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ISPRS Int. J. Geo-Inf. 2018, 7(8), 294; https://doi.org/10.3390/ijgi7080294

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

1
droneMetrics, 7 Tauvette Street, Ottawa, ON K1B 3A1, Canada
2
AirTech UAV Solutions, 1071 Kam Avenue, Inverary, ON K0H 1X0, Canada
3
Environmental and Life Sciences Graduate Program, Trent University, 1600 West Bank Drive, Peterborough, ON K9J 7B8, Canada
4
Ecological Restoration Program, Fleming College, 200 Albert Street South, Lindsay, ON K9V 5E6, Canada
*
Author to whom correspondence should be addressed.
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)
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Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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