Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

We investigate the unbounded operator obtained from the finite Hilbert transform when it acts from $L^1(-1, 1)$ into itself. A full inversion theorem is established for this operator, together with suitable extended versions of the Parseval and Poincaré-Bertrand formulae.

The talk is based on joint papers with Oleksiy Karlovych. We consider multidimensional generalisations of Wiener-Hopf operators and show that, under mild restrictions on the underlying Banach function space, their norm is estimated below by the L^infinity norm of the symbol, while their Kuratowski measure of noncompactness is estimated below by half that norm. Additionally, we show that for a classical Wiener-Hopf operator on a (separable) translation-invariant Banach function space over the half-line, its Hausdorff measure of noncompactness, its essential norm, and its norm are all equal.

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Venue: office 410, 4th floor of the "Departamento de Engenharia Mecânica e Industrial (DEMI)" Building

Statistics and Risk Management Seminar

Department of Mathematics, NOVA MATH/FCT NOVA

Title: Estimation of Above-Ground Biomass in Forests Using Satellite Data

Speaker: Ricardo Coelho, PhD Student, NOVA SST, Caparica, Portugal

Date | Time: January 22, 2026 | 14:30

Location: To be announced

Teams: https://teams.microsoft.com/meet/32754086330496?p=nspp91z49enhjNBXTR

Abstract: Monitoring carbon sequestration is essential for climate policy, carbon markets, and ecosystem assessment. Above-Ground Biomass (AGB) is a key indicator of carbon storage, where it can be estimated using direct or indirect methods. Direct methods involve the destruction of the trees, while indirect methods, like allometric equations, require the collection of forest inventory data, which is a time-consuming and costly process. With technological advances, a faster and more cost-effective alternative is to estimate AGB through remote sensing, combining satellite data with field measurements, which can lead to more accurate estimates.Machine learning (ML) models have been widely applied for AGB prediction, but many do not account for spatial autocorrelation and fail to quantify prediction uncertainty. Hybrid approaches that combine spatial statistical models with ML have emerged to address these limitations, leveraging both predictive power and spatial dependency modelling. This study first presents a simulation experiment to systematically compare the predictive performance of ML, hybrid, and spatial statistical methods under controlled geostatistical scenarios. We then apply these approaches to estimate AGB in a region of Spain, using GEOSAT-2 remote sensing data, including reflectance bands, vegetation indices, and texture metrics. 

Short Bio: Ricardo Coelho is a PhD student in the Mathematical Doctoral Programme at the NOVA School of Science and Technology (FCT-NOVA). His research work is focused on predicting forest biomass, through statistical models, using field and remote sensing data. Before enrolling in his PhD, he did a MSc in Mathematics and Applications at FCT-NOVA and a BSc in Mathematics at the same university. 

Organizers: Isabel Natário and Mina Norouzirad

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This work is funded by national funds through the FCT – Fundação para a Ciência e a Tecnologia, I.P., under the scope of the projects UID/00297/2025 (\url{https://doi.org/10.54499/UID/00297/2025}) and UID/PRR/00297/2025 (\url{https://doi.org/10.54499/UID/PRR/00297/2025}) (Center for Mathematics and Applications)