Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

This collaborative research demonstrates innovative approaches to upscaling point-based methane flux measurements to landscape scales using high-resolution satellite imagery and machine learning techniques.

Research Contributions

• Integrated closed chamber gas measurements with high-resolution vegetation mapping
• Applied PlanetScope imagery for detailed habitat classification across drainage gradients
• Developed machine learning models for methane flux estimation at landscape scale
• Improved greenhouse gas emission assessments for peatland ecosystems

Methodology

The study combined field-based gas flux measurements with high-resolution satellite imagery (3-meter PlanetScope) to model methane emissions across different vegetation communities and drainage conditions in Irish peatlands.

Significance

This work advances our understanding of spatial variability in peatland greenhouse gas emissions and provides methods for scaling up field measurements to support national-level emission inventories and climate reporting.

Recommended citation: Ingle, R., Habib, W., Connolly, J. et al. (2023). "Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools." Scientific Reports, 13, 11997.
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