Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis

Robson, Benjamin Aubrey; Hölbling, Daniel; Nuth, Christopher; Strozzi, Tazio; Dahl, Svein Olaf

doi:10.3390/rs8010067

Cited by 26 publications

(23 citation statements)

References 62 publications

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“…Due to the spectral similarity of debris on and off glaciers, there is so far no method available to automatically map debris cover over a large set of glaciers using optical satellite imagery alone. Hence, several studies have tested combined approaches that generally include topographic information derived from a DEM and other data (Robson et al, 2016;Racoviteanu and Williams, 2012;Rastner et al, 2014;Bolch et al, 2007;Paul et al, 2004). However, all methods require time-consuming manual post-processing, and the quality of the results depends to some extent on the experience of the analyst.…”

Section: Glacier Mappingmentioning

confidence: 99%

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Mölg

Bolch

Rastner

et al. 2018

Earth Syst. Sci. Data

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110

102

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Abstract. Knowledge about the coverage and characteristics of glaciers in High Mountain Asia (HMA) is still incomplete and heterogeneous. However, several applications, such as modelling of past or future glacier development, run-off, or glacier volume, rely on the existence and accessibility of complete datasets. In particular, precise outlines of glacier extent are required to spatially constrain glacier-specific calculations such as length, area, and volume changes or flow velocities. As a contribution to the Randolph Glacier Inventory (RGI) and the Global Land Ice Measurements from Space (GLIMS) glacier database, we have produced a homogeneous inventory of the Pamir and the Karakoram mountain ranges using 28 Landsat TM and ETM+ scenes acquired around the year 2000. We applied a standardized method of automated digital glacier mapping and manual correction using coherence images from the Advanced Land Observing Satellite 1 (ALOS-1) Phased Array type L-band Synthetic Aperture Radar 1 (PALSAR-1) as an additional source of information; we then (i) separated the glacier complexes into individual glaciers using drainage divides derived by watershed analysis from the ASTER global digital elevation model version 2 (GDEM2) and (ii) separately delineated all debris-covered areas. Assessment of uncertainties was performed for debris-covered and clean-ice glacier parts using the buffer method and independent multiple digitizing of three glaciers representing key challenges such as shadows and debris cover. Indeed, along with seasonal snow at high elevations, shadow and debris cover represent the largest uncertainties in our final dataset. In total, we mapped more than 27 800 glaciers >0.02 km2 covering an area of 35 520±1948 km2 and an elevation range from 2260 to 8600 m. Regional median glacier elevations vary from 4150 m (Pamir Alai) to almost 5400 m (Karakoram), which is largely due to differences in temperature and precipitation. Supraglacial debris covers an area of 3587±662 km2, i.e. 10 % of the total glacierized area. Larger glaciers have a higher share in debris-covered area (up to >20 %), making it an important factor to be considered in subsequent applications (https://doi.org/10.1594/PANGAEA.894707).

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Section: Glacier Mappingmentioning

confidence: 99%

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

Mölg

Bolch

Rastner

et al. 2018

Earth Syst. Sci. Data

Self Cite

110

102

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“…By far, this algorithm has become the backbone for delineating glacier outlines in many glacier inventories (e.g., [9,[80][81][82][83]87,88]). Other classification algorithms have also been applied, but scarcely for glacier delineation: Serandrei-Barbero et al [89] applied fuzzy contextual classification to map the glaciers in the Italian Alps; Robson et al [90] implemented Object-Based Image Analysis (OBIA) to classify more than 100 glaciers in the Austrian Alps for three time steps semi-automatically.…”

Section: Glacier Delineationmentioning

confidence: 99%

“…Since spaceborne SAR sensors are usually of single frequency and radar penetrates dry snow and ice, this limits their suitability for correctly delineating glaciers [100,101]. Nevertheless, an Interferometric Synthetic Aperture Radar (InSAR) coherence image is still useful for mapping glacier extent using low coherence (due to glacier motion and glacier surface condition changes) as an indication of glacier existence [90,102]. The concept has been realized by a four-step decision tree introduced by Atwood et al [102], including coherence indicators, surface slope indicators, morphological operations and patch size analysis.…”

mentioning

confidence: 99%

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Kuenzer

Dietz

et al. 2017

Remote Sensing

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Abstract:Cold regions affect global, regional and local climate; oftentimes they are relevant for water supply, host valuable ecosystems, and support human livelihood. They are thus eminently important for human society. In the context of ongoing climate change, monitoring and understanding cold region land surface dynamics is essential for environmental scientists, stakeholders and decision makers. However, the definition of cold regions remains inexplicit, and no up-to-date cold region maps or overarching spatial analyses exist. For example, Europe has densely populated cold regions, but hardly an article exists that provides a solid overview of Earth Observation (EO) based applications assessing cold region land surface dynamics in Europe. With this review article we aim at closing this gap by providing an overview of EO-based techniques for cold region observation in Europe, focusing on the dynamics of glaciers and snow. We present a novel spatial delineation of cold regions for Europe before analyzing the benefits and limitations of different EO sensor types and data processing methods for EO based cold region research. Furthermore, we identify research gaps and discuss challenges for future studies.

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“…Change detection is important for detecting dynamic changes of the Earth. Change detection attempts to identify land cover differences in the same geographical area across a period of time [2] and can be applied to various domains, including urban expansion [3], disaster monitoring [4], land cover map updating [5], forest degradation survey [6] and glacier melting [7]. In this context, various types of multi-temporal images are exploited to resolve the above problems.…”

Section: Introductionmentioning

confidence: 99%

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

2016

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When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors' best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection.

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Decadal Scale Changes in Glacier Area in the Hohe Tauern National Park (Austria) Determined by Object-Based Image Analysis

Cited by 26 publications

References 62 publications

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

A consistent glacier inventory for Karakoram and Pamir derived from Landsat data: distribution of debris cover and mapping challenges

The Potential of Earth Observation for the Analysis of Cold Region Land Surface Dynamics in Europe—A Review

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

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