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UID:pretalx-global-workshop-2026-9Q7PZY@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261008T180000
DTEND;TZID=Europe/Amsterdam:20261008T184500
DESCRIPTION:In this workshop\, the participants will have access to harmoni
 zed\, analysis-ready\, gap-filled and complete Landsat global mosaics from
  1997 onward in cloud-optimized GeoTIFF (COG) format (130 TB of data) in C
 DSE (https://browser.stac.dataspace.copernicus.eu). Spanning over 25 years
  and structured in 7 spectral bands (RGB\, NIR\, SWIR-1\, SWIR-2 and therm
 al)\, this data is instrumental for long-term monitoring applications of l
 and cover change\, soil proprieties\, vegetation productivity\, land degra
 dation\, vegetation height and other environmental characteristics. The gl
 obal mosaics were produced via the Time-Series Iteration-free Reconstructi
 on (TSIRF) framework over the entire Global Land Analysis and Discovery (G
 LAD) ARD Landsat archive (https://doi.org/10.7717/peerj.18585). Participan
 ts will learn about the implemented methodologies and use several python l
 ibraries (stacstac\, scikit-map) JupyterLab.
DTSTAMP:20260624T071548Z
LOCATION:Room 18
SUMMARY:Accessing global multi-decade Landsat cloud-free time-series in CDS
 E - Leandro Parente
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/9Q7PZY/
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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:20260624T071548Z
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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