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An adaptive mapping framework for the management of peat soils: A new Irish peat soils map
Abstract
We used disparate data in a decision tree to refine the mapping of Irish peat soils.
Peat Associated Landcover Class concept was applied to locate converted peatlands.
Shallow peat soils ≥ 10 cm were included in the new Irish Peat Soils Map (IPSM).
The IPSM shows a larger peat area than previous peat maps and accuracies ≥ 74 %.
The Adaptative Mapping Framework implemented can be applied in other regions
... The conversion of peatlands to other land use categories can result in peat soils being hidden or masked below the landcover e.g., grassland and forestry (Gilet et al., 2024;O'Leary et al., 2022). Additionally, the peat soil is often too thin for industrial peat extraction or mixed with mineral soil (composed of weathered geological material, i.e., sand, silt and clay), making them uneconomic for energy production. ...
... These peat soils tend to be located at the edge of commercial peatland sites, which generally are not identified on national scale maps. These transition zones from peatland to mineral soil are currently underrepresented in inventories and are only recently included in national peat soil maps Gilet et al., 2024) despite their importance in the overall carbon budget. Recent studies are starting to identify these transition zones more accurately (Beamish and White, 2024;Gilet et al., 2024), and more work is needed at various scales to represent these geographic areas for policy level planning and climate mitigation strategies. ...
... These transition zones from peatland to mineral soil are currently underrepresented in inventories and are only recently included in national peat soil maps Gilet et al., 2024) despite their importance in the overall carbon budget. Recent studies are starting to identify these transition zones more accurately (Beamish and White, 2024;Gilet et al., 2024), and more work is needed at various scales to represent these geographic areas for policy level planning and climate mitigation strategies. ...
Peat soils are high in soil organic matter (SOM) and are recognised stores of carbon. Knowledge of the spatial distribution of peat soils is becoming the focus of many studies and is related closely to peatland mapping. Accurate maps of peat soils have many applications of international importance e.g., gaseous emission inventory reporting or soil organic carbon stock accounting. Traditional mapping methods include in-situ soil auger sampling or peat probing (for depth) while modern methods also incorporate satellite data (optical and radar). However, both methods have limitations. Traditional sampling often omits boundaries and transition zones between peat and mineral soils, while satellite data only measure the surface and may not be able to penetrate landcover, potentially omitting areas of peat under, for example, grassland or forestry. Radiometrics is a measurement of naturally occurring gamma radiation. Peat soils attenuate this radiation through high soil moisture content. For the present study in Ireland, the supervised classification of gridded airborne radiometric data, acquired over multiple years, is performed using neural network pattern recognition to identify areas of peat and non-peat soils. Classification confidence values are used to identify the transition zone between these soil types, providing a simplified visualisation of this transition. Validation is performed using Loss on Ignition (LOI %) point data and several different (blanket bog, raised bog, transition zone) sites in Ireland, showing classified data can detect the presence of peat soils from broad to local scales. Airborne geophysical methods, in particular airborne radiometrics, can bridge the gap between the accuracy of point measurement and the spatial coverage of satellite data to identify peat soils by providing uniform data and objective analysis. The resulting map is a step towards understanding the true spatial distribution of peat soils in Ireland, including transition zones.
... Fens are peat forming-systems that are different to bogs (i.e., raised or blanket) as they are fed by groundwater or moving surface water and can occur as isolated pockets of peat soil in river valleys, poorly drained basin, in hollows and beside open stretches of water (Fossitt and Heritage, 2000). They are often found around the edges, or what remains, of a raised bog (Gilet et al., 2024;Minasny et al., 2023). These soils represent small pockets of organic material that are connected with groundwater and the larger landscape (Figure 1), making them difficult to rewet 95 effectively. ...
Open drainage ditch (i.e. open drain) damming aims to raise the water table in agricultural grassland peat soils thereby reducing greenhouse gas (GHG) emissions. A current knowledge gap is how to examine the spatial and temporal effectiveness of such an action i.e., assessing the behaviour of the water table in the adjoining field. To address this gap, at a drained agricultural grassland site with shallow fen peat soils (ranging from 0 to 2 m depth), water level in an open drain was raised by installing a dam. Associated changes to the water table depth (WTD) were monitored using two nests of dip wells installed at two locations (Rewetted and Normal areas) in the adjoining field. Soil profile volumetric water content (VWC) data were obtained in these two areas in addition to the temperature, salinity, pH, and electrical conductivity signature of the water in the open drain. These data were integrated with geophysical (electromagnetic induction (EMI)) survey data conducted during summer and winter. Results from the dip wells (located > 20 m from dam) indicated that no measurable change in WTD occurred due to the dam installation, aligning with previous studies suggesting limited spatial influence in agricultural fen peat soils. VWC profiles, while consistent with peat physical properties, showed no deviation attributable to drain damming. The EMI results identified a distinct zone with electrical conductivity values similar to those of open drain water, suggesting localised water infiltration within ~20 m of the dammed drain during summer. This spatial impact was less evident during winter, likely due to increased precipitation and regional groundwater influence. This study demonstrates that EMI surveys, shown here in combination with other high-resolution data capture, can detect rewetting effects when combined with neural network clustering and Multi-Cluster Average Standard Deviation analysis, highlighting its value for rapid site assessment. Moreover, the results underscore the importance of survey timing, as summer measurements provided clearer evidence of drain damming impact than winter measurements.
... Irish Peatlands: Extent, Traditional Use, and Land Ownership Irish peatlands cover circa 1.46 million ha or 21% of Ireland (Connolly & Holden 2009), with recent cover estimates of peat soil increased to circa 23.3% (Gilet et al. 2024). Largely comprising blanket bogs (upland and lowland approximately 66%), raised bogs (approximately 33%) and fens (<1%), peatlands are located across the entire island of Ireland, though in some cases they are not identified due to their scale and/or the resolution of the mapping approach used (Connolly & Holden 2009). ...
Peatlands are complex landscape ecosystems. Since the beginning of the last century, they have been viewed as wastelands, with little or no economic value of note in their natural state. This led to their hidden values, particularly their contributions to human well‐being, and global and local support systems, being completely overlooked in policy and decision‐making, both at national and global levels. In this paper, we highlight some of the complexities relating to Irish peatlands: from their traditional use, to changes in land use relating to national and European policy changes in the last century. We then outline essential supporting components of a framework for their restoration and future sustainable use. Policy relating to agriculture, forestry, and energy has driven most of the land use change in Ireland, particularly since the mid‐1900s, and this has led to dramatic changes in peatland extent and condition, with negative impacts on the flows of ecosystem services and benefits for people. Restoration of peatlands has significant potential to reverse those negative flows and deliver benefits (local and global) for carbon, water, biodiversity, and people. Local communities can, and are keen to, support the delivery of peatland restoration, but they need to be supported by national agencies and policy frameworks that address social, economic, and environmental targets. The act of restoration creates opportunities to re‐connect with peatlands in a positive way, re‐enforcing the intrinsic and reciprocal values of peatlands, and ultimately supporting their sustainable use.
