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Research progress of aquatic vegetation remote sensing in shallow lakes

  • role： First author第一作者
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

  • Email：jhluo@niglas.ac.cn
  • Introduction：罗菊花，1981年生，女，副研究员，研究方向为水生/湿生植被遥感。E-mail： jhluo@niglas.ac.cn
LUO Juhua,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

YANG Jingzhicheng,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

DUAN Hongtao,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

LU Lirong,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

SUN Zhe,  
  • Affiliation：

    Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China

XIN Yihao

resumen

In shallow lakes or reservior, aquatic vegetation plays an important role in purifying water, maintaining the balance of lake ecosystems, supporting socioeconomic functions and protecting lake ecological environment. However, an excessive amount of macrophytes, especially floating-leaved vegetation, can have some negative effects on lake ecology. For example, the addition of large amounts of plant material to the lake bottom can cause lake silting and accelerate lake swamping; the release of pollutants into the lake water when the plants die and decay can result in water pollution. Therefore, it is very important to map spatiotemporal distribution and their changes of aquatic vegetation and then to retrieve biochemical parameters such as coverage and biomass for ecological restoration and management of lakes. Remote sensing techniques have become powerful and effective tools for mapping aquatic vegetation types and their changes over a large area and a long period. In this paper, with the theme of aquatic vegetation remote sensing, we reviewed and summarized the major progresses and methods of remote sensing application in aquatic vegetation in shallow lakes by literature review. We found the research topics in aquatic vegetation remote sensing mainly included hyperspectral analyses, classification and mapping, parameter inversion, change detection, and so on. We also offered a literature statistical diagram of classification methods for mapping aquatic vegetation, and found decision tree was the most popular and machine learning was becoming more and more popular in all mapping methods. Finally, we discussed existing major challenges, potential solutions and future prospects in aquatic vegetation remote sensing, including developing a multi-parameter method for mapping different species of submerged vegetation, expanding the spatial-temporal scale of inversion models in parameters in application and making full use of the advantages of UAV (unmanned aerial vehicle) coupled with hyperspectral and multispectral sensors for mapping and parameter inversion in aquatic vegetation.

palabra clave

Aquatic vegetation;submerged aquatic vegetation;Biomass;Remote sensing;Classification;Change detection

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