Better tools for rapid and reliable assessment of global peatland extent and condition are urgently needed to support action to prevent further decline of peatlands. Peatland surface motion is a response to changes in the water and gas content of a peat body regulated by the ecology and hydrology of a peatland system. Surface motion is therefore a sensitive measure of ecohydrological condition but has traditionally been impossible to measure at the landscape scale. Here we examine the potential of surface motion metrics derived from satellite interferometric synthetic aperture radar (InSAR) to map peatland condition in a blanket bog landscape. We show that the timing of maximum seasonal swelling of the peat is characterised by a bimodal distribution. The first maximum, usually in autumn, is typical of “stiffer” peat associated with steeper topographic gradients, peatland margins, and degraded peatland and more often associated with “shrub”-dominated vegetation communities. The second maximum, usually in winter, is typically associated with “softer” peat typically found in low topographic gradients often featuring pool systems and
The conservation of well-functioning peatlands and restoration of degraded peatlands to reduce and ultimately mitigate land-use-related emissions of atmospheric carbon dioxide is now a global priority (Leifeld and Menichetti, 2018; Amelung et al., 2020; Günther et al., 2020). Furthermore, to support the implementation of national peatland management plans and restoration initiatives, cost-effective measures to record current peatland condition and restoration progress are urgently required (Crump, 2017). Mapping peatland extent and condition has long been recognised as a huge challenge over large, remote, wet, and often discontinuous peat-forming regions where field-based surveys are impractical and expensive (Lees et al., 2018). Alternatives such as thematic mapping based on optical remote sensing (visible and near-infrared) are increasingly used (Artz et al., 2019; Minasny et al., 2019; Lees et al., 2020), but the number of observations in regions with frequent cloud cover such as peatland in the northern latitudes and the tropics reduces the number of possible surface observations. Radio detection and ranging (radar), which is sensitive to physical properties of the surface, provide an effective, more frequent option, given that microwave frequencies can penetrate cloud cover and return a measured signal from the ground (Minasny et al., 2019; Poggio and Gimona, 2014). For example, using the ESA Sentinel-1 synthetic aperture radar (SAR) satellites it is now possible to observe a peatland surface anywhere at high frequency (6 to 12 d) with continuous spatial coverage. When this is combined with the technique of SAR Interferometry (InSAR), it allows detection of surface displacement, an indication of peatland condition, as a time series of observations (Sowter et al., 2013).
In peatland, the rise and fall of the surface, sometimes described as “bog breathing” (Kulczynski, 1949; Baden and Eggelsmann, 1964; Mustonen and Suena, 1971; Hutchinson, 1980; Kurimo, 1983; Almendinger et al., 1986; Price, 2003; Price and Schlotzhauer, 1999), is one of the key self-regulating feedback mechanisms in peatland, providing resilience and maintaining function during periods of hydrological stress (Money and Wheeler, 1999; Waddington et al., 2015; Mahdiyasa et al., 2022). This “surface motion”, which is a poro-elastic mechanical response to ecohydrological processes, results from the collapse and expansion of large pores in response to changes in the mass of water stored and associated stresses within the peat (Price, 2003; Mahdiyasa et al., 2022). Mechanical deformation of the peat body and consequent surface motion can also modify the ecohydrology of a peatland via compaction, slope failure, and pipe formation (Waddington et al., 2010, 2015). Small-scale field observations indicate that peat surface motion is influenced by changes in water level (Roulet, 1991; Price, 2003; Kennedy and Price, 2005; Fritz at al., 2008; Alshammari et al., 2020), vegetation composition (Howie and Hebda, 2018; Alshammari et al., 2020), micro-topography (Waddington et al., 2010), accumulation and upward migration of methane bubbles (Glaser et al., 2004; Reeve et al., 2013), and land management (Kennedy and Price, 2005).
