RT info:eu-repo/semantics/article T1 Assessing the efficacy of phenological spectral differences to detect invasive alien Acacia dealbata using Sentinel-2 data in southern Europe A1 Domingo Ruiz, Darío A1 Pérez Rodríguez, Fernando A1 Gómez García, Esteban A1 Rodríguez Puerta, Francisco K1 Invasive alien species K1 Invasive plants - Biological control K1 Animales y plantas perjudiciales, Lucha biológica contra los K1 Remote sensing K1 Artificial satellites K1 Satelites artificiales K1 Machine learning K1 Aprendizaje automático K1 Phenology K1 Fenología K1 Bosques - Gestión K1 Forests and forestry - Europe K1 Bosques y Silvicultura - Europa K1 Sustainable development K1 Desarrollo sostenible K1 Plant science K1 2506.16 Teledetección (Geología) K1 3106 Ciencia Forestal K1 3106.08 Silvicultura AB Invasive alien plants are transforming the landscapes, threatening the most vulnerable elements of local biodiversity across the globe. The monitoring of invasive species is paramount for minimizing the impact on biodiversity. In this study, we aim to discriminate and identify the spatial extent of Acacia dealbata Link from other species using RGB-NIR Sentinel-2 data based on phenological spectral peak differences. Time series were processed using the Earth Engine platform and random forest importance was used to select the most suitable Sentinel-2 derived metrics. Thereafter, a random forest machine learning algorithm was trained to discriminate between A. dealbata and native species. A flowering period was detected in March and metrics based on the spectral difference between blooming and the pre flowering (January) or post flowering (May) months were highly suitable for A. dealbata discrimination. The best-fitted classification model shows an overall accuracy of 94%, including six Sentinel-2 derived metrics. We find that 55% of A. dealbata presences were widely widespread in patches replacing Pinus pinaster Ait. stands. This invasive alien species also creates continuous monospecific stands representing 33% of the presences. This approach demonstrates its value for detecting and mapping A. dealbata based on RGB-NIR bands and phenological peak differences between blooming and pre or post flowering months providing suitable information for an early detection of invasive species to improve sustainable forest management. PB MDPI SN 2072-4292 YR 2023 FD 2023 LK https://uvadoc.uva.es/handle/10324/69475 UL https://uvadoc.uva.es/handle/10324/69475 LA eng NO Remote Sensing, 2023, Vol. 15, Nº. 3, 722 NO Producción Científica DS UVaDOC RD 27-nov-2024