Terrestrial photography combined with the recently presented Photo
Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0) has
proven to be a valuable source to derive snow cover maps in a high
temporal and spatial resolution. The areal coverage of the used
digital photographs is however strongly limited. Satellite images on
the other hand can cover larger areas but do show uncertainties with
respect to the accurate detection of the snow covered area. This is
especially the fact if user defined thresholds are needed, e.g. in
case of the frequently used normalized-difference snow index (NDSI).
The definition of this value is often not adequately defined
by either a general value from literature or over the impression of
the user, but not by reproducible independent information. PRACTISE
V.2.1 addresses this important aspect and shows additional
improvements. The Matlab-based software is now able to automatically
process and detect snow cover in satellite images. A simultaneously
captured camera-derived snow cover map is in this case utilized as
in situ information for calibrating the NDSI threshold
value. Moreover, an additional automatic snow cover classification,
specifically developed to classify shadow-affected photographs, was
included. The improved software was tested for photographs and
Landsat 7 Enhanced Thematic Mapper (ETM
Snow cover plays an important role in the Earth's climate system as direct feedback mechanisms between surface temperature, surface albedo, and snow cover exist (IPCC, 2013). These reinforcing feedback processes have significantly contributed to the observed decrease in spring snow cover in the Northern Hemisphere in the last decades (Groisman et al., 1994; IPCC, 2013). Despite this general trend in the Northern Hemisphere, the observed seasonal and altitudinal variations in snow cover changes are large for different regions (Brown and Mote, 2009). Regional studies are thus crucial to provide a more complete picture. This is of special importance for high elevation areas where large amounts of water are temporally stored as snow and which therefore supply the lowlands with fresh water during the snowmelt in spring and summer (Viviroli et al., 2007, 2011).
However, station data of snow cover in alpine regions are rare except for a few well-equipped sites (Scherrer et al., 2004; Marty, 2008; Viviroli et al., 2011; Pomeroy et al., 2015). Manual in situ measurements are often prevented for reasons of remoteness and safety by the harsh environmental conditions (Klemes, 1990). Satellite remote sensing techniques are a big step forward in these data-scarce areas but it is still a challenge to achieve snow cover products with high spatial and temporal resolutions as well as a high accuracy (Klemes, 1990; Viviroli et al., 2011). The complementary use of ground and space borne measurements for observing mountainous snow cover as highlighted by Vivirioli et al. (2011) is a promising approach and the main motivation behind this paper.
Terrestrial photography is thereby utilized as
The alpine snow cover patterns derived from terrestrial photography can then be used to evaluate spatially distributed (snow-) hydrological models like Alpine3D, SnowModel, and others (Lehning et al., 2006; Liston and Elder, 2006; Bernhardt et al., 2012). The high spatial resolution of the photograph snow cover maps is very valuable, as snow cover strongly varies over time and space and an accurate description in models is difficult (Blöschl et al., 1991; Winstral and Marks, 2002; Bernhardt and Schulz, 2010). The high temporal resolution of the terrestrial camera systems, for example on an hourly basis, further enhances the probability of at least one suitable photograph per day, despite the frequently occurring cloud and precipitation events at high altitudes (Härer et al., 2013).
To map the spatial snow cover distributions, the recorded 2-D photographs have to be classified and georectified. Corripio (2004) and Corripio et al. (2004) presented a software tool that eased the georectification process, utilizing the animation and rendering technique by Watt and Watt (1992). This also formed the basis for the Photo Rectification And ClassificaTIon SoftwarE (PRACTISE V.1.0; Härer et al., 2013). Though, the formulations for the calculation of the 3-D rotation and projection are slightly different to Corripio (2004) and Corripio et al. (2004). PRACTISE V.1.0 further simplifies and fastens the spatially distributed monitoring of snow cover patterns in mountainous terrain as it includes in addition to the georectification module routines for the identification of camera location and orientation, the viewshed computation and the snow classification of photographs. A batch mode also allows the processing of several photographs and thus the generation of multiple snow cover maps in a single program evaluation.
