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| Titel |
Spatial statistical analysis on glacier surface elevation change based on ALS data |
| VerfasserIn |
Maximilian Spross, Erik Bollmann, Andrea Fischer, Lorenzo Rieg, Rudolf Sailer, Johann Stötter  |
| Konferenz |
EGU General Assembly 2011
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 13 (2011) |
| Datensatznummer |
250056366
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| Zusammenfassung |
| Monitoring the behaviour of glaciers has gained more significance with the increasing
amount of studies on global change. Due to the sensitive reaction of glaciers to changes of
regional climate, an increasing number of scientific studies are focusing on glacier mass
balances. Glacier mass balance depends on climate, topography and surface albedo. It is
assumed, that topographic characteristics, such as slope, curvature, aspect or surface
elevation, as well as their changes, modify the local climate as well as radiation with a direct
impact on the mass balance of a glacier.
The monitoring of surface elevation changes with methods of airborne remote sensing
(e.g. airborne laser scanning (ALS)) is useful to derive topographic information with high
accuracy and high resolution which are used within the presented workflow. The
multi-temporal ALS dataset of the Hintereisferner (Ötztal, Tyrol, Austria) from the Institute of
Geography, University of Innsbruck (acquired during the OMEGA, ALS-X and C4AUSTRIA
projects), gives the opportunity to calculate spatial volume changes over the years 2001 to
2009. These volume changes are defined as the dependent variable for further regression
analysis. Additionally, significant topographic explanatory variables for each year were
calculated using the digital elevation models (DEM) with a resolution of five meters.
Explanatory variables are used for a multiple statistical analysis, with the aim of spatial
visualisation and quantification. The achieved results describe the influence of each
explanatory variable, as well as all variables combined on the annual surface elevation
change.
In order to yield results, the raster dataset has to be preprocessed due to computer-dependent
limitations and the annual glacier boundary has to be taken into account. Subsequently, the
autocorrelation structure (commonly present for correlated high resolution raster data sets)of
the predictors has to be checked. Afterwards redundant input data can be identified and
eliminated, which is important for multiple spatial regression with the consideration of
interactions between the explanatory variables. As a first result of single linear regression
modelling for each predictor, the corresponding residuals are plotted into one map applying
the rule of best fitted value. In that case, the maps allow a visual impression of the influence
of topographic parameters in the test site Hintereisferner. The second result quantifies the
dependence of surface elevation changes from at least three explanatory variables
(elevation, solar potential and slope). The according multiple regression model is well
fitted with R2= 0,67. For the interpretation of both results, it is important to keep in
mind that surface elevation changes derived from remote sensing data integrate
effects both due to mass accumulation/mass ablation and due to glacier dynamics. |
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