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OLETÍN
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EOLÓGICA
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OLUMEN
63,
NÚM
. 1, 2011,
P
. 95-107
Assessment of airborne LIDAR for snowpack depth
modeling
Ignacio Moreno Baños
1,*
, Antoni Ruiz García
2
, Jordi Marturià i Alavedra
1
,
Pere Oller i Figueras
1
, Jordi Piña Iglesias
1
, Pere Martínez i Figueras
1
, Julià
Talaya López
2
1
Institut Geològic de Catalunya, C/Balmes, 209-211, E08006, Barcelona
2
Institut Cartogràfc de Catalunya, Parc de Montjuïc, 08038, Barcelona
* natxo.moreno@gmail.com
Abstract
The Institut Geològic de Catalunya (IGC) and the Institut Cartogràfc de Catalunya (ICC) have begun a joint project to model
snowpack depth distribution in the Núria valley (a 38 km
2
basin located in the Eastern Pyrenees) in order to evaluate water reserves in
mountain watersheds . The evaluation was based on a remote sensing airborne LIDAR survey and validated with feld-work calculations.
Previous studies have applied geostatistical techniques to extrapolate sparse point data obtained From costly feld-work campaigns.
Despite being a recently developed technique, LIDAR has become a useFul method in snow sciences as it produces dense point data and
covers wide areas.
The new methodology presented here combines LIDAR data with feld-work, the use oF geographical inFormation
systems (GIS) and the stepwise regression tree (SRT), as an extrapolation technique. These methods have allowed us to map snowpack
depth distribution in high spatial resolution.
Extrapolation was necessary because raw LIDAR data was only obtained From part oF the
study area in order to minimise costs. Promising results show high correlation between LIDAR data and feld data, validating the use
oF airborne laser altimetry to estimate snow depth.
Moreover, diFFerences oF total snow volume calculated From modeled snowpack
distribution and total volume From LIDAR data diFFer by only 1 %.
Keywords: Snowpack depth, stepwise regression tree, LIDAR, GIS, Pyrenees
Resumen
El Institut Geològic de Catalunya (IGC) junto con el Institut Cartogràfc de Catalunya (ICC) han iniciado un proyecto para modelizar
el manto nival en un área piloto situada en el valle de Nuria (cuenca de 38 km
2
situada en el Pirineo Oriental) con el fn de evaluar
reservas hídricas en áreas de montaña. Para ello se realizó un vuelo LIDAR validado mediante una campaña de campo de toma de datos
puntuales. Estudios previos han consistido en la aplicación de técnicas geoestadísticas a fn de extrapolar datos puntuales dispersos
adquiridos mediante campañas de campo costosas. El uso de LIDAR aerotransportado, a pesar de ser una técnica novedosa en el
campo de la nivología, tiene la ventaja de proporcionar un gran número de datos puntuales de áreas extensas. La nueva metodología
aquí presentada combina el uso de la técnica LIDAR con trabajo de campo, el uso de los sistemas de inFormación geográfca (SIG)
y árboles de regresión (SRT, stepwise regression tree) como método de extrapolación. Estos métodos nos han permitido obtener un
mapa de resolución elevada del espesor de nieve. La extrapolación Fue necesaria ya que los datos LIDAR sólo Fueron obtenidos para
una porción del área de estudio a fn de reducir los costes. Se obtuvieron resultados alentadores en virtud de la alta correlación de los
datos LIDAR y las muestras de campo, lo cual valida el uso de la altimetría láser aerotransportada para la estimación del espesor del
manto nival. Así mismo, los resultados muestran un ajuste elevado entre el volumen de nieve calculado mediante LIDAR y el volumen
de nieve modelizado con una diFerencia de tan sólo 1 %.
Palabras clave: ProFundidad de manto nivoso, árbol de regresión,
LIDAR, SIG, Pirineos
Moreno Baños et al.
96
1. Introduction
The Mediterranean climate is characterized by high
precipitation variability (Capel Molina, 2000). As a result,
the Iberian Peninsula, including Catalonia, is affected
by frequent and serious droughts, such as those of 1973,
1985 and 1988 (ACA, 2005). The most recent signiFcant
dry period took place during 2007-2008 (SMC, 2008).
The correct management of hydric resources is of great
importance in order to administrate and limit the effect of
natural climatic variability.
Hydric resources are not only affected by natural
±uctuations, but also social pressure. Catalonia’s internal
fluvial basins (oversight responsibility of the local
government) occupy 52 % of Catalonia’s territory and
host 92 % of the total population (Sangrà, 2008), which
increases hydric stress. ²urthermore, Ulied (2003) points
out that by 2020 Catalonia’s population will have reached
7.3 million, which implies an increase in hydric stress of
water usage of 88-150 hm
3
/year
(depending on different
scenarios). As a consequence, it is necessary to quantify
hydric resources stored artiFcially in water reservoirs or
naturally in snow.
