scholarly journals Rock glacier inventory of the western Nyainqêntanglha Range, Tibetan Plateau, supported by InSAR time series and automated classification

2021 ◽  
Author(s):  
Eike Reinosch ◽  
Markus Gerke ◽  
Björn Riedel ◽  
Antje Schwalb ◽  
Qinghua Ye ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Automated Classification ◽  
Rock Glacier ◽  
Glacier Inventory

Inventory of active rock glaciers in the western Nyainqêntanglha Range, Tibetan Plateau

2021 ◽  
Author(s):  
Eike Reinosch ◽  
Markus Gerke ◽  
Björn Riedel ◽  
Antje Schwalb ◽  
Qinghua Ye ◽  
...  
Keyword(s):  
Tibetan Plateau ◽  
High Reliability ◽  
Surface Velocity ◽  
Windward Side ◽  
Mountain Range ◽  
The Tibetan Plateau ◽  
Automated Classification ◽  
Rock Glacier ◽  
North West ◽  
Rock Glaciers

<p>The western Nyainqêntanglha Range on the Tibetan Plateau (TP) reaches an elevation of 7162 m and is characterized by an extensive periglacial environment. Here, we present the first rock glacier inventory of the central TP containing 1433 rock glaciers over an area of 4622 km². The rock glaciers are identified based on their surface velocity. The surface velocity is derived from Sentinel-1 satellite data of 2016 to 2019 via InSAR time series analysis. 16.4 % of the inventoried rock glaciers are classified as active with a surface velocity above 10 cmyr<sup>-1</sup> and 80.0 % are classified as transitional with 1 to 10 cmyr<sup>-1</sup>. The western Nyainqêntanglha Range forms a climate divide between the dry continental climate brought by the Westerlies from the north-west and the Indian Summer Monsoon to the south. 89.7 % of all active rock glaciers and 74 % of the free ice glacial area are located on the southern side. The higher moisture availability on the southern (windward) side of the mountain range is likely the cause of a higher rock glacier occurrence and the greater activity.</p><p>Manually identifying and outlining rock glaciers is time consuming and subjective. To ensure a high reliability and comparability of our inventory, we therefore combined a manual approach with an automated classification. Three analysts worked in tandem to generate the manual outlines according to the guidelines of the IPA action group on ‘Rock glacier inventories and kinematics’. A subset of these outlines acted as training areas for a pixel-based maximum likelihood classification. Both the manual and the automated classification were performed based on DEM parameters (elevation, slope etc.), optical datasets (Sentinel-2 and NDVI) and surface velocity (generated with InSAR). 87.8 % of all manually outlined rock glaciers were identified successfully at a true positive rate of 69.5 %. 18 additional rock glaciers were added to the inventory based on the automated classification. This combined approach is therefore beneficial to generate a complete inventory. The automated classification can, however, not replace the expertise of an analyst as it greatly overestimates the actual rock glacier area.</p>

Creating a rock glacier inventory of the northern Nyainqêntanglha range (Tibetan Plateau) based on InSAR time-series analysis

2020 ◽  
Author(s):  
Eike Reinosch ◽  
Johannes Buckel ◽  
Markus Gerke ◽  
Jussi Baade ◽  
Björn Riedel
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Series Analysis ◽  
Microwave Remote Sensing ◽  
High Mountain ◽  
Mountain Range ◽  
Rock Glacier ◽  
Series Analysis ◽  
Ipcc Report ◽  
Rock Glaciers

<p>The northern Nyainqêntanglha range on the southern Tibetan Plateau reaches an elevation of 7150 m and is mainly characterized by a periglacial landscape. A monsoonal climate, with a wet period during the summers and arid conditions during the rest of the year governs the landscape processes. Large parts of the mountain range are considered permafrost due to the high altitude and the associated low air temperature. Rock glaciers, which are bodies of ice-rich debris, are a typical landform. The recently published IPCC report on the cryospheres of high mountain areas highlights the sensitivity of rock glaciers to climate warming and emphasizes the importance of their study.</p><p>We study the distribution of rock glaciers of the northern Nyainqêntanglha range and our aim is to produce an inventory of active rock glaciers based on their surface motion characteristics. The lack of higher order vegetation and the relatively low winter precipitation enable us to employ Interferometric Synthetic Aperture Radar (InSAR) time-series techniques to study both seasonal and multi-annual surface displacement patterns. InSAR is a powerful microwave remote sensing technique, which makes it possible to study displacement from a few millimeters to centimeters and decimeters per year. It is thus suitable to detect sliding and creeping processes related to periglacial landscapes and permafrost conditions on the Earth’s surface. We use both Sentinel-1 (2015-2019) and TerraSAR-X ScanSAR data (2017-2019) for our analysis.</p><p>In this study we differentiate rock glaciers from the surrounding seasonally sliding slopes by their significantly higher surface creeping rates with mean velocities of 5–20 cm yr<sup>-1</sup>. We also observe that the velocity of rock glaciers is less dependent on the summer monsoon, which allows us to further differentiate between rock glaciers and other landforms. This method could potentially be used to create rock glacier inventories in other remote regions, as long as the snow cover in winter is thin enough to allow continuous InSAR time-series analysis. These rock glacier inventories are necessary to assess the effects of climate change on vulnerable high mountain regions.</p>

Extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau

2021 ◽  
Author(s):  
Junyuan Fei ◽  
Jintao Liu
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Time Scale ◽  
Proper Time ◽  
Cumulative Distribution ◽  
The Tibetan Plateau ◽  
Statistical Features ◽  
Catchment Scale ◽  
Intermittent Rivers ◽  
Intermittent River

<p>Highly intermittent rivers are widespread on the Tibetan Plateau and deeply impact the ecological stability and social development downstream. Due to the highly intermittent rivers are small, seasonal variated and heavy cloud covered on the Tibetan Plateau, their distribution location is still unknown at catchment scale currently. To address these challenges, a new method is proposed for extracting the cumulative distribution location of highly intermittent river from Sentinel-1 time series in an alpine catchment on the Tibetan Plateau. The proposed method first determines the proper time scale of extracting highly intermittent river, based on which the statistical features are calculated to amplify the difference between land covers. Subsequently, the synoptic cumulative distribution location is extracted through Random Forest model using the statistical features above as explanatory variables. And the precise result is generated by combining the synoptic result with critical flow accumulation area.  The highly intermittent river segments are derived and assessed in an alpine catchment of Lhasa River Basin. The results show that the the intra-annual time scale is sufficient for highly intermittent river extraction. And the proposed method can extract highly intermittent river cumulative distribution locations with total precision of 0.62, distance error median of 64.03 m, outperforming other existing river extraction method.</p>

scholarly journals Spatiotemporal patterns of High Mountain Asia's snowmelt season identified with an automated snowmelt detection algorithm, 1987–2016

2017 ◽  
Vol 11 (5) ◽  
pp. 2329-2343 ◽  
Author(s):  
Taylor Smith ◽  
Bodo Bookhagen ◽  
Aljoscha Rheinwalt
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Climate Model ◽  
Single Point ◽  
Passive Microwave ◽  
Spatiotemporal Patterns ◽  
Signal Separation ◽  
High Mountain ◽  
The Tibetan Plateau ◽  
Microwave Data

Abstract. High Mountain Asia (HMA) – encompassing the Tibetan Plateau and surrounding mountain ranges – is the primary water source for much of Asia, serving more than a billion downstream users. Many catchments receive the majority of their yearly water budget in the form of snow, which is poorly monitored by sparse in situ weather networks. Both the timing and volume of snowmelt play critical roles in downstream water provision, as many applications – such as agriculture, drinking-water generation, and hydropower – rely on consistent and predictable snowmelt runoff. Here, we examine passive microwave data across HMA with five sensors (SSMI, SSMIS, AMSR-E, AMSR2, and GPM) from 1987 to 2016 to track the timing of the snowmelt season – defined here as the time between maximum passive microwave signal separation and snow clearance. We validated our method against climate model surface temperatures, optical remote-sensing snow-cover data, and a manual control dataset (n = 2100, 3 variables at 25 locations over 28 years); our algorithm is generally accurate within 3–5 days. Using the algorithm-generated snowmelt dates, we examine the spatiotemporal patterns of the snowmelt season across HMA. The climatically short (29-year) time series, along with complex interannual snowfall variations, makes determining trends in snowmelt dates at a single point difficult. We instead identify trends in snowmelt timing by using hierarchical clustering of the passive microwave data to determine trends in self-similar regions. We make the following four key observations. (1) The end of the snowmelt season is trending almost universally earlier in HMA (negative trends). Changes in the end of the snowmelt season are generally between 2 and 8 days decade−1 over the 29-year study period (5–25 days total). The length of the snowmelt season is thus shrinking in many, though not all, regions of HMA. Some areas exhibit later peak signal separation (positive trends), but with generally smaller magnitudes than trends in snowmelt end. (2) Areas with long snowmelt periods, such as the Tibetan Plateau, show the strongest compression of the snowmelt season (negative trends). These trends are apparent regardless of the time period over which the regression is performed. (3) While trends averaged over 3 decades indicate generally earlier snowmelt seasons, data from the last 14 years (2002–2016) exhibit positive trends in many regions, such as parts of the Pamir and Kunlun Shan. Due to the short nature of the time series, it is not clear whether this change is a reversal of a long-term trend or simply interannual variability. (4) Some regions with stable or growing glaciers – such as the Karakoram and Kunlun Shan – see slightly later snowmelt seasons and longer snowmelt periods. It is likely that changes in the snowmelt regime of HMA account for some of the observed heterogeneity in glacier response to climate change. While the decadal increases in regional temperature have in general led to earlier and shortened melt seasons, changes in HMA's cryosphere have been spatially and temporally heterogeneous.

