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基于像元二分法的冬小麦植被覆盖度提取模型

孟沌超, 赵静, 兰玉彬, 鲁力群, 杨焕波, 李志铭, 闫春雨

孟沌超, 赵静, 兰玉彬, 等. 基于像元二分法的冬小麦植被覆盖度提取模型[J]. 华南农业大学学报, 2020, 41(3): 126-132. DOI: 10.7671/j.issn.1001-411X.201909055
引用本文: 孟沌超, 赵静, 兰玉彬, 等. 基于像元二分法的冬小麦植被覆盖度提取模型[J]. 华南农业大学学报, 2020, 41(3): 126-132. DOI: 10.7671/j.issn.1001-411X.201909055
MENG Dunchao, ZHAO Jing, LAN Yubin, et al. Vegetation coverage extraction model of winter wheat based on pixel dichotomy[J]. Journal of South China Agricultural University, 2020, 41(3): 126-132. DOI: 10.7671/j.issn.1001-411X.201909055
Citation: MENG Dunchao, ZHAO Jing, LAN Yubin, et al. Vegetation coverage extraction model of winter wheat based on pixel dichotomy[J]. Journal of South China Agricultural University, 2020, 41(3): 126-132. DOI: 10.7671/j.issn.1001-411X.201909055

基于像元二分法的冬小麦植被覆盖度提取模型

基金项目: 山东省引进顶尖人才“一事一议”专项经费资助项目(鲁政办字[2018]27号);中央引导地方科技发展专项资金资助项目(2017-01—2019-12)
详细信息
    作者简介:

    孟沌超(1992—),男,硕士研究生,E-mail: 1076845228@qq.com

    通讯作者:

    赵 静(1971—),女,副教授,博士,E-mail: zbceozj@163.com

  • 中图分类号: TP79

Vegetation coverage extraction model of winter wheat based on pixel dichotomy

  • 摘要:
    目的 

    快速准确提取冬小麦返青期植被覆盖度信息。

    方法 

    利用无人机获取田间冬小麦可见光图像,提取图像中4种常见可见光植被指数;在像元二分法原理的基础上,分别构建基于差异植被指数(Visible-band difference vegetation index,VDVI)、过绿指数(Excess green,EXG)、归一化绿蓝差异指数(Normalized green-blue difference index,NGBDI)和归一化绿红差异指数(Normalized green-red difference index,NGRDI)的植被覆盖度提取模型,采用支持向量机(Support vector machine,SVM)监督分类结果作为真值对各模型进行精度验证。

    结果 

    4种模型中,利用VDVI植被覆盖度提取模型获取的植被覆盖度精度最高,提取效果较好。与监督分类结果对比,4种植被覆盖度提取模型的提取误差(EF)分别为3.36%、15.68%、8.74%和15.46%,R2分别为0.946 1、0.934 4、0.695 3和0.746 0,均方根误差(RMSE)分别为0.021 9、0.059 5、0.042 0和0.055 9。

    结论 

    采用可见光植被指数结合像元二分法构建植被覆盖度提取模型实现了冬小麦返青期植被覆盖度准确快速提取,为植被覆盖度提取提供了一种新途径,可为无人机遥感监测提供参考。

    Abstract:
    Objective 

    To quickly and accurately extract vegetation coverage information of winter wheat in turning green period.

    Method 

    The field visible light images of winter wheat were obtained by UAV, and four common visible light vegetation indices were extracted. According to the principle of pixel dichotomy model, the vegetation coverage extraction models were established based on visible-band difference vegetation index(VDVI), excess green (EXG), normalized green-blue difference index (NGBDI) and normalized green-red difference index (NGRDI) respectively. The accuracies of four models were verified using the support vector machine (SVM) supervised classification results as the truth values.

    Result 

    The VDVI vegetation coverage extraction model had the highest accuracy and the best extraction effect to extract vegetation coverage among the four models. Compared with the supervised classification results, the extraction errors (EF) of four vegetation coverage extraction models were 3.36%, 15.68%, 8.74% and 15.46% respectively, the values of R2 were 0.946 1, 0.934 4, 0.695 3 and 0.746 0 respectively, and the values of root mean square error (RMSE) were 0.021 9, 0.059 5, 0.042 0 and 0.055 9 respectively.

    Conclusion 

    The vegetation coverage extraction model based on visible vegetation index and pixel dichotomy has realized accurate and rapid extraction of vegetation coverage of winter wheat in turning green period, which provides a new way to extract vegetation coverage and a reference for UAV remote sensing monitoring vegetation coverage information.

  • 图  1   冬小麦试验区

    Figure  1.   Test area of winter wheat

    图  2   冬小麦植被覆盖度提取流程

    Figure  2.   The extracting flow of winter wheat vegetation coverage

    图  3   冬小麦监督分类结果

    Figure  3.   Supervised classification result of winter wheat

    图  4   VDVI植被覆盖图

    Figure  4.   VDVI vegetation coverage map

    图  5   不同植被覆盖度提取模型的线性拟合结果

    Figure  5.   The linear fitting results of different vegetation coverage extraction models

    图  6   冬小麦植被覆盖度等级分布

    Figure  6.   Grade distribution of winter wheat vegetation coverage

    表  1   冬小麦分类精度评价

    Table  1   Evaluation of classification accuracy of winter wheat

    类别
    Category
    小麦/像素
    Wheat
    土壤/像素
    Soil
    样本总数/像素
    Total sample size
    用户精度/%
    User accuracy
    小麦/像素 Wheat 20 779 3 20 782 99.99
    土壤/像素 Soil 92 25 806 25 898 99.64
    样本总数/像素 Total sample size 20 871 25 809 46 680
    用户精度/% User accuracy 99.56 99.99
    下载: 导出CSV

    表  2   植被覆盖度精度评价

    Table  2   Accuracy evaluation of vegetation coverage

    植被指数模型
    Vegetation index model
    覆盖度 Vegetation coverage 提取误差/%
    Extraction error
    像元二分法
    Pixel dichotomy
    监督分类
    Supervised classification
    差值
    Difference value
    VDVI 0.321 452 0.332 623 0.011 171 3.36
    NGBDI 0.303 564 0.332 623 0.029 059 8.74
    NGRDI 0.384 043 0.332 623 0.051 420 15.46
    EXG 0.384 780 0.332 623 0.052 157 15.68
    下载: 导出CSV

    表  3   植被覆盖度等级分布统计结果

    Table  3   Statistical results of vegetation coverage grade distribution

    植被覆盖度
    Vegetation coverage
    像元数
    Pixel number
    百分比/%
    Percentage
    0~0.10 1 960 500 8.15
    0.10~0.30 12 265 775 51.01
    0.30~0.45 4 166 478 17.33
    0.45~0.60 2 460 841 10.23
    0.60~1.00 3 192 675 13.28
    总计 Total 24 046 269 100
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-09-26
  • 网络出版日期:  2023-05-17
  • 刊出日期:  2020-05-09

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