The present study
was carried out
by the Institut
Geològic de Catalunya (IGC) jointly with the Institut
CartogràFc de Catalunya (ICC). The project was composed
of several stages: Frstly, to evaluate LIDAR technology as
a method of obtaining snow depth and, secondly, to model
snow depth in order to calculate water availability from
snow density data.
Despite spatial variability in snow depth, numerous
studies have dealt with modeling snowpack. Elder
et al
.
(1995) studied snow water equivalent, first on a small
basin (120 ha) through a decision tree model, and later
extending the study area to 9600 ha, and including data
from Landsat-5-TM. Erxleben
et al.
(2002) applied
geostatistical methods to 550 data points from three
experimental areas, each of 1 km
2
. The techniques applied
include inverse distance weighting (IDW)¸ modified
residual kriging and cokriging,
and decision tree models.
Interpolation techniques such as IDW showed low
adjustment (R
2
between 0.10 and 0.20) while the tree
classiFcation was the best method of modeling snow depth
variability (R
2
= 0.298; Erxleben
et al.
, 2002). Molotch
et al.
(2005) suggest interpolating residuals and adding
them to a tree model as new variables, slightly improving
adjustment (R
2
of 0.31). More recently, López-Moreno
and Nogués-Bravo (2006) and López-Moreno
et al.
(2007)
compared several statistical methods to map snow depth
distribution in the Pyrenees, using 106 data points. In that
investigation, geostatistical methods (IDW, kriging
and
cokriging) also gave poor results (R
2
between 0.04 and
0.14) compared to the tree models (R
2
=0.71). Despite this
model Ft, the authors consider that prediction accuracy
made by tree models is insufFcient because snow depth
values are limited to the number of Fnal nodes calculated
by the model. They conclude that a general additive
model (GAM) is the best approach to model non-lineal
relationships between topography and snow depth.
The main limitation of most studies is the low
availability of manual measurements of snow depth. As
a consequence, derived models over large areas have a
low prediction ability, demonstrating the need for remote
sensing techniques. Marchand and Killingtveit (2005)
made more than 100 000 measurements using geo-radar to
obtain snow depth point data. As geo-radar was mounted
on a snow mobile, the survey area was limited in extension
and accessibility to ±at areas not exceeding a few square
kilometers (preferably with no forest). The use of remote
sensing techniques includes satellite data for quantifying
the area covered by snow (Rosenthal and Dozier, 1996;
Salomonson and Appel, 2004) but not its depth.
The technique used in this research, airborne light
detection and ranging
(
LIDAR), is a new technology
in snow science. LIDAR makes it possible to calculate
snow depth over large areas with high resolution and at
relatively
low cost compared to manual surveys. That is
why numerous studies have applied LIDAR to snowpack
modeling with satisfactory results (Hopkinson
et al
., 2001;
Hopkinson y Demuth, 2007; ²assnacht and Deems, 2005;
Kaneta and Hatake, 2007).
This research focuses on the use of airborne LIDAR
to calculate snow depth across a pilot area situated in the
Eastern Pyrenees and the methodology used for modeling
snow depth over large areas using LIDAR data obtained on
a recent campaign in spring 2009.
2. Study area
A pilot area was established in Núria valley to assess
LIDAR techniques and establish a methodology applicable
to a wider area of the Eastern Pyrenees (42°23’50” N
and 2º9’13” E, ²igure 1). Avalanche forecasts have been
carried out by IGC in Núria valley and its high climatic
representativeness of the whole Eastern Pyrenees make
it the ideal site to establish a pilot area. The valley itself
covers an area of 38 km
2
with an altitude ranging from
1950 m at Núria Sanctuary to 2910 m at the summit of
Puigmal peak.
Much of the study area consists of meadows and
rocky soil above the timberline. ²orested areas formed by
mountain pine (
Pinus mugo
ssp.
uncinata
) with alpenrose
(
Rhododendron ferrugineum
) undergrowth are located in
the region surrounding the Sanctuary (DMAH, 1993).
The climate is characterized by an annual precipitation
of 1150 mm, mainly concentrated in summer months, and
a mean annual temperature of 6 ºC (ICC, 1996). During
winter, snow precipitations are more common and yearly
snowfall is about 200 cm. Another climatic variable to take
into account is wind. Núria valley has a very high wind
exposure due to its eastern location within the Pyrenees,
with wind-speeds reaching up to 200 km/h.
Assessment of airborne LIDAR for snowpack depth modeling
97
3. Data and methods
Snow depth calculation was based on subtracting two
digital elevation models (DEM) generated from LIDAR
data, with and without snow cover. LIDAR data was
acquired across the whole Núria valley pilot area, making
extrapolation unnecessary. Nevertheless, to increase
project efficiency for larger areas, LIDAR flights were
limited to a small part of the area of interest (
e. g.
a LIDAR
strip covering 15 % of the total surface, Figure 2). Thus an
extrapolation methodology was developed to obtain snow
depth data for the whole research area.
