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Creating a digital treeless peatland map usingsatellite image interpretationReija Haapanen a & Timo Tokola aa Department of Forest Resource Management , University of Helsinki , FinlandPublished online: 20 Feb 2007.
To cite this article: Reija Haapanen & Timo Tokola (2007) Creating a digital treeless peatland map using satellite imageinterpretation, Scandinavian Journal of Forest Research, 22:1, 48-59, DOI: 10.1080/02827580601168410
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ORIGINAL ARTICLE
Creating a digital treeless peatland map using satellite imageinterpretation
REIJA HAAPANEN & TIMO TOKOLA
Department of Forest Resource Management, University of Helsinki, Finland
AbstractIn the satellite image-based estimation and classification of forest variables in Finland peatlands are usually processedseparately from mineral soil forests, to improve the accuracy of the results. The division into peatlands and mineral soilforests is based on a mask provided by the National Land Survey. It would be advantageous, however, to update the maskwith the satellite imagery used for estimating forest variables. The aim here was to compare methods for treeless peatlanddetection on a Landsat ETM�/satellite image. The area concerned was located within the southern aapa mire zone inFinland. The classification methods tested included sequential maximum a posteriori (SMAP), supervised maximumlikelihood (ML) and unsupervised ML with Iso Cluster-based signatures. The unsupervised Iso Cluster ML methodperformed poorly, while the overall accuracies of SMAP and supervised ML were better and quite similar (88�94% and89�90% on forestry land, respectively). SMAP produced more usable maps, by forming compact and unspeckled treelesspeatland regions. The existing peatland mask was slightly more accurate than SMAP and ML, although it performed lesswell in the treeless peatland class. The updating of the existing mask by combining it with the best classification result didnot succeed. The main conclusion is that a peatland mask can be based on Landsat TM classification, but in areas where agood topographic mask exists the latter is more useful, and cannot easily be updated with help of satellite image data.
Keywords: Classification, Landsat, ML, SMAP, stratification.
Introduction
Peatlands are an essential part of the landscape in
Finland as they account for 29% of the land area
(Peltola, 2003). Peatlands are commonly processed
separately from mineral soil forests in satellite image-
aided forest inventory applications, as this has been
shown to reduce the biases of both strata (Katila &
Tomppo, 2001). A digital peatland mask supplied by
the National Land Survey (NLS) is used in the
stratification. Stratification increases homogeneity
within each stratum, but in this case it also helps
to avoid errors caused by the wetness of peatlands,
which means that the relationship between satellite
image intensities and growing stock properties dif-
fers between peatlands and mineral soil forests
(Saukkola, 1982; Tomppo, 1992). Of the Finnish
peatland area 19% is treeless (Peltola, 2003). Tree-
less or sparsely forested peatlands are especially
problematic in satellite image interpretation, as
reflectance may originate from the vegetation, water
or mud, or from patterns formed by these surface
features.
The peatland mask provided by the NLS is
generalized from a topographical database that is
itself reliant on visual interpretations of aerial
photographs. The applied peatland definition is
based on peat thickness (�/0.3 m) and existing
mire vegetation, whereas the silvicultural peatlands
comprise sites supporting peat-producing plant
communities. The latter definition results in larger
total peatland area, as no threshold is set to the peat
thickness. Owing to the differing definitions, artifi-
cial changes in the landscape and possible interpre-
tation errors in the mask, it would be advantageous if
the mask could be updated with the same satellite
image data that are used for forest variable estima-
tion and classification.
Several satellite image-aided forest variable esti-
mation or land cover classification studies have been
carried out in boreal forest conditions in recent
Correspondence: R. Haapanen, Karjenkoskentie 38, FI-64810 Vanhakyla, Finland. E-mail: [email protected]
Scandinavian Journal of Forest Research, 2007; 22: 48�59
ISSN 0282-7581 print/ISSN 1651-1891 online # 2007 Taylor & Francis
DOI: 10.1080/02827580601168410
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decades in which peatlands have been considered
(e.g. Saukkola, 1982; Horler & Ahern, 1985;
Tomppo et al., 1998; Boresjo Bronge, 1999;
Kurvonen et al., 2002), although it has not been
popular to concentrate on peatlands. Lahti and
Hame (1992) tested several remote-sensing data
sources for their ability to discriminate peatlands
from mineral soil lands, and also addressed the
question of discriminating between forested and
treeless peatlands, while Boresjo Bronge and
Naslund-Landenmark (2002) tested the classifica-
tion of open wetlands on Landsat Thematic Mapper
(TM) images with the help of digital map data
for the Swedish CORINE land cover survey. Poulin
et al. (2002) mapped peatland vegetation within a
mire complex using Landsat Enhanced Thematic
Mapper Plus (ETM�/) images, beginning the ana-
lysis with the preparation of a peatland mask, and
Glaser (1989) used Landsat TM images for detect-
ing biotic and hydrogeochemical processes in large
peat basins, interpreting the images for the presence
of vegetation patterns. High-resolution images have
been used for mire habitat mapping: Holopainen
and Jauhiainen (1999) used aerial photographs,
Juvonen et al. (1997) thermal infrared images and
Holopainen (1998) and Thomas et al. (2002) high-
resolution airborne imaging data.
