Using DEM Derivatives from Quanergy LiDAR Data to Classify Wetlands through Machine Learning

Shitij Govil, Aiden Lee, Aiden MacQueen, Garrison Payne

Abstract

Wetlands play a vital role in the environment, providing ecological and economic benefits for the people living on or near them. However, they are underrepresented because of outdated data collection methods, which can lead to the destruction of unprotected wetlands. As part of the Clean Water Act of 1972, the Environmental Protection Agency protects natural sources of water such as wetlands. The current wetland classification method (NWI) relies on research teams to survey an area and classify it on foot, which is inefficient and potentially harmful to wetlands. This does not apply to unidentified wetlands, which vastly outnumber the amount of identified wetlands. This project aims to utilize Unmanned Aircraft Systems (UAS) data and Light Detection and Ranging (LiDAR) technology to speed up the process of classifying wetlands and gather more accurate data. We collected DEM(Digital Elevation Model) and multi-spectral data of two wetland sites and broke the sites into hexagon tessellations of various sizes. We used this data along with tree-based machine learning algorithms such as Gradient Boosting to classify parts of the sites as wetlands or non wetlands, with the highest accuracy of 96%. This study shows a promising future for machine learning and drone-collected data for the purpose of conserving wetlands.

Faculty Advisor: Cuixian Chen

Acknowledgment: Aiden MacQueen, Dr. Cuixian Chen, Jessica Gray, Bailey Hall, Michael Suggs, Asami Minei, Dr. Narcisa Pricope, Dr. Yishi Wang

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Shitij Govil

Aidan Lee

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Aiden MacQueen

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Garrison Payne

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LinkedIn Profile: Shitij Govil

Email: aidencmacqueen@gmail.com

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