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 01-sep-2024