Many peatland areas in Europe are subject to conflicts of interest regarding land use. Robust policies are therefore required to accurately assess the regional and national consequences of peatland policies. These policies should be informed by accurate inventories that provide information about the status of peatland and the vulnerability of the carbon store. National mapping efforts are often varied in completeness and depth and are often based on historical categories and information. Harmonisation is increasingly required to enable benchmarking and compatibility with international standards and reporting.
This session welcomes contributions on peatland systems globally that address aspects of 1) GHG accounting, 2) monitoring, reporting, and verification (MRV) schemes, 3) mapping of peatland conditions, 4) regional and international standards for voluntary carbon markets, and 5) economic and social aspects of peatland rewetting/restoration. We appreciate studies on methodological development, field measurements, remote sensing, hydrological modelling, as well as interdisciplinary studies.
Orals: Mon, 4 May, 14:00–18:00 | Room 2.95
Greenhouse gas (GHG) emissions from drained peatlands account for about 7% of the total anthropogenic GHG emissions in the European Union (EU). Yet, a lack of high-resolution spatial data hampers targeted mitigation. We present results of a recent study (van Giersbergen et al. 2025 Nature Communications) where we combined soil and land use data to generate detailed maps of land use, GHG emissions, and emission hotspots for EU+ peatlands. Undrained peatlands and those drained for forestry dominate at high latitudes, while drained grasslands and croplands prevail around latitudes 50°−55°. Four main emission hotspots emerge: the North Sea region, eastern Germany, the Baltics together with eastern Poland, and north Ireland. The North Sea region is the largest, accounts for 20% of EU+ peatland emissions on just 4% of the peatland area. Our findings highlight the urgency of reducing emissions from drained peatlands to meet EU climate targets and reveal substantial underreporting in National UNFCCC inventories, amounting to 59–113 Mt CO2e annually. Our findings provide a robust and spatially explicit evidence base for policymakers to prioritize peatland rewetting to reduce GHG emissions. Recent developments on reducing EU peatland emission underreporting will be included.
How to cite: Tanneberger, F., van Giersbergen, Q., Barthelmes, A., Couwenberg, J., Lang, K., Martin, N., Tegetmeyer, C., and Fritz, C.: Hotspots of greenhouse gas emissions from drained peatlands in the European Union, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19007, https://doi.org/10.5194/egusphere-egu26-19007, 2026.
Peatlands are significant carbon sinks, and yet their carbon stocks and extents in coastal British Columbia, Canada, remain largely unquantified. We conducted a field assessment to estimate above- and belowground carbon stocks at six peatland sites across the coast of British Columbia. These values were compared with regional aboveground carbon stock estimates. We found that coastal peatlands store approximately three times more carbon than adjacent temperate rainforests. These results underscore the importance of peatlands, and highlight a need for improved mapping and assessment. In particular, our results demonstrate a substantial gap in our understanding of the carbon stocks and spatial extent of peat-forming swamps in this region.
How to cite: Martens, H. R. and Kreyling, J.: Beyond the Forests: Peatlands as Dominant Carbon Stores in Coastal British Columbia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5161, https://doi.org/10.5194/egusphere-egu26-5161, 2026.
Fire is an increasingly important disturbance in Atlantic blanket bogs, yet empirical evidence of its effects on carbon dynamics remains limited. This study quantified the immediate and short-term impacts of a burning event on ecosystem atmosphere carbon dioxide (CO2) exchange, vegetation loss, and post-fire recovery from a blanket bog in the North-West of Ireland in 2023. Continuous eddy-covariance (EC) measurements collected over three years (2023-2025) were analysed together with biomass sampling and burn severity mapping (dNBR) conducted in 2023.
Field scale measurements of CO2 by EC quantified an emission event of 42.7 g C m-2 d-1during the burning window. Footprint-weighted biomass assessments indicated an above-ground vegetation carbon loss of 28.3 g C m-2, dominated by heather and graminoids, demonstrating that surface vegetation combustion was the primary contributor to the observed emission spike. Burn severity and field observations confirmed that combustion was surface limited, with no evidence of deep peat burning. Despite this disturbance, the bog remained a net annual carbon sink in all years analysed, indicating rapid functional recovery but reduced net carbon uptake in the later post-fire year. Annual net ecosystem exchange (NEE) remained within the range reported for blanket bogs under prevailing land management conditions.
Generalized additive models (GAMs) showed that post-fire CO2 exchange was primarily controlled by solar radiation and air temperature, with moisture related controls more pronounced in 2023 when the peat surface was exposed. Rapid graminoid regrowth and persistently high-water tables supported recovery of photosynthetic function and reduced moisture sensitivity by 2024.
Overall, the fire disturbance caused a distinct temporary carbon loss, with emissions during the burn substantially exceeding pre-fire emissions. Despite this disturbance, the ecosystem remained a net annual carbon sink, and post fire carbon recovered quickly due to intact hydrology and shallow burn severity. These findings demonstrate that Atlantic blanket bogs can exhibit high resilience to low-moderate severity surface fires and highlight the importance of maintaining high water tables and peatland condition to minimize fire related carbon losses under future climate change.
Keywords: Atlantic blanket bog, fire disturbance, eddy covariance, CO2 exchange, burn severity, post-fire recovery
How to cite: Min, S., Bondi, G., Righetti, A., Rambaud, J., and Murphy, R.: Impacts of Fire Disturbance on Carbon Dynamic and Ecosystem Recovery in a Blanket Bog Ecosystem, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3561, https://doi.org/10.5194/egusphere-egu26-3561, 2026.
To mitigate agricultural greenhouse gas emissions, Danish ministerial agreements have initiated a land-use transformation of historical dimensions, focusing on restoring and rewetting extensive peatland areas currently used for agriculture. In addition, a CO2-equivalent tax on emissions from organic peatlands is scheduled to be implemented from 2028. This study informs discussions on requirements and best practices for rewetting and peatland restoration and highlights the importance of including changing climate conditions and rewetting management scenarios in future peatland management strategies.
The study integrates process-based hydrological modeling and empirical CO2 flux modeling at a daily temporal resolution to evaluate how peatland hydrology influences CO2 emissions under scenarios of rewetting and climate change.
Following the calibration of a three-dimensional transient distributed hydrological model for a peat-dominated catchment, daily groundwater table dynamics were simulated to represent hydrological conditions in drained peat soils. These simulations were coupled with an empirical CO2 flux model, developed from a comprehensive daily dataset of groundwater table depth, temperature, and soil CO2 flux measurements. The novel empirical CO2 flux model captures a clear temperature-dependent response of soil CO2 emissions to variations in groundwater table depth.
By applying this coupled modeling framework, we quantified CO2 emissions at daily timescales. The results demonstrate that incorporating both temperature sensitivity and high-resolution temporal variability in water level significantly influence projections of CO2 fluxes. In particular, high CO2 emissions are expected in cases of co-occurrence of elevated air temperature and low groundwater tables. Using 17 different climate projections from the Euro-CORDEX regional climate modeling project, we simulated future groundwater table depth and temperature-dependent CO2 emissions. We find increased emissions due to increased temperatures, which, however, can be counter-balanced (in the Danish case) or amplified depending on the future trend in groundwater table depth.
Our results further demonstrate that rewetting strategies that achieve near-surface groundwater tables mainly during winter result in only marginal emission reductions compared to drained conditions. Conversely, near-surface groundwater tables in summer offer more effective reductions (up to 50%).
The study illustrates the value of combining detailed hydrological simulations with emission models.
How to cite: Denager, T., Riis Christiansen, J., Johannes Maria Schneider, R., Langen, P., Quistgaard, T., and Stisen, S.: Water table and temperature dynamics control CO2 emission estimates from peatlands under rewetting and climate change scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4015, https://doi.org/10.5194/egusphere-egu26-4015, 2026.
Mires are among the most valuable and endangered ecosystems in the world. In the Alps, they are an integral component of the region’s natural, geographical, climatic, and cultural-historical diversity. Mires have been used by humans in many ways. Recent climatic changes and other human influences on mires are suspected of altering species composition. However, this assumption has never been comprehensively tested across a large number of mire sites in the Eastern Alps.
This study aimed to expand knowledge regarding the response and resilience of mire habitats and biodiversity after 35 years of environmental and anthropogenic influences and to determine the current state of native mire diversity in the Eastern Alps. In summer 2023, the vegetation of about 200 Austrian mires was resampled through more than 1000 vegetation surveys and compared with the plant diversity of the same mires recorded in the Austrian Mire Conservation Catalogue (Steiner, 1992) about 35 years ago (1984–1988).
Each vegetation survey was assigned EUNIS habitat types (Chytrý et al., 2020), regions, protection status, and altitude. We used indicator values of light, temperature, nutrients, reaction (pH), aeration, and moisture (Landolt et al., 2010) to examine the conservation status of these alpine mires and changes in climatic and edaphic site factors. Additionally, we analyzed long-term changes in plant diversity based on species richness, plant types, and red lists. The collected data supported selecting mires for restoration during the project period.
