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UID:pretalx-global-workshop-2026-ZWFDRC@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20261007T173500
DTEND;TZID=Europe/Amsterdam:20261007T174000
DESCRIPTION:Large-scale reforestation is central to climate mitigation and 
 ecosystem restoration\, yet monitoring when and where restoration activiti
 es occur remains a major challenge. Existing satellite based approaches ty
 pically detect forest recovery only after canopy development\, limiting th
 eir usefulness for timely monitoring\, reporting\, and verification (MRV) 
 of restoration efforts.\nWe present a scalable framework for detecting ref
 orestation interventions within one year of planting using multi-sensor sa
 tellite time series. The approach integrates Sentinel-1 radar and Sentinel
 -2 optical data to learn a characteristic temporal signature of restoratio
 n\, capturing transitions from stable pre-intervention conditions to distu
 rbance and early vegetation recovery. Training leverages a global dataset 
 of verified reforestation sites combined with a synthetic control strategy
  to generate spectrally matched non restoration samples.\nOur results show
  that early stage reforestation can be identified at the pixel level acros
 s diverse climate zones\, substantially improving the temporal resolution 
 of forest monitoring. The poster will present global scale examples\, mode
 l outputs\, and temporal signatures illustrating how restoration signals e
 merge prior to canopy closure.\nThis work supports more timely and transpa
 rent monitoring of reforestation efforts and has direct relevance for carb
 on accounting\, climate finance\, and large-scale restoration initiatives.
DTSTAMP:20260624T071046Z
LOCATION:Aula Magna
SUMMARY:Early Detection of Reforestation Interventions Using Multi-Sensor S
 atellite Time Series - Angela John
URL:https://pretalx.earthmonitor.org/global-workshop-2026/talk/ZWFDRC/
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