To meet the sustainable development goals and enable sustainable
management and protection of peatlands, there is a strong need for improving
the mapping of peatlands. Here we present a novel approach to identify peat
soils based on a high-resolution digital soil moisture map that was produced
by combining airborne laser scanning-derived terrain indices and machine
learning to model soil moisture at 2 m spatial resolution across the Swedish
landscape. As soil moisture is a key factor in peat formation, we fitted an
empirical relationship between the thickness of the organic layer (measured
at 5479 soil plots across the country) and the continuous SLU (Swedish University of Agricultural Science) soil moisture
map (R2= 0.66, p<0.001). We generated categorical maps of
peat occurrence using three different definitions of peat (30, 40, and 50 cm
thickness of the organic layer) and a continuous map of organic layer
thickness. The predicted peat maps had a higher overall quality (MCC = 0.69–0.73) compared to traditional Quaternary deposits maps (MCC = 0.65)
and topographical maps (MCC = 0.61) and captured the peatlands with a
recall of ca. 80 % compared to 50 %–70 % on the traditional maps. The
predicted peat maps identified more peatland area than previous maps, and
the areal coverage estimates fell within the same order as upscaling
estimates from national field surveys. Our method was able to identify
smaller peatlands resulting in more accurate maps of peat soils, which was
not restricted to only large peatlands that can be visually detected from
aerial imagery – the historical approach of mapping. We also provided a
continuous map of the organic layer, which ranged 6–88 cm organic layer
thickness, with an R2 of 0.67 and RMSE (root mean square error) of 19 cm. The continuous map
exhibits a smooth transition of organic layers from mineral soil to peat
soils and likely provides a more natural representation of the distribution
of soils. The continuous map also provides an intuitive uncertainty estimate
in the delineation of peat soils, critically useful for sustainable spatial
planning, e.g., greenhouse gas or biodiversity inventories and landscape
ecological research.
Introduction
Soil, i.e., the pedosphere, provides a suite of unique and essential
ecosystem services globally (Smith et al., 2021), such as food (Silver et
al., 2021) and forest production (Laamrani et al., 2014), conservation of
water resources (Cheng et al., 2021), modulation of extreme events (Saco et
al., 2021), and regulation of the global carbon cycle (Scharlemann et al.,
2014). The characteristics of the pedosphere depend on multiple soil forming
factors, i.e., parent material, climate, organisms, topography, and time
(Jenny, 1941). In the northern boreal regions, the parent material of soil
is mostly composed of Quaternary deposits that were formed during several
cycles of glaciation and deglaciation (Imbrie et al., 1993). Therefore, the
surficial sediments in these regions are mainly characterized by unsorted
deposits from preceding stadial and interstadial periods (Hirvas et al.,
1988; Olsen et al., 2013). Sorted sediments were also deposited in
glaciofluvial deposits such as eskers, as well as marine and lacustrine
environments during and after the glacial melting (Stroeven et al., 2016).
Furthermore, the humid climate of the northern boreal regions favors anoxic
soil conditions that support widespread mire formation and peat deposits
(Ivanov, 1981; Rydin and Jeglum, 2013). Altogether, the boreal ecosystem
contains one of the largest terrestrial carbon storages of the world
(Beaulne et al., 2021; Loisel et al., 2014), which makes it significantly
important for the earth system, especially under the current global warming
and climate change (Astrup et al., 2018). The boreal biome stores about 272
(±23) Pg of C and 60 % of this carbon is found in soil organic
matter (Pan et al., 2011).
The soil moisture regime, which is also a strong regulator of soil organic
matter (SOM) dynamics, is a critical factor for ecosystem functioning and
management in the boreal regions (Ivanov, 1981; Sewell et al., 2020). Soil
moisture and SOM feedback has been clearly documented, for example, in
central and northern Sweden which comprises a key boreal forest region
(Hounkpatin et al., 2021). In Swedish boreal podzols, dry sites have an
average soil organic carbon (SOC) stock of 6.7 kg C m-2 while
mesic-moist sites had 9.7 kg C m-2 in the mineral horizons and 2.0 to
4.4 kg C m-2 alone in the organic horizon (Olsson et al., 2009).
Moreover, SOC stock in peatlands of this boreal region is even higher,
ranging from 22.6 to 72.0 kg C m-2 (Beaulne et al., 2021; Loisel et
al., 2014). This clear relationship between SOM and soil moisture regime can
provide an opportunity for mapping the distribution of peat soils in the
boreal ecosystem. Soil maps detailing the distribution of mineral and peat
soils across the boreal regions could support better the sustainable
management and ecological restoration activities in these regions.
Unfortunately, availability of soil maps are substantially limited for most
regions and existing maps are mostly based on the technology and data from
the 1900s – usually a result of manual interpretation of topographical
maps, aerial photos, and field investigations (Olsson, 1999).
Recently, high-resolution topographic data from airborne laser scanning
(ALS) have provided a new avenue for producing highly accurate maps of soil
and site conditions at local to regional scales (Behrens et al., 2018;
Latifovic et al., 2018; O'Neil et al., 2020; Pouliot et al., 2019; Prince et
al., 2020). In Sweden, for instance, ALS data at 2 m spatial resolution were
combined with machine learning (Lidberg et al., 2020) to map soil moisture
condition in a recent study that exhibited both categorical soil wetness
classes and continuous moisture variation at the national scale (Ågren
et al., 2021). These maps provide new opportunities to explore the
relationship between soil moisture regime and thickness of the organic
layer, which in turn can be used to map the horizontal distribution of peat
and mineral soils at landscape scale. In this study, we used Sweden as a
test area for mapping the distribution of peat soils specifically based on
soil moisture information, where we combined data from the National Forest
Inventory (NFI) (Fridman et al., 2014), the Swedish Forest Soil Inventory
(Stendahl et al., 2017), and the nationwide soil moisture map (Ågren et
al., 2021).
This study focuses on the division between peat and mineral soils based on
soil moisture condition. Here, we define peat soils and peatlands according
to the definition provided by Rydin and Jeglum (2013).
Peat is the remains of plant and animal constituents accumulating under more or less water-saturated conditions owing to incomplete decomposition. It is the result of anoxic conditions, low decomposability of the plant material, and other complex causes. Peat is organic material that has formed in place, i.e., as sedentary material, in contrast to aquatic sedimentary deposits. Quite different plant materials may be involved in the process of peat formation, for instance, woody parts, leaves, rhizomes, roots and bryophytes (notably Sphagnum peat mosses). … Peatland generally refers to peat-covered terrain while a minimum depth of organic layer is required for a site to be classified as peatland.
