BA573403.pdf


Arctic, Antarctic, and Alpine Research, Vol. 45, No. 3, 2013, pp. 330–341

Particle Size Sampling and Object-Oriented Image
Analysis for Field Investigations of Snow Particle Size,
Shape, and Distribution

AbstractSusanne Ingvander*‡
Snow particle size is an important parameter strongly affecting snow cover broadbandIan A. Brown*
albedo from seasonally snow covered areas and ice sheets. It is also important in remote

Peter Jansson*
sensing analyses because it influences the reflectance and scattering properties of the snow.

Per Holmlund* We have developed a digital image processing method for the capture and analysis of
data of snow particle size and shape. The method is suitable for quick and reliable dataCecilia Johansson† and
capture in the field. Traditional methods based on visual inspection of samples have been

Gunhild Rosqvist*
used but do not yield quantitative data. Our method provides an alternative to both simpler

*Department of Physical Geography and and more complex methods by providing a tool that limits the subjective effect of the
Quaternary Geology, Stockholm visual analysis and provides a quantitative particle size distribution. The method involves
University, S-106 91 Stockholm, Sweden

image analysis software and field efficient instrumentation in order to develop a complete
†Department of Earth Science, Uppsala

process-chain easily implemented under field conditions. The output from the analysis isUniversity, Villavägen 16, SE- 752 36
a two-dimensional analysis of particle size, shape, and distributions for each sample. TheUppsala, Sweden

‡Corresponding author: results of the segmentation process were validated against manual delineation of snow
susanne.ingvander@natgeo.su.se particles. The developed method improves snow particle analysis because it is quantitative,

reproducible, and applicable for different types of field sites.

DOI: http://dx.doi.org/10.1657/1938-4246-45.3.330

Introduction

Snow particle size and shape analysis is a key ingredient in
snow related investigations such as, for example, avalanche risk
analysis (Lehning et al., 1999), ground control for remote sensing
investigations of snow cover and its properties (Wiscombe and
Warren, 1980; Nolin and Dozier, 1993, 2000; Dozier and Painter,
2004; Scambos et al., 2007; Dozier et al., 2009), and in accumula-
tion modeling studies (Kirnbauer et al., 1994; Fierz et al., 2009).
Traditionally snow particle studies have largely been accomplished
using either visual observations in the field (following standards
outlined in e.g. Fierz et al., 2009) or by gathering samples in the
field for subsequent laboratory analysis (e.g. Albert, 2002; Gay et
al., 2002). The visual observations have the benefit of providing
direct results but are cumbersome and may involve some subjectiv-
ity and lower accuracy (Colbeck et al., 1990; Ingvander et al.,
2010). The methods used for laboratory analysis typically involve
fixating the sample in the field (requiring chemical techniques or
specialist equipment) as well as transport of frozen samples to a
laboratory for analysis (e.g. Albert, 2002; Gay et al., 2002, Matzl
and Schneebeli, 2010). The laboratory techniques yield high-qual-
ity reproducible data but prohibit acquisition of results in the field
and typically yield small sample sizes. Alternatively, sophisticated
instrumentation using near infrared (NIR) imaging or specific sur-
face area instruments can be used (e.g. Matzl and Schneebeli, 2006;
Gallet et al., 2009), requiring increased logistical and financial
support for the field campaign. Hence there is need for quick, relia-
ble, and reproducible methods that combine the main advantages
of both methods, the speed of visual field investigations, and the
high-quality reproducible data of the laboratory methods. We have
explored the advantages of using digital photography in combina-

330 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH � 2013 Regents of the University of Colorado
1523-0430/6 $7.00

tion with digital image analysis to record and analyze snow samples
in the field.

Imaging of snow particle size has been carried out for different
purposes and has evolved over time. This evolution has resulted
from improving technology such as the introduction of charge-
coupled devices and the digital camera (Mellor, 1965; Sommerfeld
and LaChapelle, 1970; Colbeck et al., 1990; Grenfell et al., 1994;
Pulliainen, 2006; Fierz et al., 2009). Lesaffre et al. (1998) presented
a method for an objective analysis of particle size images extracting
the mean convex radius of the snow particles. To improve on the
two-dimensional analysis of images, the use of pore fillers were
employed to fixate the pore space of the snow pack in situ, hence
preserving the three-dimensional structure of the snow for later
detailed analysis (Perla, 1982). Brun and Pahaut (1991) used iso-
octane for fixation and post characterization of such snow samples.
Such three-dimensional analysis provides rich data but is cumber-
some to perform due to the fixation process and need for transport
of larger samples to an environment where final analysis can be
performed. Improving image analysis of snow particles thus pro-
vides a means to obtain good quality data in larger quantity and
with little additional effort because the researcher is already in the
field.

