Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Towards improved accounting and mitigation of greenhouse gas emissions from ditches and canals

Published in Environmental Research Letters, 2026

A multi-author synthesis identifying ditches and canals as under-accounted hotspots of greenhouse gas emissions, and outlining priorities for improved measurement, reporting and mitigation in inland-water and peatland systems.

Recommended citation: Silverthorn, T., Connolly, J., Habib, W., Kosten, S., Larmola, T., Paranaíba, J.R., Webb, J., Balathandayuthabani, S., Clarke, S.J., Tatariw, C., Cook, S.B., Williamson, J.L., Friday, L.E., Law, A., Andrews, L.O., van der Knaap, J., Evans, C.D., Fonvielle, J.A., Bryan, D., Yan, Z., Bieroza, M., van den Berg, M., Hill, M.J., Baugh, L., Evers, S., Mwanake, R.M., Baliña, S., Rahimi, H., Simpson, E., Struik, Q., Rissanen, A.J., & Peacock, M. (2026). "Towards improved accounting and mitigation of greenhouse gas emissions from ditches and canals." Environmental Research Letters.
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Peatland use and peat soil land cover types in Ireland: Implications for the calculation of GHG emissions in the context of climate action

Published in Land Use Policy, 2025

This study examines peatland use and peat soil land cover types across Ireland, providing critical insights for accurate GHG emissions calculations in support of national climate action targets and policy development.

Recommended citation: Gilet, L., Habib, W., Aitova, E., Byrne, K.A., Farrell, C.A., Fenton, O., Flynn, R., O'Leary, D., Morley, T.R., Tuohy, P., Regan, S., Renou-Wilson, F., Wilson, D., & Connolly, J. (2025). "Peatland use and peat soil land cover types in Ireland: Implications for the calculation of GHG emissions in the context of climate action." Land Use Policy, 152, 107792.
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Mapping internal farm roadways to identify runoff accumulation areas using an integrated GIS, aerial imagery and deep learning approach in grassland farms

Published in International Journal of Applied Earth Observation and Geoinformation, 2025

This study develops an automated deep learning workflow to map internal farm roadways on grassland farms and assess runoff accumulation risk, identifying critical areas where phosphorus-enriched runoff from livestock movement could impact water quality.

Recommended citation: Sifundza, L.S., Murnane, J.G., Adams, R., Daly, K., Habib, W., Tuohy, P., & Fenton, O. (2025). "Mapping internal farm roadways to identify runoff accumulation areas using an integrated GIS, aerial imagery and deep learning approach in grassland farms." International Journal of Applied Earth Observation and Geoinformation, 134, 104896.
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Lines in the landscape

Published in Communications Earth & Environment, 2025

This collaborative review examines the ecological, biogeochemical, and societal significance of agricultural ditches and drainage channels, highlighting their role as important landscape features that deserve greater scientific and conservation attention.

Recommended citation: Clifford, C., Bieroza, M., Clarke, S.J. et al. (including Habib, W. & Connolly, J.) (2025). "Lines in the landscape." Communications Earth & Environment, 6, 699.
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Mapping and monitoring peatland conditions from global to field scale

Published in Biogeochemistry, 2024

This comprehensive review examines approaches for mapping and monitoring peatland conditions across multiple spatial scales, from global to field-level assessments.

Recommended citation: Minasny, B., Adetsu, D.V., Aitkenhead, M. et al. (including Habib, W.) (2024). "Mapping and monitoring peatland conditions from global to field scale." Biogeochemistry, 167(4), 383-425.
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Mapping artificial drains in Peatlands – A national scale assessment of Irish raised bogs using sub-meter aerial imagery and deep learning methods

Published in Remote Sensing in Ecology and Conservation, 2024

This study presents the first national-scale assessment of artificial drainage networks in Irish raised bogs using very high-resolution aerial imagery and deep learning semantic segmentation models.

Recommended citation: Habib, W., Cresson, R., McGuinness, K., & Connolly, J. (2024). "Mapping artificial drains in Peatlands – A national scale assessment of Irish raised bogs using sub-meter aerial imagery and deep learning methods." Remote Sensing in Ecology and Conservation.
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Quantifying Peatland Land Use and CO2 Emissions in Irish Raised Bogs: Mapping Insights using Sentinel-2 Data and Google Earth Engine

Published in Scientific Reports, 2024

This study uses Sentinel-2 satellite imagery and machine learning in Google Earth Engine to classify peatland land use and estimate CO₂ emissions from Irish raised bogs.

Recommended citation: Habib, W., Ruchita, I., Saunders, M., & Connolly, J. (2024). "Quantifying Peatland Land Use and CO2 Emissions in Irish Raised Bogs: Mapping Insights using Sentinel-2 Data and Google Earth Engine." Scientific Reports, 14, 1171.
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A national-scale assessment of land use change in peatlands between 1989 and 2020 using Landsat data and Google Earth Engine—a case study of Ireland

Published in Regional Environmental Change, 2023

This study presents a comprehensive 31-year analysis of peatland land use change across Ireland using Landsat satellite data and machine learning in Google Earth Engine.

Recommended citation: Habib, W. & Connolly, J. (2023). "A national-scale assessment of land use change in peatlands between 1989 and 2020 using Landsat data and Google Earth Engine—a case study of Ireland." Regional Environmental Change, 23, 124.
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Upscaling methane fluxes from peatlands across a drainage gradient in Ireland using PlanetScope imagery and machine learning tools

Published in Scientific Reports, 2023

This study demonstrates the upscaling of methane flux measurements from peatlands using high-resolution PlanetScope imagery and machine learning methods across drainage gradients.

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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