Detailed methods and data used to obtain LIDAR data,
snow depth and its modeling are explained in more detail
in the following sections.
3.1. DEM generation from LIDAR survey
As mentioned above, two DEMs were generated to
calculate snow depth. The first flight was carried out
without snow on August 9
th
, 2006, and the second with
snow cover on March 27
th
, 2009.
Airborne LIDAR was incorporated into a light aircraft,
which covered the ±ight lines shown in Figure 3. Measuring
equipment was composed of a Leica ALS50-II airborne
laser scanner, a GPS system and an inertial navigation
system
(INS). Elevation was calculated by determining the
return time of emitted laser pulses together with the known
trajectory and velocity of the aircraft. An oscillating mirror
diverted the laser pulse scanning the surface in a zigzag
shape. According to this procedure and the established
parameters (Table 1), two 1 m resolution DEMs were
calculated.
Data density on each ±ight strip was 0.5 point/m
2
, but
due to ±ight line overlap of 50 %, a ²nal point data density
Figure 1. Núria valley location map.
Table 1. Flight characteristics for DEM generation.
Modeled surface
4610 ha
Point density
0.5 point/m
2
DEM control areas
2
Fligth time
4 h
Strips
9
Swath
540 m (40º)
Moreno Baños et al.
98
of 1 point/m
2
was attained. The overlapping of Fight lines
provides higher productivity because point data in the
overlapping areas are not redundant, resulting in a better
distribution of point data over the target surface with higher
homogeneity. Overlapping Fight lines also guarantees that
no gaps will be present between strips, due to summits or
navigation errors. The highest point density is obtained in
valleys where it is possible to have up to 3 overlapping
strips due to broader Fight swath with altitude. To take
advantage of these benefits it is necessary to remove
systematic errors in the strip elevation. Point data is always
affected by systematic errors from GPS and INS (Kornus
and Ruiz, 2003). In such a case, automatic classi±cation
will consider points from the more elevated strip so they
will be classi±ed as vegetation, thus eliminating data. The
processing software used was TerraMaatch® and control
point data was measured with GPS-RTK on areas without
snow with an estimated precision of 2-3cm to ensure
correct match up between the two DEMs calculated.
Each laser point has information about different
obstacles found between the airplane and ground echoes.
If the terrain is snow-covered and vegetation free, the
last laser echo corresponds to bare earth, but if obstacles
are encountered then different laser echoes correspond to
those obstacles. In this way when the ±rst laser echoes are
processed, a digital surface model (DSM) is obtained with
all elements, such as vegetation and buildings, present on
the surface, or the surface itself if there isn’t an obstacle. A
digital elevation model is calculated from the last echoes
but must be validated to remove the possible presence of
vegetation. This validation was completed using a GIS
layer containing areas of vegetation and applying a 1m
threshold between areas with and without vegetation.
Once validated, a triangulated irregular network (TIN)
was created using TerraModeler® and converted to a 1 m
regular grid.
The root mean square error (RMSE) of this process
was approximately 0.15 m but the combination of the
two DTMs (with and without snow) increased the error
slightly to 0.21 m. The sources of error are mainly steeper
slopes and areas with dense vegetation, as pointed out by
Hopkinson
et al.
(2004) and Deems and Painter (2006).
3.2. LIDAR snow depth validation.
Snow depth validation involved: 1) ±eld work to obtain
manual snow depths; 2) the capture of aerial photography
during LIDAR surveying, to determine areas with
presence/absence of snow and 3) topographical pro±les in
areas with large snow accumulations (greater than 6 m). In
the following sections these methods will be explained in
more detail.
2 km
²igure 2. Arrows show snow depth data extrapolation from one strip of LIDAR data covering 15 % of total surface area (shown in grey).
Assessment of airborne LIDAR for snowpack depth modeling
99
3.2.1. Field work
Manual snow depth was measured on the same day as
the fight. Three groups were Formed For this purpose, and
distributed throughout valleys with diFFerent topographical
characteristics (±igure 4). Each team accessed the highest
point oF each valley by helicopter and then skied down. In
this way we ensured rapid access to the respective valleys
and obtained the maximum number oF data points.
Manual snow depth was measured with 4 m long snow
probes positioned by GPS. Taking into consideration that
the DEM resolution was 1 m, the diFFerential submetric
GPS system (model Trimble GEO XH
®
) was ideal. Once
captured, point GPS data were post-processed through
public reFerence stations From the Cartographic Institute oF
Catalonia (ICC, 2010). As a result oF ²eld work, 74 manual
measurements oF snow data were obtained.
3.2.2. Aerial photography
During the LIDAR Flight, high resolution aerial
photographs were also acquired. These photographs were
used, aFter orthorecti²cation, to digitalize control areas
where snow depth was equal to 0. These control areas
with a known snow depth oF 0 m were then compared to
the LIDAR snow depth map. Within these control areas
(with real snow depth equal to 0), any value obtained From
the LIDAR other than 0 was considered an error. Through
aerial photography, the validation process was ensured due
to the high number oF point data used (680 000 in total,
±igure 5) compared to 74 manual measurements (±igure
4).
