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

Autor: Tharindu Abeysinghe, Anita Simic Milas, Kristin Arend, Breann Hohman, Patrick Reil, Andrew Gregory, Angélica Vázquez-Ortega
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Zdroj: Remote Sensing, Vol 11, Iss 11, p 1380 (2019)
Druh dokumentu: article
ISSN: 2072-4292
11111380
DOI: 10.3390/rs11111380
Popis: 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.
Databáze: Directory of Open Access Journals
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