Earlier studies of snow particle size analysis in Dronning
Maud Land, Antarctica, based on digital photography were made
by, for example, Gay et al. (2002) and Kärkäs et al. (2002). Gay et
al. (2002) focused on the methodology of snow sampling, providing
three different types of analysis based on fixation with isooctane
and sample analysis in a laboratory by using classical macro-pho-
tography and digital image acquisition. These studies show that
photography-based methods are useful parts of any sampling and
analysis program. A benefit of the digital images is that they can



easily be kept for posterity for re-analysis. Hence there is also a
documentary benefit to an image based system.

We have modified, merged, and extended existing methods
(Gay et al., 2002; Kärkäs et al., 2005) to arrive at the so-called
Digital Snow Particle Property (DSPP) method, which requires a
minimum of both instrument support and analysis time. The spe-
cific goal has been to provide opportunities for both quick field
sampling and preliminary analysis, and subsequent re-analysis of
the collected data using advanced image processing techniques.
The method is based on digital photography of snow samples on
a high-accuracy reference plate. The resulting images are processed
in an object-oriented image analysis program yielding several pa-
rameters including snow particle size and shape. The result is a
non-subjective method that yields statistical measures of the snow
particle samples allowing for quantitative analysis and inter-sample
comparison. Since the digital analysis is made using adjustable
parameters, results can be double checked or re-analyzed with dif-
ferent settings at a later stage. The main limitation of the photo-
graphic method is that the analysis is two-dimensional. The data
resulting from the analysis allow us to study the distribution within
each sample and thereby enable comparisons between distributions
from multiple samples. With our method we can obtain large quan-
tities of field data with minimal equipment requirements and can
provide quick and reproducible digital analysis of gathered data.
This method can thus be used as a simple way to gain independent
data for more complex studies of snow particle size. The novelty
of this method in comparison with previous approaches is the use
of a high-precision sampling grid for accurate pixel-size calibration
and the pixel-based object-oriented image analysis method which
enables analysis of multiple size parameters for a range of grains
in each sample. This also enables us to establish size distributions
for each sample and also minimizes the objective input by the
observer in ordinary visual analysis. Furthermore, given the ex-
tremely large number of samples resulting, the DSPP approach
facilitates the use of rigorous statistical analyses of the particle size
data in contrast to approaches that focus on single grains or limited
sample sizes.

In this paper we have chosen to use the term snow particle
size rather than snow grain size for the objects we study. A snow
grain is normally a single crystal (e.g. Fierz et al., 2009), though
multiple crystal examples have been observed (e.g. Rolland du
Roscoat et al., 2011). Furthermore, snow metamorphoses in situ
as molecules transfer between individual grains in contact resulting
in the bonding of grains across flat surfaces (Colbeck, 1998; Rol-
land du Roscoat et al., 2011). Thus, we have therefore opted to use
the more general term particle to indicate that we do not distinguish
between objects consisting of single grains (or crystals) or multi-
grain bonded particles. These different grain types are very difficult
to distinguish using traditional visual or other in situ methods and
require appropriate and detailed instrumentation for determination.

Observations of grains in situ and in laboratory environments,
using different observation techniques, have led to the development
of a wide range of descriptive parameters. Different parameters
used to describe snow particle sizes include: grain radius (Wis-
combe and Warren, 1980), mean convex radius (Fily et al., 1997),
and optically equivalent radius (Nolin and Dozier, 1993; Painter et
al., 2007). Increasingly, Specific Surface Area (SSA) is becoming a

SUSANNE INGVANDER ET AL. / 331

favored parameter (Legagneux et al., 2002; Domine et al., 2008;
Picard et al., 2009; Jacobi et al., 2010). The SSA of snow is used
in optical equivalent analysis of snow grain size as it is strongly
correlated to the spectral reflectance, an important factor in remote
sensing of snow (Domine et al., 2008; Painter et al., 2009; Gallet
et al., 2009). The SSA parameter normalizes the surface area to
the snow particle volume as SSA is a relation between the surface
area and the mass of a snow grain (Picard et al., 2009). A plethora
of SSA approaches have been implemented. SSA has been mea-
sured by extracting snow samples and placing them beneath a near-
infrared laser and measuring diode in an integrating sphere (e.g.
Gallet et al., 2009). In contrast, Legagneux et al. (2002) measured
snow SSA using methane absorption at the temperature of liquid
nitrogen (77.1 K) in the laboratory. Matzl and Schneebeli (2010)
compared gas absorption, standard micro X-ray tomography, and
stereological analysis of vertical thin sections using micro X-ray
tomography to validate the latter. This latter approach required the
estimate of three-dimensional SSA from two-dimensional data, an
approach with some relevance for the method described below.
That said, the aim of the method presented here is to improve and
reduce subjectivity in visual analysis of snow particles and not
duplicate or replace existing highly sophisticated methods such as
the measurement of specific surface area in the laboratory or the
use of highly specialized equipment.

