像元值 Pixel value for blue band 159.941 30.192 205.148 17.529
    BGRI像元值 Pixel value for BGRI 0.900 0.083 1.006 0.042
    EXG像元值 Pixel value for EXG 0.063 0.036 −0.015 0.006
    EXGR像元值 Pixel value for EXGR −0.750 0.020 −0.821 0.013
    NGBDI像元值 Pixel value for NGBDI 0.055 0.046 −0.003 0.020
    NGRDI像元值 Pixel value for NGRDI 0.039 0.016 −0.019 0.015
    RGRI像元值 Pixels value for RGRI 0.925 0.029 1.039 0.030
    VDVI像元值 Pixel value for VDVI 0.046 0.026 −0.011 0.005
    VEG像元值 Pixel value for VEG 1.093 0.045 0.973 0.009
    下载: 导出CSV

    表  3  4种可见光植被指数分类结果精度评估

    Table  3.   Accuracy evaluation of four kinds of vegetation indices of visible bands %

    项目 Item

    EXG EXGR NGRDI VDVI 监督分类
    Supervised
    classification
    Otsu法
    Otsu method
    双峰直方图法
    Histogram method
    Otsu法
    Otsu method
    双峰直方图法
    Histogram method
    Otsu法
    Otsu method
    双峰直方图法
    Histogram method
    Otsu法
    Otsu method
    双峰直方图法
    Histogram method
    样方A Quadrat A 93.53 95.41 91.80 93.55 88.35 90.14 91.12 95.86 98.93
    样方B Quadrat B 61.61 75.89 62.82 83.57 64.31 90.33 61.40 90.98 96.39
    样方C Quadrat C 91.27 92.86 85.82 85.87 78.81 81.30 92.22 92.89 96.80
    均值 Mean 82.14 88.05 80.15 87.66 77.16 87.26 81.58 93.24 97.37
    下载: 导出CSV

    表  4  样方D精度评估表

    Table  4.   Accuracy evaluation of quadrat D

    分类数据
    Classification data
    双峰直方图法 Histogram methodOtsu法 Otsu method
    植被Vegetation非植被
    Non-vegetation
    行总和
    Row total
    用户精度
    User accuracy/%
    植被
    Vegetation
    非植被
    Non-vegetation
    行总和
    Row total
    用户精度
    User accuracy/%
    植被 Vegetation516 24025 318541 55895.32385 20211 971397 17396.99
    非植被 Non-vegetation84 415747 313831 72889.85215 453760 660976 11377.93
    列总和 Column total600 655772 6311 373 286600 655772 6311 373 286
    生产者精度
    Producer accuracy/%
    85.9596.7264.1398.45
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-06-10
  • 修回日期:  2019-10-06
  • 网络出版日期:  2020-05-30
  • 刊出日期:  2020-07-01

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