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GEE平台下考虑潮位变化及植被物候特征的盐城滨海湿地精细化遥感分类

顾容 张东 钱林峰 吕林 陈艳艳 于凌程

顾容,张东,钱林峰,等. GEE平台下考虑潮位变化及植被物候特征的盐城滨海湿地精细化遥感分类[J]. 海洋学报,2024,46(x):1–13 doi: 10.12284/hyxb2024030
引用本文: 顾容,张东,钱林峰,等. GEE平台下考虑潮位变化及植被物候特征的盐城滨海湿地精细化遥感分类[J]. 海洋学报,2024,46(x):1–13 doi: 10.12284/hyxb2024030
Gu Rong,Zhang Dong,Qian Linfeng, et al. Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform[J]. Haiyang Xuebao,2024, 46(x):1–13 doi: 10.12284/hyxb2024030
Citation: Gu Rong,Zhang Dong,Qian Linfeng, et al. Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform[J]. Haiyang Xuebao,2024, 46(x):1–13 doi: 10.12284/hyxb2024030

GEE平台下考虑潮位变化及植被物候特征的盐城滨海湿地精细化遥感分类

doi: 10.12284/hyxb2024030
基金项目: 国家自然科学基金项目(41771447,41971382);事业单位研究项目(WSW5310DY2022LJ)。
详细信息
    作者简介:

    顾容(1999—),女,四川省宜宾市人,主要从事海岸带盐沼湿地生态遥感应用研究。E-mail:gurong996@163.com

    通讯作者:

    张东(1975—),男,江苏省南通市人,教授,主要从事海洋信息技术与海岸带资源环境遥感研究。E-mail: zhangdong@njnu.edu.cn

Refined remote sensing classification of Yancheng coastal wetland considering tide-level changes and vegetation phenological characteristics on the GEE platform

  • 摘要: 滨海湿地具有重要的经济价值与生态价值,快速准确地监测其现状对滨海湿地资源的保护和管理具有重要意义。由于潮汐动态变化、植被光谱相似性以及云覆盖等因素的影响,滨海湿地的遥感监测具有较大挑战。本文提出了一个综合考虑潮位变化及植被物候特征的滨海湿地遥感分类方法,基于GEE(Google Earth Engine)平台,首先引入Fmask(Function of mask)算法进行云检测与去云处理,然后利用S-G(Savitzky-Golay)滤波算法重构NDVI时间序列数据,提取植被物候特征参数,采用随机森林算法实现互花米草(Spartina alterniflora)、芦苇(Phragmites australis)、碱蓬(Suaeda salsa)与茅草(Imperata cylindrica)4种湿地植被类型的提取;最后利用最大光谱指数合成算法 (Maximum Spectral Index Composite, MSIC) 生成最高与最低潮位合成影像,结合大津算法(Otsu)提取光滩与海水,实现滨海湿地的精细化遥感分类。研究结果表明,生长季开始时间、生长季结束时间、生长季时长、基准值、振幅、小季节积分是区分滨海湿地植被的重要植被物候特征参数。利用本方法对盐城滨海湿地进行分类,湿地总体分类精度达96.50%,Kappa系数为0.957 1,湿地植被中互花米草的使用者精度最高,为96.59%;其次是芦苇与碱蓬;茅草最低,为93.55%。与面向对象分类相比,本方法不仅能够提取完整的光滩范围,而且将总体精度提高10.25%,体现出植被物候特征在滨海湿地动态变化遥感监测中的应用潜力。
  • 图  1  研究区位置

    Fig.  1  Location of the study area

    图  2  滨海湿地分类流程

    Fig.  2  Workflow of coastal wetland classification

    图  3  不同植被类型NDVI时间序列拟合曲线

    Fig.  3  Time series fitting curves of different vegetation types

    图  4  盐城滨海湿地植被物候特征参数对比

    Fig.  4  Comparison of vegetation phenological characteristics parameters of Yancheng coastal wetland vegetation