Traditional supervised classification methods such
as maximum likelihood (ML) assign the pixels to
classes based purely on their spectral properties. In a
problem with spatially contiguous regions such as
peatlands, burnt or clear-cut areas, these pixel-by-
pixel methods may show a spotty intraregional
variation that is of no interest for the end-user.
However, smoothing or segmentation can be run
afterwards. Bouman and Shapiro (1994) developed a
sequential maximum a posteriori (SMAP) method
that classifies multispectral images on the basis of
their spectral properties and spatial continuity. This
works by segmenting the image on various scales and
using the coarse-scale results to guide the finer scale
segmentations. McCauley and Engel (1994), com-
paring SMAP, ML and extraction and classification
of homogeneous objects (ECHO) for farmland
vegetation cover classification, found that the overall
accuracies achievable with SMAP were slightly
better than those of the other methods. SMAP also
resulted in a considerably smaller number of sepa-
rate regions.
The objectives of this study were to compare three
classification methods for the detection of treeless
peatlands on Landsat ETM�/ satellite images and to
create an improved peatland mask. The hypothesis
was that a method that takes into account the spatial
neighbourhood of the pixels (SMAP) would perform
better than pixel-based methods (supervised ML or
unsupervised Iso Cluster classification). Concerning
the updating of the existing peatland mask, the main
idea was that the mask itself could be used as the
basis of training data. Errors in the original mask are
likely to be insignificant compared with the total
number of pixels. Furthermore, consideration was
given to the landscape characteristics affecting the
success of the classification.
The problems caused by the spectral properties of
treeless peatlands in satellite image-aided forest
inventory work make this type of study important,
as errors in the land-use/land cover mask may result
in errors in the estimates of growing stock volumes,
especially in the small-area estimates of peatland-
rich regions.
Materials and methods
The area to be used for this purpose (Figure 1) was
defined by the intersection of Landsat 7 ETM�/
satellite image 188/15, dated 29 May 2002, with
the borders of Finland, and five subareas of approxi-
mately 2.5�/2.5 km each were selected within this
area for detailed analysis. The selection of the
subareas was based on the incidence of treeless
peatlands and on a geomorphological distribution,
as follows: subarea 2 (150 m a.s.l.) was located on
the flat plains of Ostrobothnia in the west, subareas 1
and 5 (180 and 210 m a.s.l) were located in a hilly
region in the middle of the study area, and subareas
3 and 4 (220 and 240 m a.s.l.) were located in
terrain of varying height characterized by drumlins
and eskers in the east (Fogelberg & Seppala, 1986).
Figure 1. The area studied, defined by the intersection of Landsat
ETM�/ image 188/15 and the boundaries of Finland. The
subareas (S1�S5) are also shown.
Creating a digital treeless peatland map 49
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The entire area falls within the southern aapa mire
zone. The aapa mires are flat, with string/pool
variation in the central, and are surrounded by
pine fens. The mires in the southern aapa mire
zone are relatively dry and are dominated by inter-
mediate-level vegetation, although there are also
some mires with large pools (Ruuhijarvi, 1988).
Three subzones within the southern aapa mire
zone were intersected by the study area: a subzone
characterized by Sphagnum papillosum fens (subarea
1), a subzone containing flark fens (subarea 2) and a
subzone with sloping mires or flat mires dominated
by intermediate-level vegetation (subareas 3�5).
The Landsat ETM�/ image was rectified to the
Finnish Uniform Coordinate System by reference to
130 ground control points. The rectification error
was 0.25 pixels. The cubic convolution method was
used in the resampling, and the pixel size of the
final image was 25�/25 m. Bands 1�7 were used,
which means that the thermal band (6) was also
included.
Since the aim was to update the NLS’s digital
peatland mask, this mask was used in the evaluation.
The mask was in raster format with 25�/25 m pixel
size and it included the following classes: back-
ground, paludified forest, passable (�/dry) open
peatland, passable forested peatland, impassable
(�/wet) open peatland and impassable forested
peatland. Two training data sets were tested. For
training data set 1, nine neighbouring pixels (3�/3
window) were picked out of the digital peatland
mask every fifth kilometre. Training data set 2 was
obtained by digitizing 31 treeless peatland polygons
and 78 other land cover polygons from the satellite
image. The distribution of the training data into
classes is presented in Table I.
Two supervised classification methods and one
unsupervised method were used for separating the
open peatlands from the other land cover classes:
. A SMAP estimation by Bouman and Shapiro
(1994). The method is implemented in the
open source Geographic Resources Analysis
Support System (GRASS).
. A supervised ML classification in the ArcGIS
program of Environmental Systems Research
Institute, Inc. (ESRI; Redlands, CA, USA).
. An unsupervised Iso Cluster procedure in
ESRI’s ArcGIS complemented with ML classi-
fication. The number of clusters was 40. Train-
ing data set 1 was cross-tabulated with the
clusters. The dispersion of the treeless peatland
pixels over the clusters was great. After visual
comparison of satellite image and clusters, the
clusters in which over 10% of the training data
pixels were in the treeless peatland class and
those which contained over 10% of the treeless
peatland pixels in the training data were se-
lected. A 5% threshold was also tested.