3.2.3. Topographical pro±les
The LIDAR data analysis shows areas with large
snow accumulation oF more than 6 m depth in speci²c
topographic conditions. As manual data acquisition was
not possible in these areas, snow pro²les were made to
con²rm the large snow accumulations.
Through GIS soFtware (ArcGIS 9.3.1), a total oF 17
pro²les were made in wind sheltered areas, such as streams
or downwind surFaces, which are prone to snow deposition.
Each pro²le shows terrain with and without snow. In this
manner it was easy to veriFy iF snow accumulation values
obtained From LIDAR data were Feasible or simply errors
linked to the technique.
2 km
1
0
±igure 3. Situation oF fight strips and aerial photography surveyed.
Moreno Baños et al.
100
2 km
Noucreus valley
Puigmal valley
Coma de Vaca valley
Núria Sanctuary
Topographical profiles
Figure 4. Location of snow sampling point data by ±eld work and topographical pro±les together with snow depth map.
Figure 5. Digitalized areas with known snow depth equal to 0.
Assessment of airborne LIDAR for snowpack depth modeling
101
3.3.
Snow depth modelization
In the following sections the methods used for snow
depth modeling and calculating the topographical variables
are presented. The original DEM resolution was 1m but
the working resolution was 5 m due to computational
optimization.
3.3.1. Calculation of topographical variables
Previous studies (Marchand and Killingtveit, 2005;
López-Moreno and Nogués-Bravo, 2006; and Elder
et
al.,
1998) suggest different independent variables that
have a major inFuence in snow distribution. The variables
adopted in this investigation were slope, aspect, altitude,
curvature, distance to main range and solar radiation.
Elevation, slope, curvature and distance to main range
(López-Moreno and Nogués-Bravo, 2006) were directly
calculated from the DEM with algorithms available in
ArcGIS 9.3.1.
Aspect had a higher complexity because of its circularity
(Burrough
et al
., 2000; Marchand and Killingtveit,
2005). For this reason this parameter was divided two
components: a north-south component and an east-west
component. Resulting values were standardized with
values ranging between 0 and 1.
Solar radiation calculation is a complex task which
involves several factors (Cline
et al.
, 1998), and a dense
network of weather stations is necessary in order to model
solar radiation correctly. However, in high mountain areas
such as the Pyrenees, this is generally not possible. ±or
this reason the solar radiation calculation was simpli²ed
to a global input radiation calculation using the function
available on ArcGIS, named
area solar radiation
, which
only takes in consideration topographical variables such
as elevation, slope or aspect (for more information about
calculations see software documentation). Radiation was
modeled for the winter season lasting from November, 1
st
to March, 27
th
(the day of the Fight).
In addition to these independent variables other
important factors such as wind must be taken into
account when modeling snow depth (Molotch
et al.
,
2005). Therefore upwind index
(Winstral and Marks,
2002; Winstral
et al
., 2002) was added to the independent
variables. Upwind index
measures the exposure of a cell
in a DEM depending on the prevailing wind direction and
quanti²es how wind affects snow distribution. Calculation
of upwind index
is theoretically expressed in equation
(1) and was applied through an algorithm supplied by the
author A. Winstral (Winstral and Marks, 2002; Winstral
et
al
., 2002),
,
,,
max tan
Sx
x y
xx yy
ELEV x y
ELEV x y
, max
22
0.5
Ad
i
i
vi
vi
vv
ii
=
-
+
-
-
^
^^
^^
e
h
hh
hh
o
6
=
@
G
(1)
Where
ELEV
is the altitude of interest cell;
A
the
azimuth of the search direction; (
x
i
,
y
i
) coordinates of the
cell of interest; (
x
v
,
y
v
) the coordinates of the cells found
in the same direction of prevailing wind and
dmax
is the
maximum search distance.
The application of the upwind index for the Eastern
Pyrenees was best correlated with snow depth at a
prevailing wind direction of 220º. Empirically, the
maximum distance of search vector was established at
300 m.
±inally, Pearson’s correlation between each variable
and snow depth was calculated to evaluate the relation
between them with SPSS 16.0 statistical software.
3.3.2. Snow depth modeling
As previously mentioned, modeling of snow depth
was achieved through the extrapolation of strip Fight data.
Existence of data for the whole area permitted validation
of the ²nal model.
A stepwise regression tree (SRT, Huang and
Townshend, 2003) was used to model snow depth because
it considers the non-linearity of dependent variables.
This is the most important characteristic for modeling
snow depth calculations (De’Ath and ±abricius, 2000;
Huang and Townshend, 2003). The SRT method has been
implemented by Loh (2002) and Loh
et al
. (2008) in the
GUIDE algorithm (Loh, 2011) as an evolution of the
classical tree classi²cation proposed by Breiman
et al
.
