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Optimizing Environmental Monitoring with Machine Learning and UAS.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Optimizing Environmental Monitoring with Machine Learning and UAS./
作者:	Reckling, William Joseph.
面頁冊數:	1 online resource (111 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Software. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30516475click for full text (PQDT)
ISBN:	9798379871406

Optimizing Environmental Monitoring with Machine Learning and UAS.
Reckling, William Joseph.

Optimizing Environmental Monitoring with Machine Learning and UAS. - 1 online resource (111 pages)

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--North Carolina State University, 2023.

Includes bibliographical references

Unmanned aerial systems (UAS) are ideal tools for environmental monitoring due to their low cost, ease of use, and ability to collect high-quality data. Additionally, UAS can collect data from otherwise inaccessible places without endangering personnel or disturbing the study area. However, UAS are limited by battery, payload capacity, and regulations. Additionally, data collection can be sensitive to weather, and data processing can be time and resource intensive. Pairing drone-based data collection with a machine learning predictive model to narrow the search window can help focus environmental monitoring efforts. In this dissertation, I describe methods to target UAS data collection to identify rare plants, failing septic systems, and cyanobacterial harmful algal bloom (CyanoHAB) hotspots. In Chapter 2, I present an approach to create an optimal flight area to identify rare plants from a predictive model using the collected imagery to monitor plant health and distribution. In Chapter 3, a predictive model is used to create a priority queue to identify septic system malfunctions, which are then confirmed in UAS collected imagery. Lastly, in Chapter 4, I develop and test a novel technique to rapidly create an interpolated spectral index map highlighting CyanoHAB distribution for targeted UAS water sampling with a custom-made device. The research presented in this dissertation demonstrates data-driven approaches to planning targeted UAS flight areas for improving UAS data collection and analysis for environmental monitoring applications.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379871406Subjects--Topical Terms:

619355
Software.
Index Terms--Genre/Form:

542853
Electronic books.

Optimizing Environmental Monitoring with Machine Learning and UAS.
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