Ireland has > 50% of the EU’s ocean-raised bogs; however, degradation through land-use activities has transformed them from carbon (C) sinks to sources. Given their significant role in climate mitigation, it is essential to quantify the emissions resulting from land use degradation of these ecosystems. A seven-class land-use classification system for Irish peatlands (LUCIP) was developed and mapped using Sentinel-2 imagery, random forest machine learning and Google Earth Engine. The results revealed that agricultural grassland comprised 43% of the land use on raised bogs, followed by, forestry (21%), cutover (11%), cutaway (10%) remnant peatlands (13%), waterbodies and built-up ~ 1% each. The overall accuracy of the map was 89%. The map was used to estimate CO2 emissions for four classes constituting 85% of raised bogs: cutover, cutaway, grassland, and forestry using the IPCC wetlands supplement and literature-based emission factors, we estimated emissions at ~ 1.92 (± 1.58–2.27 Mt CO2-C-yr⁻¹) and ~ 0.68 Mt CO2-C-yr⁻¹ (± 0.44–0.91 Mt CO2-C-yr⁻¹) respectively. This is the first study to spatially quantify land use and related emissions from raised bogs. The results have revealed widespread degradation of these globally rare habitats, making them net emitters of CO2. The map is vital for the conservation of these ecosystems through restoration efforts, and the methodology can also be applied to other regions with similar peatland land use issues.
Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.
Pristine peatlands being excellent storage for terrestrial Carbon (C) play a crucial role in regulating climate and water and provide several important ecosystem services. However, peatlands have been heavily altered (e.g., by draining the water table), increasing greenhouse gas (GHG) emissions. Restoring peatlands requires a comprehensive characterization, including knowledge of peat depth (PD; m). Traditionally, this requires the physical insertion of a push probe, which is time-consuming and labor-intensive. It has been shown that non-invasive proximal sensing techniques such as electromagnetic induction and ground penetrating radar can add value to PD data. In this research, we want to assess the potential of proximally sensed gamma-ray (γ-ray) spectrometry (i.e., potassium [K], thorium [Th], uranium [U], and the count rate [CR]) and terrain attributes data (i.e., elevation, slope, SAGAWI, and MRVBF) to map PD either alone or in combination across a small (10 ha) peatland area in ØBakker, Denmark. Here, the PD varies from 0.1 m in the south to 7.3 m in the north. We use various prediction models including ordinary kriging (OK) of PD, linear regression (LR), multiple LR (MLR), LR kriging (LRK), MLR kriging (MLRK) and empirical Bayesian kriging regression (EBKR). We also determine the minimum calibration sample size required by decreasing sample size in decrements (i.e., n = 100, 90, 80,…, 30). We compare these approaches using prediction agreement (Lin’s concordance correlation coefficient; LCCC) and accuracy (root mean square error; RMSE). The results show that OK using maximum calibration size (n = 108) had near perfect agreement (0.97) and accuracy (0.59 m), compared to LR (ln CR; 0.65 and 0.78 m, respectively) and MLR (ln K, Th, CR and elevation; 0.85 and 0.63 m). Improvements are achieved by adding residuals; LRK (0.95 and 0.71 m) and MLRK (0.96 and 0.51 m). The best results were obtained using EBKR (0.97 and 0.63 m) given all predictions were positive and no significant change in agreement and standard errors with the decrement of calibration sample size (e.g., n = 30). The results have implications towards C stocks assessment and improved land use planning to control GHG emissions and slow down global warming.
Over the centuries, anthropogenic pressure has severely impacted peatlands on the European continent. Peatlands cover ~ 21% (1.46 Mha) of Ireland’s land surface, but 85% have been degraded due to management activities (land use). Ireland needs to meet its 2030 climate energy framework targets related to greenhouse gas (GHG) emissions from land use, land use change and forestry, including wetlands. Despite Ireland’s voluntary decision to include peatlands in this system in 2020, information on land use activities and associated GHG emissions from peatlands is lacking. This study strives to fill this information gap by using Landsat (5, 8) data with Google Earth Engine and machine learning to examine and quantify land use on Irish peatlands across three time periods: 1990, 2005 and 2019. Four peatland land use classes were mapped and assessed: industrial peat extraction, forestry, grassland and residual peatland. The overall accuracy of the classification was 86% and 85% for the 2005 and 2019 maps, respectively. The accuracy of the 1990 dataset could not be assessed due to the unavailability of high-resolution reference data. The results indicate that extensive management activities have taken place in peatlands over the past three decades, which may have negative impacts on its ecological integrity and the many ecosystem services provided. By utilising cloud computing, temporal mosaicking and Landsat data, this study developed a robust methodology that overcomes cloud contamination and produces the first peatland land use maps of Ireland with wall-to-wall coverage. This has the potential for regional and global applications, providing maps that could help understand unsustainable management practices on peatlands and the impact on GHG emissions.
Since peatlands cover around 20 % of the land area in the Republic of Ireland, their management is of particular significance in reducing national greenhouse gases (GHG) emissions. We reviewed peatland carbon (C) flux studies within Ireland, extracting data for carbon dioxide, methane, and nitrous oxide fluxes, as well as fluvial losses and here propose preliminary country-specific emission factors (EFs) for various peatland land uses and management practices. Using our derived EFs and latest areal estimates, national emissions from peatlands (excluding horticulture and combustion) amount to 2.3 Mt C y-¹ (± 0.9–3.7 Mt C y-¹), with half of all peatland GHG emissions coming from grasslands on organic soils and nearly one-third from domestic extraction drained peatlands. Our analyses suggest that peatland management through rewetting and restoration has the potential to substantially reduce emissions from drained peatlands, and this paper attempts to quantify this reduction. This is critically important given the large areas of degraded peatlands that have been earmarked for rewetting in the next decade
Wetlands have long been drained for human use, thereby strongly affecting greenhouse gas fluxes, flood control, nutrient cycling and biodiversity1,2. Nevertheless, the global extent of natural wetland loss remains remarkably uncertain³. Here, we reconstruct the spatial distribution and timing of wetland loss through conversion to seven human land uses between 1700 and 2020, by combining national and subnational records of drainage and conversion with land-use maps and simulated wetland extents. We estimate that 3.4 million km² (confidence interval 2.9–3.8) of inland wetlands have been lost since 1700, primarily for conversion to croplands. This net loss of 21% (confidence interval 16–23%) of global wetland area is lower than that suggested previously by extrapolations of data disproportionately from high-loss regions. Wetland loss has been concentrated in Europe, the United States and China, and rapidly expanded during the mid-twentieth century. Our reconstruction elucidates the timing and land-use drivers of global wetland losses, providing an improved historical baseline to guide assessment of wetland loss impact on Earth system processes, conservation planning to protect remaining wetlands and prioritization of sites for wetland restoration⁴.
The size of peat subsidence at Solec peatland (Poland) over 50 years was determined. The field values for subsidence and mineralization were compared with estimates using 20 equations. The subsidence values derived from equations and field measurements were compared to rank the equations. The equations that include a temporal factor (time) were used to forecast subsidence (for the 20, 30 and 40 years after 2016) assuming stable climate conditions and water regime. The annual rate of subsidence ranged from 0.08 to 2.2 cm year⁻¹ (average 1.02 cm year ⁻¹). Equation proposed by Jurczuk produced the closest-matching figure (1.03 cm year⁻¹). Applying the same equation to calculate future trends indicates that the rate of soil subsidence will slow down by about 20% to 0.82 cm year⁻¹ in 2056. With the measured peat subsidence rate, the groundwater level (57–72 cm) was estimated and fed into equations to determine the contribution of chemical processes to the total size of subsidence. The applied equations produced identical results, attributing 46% of peat subsidence to chemical (organic matter mineralization) processes and 54%—to physical processes (shrinkage, organic matter consolidation). The belowground changes in soil in relation to groundwater level have been neglected lately, with GHGs emissions being the main focus.
