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首页> 外文期刊>Limnologica >Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams
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Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

机译：淹没水生植被的光学遥感：浅水清澈溪流的机会

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Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries-Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column.Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature 'length/width ratio of sub-objects' was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features.In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management.
机译：由于现有图像数据的空间分辨率不足以及水中由于吸收和散射而引起的强烈衰减相结合，遥感技术很少被用作绘制和监测河流中淹没水生植物（SAV）的工具。后一过程减少了使用光谱反射率信息对淹没物种进行准确分类的可能性。然而，超高分辨率（VHR）图像数据的可用性不断提高，可以通过采用基于对象的图像分析（OBIA）方法来利用形状和纹理特征来帮助区分物种，并克服了目前存在的一些局限性。调查使用光学遥感技术检测和绘制SAV的可能性。首先，它研究了仅基于光谱信息来区分淹没的大型植物的可能性。在一定的浸没深度范围内就地测量了三种大型植物的反射光谱。结果表明，水深将成为遥感图像物种分类的限制因素。通过ANOVA分析，仅将尖刺的水嫩叶（Myriophyllum spicatum）表示为光谱上不同，但是随后的Jeffries-Matusita距离分析并未证实这一点。尤其不能区分水（（毛an）和蓬草（Potamogeton pectinatus），其显着性水平为95％。没有上覆水柱的影响，这两个物种的光谱可分离性也是不可能的。其次，利用空间和纹理信息研究了相同SAV物种改善物种歧视的可能性。 VHR图像数据是使用近红外（NIR）敏感的DSLR相机从四个不同高度（包括伸缩杆和Helikite UAS）获取的。结果表明，形状和纹理信息可以改善从VHR图像数据中光谱相似的Pondweed和Water Crowfoot的检测。通过专家知识获得了性能最佳的特征“子对象的长宽比”。所有基于形状和纹理的特征在物种分化方面都比基于光谱的特征表现更好。总之，这项研究表明，VHR数据和OBIA的组合在浅流环境中绘制SAV具有很大的潜力，这可以使物种受益监控和管理。

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