论文总字数：15603字

摘 要

玄武湖属浅水湖泊,平均水深为1.14m,最大水深为2.31m,湖水面积为3.7km²，该湖属金川河水系,全流域汇水面积达20.1km²，湖水主要靠钟山北麓雨水供给，玄武湖水系完全处于人工控制之下,常年水位保持在9.8～10.2m。^[1]目前湖区的水下植被概度需要进行调查，为水域生态链及水生植物物种丰富度的健康程度提供一个参考标准。

在这项研究中，我们使用了一种新的集成测量系统，该系统将DIDSON声学成像声纳（双频识别声纳）与聚光透镜，运动传感器和GPS接收器相结合，以发现和识别不同种类的水生植物。在实验中，我们使用1°聚光透镜和3°聚光透镜。在使用1°聚光透镜观察时，将垂直光束集中在1°内，并用多光束图像处理用于生成水生植物的三维图像。使用3°聚光透镜观察时，对植物物种分类进行直方图和空间光谱分析。根据当前方法获得的参数对三种水生植物，即菹草，金鱼藻和狐尾藻进行分类。最后，在Windows平台基于QT与OpenCV搭建的图像处理平台对影像进行水生植物进行概度估算。之所以能够可视化水生植物的特征并进行物种分类，这归因于声学图像的空间光谱和散射分析。同时，DIDSON测量系统的高空间分辨率将有助于在快速变化的水下环境保护濒危物种。

关键词：植被概度 水生植物 DIDSON 声学图像

Application of Sonar Image in Estimation of Underwater Vegetation

Abstract

Xuanwu Lake is a shallow lake with an average water depth of 1.14m, a maximum water depth of 2.31m and a lake area of 3.7km². The lake belongs to the Jinchuan River system, and the catchment area of the whole basin reaches 20.1km². The lake water is mainly supplied by rainwater from the north of Zhongshan. The Xuanwu Lake water system is completely under artificial control, and the annual water level is maintained at 9.8 to 10.2 m. At present, the underwater vegetation probabilities in the Lake District need to be investigated to provide a reference standard for the health of the water ecosystem and the richness of aquatic plant species.

In this study, we used a new integrated measurement system that combines the acoustic imaging sonar of DIDSON (Dual-frequency Identification Sonar) with concentrator lenses, motion sensors, and GPS receivers to find and identify different species of aquatic plants. In the experiment, we used a 1-degree concentrator lens and a 3-degree concentrator lens. When viewed using a 1-degree concentrator lens, the vertical beam is concentrated within 1 degree and processed with a multi-beam image for generating a three-dimensional image of the aquatic plant. Histogram and spatial spectral analysis were performed on plant species classification when observed using a 3-degree concentrator lens. Three aquatic plants, namely Valeriana, Ceratophyllum, and Myriophyllum sp. were classified according to the parameters obtained by the current method. Finally, on the Windows platform based on QT and OpenCV built image processing platform for image estimation of aquatic plants Ability to visualize aquatic plant features and classify species due to spatial spectroscopy and scattering analysis of acoustic images. Meanwhile, the high spatial resolution of the integrated DIDSON measurement system will contribute protection of endangered species in rapidly changing underwater environment.

Key Words: Vegetation prevalence;aquatic plants;DIDSON; acoustic images

目 录

摘 要 I

Abstract II

第一章 引言 1

1.1 研究背景 1

1.2 研究目的和意义 2

1.3 国内外研究现状 2

1.4 研究内容和技术路线 3

第二章 仪器与方法 4

2.1 调查区域概况 4

2.2 调查仪器 4

2.2.1 双频识别声纳DIDSON概况 4

2.2.2 双频识别声纳DIDSON原理 5

2.2.3 双频识别声纳DIDSON特点 7

2.2.4 双频识别声纳DIDSON应用 8

2.3 DIDSON图像数据显示方法 8

2.4 现场实验和设置 9

2.5 图像处理和数据分析 11

2.5.1 生成水生植物三维视图的步骤 11

2.6.1 直方图分析 13

2.6.2 空间光谱分析 14

2.7 本章小结 16

第三章 讨论与结果 17

3.1 水生植物的3D视图 17

3.2 水生植物的物种分类 18

3.2.1 直方图分析 18

3.2.2 空间光谱分析 19

3.3水生植物的概度估算 22

3.4 本章小结 23

第四章 结论 24

参考文献 25

致谢 27

第一章 引言

1.1 研究背景

水生植物在水下生态系统中发挥着重要作用，并对水生生态系统产生影响。然而，由于富营养化、物种入侵和人类的影响干预，许多物种的数量正在减少并面临可能的灭绝。为了水生植物的保护，恢复和建立，需要对水下环境进行可持续管理。同时需要一个更准确的测绘和监测系统来评估水生植物的健康和分布。

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