**VEGETATION AND NEAR-SURFACE PERMAFROST MAPPING USING SPOT-5 IMAGES,
MACKENZIE DELTA, NORTHWEST TERRITORIES**

**T-N. Nguyen^\*^, D.J. King, and C.R. Burn**

*Department of Geography and Environmental Studies*

*Carleton University*

*Ottawa, Canada*

> ^\*^Correspondence to: Thai-Nguyen Nguyen, Geological Survey of
> Canada, Natural Resources Canada, 601 Booth Street, Ottawa, ON,
> Canada, K1A 0E8
>
> Email: [thnguyen@nrcan.gc.ca]{.underline}

**ABSTRACT**

Near-surface permafrost (NSP), or permafrost within 3 m of the ground
surface, is widely encountered in the Mackenzie River delta. NSP is
ubiquitous beneath spruce forests in the delta, but there is some
variation in its occurrence near channels, where snow drifts may
accumulate and warm the ground sufficiently to eradicate permafrost. In
order to estimate the proportion of ground underlain by NSP in the
delta, the association of NSP with riparian vegetation communities was
determined in the field, and these vegetation communities were mapped
using SPOT-5 images. There was no NSP beneath willow-horsetail
(*Salix-Equisetum*) vegetation communities throughout the delta, and
beneath horsetail (*Equisetum*) communities in the southern and central
delta. NSP was found beneath all other vegetation communities. Three
classifiers were tested: Maximum Likelihood (ML), Artificial Neural
Networks (ANN), and Linear Spectral Unmixing (LSU). ML achieved the
highest overall classification accuracies, and NSP was estimated to
occur beneath 93%, 95%, and 96% of the land surface in the southern,
central, and northern delta, respectively.

**INTRODUCTION**

This research investigates the proportion of the Mackenzie Delta,
underlain by near-surface permafrost (NSP) (Fig. 1). NSP is a key
component of northern environmental systems because it influences
terrain stability and surface hydrology (Carey and Woo, 1999; Lewkowicz
and Harris, 2005). With increasing industrial development, and a rapidly
changing climate in the North, knowledge of the spatial distribution of
NSP is critical for land-use planning, as terrain behaviour varies
significantly between frozen and unfrozen ground (Imperial Oil, 2004;
Smith *et al*., 2005). However, there have been few field and remote
sensing studies investigating permafrost extent in the delta.

The objective of this research was to assess if the distribution of
riparian vegetation communities can be used to predict NSP presence,
using first an intensive field campaign, and second remote sensing
classification methods. The resulting maps could then be used to
estimate the extent of NSP in the delta.

**VEGETATION AND PERMAFROST DISTRIBUTION IN MACKENZIE DELTA**

The Mackenzie Delta, on the southeastern Beaufort Sea coast, extends
about 200 km north from Point Separation, with an average width of 60 km
(Fig. 1). The delta is an alluvial plain containing a myriad of channels
and thousands of lakes, and is distinct from surrounding regions as it
is flooded annually (Burn, 1995). It is usually divided into three
regions, corresponding with two major drops in levee height: the upper,
or southern, delta, the central delta, and the lower, or northern delta
(Mackay, 1963). Spatial variation in channel shifting and flooding is
indicated by the patterns of vegetation (Gill, 1973). There are three
general vegetation zones in the delta (Mackay, 1963): (1) spruce (*Picea
glauca*) forests of the southern delta; (2) spruce forest to tundra
transition in the central delta, where shrub species such as willows
(*Salix* *alaxensis, S. pulchra*) and alders (*Alnus crispa*) are
widespread; and (3) tundra shrubs and herbs, dominantly willow (*S.
alaxensis, S. pulchra, S. richardsonii*) and sedge (*Carex* *aquatilis*)
species in the northern delta, north of tree line.

![](media/image1.jpeg){width="3.783333333333333in"
height="5.741666666666666in"}At a local scale, horsetail (*Equisetum
arvense, E. fluviatile*) communities, which can tolerate a high flooding
rate, are the typical emergent plant communities, whereas
willow-horsetail communities are found at greater distances from stream
channels. Alder communities occur at slightly higher elevations than the
willow-horsetail communities, since alder species are less tolerant of
sedimentation. Spruce forests represent the climax community in the
central and southern delta. North of tree line, sedges and horsetails
are common adjacent to channels, and willow-horsetail and, other low
willow (*S. richardsonii*) communities are widespread at more elevated
sites (Pearce, 1998).