Collectively these results suggest that peatland surface motion could be a
sensitive indicator of peatland function on a landscape scale. So far, InSAR
investigations have focused on discrete, small-scale (
In this paper, we determine whether surface motion measured by InSAR can be used to quantify peatland condition continuously over a complex peatland landscape. Using the APSIS (Advanced Pixel System using Intermittent Small Baseline Subset, formerly known as the Intermittent Small Baseline Subset (ISBAS)) method, which is capable of generating spatially continuous measures of vertical surface motion over peatland (Sowter et al., 2013), we measure time series of surface motion over our study site at a high spatial and temporal resolution. Specific time series metrics are then compared to independent measures of peatland condition to determine their relationship. By doing this we relate surface motion metrics to the continuum of ecohydrological conditions in this peatland landscape. Finally, we demonstrate how surface motion metrics can be used to map the ecohydrology of a peatland system. By doing so we illustrate how our new approach could be applied to monitoring the response of global peatland to restoration, management, and climate change.
The Flow Country peatlands, northern Scotland (Andersen et al., 2018), exist
in a range of topographic, hydrological, and management settings, leading to
a range of different conditions, e.g. highly eroded uplands to relatively
intact low-lying peatland with pool systems, with activities such
as forestry, drainage, and grazing superimposed. Our chosen study site is 930 km
The study location (inset) and true colour satellite image composite covering the study area, outlined, in the Flow Country, northern Scotland. Forested areas are dark green; more intensive agriculture appears as lighter greens to the north-east, with the remainder of the image mostly consisting of blanket bog. Superimposed on the image are the main river network and the location of pool features shown with sub-sites for detailed analysis marked as SS1 to SS5. Credits: Esri World Imagery; sources: ESSRI, DigitalGlobe, GeoEye, i-cubed, USDA FSA, USGS, AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community, © Crown copyright 2017. Distributed under the Open Government Licence (OGL). Ordnance Survey (Digimap Licence).
Details of the five sub-sites (SSs), which are all currently designated Site of Special Scientific Interest (SSSI), Special Protection Areas (SPA), and Special Areas of Conservation (SAC). FFNNR: Forsinard Flows National Nature Reserve.
InSAR surface motion time series were calculated at a pixel resolution of 80 m
First, using the R programming environment (R Core Team, 2013), the time series was sub-sampled into equal time intervals of 12 d, to match the longest overpass interval of Sentinel-1 images since Sentinel-1B, which reduces overpass times to 6 d, was not operational until 2016. Outliers were re-estimated using the R “tsclean” function (Box and Cox, 1964), from the R package “Forecast” (Hyndman et al., 2019). Gaps were filled with a linear interpolation using the R “approx” function (Becker et al., 1988) from the R “stats” package (R Core Team, 2020) after “spline” interpolation methods were found to produce contradictory results with adjacent time series across the largest gaps. The “detrend” R function aligned and reset each time series around zero by subtracting the mean.
Second, multichannel singular spectrum analysis (MSSA) using the SSA-MTM toolkit (Ghil et al., 2002; SPECTRA, 2021) was applied to isolate the cyclical, annual seasonal component of the time series from regional climate trends. The MSSA procedure initially calculates covariance after channel reduction with principal component analysis (PCA); then, using moving windows of 2–12 months, long enough to capture annual cycles, we recovered 80% of the signal variance in the first 20 empirical orthogonal functions (EOFs), which included the seasonal cycles in the time series. In the first instance, surface motion time series were reconstructed using EOFs 1–6. This reconstruction captured the seasonal cycles but also included longer-term climate trends, notably 3 wetter years leading to the 2018 European-wide drought (Buras et al., 2020). This climate trend causes merging and shouldering of peaks that compromised the detection of the seasonal cycles, particularly in the west of the study area, where it is wetter. To overcome this difficulty, we used a surface motion time series reconstruction using EOFs 5 and 6 (Fig. 2; Sect 1.1 and Fig. S1 in the Supplement) which extracted only the seasonal cycles. The final MSSA reconstruction provides a signal of relative movement, not absolute surface height, where a rise in the bog surface is an increase in displacement values and a fall is a decrease (Fig. 2).
