19-01-2026
The Center of Mathematics and Applications (NOVA Math) promotes the Seminar of Statistics and Risk
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.
22nd January 2026, 14:30.
Location: Hangar II, Room 3
Teams: https://teams.
| Attachment | Size |
|---|---|
| 20260122ricardocoelho.pdf | 86.32 KB |