    图  5  盐城滨海湿地分类结果

    a. 盐城滨海湿地分类结果图;b. 植被混生带;c. 植被与内陆水体相接地带;d. 植被与光滩相接地带

    Fig.  5  Classification results of Yancheng coastal wetland

    a. Classification results map of Yancheng coastal wetland; b. the mixed zone of vegetation; c. the adjacent zone of vegetation and inland water; d. the adjacent zone of vegetation and tidal flats

    图  6  面向对象方法分类结果

    a. 盐城滨海湿地分类结果图;b. 植被混生带;c. 植被与内陆水体相接地带;d. 植被与光滩相接地带

    Fig.  6  Classification results of the object-oriented methods

    a. Classification results map of Yancheng coastal wetland; b. the mixed zone of vegetation; c. the adjacent zone of vegetation and inland water; d. the adjacent zone of vegetation and tidal flats

    图  7  不同时间序列数据重构方法分类精度对比

    Fig.  7  Comparison of classification accuracy of different time series data reconstruction methods

    图  8  判别碱蓬的3个重要物候特征参数对比

    Fig.  8  Comparison of three important phenological characteristics parameters of the discriminate Suaeda salsa

    图  9  盐城滨海湿地植被分类结果图 ((a) QA60 + HANTS; (b) QA60 + S-G; (c) Fmask + HANTS)

    Fig.  9  Classification results map of coastal wetland vegetation in Yancheng

    表  1  Fmask算法的公式与阈值

    Tab.  1  Formula and threshold of Fmask algorithm

    名称 公式与阈值 公式编号
    基本检测 ${ \begin{gathered} {\rho _{SWIR2}} > 0.03{\text{ }}and{\text{ }}NDSI < 0.8{\text{ }}and{\text{ }}NDVI < 0.8,{\text{ }} \\ \left\{ \begin{gathered} NDSI = ({\rho _{Green}} - {\rho _{SWIR1}})/({\rho _{Green}} + {\rho _{SWIR1}}) \\ NDVI = ({\rho _{NIR}} - {\rho _{Red}})/({\rho _{NIR}} + {\rho _{Red}}) \\ \end{gathered} \right. \\ \end{gathered}} $ (1)
    白度检测(Whiteness) ${ \begin{gathered} Whiteness = \left| {\left( {{\rho _{Red}} - MeanVis} \right)/MeanVis} \right|+\left| {\left( {{\rho _{Green}} - MeanVis} \right)/MeanVis} \right| + \\ \left| {\left( {{\rho _{Blue}} - MeanVis} \right)/MeanVis} \right| < 0.7,{\text{ }}MeanVis = \left( {{\rho _{Red}} + {\rho _{Green}} + {\rho _{Blue}}} \right)/3 \end{gathered} }$ (2)
    霾优化转换检测(HOT) $ {HOT = {\rho _{Blue}} - 0.5 \times {\rho _{Red}} - 0.08 > 0 }$ (3)
    比值检测 $ {{\rho _{NIR}}/{\rho _{SWIR1}} > 0.75 }$ (4)
    卷云检测(Cir) ${ Cir = {\rho _{Cir}}/0.04 > 0.01 }$ (5)
    水检测(Water) ${ \begin{gathered} Water = (NDVI < 0.01{\text{ }}and{\text{ }}{\rho _{NIR}} < 0.11){\text{ or }}(NDVI < 0.1{\text{ }}and{\text{ }}{\rho _{NIR}} < 0.05) \\ {\text{ or }}(GSWO > {O_{water}}{\text{ }}and{\text{ }}snow/ice = false) \\ \end{gathered} }$ (6)
    陆地云概率(lCloudp) ${ \begin{gathered} lCloudp = lVar \times lHOT + 0.5 \times Cir > lCloudt, \\ \left\{ \begin{gathered} lHOT = \frac{{HOT - \left( {HO{T_{low}} - 0.04} \right)}}{{\left( {HO{T_{high}} + 0.04} \right) - \left( {HO{T_{low}} - 0.04} \right)}} \\ lVar = 1 - \max \left( {\left| {NDVI} \right|,\left| {NDSI} \right|,\left| {Whiteness} \right|} \right) \\ \end{gathered} \right. \\ \end{gathered}} $ (7)
    水体云概率(wCloudp) ${ wCloudp = wBright + 0.5 \times Cir > wCloudt,{\text{ }}wBright = \min ({\rho _{SWIR1}},0.11)/0.11} $ (8)
    云阴影检测 ${ \left\{ \begin{gathered} {\rho _{NIR}} < 0.25 \\ {\rho _{SWIR1}} < 0.11 \\ \end{gathered} \right. }$ (9)
      注:ρBlueρGreenρRedρNIRρSWIR1ρSWIR2ρcir分别为蓝、绿、红、近红外、短波与卷云波段的反射率。MeanVis为平均反射率。GSWO(Global Surface Water Occurrence)为全球地表水发生率数据,为每个像元提供水发生率,其中0%表示永久的陆地,100%表示永久的水体;snow/ice为雪、冰;Owater为水发生率阈值,取光谱识别的水体像元集的低值(17.5百分位值)再减去5%的GSWO数据误差。lHOT为陆地HOT云概率,HOTlow等于陆地绝对无云像元的HOT低值(17.5百分位值),HOThigh等于陆地绝对无云像元的HOT高值(82.5百分位值);lCloudt为陆地云概率阈值,等于陆地绝对无云像元的lCloudp高值(82.5百分位值)再加上常数0.2。lVar为陆地光谱差异概率。wCloudt为水体云概率阈值,等于水体绝对无云像元的wCloudp高值(82.5百分位值)再加上常数0.2。wBright为水体亮度概率。
    下载: 导出CSV