The research set-up is shown in Table II. The
classes in training data set 1 were merged to either two
or three classes, the two basic classes being treeless
peatland and all other forms of land cover, while in the
three-class classification paludified forests and
forested peatlands were separated into a class of their
own. A digital land-use mask, SLICES (Separated
Land Use/Land Cover Information System), pro-
vided by the NLS was used to distinguish between
forestry land and other land in some of the analyses, a
step that affected the ‘‘other land cover’’ class.
To evaluate the classification results and the
existing peatland mask, a net of points in a 25�/
25 m grid was laid over the subareas and the land
cover class of each point was assessed visually from
digital false-colour orthophotographs with 0.5 m
resolution. Originally as many as 13 land cover
classes were used in order to detect the problematic
ones. After the interpretation, some classes were
combined, resulting in 10 classes (Table III). No
growing stock-based limits are laid down in Finnish
mire site type classifications for distinguishing
between treeless and forested mires, the sparsely
forested mire types being composites in which
Table I. Characteristics of training data sets 1 and 2.
Training data set 1 Training data set 2
All land-use classes Forestry land All land-use classes
Pixels % Pixels % Pixels %
Background 7, 327 64.3 5, 464 57.7 130, 884 86.6
Paludified forest 538 4.7 531 5.6
Treeless peatland dry 328 2.9 328 3.5 Dry and wet:
Treeless peatland wet 77 0.7 77 0.8 29, 283 13.4
Forested peatland dry 3, 124 27.4 3, 064 32.4
Forested peatland wet 0 0.0 0 0.0
All 11, 394 100.0 9, 464 100.0 160, 617 100
50 R. Haapanen & T. Tokola
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features of both mires with and without trees are to
be found (Laine & Vasander, 1996). A division into
treeless, near-treeless and sparsely forested peatlands
was contemplated here, but the distinction between
treeless and near-treeless peatlands proved too fine
to be practicable, so that they are treated as one class
in the results. Clear-cut areas and seedling stands
were also combined into one class owing to their
small coverage.
A separate 2.5�/2.5 km area with only one small
treeless patch of peatland (covering 3 pixels) was
also selected for evaluation, in addition to which a
field data set of 532 forested plots was available.
The kappa value (Rosenfield & Fitzpatrick-Lins,
1986) was used as the main measure of classification
accuracy. First, error matrices (Campbell, 2002)
were constructed. These show the distribution of
reference data points in result classes. Kappa mea-
sures the strength of the agreement between the row
and column variables, taking into account the
estimated contribution of chance. A kappa value of
0.7 means that the classification accuracy is 70%
better than would be expected by chance assignment
of pixels into classes. When there is perfect agree-
ment, all cell counts off the diagonal are zero and
kappa is 1. In addition, the overall accuracies and
producer’s and consumer’s accuracies were calcu-
lated from the error matrices. Consumer’s accuracy
is calculated by dividing the number of correct pixels
for a class by the total pixels assigned to that class,
producer’s accuracy by dividing the number of
correct pixels for a class by the actual number of
reference data pixels for that class, and overall
accuracy by dividing the number of correctly classi-
fied pixels by the number of all pixels. All measures
were calculated based on two classes: treeless peat-
lands and other forms of land cover, even when the
classification contained three classes.
Some measures describing the landscape charac-
teristics were calculated for the subareas and a larger
area which was defined by a circle of radius 30 km
surrounding (and including) each subarea. These
landscape characteristics were: (1) percentage of
treeless peatlands; (2) percentage of wet pixels
within treeless peatlands; and (3) percentage of
non-bordering pixels within the treeless peatlands.
The outermost 1-pixel-wide zone inside the treeless
peatlands was regarded as a border zone and all the
other pixels as non-bordering.
Results
All of the following results are given for the forestry
land area only. Training data set 1 was used unless
Table II. Research set-up.
Training data set 1 Training data set 2
Class SMAP ML Iso CL SMAP ML
No land-use mask, two-class x x x x x
No land-use mask, three-class x x
Land-use mask on, two-class x x x
Land-use mask on, three-class x x
Note: in the two-class approach the classes are 1�/treeless peatland, and 2�/other land cover. In the three-class approach the third class
separates paludified forest and forested peatland from class 2.
SMAP�/sequential maximum a posteriori ; ML�/maximum likelihood; Iso CL�/Iso Cluster ML.
Table III. Distribution of visually interpreted pixels into land cover classes in the evaluation subareas.
Subarea
1 2 3 4 5
No. of visually interpreted pixels 10, 100 9, 827 10, 100 10, 800 10, 100
Percentage covered by:
Treeless or near-treeless peatland, dry 7.9 4.4 3.8 13.9 6.9
Treeless or near-treeless peatland, wet 26.9 10.8 5.2 12.0 11.6
Sparsely forested peatland 7.8 8.6 10.7 9.9 6.2
Densely forested peatland 30.5 44.8 17.5 12.9 9.4
Mineral soil forest 25.7 20.5 31.2 49.4 47.2
Rocky forest 0.0 0.1 1.4 0.0 1.9
Clear-cut area or seedling stand 0.6 5.2 27.1 0.0 12.7
Water 0.0 2.9 1.9 0.7 2.9
Peat production 0.0 2.0 0.0 0.0 0.0
Other forms of land use 0.6 0.6 1.3 1.2 1.1
Creating a digital treeless peatland map 51
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otherwise stated. Kappa values for the classification
methods are presented in Figure 2a and error
matrices as averages for the subareas in Table IV.
