2026-10-07, 17:00–17:15 (Europe/Amsterdam), Room 18
While forest monitoring has reached high levels of maturity, grassland ecosystems remain a critical "blind spot" in global conservation. To address this, Global Pasture Watch (GPW) has established a comprehensive baseline using 30m multi-decadal datasets (2000–2022) covering grassland extent, vegetation height, and livestock density. However, the inherent heterogeneity and rapid seasonality of these landscapes present significant current challenges for traditional pixel-based classification. To overcome these barriers, our next steps involve transitioning to next-generation machine learning models that utilize Sentinel-2 spatial-temporal embeddings. By moving beyond simple spectral signatures to rich, high-dimensional latent representations, we can better capture the nuances of managed vs. natural grasslands and monitor Gross Primary Productivity (GPP) with unprecedented precision. This evolution in our workflow aims to deliver near-real-time, actionable insights, transforming how we track land-use conversion and guide sustainable restoration across the world’s most vulnerable non-forest biomes.
Open-Earth-Monitor Cyberinfrastructure (Grant agreement ID: 101059548), Other
Please provide URL that you plan to use to distribute your materials (if available). –Bezos Earth Fund & Global Methane Hub
Leandro Parente is a senior researcher at OpenGeoHub Foundation with more than 15 years of experience in processing Earth Observation (EO) data and developing Machine Learning (ML) pipelines for producing continental and global maps.