海底视频图像分析有助于在空间尺度上监测Dreissena种群 | |
yaoxn@llas.ac.cn | |
2018-06-25 | |
学科主题 | 水体污染治理 |
来源网址 | http://portal.nstl.gov.cn/STMonitor/home/bianyi_recordshow.htm?id=64632&parentPageId=1530770106693&serverId=&controlType=openhome |
内容摘要 | In contrast to marine systems where remote sensing methods in studies of benthic organisms have been widely used for decades, these methods have experienced limited use in studies of freshwater benthos due to the general lack of large epifauna. The situation has changed with the introduction of dreissenid bivalves capable of creating visible aggregations on lake bottoms into North American freshwaters in the 1980s and 1990s. The need for assessment of Dreissena densities prompted exploration of videography as a potentially cost-effective tool. We developed a novel sampling method that analyzes video recorded using a GoPro camera mounted to a benthic sled to estimate Dreissena coverage, density, and biomass over relatively large areas of the lake bed in the Laurentian Great Lakes compared to traditional sampling methods. Using this method, we compared quagga mussel coverage, density, and biomass estimates based on three replicate Ponar grabs vs. 500?m-long video transects across 43 stations sampled in Lake Michigan in 2015. Our results showed that analysis of images from video transects dramatically increased the bottom area surveyed compared to Ponar grabs and increased the precision of Dreissena density and biomass estimations at monitoring stations. By substantially increasing the ability to detect relatively small (<20%) changes between years within a particular station, this method could be a useful and cost-effective addition for monitoring Dreissena populations in the Great Lakes and other freshwater systems where they occur. 来源机构:
大湖研究期刊
原文题目:
Benthic video image analysis facilitates monitoring of Dreissena populations across spatial scales
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来源 | 大湖研究期刊 |
存缴方式 | webcrawl |
内容类型 | 科技动态 |
URI标识 | http://www.corc.org.cn/handle/1471x/1632150 |
专题 | 科技动态 |
推荐引用方式 GB/T 7714 | yaoxn@llas.ac.cn. 海底视频图像分析有助于在空间尺度上监测Dreissena种群. 2018. |
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