Conference Agenda
Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).
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Daily Overview |
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📌Poster Session and Networking Aperitivo 🍷 Location: Lower Lobby | |
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Quantifying Forest Diversity through Spectral Metrics from PRISMA and Structural Information from BIOMASS P-band SAR 1: Mendel University in Brno, Faculty of Forestry and Wood Technology, Department of Forest Management and Applied Geoinformatics, Brno, Czechia; 2: Paris Lodron University of Salzburg, Faculty of Digital and Analytical Sciences, Department of Artificial Intelligence and Human Interfaces, Salzburg, Austria This study investigates the advantage of combining structural information obtained from the newly launched ESA BIOMASS (P-band) Synthetic Aperture Radar (SAR) with spectral information derived from PRISMA hyperspectral data to assess forest diversity in the Amazon region of Brazil. The study area was selected based on the overlap of available PRISMA and BIOMASS acquisitions. Structural diversity was estimated from BIOMASS data (6 June 2025) using polarisation variability, Polarimetric SAR (PolSAR) metrics, and texture of above-ground biomass to represent differences in forest vertical structure and stand complexity. The cross-polarised backscattering coefficient (HV/VH) is particularly sensitive to forest height variations due to volume scattering, providing key information on canopy structure and scattering mechanisms. Surface reflectance from PRISMA Level-2D (29 July 2025, 234 bands, 406–2497 nm) was used to derive spectral diversity indicators, including Rao’s Q, spectral variance, and clustering-based “spectral species.” These metrics describe variability in canopy composition and biochemical traits that reflect species and functional diversity across heterogeneous forest areas. The analysis evaluates relationships between spectral diversity from PRISMA and structural diversity from BIOMASS, examining how compositional and structural heterogeneity correspond across forest environments. Available LiDAR canopy height data and, where possible, field observations of species composition and functional traits will serve as supporting reference information. Correlation and multivariate analyses will assess the consistency and complementarity of spectral and radar-derived indicators. This multi-modal EO approach aims to advance satellite-based monitoring of forest biodiversity across tropical ecosystems, highlighting the potential of the ESA BIOMASS mission for forest applications. | |
