Accurate mapping is necessary for effective management of peat soils to help reduce GHG emissions and improve environmental quality. However, mapping peat soils remains a major challenge: definitions of peat soils vary substantially, field data are sparse and difficult to produce, and remote sensing of limited use for converted peatlands. Using an Adaptive Mapping Framework developed for the Derived Irish Peat Map, this study describes our work to update the map with refined and new datasets. These include incorporation of areas considered to be underlain by shallow peat soils (≥ 10 cm and ≥ 8.6 % Organic Matter content), and augmentation of the overall test dataset with an additional ~ 20,000 points.

The workflow for map generation employed 20 Decision Tree Output Maps (DTOMs), aggregated into 33 Map Combinations (MCs). The MC selected for the update had the highest accuracy metrics (≥ 80 %), consisting of DTOMs with a user accuracy ≥ 60 % and assessed over a minimum number of test points ≥ 50. The resulting map reveals peat to underlie 1.66 M ha of Ireland (~ 23.3 % of the country), with an overall accuracy of 84 % and a F1 score for peat areas of 85 %. This extent is 13.2 % larger than that delineated in previous versions and at least 18.8 % larger than areas presented in other previous maps. The methodology also allows transparency from which data sources the different peat layers of the new map are coming from and to distinguish different peat thickness ranges (≥ 10 cm, ≥ 30-40 cm).

We demonstrate the utility of the mapping framework to facilitate the production of a more reliable peat map than previous mapping attempts. This approach has potential relevance for peat mapping elsewhere, in areas containing disparate datasets (e.g., land cover, soil map, etc.), covering different time periods, or employing different production methods. The accuracy metrics generated also suggest that the approach can be used as a basis for implementing or updating European and national regulations concerning carbon-rich soils in comparable settings to those encountered in Ireland.

How to cite: Gilet, L., Morley, T., Flynn, R., and Connolly, J.: An adaptive mapping framework for the management of peat soils: a new Irish Peat Soils Map., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-720, https://doi.org/10.5194/egusphere-egu24-720, 2024.

National and international climate change mitigation plans require a knowledge of peat soil extent across large geographic areas. Peat soils, which play a vital role in carbon storage and climate regulation, have a physical margin where soils change from high to low organic content. Accurate delineation of both national extent of peat soils and peat to mineral soil transition is required for assessing land use and planning effective conservation and carbon loss mitigation strategies. This abstract presents a novel approach for defining both peat soil extent nationally and transition zones between peat and mineral soils at field scale.

At a national scale, peat soil maps are created using optical satellite remote sensing or legacy soil/quaternary maps or a combination of both. However, optical remote sensing cannot detect peatlands under landcover such as forest or grassland and legacy maps are often created from sparse in-situ auger data making the accurate delineation of the boundary between peat and mineral soils difficult and cost prohibitive.

Airborne radiometric data, which measures natural environmental radiation, has been shown to differentiate between peat and mineral soils due to high attenuation of gamma rays in organic soils. Radiometric data is considered a direct measurement of the subsurface and so is minimally affected by landcover. Additionally, as airborne radiometric data can be acquired in a spatially consistent manner, it has the potential to identify areas of peat soil across the landscape and highlight areas of transition between high and low organic soils.

In Ireland, the Tellus survey, acquired by Geological Survey Ireland (GSI) aims to acquire airborne data (including radiometric data), consistently across the country (flight line spacing of 200m) at a resolution of 50 x 50 m. Utilising this national radiometric dataset, a machine learning classification methodology is presented. Data are classified as peat (> 30 % organic material) or non-peat, with 85 % accuracy, is validated using a national soils sampling survey. A confidence value is extracted, once data are classified, which results in the identification peat soils. Several field sites across the midlands of Ireland, which are located at verified transition zones, are then used to show the effectiveness of the classification at identifying transition zones at the field scale.

The methodology is robust and can be applied in all areas where these data exist. The results highlight that inclusion of an airborne radiometric dataset in a national climate plan can be used to update national and international carbon inventories of peatlands areas and inform European policy. Understanding the location of these peat to mineral soil transitions is paramount when considering the impact on climate change mitigation strategies such as potential impact of rewetting of peat soils.

