Greining og kortlagning skógarkerfils á Stór-Reykjavíkursvæðinu með dróna og gervihnattamyndum.

Across the globe, invasive plants represent a significant threat to biodiversity and ecosystem structure. To effectively manage the spread of invasive plants, it is important to detect and map their distribution. Cow parsley (Anthriscus sylvestris), an invasive species in Iceland, typically grows in large, homogenous stands, and appears early in the growing season, making it a suitable species to detect via Remote Sensing (RS). RS and early detection allow for timely action, helping to mitigate negative impacts. The aim of this study was to test various RS techniques to detect and map cow parsley. Sites in the Greater Reykjavík area were selected: Mt. Esja, Rafstöð, Skólagarðar, and Grafarvogur. RS data was collected using Sentinel-2 data (2023, 2024), and drone imagery during the growing season (2024, 2025). Moreover, information on the spectral qualities of cow parsley and other co-occurring vegetation was gathered using a field spectroradiometer, to determine if cow parsley has a distinctive spectral signature, as well as the best time to survey cow parsley. PIX4Dfields® and ArcGIS Pro software were used to classify the data. Findings indicate that for Sentinel-2 data, cow parsley is best distinguished early in the growing season (May), as it has a significant head start on other surrounding vegetation. For drone data, the time to gather data is more flexible. However, cow parsley stands out well during its flowering in late May and June, when it forms dense white flowers. Used in tandem, these two RS techniques could be used to better map the extent of cow parsley across other sites in Iceland, and aid in more effective management.