For technical or
practical reasons, a minimum organic layer depth is commonly used to define
peatlands. However, such a technical depth-based definition of peatlands
incorporates strong biases in aerial estimates of peatland across large
regions. In addition, there is inconsistency – nationally and
internationally – about the minimum organic layer depth required for an area
to be classified as a peatland. For example, the Geological Survey of Sweden
sets a threshold of 50 cm organic layer depth for peatland. The same
threshold is also used in Scotland (Burton, 1996) while an organic layer
depth of 40 cm is recognized for peatland in Canada, England, and Wales
(Burton, 1996; Cruickshank and Tomlinson, 1990; Zoltai et al., 1975). This
40 cm limit also follows the definition of “Histosols” (i.e., soils
consisting of dominantly organic materials) according to the World Reference
Base for Soil Resources (WRB, 2015). Moreover, an organic layer thickness of
30 cm is used for defining peatland by the Swedish Forest Soil Inventory and
NFI, Geological Survey of Finland, International Mire Conservation Group,
and International Peat Society (Joosten and Clarke, 2002; Lappalainen and
Hänninen, 1993). A shallower threshold of organic layer thickness to
define a peatland will include more of the mineral-soil wetlands that often
have a substantial content of organic matter within their surface layers.
But the organic matter in these mineral-soil wetlands has not had a high
enough accumulation rate or has not had enough time for thicker peat
formation. Such soils in the boreal region may include the soil type “peaty
mor” and form landscape features such as “cryptic wetlands,” which are
usually elongated small areas with saturated soils commonly found in the
bottom of small valleys, and riparian peat (Creed et al., 2003; Ploum et
al., 2018; Kuglerova et al., 2014a). Therefore, to be useful for different
practitioner groups and the scientific community, the map of peat soil
distribution needs to incorporate multiple definitions of peatland based on
the thickness of organic layer.
In this study, we developed an approach for accurate mapping of peat soil
distribution based on the relationship between soil moisture variation and
organic layer thickness using Sweden as a test case for a peat-rich northern
landscape. The specific objectives of our study were to (i) generate
categorical maps of mineral vs. peat soils across Sweden using multiple
definitions of organic layer thickness for peatlands as described above,
(ii) produce a continuous organic layer thickness map that could visualize
and be useful for any definition of peatlands, (iii) evaluate our predicted
peatland estimate of Sweden against inventory data and compare with the
existing estimates from traditional maps, and (iv) provide the most accurate
national estimates of peatland coverage and constrain the uncertainty in the
estimates. This study provides a guide to map mineral and peat soils in any
northern boreal region that will be essential for effective ecosystem
management and for supporting sustainable development goals related to
restoration of degraded land and climate action.
MethodStudy area
Our study area, the whole of the country of Sweden (latitude
55–70∘ N, longitude 11–25∘ E) falls in the boreal and
temperate forest region of northern Europe (Fig. 1a). According to
satellite data, the land cover in Sweden is dominated by forest, covering
69 % of the country, followed by agricultural land (9 %), open peatland
(9 %), grassland (8 %), rock outcrops (5 %), and urban land (3 %)
(Schöllin and Daher, 2019). The climate according to Köppen is
classified as warm summer continental or hemiboreal climates (Dfb) and
subarctic or boreal climates (Dfc) (Beck et al., 2018). There is a notable
elevation and precipitation gradient from north to south, and from east to
west of the country, with annual precipitation ranging from 400 to 2100 mm
(1961–1990). The soil type in Sweden is dominated by Podzols, but more
complex distribution of Histosols, Gleysols, Arenosols, and Regosols are also
common (Olsson, 1999). The topogenous fens are most common wetland types in
Sweden, followed by string mixed mires and string flark fens (Gunnarsson and
Löfroth, 2009).
Swedish Forest Soil Inventory (SFSI)
The Swedish Forest Soil Inventory (SFSI) was used for organic layer
thickness data (Olsson, 1999; Stendahl et al., 2017). The spatial density of
the inventory plots varies throughout Sweden due to landscape heterogeneity,
emanating from both natural and human-induced conditions (Fig. 1b). The
SFSI is conducted on plots with a radius of 10 m. In case of heterogeneity
inside the plots, they are divided into partial plot areas and data are
recorded on the sub plots. In these plots, the organic layer thickness was
directly recorded from soil pits (a soil sampling circle with 1 m radius
which is located within the plot area). We included a total of 5479 data
points for organic layer thickness (Fig. 1b).
(a) Sweden's position in the northern boreal zone/taiga; map data
from Dinerstein et al. (2017). (b) Black dots indicate the sites for the
Swedish Forest Soil Inventory (Olsson, 1999; Stendahl et al., 2017) where
the thickness of the organic layer has been measured (n= 5479).
Generating categorical (peat vs. mineral soils) and continuous organic
layer thickness maps
This study utilized the SLU (Swedish University of Agricultural Science)
soil moisture map that exhibits soil moisture variation across Sweden on an
arbitrary scale from 1 to 100, i.e., from dry to wet (Ågren et al., 2021).
The method development of the SLU soil moisture map was described in
Ågren et al. (2021) and a previous version in Lidberg et al. (2020).
Here we give a brief introduction to the SLU soil moisture map as this study
is based on that map. It was developed using a combination of digital
terrain indices (derived from 2 m resolution digital elevation model) based
on airborne laser scanning (ALS) data and ancillary data on Quaternary
deposits, soil depth, annual and seasonal runoff etc. The topographical
indices were calculated on window sizes from 6×6 to
160×160 m to allow for both large-scale and small-scale controls
on soil moisture. By working on a higher resolution than most studies, we
aimed to improve the modeling of soil moisture in local pits and
small-scale variability in riparian zones. In total, 45 different maps (or
features) were evaluated for predicting soil moisture, and after the feature
reduction step 28 different maps was included in the final predictive
machine learning model (e.g., extreme gradient boosting model, Chen et al.,
2020) that was used to predict the soil moisture across Sweden. Top
predictors for mapping soil moisture across Sweden included depth-to-water
maps and topographic wetness index maps calculated at different scales and
resolutions, but also the autumn runoff and latitude (Ågren et al.,
2021). These maps are now publicly available (Sveriges lantbruksuniversitet/Swedish University of Agricultural Sciences, 2022). The model was
trained and tested using 19 643 field observations from the NFI of which
80 % were used for training and 20 % was used for testing. The soil
moisture map has a Cohen's kappa (Cohen, 1960) and Matthews correlation
coefficient (MCC) (Matthews, 1975) values of 0.69 and 0.68, respectively.