The Digital Snow Particle Property
(DSPP) Method

The DSPP method was developed to meet our needs for fast,
on-the-fly sampling and measurement, and for an efficient process-
ing method for field determination of sampled surface snow particle
sizes during the Swedish Japanese Antarctic Expedition 2007/2008
(Ingvander, unpublished data). There was a need to be able to gather
information in a short time period since sampling could only be
managed during brief stops of the traverse vehicles. The time con-
straints made accurate manual observations difficult. We also
wished to make a large number of observations along the traverse,
which precluded the use of the traditional sampling methods requir-
ing samples to be carried back to a laboratory for analysis. In
addition, we also aimed at gathering as much information as possi-
ble about not only size but also shape of the snow particles. The
use of digital photography hence provided most of the positive
aspects of the established sampling techniques. The speed of the
field sampling and recording of samples in the field greatly limited
the time available for sublimation that could have changed particle
size and shape (e.g. Fujii and Kusunoki, 1982; Albert, 2002; Déry
and Yau, 2002; Neumann et al., 2009. Although most of the details
of the analysis lie in the digital image analysis, here we describe
the entire workflow of the method. A digital analysis for particle
size estimates consists of three main steps: (1) digital image acquisi-
tion of the particles to be analyzed; (2) digital analysis of the im-
ages; and (3) statistical analysis of the retrieved data. The complete
workflow of the DSPP method is displayed in Figure 1.

Our field sampling instrumentation system consisted of a
Canon EOS 350D digital camera mounted on a tripod that fixed
the camera inside a transparent squared wind shelter (Fig. 2). An
�m-accurate millimeter dot grid reference plate was used to support
image calibration and rectification of the image during post-pro-



FIGURE 1. Flowchart of the digital snow grain proper-

Sample site

Metadata data parameters retrieval

Sample photography

Three parallell snow samples

Pixel size determination

Image crop 

Classification export

Image classification

Image segmentation

Class export Object export

Scale parameter
Shape
Compactness

Area
Brightness
Shape Factor

Mean
Mode
St. deviation

Individual
objects statistics

1

2

3

4

ID
Position
Elevation
Temperature
Camera height
Sun angle

ties (DSPP) method. See text for details.

FIGURE 2. Scatter plot of equation-derived pixel size (estimated
pixel size) (from correlating camera elevation and pixel size for
sample images) and actual pixel size (pixel size) derived by imple-
menting Equation (1) for 27 grid samples during the Japanese
Swedish Traverse 2007/2008. R2 � 0.84 and root mean squared
error (RMSE) � 0.0012.

332 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH

cessing. A snow sample was collected from the natural snow cover
using a silicon spatula and placed on the reference plate by a gentle
shake to disperse the particles over the glass plate. The plate was
immediately photographed to prevent melting and/or sublimation.
The image resolution was calculated in two different ways based
on the sampling procedure: either by recording the distance be-
tween the camera and the sample glass (camera height) in order to
calculate the pixel size in the images, or by counting the number
of pixels between the millimeter markers on the reference plate.

The image analysis is performed by object-oriented image
analysis software; we used Definiens Developer 7.0 in our study.
The digital analysis is based on segmentation to identify each snow
particle as an object and then apply different algorithms to extract
a series of generic parameters such as width, length, area, circum-
ference, etc. The result of the analysis is a comprehensive data set
of all measures for all identified objects. No additional light source
was added to the sample equipment as sufficient natural lighting
was present and generated adequate contrast between the snow
particles and the background in the images.

The object-oriented image analysis extracts every object in
the images generating a size range and distribution within each
sample. This enables statistical analysis of the size distribution
within each sample generating exact size ranges, in contrast to
visual/manual classification systems that generate a mean size
within fixed size ranges for each class. Measured snow particles
are thus binned based on the resolution of the image and the size
of the objects to visualize size distribution.