We compared the vegetation of mires from 1988 to 2023 and found that all six mire habitat types had degraded on average after 35 years due to less moisture, more nutrients, less light, and more aeration. The exceptions were Non-calcareous quaking mire, which showed no significant change in nutrients, and Tall-sedge bed, which showed only an average increase in aeration. An increase in the mean temperature indicator value, independent of altitude, was observed only for Poor fen in Northern and Central Alps.
On average, we observed a significant increase in plant species richness in all mire habitat types except Tall-sedge bed. The increase is attributable to species on the red list with the status “least concern”, as well as to woody plants and other herbaceous plants. This trend may not be positive for mires, as it suggests an increase in generalists rather than mire specialists.
We observed a reduction in typical mire plant types, such as a significant decline in mean peat moss (Spaghnum sp.) cover in Raised bog and a significant decline in mean sedge cover, except for Poor fen and Tall-sedge bed.
About one-third of all studied mire habitats showed negative trends regarding moisture, nutrients, and light. However, half were resilient in some way (only slight changes), and about 5 % even showed improvements (positive trends).
How to cite: Baur, P. A., Steiner, G. M., and Glatzel, S.: Climate-change-induced degradation of mires in Eastern Alps: A comprehensive resampling study of 200 mires after 35 years, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4006, https://doi.org/10.5194/egusphere-egu26-4006, 2026.
Since 2005, the Alaska Peatland Experiment (APEX) has maintained experimentally manipulated water table levels in a rich fen to investigate how these key carbon sinks will function in an uncertain climatic future. This fen, located in an area of discontinuous permafrost, is representative of similar fens across interior Alaska, where they are considered significant carbon sinks and are projected to become more common on the landscape as climate and permafrost systems shift. Leveraging nearly twenty years of chamber and tower carbon-dioxide and methane flux data, as well as nearly a decade of both multispectral and SAR satellite data,[2] we present an improved understanding of trends in trace gas fluxes in the context of a changing water table. We evaluate potential new functional patterns for rich fens, and endeavor to create time-series maps of total carbon flux using satellite systems.
While water table position and carbon flux mapping via remote sensing platforms have been successful in other peatland systems, best practices for rich fens have not yet been established. Using the impressive temporal resolution of the APEX site, we compare a suite of historically successful multispectral and SAR indices to identify and implement carbon flux mapping across the site. Sentinel-1 SAR been used to successfully map variability in Water Table position with nearly 60% accuracy.
Our research has found significant changes in the carbon flux of the fen, particularly within the last 10 years. Not only has the system overall become wetter, but the fen has begun to serve as a net source of carbon to the atmosphere, rather than a sink. [EK5] This change is largely due to increases in total methane production, as ecosystem respiration does not significantly change across both flooded conditions and water treatments. In the wettest years, when the water table remains above the soil surface for much of the growing season, CH4 accounts for nearly 8% of total carbon flux, more than four times that of the driest years. By considering both environmental and carbon flux trends across the entire data set, we are better able to understand and document the long-term changes in rich fen carbon fluxes and spatially [7] scale this understanding to the growing extent of this expansive ecosystem in interior Alaska.
How to cite: Serocki, E., Kane, E., Euskirchen, E., Dieleman, C., Bourgeau-Chavez, L., Graham, J., and Turetsky, M.: Leveraging Long-Term, Multiscale Data to Understand Increased Carbon Release in an Alaskan Boreal Fen Complex, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14082, https://doi.org/10.5194/egusphere-egu26-14082, 2026.
Drainage-driven peatland degradation in Europe has led to widespread loss of peatland functions, including important regulating ecosystem services such as climate and water regulation. European peatlands have been formed, used and managed in different ways, resulting in contrasting land-use practices and rewetting policies between countries. In this study, we investigate the differences between European countries in why and how peatlands are drained, used and restored and how these differences influence current peatland management and restoration strategies. We conduct a structured scientific literature review for ten European countries using the DPSIR (Drivers-Pressures-State-Impacts-Responses) framework. Our results from abstract screening of over 200 publications across seven countries (Denmark, Estonia, Finland, Ireland, the Netherlands, Sweden and the United Kingdom) indicate that peatland drainage is primarily driven by forestry (45%) and agriculture (40%), while peat extraction is less frequently identified as a driver (15%). Greenhouse gas emissions dominate the reported impacts of drainage (77%), whereas biodiversity loss and habitat degradation, and land subsidence are mentioned less frequently (16% and 14%, respectively). Reported responses are strongly skewed toward hydrological interventions such as rewetting (63%), with fewer studies emphasizing vegetation and biodiversity restoration (16%), land-use conversion (11%), or measures to improve water and soil quality (9%). Initial comparative analyses suggest that the relative emphasis on drivers, impacts and response strategies differ between countries, reflecting national peatland contexts and policy priorities. Although agriculture and forestry dominate as drivers, responses rarely address land-use systems directly, instead emphasizing hydrological interventions. Similarly, while biodiversity impacts are widely recognized, targeted ecological responses are seldom reported. Ongoing analysis will further explore country-specific DPSIR profiles and link dominant drivers and impacts to preferred restoration approaches. These insights are essential for targeted and appropriate restoration strategies to maximize the recovery of the regulating ecosystem services across European peatlands.
How to cite: Jongejans, L. and van der Velde, Y.: Why drained peatlands differ across Europe: drivers, impacts and responses in a DPSIR-based literature review, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19808, https://doi.org/10.5194/egusphere-egu26-19808, 2026.
Ireland’s peatlands comprise up to 23.3% of the country’s area and up to 90% of these peatlands have been degraded [1]. It is crucial to understand these through modelling, because flooding, water quality, and carbon emission risks can be mitigated through the management of these ecosystems. Formerly extracted and degraded peatlands especially pose these risks due to increased uncertainty about the manner of their influence on biodiversity, hydrology, and water chemistry regardless of if they are restored, managed, or left alone. Ireland’s post-extraction peatlands are novel habitats, which will require more explicit parametrisation of vegetation types and quantities for adequate modelling.
This research carries out remote sensing and machine learning methods to identify habitats, subdivided into ranges of Plant Functional Types, in two rewetted Irish peatlands, which had formerly been extracted for fuel: Ballycon and All Saints, Co. Offaly. It attempts to link these habitats to calibrate the process-based model PVN in an Irish context.
Six habitat classes were generated for both sites using drone imagery and ArcGIS Pro’s Deep Learning library; in parallel, the method was applied to PlanetScope imagery at Ballycon using a Python Random Forest algorithm. Results yielded 72-77% accuracy for the different products, though this is highly dependent upon scale.
(ONGOING RESEARCH BELOW)
From this, locations were chosen to scale down to a single-dimension case at each site by ‘translating’ the habitat class for a given area into a range of areal cover per Plant Functional Type. This, alongside hydrological and geotechnical data collected in the field, can be used to develop scenarios for plant growth and carbon emissions, with potential for scaling up again to develop early-stage estimates about the whole site's carbon balance.
Paper on remote sensing approaches in prep. Publications relating to developing/calibrating PVN planned, but this work is ongoing.
[1] Gilet et al., 2025: https://doi.org/10.1016/j.landusepol.2025.107792
How to cite: Silva, M.: Classification and ecohydrological modelling of Irish post-extraction peatlands using Plant Functional Types: scenarios for rehabilitation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14293, https://doi.org/10.5194/egusphere-egu26-14293, 2026.
Peat replacement has dominated research in growing media science over the past decades, driven by peatland protection policies and the need to reduce greenhouse gas emissions. At the same time, global demand for growing media is projected to increase substantially, reflecting the expansion of horticultural and ornamental plant production worldwide. Achieving net-zero emission targets while maintaining a resilient horticulture sector therefore requires re-examining existing assumptions and broadening the scope of current research strategies.
While most scientific and regulatory efforts focus on substituting peat with alternative materials, comparatively little attention has been paid to reducing the environmental impact of peat extraction itself or to exploring whether pathways exist to make peat use more sustainable. Horticultural peat is generally considered unsustainable because peat mineralisation rates exceed natural peat accumulation, leading to the release of long-stored, non-biogenic carbon. Nevertheless, peat has also been described as a slowly renewable resource, a claim that remains insufficiently examined in practical or quantitative terms.
We explore conceptual models in which peat extraction could be matched by equal or higher rates of peat accumulation across larger, managed landscapes. Such approaches would rely on long-term peatland management, compensatory restoration of degraded peatlands, or integrated landscape-scale strategies. The feasibility of these models is assessed in terms of biophysical constraints, economic costs, and competition with alternative land-use options, including carbon credit schemes, biodiversity restoration initiatives, and commercially available peat substitutes.
In addition, we discuss related approaches, including sphagnum moss cultivation in paludicultural systems, acrotelm harvesting, and the utilisation of residual peat from construction, mining/quarrying, or restoration projects. By comparing current peat extraction with potential future pathways, we aim to stimulate a broader discussion on whether or not peat use can be re-framed within sustainability goals, rather than being considered solely as a material to be phased out.
