植物生态学报 ›› 2022, Vol. 46 ›› Issue (12): 1551-1561.DOI: 10.17521/cjpe.2021.0414

所属专题: 生态遥感及应用

• 研究论文 • 上一篇    下一篇

基于Landsat影像的武汉东湖30年来水生植物动态变化

姜艳(), 陈兴芳, 杨旭杰   

  1. 华中师范大学地理过程分析与模拟湖北省重点实验室, 华中师范大学城市与环境科学学院, 武汉 430079
  • 收稿日期:2021-11-15 接受日期:2022-04-21 出版日期:2022-12-20 发布日期:2023-01-13
  • 通讯作者: *姜艳, E-mail: jlhyan@126.com
  • 基金资助:
    国家自然科学基金(31300394)

Changes of aquatic plants in Donghu Lake of Wuhan based 1990-2020 Landsat images

JIANG Yan(), CHEN Xing-Fang, YANG Xu-Jie   

  1. Hubei Province Key Laboratory for Geographical Process Analysis & Simulation, College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
  • Received:2021-11-15 Accepted:2022-04-21 Online:2022-12-20 Published:2023-01-13
  • Supported by:
    National Natural Science Foundation of China(31300394)

摘要:

水生植物的动态分布可以反映水域生态环境的变化, 掌握水生植物的时空分布情况对湖泊的管理与监测具有重要意义。该研究基于Landsat影像数据, 结合归一化水体指数、绿度指数和藻类指数, 利用决策树分类方法, 构建武汉东湖水生植物的提取模型, 绘制了东湖2020年挺水/浮水植物和沉水植物的季节分布图和1990-2020年31期年际分布图。主要结果表明, 决策树模型能较为准确地获取东湖水生植物的分布情况, 其总体精度为82.29%, Kappa系数为72.39%。东湖水生植物季节性变化分析表明, 水生植物面积呈先增大后减少的趋势, 2月份东湖水生植物分布面积较小, 4-8月面积逐渐增加, 10月以后水生植物开始衰退。水生植物面积的年际变化较大, 可分为3个阶段: 第一阶段(1990-1996年)挺水/浮水植物的面积先减少后增大, 而沉水植物的面积持续增加; 第二阶段(1997-2015年)沉水植物与挺水/浮水植物面积的年际波动较大, 在此期间, 东湖水生植物最大面积为2.61 km2, 最小面积仅为0.49 km2; 第三阶段(2016-2020年)东湖水生植物逐渐恢复, 挺水/浮水植物面积增加30%, 沉水植物面积增加18%。通过研究30年来水生植物面积与年平均气温和年降水量的关系, 发现年平均气温和年降水量对东湖水生植物的影响较小。东湖中有水生植物的分布和无水生植物分布环境指标存在差异, 总磷含量、总氮含量、水深、透明度和浊度均可能影响水生植物的分布。

关键词: 水生植物, 遥感, 水质, 武汉东湖

Abstract:

Aims Since the dynamic distribution of aquatic plants can reflect the variation of water ecological environment, it is of great significance to fully understand the spatial and temporal distribution characteristics of aquatic plants for better lake management and monitoring.

Methods On the basis of Landsat image data, this study calculated three vegetation indices, including normalized difference water index (NDWI), green veg index (Green), and macroalgae index (MAI), and constructed an aquatic plant extraction for the Donghu Lake of Wuhan using the decision tree classification method. With this method, we mapped the seasonal distribution of emergent/floating and submerged plants in the Donghu Lake in 2020, as well as their inter-annual variations during a 31-year period from 1990 to 2020.

Important findings Our results showed that the decision tree model is capable of determining the distribution of aquatic plants in the Donghu Lake accurately, with an overall accuracy of 82.29% and Kappa coefficient of 72.39%. The analysis of the seasonal variation of aquatic plants in the Donghu Lake reveal that the area of aquatic plants first increased and then decreased. In spite of being limited in February, the distribution area gradually expanded from April to August, and subsequently declined after October. The distribution and area of aquatic plants varies greatly, which can be divided into three stages regarding the long-term analysis. In the first stage (1990-1996), the area of emergent/floating plants decreased first and then increased, while that of submerged plants presented an increasing trend on a continued basis. In the second stage (1997-2015), the area of submerged plants and emergent/floating plants exhibited significant fluctuations from year to year. During this period, the area of aquatic plants reached a maximum of 2.61 km2, whereas the minimum of 0.49 km2. In the third stage (2016-2020), the aquatic plants in the Donghu Lake gradually recovered, leading to a 30% increase in the area of emergent/floating plants and a 18% increase in the area of submerged plants. Upon a study of the relationship between the area of aquatic plants, annual average temperature, and annual precipitation over the recent three decades, a conclusion can be drawn that annual average temperature and annual precipitation had little influence on the area of aquatic plants in the Donghu Lake. Instead, we found that environmental indicators, such as total phosphorus content, total nitrogen content, water depth, transparency, and turbidity, have significant spatial differences in the Donghu Lake, which are likely to affect the distribution of aquatic plants.

Key words: aquatic plant, remote sensing, water quality, Donghu Lake of Wuhan