Examples of surface motion time series and the metric definitions
for
Third, the MSSA reconstructions were then analysed for two of three surface
motion metrics used to represent the condition of the peatland within each
pixel for each motion year using the R “pracma” peak-find function (R Core
Team, 2013). Metric one is the date of the annual peak “swelling” in the
seasonal cycle (peak timing) of the MSSA reconstruction within 12 months from mid-May (Fig. 2). This has been shown to relate to peatland ecohydrology (Alshammari, et al., 2020; Tampuu et al., 2020). Metric two is the annual maximum amplitude (m) in the surface motion signal (amplitude) measured from the previous minimum of the MSSA reconstruction (Fig. 2). This is an indicator of the elastic response of the peat to changes in water storage (Roulet, 1991; Waddington et al., 2010). Metric three is the multi-annual average velocity (m yr
As the 2018 drought caused severe and widespread subsidence, it was found to have subdued the multi-annual average velocity and for this reason, we concentrated our analysis between 10 May 2015 and 9 May 2018, discarding the drought period. Multi-annual average velocity was recalculated accordingly. While the impacts of climate anomalies on the time series were noticeable and interesting, the first step is to gain an understanding of how InSAR data can be used to characterise peatland condition, and we focus on this aspect. We also screened time series with multiple peaks per annum or years where peaks were not discernible, and these pixels were classed as having irregular cycles. Irregular time series made up 8.4 % of the data set and are commonly associated with water courses and damaged bog (including agriculture and some forested pixels). Exclusion of these irregular time series does not affect our conclusions. Additionally, for the first year in the time series (2015 to 2016) many surface motion time series are truncated preventing the accurate calculation of amplitude or peak timing. In that year the mapping is incomplete, so for clarity we show results for a complete motion year from the mid-section of the concentrated analysis (2016 to 2017). To understand the relationships between the three metrics with respect to peatland condition we visualised the metrics in a three-axis plot (Fig. 3).
Characteristics of the motion metrics by sub-site (SS) calculated from MSSA reconstructions of the InSAR-detected annual motion between 10 May 2016 to 9 May 2017.
The training bed of the sub-sites SS1 to SS5 was divided manually into 130 smaller polygons (hereafter, sub-site polygons). Polygons ranged from (0.3 to 0.6 km
The full set of polygons (sub-sites and random polygons) was then passed to
one of the authors with specialist peatland knowledge and based locally for a “blind” (i.e. without prior knowledge of or information about InSAR
metrics within the polygons) ground-based ecohydrological classification. For each polygon, the cover of plant functional types (PFTs;
Using the semi-quantitative scores, the PFT and hydrology polygon attributes
were clustered by similarity using a hierarchical cluster analysis (HCA;
Supplement 1.4, Fig. S3). To avoid an overly split hierarchical tree with
only one or two members per cluster requiring complex explanation, it was
deemed more informative to analyse the PFTs, hydrology, and the topography
categories separately from each other. For the PFTs, once the clustering was
complete, the average score of the semi-quantitative scale of each PFT in the cluster was ranked. The top three PFTs were used to characterise and name the plant functional group composition (Tables S1–S4 in the Supplement). For data visualisation of the results, clusters were further grouped based on the
dominant PFT, resulting in five plant functional groups:
Percentage proportion of clusters derived from hierarchical cluster analysis based on plant functional types (PFTs) represented in the polygons
of the five sub-sites and the random polygons. Clusters are defined by the
dominant (first) and co-dominant (subsequent) PFTs. PFT notations: Sp –
We also categorised topography into equal altitude belts (0–150, 151–300, and 301–450 m) and split slope face direction into four quadrants (north, east, south, and west facing) and ran the HCA. Except for the highest most eroded SS3 site, altitude and aspect did not show any meaningful cluster
groups and played no further part in the analysis. The lack of topographic
relationships is largely due to the gentle relief of the Flow Country, which has few sheltered slopes and deep valleys. Instead, we used average gradient
(degrees) in the polygon and found a natural breakpoint at 1.5
Within the three-axis plot, we then chose a point with a winter peak timing, a high amplitude, and extreme positive velocity, normally associated with
“soft”, wet
To further validate our ecohydrological classification map, we remotely
identified and marked the central locations of all the pool systems within the study area (328 in total) using Google Earth images and determined if
these markers corresponded to the soft, wet
Distribution of mean peak timing date by dominant plant functional
clusters for polygons with pools, streams, or other hydrological features
(e.g. drains, erosion gullies, peat cutting, or no apparent features) in
either a flat (gradient
From the frequency histograms of maximum peak timing, we discovered a bimodal distribution, showing an early and late peak (Fig. 3a) and defined the motion year to begin at the least active swelling period on 10 May to avoid dividing periods of maximum swelling into consecutive calendar years. The bimodal distribution peaks fall between August to October and December to February, similarly illustrated in the three-axis plots (Fig. 3b–f). For each of the sub-sites, the plots of the three motion metrics show small variations in the shape and position of the data cluster reflecting the diversity of peatland conditions sampled across the landscape.
