Remote sensing for monitoring and assessment of invasive herbaceous plants: the case of Oenothera drummondii in coastal ecosystems

Invasive exotic species pose a serious threat to biodiversity, particularly in fragile and degraded habitats. This is the case with Oenothera drummondii, which significantly affects certain coastal areas of the Iberian Peninsula. This study area focuses on the Odiel Marshes Natural Area, where populations of this herbaceous plant are drastically affecting native vegetation. The objective is to develop a methodology for the automatic detection of individuals of this species with Unmanned Aerial Vehicles (UAV) equipped with multispectral cameras. The final goal is to optimize monitoring, control, and potential eradication activities. A photogrammetric flight was carried out over 26.26 ha using a DJI Mavic 3Multiespectral. The model used to distinguish the invasive species from the native vegetation in the area was the C5.0 classification model. A total of 800 individuals were analysed using this model, including samples of the invasive species and three of the most abundant native plant species with a similar appearance. The classification tree results were extrapolated to the entire study area. The results indicate that the use of multispectral bands and vegetation indices allows the C5.0 model to classify the studied species with an error rate of 15.4%. The subsequent application of the classification tree obtained across the entire study area resulted in the detection of O. drummondii with an accuracy rate of 83%, demonstrating that this UAV-based technique enables the identification of exotic invasive herbaceous species. This methodology could contribute to management of the species and could be easily applied to other affected areas and species worldwide.

• Publication year

  2025

• Venue

  NeoBiota

• Publication date

  2025-07-11

• Fields of study

  Not labeled

• Identifiers
  DOI 10.3897/neobiota.99.151472
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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