(1984).
The GUIDE algorithm was used for a stepwise
regression tree. The independent topographical variables
were used to explain snow depth (dependent variable).
In a classical tree classification the assigned value for
prediction is the mean of each node. Consequently, the
range of predicted values is limited to the number of ²nal
nodes of the tree. Loh (2002) and Huang and Townshend
(2003) propose a stepwise regression at each ²nal node.
The regression at each final node ensures a small and
homogeneous
sample size which implies a better accuracy
of prediction and cartography. In this way, the prediction
value is not limited to a mean on the ²nal nodes (because
a regression is made) and a range of values is permitted,
making prediction more accurate. Pruning was made
through 10 cross-validation iterations and the minimum
sample size for each node was 7500 data points.
4. Results and discussion
4.1. LIDAR snow depth model validation
The aim of snow depth validation was to establish the
RMSE of the snowpack and a feasible maximum value of
snow depth. With these values the model was validated
by removing negative and extreme positive snow depths.
Validation was carried out on the snow depth map obtained
from the subtraction of the LIDAR-generated DEMs.
Moreno Baños et al.
102
4.1.1. Validation through feld data
Despite the use of LIDAR for snow depth calculations
(Fassnacht and Deems, 2005; Deems and Painter, 2006),
few validations have been achieved with field data.
Hopkinson
et al.
(2004) estimated that differences between
LIDAR data and ±eld measurements were around 19 %.
In this study there are many factors that affect the
quality of results:
1.
Despite efforts to increase efficiency in field work
the validation data was limited to 74 points, which is
probably insuf±cient to ensure data representativeness.
2.
The field surface surveyed was uneven and rocky,
resulting in biased ±eld data. The variability of snow
depth within 1 m
2
was high and the exact place where
the snow probe was inserted is of great importance
(even though the nested method for snow sampling was
adopted during ±eld work).
3.
Snowpack conditions induce measurement errors. Pre-
sence of ice layers within the snowpack impeded the
correct penetration of the snow probe.
4.
Mountainous terrain induces GPS precision errors.
Moreover, data acquisition in steep slopes coincided
with areas where LIDAR techniques presented more
de±ciencies.
These factors resulted in unsatisfactory results which
were therefore not considered when performing validation.
Validation was therefore achieved with aerial photographs
and pro±le making in accumulation areas.
4.1.2. Validation with aerial photographs.
Although only 74 point data were obtained through
manual surveying, with the digitalization of snow-free
control areas, a total of 680 000 data points were obtained,
representing an area of 0.7 km
2
(2 % of the total study
area).
Since slope is the major source of error in LIDAR
surveys,
slope distribution at the control areas should be
very similar and representative of the entire study area.
The presence of higher slopes in control areas would mean
an increment in the calculation error of snow depth. Table
2 shows all slope classes (every 10º) for the whole area
and for the control areas. All ranges were well represented
during digitalization so deviations in the error source were
avoided.
For comparison, Table 3 shows RMSE obtained for
snow depth calculations in control areas (0 cm) from
LIDAR data depending on different slope ranges. The
results show that error increases with slope and that
low snow accumulation in slopes of more than 60º also
increases error. Thus we excluded these slopes in the ±nal
RMSE calculation. The ±nal RMSE calculated value for
control areas was 0.428 m.
4.1.3. Validation in snow accumulation areas
After subtraction of the DEM, the resulting snow depth
presented extreme values that should be investigated. A
total of 17 topographic pro±les were made for this purpose,
two of which are shown in Figures 6 and 7.
Both ±gures demonstrate how bare-earth and snowpack
follow the same topographical trend, which implies that
the snow depth values are correct measurements. The same
analysis made on 17 pro±les showed evidence that snow
depth accumulations of up to 11 meters were valid in very
speci±c topographical conditions, namely deep streams
and wind-sheltered areas. During validation all values
higher than 11 m were therefore considered erroneous and
subsequently eliminated.
4.1.4. Validated snow map.
After the validation process a range of valid values
was established and applied to the LIDAR snow model.
This range lies between -0.428 m, corresponding to the
RMSE calculated, and 11 m, from the pro±le analysis. A
value of 0 m depth was assigned to measurements between
-0.428 m and 0 m. The resulting snow model therefore
ranges from 0 m to 11 m of snow depth (Figure 5).
Table 2. Percentage of slope distribution in original DEM and
digitalized control areas. Calculated differences are low so deviation in
error calculation is minimal.