The trade-off for the high spatial resolution snow cover maps from terrestrial photography is that these maps are restricted to a comparatively small region. To monitor a complete catchment with an extent of several square kilometres and more, satellite imagery is more suitable. These data have a lower spatial and temporal resolution but it offers the advantage of long consistent time series and the coverage of large areas. The normalized-difference snow index (NDSI) formulated by Dozier in 1989 for Landsat data is thereby still a standard method to derive snow cover maps (cf. SNOMAP algorithm of the MODIS snow cover product; Hall et al., 2001; Hall and Riggs, 2007). Other promising methods like traditional supervised multispectral classifications, artificial neural networks or spectral-mixture analyses are computationally highly intensive, need lots of additional input data or are dependent on the interpreter's knowledge (Hall et al., 2001). These techniques are thus difficult to automate.
The NDSI represents the space borne component in the synthesis of
ground and satellite measurements in this study. The index relies on
a band rationing technique with a simple but effective principle that
snow is highly reflective in the visible bands (GREEN,
The NDSI threshold value of 0.4 is the standard literature value (Nolin, 2010; Dietz et al., 2012) even though Hall et al. (1995) already mention that acceptable snow cover maps were found for NDSI thresholds between 0.25 and 0.45 in a study investigating six scenes in the United States and Iceland. This threshold range corresponded to changes in snow cover extent of more than 10 % in the studied scenes. In particular for local and regional applications it is thus crucial to set the NDSI threshold accurately but in a user-friendly and standardized manner. The manual adjustment of the threshold is no option in most cases as it is not reproducible and offers the danger of adapting the resulting snow cover distribution to support a given hypothesis.
This paper presents a new method to monitor alpine snow cover patterns
with satellite data by making use of terrestrial camera
infrastructure, including webcams. The NDSI threshold value for snow
is thereby calibrated to achieve an optimal agreement in the
overlapping area of the photograph and satellite snow cover maps. Hence, an
optimal NDSI-based satellite snow cover map for the specific region
and time is produced, for example for an alpine catchment with an
extent of several square kilometres. The cameras needed for this
method are often already available or can be easily installed at many
sites. We focus on Landsat data in here, as the pixel dimensions of
30
The new approach to complementary use ground and space borne measurements to derive snow cover maps is fully implemented in PRACTISE V.2.1. The fast and easy-to-use processing includes the NDSI calculation from Landsat raw data as well as the use of NDSI maps produced externally in geoinformation systems. Optionally, it also allows for including an existing cloud mask using for example the freely available Fmask software (Zhu et al., 2015). In addition, a newly developed snow classification algorithm for shadow-affected photographs is presented in PRACTISE V.2.1. Further improvements are bug fixes and revised code of already published modules as well as increased user friendliness.
This paper is supplemented with an example data set, a manual and the associated Matlab code. The structure of the paper itself is as follows: at first, the test site and data are described. The newly developed modules and improvements in existing modules of the software are subsequently explained. Then, the resulting snow cover maps of exemplary photographs and Landsat satellite images are presented and discussed for the test area. Finally, a conclusion and an outlook are given.