How to cite: O Leary, D., Connolly, J., Gilet, L., Tuohy, P., Hodgson, J., and Daly, E.: Identifying the Transition Zone between Peat and Mineral Soils Using Airborne Radiometric Data: a national scale case study from Ireland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17791, https://doi.org/10.5194/egusphere-egu24-17791, 2024.

Peatland plays a significant role in methane (CH₄) emissions, and methane dynamics are governed by ecohydrological variables and site heterogeneity. Emission quantification from different stages of peatland is vital to understanding the impacts of peatland on climatic feedbacks for effective rehabilitation of these sensitive ecosystems. Chamber measurement and eddy covariance techniques are widely used to understand methane dynamics. These measurements are either at a point or footprint scale, making it challenging to upscale these emissions to the site scale considering the heterogeneity of peatlands. Here, we present a simple approach to upscale methane emissions from closed chambers using PlanetScope high-resolution satellite data along with the random forest algorithm and weighted-area approach. This methodology was tested at three peatlands covering near-natural, under-rehabilitation, and degraded sites in Ireland for a span of two years. The annual vegetation maps were mapped with an accuracy of 83% at the near-natural site and around 98-99% at the under-rehabilitation and degraded sites. The highest site-scale fluxes were observed at the near-natural site (2.25 and 3.80 gC m⁻² y⁻¹), and the site-scale fluxes were close to net zero for the under-rehabilitation (0.17 and 0.31 gC m⁻² y⁻¹) and the degraded site (0.15 and 0.27 gC m⁻² y⁻¹). As a step forward, this approach will be applied to upscale eddy covariance fluxes from three fen sites in the Netherlands. Overall, the easy-to-implement methodology proposed in this study shows potential to apply it across various heterogeneous land-use types to assess the impact of peatland rehabilitation on methane emissions.

How to cite: Ingle, R., Saunders, M., Habib, W., Connolly, J., Bataille, L., Hutjes, R., Biermann, J., Jans, W., Franssen, W., vander Poel, L., and Kruijt, B.: A simple approach to upscale methane emissions from peatlands using Planetscope satellite data and machine learning algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18342, https://doi.org/10.5194/egusphere-egu24-18342, 2024.

Peatland afforestation and drainage are major causes of upland peatland degradation and have resulted in ongoing issues including increased flood risk, biodiversity loss, and carbon emissions. The last decades have witnessed increasing global investments in peatland restoration, as exemplified in South Wales. Here, the peatlands of Pen y Cymoedd coexist as the UK’s highest altitude windfarm and are being restored post tree-felling through a process known as ‘forest-to-bog’ restoration. Yet, there is a definite need to improve understanding of the impacts and effectiveness of these interventions, which can be gained through the mapping and representation of peatland structure and in particular, its ecohydrological properties.

Traditional peatland investigations involving manual probing and coring are environmentally intrusive and time and labour intensive. However, recent studies have demonstrated that geophysical approaches such as ground penetrating radar offer an alternative approach, enabling peat depth to be rapidly surveyed over large areas. Afforested peatlands, however, present new challenges for both radar and probe-based approaches, for example, the presence of tree roots can obstruct the probe from reaching the true depth of the peat body and create complex reflectors on the radargram. There remains little guidance on appropriate use of ground-penetrating radar methodologies in afforested peatland settings, particularly on peatlands that have different hydrogeophysical properties resulting from various land use interventions.

In this study, ground-penetrating radar surveys were conducted on peatland sites representing four different condition states: intact, afforested, felled, and restored. The surveys aim to map peat depth and explore the structure of the shallow subsurface. We adjust the parameterisation and processing flows involved in ground-penetrating radar surveys to determine the most appropriate approach dependant on peatland condition and the purpose of survey. Furthermore, by comparing reflection properties from different peatland sites which were selected to replicate the successive stages of forest-to-bog restoration, the structural changes caused by forestry and subsequent restoration attempts are revealed. This research will therefore help to inform operational best-practice and policy of peatland restoration, both within and beyond Wales.

How to cite: Hughes-Dowdle, L., Kulessa, B., Murray, T., Walker, J., Low, R., Cox, R., and Pickard, J.: The Use of Ground-Penetrating Radar for Mapping Peatland Subsurface in Afforested Peatland Restoration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18973, https://doi.org/10.5194/egusphere-egu24-18973, 2024.

