Seminar: Liezl Vermeulen

How do you monitor a rainforest that is almost always hidden by clouds? Understanding how African tropical forests respond to climate and land-use change requires reliable, high-frequency observations of vegetation dynamics. Yet persistent cloud cover and atmospheric variability limit existing satellite products, leaving forest phenology, seasonality, and short-term variability poorly resolved. Here, we develop and evaluate a high-quality vegetation time series for Central African forests by combining hyper-temporal MSG SEVIRI observations with high-resolution Sentinel-2 and drone data from the CoForFunc project. We apply dedicated atmospheric and BRDF corrections to SEVIRI, optimised for humid tropical forest conditions, to ensure that detected signals reflect canopy dynamics rather than atmospheric or geometric artefacts. SEVIRI’s near-continuous sampling reduces cloud-related noise and improves temporal consistency, while Sentinel-2 and drone data provide spatial detail and validation. The resulting dataset provides a robust baseline for tracking forest canopy dynamics and detecting emerging impacts of climate and land-use change. Initial results show that it captures seasonal transitions more consistently than commonly used products such as MODIS, revealing phenological patterns otherwise obscured by cloud contamination and atmospheric noise. By improving functional monitoring in one of the world’s most data-limited tropical regions, this work strengthens the basis for assessing carbon dynamics, ecosystem resilience, and potential tipping behaviour in African tropical forests.