Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis
- To semi-automatically assess the changes in glacier area in Hohe Tauern National Park in the Austrian Alps by using Object-Based Image Analysis (OBIA).
- To assess the potential of using high resolution topographic data to detect debris-covered ice by using edge detection of the surface slope. Although debris-covered ice is not of a major concern in the Austrian Alps, other glacierized regions such as the Himalayas contain considerable amounts of debris-covered ice and, as such, any semi-automatic methods would be beneficial for estimating ice reserves and assessing glacier change.
2. Study Area and Data Used
|Scene ID||Date||Sensor||Resolution (m)|
|LC81920272013247LGN00||4 September 2013||Landsat 8||30 (15 pan-sharp)|
|LT51920272003236MTI01||24 August 2003||Landsat 5||30|
|LT51920271985234KIS00||22 August 1985||Landsat 5||30|
|SRTM1N47E012V3||11 February 2000||SRTM||30|
|Track 637 Frame 930||01 July 2007||ALOS PALSAR||16 m × 13 m, geo-coded to 1 arc-second (~30 m)|
|Track 637 Frame 930||16 August 2007||ALOS PALSAR||16 m × 13 m, geo-coded to 1 arc-second (~30 m)|
|Segmentation Level||Scale Parameter||Shape||Compactness||Bands Used||Purpose|
|1||3 (5 *)||0.3||0.6||Blue, Green, NIR, Red, Slope, SWIR 1, SWIR 2, Thermal||Input for level 2|
|2||5 (8 *)||0.8||0.6||Blue, Green, NIR, Red, Slope, SWIR 1, SWIR 2, Thermal||Classifying clean Ice|
|2B||Maximum spectral difference = 10||n/a||n/a||NIR||Classifying transient snowline|
|3||10 (12 *)||0.25||0.5||NIR, Red, Slope, NDVI, canny edge detection (slope) **||Classifying debris covered Ice|
3.1. Clean Ice and Transient Snowline (TSL)
- The Normalized Difference Snow Index (NDSI) with a threshold of ≥−0.05–0.1 (after )
- The Normalized Difference Water Index (NDWI) with a threshold <0.15–0.4. It has been highlighted by others that turbid proglacial meltwater can be misclassified as clean ice (for example ), and the NDWI was therefore used to exclude proglacial lakes.
- Constraints of altitude of ≥2000 m and an upper threshold for the slope between 40° and 60°.
- The classified image objects that bordered each other were then merged and clean ice smaller than 0.02 km2 was removed from the classification.
3.2. Debris-Covered Ice
- −0.05–0 ≤ NDVI values ≤0.01–0.03. The NDVI has been used by others to take advantage of the fact that debris-covered ice typically has less vegetation than the surrounding non-glacierized terrain (for example ).
- Red channel ≤59 (in the case of Landsat 8, 11000 was used). This was found to be useful in excluding some paraglacial slopes.
- An upper threshold of the slope between 12° and 20°. Surface slope has been extensively used to delineate debris-covered ice, with a threshold of 20° being used previously in the European Alps (for example, see )
- Thermal band ≤12 °C. The thermal signature has often been used to differentiate debris-covered ice (for example, see ). The strength of the thermal signature is, however, highly dependent on the thickness and distribution of the glacier debris  and care should be taken to not overly rely on the thermal signature. For this reason it was an advantage to include the thermal band as a fuzzy membership function. That way objects that met all other criteria yet did not have a distinct thermal signature could still be considered debris-covered ice.
- Normalized Difference Water Index (NDWI) ≥−0.03. A threshold in the NDWI was included to exclude marginal glacial lakes from the classification.
3.4. Manual Delineation and Accuracy Assessment
- User’s accuracy—This is an error of commission and shows the percentage of the final classification that was a glacier.
- Producer’s accuracy—This is an error of omission and describes the percentage of actual glacier area that was successfully classified.
- Overall accuracy—This considers both the user’s accuracy and the producer’s accuracy and shows the percentage of points that were correctly classified.
- Kappa coefficient—This is a measure of agreement between the classifications and the ground truth pixels, and of the classification not being due to random chance .
4. Results of Glacier Mapping
4.1. Decadal-Scale Changes in Glacier Area
4.2. Change in Transient Snowline Elevation (TSL)
4.3. Accuracy Assessment
|User’s Accuracy||Producer’s Accuracy||Overall Accuracy||Kappa|
5.1. Area Loss Compared with Other Areas in the European Alps
5.2. Use of OBIA for Glacier Mapping
5.3. Topographic Data for Debris-Covered Ice
Supplementary FilesSupplementary File 1
Conflicts of Interest
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Robson, B.A.; Hölbling, D.; Nuth, C.; Strozzi, T.; Dahl, S.O. Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis. Remote Sens. 2016, 8, 67. https://doi.org/10.3390/rs8010067
Robson BA, Hölbling D, Nuth C, Strozzi T, Dahl SO. Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis. Remote Sensing. 2016; 8(1):67. https://doi.org/10.3390/rs8010067Chicago/Turabian Style
Robson, Benjamin Aubrey, Daniel Hölbling, Christopher Nuth, Tazio Strozzi, and Svein Olaf Dahl. 2016. "Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis" Remote Sensing 8, no. 1: 67. https://doi.org/10.3390/rs8010067