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Invasive species pose a significant threat to biological diversity. Exotic and invasive grass species negatively affect urban biodiversity in Brazilian cities, since they occupy open spaces, compete with the native plants and eventually displace them. These species also increase the probability of wildfires in urban areas. Resources allocated for biodiversity conservation and invasive plants’ management purposes remain scarce in Brazilian cities. Therefore, we have developed a low-cost methodology to support the detection of invasive grass species based on using drones, as well as two frameworks for data processing, model development, and validation, that can be used anywhere in the world. Local governments could set up a basic workstation and generate geomatic products to enable the environmental management of invasive species by investing approximately 2000$. Another advantage of the presented monitoring methodology is that the approach can be adapted to demands associated with biodiversity conservation in urban ecosystems, e.g. monitoring endangered species.

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A Low-Cost Method to Detect Invasive Species in the Urban Environment—An Example of a Drone-Based Workflow Application

  • 章节

  • 发表日期：

    2025年

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  • Normandes Matos da Silva,
  • Domingos Sávio Barbosa,
  • Marcin K. Dyderski,
  • Dhonatan Diego Pessi,
  • Eliane Guaraldo,
  • Antonio Conceição Paranhos Filho,
  • Larissa Dall Agnol,
  • Cláudio Angelo Corrêa Gonzaga,
  • Camila Leonardo Mioto,
  • Fabio Angeoletto

摘要

Invasive species pose a significant threat to biological diversity. Exotic and invasive grass species negatively affect urban biodiversity in Brazilian cities, since they occupy open spaces, compete with the native plants and eventually displace them. These species also increase the probability of wildfires in urban areas. Resources allocated for biodiversity conservation and invasive plants’ management purposes remain scarce in Brazilian cities. Therefore, we have developed a low-cost methodology to support the detection of invasive grass species based on using drones, as well as two frameworks for data processing, model development, and validation, that can be used anywhere in the world. Local governments could set up a basic workstation and generate geomatic products to enable the environmental management of invasive species by investing approximately 2000$. Another advantage of the presented monitoring methodology is that the approach can be adapted to demands associated with biodiversity conservation in urban ecosystems, e.g. monitoring endangered species.