Climate is the main factor determining the continental distribution of
permafrost, and the spatial distribution of permafrost typically
increases with latitude (Zhang *et al*., 1999). However, site-specific
factors, such as snow, vegetation, hydrology, and topography interact to
buffer the effects of climate on the ground thermal regime at individual
sites. In the Mackenzie Delta, taliks occur due to deep drifts of snow
blown off channels and trapped by riparian willow communities (Gill,
1973; Smith, 1975; Dyke, 2000). These snow drifts retard heat loss from
the ground, and raise ground temperatures so that permafrost is not
sustainable.

**REMOTE SENSING OF PERMAFROST CONDITIONS**

Permafrost cannot presently be directly imaged by airborne or
satellite-based sensors, but its presence may be inferred from surface
characteristics, especially vegetation. However, in some regions
variations in subsurface properties may prevent permafrost mapping from
remotely sensed imagery (Leverington and Duguay, 1997).

In the Mackenzie Delta, subsurface materials are relatively consistent,
so ground temperatures may be well-associated with specific vegetation
communities (Johnston and Brown, 1965). In this paper, relations between
vegetation characteristics and NSP at various sites throughout the
Mackenzie Delta are reported, based on field surveys conducted in summer
2006. SPOT-5 images of the delta captured in July 2006, with a nominal
ground pixel size of 10 m x 10 m, are interpreted in terms of these
relations to estimate the proportion of ground underlain by NSP (Fig.
1).

**FIELD METHODS**

**Field site selection**

Aerial photographs and a Landsat 7 image were used to select field
sites. Sites that were chosen had several representative vegetation
communities and spectral classes, and were accessible by boat. Because
the sites were not selected randomly, a minimum distance of 1 km was
maintained between them, to reduce the potential for spatial
autocorrelation.

**Vegetation and permafrost sampling**

Given the typically muddy and bushy terrain, a line transect methodology
was adopted for efficient vegetation sampling and permafrost probing at
each site. A total of 52 transects were established, perpendicular to
the shoreline and crossing the successional sequence of vegetation (Fig.
2). Transects were divided roughly equally between the three delta
regions, and the majority of transects were between 100 m and 200 m long
depending on the width of the different vegetation communities.
Vegetation sampling was conducted within generally uniform
vegetation-cover plots, 20 x 20 m in size, along the central axes of
each plot parallel and perpendicular to the channel as denoted by the
cross in each plot in Fig. 2. The objective of the vegetation survey was
to identify the physiognomic mixtures that could be determined by
SPOT-5.

![](media/image2.jpeg){width="2.6597222222222223in"
height="2.547222222222222in"}

**The water-land boundary**

Permafrost mapping usually only considers permafrost underlying exposed
ground. In the Mackenzie Delta it is important to define the boundary
between water bodies and land because there is high seasonal variability
in water levels (Marsh and Hey, 1989). Since the satellite images were
acquired when the water level had fallen and stabilized following the
spring flood, the shorelines in the imagery were taken as the water-land
boundary. This "instantaneous shoreline" (Boak and Turner, 2005)
represents average conditions given that the water level of East Channel
near Inuvik was recorded at 12.6 m a.s.l. in July 2006 with a median of
12.3 m and a mean of 12.5 m for the open water period of May to October
2006 (Environment Canada, 2008).

**FIELD RELATIONS BETWEEN VEGETATION AND PERMAFROST**

In all three delta regions there was a clear association between
presence of permafrost in the upper 3 m at a site and the vegetation
community (Table 1). Spruce forests were not the focus of this research
as permafrost is ubiquitous beneath these communities (Kokelj and Burn,
2003). However, to validate remote sensing data, the locations of eight
spruce forest sites in the southern delta and ten in the central delta
were recorded by GPS.

Field surveys demonstrated that willow-horsetail communities in all
three delta regions, and horsetail communities in the southern and
central delta were not associated with NSP. NSP was present beneath all
other vegetation communities. Therefore, riparian vegetation communities
may be used to predict the presence of NSP in the delta.

Table 1 -- Distribution of vegetation communities plots with and without
NSP in the Mackenzie Delta. Each plot was assigned a single NSP presence
or absence label based on the rule of majority of drilled holes.

