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UID:pretalx-global-workshop-2026-E9SDB8@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T133000
DTEND;TZID=Europe/Amsterdam:20261007T134500
DESCRIPTION:Statistical modeling and uncertainty analysis plays a critical 
 role in evaluating climate and environmental data. Concepts such as standa
 rd error of the mean and design-based estimation seem to be increasingly u
 sed to manipulate prediction errors and tradable changes. Advanced trend e
 stimation and change-point models are essential for accurately identifying
  long-term shifts in essential climatic variables such as soil organic car
 bon and above ground biomass. Subtracting two above-ground biomass (AGB) m
 aps can create false data because map uncertainties propagate into the dif
 ference\, compounding the errors from both individual maps and inflating a
 pparent change signals. Rather than revealing true environmental dynamics\
 , naive subtraction often produces an apparent "change" that is actually j
 ust statistical noise. Quantile Regression Random Forests (QRRF) offer a p
 owerful\, non-parametric approach to estimating the true distribution of e
 rrors by retaining all observations within the terminal leaf nodes of the 
 forest\, rather than just calculating the conditional mean. This allows th
 e model to estimate the full conditional cumulative distribution function 
 and extract specific percentiles to form prediction intervals. We demonstr
 ate how this method can be used to determine tradable carbon sequestration
  without taking additional risks.
DTSTAMP:20260624T070204Z
LOCATION:Aula Magna
SUMMARY:Quantification of temporal changes in Earth-Observation-based estim
 ates: examples with soil carbon & above ground biomass - Tom Hengl (OpenGe
 oHub)
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/E9SDB8/
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BEGIN:VEVENT
UID:pretalx-global-workshop-2026-YKFVG9@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261008T164500
DTEND;TZID=Europe/Amsterdam:20261008T173000
DESCRIPTION:One of the most significant deliverables of the OEMC project ar
 e global\, cloud-less Landsat monthly time series from 2000–2025 at 30 m
  resolution. The Landsat global mosaics (V1) are explained in detail in Co
 nsoli et al. (2025\; https://peerj.com/articles/18585/). The Landsat V2 is
  at the order of magnitude more ambitious aiming at monthly products in 16
 bit format and will significantly less artifacts. The  pipeline uses a fou
 r-step process for improved quality\, including gap-filling using spatial 
 and temporal neighbours\, data fusion and final gap filling using global m
 odels. The results of cross-validation show improvements in accuracy in co
 nsistency. Major project challenges include needing 1PB of storage and sec
 uring post-2025 commercial services. Landsat V2 can also be used to derive
  embeddings for 2000-2025.
DTSTAMP:20260624T070204Z
LOCATION:Rooms 12+14
SUMMARY:Landsat monthly cloud-free complete consistent mosaics 2000-2025 - 
 Tom Hengl (OpenGeoHub)\, Sajed
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/YKFVG9/
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