The extraction of the sample from the snowpack using a spat-
ula may result in the destruction of bonds between grains in some



cases. Obviously, care must be taken during sample extraction.
This potential limitation to the method can be addressed in a number
of ways. Visual inspection of the photographs can be used to assess
the validity of the grain size distribution and maximum and mini-
mum particle size thresholds adjusted to exclude extraneous ob-
jects. The image may be subset to remove regions that clearly
exhibit damaged particles. Multiple samples and images are typi-
cally taken to improve the data quality and the very large number
of particles resulting from the analysis, and the potential to use
sophisticated statistical analyses post prioi means that the effects
of damaged samples can be ameliorated.

In the following we will outline the digital processing steps
and the resulting measures of particle size and shape. This is impor-
tant since we need to show how our measures tie to standard meth-
ods for snow particle analysis.

A Brief Overview of Digital Image Analysis
Object-oriented image analysis involves applying a series of

image processing operations to the images as described in the fol-
lowing processing chain and visualized in Figure 1.

IMAGE PREPARATION

The digital analysis requires a pixel-based sharp digital image.
In this study we used JPEG and RAW formats. Note that JPEG
compression is destructive, and these effects have to be considered.
The image must first be cropped in image processing software in
order to remove the reflective border areas of the calibration glass
plate and to remove areas exhibiting snow particle clustering (mul-
tiple particles in aggregates), melt features, or frost tracks on the
glass.

IMAGE RESOLUTION

The resolution (mean pixel size) (Sp) of each image is calcu-
lated by counting the number of pixels between each millimeter
marker on the sampling grid in four directions (0, 90, 180 and
270�) at three markers (P1, P2, and P3) in each image [Equation
(1)] (step 2 in the DSPP method).

S̄p �
Ptot lmax

�4
i�1

diP1 � �4
i�1

diP2 � �4
i�1

diP3

(1)

where Ptot is the total number of points where the numbers of pixels
were counted, and lmax is the number of pixel distances counted.
By dividing 1 mm by the total number of pixels divided by the
number of pixel distances counted we receive the resolution of the
image. For extensive data sets with registered camera elevation,
the pixel resolution can be calculated by correlating the camera
elevation with pixel resolution for reference images and implement-
ing the equation on remaining images. Figure 3 shows an example
of equation-derived pixel size and actual pixel size derived by using
Equation (1) generating a root mean square (RMS) error of 0.0012.

SEGMENTATION

Using Definiens Developer 7.0 we segmented, then classified
the snow particles. Definiens Developer image segmentation and

SUSANNE INGVANDER ET AL. / 333

FIGURE 3. Field implementation of the instrumentation for using
the DSPP method. (a) The fixed camera and the sample glass under-
lain by a dark reference plate to improve contrast; (b) example of
surface snow sampling for photography; and (c) the micrometer
accurate dot grid used. It is here equipped with a gray scale for
light condition comparison.



classification have been used in a range of image processing appli-
cations (Benz and Pottier, 2001; Schiewe, 2002; van der Sande et
al., 2003; Benz et al., 2004). In the following we focus on the
Definiens Developer 7.0 software parameter definitions; all infor-
mation below is based on this source unless otherwise stated. The
segmentation and classification processes are integral to the pro-
gram and are described fully in the Definiens user manual (Defini-
ens, 2008) and, with emphasis on the segmentation process, in
Baatz and Schäpe (2000). First the image is segmented using a
multi-resolution segmentation algorithm in order to extract discrete

FIGURE 4. Examples of the image analysis processes: (a) raw image, which has been cropped to reduce size and increase particle visibility;
(b) image after segmentation; (c) the segmented image after classification identifying segments corresponding to particles; and (d) resulting
distribution from the classified image (note that the distribution shape is poor due to the low number of particles (N � 88) in the sample
image.

334 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH

objects in the image (Figs. 1 and 4). Multiple-resolution segmenta-
tion is performed grouping homogeneous pixels into regions from
an evenly spaced set of seed cells in the image using user-defined
segmentation parameters (Definiens, 2008). Multiresolution seg-
mentation operates at multiple scales, thereby avoiding bias related
to segment size. In the first run, each pixel is considered as being
a seed cell. Pixels are then merged with surrounding pixels with the
same attributes as defined by the segmentation parameters chosen
(Baatz and Schäpe, 2000). The grouped pixels are then pairwise
merged into regions in iterations that continue until no further merg-



ing is possible according to the given rule set (this is the so-called
multiresolution approach). The limiting factor for the merge (reso-
lution) is based on the scale parameter set by the user which is, in
turn, based on the pixel size of the image. Higher scale parameter
values generate larger, more diverse objects compared to a smaller
scale parameter generating small homogeneous parameters. Seg-
mentation parameters are chosen in order to capture the features
in the image but constrained so as not to divide the objects to be
analyzed into smaller features. The original image is segmented
by two types of segmentation values: the shape, which determines
to what level (in percent) the shape of the object contributes to the
homogeneity of the image; and the compactness, which is a func-
tion of the homogeneity within the brightness and color of the
objects (smoothness and compactness). To summarize, when the
shape is set to 1, the segmentation is optimized for the shape of
the objects, whereas if the segmentation is set to 0, the segmentation
is optimized for the compactness and smoothness of the objects in
the image. Testing and validation is appropriate when selecting
these parameters.

