Title
Mapping Invasive Phragmites Australis Using Unoccupied Aircraft System Imagery, Canopy Height Models, And Synthetic Aperture Radar
Abstract
Invasive plant species are an increasing worldwide threat both ecologically and financially. Knowing the location of these invasive plant infestations is the first step in their control. Surveying for invasive Phragmites australis is particularly challenging due to limited accessibility in wetland environments. Unoccupied aircraft systems (UAS) are a popular choice for invasive species management due to their ability to survey challenging environments and their high spatial and temporal resolution. This study tested the utility of three-band (i.e., red, green, and blue; RGB) UAS imagery for mapping Phragmites in the St. Louis River Estuary in Minnesota, U.S.A. and Saginaw Bay in Michigan, U.S.A. Iterative object-based image analysis techniques were used to identify two classes, Phragmites and Not Phragmites. Additionally, the effectiveness of canopy height models (CHMs) created from two data types, UAS imagery and commercial satellite stereo retrievals, and the RADARSAT-2 horizontal-horizontal (HH) polarization were tested for Phragmites identification. The highest overall classification accuracy of 90% was achieved when pairing the UAS imagery with a UAS-derived CHM. Producer's accuracy for the Phragmites class ranged from 3 to 76%, and the user's accuracies were above 90%. The Not Phragmites class had user's and producer's accuracies above 88%. Inclusion of the RADARSAT-2 HH polarization caused a slight reduction in classification accuracy. Commercial satellite stereo retrievals increased commission errors due to decreased spatial resolution and vertical accuracy. The lowest classification accuracy was seen when using only the RGB UAS imagery. UAS are promising for Phragmites identification, but the imagery should be used in conjunction with a CHM.
Year
DOI
Venue
2021
10.3390/rs13163303
REMOTE SENSING
Keywords
DocType
Volume
Phragmites australis, UAS, invasive species, object-based classification, OBIA
Journal
13
Issue
Citations 
PageRank 
16
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Connor J. Anderson100.34
Daniel Heins200.34
Keith C. Pelletier300.34
Julia L. Bohnen400.34
Joseph Knight500.68