Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques:  the case of Nevado Coropuna (Peru)

Peduzzi, Pascal; Herold, Christian; Silverio, Walter

doi:10.5194/tc-4-313-2010

Cited by 45 publications

(58 citation statements)

References 12 publications

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“…Gorokhovich and Voustianiouk, 2006;Racoviteanu et al, 2007;Peduzzi et al, 2010). The reason for that is to be able to follow and understand individual error terms, and to decide individually on their correction.…”

Section: Methodsmentioning

confidence: 99%

“…The reason for that is to be able to follow and understand individual error terms, and to decide individually on their correction. Furthermore, it will become clear why a multiple regression based upon some combination of these terrain parameters will be significant, though such a correction may not be geometrically appropriate (e.g., see Peduzzi et al, 2010).…”

Section: Methodsmentioning

confidence: 99%

“…The relationship between elevation error and aspect has been described previously (Schiefer et al, 2007;Van Niel et al, 2008;Gorokhovich and Voustianiouk, 2006;Peduzzi et al, 2010), although corrections applied in the latter two studies were not analytical but based upon multiple regression adjustments to elevation. Gorokhovich and Voustianiouk (2006) showed the significance of the correlation between elevation differences and aspects on large slopes but overlooked the underlying cause as described in Kääb (2005).…”

Section: A Universal Co-registration Correctionmentioning

confidence: 99%

“…iteratively shifting one DEM to the other within a ±5 pixel window (Rodriguez et al, 2006;Berthier et al, 2007;Howat et al, 2008). Other studies have corrected DEMs using single or multiple linear regression corrections between elevation and the location and terrain parameters (Gorokhovich and Voustianiouk, 2006;Bolch et al, 2008;Peduzzi et al, 2010). In terrestrial and airborne laser scanning, 3-D Least Squares Matching (LSM) is used to minimize the Euclidean distances between the points of point clouds, often allowing not only for shifts but also for rotations and scales between the two or more datasets (Gruen and Akca, 2005;Miller et al, 2009).…”

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confidence: 99%

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Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change

2011

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Abstract.There are an increasing number of digital elevation models (DEMs) available worldwide for deriving elevation differences over time, including vertical changes on glaciers. Most of these DEMs are heavily post-processed or merged, so that physical error modelling becomes difficult and statistical error modelling is required instead. We propose a three-step methodological framework for assessing and correcting DEMs to quantify glacier elevation changes: (i) remove DEM shifts, (ii) check for elevation-dependent biases, and (iii) check for higher-order, sensor-specific biases. A simple, analytic and robust method to co-register elevation data is presented in regions where stable terrain is either plentiful (case study New Zealand) or limited (case study Svalbard). The method is demonstrated using the three global elevation data sets available to date, SRTM, ICESat and the ASTER GDEM, and with automatically generated DEMs from satellite stereo instruments of ASTER and SPOT5-HRS. After 3-D co-registration, significant biases related to elevation were found in some of the stereoscopic DEMs. Biases related to the satellite acquisition geometry (along/cross track) were detected at two frequencies in the automatically generated ASTER DEMs. The higher frequency bias seems to be related to satellite jitter, most apparent in the backlooking pass of the satellite. The origins of the more significant lower frequency bias is uncertain. ICESat-derived elevations are found to be the most consistent globally available elevation data set available so far. Before performing regional-scale glacier elevation change studies or mosaicking DEMs from multiple individual tiles (e.g. ASTER GDEM), we recommend to co-register all elevation data to ICESat as a global vertical reference system.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: A Universal Co-registration Correctionmentioning

confidence: 99%

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confidence: 99%

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Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change

2011

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“…This is the result of topographic complexity below the resolution of the sensor, radar geometry considerations (layover, foreshortening, and shadowing), and interferometric phase unwrapping errors, all most pronounced in steep mountains. Such terrain biases are demonstrated in the SRTM-C with elevation (Berthier et al, 2006;Paul, 2008), slope and aspect (Gorokhovich and Voustian-15 iouk, 2006;Van Niel et al, 2008;Peduzzi et al, 2010;Shortridge and Messina, 2011), and resolution (manifested in curvature) (Gardelle et al, 2012), and in the TanDEM-X with only slope (Purinton and Bookhagen, 2017;Wessel et al, 2018). Terrain slope-also related to relief (Fig.…”