Peatlands account for approx. 4.23 million km² of the land surface of Earth and between 5 % and 20 % of the global soil carbon stock, however much uncertainty exists. The release of carbon from modified peatlands is significant and affects the global carbon balance. The importance of conservation and rehabilitation of peatlands is clear. Global estimates currently use national scale mapping strategies that vary depending on available resources and national interest. The most up-to-date methods rely on satellite remote sensing data, which detect peat based on a multiband spectral signature, or reflected radar backscatter. However, satellite data may not be capable of detecting peat under landcover such as pasture or forest. Airborne geophysical surveys provide relevant subsurface information to update or redefine peatland extent maps at a national scale. Radiometric surveys, which measure the naturally occurring geologically sourced potassium, uranium, and thorium, offer the largest potential. Modelling of gamma ray attenuation shows that peat has a distinctive attenuation signature, due to its low bulk density, when considering all recorded radiometric data. This study exploits this signature by combining airborne radiometric data in a machine learning framework and training an artificial neural network to detect those data which have been acquired over previously mapped peatlands. A ∼95 % predictability is achieved. The trained neural network can be then used to predict the extent of all peatlands within a region, including forested and agriculturally modified peatlands, and an updated peatland map can be produced. This methodology has implications for global carbon stock assessment and rehabilitation projects where similar datasets exist or are planned, by updating the extent and boundary positions of current peatlands and uncovering previously unknown peatlands under forestry or grasslands.
One of the most commonly used techniques for validating Land Use Cover (LUC) maps are the accuracy assessment statistics derived from the cross-tabulation matrix. However, although these accuracy metrics are applied to spatial data, this does not mean that they produce spatial results. The overall, user’s and producer’s accuracy metrics provide global information for the entire area analysed, but shed no light on possible variations in accuracy at different points within this area, a shortcoming that has been widely criticized. To address this issue, a series of techniques have been developed to integrate a spatial component into these accuracy assessment statistics for the analysis and validation of LUC maps. Geographically Weighted Regression (GWR) is a local technique for estimating the relationship between a dependent variable with respect to one or more independent variables or explanatory factors. However, unlike traditional regression techniques, it considers the distance between data points when estimating the coefficients of the regression points using a moving window. Hence, it assumes that geographic data are non-stationary i.e., they vary over space. Geographically weighted methods provide a non-stationary analysis, which can reveal the spatial relationships between reference data obtained from a LUC map and classified data. Specifically, logistic GWR is used in this chapter to estimate the accuracy of each LUC data point, so allowing us to observe the spatial variation in overall, user’s and producer’s accuracies. A specific tool (Local accuracy assessment statistics) was specially developed for this practical exercise, aimed at validating a Land Use Cover map. The Marqués de Comillas region was selected as the study area for implementing this tool and demonstrating its applicability. For the calculation of the user’s and producer’s accuracy metrics, we selected the tropical rain forest category [50] as an example. Furthermore, a series of maps were obtained by interpolating the results of the tool, so enabling a visual interpretation and a description of the spatial distribution of error and accuracy.
The world’s largest tropical peatland complex is found in the central Congo Basin. However, there is a lack of in situ measurements to understand the peatland’s distribution and the amount of carbon stored in it. So far, peat in this region has been sampled only in largely rain-fed interfluvial basins in the north of the Republic of the Congo. Here we present the first extensive field surveys of peat in the Democratic Republic of the Congo, which covers two-thirds of the estimated peatland area, including from previously undocumented river-influenced settings. We use field data from both countries to compute the first spatial models of peat thickness (mean 1.7 ± 0.9 m; maximum 5.6 m) and peat carbon density (mean 1,712 ± 634 MgC ha⁻¹; maximum 3,970 MgC ha⁻¹) for the central Congo Basin. We show that the peatland complex covers 167,600 km², 36% of the world’s tropical peatland area, and that 29.0 PgC is stored below ground in peat across the region (95% confidence interval, 26.3–32.2 PgC). Our measurement-based constraints give high confidence of globally significant peat carbon stocks in the central Congo Basin, totalling approximately 28% of the world’s tropical peat carbon. Only 8% of this peat carbon lies within nationally protected areas, suggesting its vulnerability to future land-use change.
Integration of peatlands into land-use monitoring systems is central to the conservation of their carbon storage – be they conserved, degraded or restored. Healthy peatlands mitigate climate change, enhance adaptive capacity and maintain ecosystem services and biodiversity. Peatlands are starting to receive a high level of attention and the scientific basis for their monitoring has quickly developed over the last few years. This report includes robust and practical approaches and tools for developing and integrating peatland-monitoring into national monitoring and reporting frameworks is an important opportunity for countries to limit global warming to 2 °C.
In the 21st century, remote sensing (RS) has become increasingly employed in many environmental studies. This paper constitutes an overview of works utilising RS methods in studies on peatlands and investigates publications from the period 2010–2021. Based on fifty-nine case studies from different climatic zones (from subarctic to subtropical), we can indicate an increase in the use of RS methods in peatland research during the last decade, which is likely a result of the greater availability of new remote sensing data sets (Sentinel 1 and 2; Landsat 8; SPOT 6 and 7) paired with the rapid development of open-source software (ESA SNAP; QGIS and SAGA GIS). In the studied works, satellite data analyses typically encompassed the following elements: land classification/identification of peatlands, changes in water conditions in peatlands, monitoring of peatland state, peatland vegetation mapping, Gross Primary Productivity (GPP), and the estimation of carbon resources in peatlands. The most frequently employed research methods, on the other hand, included: vegetation indices, soil moisture indices, water indices, supervised classification and machine learning. Remote sensing data combined with field research is deemed helpful for peatland monitoring and multi-proxy studies, and they may offer new perspectives on research at a regional level.
The Semi-Automatic Classification Plugin is a Python plugin for the software QGIS (QGIS Development Team, 2021) developed with the overall objective to facilitate land cover monitoring by people whose main field is not strictly remote sensing, but that could benefit from remote sensing analysis. The Semi-Automatic Classification Plugin provides a set of intertwined tools and a user interface for easing and automating the phases of land cover classification, from the download of remote sensing images, to the preprocessing (i.e. tools for preparing the data to the analysis or other calculations), the processing (i.e. tools for performing the classification of land cover or perform analysis), and postprocessing (i.e. tools for assessing the classification accuracy, refining the classification, or integrating additional data). The processing of remote sensing data can be computationally intensive, therefore most of the developed tools use Python multiprocessing to exploit the system CPU and RAM by splitting the work among multiple subprocesses. The aim of this paper is to describe the main characteristics of the Semi-Automatic Classification Plugin for the land cover classification of remote sensing images.