The map displays the probability of a soil being wet (0–1), which was
rescaled to 1–100 (Fig. 2a) so the variability could be displayed without
the use of decimals which reduced file size. There is a strong correlation
with the probability of a soil being wet and the soil moisture (i.e., Fig. 6
in Ågren et al., 2021), and the use of a scale from 1–100 allows the
modeling of smooth transitions from dry to wet instead of fixed categories.
The current SLU soil moisture map version includes 98.7 % of the Swedish
landmass. The remaining 1.3 % was not laser scanned at the time of map
production. Thus, the prediction of peat soil maps in this study also
excludes those 1.3 % areas of Sweden, which added minor uncertainties in
our national estimates of peat soils.
(a) Example of the SLU soil moisture map showing the probability of
a soil being wet (0 %–100 %) (Ågren et al., 2021). (b) Green areas
indicate the forest landscape in Sweden that is sampled by the Swedish
National Forest Inventory (Fridman et al., 2014) and the Swedish Forest Soil
Inventory (Olsson, 1999; Stendahl et al., 2017).
We tested the relationship between the SLU soil moisture map and the
thickness of the organic layers. The organic layer thickness was registered
in the Swedish Forest Soil Inventory up to a maximum thickness of 99 cm. We
first divided the Swedish Forest Soil Inventory data (from Sect. 2.2) into
calibration and validation datasets using a randomized 50 % split on the IBM
SPSS statistics program. The calibration dataset was then used to establish
a relationship between the SLU soil moisture map and the thickness of the
organic layers using the curve estimation procedure in IBM SPSS statistics
version 27, which fits a total of 11 linear and non-linear models. The model
with the highest R2 was selected to describe the relationship (Fig. 3).
The categorical maps were generated based on the relationship with the
highest R2. By solving Eq. (7) for X when Y was 30, 40, and 50 cm, we could
determine the soil moisture limits for classifying peat soil. At organic
layer thickness of ≥ 30, ≥ 40, and ≥ 50 cm, the soil moisture
limits were ≥ 76 %, ≥ 83 %, and ≥ 87 %, respectively
(Fig. 3). These thresholds were used to reclassify the soil moisture map
into maps of peat extent while the remaining soil was delineated as mineral
soil. Hence, three different peatland maps were derived, which we referred to
as “peat ≥ 30 cm”, “peat ≥ 40 cm”, and “peat ≥ 50 cm”.
In addition, a continuous organic layer thickness map was generated by
applying Eq. (7) in raster calculator on the continuous soil moisture map.
This continuous map does not contain discrete classes of mineral and peat
soils, rather it presents the distribution of organic layer thickness
across the landscape. The accuracy of the maps was then tested using the
validation dataset described in Sect. 2.5.
As the data underlying the maps comes from NFI and SFSI, we lack evaluation
data from other land use types. We therefore first defined the Swedish
forest landscape to indicate where the predicted maps could be trusted. The
Swedish Forest Soil Inventory samples both productive forest land (defined
as areas with a potential wood yield capacity of > 1 m3 ha-1 yr-1) and low-productivity forest land (with lower yield
capacity), such as pastures, thin soils, non-forest peatlands, rock
outcrops, and areas close to the tree line (e.g., birch forests in the alpine
region). We generated a map of the Swedish forest landscape (Fig. 2b) by
reclassifying the National Land Cover Database (NMD), a land cover map over
the entire country in 10 m resolution (Olsson and Ledwith, 2020) where we
excluded areas outside the NFI's sampling (crop fields, urban areas, roads,
rail roads, and power lines) and the alpine region above the birch forest
(based on an elevation threshold which is a function of latitude).
Quaternary deposits maps and topographical maps
We used the Quaternary deposit and topographic maps of Sweden for comparison
with our predicted estimates of peat soil distribution. In the Quaternary
deposits map from the Geological Survey of Sweden (SGU), peatland is
delineated based on these criteria: (i) organic layer thickness ≥ 50 cm, (ii) minimum detected area of 2500 m2, and (iii) the estimated
position accuracy ranging 25–200 m (Karlsson et al., 2021). Some linear
peatlands narrower than 50 m, but important for the understanding of the
geology, were also included and expanded to 50 m width (Cecilia Karlsson, SGU, personal communication, 2022). However, there are various scales with different coverages
for the Quaternary deposit maps in Sweden, such as 1:25000 covers 1.7 %
of the area, 1:50000 covers 2.7 %, 1:100000 covers 47 %, 1:200000
covers 1.4 %, 1:250000 covers 21.2 %, 1:750000 covers 33.6 %, and 1:1000000 covers 100 %. These maps were merged together to produce a single
Quaternary deposits map for the whole country where the map with the highest
scale was always chosen in areas with overlapping maps (Lidberg et al.,
2020). This Quaternary deposit map contains five categories of deposit,
including till soils, thin soils and rock outcrops, peat, coarse sediments
(sand–gravel–boulders), and fine sediments (clay–silt). The coverage of each
category was calculated by summarizing the areas of all polygons within the
respective category. Finally, the total coverage of peat category was used
for comparison with our predicted estimate of peatland.
Another commonly used mask for delineating peatlands in Sweden is wetlands
from the topographic map, i.e., the Swedish property map (1:12500)
(Lantmäteriet, 2020). However, the wetland class in the property map is
not based on the thickness of organic layer; instead, it is defined as
peat-forming mires or watery mires and grouped into two categories – (i) wetlands that can be crossed on foot and include mires with shrubs, sedge,
and trees of variable densities, and (ii) impassible wetlands that are
inaccessible on foot and include watery mires, which are mostly fens, soft
bed without vegetation, and overgrown lakes with reed. Here we calculated
the total coverage of the wetlands and impassible wetlands by summarizing
the areas of the polygons for all of Sweden. These two categories of
wetlands were merged together to find a peatland coverage for the whole
country and compared with our predicted estimate.