CLASSIFICATION

The segmented images are classified by: (1) brightness, to
contrast the snow particle against the background; (2) area, to re-
duce the influence of clusters and remove the millimeter markers;
and (3) shape index, to determine the shape of the particle and
remove elongated reflections in the sampling glass. This operation
is performed by assigning a class with relevant threshold values
for the objects to be extracted. The snow class was determined by
extracting minimum and maximum values for snow particle objects
and determining the threshold at multiple levels, including small
snow particles and excluding clusters of particles. The classification
is mostly consistent, but occasionally the brightness has to be ad-
justed for incident light in the image or the air content in the particle,
which affects the transparency of the particles. The size range may
also be adjusted for the presence of particles of extreme sizes. The
minimum area value used in the classification of snow particles
was 0.015 mm2, which is just larger than the size of the millimeter
markers of the sampling grid. The segmentation and classification
values vary with snow and sample type, and values for the different
regions are presented in Table 1. The segmented-classification pro-

TABLE 1

Five different rules sets of segmentation and classification systems used in Definiens Developer 7.0 to optimize grain size retrieval on four
separate field sites where segmentation scale determines the size of the segmented objects, the compactness, and shape determined the
spectral or spatial homogeneity of the objects. The classification values of area, brightness, and shape of the objects determine snow particle

or not snow particle.

Region Segmentation values Classification values

Scale Compactness Shape Area (mm2) Brightness Shape
(8 bit relative

scale)

Antarctica coast 50 0.4 0.6 0.025–13.6 136–255 0–2.6
Antarctica plateau 50 0.4 0.6 0.015–13.6 145–255 0–2.6
Järämä 1 150 0.4 0.6 0.035–20 116–255 0–2.6
Järämä 2 50 0.2 0.8 0.035–20 116–255 0–2.6
Tarfala 30 0.4 0.6 0.035–13.6 200–255 0–2.6

SUSANNE INGVANDER ET AL. / 335

cessing can be automated or, if necessary, run interactively to allow
the images to be re-segmented by changing parameter settings.

PARAMETER RETRIEVAL

The size of the objects classified as snow particles are calcu-
lated by identifying separate objects and using generic shape algo-
rithms. The parameters are divided into different levels (I–III)
based on how complex the calculations of the required parameter
are (how many of the prior calculations are necessary in the calcula-
tion) (Fig. 5).

Based on the first-level data (I), size and shape parameters
can be calculated by the level II and III algorithms. The most basic
shape parameters are border length (BL) and area (A). As the pixel
resolution is set initially in the program, the area is calculated using
the area of each pixel (Ap) times the number of pixels in each object
(np). The border length is simply the perimeter of each particle.
Further algorithms at the basic level are the bounding box and
eigenvalue calculations. The bounding box (BB) is the smallest
rectangular area that encloses the object of interest. The geometrical
definition is the difference between maximum and minimum coor-
dinates in x and y direction for each object (Fig. 5). The final
algorithm on level I is based on a tilted bounding box delimited
by eigenvalues (�) derived from the spatial distribution of the ob-
ject. At the second level of calculations, the calculations are based
on the values from the first level equations. An example of second
level equations is length/width ratio, which is calculated using BB
and �: the ratio between the longest and the shortest axis is calcu-
lated for both BB and �; the smallest value is the featured value.
The largest enclosed ellipse (LEE) and smallest enclosing ellipse
(SEE) are based on the eigenvalues and an ellipse with the same
area as the object OE. The OE is downscaled in order to be com-
pletely surrounded by the object LEE or upscaled to surround the
object SEE. The returned result is the ratio between the radius of
the original ellipse and the radius of the SEE or LEE ellipse. This
value explains the complexity of the object. The third level of
equations is based on the results of the level II equations. As the
smallest feature of BB and � is determining the length-to-width
ratio, the values retrieved (depending on which is the smallest) are
the length and width, respectively. The DSPP method has been
tested in different environments to verify its applicability outside of



FIGURE 5. Basic and derived parameters in Definiens Developer 7.0 used for generating snow particle size parameter data sets from
collected images (Definiens, 2008). See text for a detailed description.