Exploring how values are assigned to cultural ecosystem services of peatlands, this paper draws on a process perspective that incorporates relational and pragmatist approaches to conceptualising value. These approaches emphasise how value is established through cultural practices and everyday lived experiences of communities who are interacting with landscapes in dynamic and novel ways. The research proposes a framework for valuing temporal dimensions of cultural ecosystem services to incorporate the dynamic and evolving nature of people’s relationships with natural environments. Using a mixed methods approach, this research reveals how participants assigned value through active processes of remembering the past, engaging and interacting with peatlands in the present, and restoring value for the future through community conservation activities. These experiences influence the formation of memories, identities, values, and emotional connections to places, and thus help in understanding people’s motivations to protect the natural environment. Research evidence is drawn from three Irish peatland case study sites which are recognised for their scientific and community value. The results demonstrate the importance of peatlands for local communities: as carriers of personal and collective memories and histories; as social spaces for health and well-being; and as learning contexts for building social and ecological capacities. Recommendations are made to support inclusion of local, contextual knowledge and cultural and temporal value dimensions of landscapes. This approach represents a broader range of values, recognising new associations between people and peatlands, and supporting communities negotiating the transition to more sustainable futures.
This report translates key findings from the International Panel on Climate Change (IPCC) Special Report on Climate Change and Land (SRCCL) into the context of the Irish land system. The SRCCL is particularly relevant for Ireland because of the specific challenges to climate change mitigation and adaptation presented by the country’s land system and land use. This report informs policy in relation to the scale of these challenges. It identifies pressures in terms of greenhouse gas (GHG) fluxes from the land system and how these emissions relate to land use and its economic outputs. Finally, the report focuses on solutions in the form of an assessment of actions that can be taken to simultaneously address climate mitigation and adaptation in the land system.
Knowledge gaps are identified and recommendations for future research are made.
Although land use in Ireland is dominated by grassland (61.0% in 2016) and related grassland-based agriculture, there is significant variation in the intensity of grassland management across farming systems. Economically, agricultural outputs are dominated
by ruminant livestock in the form of dairy and beef production. However, pigs, cereals (barley, wheat and oats), sheep, poultry, potatoes and mushrooms are also important. The area of forestry in Ireland has increased dramatically over the last century, from around 1.4% in 1918 to around 10.7% in 2016. However, the forest area remains relatively low
compared with the average for the first 28 Member States of the EU (EU-28), and the rate of afforestation has slowed in recent decades. Peatlands cover a significant area but are largely degraded by human activities such as peat extraction. A national land use map, including data on land use intensity, would enable a better understanding of the dynamics of the land system in Ireland and facilitate targeted implementation of actions.
Current data on the principal land-based GHGs – carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) – were summarised and assessed. Over the period 2010–2017, agriculture contributed 28.8% of GHG emissions in Ireland, a substantially larger portion than the EU-28 average over the same period (10.3%). This contrast was driven by the dominance of ruminant livestock production and a relatively low level of heavy industry in Ireland. Again in contrast to the EU-28, the land use, land use change and forestry (LULUCF) category was a net source of GHGs in Ireland between 2010 and 2017.
Agricultural emissions are dominated by CH4 emissions from ruminant livestock and manure management and by N2O emissions resulting from fertiliser use and soil management. Despite improvements in production efficiency in the agriculture sector, expansion and intensification have led to increases in absolute GHG emissions over the last decade. Forestry is an important net carbon sink, but its contribution is likely to decline in the coming decades as the rate of afforestation is decreasing. Peatlands and
organic soils under agricultural management are significant GHG sources, but there is large uncertainty regarding actual GHG emissions from these lands, and their extent and drainage status represents a key knowledge gap.
The potential of 40 integrated response options to contribute to climate mitigation and adaptation and other land system challenges was assessed for Ireland, with 12 of the options found to be highly applicable. Options considered highly applicable in terms of potential to mitigate climate change, particularly at an individual consumer level, include
dietary change, such as a shift towards “sustainable healthy” diets, and a reduction in food waste. Actions aimed at increased food productivity and improved grazing land and livestock management also have high potential but could lead to “rebound effects” in
terms of absolute environmental footprint. Agroforestry and agricultural diversification are highly applicable, yet these options face considerable barriers to uptake by landowners, and a review of current policies is recommended. Although afforestation and bioenergy have considerable mitigation potential, they could have negative impacts on biodiversity and increase land competition if implemented at large scales. The restoration of peatlands and organic agricultural soils represents a major opportunity to reduce GHG
emissions and create carbon sinks, but positive outcomes require major investment and may be limited by site-specific constraints. Biodiversity conservation goals may be more likely to succeed where integrated with land use planning including climate mitigation
and adaptation strategies. Implementation of land-based response options requires a sustainable land management approach, one in which local communities and landowners are actively engaged in the planning and implementation process.
This study presents a novel landscape classification map of the Republic of Ireland and is the first to identify broad landscape classes by incorporating physiographic and land cover data. The landscape classification responds to commitments to identify and classify the Irish landscape as a signatory to the European Landscape Convention. The methodology applied a series of clustering iterations to determine an objective multivariate classification of physiographic landscape units and land cover datasets. The classification results determined nine statistically significant landscape classes and the development of a landscape classification map at a national scale. A statistical breakdown of land cover area and diversity of each class was interpreted, and a comparison was extended using independent descriptive variables including farmland use intensity, elevation, and dominant soil type. Each class depicts unique spatial and composition characteristics, from coastal, lowland and elevated, to distinct and dominating land cover types, further explained by the descriptive variables. The significance of individual classes and success of the classification is discussed with particular reference to the wider applicability of the map. The transferability of the methodology to other existing physiographic maps and environmental datasets to generate new landscape classifications is also considered. This novel work facilitates the development of a strategic framework to efficiently monitor, compare and analyse ecological and other land use data that is spatially representative of the distribution and extent of land cover in the Irish countryside.
Peatlands are significant carbon (C) stores, playing a key role in nature-based climate change mitigation. While the effectiveness of non-forested peatlands as C reservoirs is increasingly recognized, the C sequestration function of forested peatlands remains poorly documented, despite their widespread distribution. Here, we evaluate the C sequestration potential of pristine boreal forested peatlands over both recent and millennial timescales. C stock estimates reveal that most of the carbon stored in these ecosystems is found in organic horizons (22.6–66.0 kg m−2), whereas tree C mass (2.8–5.7 kg m−2) decreases with thickening peat. For the first time, we compare the boreal C storage capacities of peat layers and tree biomass on the same timescale, showing that organic horizons (11.0–12.6 kg m−2) can store more carbon than tree aboveground and belowground biomass (2.8–5.7 kg m−2) even over a short time period (last 200 years). We also show that forested peatlands have similar recent rates of C accumulation to boreal non-forested peatlands but lower long-term rates, suggesting higher decay and more important peat layer combustion during fire events. Our findings highlight the significance of forested peatlands for C sequestration and suggest that greater consideration should be given to peat C stores in national greenhouse gas inventories and conservation policies.
This study aims at identifying underutilized land potentially suitable for bioenergy production in Europe by means of remote sensing time series analysis. The background is the Revised Renewable Energy Directive (REDII) requesting that 32% of Europe’s energy production shall come from renewable energy sources until 2030. In order to avoid the food versus fuel debate, we only considered land that has not been used in the previous five years. Satellite remote sensing is the only technique that allows for the assessment of the usage of land for such a long time span at the pan-European scale with reasonable efforts. We used Landsat 8 (L8) data for the full five year time period 2015–2019 and included additional Sentinel-2 (S2) data for 2018 and 2019. The analysis was based on a stratified approach for biogeographical regions and countries using Google Earth Engine. To our knowledge, this is the first work that employs high resolution time series data for pan-European mapping of underutilized land. The average patch size of underutilized land was found to be between 23.2 ha and 49.6 ha, depending on the biogeographical region. The results show an overall accuracy of more than 85% with a confidence interval (CI) of 1.55% at the 95% confidence level (CL). The classification suggests that at total of 5.3 million ha of underutilized land in Europe is potentially available for agricultural bioenergy production.
