2023-10-05, 13:45–14:05, EURAC Auditorium
While land is increasingly degrading, robust monitoring approaches are required to identify land degradation processes and to ultimately tackle those. Land degradation is commonly assessed by comparison with the immediate past or surrounding. Comparison with its natural potential, however, i.e. a state of minimal human impact, could give further insights into the full degree of degradation, accounting for the “shifting baseline syndrome”, the gradual change in perception of what is considered the reference. Primary production is one of the key indicators for determining impacts of land degradation, which can be approximated by FAPAR, the fraction of absorbed photosynthetic active radiation, a metric directly related to primary productivity. Here, we present a novel methodology to assess land degradation in reference to its natural potential. Using a machine learning model approach, global time series maps spanning 2000 - 2022+ will be generated by simulating potential natural FAPAR in the hypothetical space of minimal human impact. This will allow performing gap analyses of actual and potential natural FAPAR to monitor impacts of land degradation 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 research is carried out within the Open Earth Monitor Cyberinfrastructure project (OEMC) and received funding via the European Union's Horizon Europe programme under grant agreement No.101059548.
OEMC Grant agreement ID: 101059548
Julia is a PhD candidate and research assistant at OpenGeoHub foundation, the Netherlands. Her research focuses on mapping land potential and monitoring land degradation using earth observation data.