Peatlands, which cover a significant proportion of the wetland ecosystems globally, play a vital role in maintaining biodiversity and regulating water and climate. However, these ecosystems are currently undergoing degradation as a result of human activities, particularly the draining of peatlands for agricultural purposes, peat extraction, and forestry. Irish raised bogs, which constitute over half of the EU's oceanic raised bogs, have been extensively drained for various land-use activities. Efforts are being made to conserve these ecosystems by implementing measures such as rewetting, restoration, and rehabilitation. However, this requires the identification and accurate mapping of artificial drainage ditches. This study uses a U-net-based convolutional neural network to develop a very high-resolution map of the artificial drainage network in Irish raised bogs, covering an area of 523,000 hectares. The map also quantifies drainage in different land-use categories, such as industrial and domestic peat extraction. The results of this study will aid in implementing conservation activities, such as drain blocking to promote rewetting and improve carbon and greenhouse gas emission accounting at the national scale.

How to cite: Habib, W. and Connolly, J.: Automated Mapping of Artificial Drainage in Peatlands Using Deep Learning and Very High-Resolution Aerial Imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21619, https://doi.org/10.5194/egusphere-egu24-21619, 2024.

While we are aware that the Italian Alps host thousands of small peatlands, the precise estimate remains uncertain due to the absence of a comprehensive map. These ecosystems are extremely valuable because, in addition to storing large amounts of organic carbon, they provide many other ecosystem services. They regulate water flow, retaining it during wet seasons and releasing it during dry periods. Furthermore, they purify water by retaining nutrients such as nitrogen and phosphorus and provide water to wildlife even during droughts. Moreover, they are characterized by high biodiversity, serving as habitats for several endangered species.

Conventional approaches to mapping peatlands typically involve surveys characterized by long update cycles and considerable costs. Some remote sensing approaches, such as UAV and aerial photography, have the disadvantages of being weather dependent, and have high costs and limited coverage. In contrast, satellite remote sensing imagery presents several advantages, including broad coverage, cost-effectiveness, and frequent temporal resolution. Hence, our research emphasizes the mapping of Alpine peatlands by integrating multiple remote sensing datasets and employing machine learning algorithms. The spatial distribution of Alpine peatlands shows a correlation with topographic and hydrological conditions. These peatlands, averaging around 1 hectare in size, exhibit distinctive vegetation, topographic, and hydrological characteristics compared to non-peatland regions. Therefore, the differentiation in these features extracted from remote sensing imagery stands as a critical factor for identifying peatlands.

We present the results of integrating Sentinel-2 optical data, Sentinel-1 radar imagery, and the CLO-30 from the Copernicus digital elevation model (DEM) through the Google Earth Engine (GEE) platform. This integration aims to map Alpine peatlands employing a pixel-based Random Forest algorithm. We focus on a section of the Adige River basin, located within the Trentino Alto-Adige Region in Italy. Within this area, we collected and updated an inventory of 157 peatland sites, divided into two groups. One subset was used to calibrate the algorithm, while the other served to validate the results. Several sets of features were extracted from the multi-source remote sensing dataset. The findings suggest that both the DEM itself and the topographic features derived from it contributed most significantly to the classification results. Hydrological connectivity was also found to be a significant feature, probably due to the crucial role that water flow and retention play in the establishment and sustainability of peatlands. A key finding is the impact of these features surpassed that of optical and radar data in enhancing the accuracy of the classification. Since our peatland mapping methodology is implemented on the GEE platform and uses freely available datasets, it can be applied across the entire Alpine region and in other mountainous areas worldwide.

How to cite: Li, Q., Singh, M., and Silvestri, S.: Mapping Italian Alpine Peatlands Using Multisource Satellite Imagery and Machine Learning Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13540, https://doi.org/10.5194/egusphere-egu24-13540, 2024.

Peatlands, covering 3% of the global land area, store twice the carbon of all the world's forests combined, acting as crucial carbon sinks. However, under varying environmental conditions induced by global warming and land cover changes, they can transition into carbon sources. Monitoring gas exchanges in peatland ecosystems involves employing the eddy covariance method, often interpreted using flux footprint models. This study focuses on the application of the FFP model, specifically addressing the influence of spatially varying roughness parameters.