Accurately characterizing land surface changes with Earth Observation requires geo-located ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover and land use has been collected since 2006 under the Land Use/Cover Area frame Survey (LUCAS). A total of 1351293 observations at 651780 unique locations for 106 variables along with 5.4 million photos were collected during five LUCAS surveys. Until now, these data have never been harmonised into one database, limiting full exploitation of the information. This paper describes the LUCAS point sampling/surveying methodology, including collection of standard variables such as land cover, environmental parameters, and full resolution landscape and point photos, and then describes the harmonisation process. The resulting harmonised database is the most comprehensive in-situ dataset on land cover and use in the EU. The database is valuable for geo-spatial and statistical analysis of land use and land cover change. Furthermore, its potential to provide multi-temporal in-situ data will be enhanced by recent computational advances such as deep learning.
Peatlands are lands with a peat layer at the surface, containing a large proportion of organic carbon. Such lands cover ≈1 000 000 km² in Europe, which is almost 10% of the total surface area. In many countries, peatlands have been artificially drained over centuries, leading to not only enormous emissions of CO2 but also soil subsidence, mobilization of nutrients, higher flood risks, and loss of biodiversity. These problems can largely be solved by stopping drainage and rewetting the land. Wet peatlands do not release CO2, can potentially sequester carbon, help to improve water quality, provide habitat for rare and threatened biodiversity, and can still be used for production of biomass (“paludiculture”). Wisely adjusted land use on peatlands can substantially contribute to low‐emission goals and further benefits for farmers, the economy, society, and the environment.
Background:
Reduction of carbon emissions from peatlands is recognized as an important factor in global climate change mitigation. Within the SE Asia region, areas of deeper peat present the greatest carbon stocks, and therefore the greatest potential for future carbon emissions from degradation and fire. They also support most of the remaining lowland swamp forest and its associated biodiversity. Accurate maps of deep peat are central to providing correct estimates of peat carbon stocks and to facilitating appropriate management interventions. We present a rapid and cost-effective approach to peat thickness mapping in raised peat bogs that applies a model of peat bottom elevation based on field measurements subtracted from a surface elevation model created from airborne LiDAR data.
Results:
In two raised peat bog test areas in Indonesia, we find that field peat thickness measurements correlate well with surface elevation derived from airborne LiDAR based DTMs (R2 0.83-0.88), confirming that the peat bottom is often relatively flat. On this basis, we created a map of extent and depth of deep peat (> 3 m) from a new DTM that covers two-thirds of Sumatran peatlands, applying a flat peat bottom of 0.61 m +MSL determined from the average of 2446 field measurements. A deep peat area coverage of 2.6 Mha or 60.1% of the total peat area in eastern Sumatra is mapped, suggesting that deep peat in this region is more common than shallow peat and its extent was underestimated in earlier maps. The associated deep peat carbon stock range is 9.0-11.5 Pg C in eastern Sumatra alone.
Conclusion:
We discuss how the deep peat map may be used to identify priority areas for peat and forest conservation and thereby help prevent major potential future carbon emissions and support the safeguarding of the remaining forest and biodiversity. We propose rapid application of this method to other coastal raised bog peatland areas in SE Asia in support of improved peatland zoning and management. We demonstrate that the upcoming global ICESat-2 and GEDI satellite LiDAR coverage will likely result in a global DTM that, within a few years, will be sufficiently accurate for this application.
Land-use change disturbs the function of peatland as a natural carbon sink and triggers high GHG emissions1. Nevertheless, historical trends and future trajectories of GHG budgets from soil do not explicitly include peatlands2,3. Here, we provide an estimate of the past and future role of global peatlands as either a source or sink of GHGs based on scenario timelines of land conversion. Between 1850 and 2015, temperate and boreal regions lost 26.7 million ha, and tropical regions 24.7 million ha, of natural peatland. By 2100, peatland conversion in tropical regions might increase to 36.3 million ha. Cumulative emissions from drained sites reached 80 ± 20 PgCO2e in 2015 and will add up to 249 ± 38 Pg by 2100. At the same time, the number of intact sites accumulating peat will decline. In 1960 the global peatland biome turned from a net sink into a net source of soil-derived GHGs. Annual back-conversion of most of the drained area would render peatlands GHG neutral, whereas emissions from peatland may comprise 12–41% of the GHG emission budget for keeping global warming below +1.5 to +2 °C without rehabilitation. Natural peatlands accumulate carbon but land-use change and drainage leads to emission of GHGs from peatlands. Loss of natural peatland area globally has shifted the peatland biome from a sink to a source of carbon, but restoration of drained peatlands could make them carbon neutral.
The aim of this guidebook is to support the reduction of GHG emissions from managed peatlands and present guidance for responsible management practices that can maintain peatlands ecosystem services while sustaining and improving local livelihoods. This guidebook also provides an overview of the present knowledge on peatlands, including their geographic distribution, ecological characteristics and socio-economic importance.
This publication considers the environmental and pedological issues associated with peatland use and management before entering into the details of technical options for climate-responsible peatland use.
In addition to the largest existing expanse of tropical forests, the Brazilian Amazon has among the largest area of mangroves in the world. While recognized as important global carbon sinks that, when disturbed, are significant sources of greenhouse gases, no studies have quantified the carbon stocks of these vast mangrove forests. In this paper, we quantified total ecosystem carbon stocks of mangroves and salt marshes east of the mouth of the Amazon River, Brazil. Mean ecosystem carbon stocks of the salt marshes were 257 Mg C ha 21 while those of mangroves ranged from 361 to 746 Mg C ha 21. Although aboveground mass was high relative to many other mangrove forests (145 Mg C ha 21), soil carbon stocks were relatively low (340 Mg C ha 21). Low soil carbon stocks may be related to coarse textured soils coupled with a high tidal range. Nevertheless, the carbon stocks of the Amazon mangroves were over twice those of upland evergreen forests and almost 10-fold those of tropical dry forests.
If peatland is left without any restoration treatments after mechanical peat extraction ceases, the process of secondary transformation of peat continues. The resulting changes in peat properties severely impede the recovery of vegetation on cutover peatland. The aim of this study was to assess how secondary transformation of peat affects spontaneous revegetation, and the relative importance of different factors in controlling the re-establishment of raised bog species on previously cutover peat surfaces. The study was conducted on two sectors of a raised bog in southern Poland where peat extraction ended either 20 or 30 years ago. Where the residual peat layer was thin (~ 40 cm or less) and the water table often dropped into the mineral substratum, the development of vascular plants (including trees) was favoured, and this further promoted the secondary transformation of peat. In such locations the vegetation tended towards a pine and birch community. Revegetation by Sphagnum and other raised bog species (Eriophorum vaginatum, Vaccinium uliginosum, Ledum palustre, Oxycoccus palustris) was associated with thicker residual peat and higher water table level which, in turn, were strongly correlated with hydrophysical properties of the soil. A species-environmental factor redundancy analysis (RDA) showed that any single factor (of those considered) was not important in determining the revegetation pattern, because of their intercorrelations. However, water table level appeared to be the most important abiotic factor in determining the degree of soil aeration and, consequently, the stage of secondary transformation attained by the peat.
