國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Integrating UAV With Sensors to Monitor Harmful Algal Blooms in Surface Waters // Catherine Gottsacker.
作者:	Gottsacker, Catherine,
面頁冊數:	1 electronic resource (41 pages)
附註:	Source: Masters Abstracts International, Volume: 86-01.
Contained By:	Masters Abstracts International86-01.
標題:	Automotive engineering. -
電子資源:	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 /
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