These results were obtained with the SLICES
land-use mask applied and with a two-class classifi-
cation (SMAP and ML). The kappa values were
0.54�0.83 (overall accuracy 88�94%) for SMAP,
0.52�0.76 (overall accuracy 89�90%) for ML and
0.47�0.66 or 0.25�0.65 (overall accuracies 81�91%
or 67�89%; 10% and 5% thresholds) for unsuper-
vised Iso Cluster ML. The kappa values for the
existing peatland mask in the forestry land stratum
were 0.57�0.85 and the overall accuracies 91�96%.
Very few pixels with other land-use classes were
classified as treeless peatland in the mask, but only
53�87% of the visually interpreted treeless or near-
treeless peatlands were included in it. Since the
near-treeless peatlands of the evaluation data were
merged with the treeless ones, a comparison is
shown in Figure 2b, where the near-treeless peat-
lands were merged with the ‘‘other forestry land’’
class. All the results deteriorated and the difference
between the peatland mask and the other classifica-
tions increased, but the rank order of the methods
remained the same.
Supervised ML usually found more of the treeless
or near-treeless peatlands than SMAP with the same
input parameters (Figure 3), but included more of
the sparsely forested peatlands in the treeless peat-
land stratum as well (Figure 4). The errors in the
other forestry land classes were fairly similar in both
methods, but the order varied between the subareas.
Iso Cluster ML with a 10% threshold found fewer
peatlands and usually entailed more errors in the
other photo-interpretation classes, and there were
even more errors with a 5% threshold.
Figures 2�4 revealed differences in the classifica-
tion success between the subareas. To explain these
differences, the average satellite image digital num-
bers (DN) of some land cover classes in subareas 1
(high kappa values) and 3 (lowest kappa values) are
presented in Figure 5. In subarea 3 the signatures of
dry treeless peatland and dry near-treeless peatland
differ, unlike in subarea 1. Furthermore, the band 4
DN of both classes is closer to the other land use
classes in subarea 3 than in subarea 1. The landscape
characteristics of the subareas and their surrounding
areas are shown in Figure 6. The order of
kappa values (Figure 2) most clearly followed the
percentage of non-bordering pixels within the tree-
less peatlands, i.e. the fewer mixels, the better the
results.
The SMAP results were not sensitive to applica-
tion of the land-use mask, but since lakes caused
a bimodal distribution in the ‘‘other land cover’’
class, the removal of non-forestry land uses with the
SLICES mask improved the kappa values (Figure 7)
and overall accuracies of supervised ML. For Iso
Cluster ML with a 10% threshold the land-use mask
had a slight adverse effect, while with a 5% threshold
the effect was either positive or negative. The three-
class classification (SMAP and supervised ML)
produced slightly better results than the two-class
classification when the land-use mask was applied.
Without the land-use mask the kappa values and
overall accuracies for SMAP were also slightly higher
in the three-class classification than with two classes,
but the difference in the supervised ML results
was significant. However, the new class (paludified
forests and forested peatlands) was badly confused
with the mineral soil forests, so that SMAP found
36�84% of the paludified forests and forested
Figure 2. Kappa values in the subareas (S1�S5), SLICES land-use mask on, two-class classification. In (a) the treeless and near-treeless
peatlands are merged into one treeless peatland class, while in (b) the near-treeless peatlands are merged with the other forestry land.
52 R. Haapanen & T. Tokola
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peatlands, but at the same time included 9�60% of
the mineral soil forests in that class.
Pixels that were erroneously classified as treeless
or near-treeless peatland were closer to the borders
of treeless peatland in the SMAP classification than
in other classification results (Table V). Further-
more, SMAP resulted in fewer speckled regions than
supervised ML, which in turn was better than Iso
Cluster ML in this respect (Figure 8).
In preliminary analyses it was noticed that the
training data set 1 covered the spectral variation in
dry treeless peatland pixels better than that in wet
pixels. However, a greater percentage of the wet
pixels than of the dry ones was correctly classified as
treeless peatland with SMAP, 81.0�97.9% com-
pared with 53.8�95.6% (two classes, land-
use mask used), and this difference persisted
with ML but was smaller: 83.7�97.6% (wet) and
73.2�98.4% (dry).
In the subareas 38.8�77.3% of the treeless or
near-treeless peatlands in the SMAP output were
treeless peatlands in the existing peatland mask, the
corresponding figure for the area of the whole
satellite image being 56%, while conversely, SMAP
found 72.4�97.0% of the treeless peatlands in the
existing peatland mask in the subareas and 85% in
the whole area. The pixels missed by SMAP were
mainly treeless peatland pixels, sparsely or densely
forested peatlands and peat production areas, while
the pixels included in SMAP but not in treeless
peatland stratum defined by the peatland mask
were mainly sparsely forested, treeless and densely
forested peatlands. Based on this comparison, it was
thought that the existing peatland mask’s treeless
peatland stratum could be updated by including
pixels that were treeless peatland in SMAP and
treeless or forested peatland in the peatland mask.
However, this attempt only slightly improved the
kappa values compared with SMAP, but the result
was still inferior to the existing peatland mask.