Statistical evaluation of the accuracy of the different peat maps
We evaluated the accuracy of the categorical peatland maps using confusion
matrix and the validation dataset (see Sect. 2.4). Three predicted
peatland maps (i.e., “peat ≥ 30 cm”, “peat ≥ 40 cm”, and “peat ≥ 50 cm”) along with the peatland coverage derived from Quaternary
deposit and topographic maps were evaluated following the same approach.
More specifically, the ground truth for peat was the SFSI evaluation plots
where observed organic layer thickness was larger than the respective
thresholds, and the mineral soil ground truth was the SFSI evaluation plots
with observed organic layer thickness lower than the respective thresholds.
The following accuracy metrics were calculated based on the confusion
matrix:
1Accuracy =TP+TNTP+FP+FN+TN2Precision =TPTP+FP3Recall=TPTP+FN4Specificity=TNTN+FP5MCC=TP×TN-FP×FNTP+FP×TP+FN×TN+FP×(TN+FN)6Kappa =Po-Pe1-Pe,
where true positives (TP) is the number of observations where the field
data and map agree that soils are peat; true negatives (TN) is the number
of observations where the field data and map agree that soils are mineral
soils; false positives (FP) is the number of observations where the map
predicts peat while soils are mineral soils; false negatives (FN) is the
number of observations where the map predicts mineral soils while soils are
peat; Po= Relative observed agreement; and Pe= Hypothetical
probability of chance agreement. Additionally, the continuous map of the
organic layer thickness was evaluated by calculating the goodness of fit
(R2) and root mean square error (RMSE) from the predicted and observed
organic layer thickness.
Peatland estimates from the NFI and SFSI
To compare the predicted peat soil estimates from the maps with other
estimates of peatland coverage in Sweden, we also calculated peatland
coverage by statistical upscaling from the national inventories, i.e., NFI
and SFSI (SLU, 2021) to derive a complete coverage for the Swedish forest
landscape. From these inventories, peat coverage was estimated by the
statistical experts at NFI (Fridman et al., 2014) and SFSI (Stendahl et al.,
2017) in six different ways: (1) peat coverage was registered in the 2016–2020
NFI survey plots (7 and 10 m radius) in the following classes – peat
coverage 0 % (n= 33 161), 0 %–50 % (n= 1553), 50 %–100 % (n= 1439),
and 100 % (n= 6080). For the upscaling, the peat coverage ranges of
0 %–50 % or 50 %–100 % were assumed to cover 25 % and 75 %,
respectively, of the plot. It should be noted that isolated peatland
patches smaller than 25 m2 on plots were disregarded. Details on the
NFI data upscaling approach can be found in (Hånell, 2009). (2) NFI also
conducts assessment of cover of different species on 5.64 m plots. On
natural peatlands, the bryophytes are dominated by the genus Sphagnum or brown mosses
(Amblystegiaceae family). Polytrichum commune commonly also grows in bogs and in riparian zones. Their coverage
is measured in the NFI, here we pool the coverage of Sphagnum, brown mosses, and
Polythrichum commune into a class that we call “peat indicative mosses”. In addition to NFI,
the 2003–2012 SFSI database registered peat soils as (3) Quaternary deposits
with organic layer thickness ≥ 50 cm, and as (4) soil type histosol with
organic layer ≥ 40 cm (WRB, 2015). Moreover, SFSI classified the humus
form according to the depths of the OF (soil taxonomy Oe), OH (soil taxonomy
Oa), and H horizons, and amount of aggregates in case of an A horizon. Based
on humus form, peat soils were registered as (5) peat if organic layer ≥ 30 cm and 6), peat with no thickness restriction (i.e., peat if organic layer ≥ 30 cm + peaty mor with organic layer < 30 cm). This
upscaling from the SFSI database was performed following the approach
described in Nilsson et al. (2018). The survey data from NFI and SFSI are
based on sampling of the Swedish forest landscape, not the total land area.
In short, this includes productive forest land, pastures, mires, rock
outcrops, and alpine region below treeline but excluding arable land, alpine
region above treeline, railroads, power lines, roads, and urban areas.
However, the exact definitions of forest land differ slightly among sources
which introduces an uncertainty in the national estimates. For example, the
forest landscape mask (Fig. 2b) covers 343 000 km2, while NFI and SFSI
suggest that 338 000 and 306 000 km2, respectively, are
forest land. This can explain some smaller discrepancies between different
sources in Tables 2 and 3.
ResultsRelationship between soil moisture and thickness of organic layer
The relationship between the soil moisture variation from the SLU map
(Ågren et al., 2021) and organic layer thickness derived from the
Swedish Forest Soil Inventory was well described with a cubic relationship
(Eq. 7, R2= 0.66, p<0.001; Fig. 3).
Y=6.4145+(0.6673⋅X)+(-0.0214⋅X2)+(0.0002⋅X3),
where Y is the thickness of the organic layer (cm) and X is the soil moisture
level from the SLU soil moisture map. The s-shape of the curve is due to a
rapid increase in organic layer thickness at the high end of soil moisture
and a sharp decrease in organic layer thickness at the lower end of the
moisture spectrum. The driest sites are generally found on crests and ridges
characterized by rock outcrops with very thin organic layers.
The red line depicts the cubic relationship (R2= 0.66)
between the thickness of the organic layer in SFSI plots and the probability
for the soil being wet according to the SLU soil moisture map (Ågren et
al., 2021). The colored bars and black dots indicate the mean organic layer
thickness (in the calibration dataset) for soil moisture variation from 0
(dark orange) to 100 (dark blue) as extracted from the SLU soil moisture map
(shown using the same color code). The error bars represent the standard
error of mean of the organic layer thickness at each soil moisture level.
Note that the soil moisture in percentages denotes the probability of a soil
being wet expressed as percent, not the volumetric soil water content.
Statistical evaluation of different peatland maps
Our predicted peat maps generally performed better than the existing
topographic and Quaternary deposit map products (Table 1). Particularly, the
predicted peat ≥ 50 cm map was of highest quality in terms of accuracy,
recall, kappa, and MCC values. The prediction of peat ≥ 40 cm and peat ≥ 30 cm had equal kappa (i.e., 0.69) and MCC (i.e., 0.69) values; however, they
were about 5 % less accurate than the kappa and MCC of peat ≥ 50 cm.