Slope rank (º)
Presence in
DEM (%)
Presence in control
areas (%)
Difference (%)
<10
3.55
3.55
-0.005
10-20
14.98
10.79
-4.194
20-30
38.00
35.01
-2.990
30-40
36.63
41.88
5.245
40-50
5.40
6.41
1.009
50-60
1.19
1.90
0.711
60-70
0.21
0.41
0.197
> 70
0.03
0.05
0.025
Table 3. RMSE calculation of difference found among control areas
and LIDAR data for different slope classes
Slope rank (º)
RMSE (m)
<10
0.170
10-20
0.185
20-30
0.314
30-40
0.440
40-50
0.702
50-60
1.298
60-70
2.631
> 70
4.937
RMSE slope < 60
0.428
Assessment of airborne LIDAR for snowpack depth modeling
103
Figure 6. Coma del Clot stream profles showing snow accumulations up to 11 m.
Figure 7. Profle o± ridge near Pic del Segre showing snow accumulations o± up to 6 m.
Moreno Baños et al.
104
4.2.
Snowpack modeling
4.2.1. Snow and terrain parameters correlation
To select variables that best explained the distribution of
snow over the pilot area, the Pearson correlation coefFcient
was calculated between topographical variables and snow
depth. ±igure 8 shows these correlation coefFcients.
Major correlation was found with the upwind index (
r
=
0.447), and with curvature (
r
= -0.328). Surprisingly, there
was no important correlation with altitude (
r
= 0.116),
mainly due to speciFc distribution of snowpack over the
study area. Greater snow accumulations were situated in
wind sheltered areas and north facing slopes. In contrast,
the wind produced snow-free surfaces at higher altitudes.
Another important factor which must be considered to
fully understand correlation coefFcients is that the LIDAR
²ight was carried out on March 27
th
when melting had
already begun.
Based on the Pearson correlation, slope and altitude
were excluded from the SRT analysis due to their low
correlation with snow depth.
4.2.2. Extrapolation of snow depth
The GUIDE algorithm (Loh
et al
., 2008) permitted a
prediction of snow depth from one ²ight strip as pointed
out in the methodology. The result of the modeling process
was a classification tree composed of 19 final nodes
(Figure 9).
The model was run recursively with different parameters
(regression type, number of cross-validation iterations
etc.) until reaching the optimum model presented. The
best tree model is the one with the smallest number of
Fnal nodes. An excessive number of nodes results in the
overestimation of the model. An example of the in²uence
of the Fnal number of nodes over the model adjustment is
shown in ±igure 10. The Fgure shows how an increasing
number of final nodes implies an overestimation and
erroneous adjustment of the model.
The Fnal model, R
2
=0.44, is very signiFcant due to
the fact that we used a relatively high spatial resolution
(5 m) and a great number of data was modeled (more
than 1 million data points). Despite this, the RMSE
was significant (0.69 m), and the model fit was very
satisfactory for the study purpose (snow volume). The
ultimate aim of the project was to evaluate water supplies
stored as snow. In this sense, the difference in total snow
volume is more important than snow depth accuracy. So if
snow volume calculated from the validated LIDAR model
(33.7 hm
3
of snow) is compared to snow volume calculated
from the extrapolated Fnal model (33.9 hm
3
) the difference
is only -1 %.
Authors such as Elder
et al.
(1998) have presented an
R
2
of 0.68 with computations made on a 30 m resolution
DEM. Afterwards, in parcels of 1 km
2
, Erxleben
et al.
(2002) showed an adjusted R
2
of 0.298. López-Moreno
and Nogués-Bravo (2006), with 106 data points for the
entire Pyrenees, obtained an adjusted R
2
of 0.71 and,
finally, Molotch
et al.
(2005) present values between
0.31 and 0.39 (after performing a residual interpolation
procedure).
±igure 11 shows the Fnal map result of extrapolation.
The map re²ects high spatial variability of snow depth,
which is one of its most well known characteristics (Elder
et al.
, 1995).
Other aspects to consider in the map analysis are:
a) large accumulations in streams are only visible over
1900 m so the in²uence of curvature is restricted to high
altitudes; b) the map shows south-facing slopes situated
at lower heights without snow, which matches field
observations; and Fnally c) north-facing areas which are
topographically sheltered from wind show great snow
accumulations and are also well represented in the Fnal
model.
In summary, the snow volume difference between the
LIDAR model and extrapolated model was only -0.69
Solar radiation
±igure 8. Pearson correlation coefFcient between snow depth and topographical variables considered in analysis.
Assessment of airborne LIDAR for snowpack depth modeling
105
%. That result seems to validate the methodology and
technique used in a wide area (38 km
2
) with a large number
of data.
5. Conclusions
By using both LIDAR technology and the SRT
extrapolation modeling technique, a precise cartography
of snow depth distribution was obtained. Regression
application at each Fnal node together with a considerable
amount of data obtained with LIDAR permitted good
prediction values for snow depth and, more importantly,
total amount of accumulated snow.
Several data validation methods were applied because
Feld work results were less useful than expected. On the
other hand, despite being a new solution, the use of aerial
photographs during validation was demonstrated to be
useful. At the same time, aerial photographs ensure a high
data representativeness. Comparing topographic proFles,
with snow and without snow, it was possible to establish a
maximum limit in snow depth measurements. Application
of more sophisticated techniques, such as geo-radar, to get
data from snow depth accumulation areas and validation
with more Feld data are challenges for research in the near
future.