How to cite: Sentinella, A. and Purre, A.-H.: Is it possible to have 'sustainable' peat? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21971, https://doi.org/10.5194/egusphere-egu26-21971, 2026.
Peatlands cover just 3% of Earth's land surface, yet store an estimated 600-700 Pg carbon (PgC), approximately one-third of Earth's soil carbon, making them critical regulators of the global carbon cycle. However, peatland spatial extent remains highly uncertain, particularly at fine spatial scales and in data-sparse regions. Existing global peatland datasets rely on heterogeneous inventories and regional products, leading to large inconsistencies in both total peat area and spatial distribution. These limitations hinder accurate assessments of peatland-climate feedbacks, carbon budgets, national policy development, and restoration efforts. We propose a machine learning framework that combines a priori information from existing peat databases (PEATMAP, Global Peatland Database, and CORINE Land Cover) with satellite observations in the visible, together with topographic and hydrological information. Our methodology employs a neural network trained with 17 input variables including Landsat-8 surface reflectance, topographic attributes from the MERIT database (elevation, slope, distance to drainage, height above drainage), and water table depth data. The model first generates a continuous Peatland Index (PI) at 3 arc-second (~90m) resolution, that can be thresholded to obtain a binary peat classification. In regions with reliable coarse resolution peat information, the PI can be used to downscale it and obtain a coherent high resolution peat classification. The obtained pan-boreal/Northern Hemisphere peatland map at 90m was evaluated through both quantitative and qualitative approaches. Fully independent validation using the Peat-DBase field dataset (over 180,000 peat and non-peat observations) demonstrates an overall accuracy of 68.4% and an F1-score of 0.80. Regional assessments show 69.2% overall accuracy (F1=0.81) in Eurasia and 63.8% (F1=0.74) in North America. Qualitative spatial evaluation across multiple case-study regions reveals that the proposed map successfully captures fine-scale spatial details absent in existing inventories, including explicit delineation of open water bodies, river networks, and topographic constraints on peatland distribution. The product exhibits improved spatial coherency with high-resolution imagery while remaining consistent with large-scale patterns from current peat databases. This work provides a spatially coherent, high-resolution peatland dataset spanning the Northern Hemisphere, offering improved capabilities for carbon stock estimation, hydrological modeling, and monitoring peatland degradation. Future improvements will incorporate SAR data, additional environmental drivers, and deep learning-based feature extraction to further enhance classification accuracy, spatial details, time-evolution, and peat information.
How to cite: Chen, M., Aires, F., and Ciais, P.: Coherent Northern peatlands retrieval at 90 m using machine learning based on satellite observations and a priori information, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13217, https://doi.org/10.5194/egusphere-egu26-13217, 2026.
Wetlands provide essential ecosystem services, including atmospheric carbon sequestration. In the context of climate change, these ecosystems offer a natural solution to mitigate greenhouse gas emissions. Unlike many European or tropical regions, Quebec and Canada still maintain vast carbon stocks that are largely unaffected by anthropogenic pressures. The conservation of these stocks acts as nature-based climate solution to mitigate climate change.
This project aims to document the distribution and quantify the carbon stored in southern Quebec’s wetlands that span over 75,000 km2and covering 9 different ecoregions. The study implies the development of a new dataset on peat depth, compiling over 40,000 data across marshes, swamps, fens, bogs, and forested peatlands. Using this database, we will compare three modeling approaches (Random Forest, LightGBM, and Generalized Additive Models [GAMs]) to identify the most important predictors of carbon storage. These models integrate the new peat depth dataset, some topographic indices derived from a DEM and reconstructed paleoclimatic data.
Furthermore, the study will explain how topographic and past climatic conditions influenced carbon distribution and composition across different wetland types. The results of this research will be synthesized into a map that will support decision for wetland conservation and management strategies, as well as for assessing carbon losses due to the alteration or destruction of some of these ecosystems. This project specifically focuses on identifying carbon hot spots, the areas with the largest carbon stocks, to prioritize their conservation.
How to cite: Primeau, G., Garneau, M., and Millard, K.: Quantification and mapping of carbon stocks in wetlands from southern Quebec, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12880, https://doi.org/10.5194/egusphere-egu26-12880, 2026.
The Northwest Territories (NWT) is a large jurisdiction (>1.3 million km2) in Canada featuring vast areas of forests, expansive organic wetlands (peatlands), and open tundra. Within the mainland of 1.15 Mkm2 (excluding the Arctic Archipelago), the diversity of terrain, climate, biotic factors, and sub-surface (e.g., permafrost) conditions give rise to heterogeneous landscapes at both small and large scales. Land cover maps provide the basis for understanding how different types of vegetation and wetlands are distributed across landscapes, providing a foundation to subsequently derive information concerning climate change impacts and carbon/methane modelling. Through the joint Multisource Vegetation Inventory (MVI) project (Castilla et al. 2022), the Government of the Northwest Territories (GNWT) in partnership with the Canadian Forest Service of Natural Resources Canada (CFS) has developed land cover maps with improved classification accuracy of key vegetation types (forests, wetlands). The goal of this work is to produce and validate an updated land cover map of the entire NWT mainland including 10 forest classes as broad forest cover types (coniferous, broadleaf, mixedwood, treed wetland) combined with density classes (dense, open, sparse), as well as 10 non-treed classes. This presentation provides an overview of the major components in the land cover map development, with specific focus on improved (organic) wetland mapping. This initiative is based on a new network of land cover reference data consisting of thousands of points (n=24,865), forming the densest ever compilation of forest, wetland, and non-treed reference information available across NWT. The land cover map is produced from a random forest (RF) classification procedure using above reference land cover points and 30-m resolution rasters of predictive variables derived from satellite imagery and environmental datasets. Satellite imagery composites include cloud-free multispectral Sentinel-2 time-series of six spectral bands and six spectral indices which were temporally composited for each pixel over the 2020 to 2022 time period as i) seasonal summer (July-August) and winter (February-March) medians and ii) inter-annual statistics from full time-series including six percentiles (p5, p20, p40, p60 p80, p95) and two temporal variability measures (range, st. dev.). In addition, a single ca. 2020 PALSAR-2 L-band dual-polarized (HH, HV) summer composite was created, along with 26 terrain-derived data layers, 24 climatic layers, and three long-term spectral change metrics. The RF classification procedure included a spatially balanced split of the reference data into calibration and independent validation observations, a recursive feature elimination algorithm to iteratively remove the least important predictor variables, as well as a hyper-parameter optimization routine to further improve predictive performance. The resulting NWT land cover map product improves upon national mapping results (71 % vs 47 % overall accuracy all classes, improvements up to 20% in upland-wetland separation) and shows potential to provide an invaluable operational map of baseline forest and wetland information required to serve forest, wetland, wildfire, and wildlife management applications.
How to cite: van der Sluijs, J., Skakun, R., Groenewegen, K., Rea, T., Beaudoin, A., and Castilla, G.: Mapping the state of land cover and wetlands in the Northwest Territories, Canada, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22187, https://doi.org/10.5194/egusphere-egu26-22187, 2026.
Peatlands are among the most carbon-dense terrestrial ecosystems and play a crucial role in climate regulation and landscape hydrology, while supporting a unique and highly specialized biodiversity. In response, several European countries have developed national peatland strategies to support conservation, restoration, and sustainable management. A major limitation in implementing these strategies is the lack of consistent, up-to-date, and spatially explicit information on peatland health. Many existing peatland inventories are affected by missing data, outdated classifications, or heterogeneous data quality, which restricts their applicability for monitoring and management. In a previous study (Gross-Schmoelders et al., 2025), we demonstrated that high-resolution PlanetScope satellite imagery provides reliable information on peatland health, showing strong agreement with biogeochemical soil properties and enabling a clear distinction between pristine and drained peatland areas. Building on these findings, the present study evaluates whether freely available Sentinel-2 data are equally effective in distinguishing pristine from drained peatlands. Sentinel-2 offers moderate spatial resolution (10 m), a revisit time of approximately five days, and global open access, making it a highly attractive data source for large-scale peatland monitoring. Our analysis covers 13 peatland sites across Europe, representing a wide range of climatic conditions, peatland types, and management histories. Both, established reference sites (Gross-Schmoelders et al., 2025) and newly introduced test sites are included to enhance the robustness and transferability of the results. We analyze a suite of vegetation, moisture, and surface indices commonly applied in peatland remote sensing, including NDVI, GI, gNDVI, EVI, FAPAR, SAVI, MSAVI2, and albedo. In addition, ground motion metrics are incorporated to capture surface dynamics related to drainage and peatland health. The analysis focuses on the early growing season, when differences in vegetation structure, productivity, and moisture conditions between pristine and drained peatlands are expected to be most pronounced, consistent with findings from our previous work. Preliminary results show that both Sentinel-2 and PlanetScope data reliably differentiate between pristine and drained peatland conditions across all sites, particularly when using NDVI, GI, and EVI. Preliminary results show that FAPAR, SAVI, and MSAVI2 also exhibit consistent differences between pristine and drained conditions. Overall, these results demonstrate that Sentinel-2 represents a robust, cost-effective, and scalable data source for peatland health assessment. This has direct relevance for remote sensing–based peatland monitoring and supports the development of consistent, comparable, and transparent peatland inventories. The findings highlight the strong potential of open-access satellite data to support national peatland strategies, large-area monitoring frameworks, and evidence-based ecosystem management.
