BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//pretalx.earthmonitor.org//E9SDB8
BEGIN:VTIMEZONE
TZID:Europe/Amsterdam
BEGIN:STANDARD
DTSTART:20001029T030000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20000326T020000
RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
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:20260624T084501Z
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/
END:VEVENT
END:VCALENDAR