Sentinel-2 Time Series and Classifier Fusion to Map an Aquatic Invasive Plant Species along a River-The Case of Water-Hyacinth

• Tipo: Artigo em Revista Científica Internacional
• Data: 2023

Autores

Mouta, N (Autor) Outra; Silva, R (Autor) Outra; Pinto, EM (Autor) Outra; Vaz, AS (Autor) Outra; Alonso, JM (Autor) Outra; Goncalves, JF (Autor) Outra; Joao Honrado (Autor) FCUP; Vicente, JR (Autor) Outra

Revista

Título: Remote Sensing

Vol. 78

Página Final: 3248

ISSN: 2072-4292

Editora: MDPI

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00Y-S7H

DOI: 10.3390/rs15133248

Abstract (EN): Freshwater ecosystems host high levels of biodiversity but are also highly vulnerable to biological invasions. Aquatic Invasive Alien Plant Species (aIAPS) can cause detrimental effects on freshwater ecosystems and their services to society, raising challenges to decision-makers regarding their correct management. Spatially and temporally explicit information on the occurrence of aIAPS in dynamic freshwater systems is essential to implement efficient regional and local action plans. The use of unmanned aerial vehicle imagery synchronized with free Sentinel-2 multispectral data allied with classifier fusion techniques may support more efficient monitoring actions for non-stationary aIAPS. Here, we explore the advantages of such a novel approach for mapping the invasive water-hyacinth (Eichhornia crassipes) in the Cavado River (northern Portugal). Invaded and non-invaded areas were used to explore the evolution of spectral attributes of Eichhornia crassipes through a time series (processed by a super-resolution algorithm) that covers March 2021 to February 2022 and to build an occurrence dataset (presence or absence). Analysis of the spectral behavior throughout the year allowed the detection of spectral regions with greater capacity to distinguish the target plant from the surrounding environment. Classifier fusion techniques were implemented in the biomod2 predictive modelling package and fed with selected spectral regions to firstly extract a spectral signature from the synchronized day and secondly to identify pixels with similar reflectance values over time. Predictions from statistical and machine-learning algorithms were ensembled to map invaded spaces across the whole study area during all seasons with classifications attaining high accuracy values (True Skill Statistic, TSS: 0.932; Area Under the Receiver Operating Curve, ROC: 0.992; Kappa: 0.826). Our results provide evidence of the potential of our approach to mapping plant invaders in dynamic freshwater systems over time, applicable in the assessment of the success of control actions as well as in the implementation of long-term strategic monitoring.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 14