The HCA revealed ecological groups relating to dominant plant functional types that were comparable between the sub-site and random polygons (Table 2) as well as hydrological groups separating polygons with pool systems and polygons with streams from all other polygons (Fig. S3). When the HCA classifications and topographic information (slope) were compared to the surface motion metrics, we determined the following consistent relationships for the sub-site and random-site polygons.
Shifts in the peak timing distributions relate to a combination of topography, hydrology, and plant functional group (Fig. 4), and peak timings
themselves were consistent within groups between the 3 motion years.
Distribution density of multi-annual velocity for each plant functional group and polygon type plant functional polygons
Multi-annual average velocities that were most positive (gain of height over
time) were almost entirely dominated by
When multi-annual average velocities are compared across different management classes (Fig. 6), the least negative values are observed under conservation management and most negative values are associated with forest-to-bog management, a restoration approach that typically involves compaction from heavy machinery during the removal of conifer stands, followed by drain blocking and surface re-profiling. This restoration class shows a broader distribution in long-term multi-annual average velocity than other management classes, reflecting a variable degree of recovery associated with differing starting conditions, time since initiation (ranging from 0 to
Distribution density of multi-annual average velocity for different management groups and by polygon type (sub-site and random). Management polygons
The factors that influence amplitude can be deduced from relative annual
amplitude change and peak timing plots for the three most dominant PFT clusters (
Timing of and relative amplitude for 3 consecutive years (2015–2018) with respect to slope gradient (degrees), dominant plant
functional type (PFT; grass, shrub, and
The observed relationship between surface motion metrics and ecohydrology is
readily interpreted in the context of reported field measurements of peat
surface motion (Howie and Hebda, 2018; Morton and Heinemeyer, 2019). Flatter
sites under near-natural conditions are poorly drained, wetter, and dominated
by
Mapping and applying thresholds to the Euclidian distance calculations into
these three broad peatland classes relative to the soft peat condition
generated the peatland condition map (Fig. 8a). The classification produced a patchwork of conditions in the Flow Country, and the map evidences the widespread occurrence of the stiff peat condition associated with both
naturally drier areas on the slopes and wet, soft peat margins but also
areas made drier by a land-use history of burning and drainage. The impact of
restoration activities, following the felling of forestry on deep peat in
the last 25 years, can also be seen: recent forest-to-bog clearance is displayed as “thin/modified” peat, whilst areas well on their way to recovery are showing as either the soft or stiff condition (Fig. 8b and c). In polygons where forest is planted on peat, the signal is much more mixed with a greater proportion of the irregular class. This mixture is a result of the poorer SAR response over trees and the variable conditions encountered within forest stands. For example, in these forestry plantations, fire breaks, gaps between blocks (termed “rides”) and riparian areas that were never planted can still be wet, soft, and
The ecohydrological classification of “soft”, “thin/modified”, and “stiff” peatland condition, with respect to the location of pool systems, and areas of forest-to-bog restoration in the study area.
