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João Gonçalves
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Recent Grants
Grant: Close
Renovação de florestas nativas ibéricas de plantações de eucaliptos
Open Date: 2023-03-01
Close Date: 2024-08-31
Grant: Close
Combining microclimate sensor networks and models to uncover the vulnerability of small plants to climate change
Open Date: 2023-02-01
Close Date: 2024-07-31
Grant: Close
A biodeterioration model to predict biological impact on exposed rock surfaces and open-air rock-art
Open Date: 2023-02-01
Close Date: 2026-01-31
Grant: Close
SeverusPT - A web-based data product and service for fire severity assessment and prediction in mainland Portugal
Open Date: 2021-02-01
Close Date: 2024-01-31
Grant: Close
FirESmart - Soluções baseadas na natureza para a gestão preventiva do risco de incêndio e fornecimento sustentado de serviços ecossistémicos
Open Date: 2019-03-01
Close Date: 2022-08-31
Articles (14)
Year:
2025
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2025
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2025
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2025
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2025
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2024
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2024
Year:
2023
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 Cávado 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.
Year:
2023
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2023
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2023
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2023
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2023
Year:
2023
Collaborators (13)
Adrián Regos
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Ana Medeiros
Invited Professor Assistant
University of Porto
Albano Figueiredo
Assistant Professor
University of Coimbra
Claudia Carvalho-Santos
Assistant Professor
University of Minho
Isabel Silva
Assistant Professor
University of Porto
Joana R. Vicente
University of Porto
Peter Verburg
University of Amsterdam
Isabel Passos
Instituto Politécnico de Viana do Castelo Escola Superior Agrária
Lien Reyserhove
Research Institute for Nature and Forest
César Capinha
Ass. Professor
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Carlos Vila-Viçosa
Pos-Doc Researcher in Botany, Biogeography and Plant Systematics
Universidade do Porto Centro de Investigação em Biodiversidade e Recursos Genéticos
Nagore Garcia Medina
Researcher
Universidad Autónoma de Madrid
Tim Adriaens
Research Institute for Nature and Forest
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