DOI: 10.1139/dsa-2023-0082 ISSN: 2564-4939

Seasonal Phragmites australis classification in Long Point National Wildlife Area wetlands using a remotely piloted aircraft system and random forest machine learning

Morgan Hrynyk, Amir Behnamian, Sarah Banks, Zhaohua Chen, Taylor Harmer, Patrick Kirby, Lori White, Jon Pasher, Jason Duffe
  • Control and Optimization
  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Automotive Engineering
  • Aerospace Engineering
  • Computer Science Applications

This study produced a high-accuracy remotely piloted aircraft system (RPAS) imagery classification method for identifying the invasive reed Phragmites australis (Cav.) Trin. Ex Steud subsp. australis using Random Forest (RF) machine learning. RPAS imagery was collected in the spring and fall of 2019 using a fixed-wing RPAS equipped with a visible spectrum camera (eBee X, S.O.D.A. 3D; senseFly) in Long Point, Ontario, Canada. Imagery was used to produce separate early and late season classifications and a bi-temporal classification which used imagery from both dates. The overall accuracy achieved for each was; 97, 96, and 91%; respectively. Digital surface models (DSMs) were the most important variable for identifying Phragmites in all classifications due to their greater height when compared to surrounding herbaceous vegetation. The bi-temporal classification, which utilized change in DSM value during the growing season, resulted in an estimated 47.8% new growth of Phragmites and appeared to capture sparse growth better than traditional classification differencing alone. This study highlights the promising use of high-resolution DSMs produced from RPAS imagery to classify invasive Phragmites and monitor within-year patch expansions.

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