Reference:
Gross-Schmölders, M., Gupta, S., Grady, M., Wania, A., Bengtsson, F., and Alewell, C., (2025) Building a Framework to Differentiate between Pristine and Drained Peatlands in Europe by comparing Molecular and Spectral Data (submitted).
How to cite: Groß-Schmölders, M., Gupta, S., and Alewell, C.: Distinguishing pristine and drained peatland sites: How effective is open source Sentinel-2 imagery?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2514, https://doi.org/10.5194/egusphere-egu26-2514, 2026.
12% of the disturbed peatlands in the world are known to contribute approximately 4% to global greenhouse gas emission, according to UNEP. Northern peatlands spread above latitude 45 N (e.g. in Canada, USA, Scandinavia and United Kingdon) are biomes where climate change may occur earlier and rapidly thus contributing to greenhouse gas emissions more than many other biomes around the world. Peatlands can be classified as either “intact” or “disturbed” to determine whether they could be CO2 sources or sinks yet, the classification process requires a remote sensing approach because of limited accessibility to physical locations and intensive nature of field mapping. The study presented here uses spectral reflectance from peatland surfaces, together with topographic and climatic properties of the environment to classify peatland areas across Scotland. Distinct spectral reflectance responses in visible red between 0.63 and 0.69 µm, near-infrared between 0.85 and 0.88 µm, and short-wave infrared between 1.6 and 2.2 µm of the optical electromagnetic spectrum, topography, climate and land surface temperature have been used to discriminate between peatlands. A random forest classifier was trained using a 70/15/15 train-validation split, to predict peatland status. The classifier achieved an overall accuracy (F1 Score) of 72%, with a class-level accuracy of 94% for Forested, 84% for Drained and Eroded, 67% for Modified, and 44% for Near-Natural Peatlands at 100m resolution. Based on these results, a national Scottish peatland status map is modelled at 100-meter resolution, demonstrating the potential of using the model for large-scale peatland characterization. This work presents a remote-sensing-based classification framework to support peatland mapping and status monitoring.
How to cite: Aduse Poku, E. and Watson, L.: Spatial classification of peatland status using remote sensing and random forest, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14138, https://doi.org/10.5194/egusphere-egu26-14138, 2026.
Raised bogs are ombrotrophic peatlands defined by dome-like topography that is elevated above the water table. Once widespread in temperate northern Europe, their exploitation has necessitated special conservation status, which requires assessment of raised bog conditions at the scale of tens of thousands of hectares. Satellite remote sensing offers a solution to this challenge, but upscaling relationships between ground-based ecological surveys at temperate raised bogs and remotely sensed data are unclear.
We provide a first statistical analysis of the relationship between ecological survey data obtained at temperate raised bogs at c. 80 m2 scale to multispectral remote sensing data recorded by the Sentinel-2 satellite at 100-400 m2 scale. We analyse data from 34 images obtained in summer and winter of 2020-2025 in conjunction with 2827 ecological survey points recorded in 2023-2024 from six Irish raised bogs that cover an area of 3.91 km2. We test for correlations between ecological communities as established at each survey point and the reflectance spectra and amplitudes in the corresponding Sentinel-2 pixels.
Our results show statistically significant differences between reflectance of pixels associated with the end-member ecological communities as well as in the broader classes of ‘active’ and ‘inactive’ raised bog habitat. Differences are most pronounced in red-edge and near infrared bands as well as indices composed of these bands. A general reduction of reflectance values in winter, likely related to phenology, moisture and solar radiance impacts, does not greatly diminish the statistical strength of differentiation between ecotopes. Winter images can be prone to frost/ice, however, which produces anomalous reflectance distributions that can be detected (and thus filtered) by principal component analysis. These findings help to underpin the use of multispectral data for large scale automated mapping of these vulnerable habitats via e.g. machine learning approaches.
How to cite: Ferch, Z., Grappiolo, C., Regan, S., and Holohan, E.: An assessment of Sentinel-2 multispectral satellite reflectance data for ecological mapping of temperate raised peatlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17915, https://doi.org/10.5194/egusphere-egu26-17915, 2026.
Accurate mapping of peat depth is crucial for carbon accounting, areal planning, and land management in peatlands. However, existing maps often lack the resolution and accuracy needed for these purposes, even in countries with rich spatial data sets.
We present a study that evaluated whether digital soil mapping using remotely sensed data could improve existing maps of peat depth across two, >10 km2 sites in western and southeastern Norway. We measured peat depth by probing and ground-penetrating radar at 372 and 1878 locations at the two sites, respectively. Then we trained Random Forest models using radiometric and terrain variables, plus the national map of peat depth, to predict peat depth at 10 m resolution.
The two best models achieved mean absolute errors of 60 and 56 cm, explaining one-third of the variation in peat depth. Our remote sensing models had better accuracy than the national map of peat depth, even when we calibrated the national map to the same depth data. Terrain variables were much better predictors than radiometric variables, with elevation and valley bottom flatness showing the strongest relationships to depth. At our coastal site, peats were much deeper above the Holocene marine limit than below, emphasizing the importance of accumulation time in places that have experienced glacial isostatic adjustment. Meanwhile, the national map of peat depth itself carried much more information about peat depth at one of the sites than the other --- likely as a result of uneven historical field sampling.
Based on these findings, we conclude that digital soil mapping with DTM-derived predictors can improve the existing, national map of peat depth in Norway. Doing so would support national and regional-scale peatland carbon stock assessments and land management policies, as well as specific areal planning decisions at the municipal scale. Since our remote-sensing models relied on predictor--depth relationships that were specific to the sites we mapped, more depth measurements would be needed to expand the spatial coverage of an improved national map. A structured surveying effort coordinated in the manner of, or in conjunction with, the National Forest Inventory would be an efficient way to collect these data. Better data infrastructure for hosting and compiling peatland parameters from opportunistic measurements would also accelerate the accumulation of accuracy improvements. Besides specific accuracy improvements, digital soil mapping of peatland also offers advantages in transparency, reproducibility, and updatability.
How to cite: Vollering, J., Gatis, N., Kusk Gillespie, M., Muggerud, K.-K., Nerhus, S. D., Rydgren, K., and Sparf, M.: Improving national peat depth inventories with terrain-based digital soil mapping: evidence from Norway, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7144, https://doi.org/10.5194/egusphere-egu26-7144, 2026.
Peatlands are a well-known major global terrestrial carbon (C) sink. Most of Irish peatlands have been anthropogenically altered in past, such as by drainage for forestry, agriculture, and peat extraction. Recent restoration efforts highlight the need to better understand peatland C-dynamics and improve methods for reporting CO2 fluxes. Water-table (WT) depth is a key driver of CO2 flux exchange under different land-use types [1], making its inclusion in predictive models essential. Previous work by Premrov et al. [2], [3] assessed the application of random forest (RF) model to predict WT [2] at eight Irish peatland sites [4], using on-site measurements [4] and gridded meteorological data [5], with further details explained in Premrov, et al (2025) [2]. Findings reported in Premrov et al. (2025) [2] showed a relatively good RF- model performance at site-scale (R2 = 0.78). New work explores challenges and opportunities in upscaling the RF model from site- to national-scale. The ‘variable importance’ was assessed in order to reduce dimensionality (i.e. excluding less important variables), and to enable the upscaling. This resulted in a simplified RF model using only selected variables with the highest importance, to improve computational efficiency for upscaling model applications. While the simplified RF model showed slightly lower, but acceptable performance at site-scale compared to full RF model (including all variables) [2], the attempts to apply it at the national scale revealed challenges, and highlighted the need for further improvements. The study discusses the challenges and opportunities in upscaling the RF model to enhance the robustness of RF-based WT predictions at national scale.
Acknowledgements
The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding projects CO2PEAT (2022-CE-1100) and AUGER (2015-CCRP-MS.30) [EPA Research Programmes 2021- 2030 and 2014–2020], and to University of Limerick funding.
References
[1] Tiemeyer, B., et al., 2020. A new methodology for organic soils in national greenhouse gas inventories: Data synthesis, derivation and application,Ecological Indicators, Vol. 109, 105838, https://doi.org/10.1016/j.ecolind.2019.105838.
[2] Premrov, A., et.al., 2025. Assessing the application of random forest (RF) to predict water-table (WT) in selected Irish peatlands, EGU25-5122, https://doi.org/10.5194/egusphere-egu25-5122, 2025.