In general, fewer treeless or near-treeless peat-
lands were detected using the self-digitized training
areas. The kappa values for the SMAP classifications
using self-digitized areas were still 0.51�0.77 and the
overall accuracies 87�93%, i.e. almost as good as
with training data set 1 without the land-use mask
(0.53�0.82 and 87�94%). The same was true of the
supervised ML results, but ML did not perform well
with training data set 1 either when no land-use
mask was used.
The overall accuracy of SMAP in the separate
evaluation area with only three treeless peatland
Table IV. Results of sequential maximum a posteriori (SMAP), maximum likelihood (ML) and Iso Cluster ML 10% classifications.
Classified as:
Method Photo-interpretation class Other forestry land Treeless and near-treeless peatland All PA%
SMAP Other forestry land 7014.2 738.8 7753.0 90.5
Treeless and near-treeless peatland 169.6 1937.8 2107.4 92.0
All 7183.8 2676.6 9860.4
CA% 97.6 72.4 90.8
ML Other forestry land 6828.0 925.0 7753.0 88.1
Treeless and near-treeless peatland 139.6 1967.8 2107.4 93.4
All 6967.6 2892.8 9860.4
CA% 98.0 68.0 89.2
Iso Cl Other forestry land 6861.8 891.2 7753.0 88.5
ML Treeless and near-treeless peatland 534.0 1573.4 2107.4 74.7
All 7395.8 2464.6 9860.4
CA% 92.8 63.8 85.5
Note: the table shows error matrices on forestry land, averages of subareas: training data set 1, Separated Land Use/Land Cover Information
System (SLICES) land-use mask on, two-class classification. PA�/producer’s accuracy; CA�/consumer’s accuracy.
Figure 3. Percentages of correctly identified treeless or near-
treeless peatlands (wet and dry combined) in the subareas
(S1�S5). SLICES land-use mask on, two-class classification.
Creating a digital treeless peatland map 53
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pixels was 99.96%, that of supervised ML 99.10%
and that of Iso Cluster ML 98.02% (with a 10%
threshold). Of the field data set with 532 forested
plots, only three plots were classified as treeless
peatland by SMAP.
Discussion
The objective of this study was to test the detection
of treeless peatlands using the SMAP, supervised
ML and unsupervised Iso Cluster ML methods. A
summary of the strengths and weaknesses of these
methods is presented in Table VI.
SMAP and supervised ML gave similar results
with the best parameter settings: a three-class
classification on the forestry land stratum. The
kappa values of SMAP were slightly higher than
ML’s in subareas 1 and 2, where the peatland
regions were large and compact. In more fragmented
regions ML performed slightly better. SMAP pro-
duced unspeckled peatland regions, as some of the
variation in the spectral profile of a treeless peatland
is ignored if the pixel is connected with clearly
treeless peatland ones. The errors in SMAP classi-
fication were concentrated in the border regions,
which are usually fuzzy on the ground as well. SMAP
was able to find almost as many of the wet treeless
peatland pixels (usually in the middle of a larger
peatland region) as ML, but performed less well in
the dry treeless peatland stratum, which ML covered
well at the expense of the forested peatlands.
There was almost no difference between the
SMAP classifications with or without the land-use
mask. The supervised ML method needs homoge-
neous training regions with unimodal distributions
of intensities to perform well, and the inclusion of
either the land-use mask or more classes in the
process improved its results in the present tests.
Equal probabilities were used in ML, although the
classes with lower frequencies should have had
lower probabilities of being selected in the over-
lapping areas of distribution. Iso Cluster ML per-
formed poorly, and it is possible that more
preliminary classes might have improved the results
Figure 4. Percentages of sparsely forested peatlands, densely forested peatlands, mineral soil forests, and clear-cut areas and seedling stands
classified as treeless peatlands in the subareas (S1�S5). SLICES land-use mask on, two-class classification.
54 R. Haapanen & T. Tokola
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(Ozesmi & Bauer, 2002). The separation of forested
peatlands from mineral soil forests was tested as a
by-product of three-class SMAP, but it was not
successful. SMAP performed almost as well with
self-digitized training areas as with training data
originating from the existing peatland mask, which
means that working purely with satellite data is also
feasible. When doing so, waters must be separated to
a separate class. This was not done here, to make the
results comparable.
The existing peatland mask was slightly more
accurate than SMAP and supervised ML, but they
were able to find many treeless peatland pixels that
were missing from the existing mask. However, the
tested updating approach, in which an intersection
of the SMAP treeless peatland category with the
treeless or forested peatlands of the existing mask
was used, did not improve the kappa values relative
to the existing mask. A semi-automatic procedure
with visual checking of large treeless peatland areas
suggested by SMAP but missing from the mask and
vice versa should work better. Fully automatic
approaches require high-resolution data or a moist-
ure-sensitive sensor to improve delineation of peat-
lands with tree cover. In the case of high-resolution
data the spectral resolution is typically lower, but the
classification potential of textural features extracted
from the images is greater. The Landsat TM image
texture is defined by the mosaic of stands, while in
the case of fine-resolution images (pixel size5/2 m)
the texture is determined by the tree crowns
(St-Onge & Cavayas, 1997). However, Kushwaha
et al. (1994) found that homogeneous forest classes
were best categorized with IRS LISS-II spectral
features alone, while the textural features helped in
the case of heterogeneous forests, such as young
successional forests. This result suggests that in the
present case the textural features might have helped
to separate the forested peatland and paludified
forest class from other classes.
