語系:
繁體中文
English
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
Application of Coastal and Marine Ec...
~
Ruby, Caitlin A.
FindBook
Google Book
Amazon
博客來
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments./
作者:
Ruby, Caitlin A.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:
289 p.
附註:
Source: Masters Abstracts International, Volume: 56-04.
Contained By:
Masters Abstracts International56-04(E).
標題:
Geographic information science and geodesy. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268275
ISBN:
9781369706277
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments.
Ruby, Caitlin A.
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments.
- Ann Arbor : ProQuest Dissertations & Theses, 2017 - 289 p.
Source: Masters Abstracts International, Volume: 56-04.
Thesis (M.S.)--Mississippi State University, 2017.
The Coastal and Marine Ecological Classification Standard (CMECS) provides a comprehensive framework of common terminology for organizing physical, chemical, biological, and geological information about marine ecosystems. Federally endorsed as a dynamic content standard, all federally funded data must be compliant by 2018; however, applying CMECS to deep sea datasets and underwater video have not been extensively examined. The presented research demonstrates the extent to which CMECS can be applied to deep sea benthic habitats, assesses the feasibility of applying CMECS to remotely operated vehicle (ROV) video data in near-real-time, and establishes best practices for mapping environmental aspects and observed deep sea habitats as viewed by the ROV's forward-facing camera. All data were collected during 2014 in the Northern Gulf of Mexico by the National Oceanic and Atmospheric Administration's (NOAA) ROV Deep Discoverer and ship Okeanos Explorer..
ISBN: 9781369706277Subjects--Topical Terms:
2122917
Geographic information science and geodesy.
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments.
LDR
:02011nmm a2200313 4500
001
2200404
005
20190315110954.5
008
201008s2017 ||||||||||||||||| ||eng d
020
$a
9781369706277
035
$a
(MiAaPQ)AAI10268275
035
$a
(MiAaPQ)msstate:12940
035
$a
AAI10268275
040
$a
MiAaPQ
$c
MiAaPQ
100
1
$a
Ruby, Caitlin A.
$3
3427149
245
1 0
$a
Application of Coastal and Marine Ecological Classification Standard (CMECS) to Remotely Operated Vehicle (Rov) Video Data for Enhanced Geospatial Analysis of Deep Sea Environments.
260
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2017
300
$a
289 p.
500
$a
Source: Masters Abstracts International, Volume: 56-04.
500
$a
Adviser: Adam Skarke.
502
$a
Thesis (M.S.)--Mississippi State University, 2017.
520
$a
The Coastal and Marine Ecological Classification Standard (CMECS) provides a comprehensive framework of common terminology for organizing physical, chemical, biological, and geological information about marine ecosystems. Federally endorsed as a dynamic content standard, all federally funded data must be compliant by 2018; however, applying CMECS to deep sea datasets and underwater video have not been extensively examined. The presented research demonstrates the extent to which CMECS can be applied to deep sea benthic habitats, assesses the feasibility of applying CMECS to remotely operated vehicle (ROV) video data in near-real-time, and establishes best practices for mapping environmental aspects and observed deep sea habitats as viewed by the ROV's forward-facing camera. All data were collected during 2014 in the Northern Gulf of Mexico by the National Oceanic and Atmospheric Administration's (NOAA) ROV Deep Discoverer and ship Okeanos Explorer..
590
$a
School code: 0132.
650
4
$a
Geographic information science and geodesy.
$3
2122917
650
4
$a
Biological oceanography.
$3
2122748
650
4
$a
Physical oceanography.
$3
3168433
690
$a
0370
690
$a
0416
690
$a
0415
710
2
$a
Mississippi State University.
$b
Geosciences.
$3
1018556
773
0
$t
Masters Abstracts International
$g
56-04(E).
790
$a
0132
791
$a
M.S.
792
$a
2017
793
$a
English
856
4 0
$u
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10268275
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9376953
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入