Mapping invasive iceplant extent in southern coastal California using high-resolution aerial imagery.

Invasive species threaten natural ecosystems globally, displacing native species and causing biodiversity loss. In coastal areas with Mediterranean climate around the world, iceplant (Carpobrotus edulis) has become highly invasive, forming large monospecific zones that compete for resources with native plant species, including threatened or endangered species. Despite the widespread impact of iceplant across coastal areas with a Mediterranean climate, there is no precise information on where it is and how much it has spread. This study focuses on mapping and quantifying iceplant extent along the coast of Santa Barbara County in California, USA, by leveraging machine learning methods to identify iceplant in images from the 2020 National Agriculture Imagery Program (NAIP) archive at 0.6 m/pixel resolution, creating the most extensive assessment to date of this invasive species. Results include a map showing iceplant locations in 2020 with overall accuracy of 87.11% ± 2.45% (95% confidence interval). The estimated iceplant coverage in our region of study is 2.2 ± 0.42 km² (95% confidence interval). Additionally, this study's use of open data and reproducible data analysis and validation workflow opens the door for the methods presented to be adapted and applied across California and all other Mediterranean climatic regions. In addition, the developed approach will accelerate monitoring over time to comprehend the spread and mitigation of iceplant invasions. • Random forest model produces high-accuracy map of invasive iceplant in southern CA. • Public NAIP data used to detect invasive plant species C. edulis at high-resolution. • Iceplant covered 2.2 km² (±0.42 km² 95% conf.int.) of SB County's coast in 2020. • First-order texture and metadata as features increase model accuracy efficiently. • Integrated cloud computing and data access enable efficient, reproducible analyses. [ABSTRACT FROM AUTHOR]