    表  2  不同植被类型的植被物候特征判别参数与阈值

    Tab.  2  Discriminative parameters and thresholds for vegetation phenological characteristics of different vegetation types

    植被类型 植被物候特征判别参数与阈值
    芦苇 120 < SOS < 150
    碱蓬 SI < 50 and BV > 0.22 and AV < 0.4
    互花米草 340 < EOS < 380
    茅草 LOS < 130 and AV > 0.55
    下载: 导出CSV

    表  3  盐城滨海湿地分类精度评价结果

    Tab.  3  Accuracy evaluation of coastal wetland classification

    芦苇 碱蓬 互花米草 茅草 内陆水体 光滩 海水 使用者精度/%
    芦苇 113 1 2 1 0 0 0 96.58
    碱蓬 1 52 1 1 0 0 0 94.55
    互花米草 1 2 85 0 0 0 0 96.59
    茅草 0 0 2 29 0 0 0 93.55
    内陆水体 0 0 1 0 36 0 0 97.30
    光滩 0 0 0 0 0 35 0 100.00
    海水 0 0 0 0 0 1 36 97.30
    生产者精度/% 98.26 94.55 93.41 93.55 100.00 97.22 100.00
    总体精度/% 96.50
    Kappa系数 0.957 1
    下载: 导出CSV

    表  4  不同分类方法分类精度对比

    Tab.  4  Comparison of classification accuracy of different techniques

    指标 面向对象方法 与本文方法相比
    精度/% 芦苇 82.76 −13.82
    碱蓬 88.89 −5.66
    互花米草 82.02 −14.57
    茅草 93.33 −0.22
    内陆水体 78.57 −18.73
    光滩 96.88 −3.12
    海水 97.30 0.00
    总体精度 86.25 −10.25
    Kappa系数 0.831 6 −0.125 5
    下载: 导出CSV
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  • 收稿日期:  2023-07-31
  • 修回日期:  2023-11-22
  • 网络出版日期:  2024-03-06

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