The produced treeless peatland layer could be
used as the starting point for a hierarchical classifica-
tion, whereupon more detailed vegetation-type
analyses are run within the regions detected in the
first step. In their open wetland classification study
Boresjo Bronge and Naslund-Landenmark (2002)
Figure 5. Average satellite image digital numbers (DN) of some land cover classes. Subareas 1 and 3.
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concluded that a stepwise, semi-automatic approach
produced good results. Their starting point was the
open mire class on topographic maps. They sepa-
rated wet mires from the other mires with the use of
different Landsat TM band combinations and level
slicing rules at subsequent stages. Townsend and
Walsh (2001) similarly mapped the vegetation of
forested wetlands with a hierarchical approach,
starting with broad classes and fine-tuning the
classification using the optimal band combination
for each successive step.
The classification success differed greatly between
the subareas. It was assumed that the best classifica-
tion results would be obtained in the subareas
located in treeless peatland-rich areas (more training
data with the same spectral characteristics). The
results showed, however, that the classification
success somewhat followed the treeless peatland
percentage within the subarea, but not the percen-
tage within the larger area. The number of potential
mixels seemed be the most important factor deter-
mining the success of the classification. Boresjo
Bronge (1999) stated that the main factor reducing
the accuracy in a boreal vegetation mapping ap-
proach is the small-scale nature of the vegetation
patterns, causing mixels. Other properties connected
to the classification success were the percentage of
wet treeless peatland pixels of all treeless peatland
pixels (positive effect), and the percentage of clear-
cut areas and small seedling stands of all pixels
(negative effect). In the subarea with lowest kappa
values (subarea 3), the signatures of dry near-treeless
peatlands differed very little from those of sparsely
forested or forested peatlands. This may partly be
due to the large number of mixels, as well.
In the present study the overall accuracy within
the forestry land stratum was 88�94% with the
SMAP method and using a two-class classification
approach, which tends to achieve better overall
accuracies than when more classes are pursued.
Lahti and Hame (1992) reported an accuracy of
76.6% when separating peatlands (both forested and
treeless) from mineral soil lands within the forestry
land stratum. Aerogeophysical variables, especially
gamma radiation, were superior to other data in the
analysis. With Landsat image bands only, the classi-
fication accuracy was 62.9%. Kurvonen et al.
(2002), who used microwave remote sensing for
the recognition of land cover types, present a
detailed confusion matrix, from which an overall
accuracy of 88.7% for a two-class classification
Figure 6. Characteristics of the subareas (S1�S5) and their
surrounding areas (within a circle of radius 30 km) according to
the peatland mask. Treeless�/treeless peatland pixels as a percen-
tage of all pixels; inside�/non-bordering treeless peatland pixels as
a percentage of all treeless peatland pixels; wet�/wet treeless
peatland pixels as a percentage of all treeless peatland pixels.
Figure 7. Kappa values in the subareas (S1�S5). Sequential maximum a posteriori (SMAP) and supervised maximum likelihood (ML)
methods with two- or three-class classification, with or without the land-use mask.
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concerning open bogs and other forestry land can be
derived.
The idea of using the existing peatland mask as a
training data source worked well. Errors in the mask
were transferred onwards in the interpretation pro-
cess to some extent, but the effect on the signature
files is likely to have been small. Poulin et al. (2002)
used a random sample over a Landsat ETM�/ scene
to determine the spectral signature of their generic
class, with the same reasoning. The fact that the
kappa values of SMAP and ML were slightly lower
than those of the existing mask was probably due to
the lack of discrimination properties of the satellite
image bands and not the errors in the training data.
The main training data sample in the present study,
training data set 1, could have been larger from
the point of view of spectral separation, but if real
field data are used, the selected set is close to the
maximum obtainable size.
The vague nature of treeless peatlands is a
problem for classification and evaluation. In a hard
classification such as SMAP and ML every pixel
has a certain class. However, there is no clear
definition of the treeless peatland class, and the
practical definition depends on the end-user’s needs.
This problem can be approached by means of fuzzy
classification methods and vague geographical in-
formation system objects. The minimum allowable
size of a peatland region also depends on the end-
user, and affects the evaluation of the results. Here,
no size limit was set in the visual interpretation,
which means that the smallest treeless peatland areas
were of the size of one pixel. In the Finnish National
Forest Inventory a stand is to be separated if its size
is�/0.25 ha (�/4 pixels). Smaller regions can be
separated if the land use clearly differs from the
surrounding area.
The evaluation data inevitably contain errors. It
was often difficult to distinguish between treeless
peatland and near-treeless peatland, near-treeless
peatland and sparsely forested peatland, and tree-
less peatland and clear-cut peatland forest. The
Table V. Mean distance of all pixels from the nearest treeless peatland pixel (in the peatland mask provided by the National Land Survey) in
the five subareas and erroneously classified pixels in each class (forestry land uses).
