Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

Abeysinghe, Tharindu; Simic Milas, Anita; Arend, Kristin; Hohman, Breann; Reil, Patrick; Gregory, Andrew; Vázquez-Ortega, Angélica

doi:10.3390/rs11111380

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Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

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Angélica Vázquez-Ortega

Department of Geology, School of Earth, Environment and Society, Bowling Green State University, 190 Overman Hall, Bowling Green, OH 43403, USA

Ohio Department of Natural Resources, Old Woman Creek National Estuarine Research Reserve, Huron, OH 44839, USA

Fireland Coastal Tributaries, Erie Soil and Water Conservation District, Sandusky, OH 44870, USA

Author to whom correspondence should be addressed.

Remote Sens. 2019, 11(11), 1380; https://doi.org/10.3390/rs11111380

Received: 29 March 2019 / Revised: 26 May 2019 / Accepted: 6 June 2019 / Published: 10 June 2019

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Abstract

Unmanned aerial vehicles (UAV) are increasingly used for spatiotemporal monitoring of invasive plants in coastal wetlands. Early identification of invasive species is necessary in planning, restoring, and managing wetlands. This study assessed the effectiveness of UAV technology to identify invasive Phragmites australis in the Old Woman Creek (OWC) estuary using machine learning (ML) algorithms: Neural network (NN), support vector machine (SVM), and k-nearest neighbor (kNN). The ML algorithms were compared with the parametric maximum likelihood classifier (MLC) using pixel- and object-based methods. Pixel-based NN was identified as the best classifier with an overall accuracy of 94.80% and the lowest error of omission of 1.59%, the outcome desirable for effective eradication of Phragmites. The results were reached combining Sequoia multispectral imagery (green, red, red edge, and near-infrared bands) combined with the canopy height model (CHM) acquired in the mid-growing season and normalized difference vegetation index (NDVI) acquired later in the season. The sensitivity analysis, using various vegetation indices, image texture, CHM, and principal components (PC), demonstrated the impact of various feature layers on the classifiers. The study emphasizes the necessity of a suitable sampling and cross-validation methods, as well as the importance of optimum classification parameters. View Full-Text

Keywords: Phragmites australis; unmanned aerial vehicles; invasive; machine learning; object-based classifiers

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Abeysinghe, T.; Simic Milas, A.; Arend, K.; Hohman, B.; Reil, P.; Gregory, A.; Vázquez-Ortega, A. Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers. Remote Sens. 2019, 11, 1380. https://doi.org/10.3390/rs11111380

AMA Style

Abeysinghe T, Simic Milas A, Arend K, Hohman B, Reil P, Gregory A, Vázquez-Ortega A. Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers. Remote Sensing. 2019; 11(11):1380. https://doi.org/10.3390/rs11111380

Chicago/Turabian Style

Abeysinghe, Tharindu, Anita Simic Milas, Kristin Arend, Breann Hohman, Patrick Reil, Andrew Gregory, and Angélica Vázquez-Ortega. 2019. "Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers" Remote Sensing 11, no. 11: 1380. https://doi.org/10.3390/rs11111380

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Mapping Invasive Phragmites australis in the Old Woman Creek Estuary Using UAV Remote Sensing and Machine Learning Classifiers

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