Although all three predicted peat maps exhibited better accuracies for most
metrics, the topographic map had higher precision and specificity values.
Evaluation metrics of different maps of peat soils. TP = True
positives, TN = True negative, FP = False positives, FN = False
negatives. The other metrics are explained in Eqs. (1)–(6). Kappa refers to
Cohen's kappa and MCC to the Matthews correlation coefficient. The peat maps
that we predicted in this study are highlighted in italics. The topographic
and Quaternary deposits maps are existing national products.
TPTNFPFNAccuracyPrecisionRecallSpecificityKappaMCC(%)(%)(%)(%)Peat ≥50 cm map427221713510491.775.980.494.30.730.73Peat≥40 cm map417213316511490.171.778.592.80.690.69Peat ≥30 cm map537201319913488.572.980.091.10.690.69Topographic map26423183926689.487.149.898.40.580.61Quaternary deposits map363222713016789.773.668.594.50.650.65
To illustrate the confusion matrix for evaluating classified data, we
exemplify this in red in Fig. 4 using peat with ≥ 50 cm depth. The
model misclassifies peat as mineral soil in 104 instances (FN) and
misclassifies mineral soils as peat in 135 cases (FP). There are 427 observations
correctly classified as peat (TP) and 2217 are correctly classified as
mineral soils (TN). So even though there is a lot of scatter in Fig. 4,
2644 of 2883 soil pits are correctly classified in this example. The
prediction of continuous organic layer thickness captures the general
patterns, with a positive relationship between measured and observed
thickness of organic layer (R2 of 0.67 and p<0.001). However,
the confidence interval (CI) in Fig. 4 and a root mean square error (RMSE)
of 19 cm indicate a rather large uncertainty in the estimated thickness of
the organic soils. Moreover, the cubic relationship shown in Fig. 3 (i.e.,
Eq. 7) could not fully capture the rapid increase in organic layer thickness
that occurred with high soil moisture and the sharp decrease in organic
layer thickness with dry soils. Hence, the predictions in Fig. 4 only
ranged 6–88 cm compared with the measured data which ranged 0–99 cm. As a
result, the model systematically overestimates thin organic layers and
underestimates thick organic layers.
Predicted vs. measured continuous thickness of the organic layer in
the evaluation dataset (n= 2883). Red lines indicate the quadrants of
the confusion matrix for classified data using peat with ≥ 50 cm depth.
Dashed line indicates the 1:1 line and black line a linear regression
(R2= 0.67, p<0.001), grey lines indicate 95 % CI. Field
measurements of peat thickness that were 99 cm or above were reported as 99 cm, hence the many overlapping data points.
Visual interpretation of peatland maps
As described above, the predicted area of peat ≥ 50 cm covers larger
areas than the existing national maps (Table 2), which was also evident in
Fig. 5 (panels a and d). The peat ≥ 50 cm map captured more riparian
peat soils than the topographic and Quaternary deposit maps (Fig. 5a). It
also better delineated the mire areas that are obscured by tree canopy and
typically not captured using traditional mapping techniques based on aerial
photos (illustrated in Fig. 5d, cf. hillshade and aerial photo in Fig. A1 in Appendix A). Although there were differences in peatland coverage
between the predicted maps at different thresholds (i.e., ≥ 30, ≥ 40,
and ≥ 50 cm peat maps), they provided more or less comparable
distribution of peat across the landscape (Fig. 5b, e). A RMSE of 19 cm
for the prediction of continuous organic layer thickness (Fig. 5c, f),
indicates that the depth estimates are uncertain and should not be taken
literally. However, we argue that this map can be used to display the
horizontal distribution of peat and mineral soils, and indicates a smoother
and more realistic impression of the high variability in areal distribution
of the organic soils. The map with continuous organic layer depths exhibits
pixel-by-pixel variation in organic layer thickness ranging from 6 to 88 cm
across the country, and unlike the categorical maps, does not demonstrate
discrete soil classes which may cause misrepresentation of natural
conditions and distribution of organic soils due to oversimplification.
The left panels (a, d) show an example of two different locations
with peatlands with the following three maps overlaid on each other for
comparison: Quaternary deposits map (1:25000) (black hatched area),
topographical map (1:12500) (blue hatched area), and the predicted peat ≥ 50 cm map (2 m resolution) (brown area). The center panels (b, e)
demonstrate the difference in the predicted peat maps using 30, 40, and 50 cm organic layer thresholds by superimposing the predictions on top of each
other with peat ≥ 30 cm in the bottom (a wider distribution) and peat ≥ 50 cm at the top (a narrower distribution). The right panels (c, f)
exemplify the continuous map of organic layer thickness derived using
Eq. (7) in two different areas. Note that the thickness of the organic layer
may be underestimated in some areas as the full depth of the organic
deposits are not registered in the Swedish Forest Soil Inventory database
(ranges 0–99 cm).
Peat coverage in Sweden
We observed notable differences in peat coverage between our predicted maps
and estimates from the existing map products (Table 2). There was large
variation in our predicted estimates of peatland coverage at different
thresholds of organic layer thickness ranging from 70 000–94 000 km2,
and this suggests that 18 %–24 % of Swedish landmass is covered by peatland,
depending on the definition used, which is considerably larger than the
estimates from the existing Swedish map products. Namely, the peatland
coverage from topographic map was only 13 %, while it was just 14 %
based on the Quaternary deposit map. This is in comparison to the coverage
of other Quaternary deposits in Sweden from the calculations of the
Quaternary deposits map: till soils 53 %, thin soils and rock outcrops
18 %, coarse sediments (sand–gravel–boulders) 8 %, fine sediments
(clay–silt) 6 %, and other (ice, fillings, etc.) 1 %. The forest
landscape, according to the map in Fig. 2b, covers ca. 85 % of the land
area in Sweden. While excluding peatlands outside forest land decreased the
overall coverage of peat to 68 000–88 000 km2, we see that in
relative terms, peatlands were more common in the forest landscape
(21 %–26 %) (Table 3) compared to the national averages (Table 2). The peat
coverage estimates of the forest landscape according to upscaling from NFI
and SFSI (Sect. 2.6) ranged 55 000–91 000 km2, depending on the
definition used. The average errors from these surveys range 2 %–4 %.
Coverage of peatlands in Sweden according to different maps, in
km2 or in % of the land area in Sweden (excluding lakes and large
≥ 6 m wide rivers). The peat maps that we predicted in this study are
highlighted in italics. The topographic and Quaternary deposits maps are
existing national products.