Using the criteria for the remote validation that pool systems should always
include a pixel of the soft peat category within the 150 m buffer, 97.9 % (
Within the area, our method identifies approximately 254 km
Our most important finding is that surface motion metrics derived from APSIS InSAR time series enable almost continuous spatial and temporal characterisation of peatland condition at large scales. That the SAR data can penetrate cloud cover, measure regular physical displacement of the surface, and capture a known dynamic behaviour associated with peat resilience gives this approach a significant lead over the far more challenging effort to measure peatland condition from optical reflectance data (e.g. Artz et al., 2019). This is compounded by the fact that peatland areas are often obscured by cloud (Minasny et al., 2019). A valuable exercise would be to quantify the similarities and contrasts of motion-mapped peatland condition to optical products, and we anticipate that motion data will bring different and complementary information. This may be advantageous for restoration monitoring, and information on peatland mechanical condition from surface motion may be key to resolving weaknesses in optical studies, for example in carbon accounting (Couwenberg et al., 2011).
The sensitivity and dynamic response of surface motion metrics to changes in
the state of the peatland system should make the method ideally suited to
monitoring and informing peatland management and restoration. Globally,
large areas of northern peatlands degraded by historic drainage, grazing, and
forestry are now under or targeted for restoration (Rochefort and Andersen, 2017). Consequently, peatland conservation and restoration are increasingly perceived as critical tools in the fight against global climate change (Leifeld and Menichetti, 2018; Amelung et al., 2020; Günther et al., 2020). Restoration strategies typically involve raising water levels to re-establish wet conditions. The expectation is that this will promote
In the case of blanket bog landscapes, our finding of naturally stiff drier shrub and soft, wetter
In natural landscapes, these peatland states are a consequence of landscape
evolution in which the vertical accumulation of peat must be counterbalanced
on an appropriate spatial and temporal scale by erosion (Large et al., 2021). Drier states correspond to areas of net carbon loss due to natural drainage, incision, and erosion along peatland margins, and wetter states correspond to peatland interiors, areas with low gradient that tend towards carbon accumulation. In this context, to restore a site that is naturally stiff and dry to the soft, wet state would risk instability, while the opposite would fail to optimise carbon storage. A more suitable and sustainable ambition is to accept that restored blanket bog sites may follow different trajectories towards naturally
The approach outlined here should be readily transferable to alternative peatland settings within different parts of the global peatland climate space. Using surface motion metrics identified from the InSAR time series of peatland motion, a surface deformation space for a given peatland system can be defined. The position of ecohydrological characteristics within this space can then be deployed to quantify the state of the peatland system and map changes with respect to climate change and management intervention. This capacity to customise the approach is valuable as it provides the means to measure peatland condition at a global scale. If realised, this would enhance our understanding of the large-scale functionality of peatland landscapes and provide the robust evidence base required for sustainable peatland management.
Data sets are available at
The supplement related to this article is available online at:
AS led the processing of the InSAR data that AVB post-processed, analysed, and visualised. RA recorded the polygon attributes, mapped the pools, contributed to data visualisation, and completed the ground surveys with CM. DJL developed the overall idea of applying InSAR for this purpose. All authors were responsible for critical contributions, passing the final paper, and editing text and figures.
Andrew Sowter is affiliated with Terra Motion Limited. The APSIS (Advanced Pixel System using Intermittent SBAS) method is owned by the University of Nottingham and is the subject of a UK patent application (no. 1709525.8) with the inventor named as Dr. Andrew Sowter; it is currently patent pending.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. We are not responsible for the consequences of any decisions or actions even if they have been influenced by the material and ideas in this paper.
The authors would like to thank members of the following organisations who provided access to sites for surveys or insight and local knowledge about past and present management over the study area: NatureScot Peatland ACTION, Royal Society for the Protection of Birds, Plantlife Scotland, Forestry and Land Scotland, Scottish Forestry, Welbeck Estate and Shurrery Estate. David Gee and Ahmed Athab are thanked for their assistance with the APSIS InSAR data output. The “National Soil Map of Scotland” copyright and database right lie with the James Hutton Institute v.1_4, and it is used with the permission of the James Hutton Institute. All rights reserved. Any public sector information contained in these data is licensed under the Open Government License v.2.0.
This research has been supported by the Natural Environment Research Council (InSAR TOPS grant no. NE/P014100/1 to David J. Large, Roxane Andersen and Chris Marshall) and the Leverhulme Trust (Leverhulme Leadership award grant no. 1466NS to Roxane Andersen and Chris Marshall).
This paper was edited by Daniella Rempe and reviewed by two anonymous referees.