Potential of airborne laser scanning for geomorphologic feature and process detection and quantifications in high alpine mountains

Bollmann, E.; Sailer, Rudolf; Briese, Christian; Stötter, Johann; Fritzmann, Patrick

doi:10.1127/0372-8854/2011/0055s2-0047

Cited by 44 publications

(56 citation statements)

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“…Unrealistic data such as negative snow depths or extremely high values are rare but still need to be filtered. Such outliers mainly occur in very steep and rough terrain, which is attributed to larger measurement errors in such terrain Grünewald and Lehning, 2011;Bollmann et al, 2011;Hopkinson et al, 2012). In this study, we set negative snow depths to zero and excluded extremely high snow depths from the data.…”

Section: Airborne Laser Scanningmentioning

confidence: 99%

Statistical modelling of the snow depth distribution in open alpine terrain

Grünewald¹,

Stötter

Pomeroy

et al. 2013

Hydrol. Earth Syst. Sci.

120

142

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Abstract. The spatial distribution of alpine snow covers is characterised by large variability. Taking this variability into account is important for many tasks including hydrology, glaciology, ecology or natural hazards. Statistical modelling is frequently applied to assess the spatial variability of the snow cover. For this study, we assembled seven data sets of high-resolution snow-depth measurements from different mountain regions around the world. All data were obtained from airborne laser scanning near the time of maximum seasonal snow accumulation. Topographic parameters were used to model the snow depth distribution on the catchment-scale by applying multiple linear regressions. We found that by averaging out the substantial spatial heterogeneity at the metre scales, i.e. individual drifts and aggregating snow accumulation at the landscape or hydrological response unit scale (cell size 400 m), that 30 to 91 % of the snow depth variability can be explained by models that are calibrated to local conditions at the single study areas. As all sites were sparsely vegetated, only a few topographic variables were included as explanatory variables, including elevation, slope, the deviation of the aspect from north (northing), and a wind sheltering parameter. In most cases, elevation, slope and northing are very good predictors of snow distribution. A comparison of the models showed that importance of parameters and their coefficients differed among the catchments. A "global" model, combining all the data from all areas investigated, could only explain 23 % of the variability. It appears that local statistical models cannot be transferred to different regions. However, models developed on one peak snow season are good predictors for other peak snow seasons.

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Section: Airborne Laser Scanningmentioning

confidence: 99%

Statistical modelling of the snow depth distribution in open alpine terrain

Grünewald¹,

Stötter

Pomeroy

et al. 2013

Hydrol. Earth Syst. Sci.

120

142

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“…20 % des Volumens) betragen hat. Seit dem Jahr 2001 werden für den Hintereisferner parallel zu den glaziologischen Massenbilanzmessungen jährlich fl ugzeuggestützte Airborne Laserscanning (ALS)-Messungen durchgeführt (Geist und Stötter, 2008;Bollmann et al, 2011).…”

Section: Die Heutige Und Rezente Lage Der öSter-reichischen Gletscherunclassified

Kapitel 2: Der Einfluss des Klimawandels auf die Hydrosphäre

Nachtnebel¹,

Dokulil²,

Kuhn³

et al. 2014

Österreichischer Sachstandsbericht Klimawandel 2014

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“…The accuracy of laser measurements is influenced by the angle of the scanned slopes (Kraus, 2004). Bollmann et al (2011) found that vertical errors increase from ±0.04 m at slopes smaller than 35 • up to ±1 m at slopes of 80 • and assumed an overall vertical accuracy of the DEMs of ±0.15 m to ±0.20 m for application of multi-temporal ALS data in alpine catchments with slopes smaller than 70 • . For that assumption differences between differential Global Navigation Satellite System measurements and airborne laser data of about 0.07 m with a standard deviation of ±0.07 m were combined with errors from ALS point to raster aggregation.…”

Section: Als Datamentioning

confidence: 99%

“…The pre-processed data were used to produce high resolution raster digital elevation models (DEM, cell size 1 m) (Bollmann et al, 2011). The accuracy of the raster DEM depends on the point density and the accuracy of the laser scan point measurements.…”

Section: Als Datamentioning

confidence: 99%

Snow accumulation of a high alpine catchment derived from LiDAR measurements

et al. 2012

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Abstract. The spatial distribution of snow accumulation substantially affects the seasonal course of water storage and runoff generation in high mountain catchments. Whereas the areal extent of snow cover can be recorded by satellite data, spatial distribution of snow depth and hence snow water equivalent (SWE) is difficult to measure on catchment scale. In this study we present the application of airborne LiDAR (Light Detecting And Ranging) data to extract snow depths and accumulation distribution in an alpine catchment.Airborne LiDAR measurements were performed in a glacierized catchment in theÖtztal Alps at the beginning and the end of three accumulation seasons. The resulting digital elevation models (DEMs) were used to calculate surface elevation changes throughout the winter season. These surface elevation changes were primarily referred to as snow depths and are discussed concerning measured precipitation and the spatial characteristics of the accumulation distribution in glacierized and unglacierized areas. To determine the redistribution of catchment precipitation, snow depths were converted into SWE using a simple regression model. Snow accumulation gradients and snow redistribution were evaluated for 100 m elevation bands.Mean surface elevation changes of the whole catchment ranges from 1.97 m to 2.65 m within the analyzed accumulation seasons. By analyzing the distribution of the snow depths, elevation dependent patterns were obtained as a function of the topography in terms of aspect and slope. The high resolution DEMs show clearly the higher variation of snow depths in rough unglacierized areas compared to snow depths on smooth glacier surfaces. Mean snow depths in glacierized areas are higher than in unglacierized areas. Maximum mean snow depths of 100 m elevation bands are found between 2900 m and 3000 m a.s.l. in unglacierized areas and between 2800 m and 2900 m a.s.l. in glacierized areas, respectively. Calculated accumulation gradients range from 8 % to 13 % per 100 m elevation band in the observed catchment. Elevation distribution of accumulation calculated by applying these seasonal gradients in comparison to elevation distribution of SWE obtained from airborne laser scanning (ALS) data show the total redistribution of snow from higher to lower elevation bands.Revealing both, information about the spatial distribution of snow depths and hence the volume of the snow pack, ALS data are an important source for extensive snow accumulation measurements in high alpine catchments. These information about the spatial characteristics of snow distribution are crucial for calibrating hydrological models in order to realistically compute temporal runoff generation by snow melt.

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Potential of airborne laser scanning for geomorphologic feature and process detection and quantifications in high alpine mountains

Cited by 44 publications

References 0 publications

Statistical modelling of the snow depth distribution in open alpine terrain

Statistical modelling of the snow depth distribution in open alpine terrain

Kapitel 2: Der Einfluss des Klimawandels auf die Hydrosphäre

Snow accumulation of a high alpine catchment derived from LiDAR measurements

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