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Modelling submerged coastal environm...

Dillon, Chris.

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Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis./
作者:	Dillon, Chris.
面頁冊數:	208 p.
附註:	Source: Masters Abstracts International, Volume: 55-04.
Contained By:	Masters Abstracts International55-04(E).
標題:	Geographic information science and geodesy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10100862
ISBN:	9781339647326

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis.
Dillon, Chris.

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis. - 208 p.

Source: Masters Abstracts International, Volume: 55-04.

Thesis (M.S.)--Trent University (Canada), 2016.

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies.

ISBN: 9781339647326Subjects--Topical Terms:

2122917
Geographic information science and geodesy.

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis.
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100 1 $a Dillon, Chris. $3 1568491
245 1 0 $a Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis.
300 $a 208 p.
500 $a Source: Masters Abstracts International, Volume: 55-04.
500 $a Adviser: Raul Ponce Hernandex.
502 $a Thesis (M.S.)--Trent University (Canada), 2016.
520 $a Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies.
520 $a Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.
590 $a School code: 0513.
650 4 $a Geographic information science and geodesy. $3 2122917
650 4 $a Remote sensing. $3 535394
650 4 $a Marine geology. $3 3173821
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710 2 $a Trent University (Canada). $b Applied Modeling and Quantitative Methods. $3 2101153
773 0 $t Masters Abstracts International $g 55-04(E).
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792 $a 2016
793 $a English
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