國立虎尾科技大學 |

Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters // Catherine Gottsacker.
Author:	Gottsacker, Catherine,
Description:	1 electronic resource (41 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
Subject:	Automotive engineering. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31508078
ISBN:	9798383185865

Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters /
Gottsacker, Catherine,

Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters /Catherine Gottsacker. - 1 electronic resource (41 pages)

Source: Masters Abstracts International, Volume: 86-01.

Harmful algae blooms in surface waters are a global environmental concern and threaten both human and environmental health. By outcompeting aquatic diversity, causing dissolved oxygen levels in surface waters to fall, and secreting toxins, algae blooms stress water treatment infrastructure and result in large economic losses. To control and manage the impact of harmful algae blooms, timely detection and monitoring is critical. However, current monitoring methods, such as permanent monitoring stations or water sampling, can be very costly or time-intensive, and require direct water access. The methods become dangerous or impractical in areas surrounded by cliffs or wetlands. In this study, a flexible, efficient, and cost-effective approach for monitoring surface water quality was developed by integrating water quality sensors and unmanned aerial vehicles (UAV). The integration platform was designed, constructed, and deployed through the summer of 2023 to monitor chlorophyll, phycocyanin, and turbidity in William H. Harsha Lake of Clermont County, Ohio. The water quality parameters, used as an indicator of algae blooms, were then correlated to reflectance from Landsat 8 and 9 and Sentinel 2 satellites through single and multiple linear regressions. Multiple linear regressions using reflectance from Sentinel 2 satellites yielded the highest correlations between reflectance and water quality, with R2 values of 0.70, 0.86, and 0.97 for chlorophyll, phycocyanin and turbidity, respectively. From the regressions, visible, near infrared, and red-edge bands were identified as useful for algae detection, and commercially available multispectral cameras capable of integration with UAVs were identified for future improvement of the UAV monitoring platform.

English

ISBN: 9798383185865Subjects--Topical Terms:

1104081
Automotive engineering.
Subjects--Index Terms:

Unmanned aerial vehicles

Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters /
LDR:03265nam a22004453i 4500 001 1157854
005 20250603111429.5
006 m o d
007 cr|nu||||||||
008 250804s2024 miu||||||m |||||||eng d
020 $a 9798383185865
035 $a (MiAaPQD)AAI31508078
035 $a (MiAaPQD)OhioLINKucin1712912425666119
035 $a AAI31508078
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Gottsacker, Catherine, $e author. $3 1484136
245 1 0 $a Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters / $c Catherine Gottsacker.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (41 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 86-01.
500 $a Advisors: Feng, Dongmei Committee members: Beck, Richard; McAvoy, Drew.
502 $b M.S. $c University of Cincinnati $d 2024.
520 $a Harmful algae blooms in surface waters are a global environmental concern and threaten both human and environmental health. By outcompeting aquatic diversity, causing dissolved oxygen levels in surface waters to fall, and secreting toxins, algae blooms stress water treatment infrastructure and result in large economic losses. To control and manage the impact of harmful algae blooms, timely detection and monitoring is critical. However, current monitoring methods, such as permanent monitoring stations or water sampling, can be very costly or time-intensive, and require direct water access. The methods become dangerous or impractical in areas surrounded by cliffs or wetlands. In this study, a flexible, efficient, and cost-effective approach for monitoring surface water quality was developed by integrating water quality sensors and unmanned aerial vehicles (UAV). The integration platform was designed, constructed, and deployed through the summer of 2023 to monitor chlorophyll, phycocyanin, and turbidity in William H. Harsha Lake of Clermont County, Ohio. The water quality parameters, used as an indicator of algae blooms, were then correlated to reflectance from Landsat 8 and 9 and Sentinel 2 satellites through single and multiple linear regressions. Multiple linear regressions using reflectance from Sentinel 2 satellites yielded the highest correlations between reflectance and water quality, with R2 values of 0.70, 0.86, and 0.97 for chlorophyll, phycocyanin and turbidity, respectively. From the regressions, visible, near infrared, and red-edge bands were identified as useful for algae detection, and commercially available multispectral cameras capable of integration with UAVs were identified for future improvement of the UAV monitoring platform.
546 $a English
590 $a School code: 0045
650 4 $a Automotive engineering. $3 1104081
650 4 $a Environmental health. $3 569475
650 4 $a Environmental engineering. $3 557376
653 $a Unmanned aerial vehicles
653 $a Algae detection
653 $a Chlorophyll
653 $a Sentinel 2 satellites
653 $a Phycocyanin
690 $a 0775
690 $a 0470
690 $a 0540
710 2 $a University of Cincinnati. $b Engineering and Applied Science: Environmental Engineering. $e degree granting institution. $3 1484137
720 1 $a Feng, Dongmei $e degree supervisor.
773 0 $t Masters Abstracts International $g 86-01.
790 $a 0045
791 $a M.S.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31508078