Antarctica for which it was originally designed. We will therefore
briefly summarize our experiences from case studies.

Processing Examples
The method has so far been used at three very different sites:

Dronning Maud Land, Antarctica (Ingvander et al., 2010), as well
as Järämä (Ingvander et al., 2012) and Tarfala in northern Sweden
(Ingvander et al., 2013). The Antarctic data set consists of 62 sur-
face samples, ten 1-m pits sampled with 10-cm resolution, and 4
grid net samples (9 samples in a squared grid with 10-m resolution)
along the Japanese Swedish Traverse 2007/2008 (JASE). The Jär-
ämä data set, consisting of surface and pit samples, was collected
for a comparison between DSPP method results and a long-term
monitoring data set collected by Abisko Scientific Research Station
using their simplified classification system (Ingvander et al., 2012).
The Tarfala data set was collected on Storglaciären, a glacier in
the northern Swedish mountains where long-term snow studies
have been performed as part of the ongoing mass balance program
(e.g. Jansson and Pettersson, 2007), as part of a high-resolution
study of seasonal snow accumulation (Jansson et al., 2007). The

336 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH

data explored by us thus span several different types of snow re-
gimes from high elevation subarctic mountains to forest snow cover
as well as the extreme Antarctic snow conditions. Data from all
three field sites have been used in order to develop and improve
the method.

All data from the three sites have been collated and plotted
as size distributions in Figure 6 in order to illustrate the differences
in snow particle size between the three sampling sites. Segmenta-
tion and classification parameters, rule sets, for Antarctica and for
coastal and polar plateau areas (Järämä and Tarfala) are presented
in Table 1. The snow sampled at Järämä consisted of snow with
widely different particle sizes, which required analysis with differ-
ent rule sets (Ingvander et al., 2012).

The difference in brightness values is caused by the occurrence
of melt features when sampling at temperatures above 0 �C and
differences in lighting conditions during the different sampling ses-
sions. Figure 7 shows the size distribution within each group of
samples. The number of objects analyzed in each field campaign
is also evident and depends on the sizes of the snow particles in
each sample (small particle sizes allow more objects to be analyzed
in each sample). Despite the number of particles in each distribu-



0 2 4 6 8 10
0

2000

4000

6000

8000

10000

12000

14000

16000

Antarctic traverse - length (mm)

# 
pa

rt
ic

le
s

0 2 4 6 8 10
0

50

100

150

200

250

300

350

400

Järämä - length (mm)
0 2 4 6 8 10

0

50

100

150

200

250

Tarfala - length (mm)

N = 52347a) b) c)N = 1403 N = 1067

FIGURE 6. Snow particle size distributions from (a) Antarctica; (b) Järämä; and (c) Tarfala. See text for a discussion.

tion, there is a distinct negative skewness in all distributions. We
will discuss this further below. Two examples of different snow
size samples are presented in Figure 7.

The original sample 1 image of rounded particles (Fig. 7, part
a) are segmented and classified according to Järämä 2, whereas
the original sample 2 image of depth hoar (part b) is classified
using the rule set of Järämä 1. The length results from the classified
images (parts c and d) were binned using the maximum object size
divided by the resolution for each image. The objects are binned
separately according to resolution and object sizes. The bin sizes
used in Figure 7 are 14.66 in part e and 15.57 in part f in the size
distribution histograms. The interpreted size from visual observa-
tion and the mean DSPP size is presented in Figure 7. The results
show that visual interpretation may overestimate sizes of small
particles and underestimate the size of larger particles. Since the
segmentation and classification is automatic, clustered particles
may enter the distribution. This is the reason for the long distribu-
tion tail toward larger particle sizes (Fig. 7). This means that some
caution must be made when using a distribution to assign a particle
size value. The mode provides the dominant value while the mean
is affected by the tail.

In order to test the reproducibility, a re-analysis where images
were rotated 90� was made, which generated slightly deviating
results. This deviation is caused by the software analysis algorithm.
The software scans the image in a predetermined way; if altering
the image orientation, the seed cell positions are altered, which
means the segmentation develops differently. To obtain a length,
the Definiens Developer 7.0 software uses either the bounding box
or the eigenvalues as a basis for the calculation (Fig. 5). A rotation
of the object may thus result in a switch of base parameter for
the calculation of length, which may introduce smaller deviations.
Figure 8 shows how the length and area parameters change when
rotating the image.

