Mapping the Drainage Status of Ireland's Peatland through Drones, Vegetation, and Hydrology

ORCID

https://orcid.org/0009-0009-1937-4700

Department

Biological and Pharmaceutical Sciences

Year of Study

1

Full-time or Part-time Study

Full-time

Level

Postgraduate

Presentation Type

Poster

Supervisor

Eoin McCarthy

Supervisor

Eilish Broderick

Supervisor

Owen Fenton

Abstract

Intact peatlands play a vital role in maintaining ecosystem balance like biodiversity livelihood, regulating water, and acting as a carbon sink. Peatland degradation is largely driven by anthropogenic activities such as artificial drainage, peat extraction, and land use for agriculture. Degraded peatlands become the source of GHG emissions, especially those that are overshadowed by grasslands. Following the EU Paris Agreement to mitigate GHG emissions, several initiatives were taken for managing, restoring, and reporting emissions of Ireland’s peatlands. However, an information gap about peatland drainage conditions poses a significant challenge for mapping Ireland’s Peatlands.

This research aims to enhance the assessment and mapping of peatland drainage status (shallow or deep) through a combination of high-resolution drone imagery, vegetation surveys, and hydrological monitoring. Peatland sites will be evaluated through ground-based surveys and high-resolution drones during both summer and winter to create a digital twin. A scorecard system will be developed to indicate drainage status based on key vegetation indicators, such as Sphagnum mosses, with species weighted according to their correlation with drainage conditions. Dip wells and soil moisture sensors will be installed to monitor the water dynamics. Additionally, a field drainage survey will update historical methodologies to an electronic format, collecting data on drainage conditions, soil types, geology, and other relevant factors to create detailed drainage class maps. The ground-based vegetation and hydrology data will be integrated with drone imagery for digital mapping to facilitate machine learning, leading to more accurate peat drainage assessments. The outcome of the research will be an efficient, accurate, and cost-effective method for assessing and mapping peatland drainage status over large areas.

Keywords:

Peatlands, Drainage Status, Peatland Vegetation, Drone Imagery, GHG emissions

Start Date

16-6-2025 11:00 AM

End Date

16-6-2025 12:00 PM

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Jun 16th, 11:00 AM Jun 16th, 12:00 PM

Mapping the Drainage Status of Ireland's Peatland through Drones, Vegetation, and Hydrology

Intact peatlands play a vital role in maintaining ecosystem balance like biodiversity livelihood, regulating water, and acting as a carbon sink. Peatland degradation is largely driven by anthropogenic activities such as artificial drainage, peat extraction, and land use for agriculture. Degraded peatlands become the source of GHG emissions, especially those that are overshadowed by grasslands. Following the EU Paris Agreement to mitigate GHG emissions, several initiatives were taken for managing, restoring, and reporting emissions of Ireland’s peatlands. However, an information gap about peatland drainage conditions poses a significant challenge for mapping Ireland’s Peatlands.

This research aims to enhance the assessment and mapping of peatland drainage status (shallow or deep) through a combination of high-resolution drone imagery, vegetation surveys, and hydrological monitoring. Peatland sites will be evaluated through ground-based surveys and high-resolution drones during both summer and winter to create a digital twin. A scorecard system will be developed to indicate drainage status based on key vegetation indicators, such as Sphagnum mosses, with species weighted according to their correlation with drainage conditions. Dip wells and soil moisture sensors will be installed to monitor the water dynamics. Additionally, a field drainage survey will update historical methodologies to an electronic format, collecting data on drainage conditions, soil types, geology, and other relevant factors to create detailed drainage class maps. The ground-based vegetation and hydrology data will be integrated with drone imagery for digital mapping to facilitate machine learning, leading to more accurate peat drainage assessments. The outcome of the research will be an efficient, accurate, and cost-effective method for assessing and mapping peatland drainage status over large areas.