東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Water Quality and Algal Bloom Sensin...

Ford, Ryan.

Linked to FindBook

Google Book

Amazon

博客來

Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms./
Author:	Ford, Ryan.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2019,
Description:	161 p.
Notes:	Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
Contained By:	Dissertations Abstracts International81-05B.
Subject:	Remote sensing. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22622315
ISBN:	9781687923639

Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms.
Ford, Ryan.

Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms. - Ann Arbor : ProQuest Dissertations & Theses, 2019 - 161 p.

Source: Dissertations Abstracts International, Volume: 81-05, Section: B.

Thesis (Ph.D.)--Rochester Institute of Technology, 2019.

This item must not be sold to any third party vendors.

Harmful cyanobacteria blooms have been increasing in frequency throughout the world resulting in a greater need for water quality monitoring. Traditional methods of monitoring water quality, such as point sampling, are often resource expensive and time consuming in comparison to remote sensing approaches, however the spatial resolution of established water remote sensing satellites is often too coarse (300 m) to resolve smaller inland waterbodies. The fine scale spatial resolution and improved radiometric sensitivity of Landsat satellites (30 m) can resolve these smaller waterbodies, enabling their capability for cyanobacteria bloom monitoring.In this work, the utility of Landsat to retrieve concentrations of two cyanobacteria bloom pigments, chlorophyll-a and phycocyanin, is assessed. Concentrations of these pigments are retrieved using a spectral Look-Up-Table (LUT) matching process, where an exploration of the effects of LUT design on retrieval accuracy is performed. Potential augmentations to the spectral sampling of Landsat are also tested to determine how it can be improved for waterbody constituent concentration retrieval.Applying the LUT matching process to Landsat 8 imagery determined that concentrations of chlorophyll-a, total suspended solids, and color dissolved organic matter were retrieved with a satisfactory accuracy through appropriate choice of atmospheric compensation and LUT design, in agreement with previously reported implementations of the LUT matching process. Phycocyanin proved to be a greater challenge to this process due to its weak effect on waterbody spectrum, the lack of Landsat spectral sampling over its predominant spectral feature, and error from atmospheric compensation. From testing potential enhancements to Landsat spectral sampling, we determine that additional spectral sampling in the yellow and red edge regions of the visible/near-infrared (VNIR) spectrum can lead to improved concentration retrievals. This performance further improves when sampling is added to both regions, and when Landsat is transitioned to a VNIR imaging spectrometer, though this is dependent on band position and spacing. These results imply that Landsat can be used to monitor cyanobacteria blooms through retrieval of chlorophyll-a, and this retrieval performance can be improved in future Landsat systems, even with minor changes to spectral sampling. This includes improvement in retrieval of phycocyanin when implementing a VNIR imaging spectrometer.

ISBN: 9781687923639Subjects--Topical Terms:

535394
Remote sensing.
Subjects--Index Terms:

Cyanobacteria

Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms.
LDR:03565nmm a2200337 4500 001 2268876
005 20200824100416.5
008 220629s2019 ||||||||||||||||| ||eng d
020 $a 9781687923639
035 $a (MiAaPQ)AAI22622315
035 $a AAI22622315
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ford, Ryan. $3 3546179
245 1 0 $a Water Quality and Algal Bloom Sensing from Multiple Imaging Platforms.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2019
300 $a 161 p.
500 $a Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
500 $a Advisor: Vodacek, Anthony.
502 $a Thesis (Ph.D.)--Rochester Institute of Technology, 2019.
506 $a This item must not be sold to any third party vendors.
520 $a Harmful cyanobacteria blooms have been increasing in frequency throughout the world resulting in a greater need for water quality monitoring. Traditional methods of monitoring water quality, such as point sampling, are often resource expensive and time consuming in comparison to remote sensing approaches, however the spatial resolution of established water remote sensing satellites is often too coarse (300 m) to resolve smaller inland waterbodies. The fine scale spatial resolution and improved radiometric sensitivity of Landsat satellites (30 m) can resolve these smaller waterbodies, enabling their capability for cyanobacteria bloom monitoring.In this work, the utility of Landsat to retrieve concentrations of two cyanobacteria bloom pigments, chlorophyll-a and phycocyanin, is assessed. Concentrations of these pigments are retrieved using a spectral Look-Up-Table (LUT) matching process, where an exploration of the effects of LUT design on retrieval accuracy is performed. Potential augmentations to the spectral sampling of Landsat are also tested to determine how it can be improved for waterbody constituent concentration retrieval.Applying the LUT matching process to Landsat 8 imagery determined that concentrations of chlorophyll-a, total suspended solids, and color dissolved organic matter were retrieved with a satisfactory accuracy through appropriate choice of atmospheric compensation and LUT design, in agreement with previously reported implementations of the LUT matching process. Phycocyanin proved to be a greater challenge to this process due to its weak effect on waterbody spectrum, the lack of Landsat spectral sampling over its predominant spectral feature, and error from atmospheric compensation. From testing potential enhancements to Landsat spectral sampling, we determine that additional spectral sampling in the yellow and red edge regions of the visible/near-infrared (VNIR) spectrum can lead to improved concentration retrievals. This performance further improves when sampling is added to both regions, and when Landsat is transitioned to a VNIR imaging spectrometer, though this is dependent on band position and spacing. These results imply that Landsat can be used to monitor cyanobacteria blooms through retrieval of chlorophyll-a, and this retrieval performance can be improved in future Landsat systems, even with minor changes to spectral sampling. This includes improvement in retrieval of phycocyanin when implementing a VNIR imaging spectrometer.
590 $a School code: 0465.
650 4 $a Remote sensing. $3 535394
650 4 $a Water resources management. $3 794747
653 $a Cyanobacteria
653 $a Radiometry
653 $a Landsat
690 $a 0799
690 $a 0595
710 2 $a Rochester Institute of Technology. $b Imaging Science. $3 1019498
773 0 $t Dissertations Abstracts International $g 81-05B.
790 $a 0465
791 $a Ph.D.
792 $a 2019
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=22622315