The area does not show any appreciable deviations (R2 �
0.99), but there is a deviation in length for the larger particle sizes
although the correlation is still good (R2 � 0.97). This behavior
originates from the fact that area comes from a first-level equation,
but length is calculated from a third-level equation (Fig. 5). The
length parameter is based on the relation between the bounding
box and eigenvalues through a derived length/width ratio value.

SUSANNE INGVANDER ET AL. / 337

We deem this deviation of little significance, but it indicates how
important knowledge of software algorithms can be. The difference
in mean of area, length, and width is presented in Table 2.

We validated the method against visual interpretation using
manual digitalization of snow particles and compared the size of
the manually digitalized snow particle sizes with the DSPP-method
retrieved particle sizes generating a R2 � 0.98 and RMSE of 0.071
for the length parameter (Fig. 9).

It is important that the DSPP results are comparable to other
investigations. The length parameter is comparable to the Interna-
tional Classification of Seasonal Snow on the Ground (Colbeck et
al., 1990; Fierz et al., 2009) where the definition of snow grain
size is the greatest extension of the grain. When identifying the
average particle size we believe the mode from our distributions
is to be compared with the particle size concept of the snow classifi-
cation. Furthermore, the size parameters can be used to calculate
the geometry of the objects and thereby determine the shape of the
particles in the samples. For example, the length/width ratio can
be used to determine the shape of the objects classified as snow
particles. This shape parameter can be used to analyze morphology
and shape altering processes such as wind transport (Kikuchi et
al., 2005). Furthermore, shape differences may be useful in remote
sensing applications (Mätzler, 1996).

Discussion and Recommendations
The DSPP method seems to successfully fulfill the basic re-

quirements for analyzing snow particle size in the field. The method
is cost effective, accurate, and delivers the size range (distribution)
for each sample. It is an extension of existing methods using com-
mercial instrumentation and software, which enables reproducibil-
ity within the scientific community. The developed DSPP method
facilitates objective analysis of data from different types of snow
and geographical regions. By introducing a completely automatic
method, comparison of different data sets is possible independent
of the analyzer. The method generates extensive data sets that im-
prove the data that can be extracted and thereby offers a range of
alternative measurements. The number of data points retrieved also
improves the statistical reliability. Studying the distribution of the
data shows a negative skewness in Figure 6. This information im-



FIGURE 7. Snow grain images from Järämä in northern Sweden. The first column shows a sample of surface snow: (a) raw image; (b)
classified image; and (c) resulting particle length distribution. The second column shows a sample identified as depth hoar: (d) raw image;
(e) classified image; and (f) resulting particle length distribution. The values at the bottom of each column show in-field visually interpreted
and DSPP-derived size values.

338 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH



FIGURE 8. Scatter plot of objects classified as snow particles in
a test image before and after a 90� rotation for (a) particle length
(R2 � 0.97) and (b) particle area (R2 � 0.99).

plies that the mean value may not be representative when analyzing
snow grain size and should be taken into account when comparing
to visual analysis of snow samples that usually provide average
sizes. The primary advantages of the DSPP method are the range of
descriptive statistics retrieved and the removal of visual subjective
components of the snow particle size analysis. This allows users

TABLE 2

The resulting mean size in the three parameters (area [mm2], width
[mm], and length [mm]) in an original example image and the

results from a 90� shift of the same image.

Parameter (mean) Original 90� shift

Area (mm2) 0.31 0.35
Length (mm) 0.91 0.93
Width (mm) 0.61 0.62

SUSANNE INGVANDER ET AL. / 339

FIGURE 9. For validation purposes, images were digitized using
visual interpretation (manual digitalization of particle size) and
correlated using linear regression with the DSPP method for re-
trieved particle sizes (particle size DSPP). Twenty objects in four
images were used for the analysis of the size parameters area,
length, and width (R2 � 0.99 and RMSE � 0.044 for area, R2 �
0.98 and RMSE � 0.071 for length, and R2 � 0.91 and RMSE �
0.089 for width).

to apply the method widely. We would like to recommend that the
nomenclature used in this paper be carefully considered so as to
avoid confusion using the terms particle and grain in relation to
what is actually studied.