[3] Premrov, A., et.al, 2023. Insights into the CO2PEAT project: Improving methodologies for reporting and verifying terrestrial CO2 removals and emissions from Irish peatlands. IGRM2023, Belfast, UK. https://www.researchgate.net/publication/369061601_Insights_into_the_CO2PEAT_project_Improving_methodologies_for_reporting_and_verifying_terrestrial_CO2_removals_and_emissions_from_Irish_peatlands.
[4] Renou-Wilson, F., et. al, 2022. Peatland Properties Influencing Greenhouse Gas Emissions and Removal (AUGER Project) (2015-CCRP-MS.30), EPA Research Report, Irish Environmental Protection Agency (EPA). https://www.epa.ie/publications/research/climate-change/Research_Report_401.pdf.
[5] Copernicus Climate Change Service, Climate Data Store, (2020): E-OBS daily gridded meteorological data for Europe from 1950 to present derived from in-situ observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.151d3ec6.
[6] Kuhn, M. 2008. Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05.
How to cite: Premrov, A., Yeluripati, J., Renou-Wilson, F., Walz, K., Byrne, K. A., Wilson, D., Hyde, B., and Saunders, M.: Assessing the application of random forest (RF) to predict water-table (WT) in selected Irish peatlands: Challenges and opportunities with upscaling the model to national scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20667, https://doi.org/10.5194/egusphere-egu26-20667, 2026.
The Eddy covariance (EC) is a well-known and widely used technique for investigating ecosystem exchange of greenhouse gases (GHGs) between the biosphere and the atmosphere [1]. Long-term EC datasets frequently contain gaps due to a variety of reasons [2], which have resulted in the development of various gap-filling methods/tools. Despite the availability of various existing gap-filling methods and tools, many users still rely on spreadsheet software for gap-filling via semi-empirical and nonlinear regression computations. Transitioning to R or Python can significantly reduce computation time for large datasets; however, less experienced users of these environments may face challenges in adapting their methods [5]. The development of a user-friendly R-based tool, such as ‘miniRECgap’ [3], that enables effortless application of frequently used semi-empirical approaches [5] is therefore considered beneficial. Further details on the introduction of the ‘miniRECgap’ R-package can be found in Premrov et al. (2024) and Premrov et al. (2025) [4], [5]. One of the main purposes of designing ‘miniRECgap’ was to assist new or less experienced users in applying light- and temperature-response functions (utilised in ‘miniRECgap’) to enable gap-filling in a simple and efficient way, while also serving as a learning tool for those users transitioning from spreadsheets to R [5]. The use of the ‘miniRECgap’ package involves five simple steps and operates via GUI-supported scripts [3], [4], [5], which will be demonstrated. The approach makes the package suitable for all users, including beginners with no prior R experience. To enhance learning, a mini tutorial will be presented on how to use ‘miniRECgap’, showing a practical example of gap-filling Irish peatland ecosystem EC CO₂ flux data from Premrov et al. (2025) [5], aiming to encourage learning and adoption of R-based solutions for EC flux gap-filling tasks.
Acknowledgements
The authors are grateful to the Irish Environmental Protection Agency (EPA) for funding the CO2PEAT project (2022-CE-1100) under the EPA Research Programme 2021-2030.
References
[1] Burba, G., Anderson, D., Amen, J., (2007) Eddy Covariance Method: Overview of General Guidelines
[2] Baldocchi, D.D. (2003) Assessing the eddy covariance technique for evaluating carbon dioxide
[3] Premrov, A (2024). ‘miniRECgap’: R-Package for gap-filling of the Missing Eddy Covariance CO2 Flux Measurements Using Selected Classic Nonlinear Environmental Response Functions via Simple user-friendly GUI Supported R Scripts. (v0.1.0). https://github.com/APremrov/miniRECgap; https://doi.org/10.5281/zenodo.13228228; https://doi.org/10.5281/zenodo.13228227
[4] Premrov, A. et al. (2024): Introducing the ’miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO2 flux data, EGU24-6475, https://doi.org/10.5194/egusphere-egu24-6475, 2024.
[5] Premrov, A. et .al. (2025). Introducing ‘miniRECgap’ R package for simple gap-filling of missing eddy covariance CO2 flux measurements with classic nonlinear environmental response functions via GUI-supported R-scripts (case-study: In-sample gap-filling with ‘miniRECgap’ vs. MDS and an optimised shallow ANN in a ‘challenging’ peatland ecosystem). Environmental Modelling & Software 193 106611. https://doi.org/10.1016/j.envsoft.2025.106611
How to cite: Premrov, A.: The ‘miniRECgap’ package with GUI-supported R-scripts for simple gap-filling of Eddy Covariance CO₂ flux data: Demonstrating the application of 'miniRECgap' suitable for new users with no prior knowledge of R, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21762, https://doi.org/10.5194/egusphere-egu26-21762, 2026.
Posters on site: Mon, 4 May, 10:45–12:30 | Hall X1
Peatlands only cover 3-4% of Earth’s terrestrial surface yet are globally important carbon stores, hydrological regulators and biodiversity hotspots. Blanket bog, a type of peatland, is a rare ecosystem type within the EU. Active and degraded blanket bogs are spatially abundant in Ireland covering ~11% of the country. EU Natura 2000 protected areas includes ~167,000 ha of active blanket bog, of which 93% is in Ireland. Yet despite peat soils extending to 1.66 Mha of Ireland (~23.3%), more than 90% of these peatlands have experienced anthropogenically induced degradation. Land use change and digging artificial drainage challenges are key mechanisms of degradation: impacting biodiversity, hydrology and resulting in carbon and greenhouse gas emissions. Consequently, few actively functioning Irish upland blanket bogs remain. Rewetting, rehabilitation and restoration could be conducted to improve local biodiversity, water regulation and water purification, and reduce emissions.
Peatland rehabilitation relies on effective water table management, including blocking drainage ditches, to raise the local water table to promote wetland vegetation. Drainage ditches are extensive in Ireland and extend to thousands of kilometres, but manual mapping is expensive and time consuming. Still, these drainage ditches must be mapped to identify potential areas for rehabilitation. Potential areas to be restored must be mapped and quantified under the EU Nature Restoration Law and Biodiversity strategy.
We aim to: (1) adapt a methodological workflow using deep learning and very high resolution aerial imagery to map artificial drainage ditches in Irish upland blanket bogs ; and (2) combine the model with other GIS analyses to indicate upland bog rehabilitation potential. Our work adapts previous raised-bog drainage mapping models, and utilises recent peatland and peaty soil extent maps to delimit the analysis to these regions.
The model was tested in the Wicklow Mountain upland blanket bog region. Initial results show that the model is effective at recognising linear drainage ditches on upland blanket bogs regardless of depth. Early analysis depict 464km of drainage channels in Co. Wicklow (East Ireland). Incomplete or interrupted drainage ditches may indicate that some drains are over-grown or have filled in with peat. . Current Completeness, Correctness and Quality (CCQ) accuracy assessment indicates that the model identified drainage ditches with ~73% Completeness, ~94% Correctness and ~70% Quality. False positives seem to be limited to deer tracks or gullies. The same approach will be implemented Co. Mayo and Co. Sligo (West Ireland) demonstrating the transferability of these methods and potential for upscaling to a national level. The outputs from this study will inform policy, governance and practice as these bodies work towards meeting peatland restoration targets indicated in they implement EU and National Law.
How to cite: Watts, A., Habib, W., and Connolly, J.: Identifying upland blanket bog drainage channels using machine learning and very high resolution aerial remote sensing., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1281, https://doi.org/10.5194/egusphere-egu26-1281, 2026.
Peatlands cover around 3% of the Earth's land surface and store a significant portion of the world's soil carbon, about twice as much as the world's forests combined. These unique ecosystems are characterized by waterlogged, anoxic, and typically acidic conditions that inhibit microbial decomposition, leading to carbon accumulation and playing a vital role in the global carbon cycle. Peatlands serve as large carbon sinks; however, anthropogenic disturbances, such as draining and water table lowering for agriculture, or peat extraction for fuel and horticulture substrates, can convert them into carbon sources of greenhouse gases (GHG), such as carbon dioxide (CO2) and methane (CH4), which have led to increasing efforts to restore peatlands as a nature-based climate solution.
Although extensive research has improved our understanding of carbon dynamics in peatlands, including restored sites, long-term assessments remain limited. Yet, these assessments are essential for capturing climate variability and its impacts. Continuous gas monitoring, combined with physicochemical and microbial analyses, is therefore essential to evaluate the effectiveness of restoration efforts in terms of carbon sequestration. At the same time, there is a remaining knowledge gap regarding the role of microbial communities in carbon sequestration and GHG emissions. Therefore, this study aims to investigate how peatland restoration practices affect microbial activity and GHG fluxes in Danish peatlands across different seasons and years.