Errors in:
All pixels SMAP ML Iso Cl 10%
Mean distance (m)
Treeless or near-treeless peatland 10�67 57�134 18�121 10�95
Sparsely forested peatland 88�199 59�97 85�126 95�122
Densely forested peatland 173�438 41�181 70�299 100�302
Mineral soil forest 189�572 20�181 16�185 184�361
Cleat-cut area or seedling stand 140�765 53�562 125�621 132�604
Rocky forest 128�315 83�105 89�378 65�415
Note: the table compares sequential maximum a posteriori (SMAP), maximum likelihood (ML) and Iso Cluster ML 10%, training data
set 1, Separated Land Use/Land Cover Information System (SLICES) land-use mask on.
Figure 8. Examples of sequential maximum a posteriori (SMAP), supervised maximum likelihood (ML) and unsupervised Iso Cluster ML
classification results in subarea 1 compared with visually identified treeless or near-treeless peatlands. SLICES land-use mask on, two-class
classification (SMAP and ML).
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evaluation set was also biased in the sense that it
contained more treeless peatlands than the average
for the image area. Since the treeless peatland class is
quite small, a random sample of orthophotographs
or a sparse sample of field data would have revealed
very little regarding the accuracy of classification in
the case of treeless peatlands or the nature of the
errors (border pixels or random pixels). An idea of
the accuracy achievable in areas with fewer treeless
peatlands was obtained with two separate evaluation
data sets in which almost no treeless peatlands
existed. There the overall accuracy was better than
99%. Since the evaluation areas contained very small
amounts of non-forestry land use, the results are
given for the forestry land stratum only. Over the
entire area studied here, the SMAP’s treeless peat-
land pixels fell outside the SLICES forestry land
category in 1.8% of cases (classification without
land-use mask), accounting for 0.8% of the non-
forestry pixels in the SLICES mask.
Segmentation-based methods have been used in
forestry applications to boost estimation accuracies
or develop objective delineations of forest compart-
ments (Pekkarinen, 2004). Region growing and
merging strategies are the main alternatives, i.e. a
region can be expanded into neighbouring pixels, or
small initial regions can be combined into homo-
geneous areas. If the pixels in a region are too similar
as a result of the preconditions, a highly coherent
system will be obtained in which objects will
probably remain separate, but at the risk of over-
segmenting the image into regions that are much
smaller than the actual objects. If further flexibility is
allowed, larger regions can be produced that are
more likely to fill entire objects, but they may span
multiple objects, making boundaries indistinct. The
SMAP method used here is more closely related
to the region-merging algorithm used in some
commercial packages, e.g. eCognition (Definiens,
Munich, Germany). If a more detailed delineation is
required, region expansion features should be in-
cluded in the interpretation system.
In conclusion, it is not possible to improve
automatically the quality of peatland map using the
spectral features of Landsat-type satellite images.
Other image types, textural features or semi-auto-
matic approaches are needed. The magnitude of
forest attribute estimation errors caused by the
mask’s errors should also be studied, to determine
whether it is worth making more laborious efforts
than the one presented here. However, visually
delineated training data result in a sufficient treeless
peatland mask in areas where no peatland mask
exists.
Acknowledgements
This work was part of the ‘‘Statistically Calibrated
Digital Land-use and Forest Mapping’’ project at the
University of Helsinki, funded by the National
Technology Agency of Finland (TEKES). The
National Land Survey provided the peatland mask
and the SLICES land-use mask used in this project.
Ilkka Norjamaki, MSc (For) did much of the
preparatory work before this study was initiated,
and made several useful suggestions. Juho Heikkila,
LicSc (For), and Antti Kaartinen, MSc (For) are
also acknowledged for their helpful comments.
Table VI. Summary of the strengths and weaknesses of the studied classification methods.
SMAP ML Iso CL
Strengths
Finds treeless peatland pixels missing
from the existing mask
Yes Yes Yes
Kappa values close to those of the
existing mask
Yes Yes No
Creates compact, unspeckled regions of
treeless peatlands
Yes Depends on spectral
values
No
Errors concentrate at borders of treeless
peatlands
Yes No No
Weaknesses
Assigns pixels of other classes into treeless
peatland class
Mainly in spectrally and
spatially closest classes
Mainly in spectrally
closest classes
Yes
Small islands of other land cover may be
covered, small separate treeless peatland
regions may be missed
Yes Depends on spectral values Depends on
spectral values
Requires spectrally distinct training data
classes or a land-use mask allowing the
analyses to be run on forestry land
No Yes (No)
Note: SMAP�/sequential maximum a posteriori ; ML�/maximum likelihood; Iso CL�/Iso Cluster.
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References
Boresjo Bronge, L. (1999). Mapping boreal vegetation using
Landsat-TM and topographic map data in a stratified
approach. Canadian Journal of Remote Sensing , 25 , 460�474.
Boresjo Bronge, L. & Naslund-Landenmark, B. (2002). Wetland
classification for Swedish CORINE Land Cover adopting a
semi-automatic interactive approach. Canadian Journal of
Remote Sensing , 28 , 139�155.
Bouman, C. & Shapiro, M. (1994). A multiscale model for
Bayesian image segmentation. IEEE Transactions on Image
Processing , 3 , 162�177.
Campbell, J. B. (2002). Introduction to remote sensing (3rd ed).
New York: Guilford Press.