PRACTISE (V.2.1) was developed and tested in the Zugspitze massif,
Germany. The investigated Zugspitzplatt covers a surface area of
13.1
DEM of the Zugspitzplatt catchment at the border of Germany
and Austria and the sketched fields of view of the cameras installed
at the Environmental Research Station Schneefernerhaus (UFS;
2650
PRACTISE V.2.1 requires a digital elevation model (DEM) and the
exterior orientation parameters of the camera, i.e. the camera
position
Enlarged view of the Landsat Look images of Zugspitzplatt (in
the centre) and SLR photographs of Schneefernerkopf for
17 November 2011
The abovementioned inputs are obligatory in PRACTISE independent of
the used modules. However, the camera parameters can also be estimated
and automatically optimized if ground control points (GCPs) are
available. The use of an externally calculated viewshed is optional if
all exterior and interior camera parameters are known. Snow
classification parameters are another required input in PRACTISE but
only for the selected classification routine (cf. Sect. 3.1 and
Härer et al., 2013). If the satellite image module is in use,
radiometrically and geometrically corrected data of Landsat 5 Thematic
Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM
The functionality of the new modules of PRACTISE V.2.1 will be demonstrated on the basis of photographs and Landsat satellite images of 17 November 2011, 1 July 2013 and 7 April 2014. The dates were chosen because they represent different snow and illumination conditions at Zugspitzplatt as well as different snow cover extents and cloud coverages (Fig. 2a–f). The scenes are therefore suited to test the capabilities of PRACTISE with respect to changing surrounding conditions.
SLR photographs are available for all dates while webcam images are available for 2013 and 2014. Landsat 7 overflights have captured the test site in 2011 and 2013 (Fig. 2a and c), Landsat 8 in 2014 (Fig. 2e). Masks for clouds, shadows, and cloud shadows as well as water bodies were externally generated with the Fmask algorithm of Zhu et al. (2015). The masks are applied for the scenes on 17 November 2011 and on 1 July 2013 whereas the cloud cover is not visible in the Landsat Look image on 17 November 2011. We also want to note here that Landsat 7 imagery is affected by a failure of the Scan Line Corrector (SLC) that normally compensates for the forward motion of the Landsat satellite from 31 May 2003 onwards. But, Zugspitzplatt area is located in the centre of the scene and is therefore not affected by this error.
The inputs given for the georectification of the SLR and webcam photographs are presented in Table 1. Camera-dependent parameters were taken from the user manual of the camera systems. The focal lengths have been adjusted according to the used image. The location and target position of the camera, as well as the GCP locations have been identified combining photographs, DEM data, topographical maps and official orthophotos with a sub-metre spatial resolution. Nevertheless, the camera location and target position could only be estimated. The camera parameters in Table 1 except the camera sensor and photograph dimensions thus need to be optimized using GCPs. A separate estimation for each photograph in this study is further necessary as the locations and orientations of the cameras are changing in between the photographs due to either weather effects like wind, for maintenance reasons, or a new camera location at the UFS.
Estimated parameters of the exterior and interior camera orientation
of the SLR and webcam before the optimization: the parameter ranges in
the optimization for the cameras and dates are given as differences in m except noted otherwise to
the estimated values. For the webcam photograph on 7 April 2014, the
camera is directed towards an area outside of the DEM. Hence, the
optimization of the camera target point offset
The DEM used for the SLR photographs has a spatial resolution of
1
PRACTISE V.2.1 introduces two major enhancements compared to version
1.0; the snow classification in partially shadow-affected photographs
(Sect. 3.1) and the threshold calibration for optimal NDSI-based snow
cover maps (Sect. 3.2). In addition, all existing routines have been
refined with respect to performance and user
friendliness (Sect. 3.3). The new routines (Sect. 3.1 and 3.2) and the
flow chart (Sect. 3.3) of PRACTISE V.2.1 will be exemplarily presented
for a SLR photograph and a Landsat 7 ETM
PRACTISE V.1.0 provides two snow classification routines for terrestrial RGB photographs. The user can select between a manual routine, which basically detects snow for digital numbers (DN) above user-specific snow thresholds in the red, green, and blue (RGB) bands of the digital photograph and an algorithm developed by Salvatori et al. (2011). This algorithm is a threshold based procedure, which automatically analyses the blue band DN frequency histogram and sets the snow threshold. Both classification types of PRACTISE V.1.0 are described in detail in Härer et al. (2013).
