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首页> 外文期刊>International journal of remote sensing >Assessing relationships between Radarsat-2 C-band and structural parameters of a degraded mangrove forest
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Assessing relationships between Radarsat-2 C-band and structural parameters of a degraded mangrove forest

机译：评估Radarsat-2 C波段与退化红树林的结构参数之间的关系

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摘要

Relationships were assessed between mangrove structural data (leaf area index (LAI), stem density, basal area, diameter at breast height (DBH)) collected from 61 stands located in a black mangrove (Avicennia germinans)-dominated forest and both single polarized ultra-fine (3 m) and multipolarized fine beam (8 m) Radarsat-2 C-band synthetic aperture radar (SAR) data. The stands examined included representatives from the four types of mangroves that typify this degraded system, specifically: predominantly dead mangrove, poor-condition mangrove, healthy dwarf mangrove, and tall healthy mangrove. The results indicate that the selection of the spatial resolution (3 m vs. 8 m) of the incidence angle (27-39°) and the polarimetric mode greatly influence the relationship between the SAR and mangrove structural data. Moreover, the extent of degradation, i.e. whether dead stands are considered, also determines the strength of the relationships between the various SAR and mangrove parameters. When dead stands are included, the strongest overall relationships between the ultra-fine backscatter (incidence angle of ~32°) and the various structural parameters were found using the horizontal-horizontal (HH) polarization/horizontal-vertical (HV) polarization ratio. However, if the dead stands are not included, then significant relationships with the ultra-fine data were only calculated with the HH data. Similar results were observed using the corresponding incidence angle (~33°) of the fine beam data. When a shallower incidence angle was considered (~39°), fewer and weaker relationships were calculated. Moreover, no significant relationships were observed if the dead stands were excluded from the sample at this incidence angle. The highest correlation coefficients using the steepest incidence (~27°) were found with the co-polarized (HH, vertical-vertical (VV) polarization) modes. Several polarimetric parameters (entropy, pedestal height, surface roughness, alpha angle) based on the decomposition of the scattering matrix of the fine beam mode at this incidence angle were also found to be significantly correlated to mangrove structural data. The highest correlation (R = 0.71) was recorded for entropy and LAI. When the dead stands were excluded, volume scattering was found to be the most significant polarimetric parameter. Finally, multiple regression models, based on texture measures derived from both the grey level co-occurrence matrix (GLCM) and the sum and difference histogram (SADH) of the ultra-fine data, were developed to estimate mangrove parameters. The results indicate that only models derived from the HH data are significant and that several of these were strong predictors of all but stem density.
机译：评估了从位于黑红树林（Avicennia Germinans）为主的61个林分中收集的红树林结构数据（叶面积指数（LAI），茎密度，基础面积，胸高直径（DBH））之间的关系。 -精细（3 m）和多极化细波束（8 m）Radarsat-2 C波段合成孔径雷达（SAR）数据。考察的林分包括代表这种退化系统的四种类型的红树林的代表，主要是：死去的红树林，状况不佳的红树林，健康的矮红树林和高大的健康红树林。结果表明，入射角（27-39°）的空间分辨率（3 m对8 m）和极化模式的选择极大地影响了SAR与红树林结构数据之间的关系。此外，退化的程度，即是否考虑了枯草，还决定了各种SAR和红树林参数之间关系的强度。当包括静架时，使用水平-水平（HH）极化/水平-垂直（HV）极化比率发现超细反向散射（入射角约为32°）与各种结构参数之间最强的整体关系。但是，如果不包括死角，则仅使用HH数据计算与超精细数据的显着关系。使用细光束数据的相应入射角（〜33°）观察到相似的结果。当考虑较浅的入射角（〜39°）时，计算的关系越少越弱。而且，如果在该入射角下将死角从样品中排除，则没有观察到显着的关系。在同极化（HH，垂直-垂直（VV）极化）模式下，使用最陡的入射角（〜27°）发现了最高的相关系数。还发现了基于细光束模式散射矩阵在该入射角下的分解的几个极化参数（熵，基座高度，表面粗糙度，α角）与红树林结构数据显着相关。熵和LAI的相关性最高（R = 0.71）。当排除死角时，发现体积散射是最重要的极化参数。最后，基于从灰度共生矩阵（GLCM）和超细数据的和直方图和差直方图（SADH）派生的纹理度量，开发了多个回归模型来估计红树林参数。结果表明，只有从HH数据得出的模型才有意义，并且其中一些是除茎密度以外所有变量的强预测指标。

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  • 来源
    《International journal of remote sensing》 |2013年第20期|7002-7019|共18页
  • 作者

    John M. Kovacs; Xianfeng Jiao; Francisco Flores-de-Santiago; Chunhua Zhang; Francisco Flores-Verdugo;

  • 作者单位

    Department of Geography, Nipissing University, North Bay, Canada, ON P1B 8L7;

    Department of Geography, Nipissing University, North Bay, Canada, ON P1B 8L7;

    Department of Geography, The University of Western Ontario, London, Canada, ON N6A 5C2;

    Department of Geography and Geology, Algoma University, Sault Ste. Marie, Canada, ON P6A 2G4;

    Instituto de Ciencias del Mar y Limnologia, Mazatlan, Sinaloa 82000, Mexico;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
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