The study areas are located in Store Åmose, a Danish nature park on Zealand that comprises diverse peatland systems protected under the Natura 2000 network. Historically, these peatlands were converted to agriculture, forestry, and other land uses. Three sites have been selected: (i) a high water-table bog restored in 2017; (ii) a low water-table bog in a naturally forested state that has not been restored; and (iii) a high water-table fen with a diverse, undisturbed plant community. At each site, in situ GHG emissions of CO₂ and CH₄ are measured using gas flux chambers, and soil samples are collected at three depths to assess soil physicochemical properties and microbial activity.
Preliminary results indicate that peatland restoration reduces GHG fluxes (CO₂ and CH₄). Middle and deeper soil layers in restored and non-restored bogs show similar C:N ratios and bacterial biomass, with the high C:N ratios suggesting that substantial organic carbon remains stored in the peat. Meanwhile, bacterial growth in surface layers appears to be primarily influenced by climate and vegetation, whereas deeper layers are more similar across sites. Warmer and wetter periods seem to enhance both CO2 fluxes and microbial activity, likely driven by seasonal variations in temperature and moisture.
Understanding the relationship between microbial activity and carbon fluxes is crucial for improving our knowledge of these ecosystems and developing effective management strategies to reduce emissions and restore degraded peatlands.
How to cite: Aguilar Vilar, C., Cruz Paredes, C., and Herzog, S.: Carbon dynamics in Danish peatlands under changing climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5865, https://doi.org/10.5194/egusphere-egu26-5865, 2026.
Rewetting of agricultural peatlands is widely recognised as a key climate mitigation strategy, yet its net effect remains uncertain, particularly during the early transition years. We conducted a multi-year study using continuous eddy covariance measurements to quantify carbon dioxide (CO2) and methane (CH4) fluxes across two peatland fields undergone rewetting and an adjacent shallow-drained control field in Denmark. Rewetting raised the mean annual water table level by ~7 cm relative to the control when side-by-side comparisons were possible. Our results demonstrate that rewetting can rapidly shift the ecosystem carbon balance toward net CO2 uptake, likely due to the successful establishment of productive vegetation such as Phalaris arundinacea prior to rewetting. Early vegetation development may therefore accelerate CO2 uptake compared with slower trajectories observed in more degraded peatlands. However, this benefit can be partially offset by increased CH4 emissions, particularly during wet periods, which can rival the CO2 sink strength and reduce the overall greenhouse gas mitigation potential. Even in shallow-drained control areas, modest increases in water table during wet years were sufficient to temporarily reverse net carbon loss, highlighting the sensitivity of early outcomes to hydrological conditions. Management practices, such as autumn biomass cutting, further influenced CO2 exchange by enhancing early-season uptake, though biomass removal can partially offset these gains. Collectively, our findings underscore that the net climate effect of peatland rewetting depends strongly on interannual variability, water table dynamics, vegetation establishment, and management interventions. Long-term, ecosystem-scale monitoring is thus essential to capture the full spectrum of environmental variability and optimise restoration strategies for effective climate mitigation.
How to cite: Tong, C. H. M., Pullens, J. W. M., Petersen, R. J., Frederiksen, R. R., and Lærke, P. E.: Limited climate benefits of rewetting a shallow drained peatland when interannual variabilities in CO2 and CH4 fluxes are considered , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11927, https://doi.org/10.5194/egusphere-egu26-11927, 2026.
In Austria, peatlands cover an area of approximately 44,400 ha. Of these, two-thirds are drained and used for agriculture and thus are potential hotspots for greenhouse gas (GHG) emissions. This highlights the need for accurate GHG accounting and reporting. However, only Tier 1 IPCC emission factors are currently in use for Austrian peatlands, as the exact GHG balance is still unknown and is estimated using data from other countries. This creates substantial uncertainty, especially since Austria is home to a variety of peatland types with different characteristics and land management practices.
As part of the EU-funded LIFE project AMooRE (Austrian Moor Restoration), this research aims to fill this gap and assess, for the first time, the impact of different land management intensities on the emissions of CO2, CH4, and N2O in Austrian peatlands. A particular focus is placed on grassland management, as this is the most common practice in Austria, and on the benefits of extensification in reducing GHG emissions. By gaining an improved understanding of the relationship between abiotic factors, human management and GHG dynamics, the objective is to generate Tier 3 IPCC emission factors for Austrian peatlands subjected to varying land management intensities and therefore provide valuable data for national GHG accounting and the implementation of mitigation strategies.
Using manual dynamic gas flux chambers connected to portable CO2, CH4, and N2O gas analysers, field measurements are being conducted at five sites along a land management intensity and hydrological gradient in the Wörschacher and Irdninger Moor (Styria, Austria) over a two-year period, from January 2025 to December 2026. To complement gas flux data, meteorological conditions, important soil and hydrological parameters, and vegetation dynamics are being monitored continuously at all sites. Here we present the status of our research, including preliminary results and first modelling approaches. Initial findings show clear differences between the sites, with CO2 and CH4 emissions showing opposing behaviours and gradually changing along the gradient and clear N2O emission peaks after fertilisation events. Our results provide insights into the spatio-temporal variability in GHG fluxes of Austrian peatlands across different management intensities, highlighting the management role in influencing the dynamics of CO2, CH4, and N2O fluxes.
How to cite: Lanthaler, B., Maier, A., and Glatzel, S.: First Assessment of Greenhouse Gas Emissions from Austrian Peatlands under Different Land Management Intensities, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3011, https://doi.org/10.5194/egusphere-egu26-3011, 2026.
Impacts of peatland conservation and restoration—whether in the context of projects for carbon markets, or national and subnational policies to achieve nationally determined contributions—are important to evaluate in countries where conversion and use of peatlands contribute substantially to greenhouse gas emissions. Past and present emissions can be estimated based on emissions factors and observed land use. However, the benefits of peatland projects or policies must be evaluated relative to business-as-usual, requiring forecasting of future or counterfactual peatland conversion and use. To enable objective decision-making, forecasts should avoid overestimation of project or policy benefits and should provide some estimate of uncertainty.
We are developing an ensemble approach to generate aggregate baselines of business-as-usual greenhouse gas emissions from peatland conversion and use. Because the peatland community currently lacks a rich literature on predictors of land-use change, we apply a simple pixel-matching approach to produce an ensemble of land use trajectories collectively representing business-as-usual in a jurisdiction or region. Greenhouse gas emissions across all trajectories are averaged to produce an approximation of expected business-as-usual emissions. We believe this approach has the potential to produce better evaluations of the impacts of peatland projects and policies on greenhouse gas emissions, ecosystem services, and communities, and invite discussion regarding the role of the peatland research community in generating unbiased baselines.
How to cite: Cobb, A., Dommain, R., Ng, J., Arassah, F. I., Swails, E., and Zeng, Y.: Evaluating impacts of national and international peatland policies and projects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3262, https://doi.org/10.5194/egusphere-egu26-3262, 2026.
The Western Siberian Lowland (WSL) is the world’s largest peatland complex, containing vast areas of permafrost and non-permafrost peatland. Peatlands of the WSL feature extensive surface water cover. These pools play important roles in aquatic biodiversity and biogeochemical cycling, particularly of carbon, but are thought to be changing in response to climate. Synthetic Aperture Radar (SAR) satellite data are currently under-utilised for spatiotemporal peatland monitoring at large spatial scales. We analysed changing open-water cover across the WSL from Sentinel-1 SAR imagery. Our research illustrates the highly dynamic systems behaviour across the WSL with inter-year inundation dynamics observed. The cloud-computing basis of our method gives it clear potential for monitoring high-latitude regions. This potential will be realised through the recently funded NERC project, Antheia, which we also introduce here. Antheia will quantify recent and ongoing changes in northern peatland pools at a hemispheric scale and identify the drivers of change.
How to cite: Colson, D., Morris, P., Quincey, D., and Smith, M.: Six years of changing open-water cover across the peatlands of the Western Siberian Lowlands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7410, https://doi.org/10.5194/egusphere-egu26-7410, 2026.
Peatlands cover 12% of Canada's territory, primarily in the boreal and Arctic regions, and have the capacity to absorb and store significant amounts of carbon. They are therefore gaining attention as a nature-based climate solution, which involves protecting, managing, and restoring natural ecosystems from further degradation, thereby reducing greenhouse gas emissions. Canadian peatlands have been disturbed by human activities, which can convert peatlands from net carbon sinks (absorbing more than they release) into net carbon sources (releasing more than they absorb), and these disturbances have intensified in recent years. In this context, mapping anthropogenic disturbances in peatlands is crucial for effective monitoring and management of peatlands and ensuring they continue to serve as carbon sinks. Available maps of anthropogenic disturbances in peatlands are typically confined to specific regions, disturbance types, or time periods. Therefore, our goal is to develop a comprehensive Canada-wide map of anthropogenic disturbances, covering both historical and recent periods. To do this, we develop an automated framework for mapping current and historical anthropogenic disturbances in Canadian boreal peatlands. The framework leverages machine-learning–based image segmentation models applied to Landsat satellite imagery and is designed to process the full 40-year (1984 to 2024) satellite archive to generate multi-class disturbance maps (i.e., agriculture, forestry, resource extraction, transportation, industry, residential, seismic lines) across multiple decades. By comparing disturbance maps through time, the spatiotemporal dynamics of anthropogenic disturbances in boreal peatlands can be examined. The resulting maps provide a foundation for improved understanding of peatland disturbance patterns and support researchers investigating peatland–climate interactions, government agencies developing policies for peatland protection and restoration, and Indigenous communities working to safeguard their traditional lands.
