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新型海底观测站(OBSEA)用于远程和长期沿海生态系统监测。

The new Seafloor Observatory (OBSEA) for remote and long-term coastal ecosystem monitoring.

机构信息

Instituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain.

出版信息

Sensors (Basel). 2011;11(6):5850-72. doi: 10.3390/s110605850. Epub 2011 May 31.


DOI:10.3390/s110605850
PMID:22163931
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3231463/
Abstract

A suitable sampling technology to identify species and to estimate population dynamics based on individual counts at different temporal levels in relation to habitat variations is increasingly important for fishery management and biodiversity studies. In the past two decades, as interest in exploring the oceans for valuable resources and in protecting these resources from overexploitation have grown, the number of cabled (permanent) submarine multiparametric platforms with video stations has increased. Prior to the development of seafloor observatories, the majority of autonomous stations were battery powered and stored data locally. The recently installed low-cost, multiparametric, expandable, cabled coastal Seafloor Observatory (OBSEA), located 4 km off of Vilanova i la Gertrú, Barcelona, at a depth of 20 m, is directly connected to a ground station by a telecommunication cable; thus, it is not affected by the limitations associated with previous observation technologies. OBSEA is part of the European Multidisciplinary Seafloor Observatory (EMSO) infrastructure, and its activities are included among the Network of Excellence of the European Seas Observatory NETwork (ESONET). OBSEA enables remote, long-term, and continuous surveys of the local ecosystem by acquiring synchronous multiparametric habitat data and bio-data with the following sensors: Conductivity-Temperature-Depth (CTD) sensors for salinity, temperature, and pressure; Acoustic Doppler Current Profilers (ADCP) for current speed and direction, including a turbidity meter and a fluorometer (for the determination of chlorophyll concentration); a hydrophone; a seismometer; and finally, a video camera for automated image analysis in relation to species classification and tracking. Images can be monitored in real time, and all data can be stored for future studies. In this article, the various components of OBSEA are described, including its hardware (the sensors and the network of marine and land nodes), software (data acquisition, transmission, processing, and storage), and multiparametric measurement (habitat and bio-data time series) capabilities. A one-month multiparametric survey of habitat parameters was conducted during 2009 and 2010 to demonstrate these functions. An automated video image analysis protocol was also developed for fish counting in the water column, a method that can be used with cabled coastal observatories working with still images. Finally, bio-data time series were coupled with data from other oceanographic sensors to demonstrate the utility of OBSEA in studies of ecosystem dynamics.

摘要

一种合适的采样技术,用于根据不同时间尺度上与栖息地变化相关的个体计数来识别物种并估计种群动态,对于渔业管理和生物多样性研究变得越来越重要。在过去的二十年中,随着人们对探索海洋有价值资源以及保护这些资源免受过度开发的兴趣日益增长,带有视频站的海底多参数(永久性)海底缆索平台的数量不断增加。在海底观测站发展之前,大多数自主站都是由电池供电,并在本地存储数据。最近安装的低成本、多参数、可扩展、海底观测站(OBSEA)位于巴塞罗那的 Vilanova i la Gertrú 外海 4 公里处,水深 20 米,通过通信电缆直接连接到地面站,因此不受先前观测技术相关限制的影响。OBSEA 是欧洲多学科海底观测站(EMSO)基础设施的一部分,其活动被列入欧洲海洋观测站网络卓越中心(ESONET)的网络。OBSEA 通过获取同步多参数生境数据和生物数据,能够对当地生态系统进行远程、长期和连续调查,其中包括以下传感器:用于盐度、温度和压力的电导率-温度-深度(CTD)传感器;用于速度和方向的声学多普勒海流剖面仪(ADCP),包括浊度计和荧光计(用于测定叶绿素浓度);水听器;地震仪;最后,一个用于与物种分类和跟踪有关的自动图像分析的摄像头。图像可以实时监控,并且所有数据都可以存储以备将来研究使用。在本文中,描述了 OBSEA 的各个组成部分,包括其硬件(传感器和海洋和陆地节点网络)、软件(数据采集、传输、处理和存储)以及多参数测量(生境和生物数据时间序列)功能。在 2009 年和 2010 年进行了为期一个月的生境参数多参数调查,以展示这些功能。还开发了一种用于水中鱼类计数的自动视频图像分析协议,该方法可用于与使用静态图像工作的海底观测站一起使用。最后,将生物数据时间序列与其他海洋传感器的数据耦合,以展示 OBSEA 在生态系统动态研究中的应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/8c1314461f6a/sensors-11-05850f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/e591a06474b0/sensors-11-05850f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/606cf3db1b0f/sensors-11-05850f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/136f47a9c6ec/sensors-11-05850f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/4febcecb3a16/sensors-11-05850f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/0e90df20baee/sensors-11-05850f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/e4db53549c4a/sensors-11-05850f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/4bf1482a097f/sensors-11-05850f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/f4539049f620/sensors-11-05850f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/8c1314461f6a/sensors-11-05850f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/e591a06474b0/sensors-11-05850f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/606cf3db1b0f/sensors-11-05850f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/136f47a9c6ec/sensors-11-05850f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/4febcecb3a16/sensors-11-05850f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/0e90df20baee/sensors-11-05850f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/e4db53549c4a/sensors-11-05850f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/4bf1482a097f/sensors-11-05850f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/f4539049f620/sensors-11-05850f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a37/3231463/8c1314461f6a/sensors-11-05850f9.jpg

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[1]
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Sensors (Basel). 2009-11-18

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