Both algorithms are working well if the photography is evenly illuminated and in the absence of shadows (Härer et al., 2013). However, shadow-free situations are rare in structured terrain and clouds can reason further shadowing. In the case of shaded areas, the two included classification routines tend to only identify snow surfaces that are sunlit while the classification in shaded areas has high uncertainties. This results from similarly high blue band DN in RGB images for shaded snow cover, and illuminated rock, soil, or sparsely vegetated surfaces (Fig. 3a and b).
SLR photograph of Schneefernerkopf with large shadows on
17 November 2011:
Stepwise classification of the SLR photograph on 17 November 2011 with the new PCA-based classification: in a first step, the algorithm of Salvatori et al. (2011) is used to classify sunlit snow (red). Then, shaded snow (yellow-green) is detected with the PCA classification, and in the third step, sunny rock (blue) is classified comparing blue and red band DN. All unclassified pixels after these steps, mainly shaded rock, are subsequently classified using the blue band DN (not shown here, see Fig. 6).
Frequency histograms of the normalized PC score matrix with
decreasing explained variance from column 1 to 3
(
Results of the PCA-based classification of the SLR photograph
on 17 November 2011: snow is classified in red, snow-free areas are
depicted in blue. The pixels classified as
PRACTISE V.2.1 therefore includes a new classification routine, which automatically detects snow in shadow-affected photographs. The algorithm includes the automatic blue band classification from PRACTISE V.1.0 to identify the sunlit snow cover in the RGB images and additionally uses a principal component analysis (PCA) for separating shaded snow cover from sunlit rock surfaces. The method was developed analysing photographs in the Zugspitzplatt catchment and in the Vernagtferner area, Austria. The routine will be presented for the SLR photograph on 17 November 2011.
In a first step the algorithm of Salvatori et al. (2011) described in Härer et al. (2013) is used for classifying snow at sunny locations. Snow cover detected in this step is illustrated in red in Fig. 4.
The second step in the classification routine is the utilization of a PCA to detect snow cover in shaded areas. The PCA is a statistical method to analyse multivariate data sets. In our case, we use the PCA to orthogonally transform the axes of the RGB space to a new principal component (PC) space where the centre of the coordinate system is shifted to the mean value of the three-dimensional data set while the axis direction of the first PC (PC1) explains the largest variance in the data set. The axis of the second PC (PC2) is orthogonal to PC1 and explains the second largest variance. The axis of PC3 is again orthogonal to PC1 and PC2. Due to the decreasing explained variance in the higher components, most information of the RGB data is stored in PC1 and PC2 while PC3 mainly represents remaining noise.
For the PCA, the RGB values of all visible DEM pixels (
Frequency histograms of the normalized PC score matrix
(
The third step of the algorithm detects sunny rocks utilizing the DN
in the blue and the red band (DN
Finally, pixels not classified in the three steps before
(DN
Results of the newly implemented snow classification routine are illustrated in Fig. 6 and can be compared to the results of V1.0 in Fig. 3b. At last, we want to mention that the new routine and in particular the PC analysis step was successfully applied in at least 95 % of our shadow-affected test photographs. For shadow-free situations, it is though still recommended to use the existing classification routines presented in Härer et al. (2013).
The new approach to automatically derive an optimal NDSI-based snow cover map is implemented in the second new module of PRACTISE V.2.1. The method utilizes areas that show an overlap between a photograph snow cover map and the NDSI product of a simultaneously captured satellite scene. Then, the NDSI threshold value for snow is calibrated using the dynamically dimensioned search (DDS) optimization algorithm (Tolson and Shoemaker, 2007) to obtain an optimal agreement of photograph and satellite snow cover map.