How to cite: Song, S., Sonnentag, O., Kang, M., Fortier, M., Teng, M., and Lin, M.: Mapping Anthropogenic Disturbances in Canadian Boreal Peatlands using Satellite Imagery and a Machine Learning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15816, https://doi.org/10.5194/egusphere-egu26-15816, 2026.
Peatlands play a crucial role in carbon sequestration, covering approximately 10-15 % of Latvia’s territory, with a large proportion drained for forestry or peat extraction. Peat is currently extracted on ~0.4 % of Latvia’s territory and largely exported. Despite their relevance for national greenhouse gas (GHG) balances, continuous ecosystem-scale CO2 flux measurements are absent in Latvia. Current GHG accounting relies on chamber campaigns and emission factors, limiting monitoring, reporting, and verification for restoration and climate mitigation.
To address this gap, we initiated the first deployment of autonomous eddy covariance (EC) Carbon Node systems (LI-COR) in Latvian peatlands to measure CO2 exchange together with ancillary environmental variables. The first unit was installed in August 2025 in an active peat extraction site (cutover bog), while a second unit is devoted to a natural raised bog. The distance between sites is < 5 km, ensuring similar regional meteorological conditions and enabling better comparative assessment of land-use impacts.
We present first operational results, focusing on data continuity and system performance, including the feasibility of solar-powered operation at northern mid-latitudes, which proved challenging during the winter of 2025/2026. Preliminary CO2 flux dynamics provide early insights into emission magnitude and temporal variability at the active extraction site. This initiative represents a foundational step toward long-term EC observations in Latvia with implications for national GHG accounting and evidence-based peatland restoration policy.
The study is supported by a donation from the "Mikrotīkls", which is administered by the University of Latvia Foundation and by the project PeatTransform (No. 6.1.1.2/1/25/A/001).
How to cite: Bikše, J., Retike, I., and Stivriņš, N.: The first CO2 measurements by eddy covariance in natural and degraded peatlands in Latvia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18264, https://doi.org/10.5194/egusphere-egu26-18264, 2026.
Peatlands are complex ecosystems that provide a variety of ecosystem services, including the regulation of water. Peatlands also store huge amounts of carbon, thereby contributing to long-term climate protection. However, drainage and overuse are threatening these positive functions in many places. Consequently, numerous countries around the world have developed peatland conservation and restoration strategies. To support this process, monitoring approaches are needed that are suitable for areas that are difficult to access and for large-scale applications.
In previous studies, we conceptualized a methodology for the scalable monitoring of peatlands. A key component of this methodology is the automatic recognition of peatland plants. This methodology involves high-resolution drone images and metadata as input for an ecologically informed machine learning framework. We employ state-of-the-art deep learning segmentation architectures, such as DeepLabv3+ and OCRNet, which utilize a high-resolution network (HRNet) backbone. As a first step, we investigated the detectability of individual species, focusing on species for the initial class set for training the model.
Current project developments include expanding data-fusion strategies, model-architecture validation, and conceptualizing a new label strategy by introducing new vegetation class sets to address ecological issues and broaden applicability. We benchmarked multiple vegetation-classification architectures and optimized key hyperparameters via grid search to identify a robust domain-specific model. Auxiliary metadata (e.g., temperature sums, cloud cover) were integrated at different stages and early fusion (embedding metadata in the input data cube) techniques were compared with late-fusion approaches such as FiLM and feature weighting. Explainable AI was employed to identify the inputs that have the most significant impact on training and predictions. Vegetation indices (NDVI, EVI) were added as explicit input channels. As an additional target we evaluated plant dominance stand types instead of single species to better capture mixed stands. Furthermore, we expected dominance stands-based mapping to better support the integration into the GEST (Greenhouse-gas-Emission-Site-Type) approach and other applied peatland monitoring frameworks.
After classification, predicted vegetation/dominance patterns were combined with water-table maps. By using the GEST approach, spatially explicit peatland greenhouse gas emission estimates were derived and validated against a reference area. A Minimum Viable Product (MVP) combining vegetation maps, hydrological inference, and GEST-based emissions shall provide initial large-scale assessments of rewetting success and associated emission reductions. Further fields of application regarding the monitoring of ecosystem services and smart farming approaches for paludiculture will be investigated based on the results obtained.
How to cite: Ahlgrimm, T., Rossa, H., Husting, T. J., Trouillier, M., Bergheim, M., Oehmcke, S., Jurasinski, G., and Pönisch, D. L.: Advanced AI-Supported Peatland Vegetation Mapping using Remote Sensing for Environmental Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18584, https://doi.org/10.5194/egusphere-egu26-18584, 2026.
Peatland rewetting has been widely adopted in Germany as a climate-change mitigation strategy by reducing CO2 emissions from peat decomposition. However, rewetted peatlands may simultaneously act as sources of CH4 with varying strength. Quantifying their net greenhouse gas exchange remains challenging due to landscape heterogeneity and continuously changing vegetation, water table, and surface conditions following the rewetting. Therefore, sustained monitoring during and after the rewetting process is necessary.
In this study, we measured the fluxes of CO2 and CH4 at a rewetted peatland, Himmelmoor, in Northern Germany from 2015 to 2018, extending previous work conducted from 2012 to 2014. The site is characterized by heterogeneous terrain resulting from peat extraction and phased rewetting, which has been carried out alongside extraction activities since 2004. The objective is to provide updated greenhouse gas (GHG) estimates to assess how carbon dynamics have evolved in the rewetted section. Eddy covariance (EC) measurements of CO2 and CH4 fluxes were conducted at the center of the former mining area, allowing flux contributions from multiple surface classes, including rewetted area, vegetation strips, and bare soils to be captured within the source area of a single EC tower.
For CO2 fluxes, we apply and compare a wind-direction-based mechanistic source partitioning approach with a footprint-based source partitioning method to estimate flux contributions from multiple surface classes within the EC footprint, including the rewetted area, vegetated peat strips, and bare (non-rewetted) peat surfaces. For CH4 fluxes, a machine-learning framework based on an ensemble of multilayer perceptrons is used for gap filling and flux modeling of different surface classes. The model is driven by meteorological variables, optimally lagged predictors identified via cross-correlation, fuzzy seasonal and diurnal time variables, and class contributions derived from footprint analysis.
The processed fluxes are compared with previously published EC measurements from 2012 to 2014. Preliminary results based on tower view fluxes (without flux partitioning and gap filling) show that the mean CO2 flux during the summer months (July–September) ranged from −0.68 to −0.71 µmol m-2 s-1 for the year 2016 to 2018. In comparison, the mean summer CO2 flux in 2012 was 0.67 µmol m-2 s-1, indicating a substantial shift from a net CO2 source to a net CO2 sink. In contrast, mean winter (January, November, and December) CO2 fluxes for 2016 and 2017 were 0.70 and 0.66 µmol m-2 s-1, respectively, which are comparable to the 2012 value (0.66 µmol m-2 s-1). For CH4, the mean summer flux increased from 45 nmol m-2 s-1 in 2015 to 70 nmol m-2 s-1 in 2018, compared to 40 nmol m-2 s-1 in 2012, indicating a substantial increase in CH4 emissions following rewetting during the summer. Overall, the study suggests that rewetting reduced CO2 emissions while increasing CH4 emissions, providing new insights into the long-term impacts of peatland rewetting on climate and into the processing of EC flux data in heterogeneous landscapes.
How to cite: Zhao, Y., Holl, D., and Kutzbach, L.: Post-Rewetting Carbon Dynamics of a Temperate Peatland: Eddy Covariance–Based CO2 and CH4 Fluxes from Himmelmoor, Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19839, https://doi.org/10.5194/egusphere-egu26-19839, 2026.
There is increasingly demanding effort across Europe to restore degraded peatlands through rewetting techniques. While the measures of restoration by rewetting can effectively encourage the growth of peat-forming mosses, there have been concerns about peatland water quality problems due to the interactions between raised water table level and the release of nutrients such as ammonium (NH4+), nitrate (NO3-) and phosphate (PO43-), which may pose a significant risk to water quality in receiving water bodies. Studies have shown that drained peatlands have high concentration of DOC, NH4+, NO3-, PO43- and DON and rewetting is capable of reducing the concentration to near natural levels, however for NH4+ and PO43- studies have recorded elevated concentrations after full rewetting (WTD within 0 – 10cm) and less under partially rewetting ( WTD 20cm to surface) for which some researchers have suggested partial rewetting to curb internal release of nutrient on rewetted peatland.
