Land Degradation Neutrality: Towards an Earth Observation-based Early Warning System for Savannah Degradation

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Savannahs are important ecosystems that are found on almost half of the African continent. Unfortunately, they undergo massive conversions and are affected by land degradation processes leading to a decline in the ecosystem services they provide to some of the continent’s poorest and most vulnerable human populations. Reductions in the productivity of dryland savannahs have major social and political implications, and the United Nations (UN) has labelled the accurate quantification of savannah degradation as of high priority. Monitoring the woody component of savannah land cover is, therefore, needed to enhance the understanding of broad-scale changes in savannahs & their relationship with ecosystem degradation. Over large scales, this can only be achieved in a cost-effective manner using Earth Observation (EO) technologies. The development of SAV-EO is, therefore, proposed which will employ novel remote sensing methodologies, based on thousands of freely-available optical & radar satellite data for the mapping & monitoring of fractional woody cover over the entire southern African savannah region (1,500,000km2). A monitoring tool that identifies underlying land degradation processes, such as bush encroachment & deforestation, will also be devised based on a dense time-series analysis approach using all available data from the 45-year long Landsat archive. Another objective of SAV-EO is to provide an advancement in the design of operational indicators for the assessment & monitoring of land degradation, within the Land Degradation Neutrality framework suggested by the UN, in order to achieve the target of the Sustainable Development Goals to combat desertification by the 2030. The innovative algorithms, data & outputs will be made available open-access via a bespoke web-GIS platform & numerous dissemination & communication activities, thus providing an invaluable resource for scientists & policy makers alike.