New publication on SDG reporting in Remote Sensing of EnvironmentJuly 22, 2020
3D mapping of 15 million hectares of peatlands supporting BRG – the Indonesian Peat Restoration agencySeptember 1, 2020
Last year, ESA asked via the Open Space Innovation Platform for innovative ideas to detect and track marine plastic litter using satellites. Marine litter is one of the most pressing environmental problems and tackling the issue is gaining momentum at all levels, from political bodies to forward-thinking companies. "Following the promising results of previous ESA studies, we were searching for novel ideas to detect and possibly measure plastic concentrations, improve our understanding of how litter is transported around the world, and consequently help identify plastic sources and sinks," explains ESA Engineer Paolo Corradi who led the hunt for ideas. "This call for ideas was the first of its kind to source a wide variety of ideas for monitoring marine litter from space."
RSS’ idea “Tackling the plastic debris challenge at its source – Linking EO data with multi-source in-situ data for modelling debris pathways from source to sink” was selected by ESA and transformed into an Early Technology Development project, that was kicked-off on July 28, 2020. RSS is coordinating a consortium of seven partners from Europe, who are committed to tackling the plastic debris challenge.
Read more on the ESA website
Monitoring areas closer to plastic marine litter sources such as rivers and estuarine systems has the potential to improve mitigation strategies. Upscaling in-situ point data of litter with earth observation (EO) and hydrodynamic models is our central concept. Sentinel-2 and 3, together with data from similar satellite missions, will be used to monitor discharging rivers and their estuaries based on river plume detection inferred from suspended particulate matter maps. Multi-type in-situ data will be collected at various points along the pollution pathway. Imagery taken from installed cameras on bridges or other infrastructure will be analyzed using deep-learning approaches in order to detect floating plastic in rivers (in-situ type 1). This will provide improved inputs to transport models. Water samples from estuaries and coastal areas using manta trawls (in-situ 2) are used to quantify plastic litter abundances. High-resolution monitoring via automated analysis of drone imagery along the shoreline (in-situ 3) will be established for accumulation analyses as well as collecting beach samples through field surveys (in-situ 4).
Integration of in-situ data, multi-scale EO and hydrodynamic modelling serves as the development basis for a monitoring system of plastic debris in aquatic ecosystems, allowing for the first time an end-to-end depiction of real-world debris transport pathways. Three fundamental steps are needed: (1) scaling point measurements to large-area observations (in-situ + EO), (2) modelling transport pathways through both fore- and hindcasting (in-situ + current modelling), and (3) assimilation of EO data to improve model accuracy (current modelling + EO). Such source-to-sink monitoring systems can be used to identify environmental-, economic-, human health- and safety-related impacts of plastic litter and would support targeted efforts of both off- and onshore-based clean-up projects by focusing on smaller areas with higher plastic abundances.