MapPeat coverage of total land area (km2)(%)Peat ≥50 cm map70 00018Peat ≥40 cm map79 00020Peat ≥30 cm map94 00024Topographic map56 00013Quaternary deposits map58 00014
Peatland coverage in km2 or in percent of forest land
according to different sources. The peat maps that we predicted in this
study are marked in italics. The numbering refers to the different upscaling
estimates from inventory data as described in Sect. 2.6. Forest land
includes productive forest land, pastures, mires, rock outcrops, and alpine
region below treeline, but excludes arable land, alpine region above
treeline, railroads, power lines, roads, and urban areas. However, the exact
definitions differ slightly among sources which can explain some
inconsistences in the % cover among the three data sources in the table
(maps, NFI, and SFSI data), see Sect. 2.6.
SourcePeat coverage of forest land area (km2)(%)Peat ≥50 cm map68 00021Peat≥40 cm map76 00023Peat≥30 cm map88 00026(1) Peat coverage ≥ 30 cm according to upscaling from NFI65 00019(2) Peat indicative mosses according to upscaling from NFI65 00019(3) Peat coverage ≥ 50 cm according to upscaling from SFSI55 00018(4) Peat coverage ≥ 40 cm according to upscaling from SFSI60 00020(5) Peat coverage ≥ 30 cm according to upscaling from SFSI63 00020(6) Peat coverage with no peat thickness restriction according to upscaling from SFSI91 00030Average (standard deviation)71 000 (±12 000)22 (±4)Discussion
Using Sweden as a test case, this study provides a guide to improved mapping
of peat and mineral soils using ALS data across large areas – that can be
applied to other boreal forest regions. We have successfully shown in a
largely boreal landscape that we can use soil moisture to predict spatial
distribution of peat soils more accurately than previous techniques used for
the existing national maps. Specifically, these new maps include smaller
areas with peat or tree-covered peat soils previously overlooked in earlier
maps. This new map of peat soils was developed to support the need for land
use management optimization, by incorporating landscape sensitivity and
hydrological connectivity into a framework that promotes a rational and
sustainable management of organic and wet soil areas. Improved
decision-support tools hold the key for land-use management policies, and as
we enter the UN Decade on Ecosystem Restoration, mechanistic insights into
restoration targets become increasingly important. For example, the peat
maps can be used to plan land-use management, such as planning road
constructions or off road driving, designing riparian protection zones to
optimize the protection of water quality and biodiversity, or guiding the
restoration of drained wetlands. According to Minasny et al. (2019), global
estimates of soil C stocks have improved over the last decade (Arrouays et
al., 2014). But, as digital maps of peatlands are typically of low quality
globally, C stock estimates for peatlands vary considerably, between 113 and
612 Pg (Jackson et al., 2017). Improved mapping of peatlands can therefore
also answer more fundamental research questions such as improving future
estimates of soil carbon stocks.
Categorical maps – delineation of peat soils
Our categorical peat maps based on predictions from soil wetness were of
substantially higher quality compared to the spatial peat distribution from
the existing national topographic and Quaternary deposits maps (Table 1). In
fact, all evaluation metrics except accuracy and specificity measures (Table 1) were higher for our predicted categorical peat maps. The high specificity
for the topographic map is mostly driven by the underprediction of
peatlands. For example, the topographic map only overpredicts 39 instances
but underpredicts 266 instances, a clear bias toward underprediction (with
an overweight of 227 misclassified soil pits). So the high precision for the
topographic map is driven by the low number of FP; however, note that the
recall for the topographic map was below 50 %. The ≥ 50 cm peat
underpredicts peat in 104 instances and overpredicts peat in 135
instances, a fairly balanced distribution of errors with only a slight
overweight (n= 31) towards overpredictions. However, this problem was
larger for the peat ≥ 30 cm map, with an overweight of 65 plots towards
overprediction. The best measure of the overall performance of the map
quality (taking into account both over- and underpredictions) is the kappa
and MCC, which shows that our predicted maps outperform the topographical and
Quaternary deposits maps and that the ≥ 50 cm peat has the highest
quality. Kappa and MCC measures are better metrics for overall prediction
quality than accuracy, which can give overoptimistic results driven by the
larger class (i.e., mineral soil in our case) in this unbalanced dataset,
while we are in fact more interested in the smaller class (i.e., peat soil)
(Delgado and Tibau, 2019; Chicco and Jurman, 2020). Out of the two
measurements, kappa and MCC, MCC is considered the most informative measure
(Chicco et al., 2021). We believe it should be standard to publish the raw
confusion data (TP, TN, FP, FN) in studies evaluating map quality (Table 1),
as that will enable future metastudies to calculate all possible evaluation
metrics needed for comparison. This is often neglected in the literature
today.
Our predicted peat maps captured smaller peat areas as small as 4 m2
due to the high quality input data of 2 m spatial resolution (Fig. 5a),
while the existing traditional maps only include peatlands larger than 2500 m2. While there exist really small topographic hollows (in the order of
4 m2) that can fill up with peat, this is not the typical peat that we
were able to map with the new methodology. The visual inspection of the map
indicates that the main improvement from traditional maps is that the maps
capture the riparian peat or in smaller pockets in the bottom of small
valleys (Fig. 5a) where groundwater flow paths converge. These more local-scale peat soils are common in the boreal region, and are sometimes called
“cryptic wetlands” (Creed et al., 2003), discrete riparian input points
(DRIPS) (Ploum et al., 2018), or groundwater discharge areas (Kuglerova et
al., 2014a), and are more connected to mineral soils. Such areas often have
higher nutrient status and pH, and more nutrient-demanding plant species
(Kuglerova et al., 2016, 2014b; Rydin et al., 1999) than
larger mire complexes. In addition, we noted that peat soils seemed to be
underestimated in the forested areas in the traditional maps. Black and
white aerial photos (or color and infrared – IR) were used
for the delineation of peatlands in traditional mapping in combination with
field observations – mainly along the roads. As a result, the cartographers
interpreted many areas under dense forest canopy as mineral soils, a common
misinterpretation when mapping soils from aerial photos. A typical example
of such cartographic challenges is provided in Fig. 4d and Appendix A.