Finally, we point out that the next investigation
efforts in Núria valley will be focused on studying snow
water equivalent within the valley (this includes snow
water equivalent sampling) and modeling basin with
the installation of gauging stations and a comparison of
modeled snow volume calculations with measured runoff.
Acknowledgements
The authors would like to thank all those who
collaborated and all those in charge at the Vall de Núria
ski resort for their courtesy. ±urther thanks to A. Winstral
for providing the algorithm to calculate upwind index and
to W. Loh for freely distributing his GUIDE algorithm.
The authors are also grateful for the contributions made
by, James R. Carr and N. Glenn, who kindly improved this
paper by reviewing it.
References
Agència Catalana de l’Aigua (ACA), 2005, Memòria any 2005:
Barcelona, Departament de Medi Ambient, Generalitat de
Catalunya, 167 p.
curve
curve
curve
±igure 9. Resulting tree model through GUIDE algorithm. Grey circles indicate Fnal nodes and their values indicate mean snow depth of each Fnal node.
Near to each parent node the split variable and its value are shown.
±igure 10. Increment of model adjustment with increasing number of
Fnal nodes. Greater Fnal nodes cause the model to be overestimated.
Moreno Baños et al.
106
Breiman, L., Friedman, J.H., Olshen, R., Stone, C., 1984, Classifcation
and Regression Trees: Belmont Cali±ornia, Wadsworth International
Group, 358 p.
Burrough, P.A., van Gaans, P., Macmillan, R.A., 2000, High-resolution
land±orm classi±ication using ±uzzy k-means: Fuzzy Sets and
Systems, 113, 37-52.
Capel Molina, J.J., 2000, El clima de la península Ibérica: Barcelona,
Editorial Ariel, 281 p.
Cline, D., Elder, K., Bales, R., 1998, Scale e±±ects in a distributed
snow water equivalent and snowmelt model ±or mountain basins:
Hydrological Processes, 12, 1527-1536.
De’Ath, G., Fabricius, K., 2000, Classi±ication and regression trees:
A power±ul yet simple technique ±or ecological data analysis:
Ecology, 81, 3178-3192.
Deems, J.S., Painter T.H., 2006, LIDAR measurements o± snow
depth: accuracy and error sources,
in
Proceedings o± the 2006
International Snow Science Workshop: Telluride, Colorado, USA,
International Snow Science Workshop, 330-338.
Departament de Medi Ambient i Habitatge (DMAH), 1993, Mapa de
cobertes del sòl de Catalunya, maps 217-2-2 and 217-2-1, scale
1:25.000: Barcelona, Centre de Recerca Ecològica i Aplicacions
Forestals, Generalitat de Catalunya, 2 maps.
Elder, K., Michaelsen, J., Dozier, J., 1995, Small basin modeling
o± snow water equivalence using binary regression tree
methods,
in
Tonnessen, K.A., Williams, M.W., Tranter, M.
(eds.), Biochemistry o± Seasonally Snow-Covered Catchments:
Walling±ord, Ox±ordshire, Reino Unido, International Association
o± Hydrological Sciences, 129-139.
Elder, K., Rosenthal, W., Davis, R., 1998, Estimating the spatial
distribution o± snow water equivalence in a montane watershed:
Hydrological Processes, 12, 1793-1808.
Erxleben, J., Elder, K., Davis, R., 2002, Comparison o± spatial
interpolation methods ±or estimating snow distribution in
the Colorado Rocky Mountains: Hydrological Processes, 16,
3627-3649.
Fassnacht, S.R., Deems, J.S., 2005, Scaling associated with averaging
and resampling o± LIDAR-derived montane snow depth data,
in
Hellström, R., Frankenstein, S. (eds.), Proceedings o± the 62nd
Eastern Snow Con±erence, Waterloo, Ontario, Canada: USA,
Eastern Snow Con±erence, 163-172.
Hopkinson, C., Demuth, M., 2007, Using airborne LIDAR to assess
the in²uence o± glacier downwasting to water resources in the
Canadian Rocky Mountains: Canadian journal o± remote sensing,
32, 212-222.
Hopkinson, C., Sitar, M., Chasmer, L., Gynan, C., Agro, D., Enter,
R., Foster, J., Heels, N., Ho±±man, C., Nillson, J., Sant Pierre,
R., 2001, Mapping the spatial distribution o± snowpack depth
beneath a variable ±orest canopy using airborne laser altimetry,
in
Hardy, J., Frankenstein, S. (eds.), Proceedings o± the 58th Eastern
Snow Con±erence, Ottawa, Ontario, Canada: USA, Eastern Snow
Con±erence, 253-264.
Hopkinson, C., Sitar, M., Chasmer, L., Treitz., P., 2004, Mapping
snowpack depth beneath ±orest canopies using airborne lidar:
Photogrametric Engineering and Remote Sensing, 70, 323-330.