  ----------------------- ----------------------- -----------------------
  **Southern delta**      **Plots with NSP**      **Plots without NSP**

  Horsetail               0 (0%)                  8 (100%)

  Willow-horsetail        0 (0%)                  19 (100%)

  Alder                   24 (96%)                1 (4%)

                                                  

  **Central delta**       **Plots with NSP**      **Plots without NSP**

  Horsetail               1 (9%)                  10 (91%)

  Willow-horsetail        2 (10%)                 18 (90%)

  Alder                   26 (100%)               0 (0%)

                                                  

  **Northern delta**      **Plots with NSP**      **Plots without NSP**

  Horsetail               8 (100%)                0 (0%)

  Sedge                   10 (100%)               0 (0%)

  Willow-horsetail        2 (17%)                 10 (83%)

  *Salix-richardsonii*    15 (100%)               0 (0%)
  ----------------------- ----------------------- -----------------------

**IMAGE DATA ANALYSIS**

**Methods**

![](media/image3.jpeg){width="2.420138888888889in"
height="4.9319444444444445in"}SPOT-5 imagery was used to extrapolate the
results of field surveys (Table 2). For regional-scale mapping, SPOT-5
provides an ideal balance between high spatial resolution and wide-area
coverage (60 km x 60 km). The near-surface thermal regime of continuous
and discontinuous permafrost zones in northern Québec has also been
successfully mapped using the MODIS sensor (Hachem *et al*., 2007).
However, the MODIS spatial resolution of 1 km^2^ is too coarse for
identifying the narrow vegetation communities, averaging 50 m to 60 m,
along channels in the Mackenzie Delta. The image data analysis is
summarized in Fig. 3. A land/water mask was created to exclude all water
bodies from analysis. In addition to spectral data produced directly by
SPOT-5, data transformations via vegetation indices, texture analysis,
and Principal Component Analysis (PCA) were used to generate additional
image information for classification.

Vegetation indices deemed to be most relevant to tundra vegetation
communities and riparian vegetation mapping were derived and included:
NDVI, SAVI, and MSAVI (Laidler and Treitz, 2003; Johansen and Phinn,
2006). The soil-adjusted indices were selected because they may help in
differentiating vegetation communities with low canopy cover from denser
associations. Bare ground and non-photosynthetic litter that could
affect the green vegetation reflectance signal would be visible from
above in low-canopy cover vegetation communities. Six image texture
measures were extracted from the Green, NIR, and SWIR bands using a
window size of 3 x 3 for multispectral images and two window sizes, 3 x
3 and 5 x 5, for panchromatic images.

Training and testing data (60%/40%) were generated from the 2 x 2 pixel
windows at locations of vegetation sampling and permafrost probing.
Training and testing data were selected randomly from these plots.
Separability analyses using the Bhattacharrya Distance were carried out
to determine the combinations of image information having the best
potential to discriminate between vegetation communities (Jensen, 1996).

Table 2 -- Areal extent of the Mackenzie Delta covered by the SPOT-5
images. Four scenes comprised of four panchromatic and four
multispectral images were acquired in July 2006: one for the southern
delta, one for the northern delta, and two overlapping ones for the
central delta. For the overlapping scenes, a mosaic was created. The
satellite scenes covered approximately 37% of the Mackenzie Delta
surface area.

  ----------------------- ---------------------- ----------- -----------------------
  **Delta region**        **Area (km^2^)**       **Area of   
                                                 satellite   
                                                 image       
                                                 (km^2^)**   

  South                   3 025                              662

  Central                 6 500                              2 800

  North                   2 475                              920

  Whole delta             12 000                             4 382
  ----------------------- ---------------------- ----------- -----------------------

Maximum Likelihood (ML), Artificial Neural Network (ANN), and Linear
Spectral Unmixing (LSU) classifications were then used to generate maps
of vegetation communities. ML generally gives better results than other
parametric classifiers and is the most commonly used classifier for
mapping riparian vegetation (Ozesmi and Bauer, 2002). ANN is a
non-parametric algorithm that has been applied successfully in image
classifications, and has been better at delineating classes of high
heterogeneity and spectral complexity (Berberoglu *et al*., 2004). Eight
ANN configurations with different numbers of iterations as well as
momentum and learning rates were tested with the two best input variable
combinations used in MLC. Momentum and learning rate combinations of
0.9-0.1 and 0.6-0.2 were used with 100 and 100 000 iterations. If error
levels still exceeded the acceptable limit for individual (0.001) and
total (0.01) error then the number of iterations was increased to 250
000 for the ANN with the lowest error level.

Per-pixel or "hard" classifiers may only be appropriate for the mapping
of classes that are discrete and spatially mutually exclusive. However,
mixed pixels often occur in transition zones where classes co-exist
spatially, such as zones where one type of vegetation intergrades into
another. "Soft" classifications, such as LSU, have been proposed as
alternatives to traditional classifiers (Bastin, 1997). The UNMIX
algorithm implemented in PCI Geomatica was used to extract sub-pixel
vegetation information. Manual selection of endmembers was performed
using training data already utilized in MLCs and ANNs.