The main limitation of the developed method is the two-di-
mensional analysis of three-dimensional natural snow objects. Fur-
thermore, the georectification by pixel calculation reduces the accu-
racy, but this could be prevented by using a fixed distance between
the camera and the sample grid. Melted snow particles (water drop-
lets or refrozen water) resulting in clumping of particles affects the
image analysis as they imitate the same classified features as the
snow particles. This problem is reduced by using area, shape, and
brightness limitations and by initial crop of the particle size images.
Sufficient lighting conditions are also important to promote contrast
in the image. The possibility exists that the morphology of the
particles are altered by removal from the sampled snow; this error
is not method specific and is considered to be marginal considering
that the sample strategy is cautious and the particles are generally
robust. An exception is fresh snow which has to be analyzed imme-
diately when placed gently on the reference grid. The method facili-
tates the analysis of different types of snow (in shape and size)
that occur in different regions with various physical settings. Large
objects have a tendency to cluster and increase the mean size in
the analysis. This effect is countered by manually cropping the
image to remove clusters. This leads to fewer particles to be ana-
lyzed in each sample. We propose increasing number of parallel
samples when analyzing larger particles in order to achieve a statis-
tically reliable number of particles. The smallest particles also have
a tendency to cluster due to their size (Ingvander et al., 2012).



The DSPP method is dynamic and can be tuned to account for
differences in particle size and illumination. In the coastal region of
Antarctica large particles were sampled under overcast conditions
during the JASE traverse. The Järämä samples are separated into
Järämä 1 and Järämä 2 rule sets in order to segment samples with
small and large particles separately. The Tarfala samples were seg-
mented with a scale parameter of 30 in order to capture fresh snow
and small particles. The brightness threshold is considerably higher
in the Tarfala classification, being an effect of sampling and pho-
tography performed in a 3-m-deep snow pit with poorer light condi-
tions than surface sampling (i.e. overcast that generate whiteout
and raise the general brightness level in the images). However, even
in samples conducted with poor lighting conditions, the contrast
between the snow particles and the background has proved to be
sufficient for segmentation and classification.

Future development of the method is to establish constant eleva-
tion of the camera by fixing the reference grid plate to the tripod,
which would generate constant resolution and higher accuracy in the
images. Without tilt, the pixel resolution would be constant in the
entire image and not be scattered as in Figure 3. However, the refer-
ence grid is also used for calculating camera distortion between the
center and the border of the images [included in the process of Equa-
tion (1)], which we found negligible in this study, being less than
0.06 mm. Furthermore, photographing the snow fixed in its position
would remove the possibility of grain alteration by movement. This
would also provide information on how the particles are positioned
against each other in the snow pack and small-scale information on
the grain/air ratio within the snow. There are built in limitations in
the software as the seed cells governing the multi-resolution segmen-
tation grow in different directions depending on the angle of the
image. This problem is overcome by photographing snow in its origi-
nal position in the snow pack, which is a complicated but interesting
next step for the DSPP method. The segmentation process was vali-
dated against manual delineation of snow particles (Fig. 9). This high
accuracy is accomplished by the significant contrast in the image and
high brightness of the particles. The segmentation process would in-
duce larger errors on images with wider ranges in brightness of the
snow particles and the background.

This method aims to provide accurate measurements of snow
particles from large sample sizes in support of remote sensing in-
vestigations. The correlation of snow particle sizes with remote
sensing observation is a work in progress. Here we show the poten-
tial of the DSPP method to derive size and shape measurements
of snow particles in two dimensions; it should be noted that there
are approaches that allow for the estimation of SSA from two-
dimensional observations of snow particle size (e.g. Matzl and
Schneebeli, 2010).

Conclusions
We have presented a method that combines rapid, simple data

acquisition with sophisticated analysis for snow particle size assess-
ment. The aim of the method was to improve upon the visual inter-
pretation of snow particle size where project logistics could not
cover refined instrumentation such as integrated sphere measure-
ments of SSA or contact spectroscopy. As the DSPP method targets
snow particles, i.e. bonded grains, as reflectors of radiation, the
investigation does not aim to replace high-accuracy snow grain
size analysis such as tomography or grain growth modeling
(Schneebeli and Sokratov, 2004). However, the potential to retrieve

340 / ARCTIC, ANTARCTIC, AND ALPINE RESEARCH

multiple two-dimensional size parameters enables further calcula-
tion of sizes on a subsample scale. The large number of samples
that can be obtained offers statistical rigor not always found in
snow particle size observations. In the future, evaluation against
simultaneous measurements of DSPP photography and reliable
measurements of specific surface area is desirable.

Acknowledgments
The authors would like to thank the Swedish Polar Research

Secretariat for logistic support and Swedish Research Council,
Swedish National Space Board, Tarfala Research Station, Bert
Bolin Centre for Climate Research, SSAG, Ymer-80, and Helge
Ax:son Johnson foundation for financial support. Acknowledg-
ments to colleagues participating in the field for making the mea-
surements possible. The authors would also like to thank Dr.
Charles Fierz at the Institute for Snow and Avalanche Research,
Davos, Switzerland, for valuable comments on the method and the
nomenclature at an early stage.

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MS accepted February 2013