浅水湖泊水生植被遥感监测研究进展

在浅水湖泊中,水生植物具有净化水质、抑制藻类、提供鱼类食物和栖息环境等生态功能,同时,其过度扩张也会加速湖泊淤浅和沼泽化、引起湖泊二次污染等环境负效应.实时动态地掌握湖泊水生植被类群和种群的空间分布及其面积、生物量等指标信息,对湖泊生态修复和评估、水生植被恢复和管理等具有重要现实意义.遥感技术的大面积、实时、动态等特点...

Research progress of aquatic vegetation remote sensing in shallow lakes

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.