Soil carbon sequestration and avoidable emissions through peatland restoration are both strategies to tackle climate change. Here we compare their potential and environmental costs regarding nitrogen and land demand. In the event that no further areas are exploited, drained peatlands will cumulatively release 80.8 Gt carbon and 2.3 Gt nitrogen. This corresponds to a contemporary annual greenhouse gas emission of 1.91 (0.31–3.38) Gt CO2-eq. that could be saved with peatland restoration. Soil carbon sequestration on all agricultural land has comparable mitigation potential. However, additional nitrogen is needed to build up a similar carbon pool in organic matter of mineral soils, equivalent to 30–80% of the global fertilizer nitrogen application annually. Restoring peatlands is 3.4 times less nitrogen costly and involves a much smaller land area demand than mineral soil carbon sequestration, calling for a stronger consideration of peatland rehabilitation as a mitigation measure.
Based on the ‘European Mires Book’ of the International Mire Conservation Group (IMCG), this article provides a composite map of national datasets as the first comprehensive peatland map for the whole of Europe. We also present estimates of the extent of peatlands and mires in each European country individually and for the entire continent. A minimum peat thickness criterion has not been strictly applied, to allow for (often historically determined) country-specific definitions. Our ‘peatland’ concept includes all ‘mires’, which are peatlands where peat is being formed. The map was constructed by merging national datasets in GIS while maintaining the mapping scales of the original input data. This ‘bottom-up’ approach indicates that the overall area of peatland in Europe is 593,727 km². Mires were found to cover more than 320,000 km² (around 54 % of the total peatland area). If shallow-peat lands (< 30 cm peat) in European Russia are also taken into account, the total peatland area in Europe is more than 1,000,000 km2, which is almost 10 % of the total surface area. Composite inventories of national peatland information, as presented here for Europe, may serve to identify gaps and priority areas for field survey, and help to cross-check and calibrate remote sensing based mapping approaches.
Several studies contributed to the development of restoration techniques for open Sphagnum-dominated habitats on peat-extracted bogs. Yet, in exception to some afforestation efforts, connectivity between restored sites and surrounding landscapes has received little attention. The general goal of this study is to ameliorate management of very shallow peat fields (<30 cm) located within the margins of peat-extracted bogs. Firstly, to assist decision making in peatland management, baseline ecological conditions, peat physicochemistry and spontaneous vegetation recolonization were assessed for 18 of these fields. This first study revealed that (1) concentrations of several macro-nutrients are almost one order of magnitude lower for unrestored fields than previously characterized natural lagg habitats of the same region, and (2) there is little spontaneous colonization. In a second study, peat chemistry and soil/air microclimate were evaluated in plantations established on shallow residual peat (a 21-year-old Larix laricina plantation and an 18-year-old Picea mariana plantation) and compared to adjacent unrestored shallow bare-peat fields. This second study showed that afforested peat fields are characterized by (1) a soil enriched in nutrients, notably in N, P, and K, and (2) a more humid and cooler microclimate at the soil/air interface, with less daily humidity and temperature fluctuations. These results indicate that afforestation is an appropriate approach to start an ecological recovery. Yet, the absence of natural recolonization by herbs and mosses in the understory of afforested peat fields suggests that reintroduction of appropriate understory species should also be considered if the goal is to restore a fully functional ecosystem.
Peatlands are wetland ecosystems that accumulate dead organic matter (i.e. peat) when plant litter production outpaces peat decay, usually under conditions of frequent or continuous waterlogging. Collectively, global peatlands store vast amounts of carbon (C), equalling if not exceeding the amount of C in the Earth’s vegetation, and they encompass a remarkable diversity of forms, from the frozen palsa mires of the northern sub-arctic to the lush swamp forests of the tropics, each with their own characteristic range of fauna and flora. In this review we explain what peatlands are, how they form and the contribution that peatland science can make to our understanding of global change. We explore the variety in formation, shape, vegetation type and chemistry of peatlands across the globe while stressing the fundamental features that are common to all peat-forming ecosystems. We consider the impacts that past, present, and future environmental changes, including anthropogenic disturbances, have had and will have on peatland systems, particularly in terms of their important roles in carbon storage and the provision of ecosystem services. The most widespread uses of peatlands today are for forestry and agriculture, both of which require drainage that results in globally significant emissions of carbon dioxide (CO2), a greenhouse gas. Climatic drying and drainage also increase the risk of peat fires that are a further source of greenhouse gas emissions (CO2 and methane, CH4) to the atmosphere, as well as causing negative human health and socio-economic impacts. We conclude our review by explaining the roles that palaeoecological, experimental and modelling studies can play in allowing us to build a more secure understanding of how peatlands function, how they will respond to future climate- and land management-related disturbances, and how best we can improve their resilience in a changing world.
Forest ecosystems are recognised as Natural Climate Solutions because forest soils are such important carbon stores, containing almost half of the total soil organic carbon of terrestrial ecosystems. Here we present the results of a synthesis of soil carbon stocks by World Reference Base soil group, and forest litter carbon stocks for afforested soils in the Republic of Ireland. We report soil carbon stocks of mineral soils separately from organo-mineral soils. We estimated mean soil carbon stocks in a 100 cm deep mineral soil to be between 162 ± 87 t C/ha (Gleysols) and 416 ± 0 t C/ha (Umbrisols, n = 1), and between 173 ± 65 t C/ha (Phaeozems) and 602 ± 226 t C/ha (Regosols) in a 100 cm deep organo-mineral soil; both less than the estimated soil carbon stocks in organic soils (Histosols): 645 ± 222 t C/ha. The entire soil carbon stocks in mineral Leptosols (100 ± 0 t C/ha, n = 1), Stagnosols (144 ± 39 t C/ha), Luvisols (159 ± 52 t C/ha) and Fluvisols (231 ± 0 t C/ha, n = 1) was contained in the upper 50 cm of soil. Based on a 100 cm deep soil, Histosols hold 1.6–4 times the amount of soil C than mineral soils and 1.1–3.7 times the amount in organo-mineral soils for the same profile depth. Certain mineral (e.g. Umbrisols) and organo-mineral soils (e.g Gleysols, Regosols) contain substantial soil carbon stocks relative to Histosols. We found considerable soil carbon stocks below 30 cm depth, which highlights the importance of depth extent for cumulative soil carbon stocks estimates. The upper third of the 100 cm profile contained 33% (Histosols) to 70% (Luvisols) of the soil carbon stocks and the upper half of a 100 cm profile contained the entire soil carbon stocks for Leptosols, Stagnosols, Luvisols and Fluvisols and organo-mineral Leptosols. Unfortunately, there were few samples available for mineral Leptosols, Umbrisols, Luvisols and Fluvisols, and the organo-mineral Stagnosols and Regosols, which precludes the drawing of conclusions for these groups. Relative to the soil carbon stocks, we found low mean forest litter stocks: 4.1 ± 5.5 t C/ha, 4.8 ± 3.3 t C/ha and 2.7 ± 2.9 t C/ha for broadleaf, coniferous and mixed forests respectively. Few exceptions existed for individual sites: 22.7 and 131.3 t C/ha for broadleaf forests. Our results are evidence that soil carbon stocks in mineral, organo-mineral and organic soils need to be protected, appropriately managed, and enhanced to be beneficial for greenhouse gas mitigation. Assessments are needed to identify which soil-site-management practice combinations risk soil carbon stock depletion. The large range observed in soil and litter carbon stocks stresses the importance of adequately accounting for soil group differences when GHG inventories are compiled. The synthesised dataset will contribute to improved SCS estimation for afforested lands in Ireland.