The photograph snow cover map is the ground truth data in the
calibration and results from the georectification and classification
of a terrestrial photograph in PRACTISE V.2.1. The NDSI map is
calculated within the program evaluation for radiometrically and
geometrically corrected Landsat data. The Landsat level 1 data are
freely available from the archives of the US Geological Survey. The
top of atmosphere planetary reflectance values of the green, near
infrared, and mid-infrared bands of Landsat 5, 7, or 8 image are
automatically derived from the DN in accordance to the Landsat 5, 7, or
8 user handbook including a correction for the sun angle. For example,
for Landsat 7 imagery the metadata file and the data bands 2 (GREEN,
0.52–0.60
If the Landsat scene is partially cloud covered, an externally
generated cloud mask should be used to prevent
misclassifications. A direct input link for the cloud mask product of
the freely available Fmask software of Zhu et al. (2015) is integrated
in PRACTISE to mask clouds, cloud shadows and water. The near infrared
condition of Eq. (
Overlapping areas of terrestrial photography and the satellite image
are subsequently detected. The results of the photograph snow cover
maps are used as a baseline. It is a user's decision if pixels
classified as
Now, the DDS optimization routine, which is also implemented in the
framework of the GCP optimization (cf. Härer et al., 2013), is used
to optimize the NDSI threshold value. The seed is set to the threshold
of 0.4, recommended by Dozier (1989), and Hall et al. (1995) and the
NDSI threshold value is limited to the range of NDSI values, which can
be found in the overlapping area. The number of maximum iterations is
user dependent, but it was found that 150 optimization runs are
sufficient. A quality measure of Aronica et al. (2002), which was
successfully used in the context of snow extent evaluation in
Bernhardt and Schulz (2010) serves as the objective function value
The routine is exemplarily presented for the SLR photograph of
17 November 2011 and the simultaneously captured Landsat 7 ETM
Resulting snow cover maps of the SLR photograph and the
Landsat 7 ETM
General flow chart of PRACTISE V.2.1 for the SLR photograph
and Landsat 7 ETM
The photograph and satellite snow cover maps of the SLR photograph and
the Landsat 7 ETM
In addition to the two new routines (Sect. 3.1 and 3.2), the code and the user friendliness of the existing modules in PRACTISE V.2.1 have been improved.
Interactive modes are now available in the modules,
PRACTISE V.2.1 is now also able to process photographs that were taken from camera locations sheltered by for example a roof and thus are assumed below ground in a DEM. In this case, surrounding DEM pixels will obstruct the view in the viewshed calculation. For omitting this problem the user can now create a radial zone around the camera location where DEM pixels are assumed transparent. In addition to the improvements mentioned here, we refer the reader to the manual accompanying this paper for the description of other adaptations in the new version of PRACTISE, in particular regarding the data handling and naming conventions of input and output data.
The new routines are presented in detail for the SLR photograph and
Landsat 7 ETM
The runtime of PRACTISE V.2.1 for this set-up with a photographed area
of about 0.3
We have presented the functionality of PRACTISE V.2.1 in course of this paper. It incorporates all options available in PRACTISE V.1.0 with revised code and improved user-friendliness. Most important are, however, the new modules facilitating on the one hand the derivation of more reliable photography-based snow cover maps even in partially shaded areas. Furthermore, a completely new approach to create calibrated NDSI thresholds needed for the generation of snow cover maps based on satellite images was introduced.
While the new modules have been presented for a SLR photograph and
a Landsat 7 ETM
In a first step, the quality of the photography-based snow cover maps
was assured. The positional accuracy of the GCPs after the
optimization of camera parameters is exemplarily illustrated for the
SLR and webcam photographs on 7 April 2014 in Fig. 9a and b. The root
mean square error (RMSE) between GCPs and control points is 0.5 and
2.2
Real and calculated GCP positions for the investigated
photographs on 7 April 2014: the root mean square error (RMSE)
between real (green crosses) and calculated (red dots) GCP positions
are 0.5
Superimposed snow classifications on the SLR and webcam
photographs of 1 July 2013 and 7 April 2014: the SLR
Enlarged view of the superimposed snow classifications on the
SLR and webcam photographs of 7 April 2014 (rectangle boxes in
Fig. 11c and d):
Resulting snow cover maps of the SLR and webcam photographs,
and the Landsat images for the Zugspitze massif superimposed on the
Landsat Look images: the satellite snow cover maps are calibrated
using the SLR snow cover maps as baseline. The resulting NDSI
thresholds are 0.35 for 1 July 2013
Standard and optimized snow cover maps of the Zugspitzplatt
catchment for 17 November 2011
Misinterpretations in the georectification and as a result in the classification were only found for snow groomers and some infrastructure not represented in the DEM and viewshed. An example of these obstacles leading to misinterpretations is an antenna in the centre of the webcam photographs (cf. Fig. 9b). As the number of pixels affected by this and similar problems is less than 0.5 % of the mapped area, the georectification quality of all camera images can be summarized as very high.