The flat low-laying areas drained by ditches (Appendix A, Fig. 1c) is a
forested peatland area (Appendix A, Fig. 1d), that was misclassified on
traditional maps (5D). Therefore, such traditional mapping techniques have
likely resulted in underestimation of productive, now forested peatlands.
Our peat maps, based on predictions from soil wetness, were mainly based on
digital terrain indices and high-resolution laser scanning data (Ågren
et al., 2021) that are not restricted by dense forest canopies, and thus
could provide much more accurate estimates of peat soils. In addition, the
new peat maps also give a much more accurate delineation of the border
between for example a flat mire and surrounding drumlins. This is easy to
see based on ALS data, while this was more difficult using aerial photos. Our
predicted maps therefore capture larger areas of peat soils previously
unmapped and had a recall rate in the order of 80 % that can be compared
to ca. 50 % and 70 % on topographic and Quaternary deposits maps,
respectively.
While the peat ≥ 50 cm map had the highest overall quality, all of the
predicted maps (i.e., peat ≥ 30, ≥ 40, and ≥ 50 cm) were
qualitatively more or less comparable, at least in comparison to traditional
maps (Table 1, Fig. 5b, e), i.e., the spatial overall distribution
remained, even if the area covered by peat soils increased when moving from
≥ 50 to ≥ 30 cm peat depth. The delineation of peat soils using
peat that is 30, 40, or 50 cm can be valuable depending on the research or
management objectives. Even though the error bars in Fig. 3, and an RMSE
of 19 cm for the continuous map of the organic layer thickness, indicate
that there is some level of uncertainty for the estimates of peat soil
depth, the categorical maps still delineated peat soils better than
traditional maps (i.e., Quaternary deposits maps and topographical maps). We
deem the level of uncertainty to be satisfactory for the horizontal
delineation of peatlands; especially if the continuous map are used to
highlight the areas where the delineation is more uncertain, i.e., along the
borders of the peatlands (see Sect. 4.2). However, given the large RMSE
for the depth estimates, and the generally thin layers of organic soils
across most of the Swedish forest landscape, we do not suggest to use the
depth estimates for carbon stocks.
Map of the continuous thickness of the organic layer
Categorization is a fundamental mechanism of human cognitive construction
(by dividing the subject of interest into groups and comparing them, we form
our knowledge of the world) (McGarty et al., 2015). Such categorical divisions, however, may
cause overgeneralization and inaccurate representation of the true
distributions. We argue that in nature there is often a more gradual shift
from mineral soil to peat soil, rather than a clearly defined border.
Applying a cubic relationship (Eq. 7), we could model the thickness of the
organic layer from the SLU soil moisture map. The high-resolution (2 m) SLU
soil moisture map, displaying the probability of a soil being wet, captures
the gradual shifts in soil moisture across the natural landscape (Fig. 2).
The high quality of the SLU soil moisture map is obtained by combining data
from 24 different spatial data sources in a machine learning model (i.e.,
extreme gradient boosting; Chen et al., 2020) to adjust the map to both
regional and local conditions based on the observations from ∼ 16 000 National Forest Inventory (NFI) plots across Sweden. Ågren et al. (2021) found that the SLU soil moisture map captures 79 % of wet soils,
suggesting a significant improvement over the existing map products. Hence,
the SLU soil moisture map has enabled the possibility of predicting how
water follows the flow paths from each ridge into local valleys where the
groundwater is concentrated in swales (i.e., cryptic wetlands and riparian
peats), to further downstream into flat areas where water gets stagnant with
high groundwater levels, typically landscapes with mixed mire complexes. We
have now shown that this continuum of hydrological connectivity of the
landscape has a significant relationship with the organic layer thickness,
and thus can be effective for tracking the distribution of the organic
layer thickness in a continuous map (Fig. 5c, f). In the study region,
most organic soils are overlaying till deposits, which are highly
heterogeneous and often anisotropic. The surface roughness of the underlying
till will have a local effect on peat depth, which likely contributes to the
relatively high RMSE for the continuous peat depth map. In short, the maps
are based on modeling from soil surface data from ALS measurements, while
stones, boulders, or ridges made of till can be hidden below the flat peat
surface, affecting the peat thickness (Nijp et al., 2019). The relatively
high RMSE of 19 cm is also an indication that the delineations of peat
soils based on a defined thickness of the organic layer (i.e., ≥ 30, ≥ 40 ,or ≥ 50 cm) are uncertain. We therefore argue that a map based on
continuous organic layer thickness (Fig. 5c, f) provides a more realistic
representation of peat soil distribution in the natural landscape and
comprises a better basis for addressing specific research or management
questions. It should be noted that the map will not capture the full depth
of the peat deposits; however, this was not the purpose of this mapping
analysis. Mean peat thickness in Sweden has been estimated to be 1.52 m in
north Sweden, 1.94 m in south-central Sweden, and 2.26 m in south Sweden
(Franzen et al., 2012). Therefore, the depth of the organic layers should
not be taken literally, but the continuous map can be used to indicate the
horizontal distribution of peat soils instead of using a fixed threshold.
Light yellow areas on the continuous map are indicative of mineral soils,
and brown areas are indicative of peat soils, while the areas that show a
rapid change in color are indicative of the transition zone between mineral
soils and peatlands (Fig. 5c and f). We argue that this is an intuitive
way of illustrating the uncertainty in the borders between mineral and peat
soils.