scholarly journals Reconstruction of a Cloud-free Vegetation Index Time Series for the Tibetan Plateau

2004 ◽  
Vol 24 (4) ◽  
pp. 348-353 ◽  
Author(s):  
Wen Jun ◽  
Su Zhongbo ◽  
Ma Yaoming
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Vegetation Index ◽  
The Tibetan Plateau ◽  
Index Time Series

ALOS-derived glacier and rock glacier inventory of the Volcán Domuyo region (~36° S), southernmost Central Andes, Argentina

2016 ◽  
Vol 60 (3) ◽  
pp. 195-208 ◽  
Author(s):  
Daniel Falaschi ◽  
Mariano Masiokas ◽  
Takeo Tadono ◽  
Fleur Couvreux
Keyword(s):  
Central Andes ◽  
Rock Glacier ◽  
Glacier Inventory

scholarly journals Common Mode Component and Its Potential Effect on GPS-Inferred Three-Dimensional Crustal Deformations in the Eastern Tibetan Plateau

2019 ◽  
Vol 11 (17) ◽  
pp. 1975 ◽  
Author(s):  
Yuanjin Pan ◽  
Ruizhi Chen ◽  
Hao Ding ◽  
Xinyu Xu ◽  
Gang Zheng ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Crustal Deformation ◽  
Three Dimensional ◽  
Vertical Component ◽  
Eastern Tibetan Plateau ◽  
Frequency Spectra ◽  
Common Mode ◽  
Time Frequency ◽  
Gps Time Series

Surface and deep potential geophysical signals respond to the spatial redistribution of global mass variations, which may be monitored by geodetic observations. In this study, we analyze dense Global Positioning System (GPS) time series in the Eastern Tibetan Plateau using principal component analysis (PCA) and wavelet time-frequency spectra. The oscillations of interannual and residual signals are clearly identified in the common mode component (CMC) decomposed from the dense GPS time series from 2000 to 2018. The newly developed spherical harmonic coefficients of the Gravity Recovery and Climate Experiment Release-06 (GRACE RL06) are adopted to estimate the seasonal and interannual patterns in this region, revealing hydrologic and atmospheric/nontidal ocean loads. We stack the averaged elastic GRACE-derived loading displacements to identify the potential physical significance of the CMC in the GPS time series. Interannual nonlinear signals with a period of ~3 to ~4 years in the CMC (the scaled principal components from PC1 to PC3) are found to be predominantly related to hydrologic loading displacements, which respond to signals (El Niño/La Niña) of global climate change. We find an obvious signal with a period of ~6 yr on the vertical component that could be caused by mantle-inner core gravity coupling. Moreover, we evaluate the CMC’s effect on the GPS-derived velocities and confirm that removing the CMC can improve the recognition of nontectonic crustal deformation, especially on the vertical component. Furthermore, the effects of the CMC on the three-dimensional velocity and uncertainty are presented to reveal the significant crustal deformation and dynamic processes of the Eastern Tibetan Plateau.

60 years of rock glacier displacements and fluxes changes over Laurichard Rock glacier, French Alps.

2020 ◽  
Author(s):  
Diego Cusicanqui ◽  
Antoine Rabatel ◽  
Xavier Bodin
Keyword(s):  
Time Series ◽  
Mass Transport ◽  
Surface Flow ◽  
Aerial Images ◽  
Surface Elevation ◽  
European Alps ◽  
Rock Glacier ◽  
French Alps ◽  
High Resolution Satellite Images ◽  
Rock Glaciers

