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Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program

May 12, 2021

Higher spatial and temporal resolutions of remote sensing data are likely to be useful for ecological monitoring efforts. There are many different treatment approaches for the introduced European genotype of Phragmites australis, and adaptive management principles are being integrated in at least some long-term monitoring efforts. In this paper, we investigated how natural color and a smaller set of near-infrared (NIR) images collected with low-cost uncrewed aerial vehicles (UAVs) could help quantify the aboveground effects of management efforts at 20 sites enrolled in the Phragmites Adaptive Management Framework (PAMF) spanning the coastal Laurentian Great Lakes region. We used object-based image analysis and field ground truth data to classify the Phragmites and other cover types present at each of the sites and calculate the percent cover of Phragmites, including whether it was alive or dead, in the UAV images. The mean overall accuracy for our analysis with natural color data was 91.7% using four standardized classes (Live Phragmites, Dead Phragmites, Other Vegetation, Other Non-vegetation). The Live Phragmites class had a mean user’s accuracy of 90.3% and a mean producer’s accuracy of 90.1%, and the Dead Phragmites class had a mean user’s accuracy of 76.5% and a mean producer’s accuracy of 85.2% (not all classes existed at all sites). These results show that UAV-based imaging and object-based classification can be a useful tool to measure the extent of dead and live Phragmites at a series of sites undergoing management. Overall, these results indicate that UAV sensing appears to be a useful tool for identifying the extent of Phragmites at management sites.

Citation Information

Publication Year 2021
Title Using uncrewed aerial vehicles for identifying the extent of invasive Phragmites australis in treatment areas enrolled in an adaptive management program
DOI 10.3390/rs13101895
Authors Colin N. Brooks, Charlotte B. Weinstein, Andrew F. Poley, Amanda G. Grimm, Nicholas P. Marion, Laura Bourgeau-Chavez, Dana Hansen, Kurt P. Kowalski
Publication Type Article
Publication Subtype Journal Article
Series Title Remote Sensing
Series Number
Index ID 70221566
Record Source USGS Publications Warehouse
USGS Organization Great Lakes Science Center