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UID:pretalx-gw2023-EYEJB3@pretalx.earthmonitor.org
DTSTART;TZID=Europe/London:20231005T134500
DTEND;TZID=Europe/London:20231005T140500
DESCRIPTION:While land is increasingly degrading\, robust monitoring approa
 ches are required to identify land degradation processes and to ultimately
  tackle those. Land degradation is commonly assessed by comparison with th
 e immediate past or surrounding. Comparison with its natural potential\, h
 owever\, i.e. a state of minimal human impact\, could give further insight
 s into the full degree of degradation\, accounting for the “shifting bas
 eline syndrome”\, the gradual change in perception of what is considered
  the reference. Primary production is one of the key indicators for determ
 ining impacts of land degradation\, which can be approximated by FAPAR\, t
 he fraction of absorbed photosynthetic active radiation\, a metric directl
 y related to primary productivity. Here\, we present a novel methodology t
 o assess land degradation in reference to its natural potential. Using a m
 achine learning model approach\, global time series maps spanning 2000 - 2
 022+ will be generated by simulating potential natural FAPAR in the hypoth
 etical space of minimal human impact. This will allow performing gap analy
 ses of actual and potential natural FAPAR to monitor impacts of land degra
 dation and restoration efforts through time. Use-case scenarios on country
  level and project investment level will be demonstrated in the context of
  supporting UNCCD targets for land degradation neutrality (LDN). This rese
 arch is carried out within the Open Earth Monitor Cyberinfrastructure proj
 ect (OEMC) and received funding via the European Union's Horizon Europe pr
 ogramme under grant agreement No.101059548.
DTSTAMP:20260415T004312Z
LOCATION:EURAC Auditorium
SUMMARY:Mapping land potential and tracking land degradation using EO data 
 - Julia Hackländer
URL:https://pretalx.earthmonitor.org/gw2023/talk/EYEJB3/
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