Peatlands are carbon-rich ecosystems that comprise the largest terrestrial carbon store. Peatland preservation has been acknowledged on the global scale as a key nature-based component in addressing climate change. Despite their importance, there is no globally recognised definitions for peat or peatland, which influences efforts in quantifying global peat carbon stocks. We present a critical review on peatland definitions, including peat nomenclature and changing criteria for peatland classification through time. We focus on two important criteria: the minimum depth of the surface organic layer and the minimum percentage of organic carbon. We highlight the disparity between definitions, peatland nomenclature and peatland classifications. It is challenging to determine whether one definition should take precedence over another, even when considering the most common criteria. We propose that future peatland definitions focus on carbon storage and potential greenhouse gas emissions. This involves four physical and chemical characteristics of the peatland deposit: (1) Peatland extent, (2) peat thickness, (3) peat carbon content and (4) peat bulk density (volumetric carbon content). The growth dynamics and carbon flux of the peatland deposit should also become a routine part of inventories. In future, international technical agencies and experts can advise on the standardisation of concept definitions and methods, these must focus on the preservation of peatlands from the perspective of climate science.
Peatlands are globally important long‐term sinks of carbon, however there is concern that enhanced peat decomposition and moss moisture stress due to climate change mediated drought will reduce moss productivity making these ecosystems vulnerable to carbon loss and associated long‐term degradation. Peatlands are resilient to summer drought moss stress because of negative ecohydrological feedbacks that generally maintain a wet peat surface, but where feedbacks may be contingent on peat depth. We tested this ‘survival of the deepest’ hypothesis by examining water table position, near‐surface moisture content, and soil water tension in peatlands that differ in size, peat depth, and catchment area during a summer drought.
All shallow sites (<40 cm depth) lost their WT (i.e. the groundwater well was dry) for considerable time during the drought period. Near‐surface soil water tension increased dramatically at shallow sites following water table loss, increasing ~5–7.5× greater at shallow sites compared to deep sites (≥40 cm depth). During a mid‐summer drought intensive field survey we found that 60%–67% of plots at shallow sites exceeded a 100 mb tension threshold used to infer moss water stress. Unlike the shallow sites, tension typically did not exceed this 100 mb threshold at the deep sites. Using species dependent water content ‐ chlorophyll fluorescence thresholds and relations between volumetric water content and water table depth, Monte Carlo simulations suggest that moss had nearly twice the likelihood of being stressed at shallow sites (0.38 ± 0.24) compared to deep sites (0.22 ± 0.18). This study provides evidence that mosses in shallow peatland may be particularly vulnerable to warmer and drier climates in the future, but where species composition may play an important role. We argue that a critical ‘threshold’ peat depth specific for different hydrogeological and hydroclimatic regions can be used to assess what peatlands are especially vulnerable to climate change mediated drought.
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Accuracy assessment and land cover mapping have been inexorably linked throughout the first 50 years of publication of Remote Sensing of Environment. The earliest developers of land-cover maps recognized the importance of evaluating the quality of their maps, and the methods and reporting format of these early accuracy assessments included features that would be familiar to practitioners today. Specifically, practitioners have consistently recognized the importance of obtaining high quality reference data to which the map is compared, the need for sampling to collect these reference data, and the role of an error matrix and accuracy measures derived from the error matrix to summarize the accuracy information. Over the past half century these techniques have undergone refinements to place accuracy assessment on a more scientifically credible footing. We describe the current status of accuracy assessment that has emerged from nearly 50 years of practice and identify opportunities for future advances. The article is organized by the three major components of accuracy assessment, the sampling design, response design, and analysis, focusing on good practice methodology that contributes to a rigorous, informative, and honest assessment. The long history of research and applications underlying the current practice of accuracy assessment has advanced the field to a mature state. However, documentation of accuracy assessment methods needs to be improved to enhance reproducibility and transparency, and improved methods are required to address new challenges created by advanced technology that has expanded the capacity to map land cover extensively in space and intensively in time.
Peatlands offer a series of ecosystem services including carbon storage, biomass production, and climate regulation. Climate change and rapid land use change are degrading peatlands, liberating their stored carbon (C) into the atmosphere. To conserve peatlands and help in realising the Paris Agreement, we need to understand their extent, status, and C stocks. However, current peatland knowledge is vague—estimates of global peatland extent ranges from 1 to 4.6 million km2, and C stock estimates vary between 113 and 612 Pg (or billion tonne C). This uncertainty mostly stems from the coarse spatial scale of global soil maps. In addition, most global peatland estimates are based on rough country inventories and reports that use outdated data. This review shows that digital mapping using field observations combined with remotely-sensed images and statistical models is an avenue to more accurately map peatlands and decrease this knowledge gap. We describe peat mapping experiences from 12 countries or regions and review 90 recent studies on peatland mapping. We found that interest in mapping peat information derived from satellite imageries and other digital mapping technologies is growing. Many studies have delineated peat extent using land cover from remote sensing, ecology, and environmental field studies, but rarely perform validation, and calculating the uncertainty of prediction is rare. This paper then reviews various proximal and remote sensing techniques that can be used to map peatlands. These include geophysical measurements (electromagnetic induction, resistivity measurement, and gamma radiometrics), radar sensing (SRTM, SAR), and optical images (Visible and Infrared). Peatland is better mapped when using more than one covariate, such as optical and radar products using nonlinear machine learning algorithms. The proliferation of satellite data available in an open-access format, availability of machine learning algorithms in an open-source computing environment, and high-performance computing facilities could enhance the way peatlands are mapped. Digital soil mapping allows us to map peat in a cost-effective, objective, and accurate manner. Securing peatlands for the future, and abating their contribution to atmospheric C levels, means digitally mapping them now.
Mapping the extent and locations of peatland at landscape scale has implications for carbon inventories, conservation and ecosystem services assessments. The main aim of this paper was to model and map the extent of northern peat soils while taking into account its uncertainty, and in particular exploring:
1.
the use of radar Sentinel 1 as alternative to optical sensors to reduce problems due to clouds while taking advantage of the seasonality changes and
2.
the use of deep learning for peat classification and as application of Digital Soil Mapping.
The data sets defining presence or absence of peat in the soil were obtained from different sources and different sampling schemes, densities and distributions. Scotland was used as test case, because of its cloudy weather and fragmented distribution of different types of peat.
An extension of the scorpan-kriging approach was used. The trend was estimated with different approaches: Generalized Additive Models, RandomForest and deep learning (convolutional neural networks). Each approach produced the probability of belonging to a class and the predicted class for each pixel. The results were assessed using out-of-sample measures.
In this study 108 combinations of data sets and models (including trend approaches, sets of covariates and modelling of the spatial structure) were assessed. Overall, spatially explicit models performed better. The choice of the statistical method can have a significant impact on the predictive performances, while the sets of environmental covariates had a lower impact. Sentinel-1 with morphological features proved to be a good alternative to optical data for peat mapping. It is important to have balanced data sets representing the distribution of the data, because merging heterogeneous sources of data from different populations does not necessarily improve predictions. The use of deep learning and convolutional neural network provided initial promising results.
