Object-based analysis of UAS imagery to map emergent and submerged invasive aquatic vegetation: a case study
| Autor: | Adam Shemrock, Dominique Chabot, Oumer S. Ahmed, Christopher Dillon |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Control and Optimization
010504 meteorology & atmospheric sciences 0211 other engineering and technologies Object based Aerospace Engineering 02 engineering and technology 01 natural sciences Computer Science Applications Geography Control and Systems Engineering Aquatic plant Automotive Engineering Electrical and Electronic Engineering Cartography 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Journal of Unmanned Vehicle Systems. 5:27-33 |
| ISSN: | 2291-3467 |
| DOI: | 10.1139/juvs-2016-0009 |
| Popis: | 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. A small mapping drone was assessed 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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