National estimates of peat coverage for the forest landscape
Our new estimations of peatland coverage for all of Sweden ranged
70 000–94 000 km2 and are better than previous estimates from
Quaternary deposits map (58 000 km2) and topographical maps (56 000 km2) (Table 2), given that the maps of ≥ 50, ≥ 40, and ≥ 30 cm peat had a higher quality (Table 1). For the first time, it is possible
to produce maps that delineate each individual peat deposit and that give
more reasonable estimates of the national peat cover for Sweden. The new
maps produce peat estimates for Sweden close to some of the best estimates,
based on a combination of data sources and upscaling; 85 023 km2
(Barthelmes et al., 2015) and 63 700–69 200 km2 (Tanneberger et al.,
2017). While it is interesting to compare the peat coverage for the national
estimates, the predicted maps can only be trusted for the forest landscape,
as our study is based on sampling of the forest landscape. The peat coverage
estimates will vary depending on the definition used (Table 3), and all
sources have uncertainties. However, by calculating several measures we can
constrain the national estimates of peat soil coverage on Swedish forest
land. In general, there was a strong agreement in the total peat soil area
derived from soil wetness-based predictions and from statistical upscaling
of the national inventories. The predicted maps from soil moisture have
slightly larger areas of peat coverage (77000±10000 km2)
compared with the estimations from the NFI and SFSI surveys (67000±14000 km2), but lower than earlier estimates from NFI; 83 000 km2
(Hånell, 2009) and 100 000 km2 (including peaty mor) (Hånell,
1990). One potential explanation why the maps predict larger areas than peat
coverage from NFI can be that isolated peat soil patches smaller than 25 m2 are disregarded in the NFI survey, while our maps predict peat areas
as small as 4 m2. It can therefore be assumed that many of the smaller
peatland features such as riparian peat or peat in local pits/swales to a
certain degree are disregarded in the NFI data but are included in our new
map predictions. Given an RMSE of 19 cm for the continuous map, we argue
that one should not analyze different peat depth maps estimates in detail,
rather all the different estimates can be used to constrain the uncertainty
in the peatland estimates. The peat coverage of Swedish forest land
according to all estimates is 71000±12000 km2 (22±4 %). In addition, on agricultural land the total area of peat soil used
in agricultural production is estimated to be 2257 km2 (7 % of the
total agricultural area) of which 80% is used as arable land and 20 %
for pasture (Minasny et al., 2019). Another estimate of agricultural peat
and gyttja soils in Sweden is 3015 km2 (Berglund and Berglund, 2010).
Furthermore, the alpine region above the birch forest is estimated to have
3040 km2 of peatlands, but these numbers are uncertain due to few
observations (Löfgren, 1998). A clear advantage with our method of
mapping peatlands compared with previous maps generated from NFI data is
that our new method for the first time allows for a delineation of all peat
soils across Sweden, while maps from NFI data traditionally only show
statistical fractions of peat coverage per land area at county scale, i.e.,
not spatially explicit distribution (Nilsson et al., 2001; Olsson, 1999).
The novelty of the developed maps
A recent review of digital mapping of peatlands shows that there has been a
successive increase in our ability to map peatlands globally via digital
mapping using remote sensing and satellites such as Landsat, Sentinel, and
MODIS (10–1000 m resolution), or using satellites that measure earth's
surface moisture (Gravity Recovery And Climate Experiment (GRACE) available
at about 50 km resolution, and Soil Moisture Active Passive (SMAP) available
at 3 km resolution) (Minasny et al., 2019). While the coarse resolution
satellite data may be useful for continental or global mapping, it is not
adequate for detailed planning of land-use management. Moreover, the review
showed that while it is common to delineate peat extent, studies rarely
perform validation or calculations of the uncertainty of the predictions
(Minasny et al., 2019). Here, we present a novel way of delineating
peatlands across an entire country, at a very fine spatial resolution (2 m),
in addition we validate the maps using a separate evaluation dataset and
several evaluation metrics (Table 1). Furthermore, we calculate several
estimates of the peatland coverage of the Swedish forest landscape which
allows us to constrain the estimates (Table 3). Schönauer et al. (2022),
recently showed that by combining airborne laser data with other map sources
and AI models, they could produce accurate soil moisture maps for six study
areas in Finland, Germany, and Poland. By applying an XGBoost machine
learning model for predicting soil moisture, they predicted 74 % of wet
values correctly, a significant improvement compared to depth-to-water maps
that predicted 38 % of wet values correctly. As the number of countries
that have wide-area public lidar datasets are increasing in the northern
boreal zone (Cohen et al., 2020) and new methods of mapping soil moisture
using machine learning from a combination of data sources (Schönauer et
al., 2022; Ågren et al., 2021) are being developed, this study can
provide a benchmark for new and improved peatland maps of the northern
boreal zone at a nationwide scale. This can bridge an important research gap
between global-scale mapping using satellites on a coarse scale, and
detailed field-scale mapping (Minasny et al., 2019).
Conclusions
An empirical relationship between the thickness of the organic layer and the
continuous SLU soil moisture map (R2= 0.66, p<0.001) was
used to generate three categorical maps of peat distribution in Sweden (using
peat depths of ≥ 30, 40, or 50 cm, respectively, as thresholds). The
developed peat maps had a higher overall quality (MCC = 0.73) compared to
traditional Quaternary deposits maps (MCC = 0.65) and topographical maps
(MCC = 0.61), and captured more of the peatlands with a recall of ca. 80 %
compared to 50 %–70 % on the traditional maps. The ability to map smaller-scale peatlands as fine as 4 m2 and the fact that our predicted peat
maps were not restricted by dense forest canopies (as our maps were based on
high-resolution digital terrain indices) together provided better estimates
of peat soils that nearly doubled the accounting of peat soil areas for
Sweden compared to other national map products. We also provided a
continuous map of the organic layer depth, ranging from 6–88 cm, with an
R2 of 0.67 and RMSE of 19 cm. This continuous map exhibits a smooth
transition from mineral to peat soils and provides an intuitive uncertainty
estimate in the horizontal delineation of peat soils. Finally, by
calculating several measures of peat soils, we can constrain the
uncertainties in the national estimates of peat soils in the Swedish forest
landscape to 71000±12000 km2 or 22±4 %.
The SLU soil moisture maps that underlie this study are open data (https://www.slu.se/mfk, Sveriges lantbruksuniversitet/Swedish University of Agricultural Sciences, 2022).
Author contributions
AMÅ conceptualized the study, developed the methodology, ran the statistical analysis, produced the maps, wrote original draft, produced maps and visualizations, and acquired the funding. EMH contributed to the writing of the article and funded part of the study. JS contributed to the methodology, the SFSI upscaling peat estimates, and writing of the article. MBN contributed to the writing of the article. SSP developed the methodology, ran statistical analysis, produced maps, and contributed to the writing of the article.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would like to thank the staff at the Swedish Forest Soil Inventory for providing the data for this article and Jonas Dahlgren at NFI, who calculated the peatland cover from the NFI data (Sect. 2.6).
Financial support
This research has been supported by the Svenska Forskningsrådet Formas (grant nos. 2019-00173, 2021-00115, 2021-00713, 2018-00723, and 2016-00896) and the Knut och Alice Wallenbergs Stiftelse (grant no. 2018.0259).
Review statement
This paper was edited by David Dunkerley and reviewed by two anonymous referees.
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