There were large differences in the modelling approaches. These differences and uncertainties need to be taken into account for further modelling such as earth surface modelling or carbon accounting.
Peatlands provide globally important ecosystem services through climate and water regulation or biodiversity conservation. While covering only 3% of the earth's surface, degrading peatlands are responsible for nearly a quarter of carbon emissions from the land use sector. Bringing together world-class experts from science, policy and practice to highlight and debate the importance of peatlands from an ecological, social and economic perspective, this book focuses on how peatland restoration can foster climate change mitigation. Featuring a range of global case studies, opportunities for reclamation and sustainable management are illustrated throughout against the challenges faced by conservation biologists. Written for a global audience of environmental scientists, practitioners and policy makers, as well as graduate students from natural and social sciences, this interdisciplinary book provides vital pointers towards managing peatland conservation in a changing environment.
Peatlands provide globally important ecosystem services through climate and water regulation or biodiversity conservation. While covering only 3% of the earth's surface, degrading peatlands are responsible for nearly a quarter of carbon emissions from the land use sector. Bringing together world-class experts from science, policy and practice to highlight and debate the importance of peatlands from an ecological, social and economic perspective, this book focuses on how peatland restoration can foster climate change mitigation. Featuring a range of global case studies, opportunities for reclamation and sustainable management are illustrated throughout against the challenges faced by conservation biologists. Written for a global audience of environmental scientists, practitioners and policy makers, as well as graduate students from natural and social sciences, this interdisciplinary book provides vital pointers towards managing peatland conservation in a changing environment.
Peatlands provide globally important ecosystem services through climate and water regulation or biodiversity conservation. While covering only 3% of the earth's surface, degrading peatlands are responsible for nearly a quarter of carbon emissions from the land use sector. Bringing together world-class experts from science, policy and practice to highlight and debate the importance of peatlands from an ecological, social and economic perspective, this book focuses on how peatland restoration can foster climate change mitigation. Featuring a range of global case studies, opportunities for reclamation and sustainable management are illustrated throughout against the challenges faced by conservation biologists. Written for a global audience of environmental scientists, practitioners and policy makers, as well as graduate students from natural and social sciences, this interdisciplinary book provides vital pointers towards managing peatland conservation in a changing environment.
This book provides a comprehensive overview of pedology in Ireland. It describes the main soil types of the country, their functions, ecological use, and the conditions to which they were subjected associated with management over time. In addition, it presents a complete set of data, pictures and maps, including benchmark profiles. Factors involved in soil formation are also discussed, making use of new, unpublished data and elaborations. The book was produced with the support and sponsorship of Teagasc, The Agriculture and Food Development Authority, Ireland and the Irish Environmental Protection Agency.
Tropical peatland holds a large amount of carbon in the terrestrial ecosystem. Indonesia, responding to the global climate issues, has legislation on the protection and management of the peat ecosystem. However, this effort is hampered by the lack of fine-scale, accurate maps of peat distribution and its thickness. This paper presents an open digital mapping methodology, which utilises open data in an open-source computing environment , as a cost-effective method for mapping peat thickness and estimating carbon stock in Indonesian peatlands. The digital mapping methodology combines field observations with factors that are known to influence peat thickness distribution. These factors are represented by multi-source remotely-sensed data derived from open and freely available raster data: digital elevation models (DEM) from SRTM, geographical information , and radar images (Sentinel and ALOS PALSAR). Utilising machine-learning models from an open-source software, we derived spatial prediction functions and mapped peat thickness and its uncertainty at a grid resolution of 30 m. Peat volume can be calculated from the thickness map, and based on measurements of bulk density and carbon content, carbon stock for the area was estimated. The uncertainty of the estimates was calculated using error propagation rules. We demonstrated this approach in the eastern part of Bengkalis Island in Riau Province, covering an area around 50,000 ha. Results showed that digital mapping method can accurately predict the thickness of peat, explaining up to 98% of the variation of the data with a median relative error of 5% or an average error of 0.3 m. The accuracy of this method depends on the number of field observations. We provided an estimate of the cost and time required for map production, i.e. 2 to 4 months with a cost between 0.5/ha for an area of 50,000 ha. Obviously, there is a tradeoff between cost and accuracy. The advantages and limitations of the method were further discussed. The methodology provides a blueprint for a national-scale peat mapping.
Peatlands play important ecological, economic and cultural roles in human well-being. Although considered sensitive to climate change and anthropogenic pressures, the spatial extent of peatlands is poorly constrained. We report the development of an improved global peatland map, PEATMAP, based on a meta-analysis of geospatial information collated from a variety of sources at global, regional and national levels. We estimate total global peatland area to be 4.23 million km 2 , approximately 2.84% of the world land area. Our results suggest that previous global peatland inventories are likely to underestimate peat extent in the tropics, and to overestimate it in parts of mid-and high-latitudes of the Northern Hemisphere. Global wetland and soil datasets are poorly suited to estimating peatland distribution. For instance, tropical peatland extents are overestimated by Global Lakes and Wetlands Database – Level 3 (GLWD-3) due to the lack of ground-truthing data; and underestimated by the use of histosols to represent peatlands in the Harmonized World Soil Database (HWSD) v1.2, as large areas of swamp forest peat in the humid tropics are omitted. PEATMAP and its underlying data are freely available as a potentially useful tool for scientists and policy makers with interests in peatlands or wetlands. PEATMAP's data format and file structure are intended to allow it to be readily updated when previously undocumented peatlands are found and mapped, and when regional or national land cover maps are updated and refined.
Spatially distributed data exhibit particular characteristics that should be considered when designing a survey of spatial units. Unfortunately, traditional sampling designs generally do not allow for spatial features, even though it is usually desirable to use information concerning spatial dependence in a sampling design. This paper reviews and compares some recently developed randomised spatial sampling procedures, using simple random sampling without replacement as a benchmark for comparison. The approach taken is design-based and serves to corroborate intuitive arguments about the need to explicitly integrate spatial dependence into sampling survey theory. Some guidance for choosing an appropriate spatial sampling design is provided, and some empirical evidence for the gains from using these designs with spatial populations is presented, using two datasets as illustrations.
In this review paper, we identify and address key uncertainties related to four local and global controls of Holocene northern peatland carbon stocks and fluxes. First, we provide up-to-date estimates of the current northern peatland area (3.2 Mkm²) and propose a novel approach to reconstruct changes in the northern peatland area over time (Section 2). Second, we review the key methods and models that have been used to quantify total carbon stocks and methane emissions over time at the hemispheric scale, and offer new research directions to improve these calculations (Section 3). Our main proposed improvement relates to allocating different carbon stock and emission values for each of the two dominant vegetation assemblages (sedge and brown moss-dominated vs. Sphagnum-dominated peat). Third, we discuss and quantify the importance of basin heterogeneity in estimating peat volume at the local scale (Section 4.1). We also highlight the importance of age model selection when reconstructing carbon accumulation rates from a peat core (Section 4.2). Lastly, we introduce the role of biogeomorphological agents such as beaver activity in controlling carbon dynamics (Section 5.1) and review the newest research related to permafrost thaw (Section 5.2) and peat fire (Section 5.3) under climate change. Overall, this review summarizes new information from a broad range of peat-carbon studies, provides novel analysis of hemispheric-scale paleo datasets, and proposes new insights on how to translate peat-core data into carbon fluxes. It also identifies critical data gaps and research priorities, and many ways to consider and address them.
