Object-based analysis of UAS imagery to map emergent and submerged invasive aquatic vegetation: a case study

Small unmanned aircraft systems (UAS) combined with automated image analysis may provide an efficient alternative or complement to labour-intensive boat-based monitoring of invasive aquatic vegetation. We trialled a small mapping drone for collecting high-resolution (≤5 cm/pixel) true-colour and near-infrared imagery revealing the distribution of invasive water soldier (Stratiotes aloides) in the Trent-Severn Waterway, Ontario (Canada). We further evaluated the capacity of an object-based image analysis approach based on the Random Forests classification algorithm to map features in the imagery, chiefly emergent and submerged water soldier colonies. The imagery contained flaws and inconsistencies resulting from data collection in suboptimal weather conditions that likely negatively impacted classification performance. Nevertheless, our best-performing classification had a producer’s and user’s accuracy for water soldier of 81% and 74%, respectively, an overall accuracy of 78%, and a kappa value of 61%, indicating “substantial” accuracy. This trial provides an instructive case study on results achieved in a “real-world” application of a UAS for environmental monitoring, notably characterized by time constraints for data collection and analysis. Beyond avoiding data collection in unfavourable weather conditions, adaptations of the image segmentation process and use of a true discrete-band multispectral camera may help to improve classification accuracy, particularly of submerged vegetation.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author