Fogelberg, P. & Seppala, M. (1986). Geomorfologinen aluejako
[Geomorphological regions]. In P. Alalammi (Ed.), Suomen
kartasto, 122, Geomorfologia (pp. 17�18) (5th ed.). Helsinki:
National Land Survey of Finland and Geographical Society
of Finland. (In Finnish.)
Glaser, P. H. (1989). Detecting biotic and hydrogeochemical
processes in large peat basins with Landsat TM imagery.
Remote Sensing of Environment , 28 , 109�119.
Holopainen, M. (1998). Forest habitat classification by CASI
airborne measurements. Forest and Landscape Research , 1 ,
431�446.
Holopainen, M. & Jauhiainen, S. (1999). Detection of peatland
vegetation types using digitized aerial photographs. Canadian
Journal of Remote Sensing , 25 , 475�485.
Horler, D. N. H. & Ahern, F. J. (1985). Forestry information
content of Thematic Mapper data. International Journal of
Remote Sensing , 7 , 405�428.
Juvonen, T., Ojanen, M., Tanttu, J. & Rosnell, J. (1997).
Environmental applications of remote sensing methods:
Discriminating mire site types by surface temperatures.
Suo , 48 , 9�19. (In Finnish with English summary.)
Katila, M. & Tomppo, E. (2001). Selecting estimation parameters
for the Finnish multisource National Forest Inventory.
Remote Sensing of Environment , 76 , 16�32.
Kurvonen, L., Pulliainen, J. & Hallikainen, M. (2002). Active and
passive microwave remote sensing of boreal forests. Acta
Astronautica , 51 , 707�713.
Kushwaha, S. P. S, Kuntz, S. & Oesten, G. (1994). Applications
of image texture in forest classification. International Journal
of Remote Sensing , 15 , 2273�2284.
Lahti, K. & Hame, T. (1992). Discrimination of peatland and
mineral soil lands using multisource remote sensing data. In
L. W. Fritz & J. R. Lucas (Eds.), International Archives of
Photogrammetry and Remote Sensing, XVIIth Congress of
ISPRS: Vol. XXIX, Part B7 (pp. 452�456). Washington
DC: Committee of the XVII International Congress for
Photogrammetry and Remote Sensing.
Laine, J. & Vasander, H. (1996). Ecology and vegetation gradients
of peatlands. In H. Vasander (Ed.), Peatlands in Finland
(pp. 10�19). Helsinki: Finnish Peatland Society.
McCauley, J. D. & Engel, B. A. (1994). Comparison of scene
segmentations: SMAP, ECHO and Maximum Likelihood.
IEEE Transactions on Image Processing , 6 , 1�4.
Ozesmi, S. L. & Bauer, M. E. (2002). Satellite remote sensing of
wetlands. Wetlands Ecology and Management , 10 , 381�402.
Pekkarinen, A. (2004). Image segmentation in multi-source forest
inventory. Finnish Forest Research Institute, Research Papers ,
926 .
Peltola, A. (2003). Forest resources. In A. Peltola (Ed.), Finnish
Statistical Yearbook of Forestry 2003 (pp. 31�70). Vantaa:
Finnish Forest Research Institute. (In Finnish.).
Poulin, M., Careau, D., Rochefort, L. & Desrochers, A. (2002).
From satellite imagery to peatland vegetation diversity: How
reliable are habitat maps? Conservation Ecology, 6 (Article
16). Retrieved 1 August 2005, from http://www.consecol.org/
vol6/iss2/art16.
Rosenfield, G. H. & Fitzpatrick-Lins, K. (1986). A coefficient
of agreement as a measure of thematic classification accu-
racy. Photogrammetric Engineering and Remote Sensing , 52 ,
223�227.
Ruuhijarvi, R. (1988). Suokasvillisuus [Mire vegetation]. In P.
Alalammi (Ed.), Suomen kartasto , 141 (pp. 2�6). Helsinki:
National Land Survey of Finland and Geographical Society
of Finland. (In Finnish.)
Saukkola, P. (1982). Monitoring regeneration fellings by satellite
imagery (Research Rep. 89). Espoo: Technical Research
Centre of Finland. (In Finnish with English summary.)
St-Onge, B. A. & Cavayas, F. (1997). Automated forest structure
mapping from high resolution imagery based on directional
semivariogram estimates. Remote Sensing of Environment , 61 ,
82�95.
Thomas, V., Treitz, P., Jelinski, D., Miller, J., Lafleur, P. &
McCaughey, H. (2002). Image classification of a northern
peatland complex using spectral and plant community data.
Remote Sensing of Environment , 84 , 83�99.
Tomppo, E. (1992). Satellite image aided forest site quality
estimation for forest income taxation. Acta Forestalia Fennica ,
229 .
Tomppo, E., Katila, M., Moilanen, J., Makela, H. & Perasaari, J.
(1998). Kunnittaiset metsavaratiedot 1990�94 [Forest re-
sources by municipalities 1990�94]. Metsatieteen aikakaus-
kirja, 4B/1998 , 619�839. (In Finnish.)
Townsend, P. A. & Walsh, S. J. (2001). Remote sensing of forested
wetlands: Application of multitemporal and multispectral
satellite imagery to determine plant community compo-
sition and structure in southeastern USA. Plant Ecology,
157 , 129�149.
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Uni
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ity o
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da]
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