Utilizing Unoccupied Aerial Vehicle (UAS)-based high-resolution LIDAR data, we incorporated spatially varying roughness values into the FFP model, comparing the results with traditional scalar roughness length values. Our findings reveal that spatially varying roughness introduces spatial heterogeneity, resulting in more irregular and smaller footprints. The inclusion of spatially varying roughness based on the surface reduced the area contribution to fluxes by 40%, emphasizing the significance of accounting for this spatial variability.

Moreover, we investigated the impact of surface and terrain conditions on footprint modeling in a peatland previously subjected to extraction. Our analysis indicates that variations in terrain (both natural and extraction-induced) reduced the footprint contours by 18% compared to the original FFP model footprints. This underscores the importance of considering terrain changes in footprint modeling, especially in peatlands with a history of extraction activities.

In conclusion, this research enhances our understanding of (1) the impacts of spatially varying roughness on modeled footprints and (2) the influences of surface and terrain on footprint size in peatland ecosystems. These insights contribute to improved modeling accuracy and aid in effective carbon management strategies for peatland conservation.

How to cite: Lehmann, J. R. K., Arulmozhi Nambi, V., Giese, L., Meyer, H., and Gharun, M.: Integrating UAS-based lidar data in eddy covariance flux footprint modelling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9398, https://doi.org/10.5194/egusphere-egu24-9398, 2024.

Being able to identify, map and monitor areas of different ecological quality of peatland habitats, or ecotypes, provides important information on spatial peatland condition, the potential for restoration of degraded areas and ecotype carbon (C) emission and/or sequestration capacity when coupled with known C-flux factors. Regular and accurate mapping of such ecotypes is also a requirement under the European Union (EU) Habitats Directive, and will be required in some form to help guide the framework and implementation of the upcoming EU Nature Restoration Law.

Although the most precise way to identify the presence of certain ecotypes is via in-situ surveying, this approach clearly suffers from scaling issues, as it is only feasible in small selected peatlands (or even portions of them) and requires a lot of resources, e.g. skilled domain experts and time. A solution might come from remote sensing and Earth Observation technologies, which have been increasingly utilised to map the occurrence and extent of peatland environments in recent years. With this respect, the European Space Agency's Copernicus Program's Sentinel-2 satellite constellation could be a viable data source, as it allows for a multi-spectral, systematic and regular coverage of land surfaces with a spatial resolution up to 10 square metres and 5 days of revisit frequency. Nevertheless, the remote detection and mapping of ecotypes within the peatland complex itself is relatively under-studied and there is no currently accepted method that can be deployed at landscape scale. 

In this work we present a rather simple machine learning pipeline for ecotype detection at scale. The focus of this study are lowland peatlands, or raised bogs, in the Republic of Ireland. The pipeline assumes the existence of ground truth ecotype data (for machine learning training purposes), raised bogs map boundaries (shapefiles) and Sentinel-2 imagery. Both training, testing and validation datasets undergo the same pre-processing procedure. In the training step we train an ensemble of binary classifiers - specifically one multilayer perceptron network per ecotype - organised in a hierarchical fashion, to reduce the complexity of the problem. The ecotype classification would be done in a cascade - in accordance with the hierarchy - via canonical ensemble learning classification. 

The preliminary results gathered not only seems to indicate that our approach could provide reliable estimations about raised bog ecotype composition at scale, they also highlight the potential need for seasonal ensembles. Furthermore, we will present the results of a crowdsourcing experiment, in which domain experts were: (1) presented with ecotype map images, resulting from the inference of a plethora of ensemble classifiers of different settings and hyperparameters, and (2) asked to cast a vote on which image most closely resembled the related ground truth image.

How to cite: Grappiolo, C., Gurusiddappa, V., Regan, S., Boydell, O., and Holohan, E.: A Satellite-derived Peatland Ecotype Classification Method Using Artificial Neural Network Hierarchical Ensembles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19643, https://doi.org/10.5194/egusphere-egu24-19643, 2024.

