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Article

An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery

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droneMetrics, 7 Tauvette Street, Ottawa, ON K1B 3A1, Canada
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AirTech UAV Solutions, 1071 Kam Avenue, Inverary, ON K0H 1X0, Canada
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Environmental and Life Sciences Graduate Program, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada
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Ecological Restoration Program, Fleming College, 200 Albert Street South, Lindsay, ON K9V 5E6, Canada
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(8), 294; https://doi.org/10.3390/ijgi7080294
Received: 31 May 2018 / Revised: 22 June 2018 / Accepted: 19 July 2018 / Published: 24 July 2018
(This article belongs to the Special Issue GEOBIA in a Changing World)
High-resolution drone aerial surveys combined with object-based image analysis are transforming our capacity to monitor and manage aquatic vegetation in an era of invasive species. To better exploit the potential of these technologies, there is a need to develop more efficient and accessible analysis workflows and focus more efforts on the distinct challenge of mapping submerged vegetation. We present a straightforward workflow developed to monitor emergent and submerged invasive water soldier (Stratiotes aloides) in shallow waters of the Trent-Severn Waterway in Ontario, Canada. The main elements of the workflow are: (1) collection of radiometrically calibrated multispectral imagery including a near-infrared band; (2) multistage segmentation of the imagery involving an initial separation of above-water from submerged features; and (3) automated classification of features with a supervised machine-learning classifier. The approach yielded excellent classification accuracy for emergent features (overall accuracy = 92%; kappa = 88%; water soldier producer’s accuracy = 92%; user’s accuracy = 91%) and good accuracy for submerged features (overall accuracy = 84%; kappa = 75%; water soldier producer’s accuracy = 71%; user’s accuracy = 84%). The workflow employs off-the-shelf graphical software tools requiring no programming or coding, and could therefore be used by anyone with basic GIS and image analysis skills for a potentially wide variety of aquatic vegetation monitoring operations. View Full-Text
Keywords: environmental monitoring; freshwater ecosystems; OBIA; random forests; remote sensing; rivers; unmanned aircraft; UAS; UAV; wetlands environmental monitoring; freshwater ecosystems; OBIA; random forests; remote sensing; rivers; unmanned aircraft; UAS; UAV; wetlands
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MDPI and ACS Style

Chabot, D.; Dillon, C.; Shemrock, A.; Weissflog, N.; Sager, E.P.S. An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery. ISPRS Int. J. Geo-Inf. 2018, 7, 294. https://doi.org/10.3390/ijgi7080294

AMA Style

Chabot D, Dillon C, Shemrock A, Weissflog N, Sager EPS. An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery. ISPRS International Journal of Geo-Information. 2018; 7(8):294. https://doi.org/10.3390/ijgi7080294

Chicago/Turabian Style

Chabot, Dominique, Christopher Dillon, Adam Shemrock, Nicholas Weissflog, and Eric P. S. Sager. 2018. "An Object-Based Image Analysis Workflow for Monitoring Shallow-Water Aquatic Vegetation in Multispectral Drone Imagery" ISPRS International Journal of Geo-Information 7, no. 8: 294. https://doi.org/10.3390/ijgi7080294

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