<p>Recent acceleration of rock glaciers has been largely documented in the European Alps, hence highlighting an increase in flow speed of stable rock glaciers and some anomalous behaviors called destabilization (development of landslides-like features on the rock glacier surface).  In this study, we focus on Laurichard active rock glacier, 225 m long, up to 75 m wide, which covers an area of 0.084 km2 and has the longest measurement time-series in the French Alps. Here we aim to understand the causes of the changes in ice velocity of Laurichard rock glacier. We investigate the changes in the fluxes of ice masses across longitudinal and transversal profiles in order to be able to analyze in details the differences between the upper part and the front of the glacier. Using a combination of remote sensing data from 1952 (historical aerial images) until 2018 (Pléiades high-resolution satellite images), we documented the three-dimensional evolution of the Laurichard rock glacier during the last 60 years. We calculated the surface flow velocity between 1952 and 2018 using a feature-tracking algorithm at a resolution of 1 m and a precision of 0.5 m. Digital elevation models were assembled using the SfM techniques for aerial images, and the AMES stereo pipeline for Pléiades data. In addition, we made the analysis using in-situ annual velocities and temperatures data allowing to understand better which factors mostly explain the kinematic behavior.  We reconstructed a time series of changes in surface elevation by systematically co-registering and differencing DEMs between 1952 and 2018, with an average precision of 1 m. We first observed that the average annual horizontal velocity measured had increased progressively from 0.65 m yr<sup>-1</sup> to 1.1 m yr<sup>-1</sup> to 1.5 m yr<sup>-1</sup> for the periods 1952-1960, 1994-2003 and 2013-2018, respectively. On the other hand, the surface mass changes and long term monitoring of mass transport show for all analyzed periods a clear negative surface elevation change of 2 m on average, between 1952 and 2018. The area with most of the elevation changes is the frontal part of the glacier, which is consistent with the increase in speed, which represents a mass exchange from the upper part to the front. We conclude that the rates of rock glacier mass transport have increased during the last 20 years and hypothetize, for this rock glacier, a transition state controlled mainly by local topographical factors which will eventually lead to high speed rock glacier or rock glacier destabilization.</p>

Using InSAR to asses rock glacier movement in the Uinta Mountains, Utah

2020 ◽  
Author(s):  
George Brencher ◽  
Alexander Handwerger ◽  
Jeffrey Munroe
Keyword(s):  
Arid Regions ◽  
Interferometric Synthetic Aperture Radar ◽  
Rock Glacier ◽  
Uinta Mountains ◽  
Glacier Inventory ◽  
Rock Glaciers ◽  
Climatic Drivers ◽  
Glacier Velocity ◽  
Alpine Hydrology ◽  
Insight Into

<p>Rock glaciers are perennially frozen bodies of ice and rock debris that move downslope primarily due to deformation of internal ice. These features play an important role in alpine hydrology and landscape evolution, and constitute a significant water resource in arid regions. In the Uinta Mountains, Utah, nearly 400 rock glaciers have been identified on the basis of morphology, but the presence of ice has been investigated in only two. Here, I use satellite-based interferometric synthetic-aperture radar (InSAR) from the Copernicus Sentinel-1 satellites to identify and monitor active rock glaciers over a 10,000 km<sup>2 </sup>area. I also compare the time-dependent motion of several individual rock glaciers over the summers of 2016-2019 to search for relationships with climatic drivers such as precipitation and temperature. Sentinel-1 data from the August-October of 2016-2019 are used to create 79 interferograms of the entire Uinta range and are processed with the NASA/JPL/Stanford InSAR Scientific Computing Environment (ISCE) software package. Temporal baselines of intrayear interferograms range from 6-72 days. We use average velocity maps to generate an active rock glacier inventory for the Uinta Mountains containing 196 active rock glaciers. Average rock glacier velocity is 3 cm/yr in the line-of-sight direction, but individual rock glaciers have velocities ranging from 0.3-15 cm/yr. Rock glacier speeds do have a seasonal component, and were fastest in August across all years. One rock glacier reached a speed of 40 cm/yr over a 12 day interval from August 5 to August 17 of 2017. Preliminary results suggest that active rock glaciers are found at altitudes 10 m higher on average than inactive and relic rock glaciers identified in the previous inventory. Rock glacier movement did not accelerate between 2016 and 2019, suggesting that rock glaciers in this part of the Rocky Mountains are not speeding up over time. Our results highlight the ability to use satellite InSAR to monitor rock glaciers over large areas and provide insight into the factors that control their kinematics.</p>

Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series

2014 ◽  
Vol 59 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Cuizhen Wang ◽  
Huadong Guo ◽  
Li Zhang ◽  
Shuangyu Liu ◽  
Yubao Qiu ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
The Tibetan Plateau ◽  
Alpine Grasslands ◽  
Climatic Control
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