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UID:pretalx-global-workshop-2026-HGTPJN@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T170000
DTEND;TZID=Europe/Amsterdam:20261007T171500
DESCRIPTION:While forest monitoring has reached high levels of maturity\, g
 rassland ecosystems remain a critical "blind spot" in global conservation.
  To address this\, Global Pasture Watch (GPW) has established a comprehens
 ive baseline using 30m multi-decadal datasets (2000–2022) covering grass
 land extent\, vegetation height\, and livestock density. However\, the inh
 erent heterogeneity and rapid seasonality of these landscapes present sign
 ificant current challenges for traditional pixel-based classification. To 
 overcome these barriers\, our next steps involve transitioning to next-gen
 eration machine learning models that utilize Sentinel-2 spatial-temporal e
 mbeddings. By moving beyond simple spectral signatures to rich\, high-dime
 nsional latent representations\, we can better capture the nuances of mana
 ged vs. natural grasslands and monitor Gross Primary Productivity (GPP) wi
 th 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 vu
 lnerable non-forest biomes.
DTSTAMP:20260624T083952Z
LOCATION:Room 18
SUMMARY:Global monitoring of grassland and livestock: Current status\, chal
 lenges and next steps - Leandro Parente
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/HGTPJN/
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