Sustainable management of non-timber forest products, as a means to increase the value of standing forest, has long been a goal of conservation in the tropics. However, there are few studies of the long-term ecological, social, and economic impacts of sustainable management initiatives. This study addresses this issue in the context of fruit harvesting of the arborescent palm, Mauritia flexuosa. In Amazonia, M. flexuosa grows naturally at high densities in carbon-rich peatland ecosystems and its fruit is an important resource for local communities. Typically, the fruit has been harvested by felling the trees. However, over recent decades, some communities have adopted climbing techniques to harvest the fruits. We analyse for the first time the potential of M. flexuosa populations and fruit production to recover in two rural communities in Peruvian Amazonia where climbing palms was adopted between 1999 and 2002. Since then, these communities have been supported by conservation and development projects.

In both communities, we conducted interviews to assess the perceptions of change after the introduction of climbing and carried out forest inventories to estimate changes in two socio-economic indicators (volume of harvested M. flexuosa fruits and income) and three ecological indicators (pole stem density of M. flexuosa, seedling and sapling density, and the sex ratio of adult palms). Our results highlight the positive impacts of the use of climbing to harvest fruits on a range of both ecological and socio-economic indicators in these communities. These results demonstrate that sustainable fruit production is a viable way to conserve the forests, the high carbon stocks beneath the ground and the livelihood of people living in these ecosystems. These findings therefore will be of interest to a wide range of researchers, policymakers, and practitioners seeking to promote sustainable practices in these, and similar, ecosystems across the world and provide support for community-led conservation across the tropics.

How to cite: Honorio Coronado, E. and the Research team: Long-term interventions by conservation and development projects support successful recovery of tropical peatlands in Amazonia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1164, https://doi.org/10.5194/egusphere-egu24-1164, 2024.

Tropical peatlands contain approximately 17% of the total global peat carbon and are under pressure for deforestation and the formation of oil palm plantations. The conversion of large peatland forests in Malaysia and Indonesia has resulted in these plantations becoming substantial sources of greenhouse gases. While previous research has focused on estimating the C loss from the soil, the impact of drainage ditches on the overall C budget remains largely unexplored. However, on average, drainage ditches with free surface water cover roughly one-third of the total drained land. Hence, these ditches could be significant CO₂ and CH₄ sources and while not considered for C budget calculation it could lead to significant underestimation of total C loss from these ecosystems. Here we represent the CO₂ and CH₄ emissions from drainage ditches in an oil palm plantation located in Sarawak, Malaysia. CO₂ and CH₄ samples (n=107) were collected from a recently created plantation (~5 y.o.) and from the plantation, which is under second rotation using a floating chamber and LI7810 analyzer (LICOR Biosciences). Additional parameters such as water pH, electrical conductivity, dissolved oxygen concentration, temperature, turbidity, salinity, water level, and dissolved gas concentration (dCO₂ and dCH₄; analyzed in the lab with GC-2014, Shimadzu) were measured from each sampling spot. After measurements, we collected sediment samples for soil TN, TOC, TIC, DOC, DIC, and DN analyses. The results revealed that the average net CH₄ flux (combining both diffusive and ebullitive emissions) from drainage ditches in the first and second rotations was 0.31 ± 0.65 g m^-2 d^-1 and 0.29 ± 0.54 g CH₄-C m^-2 d^-1, respectively. The average CO₂ flux from the first and second rotations was 4.27 ± 2.1 g CO₂-C m^-2 d^-1 and 4.4 ± 2.5 g CO₂-C m^-2 d^-1, respectively. To estimate surface water coverage at the whole site, green vegetation, open water, and bare soil were mapped from the site drone imagery collected in Spring 2023 using object-based supervised classification and spectral indicators computed from red, green, and blue image bands. The total surface water coverage will give us an understanding of the total CO₂ and CH₄ flux in the entire region that originates from drainage ditches. Our results strongly underscore the significant role of drainage ditches in contributing to the overall carbon loss from oil palm plantations on organic soils. Proper consideration of these emissions is essential for accurate carbon budget calculations and for devising effective strategies to mitigate greenhouse gas emissions in these ecosystems.

How to cite: Kasak, K., Soosaar, K., Dronova, I., Melling, L., Wong, G., Sangok, F., Ranniku, R., Villa, J., and Mander, Ü.: Large CO2 and CH4 emissions from drainage ditches in oil palm plantations on peat soil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4700, https://doi.org/10.5194/egusphere-egu24-4700, 2024.

