東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Evolution and field application of a...

Remsen, Andrew Walker.

FindBook

Google Book

Amazon

博客來

Evolution and field application of a plankton imaging system .

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Evolution and field application of a plankton imaging system ./
作者:	Remsen, Andrew Walker.
面頁冊數:	145 p.
附註:	Adviser: Thomas L. Hopkins.
Contained By:	Dissertation Abstracts International69-08B.
標題:	Biology, Oceanography. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3326092
ISBN:	9780549772156

Evolution and field application of a plankton imaging system .
Remsen, Andrew Walker.

Evolution and field application of a plankton imaging system . - 145 p.

Adviser: Thomas L. Hopkins.

Thesis (Ph.D.)--University of South Florida, 2008.

Understanding the processes controlling the distribution and abundance of zooplankton has been a primary concern of oceanographers and has driven the development of numerous technologies to more accurately quantify these parameters. This study investigates the potential of a new plankton imaging sensor, the shadowed image particle profiling and evaluation recorder (SIPPER), that I helped develop at the University of South Florida, to address that concern. In the first chapter, results from the SIPPER are compared against concurrently sampling plankton nets and the optical plankton counter (OPC), the most widely used optical zooplankton sampling sensor in the field. It was found that plankton nets and the SIPPER sampled robust and hard-bodied zooplankton taxa similarly while nets significantly underestimated the abundance of fragile and gelatinous taxa imaged by the SIPPER such that nets might underestimate zooplankton biomass by greater than 50%. Similarly, it was determined that the OPC misses greater than a quarter of resolvable particles due to coincident counting and that it can not distinguish between zooplankton and other abundant suspended particles such as marine snow and Trichodesmium that are difficult to quantify with traditional sampling methods. Therefore the standard method of using net samples to ground truth OPC data should be reevaluated. In the second chapter, a new automated plankton classification system was utilized to see if it was possible to use machine learning methods to classify SIPPER-imaged plankton from a diverse subtropical assemblage on the West Florida Shelf and describe their distribution during a 24 hour period. Classification accuracy for this study was similar to that of other studies in less diverse environments and similar to what could be expected by a human expert for a complex dataset. Fragile plankton taxa such as larvaceans, hydromedusae, sarcodine protoctists and Trichodesmium were found at significantly higher concentrations than previously reported in the region and thus could play more important roles in WFS plankton dynamics. Most observed plankton classes were found to be randomly distributed at the fine scale (mm-100 m) and that greatest variability within plankton abundances would be encountered vertically rather than horizontally through the water column.

ISBN: 9780549772156Subjects--Topical Terms:

783691
Biology, Oceanography.

Evolution and field application of a plankton imaging system .
LDR:03240nam 2200289 a 45 001 953893
005 20110621
008 110622s2008 ||||||||||||||||| ||eng d
020 $a 9780549772156
035 $a (UMI)AAI3326092
035 $a AAI3326092
040 $a UMI $c UMI
100 1 $a Remsen, Andrew Walker. $3 1277370
245 1 0 $a Evolution and field application of a plankton imaging system .
300 $a 145 p.
500 $a Adviser: Thomas L. Hopkins.
500 $a Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: .
502 $a Thesis (Ph.D.)--University of South Florida, 2008.
520 $a Understanding the processes controlling the distribution and abundance of zooplankton has been a primary concern of oceanographers and has driven the development of numerous technologies to more accurately quantify these parameters. This study investigates the potential of a new plankton imaging sensor, the shadowed image particle profiling and evaluation recorder (SIPPER), that I helped develop at the University of South Florida, to address that concern. In the first chapter, results from the SIPPER are compared against concurrently sampling plankton nets and the optical plankton counter (OPC), the most widely used optical zooplankton sampling sensor in the field. It was found that plankton nets and the SIPPER sampled robust and hard-bodied zooplankton taxa similarly while nets significantly underestimated the abundance of fragile and gelatinous taxa imaged by the SIPPER such that nets might underestimate zooplankton biomass by greater than 50%. Similarly, it was determined that the OPC misses greater than a quarter of resolvable particles due to coincident counting and that it can not distinguish between zooplankton and other abundant suspended particles such as marine snow and Trichodesmium that are difficult to quantify with traditional sampling methods. Therefore the standard method of using net samples to ground truth OPC data should be reevaluated. In the second chapter, a new automated plankton classification system was utilized to see if it was possible to use machine learning methods to classify SIPPER-imaged plankton from a diverse subtropical assemblage on the West Florida Shelf and describe their distribution during a 24 hour period. Classification accuracy for this study was similar to that of other studies in less diverse environments and similar to what could be expected by a human expert for a complex dataset. Fragile plankton taxa such as larvaceans, hydromedusae, sarcodine protoctists and Trichodesmium were found at significantly higher concentrations than previously reported in the region and thus could play more important roles in WFS plankton dynamics. Most observed plankton classes were found to be randomly distributed at the fine scale (mm-100 m) and that greatest variability within plankton abundances would be encountered vertically rather than horizontally through the water column.
590 $a School code: 0206.
650 4 $a Biology, Oceanography. $3 783691
650 4 $a Engineering, Marine and Ocean. $3 1019064
650 4 $a Physics, Optics. $3 1018756
690 $a 0416
690 $a 0547
690 $a 0752
710 2 $a University of South Florida. $3 1020446
773 0 $t Dissertation Abstracts International $g 69-08B.
790 $a 0206
790 1 0 $a Hopkins, Thomas L., $e advisor
791 $a Ph.D.
792 $a 2008
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3326092