Many studies using LSU do not actually focus on unmixing land cover
types but rather radiometric scene components such as green
photosynthetic vegetation, soil, and shadow (Pasher and King, 2007). In
this study, endmembers were selected as vegetation communities. This
endmember selection technique was used successfully to map five land
cover fractions along an 1100 km north-south transect crossing the tree
line of northern Canada (Olthof and Fraser, 2007).

**Results**

It was difficult to discriminate between the horsetail and
willow-horsetail communities in the southern and central delta. However,
as field results indicated that both of these vegetation communities
were not underlain by NSP, the two classes were merged into a single
willow-horsetail class. In the northern delta, horsetail and sedge
communities were also difficult to separate, but as both horsetail and
sedge communities were underlain by NSP and they are the herbaceous
classes located closest to shorelines, they were also merged into one
horsetail-sedge class.

The ML classification indicated that the inclusion of image texture
information and vegetation indices was essential to the separability of
different vegetation communities. Input combinations comprising
lowly-correlated spectral and textural variables as well as MSAVI and
NDVI led to classification of vegetation communities with accuracy
levels of 84%, 82%, and 83% for the southern, central, and northern
delta images, respectively. In the southern and central delta, the
majority of classification errors were between the willow-horsetail and
alder classes. These two classes are both shrubby vegetation and are
usually located in sequential order (Fig. 2). In the northern delta,
most of classification errors occurred between the willow-horsetail and
the *Salix richardsonii* class. Figure 4 shows images of the delta
classified using the ML algorithm.

Overall classification accuracies were lower for ANN than the ML
results. However, higher accuracies were achieved by ANNs for some
individual classes. The lower overall accuracies of ANN classifications
may be due to the high variability of spectral reflectance in the
high-resolution SPOT-5 pixels
![](media/image4.jpeg){width="2.0756944444444443in"
height="5.686111111111111in"}as determined by Mills *et al*. (2006) for
IKONOS imagery.

![](media/image5.jpeg){width="4.007638888888889in"
height="4.384722222222222in"}

Figure 5 shows the fraction images resulting from LSU analysis for the
southern delta image. Visually, they appeared correct with greater
brightness for fraction image X in locations where an endmember X was
more prevalent. Comparison of Figs. 4a and 5a indicates that
willow-horsetail vegetation was more widespread using spectral unmixing.
With the MLC-derived map, alder zones appeared more extensive and the
presence of willows-horsetail patches within these zones was veiled by
the dominant alders. Since willows and horsetails were dominant in
narrow bands near shorelines, they did not clearly appear in neither
Fig. 4a nor Fig. 5a, with the exception of the alluvial islands by
Mackenzie River. Fig. 5c also shows a greater presence of spruce
throughout the southern delta image.

As with ANNs, the overall classification accuracies were lower for LSU
comparatively to ML results. However, the vegetation gradation from one
community to another could be examined with spectral unmixing (Fig. 6).
In willow-horsetail to alder transition window, there was a mixture of
willows, horsetails and alders. In the transition zone between alder and
spruce, alders were still dominant but the presence of spruce trees was
also clearly noticeable.

![](media/image6.jpeg){width="3.7576388888888888in"
height="2.311111111111111in"}

**ESTIMATION OF NEAR-SURFACE PERMAFROST EXTENT**

To estimate the extent of NSP in the delta based on ML results, the area
was divided into three regions (*R*): the southern, central and northern
delta. Next, the areal extent of vegetation (*V*) classes underlain by
NSP was identified for each delta region (*P*).

The areal extent of NSP in the delta then is:

$\sum_{i = 1}^{R}(\sum_{j = 1}^{V}P_{i,j})$ (1)

In the southern delta, 93% of the land surface was found to be underlain
by NSP. In the central and northern delta, 95% and 96% of the land
surface was underlain by NSP, respectively.

**CONCLUSIONS**

\(1\) Permafrost was absent from the upper 3 m beneath horsetail
communities in the southern and central delta, and beneath all
willow-horsetail communities. NSP was present beneath all other
vegetation communities.

\(2\) NDVI, MSAVI and image texture information were essential for
discriminating between vegetation communities in the delta using SPOT-5
images.

\(3\) Zones with presence of NSP occupied about 93%, 95%, and 96% of the
land surface, in the southern, central, and northern delta,
respectively.

\(4\) The technique developed in this study was useful in the mapping of
distribution of NSP over large areas in a dynamic environment. Since the
presence of permafrost in the upper 3 m is important for engineering
design and biophysical applications, this research could form the basis
of a mapping tool to assist land use planning.

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