Tropical peat swamp deforestation followed by extensive drainage and recurrence of fires leads to peat subsidence and subsequently carbon release to the atmosphere. While many previous studies have assessed the positive relationship between greenhouse gas (GHG) emissions and peat subsidence, an accurate field-based measurement of peat subsidence remains methodologically challenging. Between 2015 and 2020, we monitored peat subsidence (surface elevation change) in tropical peatlands of Central Kalimantan by using Rod Surface Elevation Table (RSET) installed across 22 locations representing range of land use types, including natural forest as reference. We observed that the largest net surface elevation loss was found in burned areas of -35.1 ± 87.2 mm yr^-1, followed by the drained peatland sites with -11.1 ± 16.9 mm yr^-1 and the agricultural impacted sites with -6.3 ± 13.1 mm yr^-1. Further, we observed substantial net surface elevation loss in secondary protected forests by -12.1 ± 77.2 mm yr^-1. By contrast, natural forest reference experienced net surface elevation loss as much as -8.8 ± 24.4 mm yr-¹. Our findings suggest that all the study sites in the tropical peatlands of Central Kalimantan have experienced net surface elevation loss with their degree of losses vary depending on past land use and current land management.

Keywords: peat subsidence, peat compaction, peat drainage, peat conversion, GHG emission

How to cite: Murdiyarso, D., Saragi-Sasmito, M. F., Sasmito, S. D., Rumbang, N., and Jaya, A.: How does land use change impact tropical peatland surface elevation changes? , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7452, https://doi.org/10.5194/egusphere-egu24-7452, 2024.

Tropical peat swamp forests are crucial global carbon (C) reserves. Prevailing waterlogged conditions in peat soils prevent the complete decomposition of dead plant material. As a result, more organic matter is produced than decomposed, leading to the gradual accumulation of peat. However, the destabilisation of tropical peatlands through climate warming, droughts, and changes in land use threaten this C sink capacity. Anaerobic conditions in peat soils lead to methane (CH₄) production through decomposition and nitrous oxide (N₂O) production under moderate levels of soil oxygen content. Earlier evidence suggests that tree stems in tropical peat swamp forests are significant sources of CH₄; however, little information is available on their exchange of N₂O.
This study investigated CH₄ and N₂O exchange of soil and stems of Combretocarpus rotunditus and Shorea albida trees in a peat swamp forest in Sarawak, Malaysia, from September 2022 to September 2023. To describe the temporal dynamics of greenhouse gas (GHG) exchange, we measured gas fluxes from the soil and stems at different heights (10, 80 and 170 cm from the tree's base) using the manual static chamber method and spectroscopic gas analysis. The chemical composition of the soil was analysed and several environmental parameters, including groundwater level, soil moisture content, soil and air temperature, were simultaneously measured with the GHG fluxes to determine the relationships between the fluxes and environmental factors.
Soil CH₄ emissions varied between 52.3 and 807 μg C m⁻² h⁻¹, with higher values observed during the wet season in conjunction with higher groundwater levels. On the other hand, the soil N₂O fluxes were relatively low and did not show a distinct seasonal pattern, ranging from -1.33 to 3.54 μg N m⁻² h⁻¹. Annual average soil CH₄ and N₂O emissions were 392 μg C m⁻² h⁻¹ and 0.65 μg N m⁻² h⁻¹, respectively. The highest average stem CH₄ emissions (1.48 μg C m⁻² h⁻¹) were recorded at the lowest parts of trees, with a vertical decrease in emissions and an overall uptake observed at the highest measurement point. In contrast, stem N₂O emissions were small, with no clear trend with measurement height.

In summary, we observed moderate and variable soil CH₄ emissions with limited generalisable relation to measured environmental parameters. Soil and stem N₂O emissions were relatively small. These results indicate the need for further comprehensive soil and stem GHG analyses in tropical peat swamp systems to better understand the GHG dynamics of this critical ecosystem.

