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An integrated approach to a biophysiologically based classification of floating aquatic macrophytes

机译:一种基于生物生理学的漂浮水生植物分类的综合方法

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摘要

Globally, invasive species are identified as one of the most serious threats to ecological stability and biodiversity. Water hyacinth (Eichhornia crassipes), an aggressive invasive aquatic species, has caused severe economic and ecological impacts in the Sacramento-San Joaquin River Delta in California, in the Delta, water hyacinth co-occurs with native pennywort (Hydrocotyle umbellata L.) and non-native water primrose (Ludwigia spp.). All of the species express a wide range of phenotypic variability, making it difficult to map them with remote sensing techniques because their spectral response is highly variable. We present an integrated approach to mapping these floating species using a sequence of hyperspec-tral methods, such as spectral angle mapper (SAM), linear spectral unmixing (LSU), continuum removal and several indices in a decision tree format. The ensuing tree, based on biophysiological differences between the species, was robust and consistent across three separate years and over multiple flightlines each year, spread across an area of approximately 2500 km~2. The most important inputs used to create the tree were reflectance in the short-wave infrared (SW1R), Red Edge Index, near-infrared (NIR) reflectance, LSU fractions and SAM rule values. The floating species were mapped with average accuracy of 88% for water hyacinth, 87% for pennywort and 71% for water primrose.
机译:在全球范围内,入侵物种被认为是对生态稳定和生物多样性的最严重威胁之一。风信子(Eichhornia crassipes)是一种侵略性入侵水生物种,已在加利福尼亚的萨克拉曼多-圣华金河三角洲造成严重的经济和生态影响,在该三角洲,风信子与当地的彭妮草(Hydrocotyle umbellata L.)同时发生。非天然水樱草(Ludwigia spp。)。所有物种都表现出广泛的表型变异性,由于其光谱响应高度可变,因此很难用遥感技术对其进行制图。我们提出了一种使用一系列高光谱方法(例如光谱角度映射器(SAM),线性光谱解混(LSU),连续谱去除和决策树格式的多个索引)来映射这些浮动物种的集成方法。随后的树根据物种之间的生物生理学差异,在三年内稳健且一致,并且每年跨越多个飞行路线,分布在约2500 km〜2的区域内。用于创建树的最重要的输入是短波红外(SW1R)的反射率,红边索引,近红外(NIR)反射率,LSU分数和SAM规则值。绘制的浮游物种的水葫芦的平均准确度为88%,细叶草的平均准确度为87%,水樱草的平均准确度为71%。

著录项

  • 来源
    《International journal of remote sensing》 |2011年第4期|p.1067-1094|共28页
  • 作者单位

    Center for Spatial Technologies and Remote Sensing, University of California, Davis, USA;

    Center for Spatial Technologies and Remote Sensing, University of California, Davis, USA;

    Center for Spatial Technologies and Remote Sensing, University of California, Davis, USA;

    Center for Spatial Technologies and Remote Sensing, University of California, Davis, USA;

  • 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
  • 关键词

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