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基于无人机高光谱特征的红树林种群识别研究

周在明 陈本清 徐冉 方维

周在明,陈本清,徐冉,等. 基于无人机高光谱特征的红树林种群识别研究−以漳江口红树林国家级自然保护区为例[J]. 海洋学报,2021,43(9):137–145 doi: 10.12284/hyxb2021136
引用本文: 周在明,陈本清,徐冉,等. 基于无人机高光谱特征的红树林种群识别研究−以漳江口红树林国家级自然保护区为例[J]. 海洋学报,2021,43(9):137–145 doi: 10.12284/hyxb2021136
Zhou Zaiming,Chen Benqing,Xu Ran, et al. Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve[J]. Haiyang Xuebao,2021, 43(9):137–145 doi: 10.12284/hyxb2021136
Citation: Zhou Zaiming,Chen Benqing,Xu Ran, et al. Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve[J]. Haiyang Xuebao,2021, 43(9):137–145 doi: 10.12284/hyxb2021136

基于无人机高光谱特征的红树林种群识别研究以漳江口红树林国家级自然保护区为例

doi: 10.12284/hyxb2021136
基金项目: NSFC-山东联合基金(U1806203)
详细信息
    作者简介:

    周在明(1980—),男,山东省淄博市人,副研究员,主要从事生态环境遥感研究工作。E-mail:zhouzaiming@tio.org.cn

  • 中图分类号: P407.8; S718.54

Identification of the mangrove species using UAV hyperspectral images: A case study of Zhangjiangkou mangrove national nature reserve

  • 摘要: 红树林种群的组成和分布对于红树林生态系统的保护和恢复至关重要。本研究以漳江口红树林保护区为研究对象,通过获取无人机高光谱影像,进行光谱特征分析、光谱微分变换和包络线去除,提取了911组17个光谱特征参数,通过逐步判别分析筛选出13个用于决策树构建的特征参数,最终通过C5.0决策树模型获得了研究区红树林种群的分布状况。结果表明,漳江口红树林保护区植被种群呈现自上到下不同类型的分布情况,研究区上部以桐花树和秋茄混合类型为主,中间区域呈现白骨壤、桐花树和秋茄三者共生的现状,研究区下部则以白骨壤分布为主,伴生有少量的秋茄。通过混淆矩阵计算,得到研究区总体分类精度为 87.95%,Kappa系数为 83.81%,具有较好的精度。研究结果可为区域红树林湿地保护提供数据支撑,为红树林种群识别研究提供方法参考。
  • 图  1  漳江口红树林研究区位置

    Fig.  1  The location of Zhangjiangkou mangrove in the study area

    图  2  研究区无人机高光谱影像图(a)和普通光学影像(b)

    Fig.  2  Unmanned aerial vehicle hyperspectral image (a) and RGB image (b) in the study area

    图  3  研究区典型植被类型光谱反射率曲线

    Fig.  3  Spectral reflectance curves of typical vegetation species in the study area

    图  4  包络线去除光谱反射率变换曲线

    Fig.  4  Spectral reflectance curves of continuum removal

    图  5  研究区典型植被类型逐步判别分析结果

    Fig.  5  Stepwise discriminant analysis result of the typical vegetation species in the study area

    图  6  研究区典型植被类型决策树模型示意图

    Fig.  6  The sketch map of decision tree classification model of the typical vegetation species in the study area

    图  7  研究区植被类型分类识别结果

    Fig.  7  Identification and classification results of the typical vegetation species in the study area

    表  1  研究区典型植被类型样本情况表

    Tab.  1  The information sheet of sample of the typical vegetation species in the study area

    植被类型桐花树白骨壤秋茄互花米草
    训练样本数458188102163
    验证样本数121948395
    下载: 导出CSV

    表  2  研究区典型植被类型“三边”参数

    Tab.  2  Three sides spectral parameters of the typical vegetation species in the study area

    植被类型DbDyDrSbSySr
    桐花树0.002−0.0140.726−0.239−0.1898.892
    白骨壤0.006−0.0310.791−0.398−0.31210.313
    秋茄0.009−0.0350.827−0.458−0.30710.292
    互花米草0.026−0.0010.245−0.076−0.0983.047
    下载: 导出CSV

    表  3  研究区典型植被类型最大峰度统计

    Tab.  3  The maximum kurtosis of the typical vegetation species in the study area

    植被类型650~700 nm700~720 nm720~750 nm
    K1B1K2B2K3B3
    桐花树0.019 96900.024 87180.031 6738
    白骨壤0.027 86900.019 47140.029 6738
    秋茄0.029 16900.023 17140.032 4730
    互花米草0.009 36900.009 37180.015 4742
      注:K1、K2、K3分别为各波段范围内的最大峰度值;B1、B2、B3分别为各峰度对应的波段值。
    下载: 导出CSV

    表  4  研究区典型植被类型包络线去除光谱吸收参数

    Tab.  4  Spectral absorption parameters after continuum removal of typical species in the study area

    植被类型H1H2AL1AL2A1A2S1S2
    桐花树0.446 70.916 015.061 086.550 724.359 9127.111 50.618 30.680 9
    白骨壤0.420 40.849 313.791 876.755 424.419 1112.484 60.564 80.682 4
    秋茄0.507 40.893 815.476 284.054 630.705 8119.929 10.504 00.700 9
    互花米草0.114 70.479 62.587 436.971 96.328 556.787 70.408 90.651 1
      注:H1、AL1、A1、S1为450~550 nm波段范围内的参数值;H2、AL2、A2、S2为550~750 nm波段范围内的参数值。
    下载: 导出CSV

    表  5  研究区典型植被分类结果混淆矩阵

    Tab.  5  Confusion matrix of classification results of the typical vegetation species in the study area

    桐花树白骨壤秋茄互花米草总计用户精度/%
    桐花树131312014689.72
    白骨壤31084612189.25
    秋茄124113313285.60
    互花米草274869986.86
    总计14812213395498
    生产精度/%88.5188.5284.9690.52
      注:−代表空值。
    下载: 导出CSV
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  • 收稿日期:  2020-12-08
  • 修回日期:  2021-05-06
  • 网络出版日期:  2021-06-17
  • 刊出日期:  2021-09-06

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