Figure 10a to d show the superimposed snow classifications (snow in red, no snow in blue) on the SLR and webcam photographs of 1 July 2013 and 7 April 2014. The July photographs in Fig. 10a and b do not show strong shadowing effects due to the high sun angle at this date. Hence, the automatic blue band classification algorithm was used. The resulting classification visually indicates a high quality and will not be further discussed here as the method was evaluated before in Salvatori et al. (2011) and Härer et al. (2013).
For the photographs of 7 April 2014 the PCA-based classification
algorithm was applied to reduce shadow-related misclassifications
(Fig. 10c and d). The detailed visual analysis of the pixels in the
two April photographs showed the high quality of the new classification
routine for pixels identified as
Misclassifications in the main classification categories snow and free of snow are rare with less than 0.3 % of classified pixels in the SLR photograph and less than 1 % in the webcam photograph. The reasons for misclassifications are, however, different in both photographs. In the SLR photograph, the misclassifications can mainly be attributed to the light-coloured bare rock (limestone) in the Zugspitzplatt area, which is mistakenly classified as snow. This issue has already been discussed in detail in Härer et al. (2013, PRACTISE V.1.0) and is a weakness of the blue band classification method, which represents one of the classification steps in the PCA-based classification routine. The misclassifications in the webcam photograph have two main origins: a georectification problem due to infrastructure, which has already been mentioned above, and another problem, as shaded areas, in particular in the valley below the Zugspitzplatt, are difficult to classify as snow and no snow, even with the human eye.
In addition to the two main classification categories, the three unsure categories need to be discussed for the April photographs; 1.9 % of classified pixels in the SLR photograph and 7.8 % in the webcam image are assigned probability values. The low percentages emphasize that the assignment rules in the PCA-based classification routine seem to describe the RGB characteristics of the different surfaces well. In addition, most pixels classified as unsure in the SLR photograph are exactly located at the transitional area between snow patches and snow-free areas in the photographs, and can therefore be seen as mixed pixels (Figs. 10c and 11a). The classification of the SLR photograph on 17 November 2011 (Fig. 6) has also attested this finding.
In the webcam photograph, more pixels are classified as unsure in
particular as probably no snow (Figs. 10d and 11b). The detailed
analysis also shows some
In a second step, the calibration of the NDSI threshold of the Landsat
images was evaluated. At first, the results of the Landsat 7 ETM
We want to emphasize here that the percentage of pixels identically
classified in photograph and satellite image maps is enormously high,
keeping in mind the different horizontal resolutions of photograph map
(SLR: 1
Another important finding is that the calibration of the NDSI threshold using SLR and webcam results in almost identical NDSI thresholds. As the differences are insignificant the NDSI threshold calibration seems to be robust in the Zugspitzplatt area independent of the used camera system and field of view.
At last, the changing NDSI thresholds of 0.18 on 17 November 2011, 0.35 on 1 July 2013, and 0.23 on 7 April 2014 calibrated with the SLR camera need to be discussed. All thresholds are below the value of 0.4 from Dozier (1989) and Hall et al. (1995) and the increases of snow cover extent in the Zugspitzplatt catchment are between 3.7 % on 1 July 2013 and 26.7 % on 17 November 2011 using the calibrated NDSI threshold values instead of the literature value of 0.4. Consequently, larger differences between the optimized and the standard NDSI threshold value lead to a higher percentage change of snow cover.