How to cite: Soosaar, K., Melling, L., Ranniku, R., Sangok, F. E., Pärn, J., Wong, G. X., William, S. K., Kasak, K., Espenberg, M., Öpik, M., and Mander, Ü.: Methane and Nitrous Oxide Fluxes in Soil and Stems of Malaysian Tropical Peat Swamp Forest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14157, https://doi.org/10.5194/egusphere-egu24-14157, 2024.

The chemistry of tropical peat has been shown to vary significantly according to differences in plant litter chemistry and hence the composition of the peat-forming vegetation. In the Central Congo peatlands, peat forms under two forest types; hardwood and palm swamp forests.

The complex chemistry of the organic matter (OM) in peat involves multiple compounds including; carbohydrates, cellulose, lipids, lignin and various secondary metabolites, each with different decay rates. Thus, the chemical nature of the OM determines its recalcitrance. Likewise, due to both chemical and structural differences between plant material from different species and between different litter components, not all plant fractions are conducive to or make a similar contribution to peat production. Previous studies suggest there is a relatively greater accumulation of root material in tropical peats compared to other fractions due to its high lignin content, which renders it more resistant to decay, but linking the physiochemical properties of tropical peats and their decomposability to the botanical origins of the plant litter remains understudied. As a result, there are significant gaps in our knowledge regarding the links between plant litter inputs, peat organic geochemistry and our understanding of their role in both peat formation and GHG emissions.

Important recalcitrant moieties such as lignin can be analysed via geochemical analytical methods such as pyrolysis GC-MS, to provide insights into peat composition and vegetative origin. Lignin is an abundant and complex class of organic polymer, that forms key structural plant tissues. Categorised into three monolignols: coniferyl alcohol (Guaiacyl type; G), sinapyl alcohol (syringyl type; S) and p-coumaryl alcohol (p-hydroxyphenyl type; P). The amalgamation of these monolignols results in the creation of complex and diverse lignin structures, related to plant physiology for example between monocot and dicot angiosperms and to tissue type e.g. woody and non-woody. Consequently, key vegetation types exhibit varying concentrations of these monolignols, resulting in variations in relative proportions of G, S & P – which have identifiable pyrolysis signatures, and thus can be used to differentiate between types of lignin in the Congo peat. By examining their relative concentrations, this method allows the discrimination of plant inputs and their subsequent influence on peat organic geochemistry.

This study aims to characterise the organic geochemistry of peat from various locations across the Congo Basin; investigating the vegetative origin of the peat and OM transformations in both palm and hardwood dominated swamp forests. Initial results demonstrate distinct chemical (pyrolysis) signatures reflective of plant inputs and type, leading to discernible variations in peat chemistry over short distances and significant differences in the lignin composition corresponding to hardwood and palm dominated peat. The relevance of these findings for improved understanding of peat formation in this location is discussed.

How to cite: Robinson, K., Page, S., Girkin, N., Duffy, L., and Boom, A.: Deciphering Congo Peat Chemistry; Using Plants to Understand the Peat., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18587, https://doi.org/10.5194/egusphere-egu24-18587, 2024.

Peatlands, occupying just 3–4% of the Earth's surface area, are remarkable for holding nearly 30% of the world's terrestrial carbon (C), securely stored in their soil. These ecosystems are incredibly diverse, found from the Arctic to the Tropics and at various elevations. They perform numerous critical functions and ecosystem services, crucial for achieving the Sustainable Development Goals.

The 2022 Global Peatland Assessment reported over 500 million hectares of peatlands worldwide, emphasising the importance of evaluating their baseline status and routinely assessing their conditions. This is vital for the conservation of these significant ecosystems. While global and national extent maps of peatlands exist, there's a notable gap in information regarding global peatland thickness and carbon stock.

This study aims to perform a preliminary evaluation of global peatland thickness and carbon stock by employing digital mapping techniques. We gathered over 5,000 data points on peatland characteristics (including thickness, carbon content, and bulk density) from existing observations and maps worldwide.  We combined these observations with spatial data from earth observations representing climate, topography, and vegetation as covariates for use in machine learning methods to explicitly estimate peatland thickness and carbon stock globally at a 1 km resolution. The outcome of this work provides a first comprehensive global quantification of peat thickness, carbon content, and stock, aiding in the global modelling of peatland status and conditions.

