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水生植被的遥感:理论与应用

Remote sensing of aquatic vegetation: theory and applications.

作者信息

Silva Thiago S F, Costa Maycira P F, Melack John M, Novo Evlyn M L M

机构信息

Department of Geography, University of Victoria, P.O. Box 3050 STN CSC, Victoria, BC V8W 3P5, Canada.

出版信息

Environ Monit Assess. 2008 May;140(1-3):131-45. doi: 10.1007/s10661-007-9855-3. Epub 2007 Jun 26.

DOI:10.1007/s10661-007-9855-3
PMID:17593532
Abstract

Aquatic vegetation is an important component of wetland and coastal ecosystems, playing a key role in the ecological functions of these environments. Surveys of macrophyte communities are commonly hindered by logistic problems, and remote sensing represents a powerful alternative, allowing comprehensive assessment and monitoring. Also, many vegetation characteristics can be estimated from reflectance measurements, such as species composition, vegetation structure, biomass, and plant physiological parameters. However, proper use of these methods requires an understanding of the physical processes behind the interaction between electromagnetic radiation and vegetation, and remote sensing of aquatic plants have some particular difficulties that have to be properly addressed in order to obtain successful results. The present paper reviews the theoretical background and possible applications of remote sensing techniques to the study of aquatic vegetation.

摘要

水生植被是湿地和沿海生态系统的重要组成部分,在这些环境的生态功能中发挥着关键作用。大型植物群落调查通常受到后勤问题的阻碍,而遥感是一种强大的替代方法,能够进行全面评估和监测。此外,许多植被特征可以通过反射率测量来估算,如物种组成、植被结构、生物量和植物生理参数。然而,正确使用这些方法需要了解电磁辐射与植被相互作用背后的物理过程,并且水生植物遥感存在一些特殊困难,必须妥善解决才能获得成功结果。本文综述了遥感技术在水生植被研究中的理论背景和可能的应用。

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本文引用的文献

1
Passive remote sensing techniques for mapping water depth and bottom features.用于绘制水深和底部特征的被动遥感技术。
Appl Opt. 1978 Feb 1;17(3):379-83. doi: 10.1364/AO.17.000379.
2
Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.利用高光谱遥感技术进行沉水植被制图的初步研究。
Environ Monit Assess. 2003 Jan-Feb;81(1-3):383-92. doi: 10.1023/a:1021318217654.
新型大规模水下植被监测方法:来自瑞典的全国性实例。
Integr Environ Assess Manag. 2022 Jun;18(4):909-920. doi: 10.1002/ieam.4493. Epub 2021 Aug 20.
4
Mapping the spatio-temporal distribution of key vegetation cover properties in lowland river reaches, using digital photography.利用数码摄影绘制低地河段关键植被覆盖特征的时空分布图。
Environ Monit Assess. 2017 Jun;189(6):294. doi: 10.1007/s10661-017-6004-5. Epub 2017 May 26.
5
Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques.结合高光谱遥感、机器学习和回归技术预测海洋大型植物和无脊椎动物物种的覆盖度。
PLoS One. 2013 Jun 3;8(6):e63946. doi: 10.1371/journal.pone.0063946. Print 2014.
6
Relating remotely sensed optical variability to marine benthic biodiversity.遥感光学变异性与海洋底栖生物多样性的关系。
PLoS One. 2013;8(2):e55624. doi: 10.1371/journal.pone.0055624. Epub 2013 Feb 6.
7
Assessment of water quality parameters of the Harike wetland in India, a Ramsar site, using IRS LISS IV satellite data.利用 IRS LISS IV 卫星数据评估印度哈尔基湿地(拉姆萨尔湿地)的水质参数。
Environ Monit Assess. 2010 Nov;170(1-4):117-28. doi: 10.1007/s10661-009-1220-2. Epub 2009 Oct 31.