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confidence: 90%

Measuring Decadal Vertical Land-level Changes from SRTM-C (2000) and TanDEM-X (~ 2015) in the South-Central Andes

Purinton¹,

Bookhagen²

2018

Preprint

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Abstract.Vertical change is often measured in the cryosphere via digital elevation model (DEM) differencing to assess glacier and icesheet mass balances. This requires the signal of change to outweigh the noise associated with the datasets. On the ice-free earth, land-level change is much smaller in magnitude and thus requires more accurate DEMs for differencing and identification of change. Previously, this has required high-resolution data at small scales. For the first time we measure land-level changes at the 5 scale of entire mountain belts in the south-central Andes using the SRTM-C (collected in 2000) and the TanDEM-X (collected from 2010-2015), both spaceborne radar DEMs. Long-standing errors in the SRTM-C are corrected using the TanDEM-X as a control surface and applying cosine-fit co-registration to remove~1/10 pixel (~3 m) shifts, Fast Fourier Transform and filtering to remove SRTM-C short-and long-wavelength stripes, and blocked shifting to remove remaining complex biases.The datasets are then differenced and outlier pixels are identified as potential signal for the case of gravel-bed channels and 10 hillslopes. We are able to identify signals of incision and aggradation (with magnitudes down to~3 m in best case) in two > 100 km river reaches, with increased geomorphic activity downstream of knickpoints. Anthropogenic gravel excavation and piling is prominently measured, with magnitudes exceeding ±5 m (up to > 10 m for large piles). These values correspond to conservative rates of 0.2 to > 0.5 m/yr for vertical changes in gravel-bed rivers. For hillslopes, since we require stricter cutoffs for noise, we are only able to identify one major landslide with a deposit volume of 16±0.15×10 change can be garnered from TanDEM-X auxiliary layers, however, these are more difficult to quantify. The methods presented can be extended to any region of the world with SRTM-C and TanDEM-X coverage where vertical land-level changes are of interest, with the caveat that remaining vertical uncertainties in primarily the SRTM-C limit detection in steep and complex topography.

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A snow climatology of the Andes Mountains from MODIS snow cover data

Saavedra

Kampf

Fassnacht

et al. 2016

Intl Journal of Climatology

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This study develops a method for characterizing snow climatology in the Andes Mountains using the 8‐day maximum binary snow cover product from the Moderate Resolution Imaging Spectroradiometer sensor. The objectives are to: (1) identify regions with similar snow patterns and (2) identify snow persistence zones within these regions. Within a study area between 8° and 39°S, snow regions are defined using the (1) minimum elevation of snow cover, (2) rate of change of snow persistence with elevation, and (3) timing of the minimum elevation snow cover. In tropical latitudes (8°–23°S), snow cover is constrained to high elevations (>5000 m), and these areas have steep changes in snow persistence with elevation. Minimal differences in the elevation of snow on the east and west sides of the range suggest that temperature is a primary control on snow presence. Snow cover has minimal seasonal variability in the Tropics between 8° and 14°S, but it peaks in austral fall (March) after the wet season from 14° to 23°S. In mid‐latitudes (south of 23°S) snowline decreases in elevation with increasing latitude, and snow persistence changes with elevation are more gradual than in tropical regions. Snow cover peaks in the austral winter throughout the mid‐latitudes. Differences in elevations of snow accumulation between the east and west sides of the Andes are greatest between 28° and 37°S, where high mountain peaks produce a strong orographic effect and precipitation shadow. Within the snow regions, four snow zones are defined based on the average fraction of the year that snow persists: (1) little or no snow, (2) intermittent, (3) seasonal, and (4) permanent snow zones. Tropical latitudes have snow cover only on the highest peaks. Areas of seasonal and permanent snow zones are greatest between latitudes 28° and 37°S as a result of higher precipitation than mountains further north and higher elevations than mountains further south.

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Assessing high altitude glacier thickness, volume and area changes using field, GIS and remote sensing techniques: the case of Nevado Coropuna (Peru)

Cited by 45 publications

References 12 publications

Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change

Co-registration and bias corrections of satellite elevation data sets for quantifying glacier thickness change

Measuring Decadal Vertical Land-level Changes from SRTM-C (2000) and TanDEM-X (~ 2015) in the South-Central Andes

A snow climatology of the Andes Mountains from MODIS snow cover data

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