To date, there is no peatland-specific water quality model. Although some existing water quality models have been applied to peatlands, the complex adaptive nature of peatland is oversimplified, e.g. complex interaction between fluctuating water table levels and biogeochemical processes that affect nutrients concentration are either neglected or not explicitly represented in the model. To address this knowledge gap, the objective of this study is to develop an integrated water quality model for peatlands, considering the advection-dispersion processes of solute transport, biogeochemical processes and related environmental factors that affect the evolution and variation of nutrients.
The solutions of the system of governing partial differential equations were implemented by using the finite volume method (FVM). While the overall solver was based on an explicit scheme, e.g. by using the Euler’s forward method, a second-order central differencing scheme was applied to discretise the dispersion term. In order to make the solver stable, the advection term was discretised using the second-order upwind total variation diminishing (TVD) method. The model was verified by comparing the numerical modelling results of a 1D benchmark problem with related analytical solutions, getting a coefficient of determination of R2 > 0.99. The 2D version of the water quality model has been coupled with DigiBog_Hydro model which simulates groundwater flow processes. This integrated water quality model will be applied to some selected bogs in Ireland which are under rehabilitation to investigate the hydrological and water quality responses to related restoration methods such as drain blocking and bunds creation. The results of integrated modelling will be compared to the experimental results of water quality measurements of related peatlands.
How to cite: Opoku-Agyemang, E.: Development of integrated water quality model for evaluating the peatland rehabilitation measures in Ireland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20516, https://doi.org/10.5194/egusphere-egu26-20516, 2026.
Peatlands are significant carbon sinks, heat waves and drought threatens their function as long-term carbon sinks. Detecting stress in Sphagnum-dominated peatlands before damage occurs is critical for conservation and carbon accounting. Here, we present a trial framework combining high-resolution hyperspectral remote sensing with machine learning to identify minimal spectral band sets and develop peatland-specific indices for early stress detection.
This study was at the Rzecin peatland in Poland (52°45'N, 16°18'E), a poor fen. Hyperspectral measurements (350–1000nm) were acquired across 13 plots over 22 measurement campaigns, using a Piccolo Doppio dual-field-of-view spectrometer. environmental monitoring included water table depth (WTD) from plots nine using TD divers and meteorological variables (air temperature, vapour pressure deficit, precipitation) recorded at half-hourly intervals from 2020–2024.
We defined heat stress events using a compound threshold approach requiring exceedance of the 90th percentile for both daily maximum air temperature (Tair > 29.1°C) and vapour pressure deficit (VPD > 2.90 kPa) for a minimum of three consecutive days. Drought stress was characterised by plot-specific 10th percentile WTD thresholds (site median: −25.4 cm) sustained for at least five consecutive days. Bootstrap resampling (n = 1,000) quantified threshold uncertainty, yielding 95% confidence intervals of 28.6–29.6°C for temperature and 2.71–2.99 kPa for VPD thresholds.
To address the hyperspectral multicollinearity, we applied correlation-based filtering (ρ > 0.98), reducing the original 921 spectral bands to 9 representative wavelengths while preserving spectral diversity. Recursive Feature Elimination with Random Forest, validated through leave-one-plot-out cross-validation to ensure spatial independence, identified an optimal subset of eight features: Water Index (WI), Photochemical Reflectance Index (PRI), Peatland Stress-Water Index (PSWI), Normalised Difference Red-Edge Index (NDRE), Peatland Drought Index (PDI), Normalised Difference Vegetation Index (NDVI), reflectance at 800 nm, and the MERIS Terrestrial Chlorophyll Index (MTCI).
We are trying to build a peatland-specific spectral indices. The Peatland Drought Index (PDI), calculated as (R705 − R750)/(R705 + R750), exploits the red-edge region's sensitivity to both chlorophyll content and leaf water status. The Peatland Stress-Water Index (PSWI), formulated as (R860 − R550)/(R750 − R670), combines NIR water sensitivity with red-edge slope normalisation. Permutation tests (n = 1,000) demonstrated that PDI significantly outperformed NDVI in detecting VPD-related stress (Δρ = 0.054, p = 0.009), supporting the development of ecosystem-specific rather than generic vegetation indices.
Random Forest and XGBoost classifiers achieved strong discrimination between stressed and non-stressed conditions, with areas under the receiver operating characteristic curve (AUC) of 0.836 and 0.851, respectively. The water-related indices (WI, PSWI, PDI) among top-ranked features underscores the primacy of hydrological stress in peatland ecosystems. Sensitivity analysis across varying threshold percentiles (85–95%) and duration requirements (2–7 days) revealed that stress classification varied up to 10-fold, emphasising the critical importance of transparent methodological reporting in peatland remote sensing studies.
Our findings demonstrate that reliable stress detection in our peatlands can be achieved with eight spectral features, enabling potential deployment on multispectral sensor platforms. This framework could offer a transferable approach for early-warning systems in peatland conservation, supporting climate adaptation strategies for these critical ecosystem.
Acknowledgement: Acknowledgement: Funded by NCN (2020/39/O/ST10/00775), NAWA (BPN/PRE/2022/1/00102), DDSA (2025‑5687), and PANGEOS (CA22136‑80fe26e2).
How to cite: Abdelmajeed, A., Antala, M., Feng, S., Frem, C. E., Stróżecki, M., Rastogid, A., Wang, S., and Juszczak, R.: Preliminary machine‑learning study of minimal hyperspectral bands for heat and drought stress in the Rzecin peatland, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21353, https://doi.org/10.5194/egusphere-egu26-21353, 2026.
The ITMS (Integriertes Treibhausgas Monitoring System) Sources and Sinks module, funded by the German Federal Ministry of Research, Technology and Space, develops modelling approaches to simulate greenhouse gas (GHG) fluxes in Germany at high spatial and temporal resolution. By integrating existing measurement data from national and Bavarian research initiatives with new field observations from natural, drained, and rewetted peatlands collected in the MODELPEAT project, we aim to refine statistical modeling approaches of peatland GHG exchange. While the current German national GHG inventory approach for landuse specific peatlands relies on functional relationships in dependency on water table depth and the type of organic soil (Tiemeyer et al. 2020), this project introduces a machine learning framework that leverages an extensive monthly dataset (approximately 190 site years) to capture peatland GHG dynamics in more detail. The poster presents the methodological implementation of a eXtreme Gradient Boosting (XGB) decision tree model, which incorporates predictors representing seasonal dynamics, vegetation activity, meteorological conditions, and management practices, along with initial findings. As the project progresses, the approach is aimed to be applied across Bavaria on a 30×30 m grid to generate spatially explicit simulations of peatland GHG fluxes (CO2, CH4, N2O). This work is essential for identifying emission hotspots and supporting the development of effective mitigation strategies.
How to cite: Braumann, F., Klatt, J., Friedrich, S., Blagodatsky, S., Scheer, C., Kiese, R., and Drösler, M.: Modeling of Greenhouse Gas Emissions from Peatlands in Germany: A Machine Learning Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21947, https://doi.org/10.5194/egusphere-egu26-21947, 2026.
Knowledge about the extent, distribution and the state of drained peat soils as well as the suitability for measures to promote biodiversity and terrestrial carbon storage is urgently needed for the formulation of restoration plans for organic soils in the context of the EU nature restoration regulation. Recently, a probability map showing the potential distribution of peat soils in Austria was developed via modelling within the project MOIST (Forstner et al., in preparation). This map predicts the likelihood of occurrence of peat soil based on physical properties such as soil properties, climate, relief features, vegetation indices, parent material. However, factors like current land use intensity or drainage were not considered in the probability map. For the localisation of areas potentially relevant for the improvement of biodiversity and carbon storage via restoration measures, a suitability assessment was developed within the same project by an expert-based approach. It categorizes areas by their potential suitability for restoration depending on factors such as land use intensity and drainage. So far, the probability map and the suitability layer have not yet been combined to localise areas suitable and important for restoration measures.
In this study we want to methodologically analyse the combination of the probability map with the suitability layer. The results will be critically compared with other approaches for localising peat soils, e.g. the map of organic soils developed for the Austrian greenhouse gas inventory and the Mire Inventory Austria, as well as European maps. The study will compare the extent of (drained) peat soils in NUTS3-Regions in Austria determined by the different approaches and critically discuss differences and uncertainties. Furthermore, the predictions will be compared with ten on-site data collections.
The analysis will help to identify limitations and potentials of the probability map and the suitability layer for effectively and correctly using these tools to support decisions and planning restoration measures.
How to cite: Hummel, C., Forstner, S., Brunner, T., Schwarz, M., Schwaighofer, I., Weiß, M., Haslmayr, H.-P., and Weninger, T.: Mapping Peat Soils and their Restoration Potential in Austria: Comparing Approaches and Limitations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22920, https://doi.org/10.5194/egusphere-egu26-22920, 2026.