How to cite: Widyastuti, M. T., Padarian, J., Maggi, F., and Minasny, B.: Mapping global peatlands thickness and carbon stock, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21989, https://doi.org/10.5194/egusphere-egu24-21989, 2024.

While mapping peatlands worldwide remains an important task, capturing their status using earth observation technologies has received less attention. Approximately 500,000 km² of degraded peatland worldwide contribute an excessive 5% of global greenhouse gas emissions. Most human use of peatlands remains unsustainable and can disrupt the balance of peat, water, and vegetation that maintain a stable or even growing peatland. With growing threats like the climate crisis and a need for safe water supplies and other ecosystem services, the restoration of degraded areas becomes ever more eminent. Standardized degradation classifications and land cover mapping techniques that address the severe outcomes of degraded peatlands are important tools but lacking.

The temperate and boreal zones of Northern America, Europe and Asia host a large proportion of the worlds’ peatland area. While temperate regions are often densely populated, causing high pressures on peatlands, the far north is facing increasing challenges such as permafrost melt, intensification of fire, mining, and wood harvesting. Based on a Web of Science literature search, this review identified and analyzed articles with a focus on peatland degradation research using remote sensing. 115 articles with study areas across the northern hemisphere were identified. Using a new approach to cluster this research based on a three-dimensional cube, each study’s degradation foci were evaluated along three directions of peatland degradation that build the three directions of the cube: peat, hydrology, and vegetation.

Five clusters of different weights emerge from this concept, including two-dimensional and three-dimensional research. Vegetation-focused research dominates, while there are only few holistic approaches (12 of 115) that address peatland degradation along all three dimensions. Almost 80% of all research papers between 1981 and 2023 were published on eight hotspot regions across the northern hemisphere. While there is a general increase in article numbers over the last years, publications from other countries decrease. Restoring peatland ecosystem functions after degradation presents a significant challenge. With this review, we aim to highlight cold- and hotspots of research with regard to geography, research topics and used remote sensing tools to help improving peatland degradation research using remote sensing.

How to cite: de Waard, F., Barthelmes, A., Joosten, H., Connolly, J., and van der Linden, S.: Remote sensing of peatland degradation – a review on gaps and hotspots of research across the northern hemisphere, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22001, https://doi.org/10.5194/egusphere-egu24-22001, 2024.

Tropical peatlands, covering approximately 23 million hectares, constitute 6% of the global peatlands, predominantly situated in low-lying coastal regions of Indonesia, Malaysia, Borneo, and Papua. Unfortunately, due to land-use change and accompanying subsidence, these low-lying coastal peatlands may be inundated with seawater due to sea level rise in response to climate warming in the coming decades. This would not only result in carbon losses in fluvial form but may also alter the biogeochemistry of peat, thereby affecting the peat decomposition process. Therefore, in this peat incubation study, we attempted to simulate the effect of saltwater intrusion on CO₂ emissions under a factorial setup of two salinity levels (15 ppt, 30 ppt), tidal cycle (high tide as flooded peat vs low tide as mesic peat), and labile carbon mimicking plant root exudates (in the form of glucose addition @ 0.1 mgC/g of peat/day) with peat incubated at 28 °C (the long-term average temperature at site).

            We found that salinity and carbon addition significantly (p < 0.01) affected the rate of CO₂ emissions with the highest mean values for treatment with 30 ppt salinity (251.7 ± 61.3 mgCO₂/g of peat/hr), which was approximately three times higher than the control (72.3 ± 9.3 mgCO₂/g of peat/hr). Similarly, we found that the mesic peat (reflecting low tide) showed almost twice the mean CO₂ values (150 ± 36 mgCO₂/g of peat/hr) compared to flooded peat (79.9 ± 15.1 mgCO₂/g of peat/hr). These results underscore the vulnerability of these ecosystems to future sea level rise, potentially transforming them into a significant carbon source. The urgency to conserve these vital terrestrial carbon reserves is further emphasized by the implications of our study, emphasizing the need for proactive measures to mitigate the impact of land use and climate change on tropical peatlands.

How to cite: Akhtar, H., Jaafar, S. M., Sukri, R. S., and Lupascu, M.: Saltwater intrusion may aggravate carbon loss from tropical peatlands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14254, https://doi.org/10.5194/egusphere-egu24-14254, 2024.