Figure 13a to c display the optimized snow cover maps (light blue) in the Zugspitzplatt for the three investigated dates in chronological order. They are superimposed with the standard snow cover maps (dark blue). The visual comparison of the snow cover maps clearly shows that especially the edges of the snow cover are reclassified to snow whereas no new large snow patches are identified. This demonstrates on the one hand that the core snow cover areas are already correctly classified using the standard threshold. On the other hand this result also highlights that Landsat snow pixels at the snow cover edge, and hence probably mixed pixels, represent a substantial portion of snow cover in alpine areas and thus have to be correctly classified for optimum results.
In addition to the visual analysis, we analysed changes in the
elevation distribution of snow covered pixels in the Zugspitzplatt
area, in particular of the lower elevation snow cover. As the lowest
elevation where snow cover is detected is not necessarily
representative for the current snow cover distribution in the
investigation area, the 10 % quantile of the elevation values
of snow covered pixels was calculated. The resulting elevations for
the 10 % quantile are 2390.6
The presented values and findings underline that the strong temporal variations found in NDSI thresholds transfer to large uncertainties in the derivation of snow cover extents and studies relying on these snow cover products. A spatial and temporal adjustment of NDSI thresholds is therefore important to ensure optimum results in the snow cover mapping of specific areas, for example of the studied alpine catchment.
PRACTISE V.2.1 was already in the previous version a fast and user-friendly tool to georectify and classify photographs, but now further provides a new and objective method to automatically calibrate NDSI thresholds in satellite images and thus to create reliable, spatially, and temporally specific NDSI-based satellite snow cover maps. The snow classification of photographs has moreover become more flexible with the additional opportunity to classify partially shadow-affected photographs. The code of the old version has additionally been revised and the user-friendliness has been improved while the functionality of all existing routines in PRACTISE V.1.0 remained.
PRACTISE V.2.1 is thus a simple- and ready-to-use software tool that
was developed and tested for SLR and webcam photographs, as well as Landsat 7
ETM
Our next step will be to apply PRACTISE and the integrated new approach to the complete available time series of photographs and satellite images in the Zugspitzplatt area. In addition, we will process another long-term time series of photographs in the alpine Vernagtferner area, Austria, which is located in the same Landsat scene as the Zugspitzplatt. We think that this experimental set-up will be a first step towards understanding the temporal variability of the calibrated NDSI thresholds in alpine areas. Furthermore, the set-up will also allow for testing spatial representativeness of the optimal NDSI threshold on the regional scale as this is another topic of ongoing discussion. This will be especially important as the spatio-temporal extrapolation possibilities and limits of the presented method are as yet unknown. Further research will also be necessary to verify if the synthesis of terrestrial photograph and satellite image is applicable in a modified form to other research fields like thermal photography and satellite imagery.
The source code of PRACTISE V.2.1 is distributed under the Creative
Commons license (CC-BY-NC-SA 4.0) and together with a manual and
an example data set available online here:
The software is executable on any Windows or UNIX computer with a
basic Matlab installation and at least 2
Please visit
The work described in this paper was supported by the doctoral scholarship program “Deutsche Bundesstiftung Umwelt” (DBU), the Helmholtz Research School “Mechanisms and Interactions of Climate Change in Mountain Regions” (MICMoR) and the Environmental Research Station Schneefernerhaus (UFS), as well as our previous palace of employment the Department of Geography at the LMU Munich. We also thank David Morche for providing the DEM, Michael Weber for occasional maintenance of the SLR camera, and Ben Müller, Nick Rutter and Karl-Friedrich Wetzel for thoughtful discussions. Edited by: R. Sander