Huang, C., Townshend, J.R.G., 2003, A stepwise regression tree ±or
nonlinear approximation: applications to estimating subpixel land
cover: International Journal o± Remote Sensing, 24, 75-90.
2 km
Figure 11. Snow depth map resulting ±rom extrapolation. Grey areas indicate no snow present.
Assessment of airborne LIDAR for snowpack depth modeling
107
Institut Cartogràfic de Catalunya (ICC), 1996, Atles climàtic de
Catalunya: Barcelona, Departament de Política Territorial i Obres
Públiques Generalitat de Catalunya, 42 maps.
Institut CartogràFc de Catalunya (ICC), 2010, Webserver de les estacions
de referència GPS (website): Barcelona, Institut CartogràFc de
Catalunya, available at <http://catnet-ip.icc.es/>.
Kaneta, S., Hatake, S., 2007, Snow depth distribution by airborne LIDAR
data (on-line document): Noida, India GIS Dvelopment, available
at <www.gisdevelopment.net/technology/survey/ma07131.htm>.
Kornus, W., Ruiz, A., 2003, Strip adjustment of LIDAR data,
in
Maass,
H.G., Vosselman, G., Streilein, A. (eds.), Proceedings of the ISPRS
working group III/3 workshop: Dresden, Germany, International
Society of Photogrammetry and Remote Sensing (ISPRS), .
Loh, W., 2002, Regression trees with unbiased variable selection and
interaction detection: Statistica Sinica, 12, 361-386.
Loh, W., Chen, C.W., Zheng, W., 2008, Extrapolation errors in linear
model trees: ACM Transactions on Knowledge Discovery from
Data, 1, 1-17.
Loh, W., 2011, GUIDE Classification and Regression Trees (on-line
algorithm): Madison, Wisconsin, University of Wisconsin-
Madison, available at <www.stat.wisc.edu/~loh/guide.html>,
accessed 17th April 2011.
López-Moreno, J., Nogués-Bravo, D., 2006, Interpolating snow depth
data: a comparison of methods: Hydrological Processes 20,
2217-2232.
López-Moreno, J., Vicente-Serrano, S.M., Lanjeri, S., 2007, Mapping
snowpack distribution over large areas using GIS and interpolation
techniques: Climate Research, 33, 257-270.
Marchand, W.D., Killingtveit, A., 2001, Analyses of the relation between
spatial snow distribution and Terrain Chacarcteristics,
in
Hardy,
J., ±rankenstein, S. (eds.), Proceedings of the 58th Eastern Snow
Conference, Ottawa, Ontario, Canada: USA, Eastern Snow
Conference, 71-84.
Marchand, W.D., Killingtveit, A., 2005, Statistical probability distribution
of snow depth at the model sub-grid cell spatial scale: Hydrological
Processes 19, 355-369.
Molotch, N.P., Colee, M.T., Bales, R.C., Dozier, J., 2005, Estimating the
spatial distribution of snow water equivalent in an alpine basin
using binary regression tree models: the impact of digital elevation
data and independent variable selection: Hydrological Processes,
19, 1459-1479.
Rosenthal, W., Dozier, J., 1996, Automated mapping of montane snow
cover at subpixel resolution from the Landsat Thematic Mapper:
Water Resources Research, 32,
115-130.
Salomonson, V.V. , Appel, I., 2004, Estimating fractional snow cover
from MODIS using the normalized difference snow index: Remote
Sensing of Environment, 89, 351-360.
Sangrà, J.M., 2008, Qui paga l’aigua? in Aigua: font de vida, font de
risc. Informe 2008 de l’Observatori del Risc: Barcelona, Institut
d’Estudis de la Seguretat.
Servei Meteorològic de Catalunya (SMC), 2008, Butlletí climàtic de
l’any 2007 (on-line document): Barcelona, Departament de Medi
Ambient, published 13 ±ebruary 2008, available at <www20.
gencat.cat/docs/meteocat/Continguts/Climatologia/Butlletins%20
i%20resums%20climatics/Butlletins%20anuals/2007/Butlleti_
climatic_2007.pdf>.
Ulied, A. (coord.), 2003, Catalunya cap al 2020. Visions sobre el futur
del territori: Barcelona, Departament de Presidencia, Generalitat
de Catalunya, 160 p.
Winstral, A., Marks, D., 2002, Simulating wind fields and snow
redistribution using terrain-based parameters to model snow
accumulation and melt over a semi-arid mountain catchment:
Hydrological Processes, 16, 3585-3603.
Winstral, A., Elder, K., Davis, R.E., 2002, Spatial snow modeling of
wind-redistributed snow using terrain-based parameters. Journal
of Hydrometeorology, 3, 524-538.
Manuscrito recibido:
Septiembre 27, 2009
Manuscrito corregido recibido: Diciembre 20, 2009
Manuscrito aceptado: Diciembre
30, 2009
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